xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

76a38203ef0571fd35c823e43690344137bc333b

19,893

py

Python

tests/unit/test_models_tensorflow.py

brendanhasz/probflow-v2

a16b6ec6f1dd7e2fea0b6698928ed23e09b69a6e

[ "MIT" ]

2

2019-09-13T10:16:32.000Z

2019-09-22T11:35:07.000Z

tests/unit/test_models_tensorflow.py

brendanhasz/probflow-v2

a16b6ec6f1dd7e2fea0b6698928ed23e09b69a6e

[ "MIT" ]

null

tests/unit/test_models_tensorflow.py

brendanhasz/probflow-v2

a16b6ec6f1dd7e2fea0b6698928ed23e09b69a6e

[ "MIT" ]

null

"""Tests the probflow.models module when backend = tensorflow""" import pytest import numpy as np import tensorflow as tf import tensorflow_probability as tfp tfd = tfp.distributions from probflow.core.settings import Sampling import probflow.core.ops as O from probflow.distributions import Normal from probflow.parameters import * from probflow.modules import * from probflow.models import * from probflow.data import DataGenerator def is_close(a, b, tol=1e-3): return np.abs(a-b) < tol def test_Model_0D(): """Tests the probflow.models.Model abstract base class""" class MyModel(Model): def __init__(self): self.weight = Parameter(name='Weight') self.bias = Parameter(name='Bias') self.std = ScaleParameter(name='Std') def __call__(self, x): return Normal(x*self.weight() + self.bias(), self.std()) # Instantiate the model my_model = MyModel() # Shouldn't be training assert my_model._is_training is False # Fit the model x = np.random.randn(100).astype('float32') y = -x + 1 my_model.fit(x, y, batch_size=5, epochs=10) # Shouldn't be training assert my_model._is_training is False # Should be able to set learning rate lr = my_model._learning_rate my_model.set_learning_rate(lr+1.0) assert lr != my_model._learning_rate # predictive samples samples = my_model.predictive_sample(x[:30], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 50 assert samples.shape[1] == 30 # aleatoric samples samples = my_model.aleatoric_sample(x[:30], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 50 assert samples.shape[1] == 30 # epistemic samples samples = my_model.epistemic_sample(x[:30], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 50 assert samples.shape[1] == 30 # predict samples = my_model.predict(x[:30]) assert isinstance(samples, np.ndarray) assert samples.ndim == 1 assert samples.shape[0] == 30 # metric metric = my_model.metric('mae', x[:30], y[:30]) assert isinstance(metric, np.floating) metric = my_model.metric('mse', x[:30], y[:30]) assert isinstance(metric, np.floating) assert metric >= 0 # posterior_mean w/ no args should return all params val = my_model.posterior_mean() assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 1 for v in val) # posterior_mean w/ str should return value of that param val = my_model.posterior_mean('Weight') assert isinstance(val, np.ndarray) assert val.ndim == 1 # posterior_mean w/ list of params should return only those params val = my_model.posterior_mean(['Weight', 'Std']) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 1 for v in val) # posterior_sample w/ no args should return all params val = my_model.posterior_sample(n=20) assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 2 for v in val) assert all(val[v].shape[0] == 20 for v in val) assert all(val[v].shape[1] == 1 for v in val) # posterior_sample w/ str should return sample of that param val = my_model.posterior_sample('Weight', n=20) assert isinstance(val, np.ndarray) assert val.ndim == 2 assert val.shape[0] == 20 assert val.shape[1] == 1 # posterior_sample w/ list of params should return only those params val = my_model.posterior_sample(['Weight', 'Std'], n=20) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 2 for v in val) assert all(val[v].shape[0] == 20 for v in val) assert all(val[v].shape[1] == 1 for v in val) # posterior_ci should return confidence intervals of all params by def val = my_model.posterior_ci(n=20) assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], tuple) for v in val) assert all(isinstance(val[v][0], np.ndarray) for v in val) assert all(isinstance(val[v][1], np.ndarray) for v in val) assert all(val[v][0].ndim == 1 for v in val) assert all(val[v][1].ndim == 1 for v in val) assert all(val[v][0].shape[0] == 1 for v in val) assert all(val[v][1].shape[0] == 1 for v in val) # posterior_ci should return ci of only 1 if passed str val = my_model.posterior_ci('Weight', n=20) assert isinstance(val, tuple) assert isinstance(val[0], np.ndarray) assert isinstance(val[1], np.ndarray) # posterior_ci should return specified cis if passed list of params val = my_model.posterior_ci(['Weight', 'Std'], n=20) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], tuple) for v in val) assert all(isinstance(val[v][0], np.ndarray) for v in val) assert all(isinstance(val[v][1], np.ndarray) for v in val) assert all(val[v][0].ndim == 1 for v in val) assert all(val[v][1].ndim == 1 for v in val) assert all(val[v][0].shape[0] == 1 for v in val) assert all(val[v][1].shape[0] == 1 for v in val) # prior_sample w/ no args should return all params val = my_model.prior_sample(n=20) assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 1 for v in val) assert all(val[v].shape[0] == 20 for v in val) # prior_sample w/ str should return sample of that param val = my_model.prior_sample('Weight', n=20) assert isinstance(val, np.ndarray) assert val.ndim == 1 assert val.shape[0] == 20 # prior_sample w/ list of params should return only those params val = my_model.prior_sample(['Weight', 'Std'], n=20) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 1 for v in val) assert all(val[v].shape[0] == 20 for v in val) # log_prob should return log prob of each sample by default probs = my_model.log_prob(x[:30], y[:30]) assert isinstance(probs, np.ndarray) assert probs.ndim == 1 assert probs.shape[0] == 30 # log_prob should return sum if individually = False s_prob = my_model.log_prob(x[:30], y[:30], individually=False) assert isinstance(s_prob, np.floating) assert s_prob == np.sum(probs) # log_prob should return samples w/ distribution = True probs = my_model.log_prob(x[:30], y[:30], n=10, distribution=True) assert isinstance(probs, np.ndarray) assert probs.ndim == 2 assert probs.shape[0] == 30 assert probs.shape[1] == 10 # log_prob should return samples w/ distribution = True probs = my_model.log_prob(x[:30], y[:30], n=10, distribution=True, individually=False) assert isinstance(probs, np.ndarray) assert probs.ndim == 1 assert probs.shape[0] == 10 # prob should return prob of each sample by default probs = my_model.prob(x[:30], y[:30]) assert isinstance(probs, np.ndarray) assert probs.ndim == 1 assert probs.shape[0] == 30 assert np.all(probs >= 0) # prob should return sum if individually = False s_prob = my_model.prob(x[:30], y[:30], individually=False) assert isinstance(s_prob, np.floating) # prob should return samples w/ distribution = True probs = my_model.prob(x[:30], y[:30], n=10, distribution=True) assert isinstance(probs, np.ndarray) assert probs.ndim == 2 assert probs.shape[0] == 30 assert probs.shape[1] == 10 assert np.all(probs >= 0) # prob should return samples w/ distribution = True probs = my_model.prob(x[:30], y[:30], n=10, distribution=True, individually=False) assert isinstance(probs, np.ndarray) assert probs.ndim == 1 assert probs.shape[0] == 10 assert np.all(probs >= 0) def test_Model_DataGenerators(): """Tests the probflow.models.Model sampling/predictive methods when passed DataGenerators""" class MyModel(Model): def __init__(self): self.weight = Parameter(name='Weight') self.bias = Parameter(name='Bias') self.std = ScaleParameter(name='Std') def __call__(self, x): return Normal(x*self.weight() + self.bias(), self.std()) # Instantiate the model my_model = MyModel() # Make a DataGenerator x = np.random.randn(100).astype('float32') y = -x + 1 data = DataGenerator(x, y, batch_size=5) # Fit the model my_model.fit(data, epochs=10) # predictive samples samples = my_model.predictive_sample(data, n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 50 assert samples.shape[1] == 100 # aleatoric samples samples = my_model.aleatoric_sample(data, n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 50 assert samples.shape[1] == 100 # epistemic samples samples = my_model.epistemic_sample(data, n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 50 assert samples.shape[1] == 100 # predict samples = my_model.predict(data) assert isinstance(samples, np.ndarray) assert samples.ndim == 1 assert samples.shape[0] == 100 # metric metric = my_model.metric('mae', data) assert isinstance(metric, np.floating) metric = my_model.metric('mse', data) assert isinstance(metric, np.floating) assert metric >= 0 def test_Model_1D(): """Tests the probflow.models.Model abstract base class""" class MyModel(Model): def __init__(self): self.weight = Parameter([5, 1], name='Weight') self.bias = Parameter([1, 1], name='Bias') self.std = ScaleParameter([1, 1], name='Std') def __call__(self, x): return Normal(x@self.weight() + self.bias(), self.std()) # Instantiate the model my_model = MyModel() # Shouldn't be training assert my_model._is_training is False # Data x = np.random.randn(100, 5).astype('float32') w = np.random.randn(5, 1).astype('float32') y = x@w + 1 # Fit the model my_model.fit(x, y, batch_size=5, epochs=10) # predictive samples samples = my_model.predictive_sample(x[:30, :], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 3 assert samples.shape[0] == 50 assert samples.shape[1] == 30 assert samples.shape[2] == 1 # aleatoric samples samples = my_model.aleatoric_sample(x[:30, :], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 3 assert samples.shape[0] == 50 assert samples.shape[1] == 30 assert samples.shape[2] == 1 # epistemic samples samples = my_model.epistemic_sample(x[:30, :], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 3 assert samples.shape[0] == 50 assert samples.shape[1] == 30 assert samples.shape[2] == 1 # predict samples = my_model.predict(x[:30, :]) assert isinstance(samples, np.ndarray) assert samples.ndim == 2 assert samples.shape[0] == 30 assert samples.shape[1] == 1 # metric metric = my_model.metric('mse', x[:30, :], y[:30, :]) assert isinstance(metric, np.floating) metric = my_model.metric('mae', x[:30, :], y[:30, :]) assert isinstance(metric, np.floating) assert metric >= 0 # posterior_mean w/ no args should return all params val = my_model.posterior_mean() assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 2 for v in val) assert val['Weight'].shape[0] == 5 assert val['Weight'].shape[1] == 1 assert val['Bias'].shape[0] == 1 assert val['Bias'].shape[1] == 1 assert val['Std'].shape[0] == 1 assert val['Std'].shape[1] == 1 # posterior_mean w/ str should return value of that param val = my_model.posterior_mean('Weight') assert isinstance(val, np.ndarray) assert val.ndim == 2 assert val.shape[0] == 5 assert val.shape[1] == 1 # posterior_mean w/ list of params should return only those params val = my_model.posterior_mean(['Weight', 'Std']) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 2 for v in val) assert val['Weight'].shape[0] == 5 assert val['Weight'].shape[1] == 1 assert val['Std'].shape[0] == 1 assert val['Std'].shape[1] == 1 # posterior_sample w/ no args should return all params val = my_model.posterior_sample(n=20) assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 3 for v in val) assert val['Weight'].shape[0] == 20 assert val['Weight'].shape[1] == 5 assert val['Weight'].shape[2] == 1 assert val['Bias'].shape[0] == 20 assert val['Bias'].shape[1] == 1 assert val['Bias'].shape[2] == 1 assert val['Std'].shape[0] == 20 assert val['Std'].shape[1] == 1 assert val['Std'].shape[2] == 1 # posterior_sample w/ str should return sample of that param val = my_model.posterior_sample('Weight', n=20) assert isinstance(val, np.ndarray) assert val.ndim == 3 assert val.shape[0] == 20 assert val.shape[1] == 5 assert val.shape[2] == 1 # posterior_sample w/ list of params should return only those params val = my_model.posterior_sample(['Weight', 'Std'], n=20) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 3 for v in val) assert val['Weight'].shape[0] == 20 assert val['Weight'].shape[1] == 5 assert val['Weight'].shape[2] == 1 assert val['Std'].shape[0] == 20 assert val['Std'].shape[1] == 1 assert val['Std'].shape[2] == 1 # posterior_ci should return confidence intervals of all params by def val = my_model.posterior_ci(n=20) assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], tuple) for v in val) assert all(isinstance(val[v][0], np.ndarray) for v in val) assert all(isinstance(val[v][1], np.ndarray) for v in val) assert all(val[v][0].ndim == 2 for v in val) assert all(val[v][1].ndim == 2 for v in val) for i in range(1): assert val['Weight'][i].shape[0] == 5 assert val['Weight'][i].shape[1] == 1 assert val['Bias'][i].shape[0] == 1 assert val['Bias'][i].shape[1] == 1 assert val['Std'][i].shape[0] == 1 assert val['Std'][i].shape[1] == 1 # posterior_ci should return ci of only 1 if passed str val = my_model.posterior_ci('Weight', n=20) assert isinstance(val, tuple) assert isinstance(val[0], np.ndarray) assert isinstance(val[1], np.ndarray) # posterior_ci should return specified cis if passed list of params val = my_model.posterior_ci(['Weight', 'Std'], n=20) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], tuple) for v in val) assert all(isinstance(val[v][0], np.ndarray) for v in val) assert all(isinstance(val[v][1], np.ndarray) for v in val) assert all(val[v][0].ndim == 2 for v in val) assert all(val[v][1].ndim == 2 for v in val) for i in range(1): assert val['Weight'][i].shape[0] == 5 assert val['Weight'][i].shape[1] == 1 assert val['Std'][i].shape[0] == 1 assert val['Std'][i].shape[1] == 1 # prior_sample w/ no args should return all params val = my_model.prior_sample(n=20) assert isinstance(val, dict) assert len(val) == 3 assert 'Weight' in val assert 'Bias' in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 1 for v in val) assert all(val[v].shape[0] == 20 for v in val) # prior_sample w/ str should return sample of that param val = my_model.prior_sample('Weight', n=20) assert isinstance(val, np.ndarray) assert val.ndim == 1 assert val.shape[0] == 20 # prior_sample w/ list of params should return only those params val = my_model.prior_sample(['Weight', 'Std'], n=20) assert isinstance(val, dict) assert len(val) == 2 assert 'Weight' in val assert 'Bias' not in val assert 'Std' in val assert all(isinstance(val[v], np.ndarray) for v in val) assert all(val[v].ndim == 1 for v in val) assert all(val[v].shape[0] == 20 for v in val) def test_Model_nesting(): """Tests Model when it contains Modules and sub-modules""" class MyModule(Module): def __init__(self): self.weight = Parameter([5, 1], name='Weight') self.bias = Parameter([1, 1], name='Bias') def __call__(self, x): return x@self.weight() + self.bias() class MyModel(Model): def __init__(self): self.module = MyModule() self.std = ScaleParameter([1, 1], name='Std') def __call__(self, x): return Normal(self.module(x), self.std()) # Instantiate the model my_model = MyModel() # Shouldn't be training assert my_model._is_training is False # Data x = np.random.randn(100, 5).astype('float32') w = np.random.randn(5, 1).astype('float32') y = x@w + 1 # Fit the model my_model.fit(x, y, batch_size=5, epochs=10) # predictive samples samples = my_model.predictive_sample(x[:30, :], n=50) assert isinstance(samples, np.ndarray) assert samples.ndim == 3 assert samples.shape[0] == 50 assert samples.shape[1] == 30 assert samples.shape[2] == 1 # kl loss should be greater for outer model assert my_model.kl_loss().numpy() > my_model.module.kl_loss().numpy() def test_ContinuousModel(): """Tests probflow.models.ContinuousModel""" pass #TODO def test_DiscreteModel(): """Tests probflow.models.DiscreteModel""" pass #TODO def test_CategoricalModel(): """Tests probflow.models.CategoricalModel""" pass #TODO

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1

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null

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8

4f89d59d48803fcad093efdb4b67350add3ef879

31,142

py

Python

keepit/db.py

franklinmatheus/sistema-gerenciamento-economico

6e2565eedd644469d22da3ea58141bcedb46da97

[ "Apache-2.0" ]

null

keepit/db.py

franklinmatheus/sistema-gerenciamento-economico

6e2565eedd644469d22da3ea58141bcedb46da97

[ "Apache-2.0" ]

1

2019-06-10T23:49:34.000Z

2020-09-23T01:10:06.000Z

keepit/db.py

franklinmatheus/sistema-gerenciamento-economico

6e2565eedd644469d22da3ea58141bcedb46da97

[ "Apache-2.0" ]

1

2020-07-20T07:20:15.000Z

import mysql.connector import datetime from flask import g def get_db(): db = mysql.connector.connect(user='root', password='admin',host='127.0.0.1',port='3306',database='keepit') return db def close_db(e=None): db = g.pop('db', None) if db is not None: db.close() ''' keepit.usuario clue +------------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+-------------+------+-----+---------+----------------+ | id_usuario | int(11) | NO | PRI | NULL | auto_increment | | fnome | varchar(45) | NO | | NULL | | | lnome | varchar(45) | NO | | NULL | | | nascimento | date | NO | | NULL | | +------------+-------------+------+-----+---------+----------------+ keepit.credenciais_usuario clue +------------+--------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +------------+--------------+------+-----+---------+-------+ | id_usuario | int(11) | NO | PRI | NULL | | | login | varchar(45) | NO | | NULL | | | senha | varchar(150) | NO | | NULL | | +------------+--------------+------+-----+---------+-------+ ''' def insert_user(data: dict): db = get_db() cursor = db.cursor(dictionary=True) insert_user = ('INSERT INTO keepit.usuario (fnome, lnome, nascimento) VALUES (%s, %s, %s)') data_user = (data['fname'], data['lname'], data['born']) cursor.execute(insert_user,data_user) id_user = cursor.lastrowid insert_credentials = ('INSERT INTO keepit.credenciais_usuario (id_usuario,login,senha) VALUES (%s, %s, %s)') data_credentials = (id_user,data['username'],data['password']) cursor.execute(insert_credentials,data_credentials) db.commit() cursor.close() db.close() def check_login(login: str): db = get_db() cursor = db.cursor(dictionary=True) select_user = ('SELECT * FROM keepit.credenciais_usuario WHERE keepit.credenciais_usuario.login = %s') data_user = (login,) cursor.execute(select_user,data_user) user = cursor.fetchone() cursor.close() db.close() return user def select_user_by_id(id: int): db = get_db() cursor = db.cursor(dictionary=True) select_user = ('''SELECT * FROM (keepit.usuario JOIN keepit.credenciais_usuario ON keepit.usuario.id_usuario=keepit.credenciais_usuario.id_usuario) WHERE keepit.usuario.id_usuario = %s''') data_user = (id,) cursor.execute(select_user,data_user) user = cursor.fetchone() cursor.close() db.close() return user def select_user_by_credentials(username: str, password: str): db = get_db() cursor = db.cursor(dictionary=True) select_user = ('''SELECT * FROM (keepit.usuario JOIN keepit.credenciais_usuario ON keepit.usuario.id_usuario=keepit.credenciais_usuario.id_usuario) WHERE keepit.credenciais_usuario.login = %s AND keepit.credenciais_usuario.senha = %s''') data_user = (username,password) cursor.execute(select_user,data_user) user = cursor.fetchone() cursor.close() db.close() return user ''' keepit.recurso clue +-------------------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------------+-------------+------+-----+---------+----------------+ | id_recurso | int(11) | NO | PRI | NULL | auto_increment | | id_usuario | int(11) | NO | PRI | NULL | | | nome | varchar(45) | NO | | NULL | | | data_cancelamento | date | YES | | NULL | | | data_anotacao | datetime | NO | | NULL | | +-------------------+-------------+------+-----+---------+----------------+ keepit.pagamento_recurso clue +----------------+---------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +----------------+---------+------+-----+---------+-------+ | id_recurso | int(11) | NO | PRI | NULL | | | data_pagamento | date | NO | | NULL | | | valor | float | NO | | NULL | | +----------------+---------+------+-----+---------+-------+ keepit.despesa clue +------------+---------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+---------+------+-----+---------+----------------+ | id_despesa | int(11) | NO | PRI | NULL | auto_increment | | id_recurso | int(11) | NO | MUL | NULL | | +------------+---------+------+-----+---------+----------------+ keepit.despesa_comum clue +------------+------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +------------+------------+------+-----+---------+-------+ | id_despesa | int(11) | NO | PRI | NULL | | | constante | tinyint(1) | NO | | NULL | | | automatica | tinyint(1) | NO | | NULL | | | dia_mes | int(11) | NO | | NULL | | | status | tinyint(1) | NO | | NULL | | +------------+------------+------+-----+---------+-------+ ''' def insert_expense_common(id_user: int, data: dict): ''' The dictionary has to be as shown below: { 'value': 0, 'payment_date': 'dd/mm/yyyy', 'name': 'abc', 'cancelation_date': 'dd/mm/yyyy', 'annotation_date': 'dd/mm/yyyy', 'constant': 0, 'automatic': 0, 'month_day': 0, 'status': 0 } ''' db = get_db() cursor = db.cursor(dictionary=True) insert_query = ('''INSERT INTO keepit.recurso (id_usuario, nome, data_cancelamento, data_anotacao) VALUES (%s, %s, %s, %s)''') data_insert = (id_user,data['name'],data['cancelation_date'],data['annotation_date']) cursor.execute(insert_query,data_insert) id_resource = cursor.lastrowid insert_query = ('''INSERT INTO keepit.pagamento_recurso (id_recurso, data_pagamento, valor) VALUES (%s, %s, %s)''') data_insert = (id_resource,data['payment_date'],data['value']) cursor.execute(insert_query,data_insert) insert_query = ('INSERT INTO keepit.despesa (id_recurso) VALUES (%s)') data_insert = (id_resource,) cursor.execute(insert_query,data_insert) id_expense = cursor.lastrowid insert_query = ('''INSERT INTO keepit.despesa_comum (id_despesa, constante, automatica, dia_mes, status) VALUES (%s, %s, %s, %s, %s)''') data_insert = (id_expense,data['constant'],data['automatic'],data['month_day'],data['status']) cursor.execute(insert_query,data_insert) db.commit() cursor.close() db.close() def select_expense_common(id_user: int): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT * FROM (((keepit.recurso JOIN keepit.despesa ON keepit.recurso.id_recurso=keepit.despesa.id_recurso) JOIN keepit.despesa_comum ON keepit.despesa.id_despesa=keepit.despesa_comum.id_despesa) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_usuario=%s ORDER BY keepit.recurso.data_anotacao DESC''') select_data = (id_user,) cursor.execute(select_query,select_data) results = cursor.fetchall() cursor.close() db.close() return results def update_common_expenses(id_user: int, today): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT keepit.despesa_comum.id_despesa,keepit.despesa_comum.automatica, keepit.recurso.id_recurso,keepit.pagamento_recurso.valor,keepit.pagamento_recurso.data_pagamento FROM (((keepit.recurso JOIN keepit.despesa ON keepit.recurso.id_recurso=keepit.despesa.id_recurso) JOIN keepit.despesa_comum ON keepit.despesa.id_despesa=keepit.despesa_comum.id_despesa) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE TIMESTAMPDIFF(MONTH,keepit.pagamento_recurso.data_pagamento,%s) > 0 AND keepit.recurso.id_usuario=%s''') select_data = (today,id_user) cursor.execute(select_query,select_data) results = cursor.fetchall() automatic = [] nonautomatic = [] for result in results: if result['automatica'] == 1: automatic.append(result) else: nonautomatic.append(result) for curr in nonautomatic: update_query = ('UPDATE keepit.despesa_comum SET keepit.despesa_comum.status = 0 WHERE keepit.despesa_comum.id_despesa = %s') update_data = (curr['id_despesa'],) cursor.execute(update_query,update_data) for curr in automatic: payment_date = None last_update = curr['data_pagamento'] if last_update.month == 12: payment_date = datetime.datetime(last_update.year+1, 1, last_update.day) else: payment_date = datetime.datetime(last_update.year, last_update.month+1, last_update.day) insert_query = ('INSERT INTO keepit.pagamento_recurso (id_recurso,data_pagamento,valor) values (%s, %s, %s)') inser_data = (curr['id_recurso'],payment_date,curr['valor']) cursor.execute(insert_query,inser_data) db.commit() cursor.close() db.close() def update_common_expense_constant(id_resource: int): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT keepit.pagamento_recurso.valor FROM (((keepit.recurso JOIN keepit.despesa ON keepit.recurso.id_recurso=keepit.despesa.id_recurso) JOIN keepit.despesa_comum ON keepit.despesa.id_despesa=keepit.despesa_comum.id_despesa) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_recurso=%s''') select_data = (id_resource,) cursor.execute(select_query,select_data) resource = cursor.fetchone() cursor.close() db.close() update_common_expense_inconstant(id_resource,resource['valor']) def update_common_expense_inconstant(id_resource: int, value: float): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT keepit.despesa_comum.dia_mes,keepit.despesa_comum.id_despesa,keepit.pagamento_recurso.data_pagamento FROM (((keepit.recurso JOIN keepit.despesa ON keepit.recurso.id_recurso=keepit.despesa.id_recurso) JOIN keepit.despesa_comum ON keepit.despesa.id_despesa=keepit.despesa_comum.id_despesa) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_recurso=%s''') select_data = (id_resource,) cursor.execute(select_query,select_data) resource = cursor.fetchone() payment_date = None if resource['data_pagamento'] is not None: if resource['data_pagamento'].month == 12: payment_date = datetime.datetime(resource['data_pagamento'].year+1, 1, resource['data_pagamento'].day) else: payment_date = datetime.datetime(resource['data_pagamento'].year, resource['data_pagamento'].month+1, resource['data_pagamento'].day) else: temp_date = datetime.datetime.now() temp_day = resource['dia_mes'] if temp_day < temp_date.day: payment_date = datetime.datetime(temp_date.year, temp_date.month, temp_day) elif temp_day > temp_date.day: if temp_date.month == 1: payment_date = datetime.datetime(temp_date.year-1, 12, temp_day) else: payment_date = datetime.datetime(temp_date.year, temp_date.month-1, temp_day) else: payment_date = datetime.datetime.now().strftime("%Y-%m-%d") insert_query = ('INSERT INTO keepit.pagamento_recurso (id_recurso,data_pagamento,valor) values (%s, %s, %s)') inser_data = (id_resource,payment_date,value) cursor.execute(insert_query,inser_data) update_query = ('UPDATE keepit.despesa_comum SET keepit.despesa_comum.status = 1 WHERE keepit.despesa_comum.id_despesa = %s') update_data = (resource['id_despesa'],) cursor.execute(update_query,update_data) db.commit() cursor.close() db.close() ''' keepit.recurso clue +-------------------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------------+-------------+------+-----+---------+----------------+ | id_recurso | int(11) | NO | PRI | NULL | auto_increment | | id_usuario | int(11) | NO | PRI | NULL | | | valor | int(11) | NO | | NULL | | | data_pagamento | date | NO | | NULL | | | nome | varchar(45) | NO | | NULL | | | data_cancelamento | date | NO | | NULL | | | data_anotacao | date | NO | | NULL | | +-------------------+-------------+------+-----+---------+----------------+ keepit.pagamento_recurso clue +----------------+---------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +----------------+---------+------+-----+---------+-------+ | id_recurso | int(11) | NO | PRI | NULL | | | data_pagamento | date | NO | | NULL | | | valor | float | NO | | NULL | | +----------------+---------+------+-----+---------+-------+ keepit.despesa clue +------------+---------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+---------+------+-----+---------+----------------+ | id_despesa | int(11) | NO | PRI | NULL | auto_increment | | id_recurso | int(11) | NO | MUL | NULL | | +------------+---------+------+-----+---------+----------------+ keepit.despesa_incomum clue +------------+-------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +------------+-------------+------+-----+---------+-------+ | id_despesa | int(11) | NO | PRI | NULL | | | destino | varchar(45) | NO | | NULL | | +------------+-------------+------+-----+---------+-------+ ''' def insert_expense_uncommon(id_user: int, data: dict): ''' The dictionary has to be as shown below: { 'value': 0, 'payment_date': 'dd/mm/yyyy', 'name': 'abc', 'cancelation_date': 'dd/mm/yyyy', 'annotation_date': 'dd/mm/yyyy', 'destination': 'abc' } ''' db = get_db() cursor = db.cursor(dictionary=True) insert_query = ('''INSERT INTO keepit.recurso (id_usuario, nome, data_cancelamento, data_anotacao) VALUES (%s, %s, %s, %s)''') data_insert = (id_user,data['name'],data['cancelation_date'],data['annotation_date']) cursor.execute(insert_query,data_insert) id_resource = cursor.lastrowid insert_query = ('''INSERT INTO keepit.pagamento_recurso (id_recurso, data_pagamento, valor) VALUES (%s, %s, %s)''') data_insert = (id_resource,data['payment_date'],data['value']) cursor.execute(insert_query,data_insert) insert_query = ('INSERT INTO keepit.despesa (id_recurso) VALUES (%s)') data_insert = (id_resource,) cursor.execute(insert_query,data_insert) id_expense = cursor.lastrowid insert_query = ('INSERT INTO keepit.despesa_incomum (id_despesa, destino) VALUES (%s, %s)') data_insert = (id_expense,data['destination']) cursor.execute(insert_query,data_insert) db.commit() cursor.close() db.close() def select_expense_uncommon(id_user: int): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT * FROM (((keepit.recurso JOIN keepit.despesa ON keepit.recurso.id_recurso=keepit.despesa.id_recurso) JOIN keepit.despesa_incomum ON keepit.despesa.id_despesa=keepit.despesa_incomum.id_despesa) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_usuario=%s ORDER BY keepit.recurso.data_anotacao DESC''') select_data = (id_user,) cursor.execute(select_query,select_data) results = cursor.fetchall() cursor.close() db.close() return results ''' keepit.recurso clue +-------------------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------------+-------------+------+-----+---------+----------------+ | id_recurso | int(11) | NO | PRI | NULL | auto_increment | | id_usuario | int(11) | NO | PRI | NULL | | | nome | varchar(45) | NO | | NULL | | | data_cancelamento | date | YES | | NULL | | | data_anotacao | datetime | NO | | NULL | | +-------------------+-------------+------+-----+---------+----------------+ keepit.pagamento_recurso clue +----------------+---------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +----------------+---------+------+-----+---------+-------+ | id_recurso | int(11) | NO | PRI | NULL | | | data_pagamento | date | NO | | NULL | | | valor | float | NO | | NULL | | +----------------+---------+------+-----+---------+-------+ keepit.receita clue +------------+---------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+---------+------+-----+---------+----------------+ | id_receita | int(11) | NO | PRI | NULL | auto_increment | | id_recurso | int(11) | NO | MUL | NULL | | +------------+---------+------+-----+---------+----------------+ keepit.receita_comum clue +------------+------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +------------+------------+------+-----+---------+-------+ | id_receita | int(11) | NO | PRI | NULL | | | constante | tinyint(1) | NO | | NULL | | | automatica | tinyint(1) | NO | | NULL | | | dia_mes | int(11) | NO | | NULL | | | status | tinyint(1) | NO | | NULL | | +------------+------------+------+-----+---------+-------+ ''' def insert_revenue_common(id_user: int, data: dict): ''' The dictionary has to be as shown below: { 'value': 0, 'payment_date': 'dd/mm/yyyy', 'name': 'abc', 'cancelation_date': 'dd/mm/yyyy', 'annotation_date': 'dd/mm/yyyy', 'constant': 0, 'automatic': 0, 'month_day': 0, 'status': 0 } ''' db = get_db() cursor = db.cursor(dictionary=True) insert_query = ('''INSERT INTO keepit.recurso (id_usuario, nome, data_cancelamento, data_anotacao) VALUES (%s, %s, %s, %s)''') data_insert = (id_user,data['name'],data['cancelation_date'],data['annotation_date']) cursor.execute(insert_query,data_insert) id_resource = cursor.lastrowid insert_query = ('''INSERT INTO keepit.pagamento_recurso (id_recurso, data_pagamento, valor) VALUES (%s, %s, %s)''') data_insert = (id_resource,data['payment_date'],data['value']) cursor.execute(insert_query,data_insert) insert_query = ('INSERT INTO keepit.receita (id_recurso) VALUES (%s)') data_insert = (id_resource,) cursor.execute(insert_query,data_insert) id_revenue = cursor.lastrowid insert_query = ('''INSERT INTO keepit.receita_comum (id_receita, constante, automatica, dia_mes, status) VALUES (%s, %s, %s, %s, %s)''') data_insert = (id_revenue,data['constant'],data['automatic'],data['month_day'],data['status']) cursor.execute(insert_query,data_insert) db.commit() cursor.close() db.close() def select_revenue_common(id_user: int): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT * FROM (((keepit.recurso JOIN keepit.receita ON keepit.recurso.id_recurso=keepit.receita.id_recurso) JOIN keepit.receita_comum ON keepit.receita.id_receita=keepit.receita_comum.id_receita) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_usuario=%s ORDER BY keepit.recurso.data_anotacao DESC''') select_data = (id_user,) cursor.execute(select_query,select_data) results = cursor.fetchall() cursor.close() db.close() return results def update_common_revenues(id_user: int, today): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT keepit.receita_comum.id_receita,keepit.receita_comum.automatica, keepit.recurso.id_recurso,keepit.pagamento_recurso.valor,keepit.pagamento_recurso.data_pagamento FROM (((keepit.recurso JOIN keepit.receita ON keepit.recurso.id_recurso=keepit.receita.id_recurso) JOIN keepit.receita_comum ON keepit.receita.id_receita=keepit.receita_comum.id_receita) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE TIMESTAMPDIFF(MONTH,keepit.pagamento_recurso.data_pagamento,%s) > 0 AND keepit.recurso.id_usuario=%s''') select_data = (today,id_user) cursor.execute(select_query,select_data) results = cursor.fetchall() automatic = [] nonautomatic = [] for result in results: if result['automatica'] == 1: automatic.append(result) else: nonautomatic.append(result) for curr in nonautomatic: update_query = ('UPDATE keepit.receita_comum SET keepit.receita_comum.status = 0 WHERE keepit.receita_comum.id_receita = %s') update_data = (curr['id_receita'],) cursor.execute(update_query,update_data) for curr in automatic: payment_date = None last_update = curr['data_pagamento'] if last_update.month == 12: payment_date = datetime.datetime(last_update.year+1, 1, last_update.day) else: payment_date = datetime.datetime(last_update.year, last_update.month+1, last_update.day) insert_query = ('INSERT INTO keepit.pagamento_recurso (id_recurso,data_pagamento,valor) values (%s, %s, %s)') inser_data = (curr['id_recurso'],payment_date,curr['valor']) cursor.execute(insert_query,inser_data) db.commit() cursor.close() db.close() def update_common_revenue_constant(id_resource: int): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT keepit.pagamento_recurso.valor FROM (((keepit.recurso JOIN keepit.receita ON keepit.recurso.id_recurso=keepit.receita.id_recurso) JOIN keepit.receita_comum ON keepit.receita.id_receita=keepit.receita_comum.id_receita) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_recurso=%s''') select_data = (id_resource,) cursor.execute(select_query,select_data) resource = cursor.fetchone() update_common_revenue_inconstant(id_resource,resource['valor']) def update_common_revenue_inconstant(id_resource: int, value: float): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT keepit.receita_comum.dia_mes,keepit.receita_comum.id_receita,keepit.pagamento_recurso.data_pagamento FROM (((keepit.recurso JOIN keepit.receita ON keepit.recurso.id_recurso=keepit.receita.id_recurso) JOIN keepit.receita_comum ON keepit.receita.id_receita=keepit.receita_comum.id_receita) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_recurso=%s''') select_data = (id_resource,) cursor.execute(select_query,select_data) resource = cursor.fetchone() payment_date = None if resource['data_pagamento'] is not None: if resource['data_pagamento'].month == 12: payment_date = datetime.datetime(resource['data_pagamento'].year+1, 1, resource['data_pagamento'].day) else: payment_date = datetime.datetime(resource['data_pagamento'].year, resource['data_pagamento'].month+1, resource['data_pagamento'].day) else: temp_date = datetime.datetime.now() temp_day = resource['dia_mes'] if temp_day < temp_date.day: payment_date = datetime.datetime(temp_date.year, temp_date.month, temp_day) elif temp_day > temp_date.day: if temp_date.month == 1: payment_date = datetime.datetime(temp_date.year-1, 12, temp_day) else: payment_date = datetime.datetime(temp_date.year, temp_date.month-1, temp_day) else: payment_date = datetime.datetime.now().strftime("%Y-%m-%d") insert_query = ('INSERT INTO keepit.pagamento_recurso (id_recurso,data_pagamento,valor) values (%s, %s, %s)') inser_data = (id_resource,payment_date,value) cursor.execute(insert_query,inser_data) update_query = ('UPDATE keepit.receita_comum SET keepit.receita_comum.status = 1 WHERE keepit.receita_comum.id_receita = %s') update_data = (resource['id_receita'],) cursor.execute(update_query,update_data) db.commit() cursor.close() db.close() ''' keepit.recurso clue +-------------------+-------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +-------------------+-------------+------+-----+---------+----------------+ | id_recurso | int(11) | NO | PRI | NULL | auto_increment | | id_usuario | int(11) | NO | PRI | NULL | | | valor | int(11) | NO | | NULL | | | data_pagamento | date | NO | | NULL | | | nome | varchar(45) | NO | | NULL | | | data_cancelamento | date | NO | | NULL | | | data_anotacao | date | NO | | NULL | | +-------------------+-------------+------+-----+---------+----------------+ keepit.pagamento_recurso clue +----------------+---------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +----------------+---------+------+-----+---------+-------+ | id_recurso | int(11) | NO | PRI | NULL | | | data_pagamento | date | NO | | NULL | | | valor | float | NO | | NULL | | +----------------+---------+------+-----+---------+-------+ keepit.receita clue +------------+---------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------+---------+------+-----+---------+----------------+ | id_receita | int(11) | NO | PRI | NULL | auto_increment | | id_recurso | int(11) | NO | MUL | NULL | | +------------+---------+------+-----+---------+----------------+ keepit.receita_incomum clue +------------+--------------+------+-----+---------+-------+ | Field | Type | Null | Key | Default | Extra | +------------+--------------+------+-----+---------+-------+ | id_receita | int(11) | NO | PRI | NULL | | | emissor | varchar(100) | NO | | NULL | | | motivo | varchar(100) | NO | | NULL | | +------------+--------------+------+-----+---------+-------+ ''' def insert_revenue_uncommon(id_user: int, data: dict): ''' The dictionary has to be as shown below: { 'value': 0, 'payment_date': 'dd/mm/yyyy', 'name': 'abc', 'cancelation_date': 'dd/mm/yyyy', 'annotation_date': 'dd/mm/yyyy', 'emitter': 'abc', 'reason': 'abc' } ''' db = get_db() cursor = db.cursor(dictionary=True) insert_query = ('''INSERT INTO keepit.recurso (id_usuario, nome, data_cancelamento, data_anotacao) VALUES (%s, %s, %s, %s)''') data_insert = (id_user,data['name'],data['cancelation_date'],data['annotation_date']) cursor.execute(insert_query,data_insert) id_resource = cursor.lastrowid insert_query = ('''INSERT INTO keepit.pagamento_recurso (id_recurso, data_pagamento, valor) VALUES (%s, %s, %s)''') data_insert = (id_resource,data['payment_date'],data['value']) cursor.execute(insert_query,data_insert) insert_query = ('INSERT INTO keepit.receita (id_recurso) VALUES (%s)') data_insert = (id_resource,) cursor.execute(insert_query,data_insert) id_revenue = cursor.lastrowid insert_query = ('INSERT INTO keepit.receita_incomum (id_receita, emissor, motivo) VALUES (%s, %s, %s)') data_insert = (id_revenue,data['emitter'],data['reason']) cursor.execute(insert_query,data_insert) db.commit() cursor.close() db.close() def select_revenue_uncommon(id_user: int): db = get_db() cursor = db.cursor(dictionary=True) select_query = ('''SELECT * FROM (((keepit.recurso JOIN keepit.receita ON keepit.recurso.id_recurso=keepit.receita.id_recurso) JOIN keepit.receita_incomum ON keepit.receita.id_receita=keepit.receita_incomum.id_receita) JOIN keepit.pagamento_recurso ON keepit.pagamento_recurso.id_pagamento = (SELECT keepit.pagamento_recurso.id_pagamento FROM keepit.pagamento_recurso WHERE keepit.pagamento_recurso.id_recurso=keepit.recurso.id_recurso ORDER BY keepit.pagamento_recurso.data_pagamento DESC LIMIT 1)) WHERE keepit.recurso.id_usuario=%s ORDER BY keepit.recurso.data_anotacao DESC''') select_data = (id_user,) cursor.execute(select_query,select_data) results = cursor.fetchall() cursor.close() db.close() return results def remove_resource(id_resource: int): db = get_db() cursor = db.cursor(dictionary=True) delete_query = ('DELETE FROM keepit.recurso WHERE keepit.recurso.id_recurso = %s') delete_data = (id_resource,) cursor.execute(delete_query,delete_data) db.commit() cursor.close() db.close() def cancel_resource(id_resource: int, cancelation_date): db = get_db() cursor = db.cursor(dictionary=True) update_query = ('UPDATE keepit.recurso SET keepit.recurso.data_cancelamento=%s WHERE keepit.recurso.id_recurso=%s') update_data = (cancelation_date,id_resource) cursor.execute(update_query,update_data) db.commit() cursor.close() db.close()

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1

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null

0

7

4f8aac8f02bbaf5fe53d9af28a3d42583a57b717

138

py

Python

nets/__init__.py

vedenev/receptive_field_research

0811daeef16f47ead99be56e6e497261d822c242

[ "MIT" ]

null

nets/__init__.py

vedenev/receptive_field_research

0811daeef16f47ead99be56e6e497261d822c242

[ "MIT" ]

null

nets/__init__.py

vedenev/receptive_field_research

0811daeef16f47ead99be56e6e497261d822c242

[ "MIT" ]

null

from .no_pools_net import NoPoolsNet from .no_pools_net_res import NoPoolsNetRes from .get_resnet_50_adapted import get_resnet_50_adapted

34.5

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1

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1

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1

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7

8c131a4ab7df00539b2c23b9070e279bec74b778

1,386

py

Python

backend/apps/proyecto/migrations/0002_detalleproyecto_miembro_riesgo_riesgoproyecto_rol.py

nepta1828/prueba

7226b888dd5b52c8d8625b90a57a2c4bc31b09a9

[ "CC0-1.0" ]

1

2020-06-11T23:10:58.000Z

backend/apps/proyecto/migrations/0002_detalleproyecto_miembro_riesgo_riesgoproyecto_rol.py

nepta1828/prueba

7226b888dd5b52c8d8625b90a57a2c4bc31b09a9

[ "CC0-1.0" ]

8

2020-06-07T21:40:19.000Z

2021-09-22T19:10:14.000Z

backend/apps/proyecto/migrations/0002_detalleproyecto_miembro_riesgo_riesgoproyecto_rol.py

Systeem3/phoenixware-backend-api

7226b888dd5b52c8d8625b90a57a2c4bc31b09a9

[ "CC0-1.0" ]

null

# Generated by Django 3.0.5 on 2020-06-07 20:19 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('proyecto', '0001_initial'), ] operations = [ migrations.CreateModel( name='detalleProyecto', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), migrations.CreateModel( name='Miembro', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), migrations.CreateModel( name='Riesgo', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), migrations.CreateModel( name='RiesgoProyecto', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), migrations.CreateModel( name='Rol', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), ]

31.5

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0

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1

0

1

0

null

0

7

8c152e95228a28a7013c26457271ad76c6e75990

9,605

py

Python

__tests__/test_credit_card_model.py

shadowlion/pydantic-models

c64994f0f557db029a07a3b0b8c20eab7a99468f

[ "MIT" ]

null

__tests__/test_credit_card_model.py

shadowlion/pydantic-models

c64994f0f557db029a07a3b0b8c20eab7a99468f

[ "MIT" ]

5

2022-02-02T17:39:38.000Z

2022-03-16T12:13:15.000Z

__tests__/test_credit_card_model.py

shadowlion/pydantic-models

c64994f0f557db029a07a3b0b8c20eab7a99468f

[ "MIT" ]

null

from datetime import date import pytest from pydantic import ValidationError from app.helpers import CreditCardBrand from app.models import CreditCard def test_model_instance(): cc = CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="000", ) assert isinstance(cc, CreditCard) def test_model_no_inputs(): with pytest.raises(ValidationError): CreditCard() def test_model_cardholder_name_too_short(): with pytest.raises(ValidationError): CreditCard( name="T", number="4242424242424242", month="01", year=str(date.today().year), cvv="000", ) def test_model_has_number_in_cardholder_name(): with pytest.raises(ValidationError): CreditCard( name="T2", number="4242424242424242", month="01", year=str(date.today().year), cvv="000", ) def test_model_cardholder_number_has_fifteen_digits(): with pytest.raises(ValidationError): CreditCard( name="Test", number="424242424242424", month="01", year=str(date.today().year), cvv="000", ) def test_model_cardholder_number_has_sixteen_digits(): cc = CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_cardholder_number_has_seventeen_digits(): with pytest.raises(ValidationError): CreditCard( name="Test", number="42424242424242424", month="01", year=str(date.today().year), cvv="000", ) def test_model_cardholder_number_is_visa(): test_numbers = [ "4242424242424242", "4000056655665556", "4012888888881881", ] for num in test_numbers: cc = CreditCard( name="Test", number=num, month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == num assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_cardholder_number_is_discover(): test_numbers = [ "6011111111111117", "6011000990139424", "6011000990139424", ] for num in test_numbers: cc = CreditCard( name="Test", number=num, month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == num assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.DISCOVER def test_model_cardholder_number_is_mastercard(): test_numbers = [ "5555555555554444", "2223003122003222", "5200828282828210", "5105105105105100", ] for num in test_numbers: cc = CreditCard( name="Test", number=num, month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == num assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.MASTERCARD def test_model_cardholder_number_is_american_express(): test_numbers = [ "378282246310005", "371449635398431", "371449635398431", ] for num in test_numbers: cc = CreditCard( name="Test", number=num, month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == num assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.AMERICAN_EXPRESS def test_model_cardholder_number_is_invalid_card(): with pytest.raises(ValidationError): test_numbers = [ "38520000023237", # Diners Club "3056930009020004", # Diners Club "36227206271667", # Diners Club (14 digit) "3566002020360505", # JCB "3566002020360505", # JCB "6200000000000005", # UnionPay ] for num in test_numbers: CreditCard( name="Test", number=num, month="01", year=str(date.today().year), cvv="123", ) def test_model_expiry_month_below_min_limit(): with pytest.raises(ValidationError): CreditCard( name="Test", number="4242424242424242", month="00", year=str(date.today().year), cvv="000", ) def test_model_expiry_month_at_min_limit(): cc = CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_expiry_month_above_min_limit(): cc = CreditCard( name="Test", number="4242424242424242", month="02", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "02" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_expiry_month_below_max_limit(): cc = CreditCard( name="Test", number="4242424242424242", month="11", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "11" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_expiry_month_at_max_limit(): cc = CreditCard( name="Test", number="4242424242424242", month="12", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "12" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_expiry_month_above_max_limit(): with pytest.raises(ValidationError): CreditCard( name="Test", number="4242424242424242", month="13", year=str(date.today().year), cvv="000", ) def test_model_expiry_year_previous_year(): with pytest.raises(ValidationError): CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year - 1), cvv="000", ) def test_model_expiry_year_current_year(): cc = CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_expiry_year_ten_years_from_now(): cc = CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year + 10), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "01" assert cc.year == str(date.today().year + 10) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_expiry_year_eleven_years_from_now(): with pytest.raises(ValidationError): CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year + 11), cvv="000", ) def test_model_cvv_two_digits(): with pytest.raises(ValidationError): CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="12", ) def test_model_cvv_three_digits(): cc = CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="123", ) assert cc.name == "Test" assert cc.number == "4242424242424242" assert cc.month == "01" assert cc.year == str(date.today().year) assert cc.cvv == "123" assert cc.brand == CreditCardBrand.VISA def test_model_cvv_four_digits(): with pytest.raises(ValidationError): CreditCard( name="Test", number="4242424242424242", month="01", year=str(date.today().year), cvv="1234", )

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0.110451

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0.802685

0.758006

0.75187

0.695302

0

0.13939

0.313587

9,605

370

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0

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1

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false

0

0.015723

0

0.09434

0

null

0

1

0

1

0

null

0

7

8c4b200d7846ef1aa91f2d7083a89e36d3e46bc0

9,782

py

Python

tests/tools/test_atomicswap.py

threefoldtech/jumpscale_lib9

03c1451133d777e5af106fcc6f75c1138bb997f2

[ "Apache-2.0" ]

null

tests/tools/test_atomicswap.py

threefoldtech/jumpscale_lib9

03c1451133d777e5af106fcc6f75c1138bb997f2

[ "Apache-2.0" ]

220

2018-07-29T08:37:17.000Z

2019-08-05T15:01:27.000Z

tests/tools/test_atomicswap.py

threefoldtech/jumpscale_lib9

03c1451133d777e5af106fcc6f75c1138bb997f2

[ "Apache-2.0" ]

1

2018-08-20T09:16:08.000Z

""" Test module for atomicswap """ import re from jumpscale import j import os from JumpscaleLib.tools.atomicswap.AtomicSwapFactory import BTCInitiator, TFTParticipant os.environ.setdefault('BTC_MIN_CONFIRMATIONS', '1') os.environ.setdefault('TFT_MIN_CONFIRMATION_HEIGHT', '2') participant_address = 'mhv5Tve23BkVD6a8tpWKFxzWkub2sfiRqv' initiator_address = '013803e566cdc065c415b14a1f082f240e8cc81a6f13fd01ef59e94620601a5a2f26e83b806091' initiator_amount = 0.01234 participant_amount = 0.5 testnet = True # initiator = j.tools.atomicswap.get_btc_initiator('localhost:2200', initiator_amount, # participant_address, testnet) # participant = j.tools.atomicswap.get_tft_participant('localhost:2222', participant_amount, # initiator_address, testnet) # import IPython # IPython.embed() # import pdb; pdb.set_trace() btc_prefab = j.tools.prefab.getFromSSH('localhost', port=2200) tft_prefab = j.tools.prefab.getFromSSH('localhost', port=2222) j.tools.atomicswap.execute(initiator_prefab=btc_prefab, initiator_address=initiator_address, initiator_amount='{}BTC'.format(initiator_amount), participant_prefab=tft_prefab, participant_address=participant_address, participant_amount='{}TFT'.format(participant_amount), testnet=testnet) # initiate_cmd = 'btcatomicswap --testnet --rpcuser=user --rpcpass=pass -s localhost:8332 --force-yes initiate {} 0.01234'.format(participant_address) # # rc, out, err = btc_prefab.core.run(initiate_cmd) # # out= """Secret: d65fa37cf91c74a917d3bbde2a031ccbd11538babc3057cd4bc2f9b0809e90ef\nSecret hash: 0900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e\n\nContract fee: 0.00000166 BTC (0.00000672 BTC/kB)\nRefund fee: 0.00000297 BTC (0.00001021 BTC/kB)\n\nContract (2MvpTsm9wXHNYWaQCGs9jHoaYJ42cieRjfd):\n6382012088a8200900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e8876a9145d4e3d61ad2fa7888473e99007280b3161e7fdf2670494d7335bb17576a9147c6076e1594229d2e3267a2b67102045397a232c6888ac\n\nContract transaction (7daaac73d7ed508f6fae3fe03d397a0fdc423b84dd19127c9882dc9e8b084a2d):\n02000000000101da2ad7719034ff29c595997ba5cbf1b26d9037e5bc81b0b0f4f8b7a43e679b5a0000000017160014547e2929999a8ae11963ad285e07fd8789d0145afeffffff02fb4a64030000000017a914fcc15ebf5dce3f55e7eb11b69e65a95d263a55ac8750d412000000000017a9142731b263e593f1d8c1c00d0287169460b1643d7a870247304402202c69331697c856f43d8ccfdf90ae5dc5b083384028a1feb8d6558aa9ebf2471702201b398d68d36f5199ded51cd1849927567913e507a74e345bb2d9f6031d86cca50121022aae89285d0f5996f1fe7dd004e21883d40a2ad6dba7b94bba0ea219fd9595a300000000\n\nRefund transaction (a36e15da8d7aa3d304db61c5df72b581a83686b71d4000c4e532fe723ab7161a):\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\n\nPublished contract transaction (7daaac73d7ed508f6fae3fe03d397a0fdc423b84dd19127c9882dc9e8b084a2d)""" # # # {'secret': 'd65fa37cf91c74a917d3bbde2a031ccbd11538babc3057cd4bc2f9b0809e90ef', # # 'secret_hash': '0900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e', # # 'contract_addr': '2MvpTsm9wXHNYWaQCGs9jHoaYJ42cieRjfd', # # 'contract': '6382012088a8200900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e8876a9145d4e3d61ad2fa7888473e99007280b3161e7fdf2670494d7335bb17576a9147c6076e1594229d2e3267a2b67102045397a232c6888ac', # # 'contract_txn_addr': '7daaac73d7ed508f6fae3fe03d397a0fdc423b84dd19127c9882dc9e8b084a2d', # # 'contract_txn': '02000000000101da2ad7719034ff29c595997ba5cbf1b26d9037e5bc81b0b0f4f8b7a43e679b5a0000000017160014547e2929999a8ae11963ad285e07fd8789d0145afeffffff02fb4a64030000000017a914fcc15ebf5dce3f55e7eb11b69e65a95d263a55ac8750d412000000000017a9142731b263e593f1d8c1c00d0287169460b1643d7a870247304402202c69331697c856f43d8ccfdf90ae5dc5b083384028a1feb8d6558aa9ebf2471702201b398d68d36f5199ded51cd1849927567913e507a74e345bb2d9f6031d86cca50121022aae89285d0f5996f1fe7dd004e21883d40a2ad6dba7b94bba0ea219fd9595a300000000', # # 'refund_txn_addr': 'a36e15da8d7aa3d304db61c5df72b581a83686b71d4000c4e532fe723ab7161a', # # 'refund_txn': '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', # # 'published_contract_txn_address': '7daaac73d7ed508f6fae3fe03d397a0fdc423b84dd19127c9882dc9e8b084a2d'} # # # contract = '6382012088a8200900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e8876a9145d4e3d61ad2fa7888473e99007280b3161e7fdf2670494d7335bb17576a9147c6076e1594229d2e3267a2b67102045397a232c6888ac' # contract_txn = '02000000000101da2ad7719034ff29c595997ba5cbf1b26d9037e5bc81b0b0f4f8b7a43e679b5a0000000017160014547e2929999a8ae11963ad285e07fd8789d0145afeffffff02fb4a64030000000017a914fcc15ebf5dce3f55e7eb11b69e65a95d263a55ac8750d412000000000017a9142731b263e593f1d8c1c00d0287169460b1643d7a870247304402202c69331697c856f43d8ccfdf90ae5dc5b083384028a1feb8d6558aa9ebf2471702201b398d68d36f5199ded51cd1849927567913e507a74e345bb2d9f6031d86cca50121022aae89285d0f5996f1fe7dd004e21883d40a2ad6dba7b94bba0ea219fd9595a300000000' # audit_cmd = 'btcatomicswap --testnet -s localhost:8332 auditcontract {} {}'.format(contract, contract_txn) # # rc, out, err = tft_prefab.core.run(audit_cmd) # # out = """Contract address: 2MvpTsm9wXHNYWaQCGs9jHoaYJ42cieRjfd\nContract value: 0.01234 BTC\nRecipient address: mp2Js67GxeneGYzPeh9zZqYdikr4kFphCq\nAuthor's refund address: mrrbeJYhWD3hrAwa1ikLogwySrCs8Cc2fc\n\nSecret hash: 0900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e\n\nLocktime: 2018-06-27 18:29:40 +0000 UTC\nLocktime reached in 47h33m57s""" # # secret_hash = '0900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e' # # participate_cmd = "tfchainc atomicswap -y --encoding json participate {} .5 {}".format(initiator_address, secret_hash) # rc, out, err = tft_prefab.core.run(participate_cmd) # out = """{"coins":"500000000","contract":{"sender":"012ffd03d1b4d39ba9df8294bb5135a0a69768494a54e4df0c0eb817309b6a7fba795e4ac1f4ff","receiver":"0108031a2111cec5427954fae23fdd6a0cc21d9ab91cf0e878af9d2bb0081e9c1246da7c1e2346","hashedsecret":"0900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e","timelock":1530126270},"contractid":"02806e2cfa3aa87e2ea41d4c1f1bf8bf2b73d167eb3df610b7b364633426b8215e607e40db08b1","outputid":"e27bfac78c16e7690b5cb477f1602e0f6b074522d198d4733dd03d148cac4024","transactionid":"09bb77d6555488103f59709d27f0679fcf4d86dfa3ae77dbb06d976aeccc947e"}""" # # output_id = 'e27bfac78c16e7690b5cb477f1602e0f6b074522d198d4733dd03d148cac4024' # recipient_addr = '0108031a2111cec5427954fae23fdd6a0cc21d9ab91cf0e878af9d2bb0081e9c1246da7c1e2346' # refund_addr = '012ffd03d1b4d39ba9df8294bb5135a0a69768494a54e4df0c0eb817309b6a7fba795e4ac1f4ff' # timelock = 1530126270 # secret_hash = '0900e02c2b413ad422c107862b670c7980fa24956e60699436f652ff56d98d4e' # amount = .5 # # audit_cmd = 'tfchainc atomicswap -y --encoding json auditcontract {} --receiver {} --secrethash {} --amount {}'.format(output_id, recipient_addr, secret_hash, amount) # # rc, out, err = btc_prefab.core.run(audit_cmd) # # secret = 'd65fa37cf91c74a917d3bbde2a031ccbd11538babc3057cd4bc2f9b0809e90ef' # redeem_cmd = 'tfchainc atomicswap redeem {} {} -y --encoding json '.format(output_id, secret) # rc, out, err = btc_prefab.core.run(redeem_cmd) # # rpcuser, rpcpass, addr = 'user', 'pass', 'localhost:8332' # reverse_redeem_cmd = 'btcatomicswap --testnet -s {} --rpcuser={} --rpcpass={} --force-yes redeem {} {} {}'.format(addr, # rpcuser , rpcpass, contract, contract_txn, secret) # # _, out, _, tft_prefab.core.run(reverse_redeem_cmd) # # In [97]: out # # Out[97]: 'Redeem fee: 0.0000033 BTC (0.00001015 BTC/kB)\n\nRedeem transaction (f202c74569424241c9f75f432af838a297399e139a2928c396d5ad0606212057):\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\n\nPublished redeem transaction (f202c74569424241c9f75f432af838a297399e139a2928c396d5ad0606212057)'

108.688889

1,882

0.8532

471

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17.549894

0.312102

0.007259

0.009436

0.003992

0.0346

0.032664

0.024437

0

0.452567

0.084032

9,782

89

1,883

109.910112

0.469978

0.871805

0

0.174793

0.147194

0

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0

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0

false

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null

1

0

7

4fecd69ff8078a7e37e92b77833c1e52cb2b8211

157,544

py

Python

classifier-analysis/decoder_analysis.py

spagliarini/low-dimensional-canary-GAN

4a400530e91630bc96dc1b56fab7164b10aa7cdb

[ "MIT" ]

2

2021-12-08T16:16:11.000Z

2021-12-09T10:07:41.000Z

classifier-analysis/decoder_analysis.py

neuronalX/low-dimensional-canary-GAN

d8e5258c261588856e58b69bcc36b924f24746a0

[ "MIT" ]

null

classifier-analysis/decoder_analysis.py

neuronalX/low-dimensional-canary-GAN

d8e5258c261588856e58b69bcc36b924f24746a0

[ "MIT" ]

1

2021-12-08T16:14:21.000Z

import glob import os import pickle import scipy as sp from scipy import special import scipy.io.wavfile as wav import numpy as np import librosa import shutil from math import log2 import sklearn.metrics import matplotlib.colors as colors import matplotlib.pyplot as plt import matplotlib.cm as cm import plots import statistics as stat def open_pkl(name): data = open(name, 'rb') z = pickle.load(data) return z def create_annotations(args): """ The input directory contains one or more audio files (.wav) of duration 1s. This function creates a dictionary containing for each .wav file the following elements: - name (path of the recording) - vocab: the list of the whole vocabulary - raw: this entry stores the raw outputs produced by the ESN. The ESN produce one annotation vector per timestep; the raw output does the same thing but for each timestep of input audio The output is saved in the same directory of the data (outside to be able to use the function somewhere else). """ from canarydecoder import load # Load the model # decoder = load('canary16-deltas') # decoder = load('canarygan-3-d') # decoder = load('canary16-clean-d') # decoder = load('canary16-clean-d-notrim') # decoder = load('canary16-filtered') # decoder = load('canary16-filtered-notrim') #THIS ONE TO COMPUTE IS CLASSIFIER - REAL # decoder = load('canarygan-e-ot-noise') # decoder = load('canarygan-clean-e-ot-noise-notrim') # decoder = load('canarygan-clean-e-ot-noise') # decoder = load('canarygan-f-3e-ot-noise') decoder = load('canarygan-f-3e-ot-noise-notrim') #CLASSIFIER - EXT # decoder = load('canarygan-2e-ot-noise') # decoder = load('canarygan-8e-ot-noise') # decoder = load('canarygan-8e-ot-noise-v2') # decoder = load('021220-1e') # decoder = load('021220-1e-balanced') # decoder = load('021220-8e') # decoder = load('021220-8e-balanced') # Create dictionary #annotations = decoder(args.data_dir + '/' + 'generation_' + str(args.ckpt_n)) # if loop on ckpt_n annotations = decoder(args.data_dir) # if only one dir as input dir return annotations def analysis_dataset(annotations_dataset, legend_list, args): """ The input directory contains one or more annotations. This function is meant to analyse the datasets used to train the GAN and see how the decoder works on a known set of data. """ # Classes annotations_aux = open_pkl(annotations_dataset[0]) classes = annotations_aux[0].vocab print(classes) GAN_class = np.zeros((np.size(args.GAN_classes),)) classes_noGAN = classes if np.size(args.GAN_classes)>0: for gc in range(0,np.size(args.GAN_classes)): GAN_class[gc] = np.where(classes == args.GAN_classes[gc])[0][0] classes_noGAN = np.delete(classes_noGAN, np.argwhere(classes_noGAN == args.GAN_classes[gc])) classes_noGAN_index = np.zeros((np.size(classes) - np.size(args.GAN_classes),)) for c in range(0,np.size(classes) - np.size(args.GAN_classes)): classes_noGAN_index[c] = np.where(classes == classes_noGAN[c])[0][0] n_classes = len(annotations_aux[0].vocab) # Real distributions data_real = [] # Whole dataset here because the number of samples per class is not exactly a multiple of the number of classes data_real_distr = np.ones((n_classes, )) * 1000 if np.size(args.GAN_classes)>0: for gc in range(0,np.size(args.GAN_classes)): data_real_distr[np.where(classes == args.GAN_classes[gc])[0][0]] = 0 # data_real_distr[4] = 1175 # data_real_distr[16] = 1281 # data_real_distr = [1488, 1488, 1497, 1487, 1034, 1478, 0, 0, 0, 1465, 1430, 1497, 1496, 1301, 1127, 1462, 1232, 1466, 1432] # data_real_distr = [1488, 931, 1497, 1487, 1034, 1478, 0, 0, 0, 1465, 1430, 1497, 1496, 1301, 1127, 1462, 1232, 1466, 1432] # data_real_distr = [1488, 931, 1497, 1500, 1034, 1478, 0, 0, 0, 1465, 1430, 1497, 1496, 1301, 1127, 1462, 1232, 1466, 1432] fig, ax = plt.subplots() ax.bar(classes, data_real_distr) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.xticks(rotation='45') plt.ylabel('Number of samples') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'How_many_syllables_1.' + args.format) data_real.append(data_real_distr/np.sum(data_real_distr)) dataset_dim = np.zeros((np.size(legend_list))) whole_list_of_recordings = [] whole_decoder_list_of_recordings = [] IS = np.zeros((np.size(legend_list),)) for ds in range(0, np.size(legend_list)): annotations_aux = open_pkl(annotations_dataset[ds]) dataset_dim[ds] = len(annotations_aux) if ds > 0: data_real_distr = np.ones((n_classes,)) * ((len(annotations_aux))/np.size(classes_noGAN)) data_real_distr[6:9] = 0 fig, ax = plt.subplots() ax.bar(classes, data_real_distr) ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.ylabel('Number of samples') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'How_many_syllables_' + str(legend_list[ds]) + '.' + args.format) data_real.append(data_real_distr / np.sum(data_real_distr)) list_of_recordings = [] annotations_raw = [] decoder_list_of_recordings = [] decoder_list_of_recordings_index= [] raw_sum_dataset= np.zeros((int(dataset_dim[ds]), n_classes)) raw_sum_distr= np.zeros((int(dataset_dim[ds]), n_classes)) raw_max_dataset = np.zeros((int(dataset_dim[ds]),)) raw_max_indices_dataset = np.zeros((int(dataset_dim[ds]),)) raw_max_dataset_noGAN = np.zeros((int(dataset_dim[ds]),)) raw_max_dataset_onlyGAN = np.zeros((int(dataset_dim[ds]),)) raw_max_indices_dataset_noGAN = np.zeros((int(dataset_dim[ds]),)) raw_max_indices_dataset_onlyGAN = np.zeros((int(dataset_dim[ds]),)) for i in range(0, int(dataset_dim[ds])): list_of_recordings.append(annotations_aux[i].id) annotations_raw.append(annotations_aux[i].vect) # This operation should give me an idea of which class is represented the most in my generations raw_sum_dataset[i, :] = np.sum(annotations_raw[i], axis=0) raw_sum_distr[i,]= sp.special.softmax(raw_sum_dataset[i, :]) raw_max_dataset[i] = np.max(raw_sum_dataset[i, :]) raw_max_indices_dataset[i] = np.where(raw_sum_dataset[i, :] == np.max(raw_sum_dataset[i, :]))[0][0] # without the GAN classes if np.size(args.GAN_classes)>0: raw_max_dataset_noGAN[i] = np.max(np.delete(raw_sum_dataset[i, :], GAN_class)) raw_max_indices_dataset_noGAN[i] = np.where(np.delete(raw_sum_dataset[i,:], GAN_class) == np.max(np.delete(raw_sum_dataset[i,:], GAN_class)))[0][0] # only GAN raw_max_dataset_onlyGAN[i] = np.max(np.delete(raw_sum_dataset[i, :], classes_noGAN_index)) raw_max_indices_dataset_onlyGAN[i] = np.where(np.delete(raw_sum_dataset[i, :], classes_noGAN_index) == np.max(np.delete(raw_sum_dataset[i, :], classes_noGAN_index)))[0][0] # Assigned class to each recording decoder_list_of_recordings.append(classes[int(raw_max_indices_dataset[i])]) decoder_list_of_recordings_index.append(int(raw_max_indices_dataset[i])) original_list_of_recordings = [] original_list_of_recordings_index = [] for i in range(0, int(dataset_dim[ds])): for c in range(0, n_classes - np.size(args.GAN_classes)): if (list_of_recordings[i].find('NEW_' + classes_noGAN[c]) != -1): original_list_of_recordings.append(classes_noGAN[c]) original_list_of_recordings_index.append(c) pass # Inception score IS[ds] = stat.inception_score(raw_sum_distr[:, :]) # PLOT # Plot the distribution of syllables in the dataset, with and without the garbage classes title = 'Dataset' fig, dataset_h, dataset_mean, dataset_std, dataset_var = plots.plot_distribution_classes(raw_max_indices_dataset, classes, title, n_classes) fig.savefig( args.data_dir + '/' + args.output_dir + '/' + 'Classes_syllables_dataset_' + legend_list[ds] + '.' + args.format) # The next plot does not really work (understand WHY) classes_order = np.append(classes_noGAN, args.GAN_classes) title = 'Dataset' fig, dataset_h, dataset_mean, dataset_std, dataset_var = plots.plot_distribution_classes( np.append(raw_max_indices_dataset_noGAN, raw_max_indices_dataset_onlyGAN), classes_order, title, n_classes) fig.savefig( args.data_dir + '/' + args.output_dir + '/' + 'Classes_syllables_dataset_order_' + legend_list[ ds] + '.' + args.format) # Manual operation to get the plot # h, bins = np.histogram(raw_max_indices_dataset, bins=range(n_classes + 1)) # h = np.append(h[0:6], np.append(h[9:16], np.append(h[17:20], np.append(h[6:9], np.append(h[16], h[20]))))) # fig, ax = plt.subplots() # ax.bar(bins[:-1], h, width=0.8, color='b', alpha=0.6, label='Syllable classes distribution', align='center') # ax.spines['top'].set_color('none') # ax.spines['right'].set_color('none') # # plt.legend(loc='upper right', fontsize=8, ncol=1, shadow=True, fancybox=True) # plt.xticks(bins[:-1], np.append(classes_noGAN, args.GAN_classes), rotation='vertical', fontsize=6) # plt.xlabel('Classes of syllables', fontsize=15) # plt.ylabel('Number of occurences', fontsize=15) # plt.title(title, fontsize=15) # plt.tight_layout() # to avoid the cut of labels # plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Classes_syllables_dataset_order_' + legend_list[ds] + '.' + args.format) if np.size(args.GAN_classes) > 0: title = 'Dataset without GAN class' fig, dataset_h_noGAN, dataset_mean, dataset_std, dataset_var = plots.plot_distribution_classes( raw_max_indices_dataset_noGAN, np.delete(classes, GAN_class), title, n_classes - np.size(args.GAN_classes)) fig.savefig( args.data_dir + '/' + args.output_dir + '/' + 'Classes_syllables_dataset_noGAN' + legend_list[ds] + '.' + args.format) # In case we need to save all the recordings whole_list_of_recordings.append(list_of_recordings) whole_decoder_list_of_recordings.append(decoder_list_of_recordings) # Dataset distribution dataset_distr = dataset_h / np.sum(dataset_h) dataset_summary = {'File_name': list_of_recordings, 'Real_name': original_list_of_recordings, 'Decoder_name': decoder_list_of_recordings, 'Annotations':raw_sum_distr, 'Dataset_distr': dataset_distr, 'Dataset_real_distr': data_real, 'IS': IS[ds]} print(legend_list[ds]) np.save(args.data_dir + '/' + 'Dataset_summaryNEW' + legend_list[ds] + '.npy', dataset_summary) print('Done') def analysis_error(annotations_dataset, legend_list, args): """ The input directory contains one or more annotations. This function is meant to analyse the errors of the dataset used to train the GAN and see how the decoder works on this set of data. """ # Classes annotations_aux = open_pkl(annotations_dataset[0][0]) classes = annotations_aux[0].vocab GAN_class = np.zeros((np.size(args.GAN_classes),)) classes_noGAN = classes if np.size(args.GAN_classes)>0: for gc in range(0,np.size(args.GAN_classes)): GAN_class[gc] = np.where(classes == args.GAN_classes[gc])[0][0] classes_noGAN = np.delete(classes_noGAN, np.argwhere(classes_noGAN == args.GAN_classes[gc])) n_classes = len(annotations_aux[0].vocab) # Distribution dataset_dim = np.zeros((np.size(legend_list))) whole_list_of_recordings = [] whole_decoder_list_of_recordings = [] IS = np.zeros((np.size(legend_list),)) for ds in range(0, np.size(legend_list)): annotations_aux = open_pkl(annotations_dataset[ds][0]) dataset_dim[ds] = len(annotations_aux) list_of_recordings = [] annotations_raw = [] decoder_list_of_recordings = [] decoder_list_of_recordings_index= [] raw_sum_dataset= np.zeros((int(dataset_dim[ds]), n_classes)) raw_sum_distr= np.zeros((int(dataset_dim[ds]), n_classes)) raw_max_dataset = np.zeros((int(dataset_dim[ds]),)) raw_max_indices_dataset = np.zeros((int(dataset_dim[ds]),)) raw_max_dataset_noGAN = np.zeros((int(dataset_dim[ds]),)) raw_max_indices_dataset_noGAN = np.zeros((int(dataset_dim[ds]),)) for i in range(0, int(dataset_dim[ds])): list_of_recordings.append(annotations_aux[i].id) annotations_raw.append(annotations_aux[i].vect) # This operation should give me an idea of which class is represented the most in my generations raw_sum_dataset[i, :] = np.sum(annotations_raw[i], axis=0) raw_sum_distr[i,]= sp.special.softmax(raw_sum_dataset[i, :]) raw_max_dataset[i] = np.max(raw_sum_dataset[i, :]) raw_max_indices_dataset[i] = np.where(raw_sum_dataset[i, :] == np.max(raw_sum_dataset[i, :]))[0][0] # without the GAN classes raw_max_dataset_noGAN[i] = np.max(np.delete(raw_sum_dataset[i, :], GAN_class)) raw_max_indices_dataset_noGAN[i] = np.where(np.delete(raw_sum_dataset[i,:], GAN_class) == np.max(np.delete(raw_sum_dataset[i,:], GAN_class)))[0][0] # Assigned class to each recording decoder_list_of_recordings.append(classes[int(raw_max_indices_dataset[i])]) decoder_list_of_recordings_index.append(int(raw_max_indices_dataset[i])) original_list_of_recordings = [] original_list_of_recordings_index = [] for i in range(0, int(dataset_dim[ds])): for c in range(0, n_classes - np.size(args.GAN_classes)): if (list_of_recordings[i].find('NEW_' + classes_noGAN[c]) != -1): original_list_of_recordings.append(classes_noGAN[c]) original_list_of_recordings_index.append(c) pass # Inception score IS[ds] = stat.inception_score(raw_sum_distr[:, :]) # Control the error of the decoder decoder_syll_error = np.zeros((n_classes - np.size(args.GAN_classes),)) # not including the GAN class pos_error = [] for i in range(0,int(dataset_dim[ds])): if original_list_of_recordings[i] != decoder_list_of_recordings[i]: decoder_syll_error[int(original_list_of_recordings_index[i])] = decoder_syll_error[int(original_list_of_recordings_index[i])] + 1 pos_error.append(i) decoder_total_error = np.sum(decoder_syll_error) # PLOT # Plot the distribution of syllables in the dataset, with and without the garbage classes title = 'Dataset' fig, dataset_h, dataset_mean, dataset_std, dataset_var = plots.plot_distribution_classes(raw_max_indices_dataset, classes, title, n_classes) fig.savefig( args.data_dir + '/' + args.output_dir + '/' + 'Classes_syllables_dataset_' + legend_list[ds] + '.' + args.format) if np.size(args.GAN_classes) > 0: title = 'Dataset without GAN class' fig, dataset_h_noGAN, dataset_mean, dataset_std, dataset_var = plots.plot_distribution_classes( raw_max_indices_dataset_noGAN, np.delete(classes, GAN_class), title, n_classes - np.size(args.GAN_classes)) #fig.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Classes_syllables_dataset_noGAN_' + legend_list[ds] + '.' + args.format) # Plot of the error in the dataset fig, ax = plt.subplots() ax.bar(classes_noGAN, decoder_syll_error) plt.ylabel('Number of samples') plt.xticks(range(n_classes - np.size(args.GAN_classes)), classes_noGAN, rotation='vertical', fontsize=15) plt.ylabel('Number of errors', fontsize=15) plt.xlabel('Classes of syllables') plt.legend(['decoder_total_error=' + str(decoder_total_error)]) plt.tight_layout() if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Error in the dataset_' + legend_list[ds] + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Error in the dataset_' + legend_list[ds] + '.' + 'png') # In case we need to save all the recordings whole_list_of_recordings.append(list_of_recordings) whole_decoder_list_of_recordings.append(decoder_list_of_recordings) # Dataset distribution dataset_distr = dataset_h / np.sum(dataset_h) dataset_summary = {'File_name': list_of_recordings, 'Real_name': original_list_of_recordings, 'Decoder_name': decoder_list_of_recordings, 'Annotations':raw_sum_distr, 'Dataset_distr': dataset_distr} np.save(args.data_dir + '/' + args.output_dir + '/' + 'Error_summary_' + legend_list[ds] + '.npy', dataset_summary) plt.close('all') print('Done') def analysis_generation(annotations_generation, summary_dataset, args): """ :param annotations_generation: list of the annotations (one per epoch) :param summary_dataset: dictionary containing the info about the training dataset :param args: all the parameters, see below Also, the npy dictionary containing the distribution of the dataset needs to be in the same directory (to compute the cross entropy). :return: save npy dictionary with the summary of the analysis """ annotations_aux = open_pkl(annotations_generation[0]) classes = annotations_aux[0].vocab GAN_class = np.zeros((np.size(args.GAN_classes),)) classes_noGAN = classes if np.size(args.GAN_classes)>0: for gc in range(0,np.size(args.GAN_classes)): GAN_class[gc] = np.where(classes == args.GAN_classes[gc])[0][0] classes_noGAN = np.delete(classes_noGAN, np.argwhere(classes_noGAN == args.GAN_classes[gc])) n_classes = len(annotations_aux[0].vocab) annotations_aux = open_pkl(annotations_generation[0]) n_syll = len(annotations_aux) ld_generation = np.zeros((np.size(annotations_generation),)) ckpt_generation = np.zeros((np.size(annotations_generation),)) epoch = np.zeros((np.size(annotations_generation),)) raw_sum = np.zeros((np.size(annotations_generation), n_syll, n_classes)) raw_sum_distr = np.zeros((np.size(annotations_generation), n_syll, n_classes)) raw_max = np.zeros((np.size(annotations_generation), n_syll)) raw_max_indices = np.zeros((np.size(annotations_generation), n_syll)) raw_max_noGAN = np.zeros((np.size(annotations_generation), n_syll)) raw_max_indices_noGAN = np.zeros((np.size(annotations_generation), n_syll)) IS = np.zeros((np.size(annotations_generation),)) for a in range(0,np.size(annotations_generation)): ld_generation[a] = annotations_generation[a][annotations_generation[a].find('_ld') + 3] ckpt_generation[a] = annotations_generation[a][annotations_generation[a].find('ckpt') + 4 :annotations_generation[a].find('ckpt')+ 9] epoch[a] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) annotations_aux = open_pkl(annotations_generation[a]) GAN_list_of_recordings = [] GAN_decoder_list_of_recordings = [] annotations_raw = [] for i in range(0,n_syll): GAN_list_of_recordings.append(annotations_aux[i].id) annotations_raw.append(annotations_aux[i].vect) # This operation should give me an idea of which class is represented the most in my generations raw_sum[a,i,:] = np.sum(annotations_raw[i], axis=0) raw_max[a,i] = np.max(raw_sum[a,i,:]) raw_max_indices[a,i] = np.where(raw_sum[a,i,:] == np.max(raw_sum[a,i,:]))[0][0] # Probability distribution to compute the inception score raw_sum_distr[a, i, :] = special.softmax(raw_sum[a, i, :]) # without the GAN class raw_max_noGAN[a, i] = np.max(np.delete(raw_sum[a, i, :], GAN_class)) raw_max_indices_noGAN[a, i] = np.where(raw_sum[a, i, :] == np.max(raw_sum[a, i, :]))[0][0] GAN_decoder_list_of_recordings.append(classes[int(raw_max_indices[a,i])]) # Inception score IS[a] = stat.inception_score(raw_sum_distr[a,:,:]) GAN_summary = {'File_name': GAN_list_of_recordings, 'Decoder_name': GAN_decoder_list_of_recordings, 'Decoder_index': raw_max_indices[a,:], 'Latent_dim': ld_generation[a], 'Epoch': epoch[a], 'IS': IS[a], 'Annotations': raw_sum_distr[a, :, :]} if int(epoch[a])<100: np.save(args.data_dir + '/' + 'Generation_summary' + '_ep00' + str(round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim)) + '_ld' + str(annotations_generation[a][annotations_generation[a].find('_ld') + 3]) + '.npy', GAN_summary) elif int(epoch[a])<1000: np.save(args.data_dir + '/' + 'Generation_summary' + '_ep0' + str(round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim)) + '_ld' + str(annotations_generation[a][annotations_generation[a].find('_ld') + 3]) + '.npy', GAN_summary) else: np.save(args.data_dir + '/' + 'Generation_summary' + '_ep' + str(round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim)) + '_ld' + str(annotations_generation[a][annotations_generation[a].find('_ld') + 3]) + '.npy', GAN_summary) # PLOT # Generation from GAN h = np.zeros((np.size(ld_generation), n_classes)) h_mean = np.zeros((np.size(ld_generation),3)) # column 1 = mean value, column 2 = which dim, column 3 = at which epoch h_std = np.zeros((np.size(ld_generation), 3)) h_var = np.zeros((np.size(ld_generation), 3)) h_mean[:,1] = ld_generation h_std[:, 1] = ld_generation h_var[:,1] = ld_generation h_noGAN = np.zeros((np.size(ld_generation), n_classes - np.size(args.GAN_classes))) h_meannoGAN = np.zeros((np.size(ld_generation),3)) h_stdnoGAN = np.zeros((np.size(ld_generation), 3)) h_varnoGAN = np.zeros((np.size(ld_generation), 3)) h_percentile5noGAN = np.zeros((np.size(ld_generation), 3)) h_percentile95noGAN = np.zeros((np.size(ld_generation), 3)) h_maxnoGAN = np.zeros((np.size(ld_generation), 3)) h_minnoGAN = np.zeros((np.size(ld_generation), 3)) h_mediannoGAN = np.zeros((np.size(ld_generation), 3)) h_meannoGAN[:, 1] = ld_generation h_stdnoGAN[:, 1] = ld_generation h_varnoGAN[:, 1] = ld_generation h_mediannoGAN[:, 1] = ld_generation h_percentile5noGAN[:, 1] = ld_generation h_percentile95noGAN[:, 1] = ld_generation h_maxnoGAN[:, 1] = ld_generation h_minnoGAN[:, 1] = ld_generation rep_classes = np.zeros((np.size(ld_generation),3)) # column 1 = how many classes are represented, column 2 = which dim , column 3 = which epoch rep_classes[:,1] = ld_generation for a in range(0,np.size(ld_generation)): #title = 'Latent space dimension = ' + str(int(ld_generation[a])) + 'at epoch_' + str(round(int(ckpt_generation[a])*5*64/args.dataset_dim)) title = 'Epoch ' + str(round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim)) # Plot using mean fig, gan_h, gan_mean, gan_std, gan_var = plots.plot_distribution_classes(raw_max_indices[a, :], np.append(classes_noGAN, args.GAN_classes), title, n_classes) if a%1 == 0: if args.format != 'png': fig.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Classes of syllables_ld' + str(int(ld_generation[a])) + '_epoch_' + str(round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim)) + '.' + args.format) fig.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Classes of syllables_ld' + str(int(ld_generation[a])) + '_epoch_' + str(round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim)) + '.' + 'png') h_mean[a, 0] = gan_mean h_mean[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_std[a, 0] = gan_std h_std[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_var[a, 0] = gan_var h_var[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h[a, :] = gan_h # Distribution of the classes (how many classes are represented at each epoch) rep_classes[a, 0] = np.count_nonzero(np.delete(gan_h, GAN_class)) rep_classes[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) # NoGAN data h_noGAN[a, :] = np.delete(gan_h, GAN_class) h_meannoGAN[a, 0] = np.mean(np.delete(gan_h, GAN_class)) h_meannoGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_stdnoGAN[a, 0] = np.std(np.delete(gan_h, GAN_class)) h_stdnoGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_varnoGAN[a, 0] = np.var(np.delete(gan_h, GAN_class)) h_varnoGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_mediannoGAN[a, 0] = np.percentile(np.delete(gan_h, GAN_class),50) h_mediannoGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_percentile5noGAN[a, 0] = np.percentile(np.delete(gan_h, GAN_class), 5) h_percentile5noGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_percentile95noGAN[a, 0] = np.percentile(np.delete(gan_h, GAN_class), 95) h_percentile95noGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_maxnoGAN[a, 0] = np.max(np.delete(gan_h, GAN_class)) h_maxnoGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) h_minnoGAN[a, 0] = np.min(np.delete(gan_h, GAN_class)) h_minnoGAN[a, 2] = round(int(ckpt_generation[a]) * 5 * 64 / args.dataset_dim) plt.close('all') # Distribution of the classes # Collect all the data in a useful way mean_across_time = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)),3)) std_across_time = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)),3)) var_across_time = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)),3)) mean_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) std_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) var_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) median_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) percentile5_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) percentile95_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) max_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) min_across_time_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), 3)) rep_classes_time = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)),3)) # how many different classes are represented in the generated data classes_time = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation) / np.size(args.n_ld_dim)), n_classes)) # how many generations per class across dimensions and time for ld in range(0,np.size(args.n_ld_dim)): mean_across_time[ld, :, :] = h_mean[np.where(h_mean[:,1] == args.n_ld_dim[ld]),:] std_across_time[ld,:,:] = h_std[np.where(h_std[:,1] == args.n_ld_dim[ld]),:] var_across_time[ld,:,:] = h_var[np.where(h_var[:,1] == args.n_ld_dim[ld]),:] mean_across_time_noGAN[ld, :, :] = h_meannoGAN[np.where(h_meannoGAN[:, 1] == args.n_ld_dim[ld]), :] std_across_time_noGAN[ld, :, :] = h_stdnoGAN[np.where(h_stdnoGAN[:, 1] == args.n_ld_dim[ld]), :] var_across_time_noGAN[ld, :, :] = h_varnoGAN[np.where(h_varnoGAN[:, 1] == args.n_ld_dim[ld]), :] median_across_time_noGAN[ld, :, :] = h_mediannoGAN[np.where(h_mediannoGAN[:, 1] == args.n_ld_dim[ld]), :] percentile5_across_time_noGAN[ld, :, :] = h_percentile5noGAN[np.where(h_percentile5noGAN[:, 1] == args.n_ld_dim[ld]), :] percentile95_across_time_noGAN[ld, :, :] = h_percentile95noGAN[np.where(h_percentile95noGAN[:, 1] == args.n_ld_dim[ld]), :] max_across_time_noGAN[ld, :, :] = h_maxnoGAN[np.where(h_maxnoGAN[:, 1] == args.n_ld_dim[ld]), :] min_across_time_noGAN[ld, :, :] = h_minnoGAN[np.where(h_minnoGAN[:, 1] == args.n_ld_dim[ld]), :] rep_classes_time[ld,:,:] = rep_classes[np.where(rep_classes[:,1] == args.n_ld_dim[ld]),:] classes_time[ld, :, :] = h[np.where(ld_generation == args.n_ld_dim[ld]),:] # Distribution of the data load_data = np.load(summary_dataset[0], allow_pickle=True) load_data = load_data.item() dataset_distr = load_data['Dataset_distr'] dataset_real_distr = load_data['Dataset_real_distr'] GAN_distr = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)), n_classes)) for ld in range(0, np.size(args.n_ld_dim)): GAN_distr_aux = h[np.where(ld_generation == args.n_ld_dim[ld]), :] for ep in range(0,int(np.size(ld_generation)/np.size(args.n_ld_dim))): if np.sum(GAN_distr_aux[0,ep,:]) != 0: GAN_distr[ld,ep,:] = GAN_distr_aux[0,ep,:]/np.sum(GAN_distr_aux[0,ep,:]) # probability distribution GAN_distr_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)), n_classes-np.size(args.GAN_classes))) for ld in range(0, np.size(args.n_ld_dim)): GAN_distr_aux = h_noGAN[np.where(ld_generation == args.n_ld_dim[ld]), :] for ep in range(0,int(np.size(ld_generation)/np.size(args.n_ld_dim))): if np.sum(GAN_distr_aux[0,ep,:]) != 0: GAN_distr_noGAN[ld,ep,:] = GAN_distr_aux[0,ep,:]/np.sum(GAN_distr_aux[0,ep,:]) # probability distribution # Cross-entropy between the dataset and the generated data # Cross entropy using the classic definition classic_cross_entropy = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)))) classic_cross_entropy_noGAN = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)))) for ld in range(0, np.size(args.n_ld_dim)): for ep in range(0, int(np.size(ld_generation)/np.size(args.n_ld_dim))): classic_cross_entropy[ld,ep] = stat.cross_entropy(dataset_distr, GAN_distr[ld,ep,:]) classic_cross_entropy_noGAN[ld,ep] = stat.cross_entropy(np.delete(dataset_distr,GAN_class), GAN_distr_noGAN[ld,ep,:]) # Cross-entropy between the real dataset distribution and the generated data # Cross entropy using the classic definition classic_cross_entropy_real = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)))) classic_cross_entropy_noGAN_real = np.zeros((np.size(args.n_ld_dim), int(np.size(ld_generation)/np.size(args.n_ld_dim)))) for ld in range(0, np.size(args.n_ld_dim)): for ep in range(0, int(np.size(ld_generation)/np.size(args.n_ld_dim))): classic_cross_entropy_real[ld,ep] = stat.cross_entropy(dataset_real_distr[0], GAN_distr[ld,ep,:]) classic_cross_entropy_noGAN_real[ld,ep] = stat.cross_entropy(np.delete(dataset_real_distr[0],GAN_class), GAN_distr_noGAN[ld,ep,:]) generation_summary = {'mean_across_time': mean_across_time, 'std_across_time': std_across_time, 'var_across_time': var_across_time, 'mean_across_time_noGAN': mean_across_time_noGAN, 'std_across_time_noGAN': std_across_time_noGAN, 'median_across_time_noGAN': median_across_time_noGAN, 'var_across_time_noGAN': var_across_time_noGAN, 'percentile5_across_time_noGAN': percentile5_across_time_noGAN, 'percentile95_across_time_noGAN': percentile95_across_time_noGAN, 'max_across_time_noGAN': max_across_time_noGAN, 'min_across_time_noGAN': min_across_time_noGAN, 'rep_classes_time': rep_classes_time, 'classes_time': classes_time, 'GAN_distr': GAN_distr, 'GAN_distr_noGAN': GAN_distr_noGAN, 'cross_entropy': classic_cross_entropy, 'cross_entropy_noGAN':classic_cross_entropy_noGAN, 'cross_entropy_real': classic_cross_entropy_real, 'cross_entropy_noGAN_real':classic_cross_entropy_noGAN_real, 'classes': classes, 'classes_no_GAN': classes_noGAN} np.save(args.data_dir + '/' + 'Generation_summary_all' + str(args.n_ld_dim[0]) + '.npy', generation_summary) print('Done') def analysis_latent(generation_data, summary_dataset, legend_list, colors_list, args): """ The input directory contains one or more annotations. This function is meant to analyse the output of the GAN and see how it is able to reproduce and or differentiate from the dataset depending on a correlation with the input classes. """ # DATASET load_data = np.load(summary_dataset[0],allow_pickle=True) load_data = load_data.item() dataset_distr = load_data['Dataset_distr'] dataset_distr_real = load_data['Dataset_real_distr'] data_entropy = stat.cross_entropy(dataset_distr_real[0],dataset_distr) colors_list = list(colors._colors_full_map.values())[0::5] # GENERATED DATA load_data = np.load(generation_data[0][0], allow_pickle=True) load_data = load_data.item() classes = load_data['classes'] GAN_class = np.zeros((np.size(args.GAN_classes),)) for gc in range(0, np.size(args.GAN_classes)): GAN_class[gc] = np.where(classes == args.GAN_classes[gc])[0][0] # Load the data mean_across_time = [] std_across_time = [] var_across_time = [] mean_across_time_noGAN = [] std_across_time_noGAN = [] var_across_time_noGAN = [] median_across_time_noGAN = [] percentile5_across_time_noGAN = [] percentile95_across_time_noGAN = [] max_across_time_noGAN = [] min_across_time_noGAN = [] rep_classes_time = [] classes_time = [] GAN_distr = [] GAN_distr_noGAN = [] classic_cross_entropy = [] classic_cross_entropy_noGAN = [] classic_cross_entropy_noGAN_real = [] classic_cross_entropy_real = [] for ld in range(0,np.size(args.n_ld_dim)): mean_across_time_aux = [] std_across_time_aux = [] var_across_time_aux = [] mean_across_time_noGAN_aux = [] std_across_time_noGAN_aux = [] var_across_time_noGAN_aux = [] median_across_time_noGAN_aux = [] percentile5_across_time_noGAN_aux = [] percentile95_across_time_noGAN_aux = [] max_across_time_noGAN_aux = [] min_across_time_noGAN_aux = [] rep_classes_time_aux = [] classes_time_aux = [] GAN_distr_aux = [] GAN_distr_noGAN_aux = [] classic_cross_entropy_aux = [] classic_cross_entropy_real_aux = [] classic_cross_entropy_noGAN_aux = [] classic_cross_entropy_noGAN_real_aux = [] for gd in range(0,np.size(generation_data[ld])): load_data = np.load(generation_data[ld][gd], allow_pickle=True) load_data = load_data.item() mean_across_time_aux.append(load_data['mean_across_time']) std_across_time_aux.append(load_data['std_across_time']) var_across_time_aux.append(load_data['var_across_time']) mean_across_time_noGAN_aux.append(load_data['mean_across_time_noGAN']) std_across_time_noGAN_aux.append(load_data['std_across_time_noGAN']) rep_classes_time_aux.append(load_data['rep_classes_time']) var_across_time_noGAN_aux.append(load_data['var_across_time_noGAN']) median_across_time_noGAN_aux.append(load_data['median_across_time_noGAN']) percentile5_across_time_noGAN_aux.append(load_data['percentile5_across_time_noGAN']) percentile95_across_time_noGAN_aux.append(load_data['percentile95_across_time_noGAN']) max_across_time_noGAN_aux.append(load_data['max_across_time_noGAN']) min_across_time_noGAN_aux.append(load_data['min_across_time_noGAN']) GAN_distr_noGAN_aux.append(load_data['GAN_distr_noGAN']) classes_time_aux.append(load_data['classes_time']) GAN_distr_aux.append(load_data['GAN_distr']) classic_cross_entropy_aux.append(load_data['cross_entropy']) classic_cross_entropy_real_aux.append(load_data['cross_entropy_real']) classic_cross_entropy_noGAN_aux.append(load_data['cross_entropy_noGAN']) classic_cross_entropy_noGAN_real_aux.append(load_data['cross_entropy_noGAN_real']) mean_across_time.append(mean_across_time_aux) std_across_time.append(std_across_time_aux) var_across_time.append(var_across_time_aux) mean_across_time_noGAN.append(mean_across_time_noGAN_aux) std_across_time_noGAN.append(std_across_time_noGAN_aux) var_across_time_noGAN.append(var_across_time_noGAN_aux) median_across_time_noGAN.append(median_across_time_noGAN_aux) percentile5_across_time_noGAN.append(percentile5_across_time_noGAN_aux) percentile95_across_time_noGAN.append(percentile95_across_time_noGAN_aux) max_across_time_noGAN.append(max_across_time_noGAN_aux) min_across_time_noGAN.append(min_across_time_noGAN_aux) rep_classes_time.append(rep_classes_time_aux) classes_time.append(classes_time_aux) GAN_distr.append(GAN_distr_aux) GAN_distr_noGAN.append(GAN_distr_noGAN_aux) classic_cross_entropy.append(classic_cross_entropy_aux) classic_cross_entropy_real.append(classic_cross_entropy_real_aux) classic_cross_entropy_noGAN.append(classic_cross_entropy_noGAN_aux) classic_cross_entropy_noGAN_real.append(classic_cross_entropy_noGAN_real_aux) # Plots across time for a fixed dataset dimension, comparison between different instances for ld in range(0,np.size(args.n_ld_dim)): # Mean and variance distribution # with GAN class fig,ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(mean_across_time[ld][gd][0,:,2], mean_across_time[ld][gd][0,:,0], colors_list[ld]) ax.fill_between(mean_across_time[ld][gd][0,:,2], mean_across_time[ld][gd][0,:,0] - std_across_time[ld][gd][0,:,0], mean_across_time[ld][gd][0,:,0] + std_across_time[ld][gd][0,:,0], colors_list[gd], alpha = 0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) plt.title('Evolution of mean and std across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['mean' , 'Std']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_std_for_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) fig,ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(var_across_time[ld][gd][0,:,2], var_across_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of variance', fontsize=15) plt.title('Evolution of variance across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['ld dim' + str(args.n_ld_dim[ld])]) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) # without GAN class # Mean + std fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0] - std_across_time_noGAN[ld][gd][0, :, 0], mean_across_time_noGAN[ld][gd][0, :, 0] + std_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) plt.title('Evolution of mean and std across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'Std']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_std_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Mean + median fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], median_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and median', fontsize=15) plt.title('Evolution of mean and median across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'Median']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_median_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Mean + min/max fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], min_across_time_noGAN[ld][gd][0, :, 0], max_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) plt.title('Evolution of mean across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'p50']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_min_max_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Mean + p5 - p95 fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], percentile5_across_time_noGAN[ld][gd][0, :, 0], percentile95_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) plt.title('Evolution of mean across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'p5-p95']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_p5p95_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Var fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(var_across_time_noGAN[ld][gd][0, :, 2], var_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)') plt.ylabel('Evolution of variance') plt.title('Evolution of variance across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.close('all') # Distribution of classes represented by the dataset for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], rep_classes_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of classes', fontsize=15) plt.title('Number of represented classes across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Distr_classes_across_latent_' + str(args.n_ld_dim[ld]) + '.' + args.format) # GAN class for gc in range(0,np.size(args.GAN_classes)): for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0, :, 2], classes_time[ld][gd][0, :, int(GAN_class[gc])].T, colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable (in percentage)', fontsize=15) plt.legend(legend_list) plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class for latent dim = ' + str(args.n_ld_dim[ld])) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_class_across_time_and_latent_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0, :, 2], classes_time[ld][gd][0, :, int(GAN_class[gc])].T/1000, colors_list[gd]) plt.axhline(dataset_distr[int(GAN_class[gc])], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable (in percentage)', fontsize=15) plt.legend(np.append(legend_list, 'Training')) plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class for latent dim = ' + str(args.n_ld_dim[ld])) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_class_across_time_and_latent_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.close('all') # Cross-Entropy for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) plt.title('Cross-entropy across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) plt.title('Cross-entropy across time (without GAN class) for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN_real[ld][gd][0,:], colors_list[gd]) plt.axhline(data_entropy, color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) plt.title('Cross-entropy across time (without GAN class) for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(np.append(legend_list, 'Training')) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.close('all') # All together fig,ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(mean_across_time[ld][gd][0,:,2], mean_across_time[ld][gd][0,:,0], colors_list[gd]) ax.fill_between(mean_across_time[ld][gd][0,:,2], mean_across_time[ld][gd][0,:,0] - std_across_time[ld][gd][0,:,0], mean_across_time[ld][gd][0,:,0] + std_across_time[ld][gd][0,:,0], colors_list[gd], alpha = 0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) plt.title('Evolution of mean and std across time') plt.legend(['mean' , 'Std']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_std_for_dim_all.' + args.format) fig,ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(var_across_time[ld][gd][0,:,2], var_across_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of variance', fontsize=15) plt.title('Evolution of variance across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['ld dim' + str(args.n_ld_dim[ld])]) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) # without GAN class # Mean + std fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0] - std_across_time_noGAN[ld][gd][0, :, 0], mean_across_time_noGAN[ld][gd][0, :, 0] + std_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) plt.title('Evolution of mean and std across time') plt.legend(['Mean', 'Std']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_std_for_dim_all_noGAN.' + args.format) # Mean + median fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], median_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and median', fontsize=15) plt.title('Evolution of mean and median across time') plt.legend(['Mean', 'Median']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_median_for_dim_all_noGAN.' + args.format) # Mean + min/max fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], min_across_time_noGAN[ld][gd][0, :, 0], max_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) plt.title('Evolution of mean across time') plt.legend(['Mean', 'p50']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_min_max_for_dim_all_noGAN.' + args.format) # Mean + p5 - p95 fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], percentile5_across_time_noGAN[ld][gd][0, :, 0], percentile95_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) plt.title('Evolution of mean across time') plt.legend(['Mean', 'p5-p95']) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_p5p95_for_dim_all_noGAN.' + args.format) # Var fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(var_across_time_noGAN[ld][gd][0, :, 2], var_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)') plt.ylabel('Evolution of variance') plt.title('Evolution of variance across time') plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dim_all_noGAN.' + args.format) plt.close('all') # Distribution of classes represented by the dataset fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], rep_classes_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of classes', fontsize=15) plt.title('Number of represented classes across time') plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Distr_classes_across_latent_all.' + args.format) # GAN class for gc in range(0,np.size(args.GAN_classes)): fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0, :, 2], classes_time[ld][gd][0, :, int(GAN_class[gc])].T, colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable (in percentage)', fontsize=15) plt.legend(legend_list) plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class') plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_class_across_time_all.' + args.format) fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data)/np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0, :, 2], classes_time[ld][gd][0, :, int(GAN_class[gc])].T/1000, colors_list[gd]) plt.axhline(dataset_distr[int(GAN_class[gc])], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable (in percentage)', fontsize=15) plt.legend(np.append(legend_list, 'Training')) plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class') plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_class_across_time_all.' + args.format) plt.close('all') # Cross-Entropy plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) plt.title('Cross-entropy across time') plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_all.' + args.format) plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) plt.title('Cross-entropy across time (without GAN class)') plt.legend(legend_list) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_all.' + args.format) plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): for gd in range(0, round(np.size(generation_data) / np.size(args.n_ld_dim))): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN_real[ld][gd][0,:], colors_list[gd]) plt.axhline(data_entropy, color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) plt.title('Cross-entropy across time (without GAN class)') plt.legend(np.append(legend_list, 'Training')) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real_all.' + args.format) print('Done') def analysis_dim(generation_data, summary_dataset, legend_list_instances, legend_list, colors_list, args): load_data = np.load(generation_data[0][0], allow_pickle=True) load_data = load_data.item() classes = load_data['classes'] GAN_class = np.zeros((np.size(args.GAN_classes),)) for gc in range(0, np.size(args.GAN_classes)): GAN_class[gc] = np.where(classes == args.GAN_classes[gc])[0][0] colors_list_long = list(colors._colors_full_map.values())[0::5] colors_list = ['b', 'r', 'g'] # DATASET dataset_distr = [] dataset_real_distr = [] data_entropy = [] data_entropy_noGAN = [] for sd in range(0,np.size(args.n_ld_dim)): load_data = np.load(summary_dataset[sd][0],allow_pickle=True) load_data = load_data.item() dataset_distr.append(load_data['Dataset_distr']) dataset_real_distr.append(load_data['Dataset_real_distr']) data_entropy.append(stat.cross_entropy(dataset_real_distr[sd][0],dataset_distr[sd])) data_entropy_noGAN.append(stat.cross_entropy(np.delete(dataset_real_distr[sd][0], GAN_class), np.delete(dataset_distr[sd], GAN_class))) # GENERATED DATA mean_across_time = [] std_across_time = [] var_across_time = [] mean_across_time_noGAN = [] std_across_time_noGAN = [] var_across_time_noGAN = [] median_across_time_noGAN = [] percentile5_across_time_noGAN = [] percentile95_across_time_noGAN = [] max_across_time_noGAN = [] min_across_time_noGAN = [] rep_classes_time = [] classes_time = [] GAN_distr = [] GAN_distr_noGAN = [] classic_cross_entropy = [] classic_cross_entropy_real = [] classic_cross_entropy_noGAN = [] classic_cross_entropy_noGAN_real = [] for ld in range(0,np.size(args.n_ld_dim)): mean_across_time_aux = [] std_across_time_aux = [] var_across_time_aux = [] mean_across_time_noGAN_aux = [] std_across_time_noGAN_aux = [] var_across_time_noGAN_aux = [] median_across_time_noGAN_aux = [] percentile5_across_time_noGAN_aux = [] percentile95_across_time_noGAN_aux = [] max_across_time_noGAN_aux = [] min_across_time_noGAN_aux = [] rep_classes_time_aux = [] classes_time_aux = [] GAN_distr_aux = [] GAN_distr_noGAN_aux = [] classic_cross_entropy_aux = [] classic_cross_entropy_real_aux = [] classic_cross_entropy_noGAN_aux = [] classic_cross_entropy_noGAN_real_aux = [] for gd in range(0,np.size(generation_data[ld])): load_data = np.load(generation_data[ld][gd], allow_pickle=True) load_data = load_data.item() mean_across_time_aux.append(load_data['mean_across_time']) std_across_time_aux.append(load_data['std_across_time']) var_across_time_aux.append(load_data['var_across_time']) mean_across_time_noGAN_aux.append(load_data['mean_across_time_noGAN']) std_across_time_noGAN_aux.append(load_data['std_across_time_noGAN']) rep_classes_time_aux.append(load_data['rep_classes_time']) var_across_time_noGAN_aux.append(load_data['var_across_time_noGAN']) median_across_time_noGAN_aux.append(load_data['median_across_time_noGAN']) min_across_time_noGAN_aux.append(load_data['min_across_time_noGAN']) max_across_time_noGAN_aux.append(load_data['max_across_time_noGAN']) percentile5_across_time_noGAN_aux.append(load_data['percentile5_across_time_noGAN']) percentile95_across_time_noGAN_aux.append(load_data['percentile95_across_time_noGAN']) GAN_distr_noGAN_aux.append(load_data['GAN_distr_noGAN']) classes_time_aux.append(load_data['classes_time']) GAN_distr_aux.append(load_data['GAN_distr']) classic_cross_entropy_aux.append(load_data['cross_entropy']) classic_cross_entropy_real_aux.append(load_data['cross_entropy_real']) classic_cross_entropy_noGAN_aux.append(load_data['cross_entropy_noGAN']) classic_cross_entropy_noGAN_real_aux.append(load_data['cross_entropy_noGAN_real']) mean_across_time.append(mean_across_time_aux) std_across_time.append(std_across_time_aux) var_across_time.append(var_across_time_aux) mean_across_time_noGAN.append(mean_across_time_noGAN_aux) std_across_time_noGAN.append(std_across_time_noGAN_aux) var_across_time_noGAN.append(var_across_time_noGAN_aux) median_across_time_noGAN.append(median_across_time_noGAN_aux) min_across_time_noGAN.append(min_across_time_noGAN_aux) max_across_time_noGAN.append(max_across_time_noGAN_aux) percentile5_across_time_noGAN.append(percentile5_across_time_noGAN_aux) percentile95_across_time_noGAN.append(percentile95_across_time_noGAN_aux) rep_classes_time.append(rep_classes_time_aux) classes_time.append(classes_time_aux) GAN_distr.append(GAN_distr_aux) GAN_distr_noGAN.append(GAN_distr_noGAN_aux) classic_cross_entropy.append(classic_cross_entropy_aux) classic_cross_entropy_real.append(classic_cross_entropy_real_aux) classic_cross_entropy_noGAN.append(classic_cross_entropy_noGAN_aux) classic_cross_entropy_noGAN_real.append(classic_cross_entropy_noGAN_real_aux) # Plots across time for a fixed dataset dimension, comparison between different instances for ld in range(0,np.size(args.n_ld_dim)): # Mean and variance distribution fig,ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(var_across_time[ld][gd][0,:,2], var_across_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Variance', fontsize=15) plt.title('Evolution of variance across time for dataset dim = ' + str(args.n_ld_dim[ld])) if args.title == 'on': plt.legend(['Dataset', 'ld dim' + str(args.n_ld_dim[ld])]) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dd_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dd_dim_' + str(args.n_ld_dim[ld]) + '.' + 'png') # without GAN class # Mean + std fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) #ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0] - std_across_time_noGAN[ld][gd][0, :, 0], mean_across_time_noGAN[ld][gd][0, :, 0] + std_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and std across time for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'Std']) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_std_for_dd_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_std_for_dd_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + 'png') # Mean + median fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], median_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Mean and median', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and median across time for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'Median']) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_median_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_median_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + 'png') # Mean + min/max fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) #ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], min_across_time_noGAN[ld][gd][0, :, 0], max_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean across time for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'p50']) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_min_max_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_min_max_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + 'png') # Mean + p5 - p95 fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) #ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], percentile5_across_time_noGAN[ld][gd][0, :, 0], percentile95_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean across time for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'p5-p95']) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_p5p95_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_p5p95_for_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + 'png') # Var fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(var_across_time_noGAN[ld][gd][0, :, 2], var_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)') plt.ylabel('Evolution of variance') if args.title == 'on': plt.title('Evolution of variance across time for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dd_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_dd_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + 'png') plt.close('all') # Distribution of classes represented by the dataset for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0,:,2], rep_classes_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of classes', fontsize=15) if args.title == 'on': plt.title('Number of represented classes across time for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Distr_classes_across_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Distr_classes_across_dim_' + str(args.n_ld_dim[ld]) + '.' + 'png') # GAN class for gc in range(0,np.size(args.GAN_classes)): for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0, :, 2], classes_time[ld][gd][0, :, int(GAN_class[gc])].T, colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable', fontsize=15) plt.legend(legend_list_instances) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class for dataset dim = ' + str(args.n_ld_dim[ld])) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + 'png') for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0, :, 2], (classes_time[ld][gd][0, :, int(GAN_class[gc])].T/1000)*100, colors_list[gd]) plt.axhline(dataset_distr[ld][int(GAN_class[gc])], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0,100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllables', fontsize=15) plt.legend(np.append(legend_list_instances, 'Training')) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class for dataset dim = ' + str(args.n_ld_dim[ld])) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + 'png') plt.close('all') # Cross-Entropy for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_' + str(args.n_ld_dim[ld]) + '.' + 'png') for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_real[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy real across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_real_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_real_' + str(args.n_ld_dim[ld]) + '.' + 'png') for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN[ld][gd][0,:], colors_list[gd]) plt.axhline(data_entropy[ld], color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class) for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(np.append(legend_list_instances, 'Training')) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_' + str(args.n_ld_dim[ld]) + '.' + 'png') for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(generation_data[ld])): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN_real[ld][gd][0,:], colors_list[gd]) plt.axhline(data_entropy_noGAN[ld], color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class) for dataset dim = ' + str(args.n_ld_dim[ld])) plt.legend(np.append(legend_list_instances, 'Training')) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real_' + str(args.n_ld_dim[ld]) + '.' + 'png') plt.close('all') # Cumulative results with average of the instances avg_mean_across_time = [] avg_std_across_time = [] avg_var_across_time = [] avg_mean_across_time_noGAN = [] avg_std_across_time_noGAN = [] avg_var_across_time_noGAN = [] avg_median_across_time_noGAN = [] avg_percentile5_across_time_noGAN = [] avg_percentile95_across_time_noGAN = [] avg_max_across_time_noGAN = [] avg_min_across_time_noGAN = [] avg_rep_classes_time = [] avg_classes_time = [] avg_GAN_distr_noGAN = [] avg_classic_cross_entropy = [] avg_classic_cross_entropy_real = [] avg_classic_cross_entropy_noGAN = [] avg_classic_cross_entropy_noGAN_real = [] avg_classes_time_onlyGAN = [] for ld in range(0, np.size(args.n_ld_dim)): avg_mean_aux = 0 avg_std_aux = 0 avg_var_aux = 0 avg_mean_noGAN_aux = 0 avg_std_noGAN_aux = 0 avg_var_noGAN_aux = 0 avg_median_across_time_noGAN_aux = 0 avg_percentile5_across_time_noGAN_aux = 0 avg_percentile95_across_time_noGAN_aux = 0 avg_max_across_time_noGAN_aux = 0 avg_min_across_time_noGAN_aux = 0 avg_rep_classes_aux = 0 avg_GAN_distr_noGAN_aux = 0 avg_classic_cross_entropy_aux = 0 avg_classic_cross_entropy_real_aux = 0 avg_classic_cross_entropy_noGAN_aux = 0 avg_classic_cross_entropy_noGAN_real_aux = 0 for gd in range(0, np.size(generation_data[ld])): avg_mean_aux = avg_mean_aux + mean_across_time[ld][gd][0, :, 0] avg_mean_noGAN_aux = avg_mean_noGAN_aux + mean_across_time_noGAN[ld][gd][0, :, 0] avg_std_aux = avg_std_aux + std_across_time_noGAN[ld][gd][0, :, 0] avg_std_noGAN_aux = avg_std_noGAN_aux + std_across_time[ld][gd][0, :, 0] avg_var_aux = avg_var_aux + var_across_time[ld][gd][0, :, 0] avg_var_noGAN_aux = avg_var_noGAN_aux + var_across_time_noGAN[ld][gd][0, :, 0] avg_median_across_time_noGAN_aux = avg_median_across_time_noGAN_aux + median_across_time_noGAN[ld][gd][0, :, 0] avg_percentile5_across_time_noGAN_aux = avg_percentile5_across_time_noGAN_aux + percentile5_across_time_noGAN[ld][gd][0, :, 0] avg_percentile95_across_time_noGAN_aux = avg_percentile95_across_time_noGAN_aux + percentile95_across_time_noGAN[ld][gd][0, :, 0] avg_max_across_time_noGAN_aux = avg_max_across_time_noGAN_aux + max_across_time_noGAN[ld][gd][0, :, 0] avg_min_across_time_noGAN_aux = avg_min_across_time_noGAN_aux + min_across_time_noGAN[ld][gd][0, :, 0] avg_rep_classes_aux = avg_rep_classes_aux + rep_classes_time[ld][gd][0, :, 0] avg_GAN_distr_noGAN_aux = avg_GAN_distr_noGAN_aux + mean_across_time[ld][gd][0, :, 0] avg_classic_cross_entropy_aux = avg_classic_cross_entropy_aux + classic_cross_entropy[ld][gd][0, :] avg_classic_cross_entropy_real_aux = avg_classic_cross_entropy_real_aux + classic_cross_entropy_real[ld][gd][0, :] avg_classic_cross_entropy_noGAN_aux = avg_classic_cross_entropy_noGAN_aux + classic_cross_entropy_noGAN[ld][gd][0, :] avg_classic_cross_entropy_noGAN_real_aux = avg_classic_cross_entropy_noGAN_real_aux + classic_cross_entropy_noGAN_real[ld][gd][0, :] avg_mean_across_time.append(avg_mean_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_std_across_time.append(avg_std_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_var_across_time.append(avg_var_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_mean_across_time_noGAN.append(avg_mean_noGAN_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_std_across_time_noGAN.append(avg_std_noGAN_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_var_across_time_noGAN.append(avg_var_noGAN_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_median_across_time_noGAN.append(avg_median_across_time_noGAN_aux/(np.size(generation_data) / np.size(args.n_ld_dim))) avg_percentile5_across_time_noGAN.append(avg_percentile5_across_time_noGAN_aux/(np.size(generation_data) / np.size(args.n_ld_dim))) avg_percentile95_across_time_noGAN.append(avg_percentile95_across_time_noGAN_aux/(np.size(generation_data) / np.size(args.n_ld_dim))) avg_max_across_time_noGAN.append(avg_max_across_time_noGAN_aux/(np.size(generation_data) / np.size(args.n_ld_dim))) avg_min_across_time_noGAN.append(avg_min_across_time_noGAN_aux/(np.size(generation_data) / np.size(args.n_ld_dim))) avg_rep_classes_time.append(avg_rep_classes_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_GAN_distr_noGAN.append(avg_GAN_distr_noGAN_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_classic_cross_entropy.append( avg_classic_cross_entropy_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_classic_cross_entropy_real.append( avg_classic_cross_entropy_real_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_classic_cross_entropy_noGAN.append( avg_classic_cross_entropy_noGAN_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_classic_cross_entropy_noGAN_real.append( avg_classic_cross_entropy_noGAN_real_aux / (np.size(generation_data) / np.size(args.n_ld_dim))) avg_classes_time_onlyGAN_aux = np.zeros((np.size(GAN_class), np.size(rep_classes_time[ld][0][0, :, 2]))) for gc in range(0, np.size(GAN_class)): aux = [] for gd in range(0, np.size(legend_list_instances)): aux.append(classes_time[ld][gd][0, :, int(GAN_class[gc])].T) avg_classes_time_onlyGAN_aux[gc, :] = np.mean(aux, axis=0) avg_classes_time_onlyGAN.append(avg_classes_time_onlyGAN_aux) avg_classes_time_aux = np.zeros((np.size(args.GAN_classes), np.size(rep_classes_time[ld][0][0, :, 2]))) for gc in range(0, np.size(args.GAN_classes)): aux = [] for gd in range(0, np.size(generation_data[ld])): aux.append(classes_time[ld][gd][0, :, int(GAN_class[gc])]) avg_classes_time_aux[gc, :] = np.mean(aux, axis=0) avg_classes_time.append(avg_classes_time_aux) # Distribution of classes represented by the dataset fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_rep_classes_time[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of classes', fontsize=15) if args.title == 'on': plt.title('Number of represented classes across time') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_dstr_classes_across_dim_.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_dstr_classes_across_dim_.' + 'png') # Mean and std across time # with GAN class fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(mean_across_time[ld][0][0, :, 2], avg_mean_across_time[ld], colors_list[ld]) #ax.fill_between(mean_across_time[ld][0][0, :, 2], avg_mean_across_time[ld] - avg_std_across_time[ld], avg_mean_across_time[ld] + avg_std_across_time[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and std across dimension') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_std_across_dim.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_std_across_dim.' + 'png') # without GAN class fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) #ax.fill_between(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld] - avg_std_across_time_noGAN[ld], avg_mean_across_time_noGAN[ld] + avg_std_across_time_noGAN[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and std across dimension') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_std_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_std_across_dim_noGAN.' + 'png') legend_aux = [] fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_median_across_time_noGAN[ld], colors_list[ld], alpha=0.3) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Mean and median', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and median across dimension') plt.legend(handles = legend_aux, labels = legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_median_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_median_across_dim_noGAN.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_median_across_time_noGAN[ld], colors_list[ld]) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Median', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and median across dimension') plt.legend(handles = legend_aux, labels = legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_median_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_median_across_dim_noGAN.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) #ax.fill_between(mean_across_time_noGAN[ld][0][0, :, 2], avg_min_across_time_noGAN[ld], avg_max_across_time_noGAN[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and min/max across dimension') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_min_max_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_min_max_across_dim_noGAN.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) #ax.fill_between(mean_across_time_noGAN[ld][0][0, :, 2], avg_percentile5_across_time_noGAN[ld], avg_percentile95_across_time_noGAN[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and p5/p95 across dimension') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_p5p95_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_p5p95_across_dim_noGAN.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(median_across_time_noGAN[ld][0][0, :, 2], avg_median_across_time_noGAN[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Median', fontsize=15) if args.title == 'on': plt.title('Evolution of median across time') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_median_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_median_across_dim_noGAN.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(var_across_time_noGAN[ld][0][0, :, 2], avg_var_across_time_noGAN[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)') plt.ylabel('Variance') if args.title == 'on': plt.title('Evolution of variance across time') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_var_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_var_across_dim_noGAN.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(var_across_time[ld][0][0, :, 2], avg_var_across_time[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Variance', fontsize=15) if args.title == 'on': plt.title('Evolution of variance across time') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_var_across_dim.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_var_across_dim.' + 'png') # GAN class for gc in range(0, np.size(args.GAN_classes)): fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classes_time[ld][gc, :].T, colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable', fontsize=15) plt.legend(legend_list) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class across dim') if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_avg_class_across_time_and_dim.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_avg_class_across_time_and_dim.' + 'png') fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], (avg_classes_time[ld][gc, :].T / 1000)*100, colors_list[ld]) plt.axhline(dataset_distr[ld][int(GAN_class[gc])], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0,100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list, 'Training')) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class across dim') if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_avg_class_across_time_and_dim.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_avg_class_across_time_and_dim.' + 'png') # Cumulative GAN classes fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): sum = 0 for gc in range(0, np.size(args.GAN_classes)): sum = sum + avg_classes_time_onlyGAN[ld][gc, :].T / 1000 plt.plot(rep_classes_time[ld][0][0, :, 2], sum * 100, colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0, 100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list, 'Training')) if args.title == 'on': plt.title('Number of syllable in ALL class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_ALL_avg_class_across_time_and_dim.' + args.format) # Cross-Entropy plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classic_cross_entropy[ld], colors_list[ld]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy.' + 'png') plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classic_cross_entropy_real[ld], colors_list[ld]) plt.axhline(data_entropy[ld], color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy real across time') plt.legend(np.append(legend_list, 'Training')) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_real.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_real.' + 'png') plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classic_cross_entropy_noGAN[ld], colors_list[ld]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class)') plt.legend(legend_list) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_noGAN.' + 'png') plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classic_cross_entropy_noGAN_real[ld], colors_list[ld]) plt.axhline(data_entropy_noGAN[ld], color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class)') plt.legend(np.append(legend_list, 'Training')) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_noGAN_real.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_noGAN_real.' + 'png') plt.close('all') print('Done') def several_instances(generation_data, summary_dataset, legend_list_instances, legend_list_avg, colors_list, classes_colors, args): """ This function allows to compare several instances between them and to visualize an average plot :param generation_data: summary of the generations (one per training) :param summary_dataset: to get the distribution of the training data :param legend_list_avg: name of latent space dim if multiple :param legend_list_instances: name of the instances :param: colors_list : to have uniform plots across different analysis (for the instances) :param classes_colors: to have uniform plots across different analysis (for the classes) :return: comparison figures """ load_data = np.load(generation_data[0][0], allow_pickle=True) load_data = load_data.item() classes = load_data['classes'] GAN_class = np.zeros((np.size(args.GAN_classes),)) for gc in range(0, np.size(args.GAN_classes)): GAN_class[gc] = np.where(classes == args.GAN_classes[gc])[0][0] # DATASET load_data = np.load(summary_dataset[0],allow_pickle=True) load_data = load_data.item() dataset_distr = load_data['Dataset_distr'] dataset_distr_real = load_data['Dataset_real_distr'] data_entropy = stat.cross_entropy(dataset_distr_real[0],dataset_distr) data_entropy_noGAN = stat.cross_entropy(np.delete(dataset_distr_real[0],GAN_class),np.delete(dataset_distr,GAN_class)) # GENERATED DATA mean_across_time = [] std_across_time = [] var_across_time = [] mean_across_time_noGAN = [] std_across_time_noGAN = [] var_across_time_noGAN = [] median_across_time_noGAN = [] percentile5_across_time_noGAN = [] percentile95_across_time_noGAN = [] max_across_time_noGAN = [] min_across_time_noGAN = [] rep_classes_time = [] classes_time = [] GAN_distr = [] GAN_distr_noGAN = [] classic_cross_entropy = [] classic_cross_entropy_real = [] classic_cross_entropy_noGAN = [] instances = [] classic_cross_entropy_noGAN_real = [] for ld in range(0,np.size(args.n_ld_dim)): mean_across_time_aux = [] std_across_time_aux = [] var_across_time_aux = [] mean_across_time_noGAN_aux = [] std_across_time_noGAN_aux = [] var_across_time_noGAN_aux = [] median_across_time_noGAN_aux = [] percentile5_across_time_noGAN_aux = [] percentile95_across_time_noGAN_aux = [] max_across_time_noGAN_aux = [] min_across_time_noGAN_aux = [] rep_classes_time_aux = [] classes_time_aux = [] GAN_distr_aux = [] GAN_distr_noGAN_aux = [] classic_cross_entropy_aux = [] classic_cross_entropy_real_aux = [] classic_cross_entropy_noGAN_aux = [] classic_cross_entropy_noGAN_real_aux = [] instances_aux = np.zeros((np.size(generation_data),)) for gd in range(0,np.size(generation_data[ld])): load_data = np.load(generation_data[ld][gd], allow_pickle=True) load_data = load_data.item() mean_across_time_aux.append(load_data['mean_across_time']) std_across_time_aux.append(load_data['std_across_time']) var_across_time_aux.append(load_data['var_across_time']) median_across_time_noGAN_aux.append(load_data['median_across_time_noGAN']) percentile5_across_time_noGAN_aux.append(load_data['percentile5_across_time_noGAN']) percentile95_across_time_noGAN_aux.append(load_data['percentile95_across_time_noGAN']) max_across_time_noGAN_aux.append(load_data['max_across_time_noGAN']) min_across_time_noGAN_aux.append(load_data['min_across_time_noGAN']) mean_across_time_noGAN_aux.append(load_data['mean_across_time_noGAN']) std_across_time_noGAN_aux.append(load_data['std_across_time_noGAN']) rep_classes_time_aux.append(load_data['rep_classes_time']) var_across_time_noGAN_aux.append(load_data['var_across_time_noGAN']) GAN_distr_noGAN_aux.append(load_data['GAN_distr_noGAN']) classes_time_aux.append(load_data['classes_time']) GAN_distr_aux.append(load_data['GAN_distr']) classic_cross_entropy_aux.append(load_data['cross_entropy']) classic_cross_entropy_real_aux.append(load_data['cross_entropy_real']) classic_cross_entropy_noGAN_aux.append(load_data['cross_entropy_noGAN']) classic_cross_entropy_noGAN_real_aux.append(load_data['cross_entropy_noGAN_real']) instances_aux[gd] = generation_data[ld][gd][generation_data[ld][gd].find('Generation_summary_i') + len('Generation_summary_i')] mean_across_time.append(mean_across_time_aux) std_across_time.append(std_across_time_aux) var_across_time.append(var_across_time_aux) mean_across_time_noGAN.append(mean_across_time_noGAN_aux) std_across_time_noGAN.append(std_across_time_noGAN_aux) var_across_time_noGAN.append(var_across_time_noGAN_aux) median_across_time_noGAN.append(median_across_time_noGAN_aux) percentile5_across_time_noGAN.append(percentile5_across_time_noGAN_aux) percentile95_across_time_noGAN.append(percentile95_across_time_noGAN_aux) max_across_time_noGAN.append(max_across_time_noGAN_aux) min_across_time_noGAN.append(min_across_time_noGAN_aux) rep_classes_time.append(rep_classes_time_aux) classes_time.append(classes_time_aux) GAN_distr.append(GAN_distr_aux) GAN_distr_noGAN.append(GAN_distr_noGAN_aux) classic_cross_entropy.append(classic_cross_entropy_aux) classic_cross_entropy_real.append(classic_cross_entropy_real_aux) classic_cross_entropy_noGAN.append(classic_cross_entropy_noGAN_aux) classic_cross_entropy_noGAN_real.append(classic_cross_entropy_noGAN_real_aux) instances.append(instances_aux) # All the instances all the dim together # legend_aux = [] # plt.subplots() # for ld in range(0, np.size(args.n_ld_dim)): # for gd in range(0, np.size(legend_list_instances)): # plot_aux, = plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN_real[ld][gd][0,:], classes_colors[ld]) # legend_aux.append(plot_aux) # plot_aux = plt.axhline(data_entropy, color='k') # legend_aux.append(plot_aux) # plt.ylabel('Cross entropy', fontsize=15) # plt.xlabel('Time (in epochs)', fontsize=15) # if args.title == 'on': # plt.title('Cross-entropy across time (without GAN class)') # plt.legend(handles = legend_aux, labels = list(np.append(legend_list_avg, 'Training'))) # plt.tight_layout() # if args.format != 'png': # plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real_ALL.' + args.format) # plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real_ALL.' + 'png') # plt.subplots() # for ld in range(0, np.size(args.n_ld_dim)): # for gd in range(0, np.size(legend_list_instances)): # plot_aux, = plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_real[ld][gd][0,:], colors_list[ld]) # legend_aux.append(plot_aux) # plot_aux = plt.axhline(data_entropy, color='k') # legend_aux.append(plot_aux) # plt.ylabel('Cross entropy', fontsize=15) # plt.xlabel('Time (in epochs)', fontsize=15) # if args.title == 'on': # plt.title('Cross-entropy across time') # plt.legend(handles = legend_aux, labels = list(np.append(legend_list_avg, 'Training'))) # plt.tight_layout() # if args.format != 'png': # plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_real_ALL.' + args.format) # plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_real_ALL.' + 'png') # # plt.close('all') # Plots across time for a fixed latent space dimension, comparison between different instances for ld in range(0,np.size(args.n_ld_dim)): # Mean + median legend_aux = [] fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], median_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Mean and median', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and median across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_median_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Variance fig,ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(var_across_time[ld][gd][0,:,2], var_across_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Variance', fontsize=15) if args.title == 'on': plt.title('Evolution of variance across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_ld_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) # Mean legend_aux = [] fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Median legend_aux = [] fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], median_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Median', fontsize=15) if args.title == 'on': plt.title('Evolution of median across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_median_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Mean + min/max fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) #ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], min_across_time_noGAN[ld][gd][0, :, 0], max_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'p50']) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_min_max_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Mean + p5 - p95 fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(mean_across_time_noGAN[ld][gd][0, :, 2], mean_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) #ax.fill_between(mean_across_time_noGAN[ld][gd][0, :, 2], percentile5_across_time_noGAN[ld][gd][0, :, 0], percentile95_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(['Mean', 'p5-p95']) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_mean_p5p95_for_dim_' + str( args.n_ld_dim[ld]) + '_noGAN.' + args.format) # Var fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(var_across_time_noGAN[ld][gd][0, :, 2], var_across_time_noGAN[ld][gd][0, :, 0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Variance', fontsize=15) if args.title == 'on': plt.title('Evolution of variance across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Evolution_var_for_ld_dim_' + str(args.n_ld_dim[ld]) + '_noGAN.' + args.format) plt.close('all') # Distribution of classes represented by the dataset for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0,:,2], rep_classes_time[ld][gd][0,:,0], colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of classes', fontsize=15) if args.title == 'on': plt.title('Number of represented classes across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Distr_classes_across_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) # GAN class for gc in range(0,np.size(args.GAN_classes)): for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0, :, 2], classes_time[ld][gd][0, :, int(GAN_class[gc])].T, colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable', fontsize=15) plt.legend(legend_list_instances) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class for latent dim = ' + str(args.n_ld_dim[ld])) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0, :, 2], (classes_time[ld][gd][0, :, int(GAN_class[gc])].T/1000)*100, colors_list[gd]) plt.axhline(dataset_distr[int(GAN_class[gc])], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0,100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list_instances, 'Training')) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class for latent dim = ' + str(args.n_ld_dim[ld])) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.close('all') # Cumulative GAN classes for ld in range(0, np.size(args.n_ld_dim)): fig, ax = plt.subplots() for gd in range(0, np.size(legend_list_instances)): sum = 0 for gc in range(0, np.size(args.GAN_classes)): sum = sum + classes_time[ld][gd][0, :, int(GAN_class[gc])].T/1000 plt.plot(rep_classes_time[ld][0][0, :, 2], sum * 100,colors_list[gd]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0, 100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list_instances, 'Training')) if args.title == 'on': plt.title('Number of syllable in ALL class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_ALL_class_across_time_and_dim_' + str(args.n_ld_dim[ld]) + '.' + args.format) # Cross-Entropy for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_real[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy real across time for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_real_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN[ld][gd][0,:], colors_list[gd]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class) for latent dim = ' + str(args.n_ld_dim[ld])) plt.legend(legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_' + str(args.n_ld_dim[ld]) + '.' + args.format) for ld in range(0, np.size(args.n_ld_dim)): plt.subplots() for gd in range(0, np.size(legend_list_instances)): plt.plot(rep_classes_time[ld][gd][0,:,2], classic_cross_entropy_noGAN_real[ld][gd][0,:], colors_list[gd]) plt.axhline(data_entropy, color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class) for latent dim = ' + str(args.n_ld_dim[ld])) #plt.legend(np.append(legend_list_avg, 'Training')) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Cross_entropy_noGAN_real' + str(args.n_ld_dim[ld]) + '.' + args.format) plt.close('all') # Cumulative results with average of the instances avg_mean_across_time = [] avg_std_across_time = [] avg_var_across_time = [] avg_mean_across_time_noGAN = [] avg_std_across_time_noGAN = [] avg_var_across_time_noGAN = [] avg_median_across_time_noGAN = [] avg_percentile5_across_time_noGAN = [] avg_percentile95_across_time_noGAN = [] avg_max_across_time_noGAN = [] avg_min_across_time_noGAN = [] avg_rep_classes_time = [] avg_classes_time = [] avg_classes_time_onlyGAN = [] avg_GAN_distr_noGAN = [] avg_classic_cross_entropy = [] avg_classic_cross_entropy_real = [] avg_classic_cross_entropy_noGAN = [] avg_classic_cross_entropy_noGAN_real = [] for ld in range(0, np.size(args.n_ld_dim)): avg_mean_aux = 0 avg_std_aux = 0 avg_var_aux = 0 avg_mean_noGAN_aux = 0 avg_std_noGAN_aux = 0 avg_var_noGAN_aux = 0 avg_median_across_time_noGAN_aux = 0 avg_percentile5_across_time_noGAN_aux = 0 avg_percentile95_across_time_noGAN_aux = 0 avg_max_across_time_noGAN_aux = 0 avg_min_across_time_noGAN_aux = 0 avg_rep_classes_aux = 0 avg_GAN_distr_noGAN_aux = 0 avg_classic_cross_entropy_aux = 0 avg_classic_cross_entropy_real_aux = 0 avg_classic_cross_entropy_noGAN_aux = 0 avg_classic_cross_entropy_noGAN_real_aux = 0 for gd in range(0, np.size(legend_list_instances)): avg_mean_aux = avg_mean_aux + mean_across_time[ld][gd][0, :, 0] avg_mean_noGAN_aux = avg_mean_noGAN_aux + mean_across_time_noGAN[ld][gd][0, :, 0] avg_std_aux = avg_std_aux + std_across_time_noGAN[ld][gd][0, :, 0] avg_std_noGAN_aux = avg_std_noGAN_aux + std_across_time[ld][gd][0, :, 0] avg_var_aux = avg_var_aux + var_across_time[ld][gd][0, :, 0] avg_var_noGAN_aux = avg_var_noGAN_aux + var_across_time_noGAN[ld][gd][0, :, 0] avg_median_across_time_noGAN_aux = avg_median_across_time_noGAN_aux + median_across_time_noGAN[ld][gd][0, :, 0] avg_percentile5_across_time_noGAN_aux = avg_percentile5_across_time_noGAN_aux + percentile5_across_time_noGAN[ld][gd][0, :, 0] avg_percentile95_across_time_noGAN_aux = avg_percentile95_across_time_noGAN_aux + percentile95_across_time_noGAN[ld][gd][0, :, 0] avg_max_across_time_noGAN_aux = avg_max_across_time_noGAN_aux + max_across_time_noGAN[ld][gd][0, :, 0] avg_min_across_time_noGAN_aux = avg_min_across_time_noGAN_aux + min_across_time_noGAN[ld][gd][0, :, 0] avg_rep_classes_aux = avg_rep_classes_aux + rep_classes_time[ld][gd][0, :, 0] avg_GAN_distr_noGAN_aux = avg_GAN_distr_noGAN_aux + mean_across_time[ld][gd][0, :, 0] avg_classic_cross_entropy_aux = avg_classic_cross_entropy_aux + classic_cross_entropy[ld][gd][0, :] avg_classic_cross_entropy_real_aux = avg_classic_cross_entropy_real_aux + classic_cross_entropy_real[ld][gd][0, :] avg_classic_cross_entropy_noGAN_aux = avg_classic_cross_entropy_noGAN_aux + classic_cross_entropy_noGAN[ld][gd][0, :] avg_classic_cross_entropy_noGAN_real_aux = avg_classic_cross_entropy_noGAN_real_aux + classic_cross_entropy_noGAN_real[ld][gd][0, :] avg_mean_across_time.append(avg_mean_aux/args.n_instances) avg_std_across_time.append(avg_std_aux/args.n_instances) avg_var_across_time.append(avg_var_aux/args.n_instances) avg_mean_across_time_noGAN.append(avg_mean_noGAN_aux/args.n_instances) avg_std_across_time_noGAN.append(avg_std_noGAN_aux/args.n_instances) avg_var_across_time_noGAN.append(avg_var_noGAN_aux/args.n_instances) avg_median_across_time_noGAN.append(avg_median_across_time_noGAN_aux/args.n_instances) avg_percentile5_across_time_noGAN.append(avg_percentile5_across_time_noGAN_aux/args.n_instances) avg_percentile95_across_time_noGAN.append(avg_percentile95_across_time_noGAN_aux/args.n_instances) avg_max_across_time_noGAN.append(avg_max_across_time_noGAN_aux/args.n_instances) avg_min_across_time_noGAN.append(avg_min_across_time_noGAN_aux/args.n_instances) avg_rep_classes_time.append(avg_rep_classes_aux/args.n_instances) avg_GAN_distr_noGAN.append(avg_GAN_distr_noGAN_aux/args.n_instances) avg_classic_cross_entropy.append(avg_classic_cross_entropy_aux/args.n_instances) avg_classic_cross_entropy_real.append(avg_classic_cross_entropy_real_aux/args.n_instances) avg_classic_cross_entropy_noGAN.append(avg_classic_cross_entropy_noGAN_aux/args.n_instances) avg_classic_cross_entropy_noGAN_real.append(avg_classic_cross_entropy_noGAN_real_aux/args.n_instances) avg_classes_time_onlyGAN_aux = np.zeros((np.size(GAN_class), np.size(rep_classes_time[ld][0][0, :, 2]))) for gc in range(0, np.size(GAN_class)): aux = [] for gd in range(0, np.size(legend_list_instances)): aux.append(classes_time[ld][gd][0, :, int(GAN_class[gc])].T) avg_classes_time_onlyGAN_aux[gc, :] = np.mean(aux, axis = 0) avg_classes_time_onlyGAN.append(avg_classes_time_onlyGAN_aux) avg_classes_time_aux = np.zeros((np.size(classes), np.size(rep_classes_time[ld][0][0, :, 2]))) for gc in range(0, np.size(classes)): aux = [] for gd in range(0, np.size(legend_list_instances)): aux.append(classes_time[ld][gd][0, :, gc]) avg_classes_time_aux[gc, :] = np.mean(aux, axis=0) avg_classes_time.append(avg_classes_time_aux) # Distribution of classes represented by the dataset fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0,:,2], avg_rep_classes_time[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of classes', fontsize=15) if args.title == 'on': plt.title('Number of represented classes across time') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_distr_classes_across_dim_.' + args.format) # Mean and std across time # with GAN class fig, ax = plt.subplots() for ld in range(0,np.size(args.n_ld_dim)): plt.plot(mean_across_time[ld][0][0, :, 2], avg_mean_across_time[ld], colors_list[ld]) #ax.fill_between(mean_across_time[ld][0][0, :, 2], avg_mean_across_time[ld] - avg_std_across_time[ld], avg_mean_across_time[ld] + avg_std_across_time[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and std across dimension') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_std_across_dim.' + args.format) # without GAN class fig, ax = plt.subplots() for ld in range(0,np.size(args.n_ld_dim)): plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) #ax.fill_between(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld] - avg_std_across_time_noGAN[ld], vg_mean_across_time_noGAN[ld] + avg_std_across_time_noGAN[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean and std', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and std across dimension') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_std_across_dim_noGAN.' + args.format) legend_aux = [] fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_median_across_time_noGAN[ld], colors_list[ld], alpha=0.3) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Mean and median', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and median across dimension') plt.legend(handles = legend_aux, labels = legend_list_avg) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_median_across_dim_noGAN.' + args.format) fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plot_aux, = plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_median_across_time_noGAN[ld], colors_list[ld]) legend_aux.append(plot_aux) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Median and median', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and median across dimension') plt.legend(handles = legend_aux, labels = legend_list_avg) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_median_across_dim_noGAN.' + args.format) fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) #ax.fill_between(mean_across_time_noGAN[ld][0][0, :, 2], avg_min_across_time_noGAN[ld], avg_max_across_time_noGAN[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and min/max across dimension') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_minmax_across_dim_noGAN.' + args.format) fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(mean_across_time_noGAN[ld][0][0, :, 2], avg_mean_across_time_noGAN[ld], colors_list[ld]) #ax.fill_between(mean_across_time_noGAN[ld][0][0, :, 2], avg_percentile5_across_time_noGAN[ld], avg_percentile95_across_time_noGAN[ld], colors_list[ld], alpha=0.3) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Evolution of mean', fontsize=15) if args.title == 'on': plt.title('Evolution of mean and p5/p95 across dimension') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_mean_p5p95_across_dim_noGAN.' + args.format) fig, ax =plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(var_across_time_noGAN[ld][0][0, :, 2], avg_var_across_time_noGAN[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Variance', fontsize=15) if args.title == 'on': plt.title('Evolution of variance across time') plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_var_across_dim_noGAN.' + args.format) fig, ax =plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(var_across_time[ld][0][0, :, 2], avg_var_across_time[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Variance', fontsize=15) if args.title == 'on': plt.title('Evolution of variance across time') plt.legend(handles = legend_aux, labels = legend_list_instances) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_var_across_dim.' + args.format) fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(median_across_time_noGAN[ld][0][0, :, 2], avg_median_across_time_noGAN[ld], colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)') plt.ylabel('Median') if args.title == 'on': plt.title('Evolution of median across time') plt.legend(handles=legend_aux, labels=legend_list_instances) if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_median_across_dim_noGAN.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_evolution_media_across_dim_noGAN.' + 'png') # ALL the classes fig, ax = plt.subplots() for gc in range(0, np.size(classes)): for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classes_time[ld][gc, :].T, classes_colors[gc]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable', fontsize=15) plt.legend(legend_list_avg) if args.title == 'on': plt.title('Number of syllable per class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'avg_class_across_time_and_dim.' + args.format) fig, ax = plt.subplots() for gc in range(0, np.size(classes)): for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], (avg_classes_time[ld][gc, :].T / 1000) * 100, classes_colors[gc]) plt.axhline(dataset_distr[gc], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list_avg, 'Training')) if args.title == 'on': plt.title('Number of syllable per class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_class_across_time_and_dim.' + args.format) # ALL the classes NOGAN fig, ax = plt.subplots() for gc in range(0, np.size(classes)): for ld in range(0, np.size(args.n_ld_dim)): if np.size(GAN_class) == 5: if gc == 6 or gc == 7 or gc == 8 or gc == 16 or gc == 20: pass else: plt.plot(rep_classes_time[ld][0][0, :, 2], (avg_classes_time[ld][gc, :].T / 1000)*100, classes_colors[gc]) if np.size(GAN_class) == 10: if gc == 6 or gc == 7 or gc == 8 or gc == 9 or gc == 10 or gc == 11 or gc == 12 or gc == 13 or gc == 21 or gc == 25: pass else: plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classes_time[ld][gc, :].T / 1000, classes_colors[gc]) elif np.size(GAN_class) == 0: plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classes_time[ld][gc, :].T, classes_colors[gc]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable (in percentage)', fontsize=15) plt.legend(['A', 'B1', 'B2', 'C', 'D', 'E', 'H', 'J1', 'J2', 'L', 'M', 'N', 'O', 'Q', 'R', 'V']) if args.title == 'on': plt.title('Number of syllable per class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'avg_class_NOGAN_across_time_and_dim.' + args.format) fig, ax = plt.subplots() for gc in range(0, np.size(classes)): for ld in range(0, np.size(args.n_ld_dim)): if np.size(GAN_class) == 5: if gc == 6 or gc == 7 or gc == 8 or gc == 16 or gc == 20: pass else: plt.plot(rep_classes_time[ld][0][0, :, 2], (avg_classes_time[ld][gc, :].T / 1000)*100, classes_colors[gc]) if np.size(GAN_class) == 10: if gc == 6 or gc == 7 or gc == 8 or gc == 9 or gc == 10 or gc == 11 or gc == 12 or gc == 13 or gc == 21 or gc == 25: pass else: plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classes_time[ld][gc, :].T / 1000, classes_colors[gc]) elif np.size(GAN_class) == 0: plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classes_time[ld][gc, :].T, classes_colors[gc]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable (in percentage)', fontsize=15) plt.legend(['A', 'B1', 'B2', 'C', 'D', 'E', 'H', 'J1', 'J2', 'L', 'M', 'N', 'O', 'Q', 'R', 'V']) if args.title == 'on': plt.title('Number of syllable per class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_class_NOGAN_across_time_and_dim.' + args.format) # GAN classes ONLY for gc in range(0,np.size(args.GAN_classes)): fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0,:,2], avg_classes_time_onlyGAN[ld][gc,:].T, colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Number of syllable', fontsize=15) plt.legend(legend_list_avg) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + classes[int(GAN_class[gc])] + '_avg_class_across_time_and_dim.' + args.format) fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0,:,2], (avg_classes_time_onlyGAN[ld][gc,:].T/1000)*100, colors_list[ld]) plt.axhline(dataset_distr[int(GAN_class[gc])], color='k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0,100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list_avg, 'Training')) if args.title == 'on': plt.title('Number of syllable in ' + classes[int(GAN_class[gc])] + ' class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_' + classes[int(GAN_class[gc])] + '_avg_class_across_time_and_dim.' + args.format) # Cumulative GAN classes fig, ax = plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): sum = 0 for gc in range(0, np.size(args.GAN_classes)): sum = sum + avg_classes_time_onlyGAN[ld][gc, :].T / 1000 plt.plot(rep_classes_time[ld][0][0, :, 2], sum * 100, colors_list[ld]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') ax.set_ylim([0, 100]) plt.xlabel('Time (in number of epochs)', fontsize=15) plt.ylabel('Percentage of syllable', fontsize=15) plt.legend(np.append(legend_list_avg, 'Training')) if args.title == 'on': plt.title('Number of syllable in ALL class across dim') plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Percentage_ALL_avg_class_across_time_and_dim.' + args.format) # Cross-Entropy plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0,:,2], avg_classic_cross_entropy[ld], colors_list[ld]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy.' + args.format) plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0,:,2], avg_classic_cross_entropy_real[ld], colors_list[ld]) plt.axhline(data_entropy, color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time') plt.legend(np.append(legend_list_avg, 'Training')) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_real.' + args.format) plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0,:,2], avg_classic_cross_entropy_noGAN[ld], colors_list[ld]) plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class)') plt.legend(legend_list_avg) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_noGAN.' + args.format) plt.subplots() for ld in range(0, np.size(args.n_ld_dim)): plt.plot(rep_classes_time[ld][0][0, :, 2], avg_classic_cross_entropy_noGAN_real[ld], colors_list[ld]) plt.axhline(data_entropy_noGAN, color='k') plt.ylabel('Cross entropy', fontsize=15) plt.xlabel('Time (in epochs)', fontsize=15) if args.title == 'on': plt.title('Cross-entropy across time (without GAN class)') plt.legend(np.append(legend_list_avg, 'Training')) plt.tight_layout() plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Avg_cross_entropy_noGAN_real.' + args.format) plt.close('all') print('Done') def plot_inception(generation_summary_list, dataset_summary, all_IS, colors_list, legend_list, args): if len(generation_summary_list) > 0: # Training #aux = np.load(dataset_summary[0], allow_pickle=True) #aux = aux.item() #IS_train = aux['IS'] #print(IS_train) # Generated IS_aux = np.zeros((np.size(generation_summary_list),)) lat_dim = np.zeros((np.size(generation_summary_list),)) epoch = np.zeros((np.size(generation_summary_list),)) for sl in range(0, np.size(generation_summary_list)): aux = np.load(generation_summary_list[sl], allow_pickle=True) aux = aux.item() IS_aux[sl] = aux['IS'] lat_dim[sl]= aux['Latent_dim'] epoch[sl] = aux['Epoch'] IS = np.zeros((int(np.size(generation_summary_list)/np.size(args.n_ld_dim)),np.size(args.n_ld_dim))) for d in range(0, np.size(args.n_ld_dim)): IS[:,d] = IS_aux[lat_dim==args.n_ld_dim[d]] print(IS) IS_summary = {'IS': IS, 'epochs': epoch,} # 'IS_train': IS_train} np.save(args.data_dir + '/' + 'IS.npy', IS_summary) fig, ax = plt.subplots() for d in range(0, np.size(args.n_ld_dim)): plt.plot(epoch[lat_dim==args.n_ld_dim[d]], IS[:,d], color=colors_list[d]) #plt.plot(epoch[lat_dim==args.n_ld_dim[d]], np.ones((np.size(epoch[lat_dim==args.n_ld_dim[d]]),))*IS_train, 'k') ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.ylabel('Inception Score', fontsize=15) plt.xlabel('Time (in number of epochs)', fontsize=15) if args.title == 'on': plt.title('Inception score across time') #plt.legend([legend_list[d], 'Training']) plt.tight_layout() if args.format != 'png': plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Inception_score.' + args.format) plt.savefig(args.data_dir + '/' + args.output_dir + '/' + 'Inception_score.' + 'png') if len(all_IS)>0: IS = [] epochs = [] for i in range(0, len(all_IS)): aux = np.load(all_IS[i], allow_pickle=True) aux = aux.item() IS.append(aux['IS']) epochs.append(aux['epochs']) print(epochs) print(IS) if i == 0: IS_train = aux['IS_train'] fig, ax = plt.subplots() plt.plot(epochs[0], np.ones((np.size(epochs[0]),)) * IS_train, 'k') for i in range(0, len(all_IS)): plt.plot(epochs[i], IS[i], color=colors_list[i]) ax.spines['top'].set_color('none') ax.spines['right'].set_color('none') plt.ylabel('Inception Score', fontsize=15) plt.xlabel('Time (in number of epochs)', fontsize=15) if args.title == 'on': plt.title('Inception score across time') plt.legend(np.append('Training', legend_list), loc='upper left') plt.tight_layout() if args.format != 'png': plt.savefig(args.data_dir + '/' + 'Inception_score.' + args.format) plt.savefig(args.data_dir + '/' + 'Inception_score.' + 'png') print('Done') if __name__ == '__main__': import argparse import glob import sys parser = argparse.ArgumentParser() parser.add_argument('--option', type=str, choices=['annotations', 'analysis_dataset', 'analysis_error', 'analysis_gen', 'single', 'analysis_dim', 'instances', 'IS']) parser.add_argument('--data_dir', type=str, help='Directory containing the data', default=None) parser.add_argument('--output_dir', type=str, help='Directory where to save the output', default=None) dataset_args = parser.add_argument_group('Dataset') dataset_args.add_argument('--wavegan_latent_dim', type=int, help='Number of dimensions of the latent space', default=2) dataset_args.add_argument('--dd', type=int, help='Scaling parameter to reduce the number of elements in the dataset', default=1) dataset_args.add_argument('--dataset_dim', type=int, help='How many elements in the dataset', default=16000) #23456 16000 dataset_args.add_argument('--ckpt_n', type=int, help='At which chekpoint', default=False) dataset_args.add_argument('--n_ld_dim', type=list, help='How many dimension for the analysis or different dataset to be compared (depending on the scaling factor) for the analysis', default=[3]) # if dataset size cond: [1,2,4] # if latent space dim all [1,2,3,4,5,6] dataset_args.add_argument('--n_instances', type=int, help='How many instances per type', default=1) dataset_args.add_argument('--GAN_classes', type=list, help='GAN classes in the decoder', default=['EARLY15', 'EARLY30', 'EARLY45', 'OT', 'WN']) # 'EARLY', 'OT', 'WN' #'EARLY15', 'EARLY30', 'OT', 'WN' #'EARLY15_1', 'EARLY15_2', 'EARLY15_3', 'EARLY15_4', 'EARLY30_1', 'EARLY30_2', 'EARLY30_3', 'EARLY30_4', 'OT', 'WN' #'EARLY15', 'EARLY30', 'EARLY45', 'OT', 'WN' #'GAN1', 'GAN2', 'GAN3' plot_args = parser.add_argument_group('Plot') plot_args.add_argument('--format', type=str, help='Saving format', default='png') plot_args.add_argument('--title', type=str, help='Add the title or not: default yes', default='off') args = parser.parse_args() # Output direction creation if args.output_dir != None: if not os.path.isdir(args.data_dir + '/' + args.output_dir): os.makedirs(args.data_dir + '/' + args.output_dir) if args.option == 'annotations': annotations = create_annotations(args) epoch = int((args.ckpt_n * 5 * 64) / args.dataset_dim) if len(str(args.ckpt_n)) == 2: with open(args.data_dir + '/' + 'annotations_generation_ckpt000' + str(args.ckpt_n) + '_ld' + str(args.wavegan_latent_dim) + '_dd' + str(args.dd) + '_ep' + str(epoch) + '.pkl', 'wb+') as f: pickle.dump(annotations, f) elif len(str(args.ckpt_n)) == 3: with open(args.data_dir + '/' + 'annotations_generation_ckpt00' + str(args.ckpt_n)+ '_ld' + str(args.wavegan_latent_dim) + '_dd' + str(args.dd) + '_ep' + str(epoch) + '.pkl', 'wb+') as f: pickle.dump(annotations, f) elif len(str(args.ckpt_n)) == 4: with open(args.data_dir + '/' + 'annotations_generation_ckpt0' + str(args.ckpt_n)+ '_ld' + str(args.wavegan_latent_dim) + '_dd' + str(args.dd) + '_ep' + str(epoch) + '.pkl', 'wb+') as f: pickle.dump(annotations, f) else: with open(args.data_dir + '/' + 'annotations_generation_ckpt' + str(args.ckpt_n) + '_ld' + str(args.wavegan_latent_dim) + '_dd' + str(args.dd) + '_ep' + str(epoch) + '.pkl', 'wb+') as f: pickle.dump(annotations, f) if args.option == 'analysis_dataset': # List of the dataset we want to compare (to have the right legend in the figures) legend_list = ['Training dataset'] # List of the annotations ( dataset) #annotations_dataset = sorted(glob.glob(args.data_dir + '/' + 'annotations_training_dataset_' + str(legend_list[0]) + '_*.pkl')) # annotations_dataset = sorted(glob.glob(args.data_dir + '/' + 'annotations_training_dataset_*.pkl')) annotations_dataset= glob.glob(args.data_dir + '/' + 'annotations_training_dataset_2_16.pkl') analysis_dataset(annotations_dataset, legend_list, args) if args.option == 'analysis_error': # List of the dataset we want to compare (to have the right legend in the figures) legend_list = ['A_noisy', 'A_notA', 'B1_noisy', 'B1_not_complete_ex', 'B1_not_cut', 'B1_too_short', 'B2_noisy', 'C_noisy', 'C_not_cut', 'C1', 'C2', 'D_not_cut', 'D_notD', 'E_noisy', 'E_not_cut', 'E_notE', 'E_too_long', 'E_too_short', 'H_noisy', 'H_not_cut', 'H_notH', 'H_prova', 'H_too_long', 'J1_noisy', 'J1_not_cut', 'J2_noisy', 'L_noisy', 'M_noisy', 'M_not_cut', 'M_notM', 'M_too_short', 'N_noisy', 'N_not_cut', 'N_too_short', 'O_noisy', 'O_notO', 'Q_noisy', 'Q_too_short', 'R_noisy', 'R_notR', 'R_too_short', 'V_noisy', 'V_notV', 'V_too_short'] legend_list = ['H'] # List of the annotations (dataset) annotations_dataset = [] for ll in range(0, np.size(legend_list)): #annotations_dataset_aux = glob.glob(args.data_dir + '/' + 'annotations_errors_training_' + legend_list[ll] + '.pkl') annotations_dataset_aux = glob.glob(args.data_dir + '/' + 'annotations_dataset_' + legend_list[ll] + '*.pkl') annotations_dataset.append(annotations_dataset_aux) analysis_error(annotations_dataset, legend_list, args) if args.option == 'analysis_gen': # List of the annotations (generations and dataset) summary_dataset = sorted(glob.glob(args.data_dir + '/' + 'Dataset_summary*.npy')) annotations_generation = sorted(glob.glob(args.data_dir + '/' + 'annotations_generation*.pkl')) analysis_generation(annotations_generation, summary_dataset, args) if args.option == 'single': # List of the dataset we want to compare (to have the right legend in the figures) legend_list = ['Dim 1', 'Dim 2', 'Dim 3', 'Dim 4', 'Dim 5', 'Dim 6'] # List of the generated data generation_data = [] for ld in range(0, np.size(args.n_ld_dim)): generation_data_aux = sorted( glob.glob(args.data_dir + '/' + 'Generation_summary_all' + str(args.n_ld_dim[ld]) + '*.npy')) generation_data.append(generation_data_aux) # Training dataset summary_dataset = sorted(glob.glob(args.data_dir + '/' + 'Dataset_summary*.npy')) analysis_latent(generation_data, summary_dataset, legend_list, args) if args.option == 'analysis_dim': # List of the dataset we want to compare (to have the right legend in the figures) legend_list_instances = ['Ex 0'] #, 'Ex 1', 'Ex 2'] #['NEW', 'OLD'] # legend_list = ['1', '1/2', '1/4'] #[3, 4] dd_dim = [16000, 8000, 4000] #[23456, 3600, 1600] colors_list = ['b', 'r', 'g'] # List of the generated data generation_data = [] for ld in range(0, np.size(args.n_ld_dim)): generation_data_aux = sorted(glob.glob(args.data_dir + '/' + 'Generation_summary_dd' + str(args.n_ld_dim[ld]) +'*.npy')) generation_data.append(generation_data_aux) # Training dataset summary_dataset = [] for ld in range(0, np.size(args.n_ld_dim)): summary_dataset_aux = sorted(glob.glob(args.data_dir + '/' + 'Dataset_summary' + str(args.n_ld_dim[ld]) + '.npy')) summary_dataset.append(summary_dataset_aux) analysis_dim(generation_data, summary_dataset, legend_list_instances, legend_list, colors_list, args) if args.option == 'instances': # Color map color_list = ['purple', 'gold', 'orange', 'blue', 'darkgreen', 'magenta', 'darkcyan', 'gray', 'brown', 'red'] #color_list = ['darkcyan'] classes_colors = ['grey', 'sandybrown', 'orange', 'gold', 'olive', 'lightcoral', 'brown', 'red', 'limegreen', 'darkgreen', 'turquoise', 'darkcyan', 'blue', 'navy', 'purple', 'indigo', 'violet', 'deeppink', 'white', 'magenta', 'black'] # List of the generated data generation_data = [] for ld in range(0, np.size(args.n_ld_dim)): generation_data_aux = sorted(glob.glob(args.data_dir + '/' + '*_ld' + str(args.n_ld_dim[ld]) +'.npy')) generation_data.append(generation_data_aux) # Training dataset summary_dataset = sorted(glob.glob(args.data_dir + '/' + 'Dataset_summary*.npy')) # List of the dataset we want to compare (to have the right legend in the figures) legend_list_instances = [] for l in range(0, np.size(generation_data)): legend_list_instances.append('Ex ' + str(l)) # legend_list_avg = ['Dim 3'] legend_list_avg = ['Dim 1', 'Dim 2', 'Dim 3', 'Dim 4', 'Dim 5', 'Dim 6'] #'Dim 4', 'Dim 5', legend_list_instances = ['Ex 0'] # legend_list_instances = ['Ex 0', 'Ex 1', 'Ex 2', 'Ex 3', 'Ex 4'] several_instances(generation_data, summary_dataset, legend_list_instances, legend_list_avg, color_list, classes_colors, args) if args.option == 'IS': # Load data generation_summary_list = sorted(glob.glob(args.data_dir + '/' + 'Generation_summary_ep*.npy')) dataset_summary = sorted(glob.glob(args.data_dir + '/' + 'Dataset_summary*.npy')) all_IS = sorted(glob.glob(args.data_dir + '/' + '*IS_*.npy')) # Legend to plot legend_list = [] for l in range(0, np.size(all_IS)): legend_list. append('Ex ' + str(l+1)) #legend_list = ['Ex 0'] #legend_list = ['1', '1/2', '1/4'] #legend_list = ['Dim 1', 'Dim 2', 'Dim 3', 'Dim4', 'Dim5', 'Dim 6'] # Colors list colors_list = ['purple', 'gold', 'orange', 'blue', 'darkgreen', 'magenta', 'darkcyan', 'gray', 'brown', 'red'] #10 instances dim 3 #colors_list = ['darkcyan'] # example dim 3 #colors_list = ['purple' , 'gold', 'orange', 'blue', 'darkgreen', 'magenta'] # different dim comparison # colors_list = ['b', 'r', 'g'] # dimension dataset comparison #colors_list = ['r'] # just to set one color plot_inception(generation_summary_list, dataset_summary, all_IS, colors_list, legend_list, args)

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4ff753487f7fd4639a9f348f486c401decd3f797

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py

Python

tests/workspaces/shared/input_1.py

shiwano/misc_rules

c45d5666cbb303751918b9f7dc9b00e314f9ed46

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

8

2019-11-01T09:54:09.000Z

2021-07-06T00:57:42.000Z

tests/workspaces/shared/input_1.py

andyscott/rules_adroit

0bc662f896bf0e3841a13864482b6e4f4e6eca90

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

2

2020-07-30T20:16:04.000Z

2021-10-12T01:54:17.000Z

tests/workspaces/shared/input_1.py

andyscott/rules_adroit

0bc662f896bf0e3841a13864482b6e4f4e6eca90

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

1

2021-03-08T15:24:32.000Z

def foo(): return 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1

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56

0.352941

15

68

1.6

0.266667

0.916667

1.25

1.5

0.5

0

0.315789

0.441176

68

2

57

34

0.315789

0

1

0.5

true

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0

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12

8b4484b8a57aa63e59d917da67647303e7e812c2

123

py

Python

wacky_rl/memory/__init__.py

maik97/wacky-rl

7fde3bc473387f69deb69fadf21db1e82479516c

[ "MIT" ]

null

wacky_rl/memory/__init__.py

maik97/wacky-rl

7fde3bc473387f69deb69fadf21db1e82479516c

[ "MIT" ]

1

2021-09-19T06:38:33.000Z

2021-09-29T21:25:54.000Z

wacky_rl/memory/__init__.py

maik97/wacky-rl

7fde3bc473387f69deb69fadf21db1e82479516c

[ "MIT" ]

null

from wacky_rl.memory.memory import BufferMemory from wacky_rl.memory.prio_experience_replay import PrioritizedReplayBuffer

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8ca1b9571d02561b9ceb59993c2559e77ab0b1e0

582

py

Python

eval.py

wayne980/PolyLoss

97ff26a5a15cf542cb95a8650f556b79f3342891

[ "Apache-2.0" ]

13

2020-10-09T03:30:52.000Z

2022-03-16T12:53:33.000Z

eval.py

wayne980/Universal-Weighting-Metric-Learning-for-Cross-Modal-Matching

97ff26a5a15cf542cb95a8650f556b79f3342891

[ "Apache-2.0" ]

2

2021-04-07T09:11:06.000Z

2021-11-12T03:51:43.000Z

eval.py

wayne980/Universal-Weighting-Metric-Learning-for-Cross-Modal-Matching

97ff26a5a15cf542cb95a8650f556b79f3342891

[ "Apache-2.0" ]

5

2020-10-20T05:28:49.000Z

2022-01-07T08:20:57.000Z

<<<<<<< HEAD # -*- coding: utf-8 -*- """ Created on Fri Oct 18 19:16:47 2019 @author: FMC_417_1 """ from vocab import Vocabulary import visualscan visualscan.evalrank("./runs/coco/ckpt1/model_best.pth.tar", data_path="/data4/weijiwei/mtfn", split="test",fold5=False) ======= # -*- coding: utf-8 -*- """ Created on Fri Oct 18 19:16:47 2019 @author: FMC_417_1 """ from vocab import Vocabulary import visualscan visualscan.evalrank("./runs/coco/ckpt1/model_best.pth.tar", data_path="/data4/weijiwei/mtfn", split="test",fold5=False) >>>>>>> 85be29440e7baea43633b1a5958249538d2edae2

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8

8ca3325237b042b1833388938b4dc6f2d8bfc914

19,123

py

Python

py_tests/end_to_end_tests/test_interaction.py

joahim/django-convenient-formsets

aeb7e009b657341f245644f7cc9214c94697fb37

[ "BSD-3-Clause" ]

6

2021-05-04T01:20:49.000Z

2021-12-22T19:07:47.000Z

py_tests/end_to_end_tests/test_interaction.py

joahim/django-convenient-formsets

aeb7e009b657341f245644f7cc9214c94697fb37

[ "BSD-3-Clause" ]

1

2021-11-11T16:01:01.000Z

py_tests/end_to_end_tests/test_interaction.py

joahim/django-convenient-formsets

aeb7e009b657341f245644f7cc9214c94697fb37

[ "BSD-3-Clause" ]

3

2021-08-29T16:33:28.000Z

2022-01-10T23:11:53.000Z

from urllib.parse import urlencode from selenium.webdriver.common.by import By def test_adding_forms1(live_server, selenium, is_legacy_edge): """ Test behavior when adding multiple forms to a formset with 0 initial forms, while keeping the add form button visible upon reaching the maximum number of forms. """ # Load webpage for test params = {'template_name': 'interaction/adding_forms_1.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate 6 clicks on `addFormButton` (one too many) add_form_button = selenium.find_element( By.CSS_SELECTOR, '#formset #add-form-button') for _ in range(6): add_form_button.click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_text_values = ['', '', '', '', ''] expected_order_values = ['1', '2', '3', '4', '5'] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 5 for i, form in enumerate(forms): # Label assert not len(form.find_elements(By.CSS_SELECTOR, 'label')) # Text input element = form.find_element(By.CSS_SELECTOR, 'input[type="text"]') assert element.get_attribute('id') == f'id_formset-{i}-user' assert element.get_attribute('name') == f'formset-{i}-user' assert element.get_attribute('value') == f'{expected_text_values[i]}' # Delete flag assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=DELETE]')) # Order index element = form.find_element(By.CSS_SELECTOR, 'input[name$="ORDER"]') assert element.get_attribute('id') == f'id_formset-{i}-ORDER' assert element.get_attribute('name') == f'formset-{i}-ORDER' assert element.get_attribute('value') == f'{expected_order_values[i]}' # Assert management form values total_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-TOTAL_FORMS"]') assert total_forms_input.get_attribute('value') == '5' initial_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-INITIAL_FORMS"]') assert initial_forms_input.get_attribute('value') == '0' min_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MIN_NUM_FORMS"]') assert min_num_forms_input.get_attribute('value') == '0' max_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MAX_NUM_FORMS"]') assert max_num_forms_input.get_attribute('value') == '5' # Assert that add form button does not have the `hidden` attribute set if is_legacy_edge(getattr(selenium, 'desired_capabilities', {})): assert add_form_button.get_attribute('hidden') == 'false' else: assert add_form_button.get_attribute('hidden') is None def test_adding_forms2(live_server, selenium, is_legacy_edge): """ Test behavior when adding a form to a formset with 2 visible forms of 5 initial forms. """ # Load webpage for test params = {'template_name': 'interaction/adding_forms_2.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate click on add form button add_form_button = selenium.find_element( By.CSS_SELECTOR, '#formset #add-form-button') add_form_button.click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_text_values = ['user0', 'user1', 'user2', 'user3', 'user4', ''] expected_delete_values = ['on', '', 'on', '', 'on'] expected_order_values = ['1', '2', '3', '4', '5', '5'] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 6 for i, form in enumerate(forms): # Label assert not len(form.find_elements(By.CSS_SELECTOR, 'label')) # Text input element = form.find_element(By.CSS_SELECTOR, '[type="text"]') assert element.get_attribute('name') == f'formset-{i}-user' assert element.get_attribute('value') == f'{expected_text_values[i]}' # Delete flag if i < len(expected_delete_values): # only for the 5 initial forms element = form.find_element(By.CSS_SELECTOR, '[name$=DELETE]') assert element.get_attribute('name') == f'formset-{i}-DELETE' assert element.get_attribute('value') == f'{expected_delete_values[i]}' else: assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=DELETE]')) # Order index element = form.find_element(By.CSS_SELECTOR, '[name$=ORDER]') assert element.get_attribute('name') == f'formset-{i}-ORDER' assert element.get_attribute('value') == f'{expected_order_values[i]}' # Assert management form values total_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-TOTAL_FORMS"]') assert total_forms_input.get_attribute('value') == '6' initial_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-INITIAL_FORMS"]') assert initial_forms_input.get_attribute('value') == '5' min_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MIN_NUM_FORMS"]') assert min_num_forms_input.get_attribute('value') == '0' max_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MAX_NUM_FORMS"]') assert max_num_forms_input.get_attribute('value') == '5' # Assert that add form button does not have the `hidden` attribute set if is_legacy_edge(getattr(selenium, 'desired_capabilities', {})): assert add_form_button.get_attribute('hidden') == 'false' else: assert add_form_button.get_attribute('hidden') is None def test_adding_forms3(live_server, selenium, is_legacy_edge): """ Test behavior when adding a form to a formset with 4 visible forms of 4 initial forms. """ # Load webpage for test params = {'template_name': 'interaction/adding_forms_3.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate click on add form button add_form_button = selenium.find_element( By.CSS_SELECTOR, '#formset #add-form-button') add_form_button.click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_values = ['user0', 'user1', 'user2', 'user3', ''] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 5 for i, form in enumerate(forms): # Label element = form.find_element(By.CSS_SELECTOR, 'label') assert element.get_attribute('for') == f'id_formset-{i}-user' # Text input text_input = form.find_element(By.CSS_SELECTOR, 'input[type="text"]') assert text_input.get_attribute('id') == f'id_formset-{i}-user' assert text_input.get_attribute('value') == f'{expected_values[i]}' # Delete flag & order index assert not len(form.find_elements(By.CSS_SELECTOR, '[type="hidden"]')) # Assert management form values total_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-TOTAL_FORMS"]') assert total_forms_input.get_attribute('value') == '5' initial_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-INITIAL_FORMS"]') assert initial_forms_input.get_attribute('value') == '4' min_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MIN_NUM_FORMS"]') assert min_num_forms_input.get_attribute('value') == '0' max_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MAX_NUM_FORMS"]') assert max_num_forms_input.get_attribute('value') == '5' # Assert that add form button does have the `hidden` attribute set if is_legacy_edge(getattr(selenium, 'desired_capabilities', {})): assert add_form_button.get_attribute('hidden') == 'true' else: assert add_form_button.get_attribute('hidden') is not None def test_deleting_forms(live_server, selenium, is_legacy_edge): """ Test behavior when deleting a form from a formset with 5 visible forms of 2 initial forms. """ # Load webpage for test params = {'template_name': 'interaction/deleting_forms.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate click on delete form button of 2nd & 3rd form forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') forms[1].find_element(By.CSS_SELECTOR, '#delete-form-button').click() forms[2].find_element(By.CSS_SELECTOR, '#delete-form-button').click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_text_values = ['user0', 'user1', 'user3', 'user4'] expected_delete_values = ['', 'on'] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 4 for i, form in enumerate(forms): # Label assert not len(form.find_elements(By.CSS_SELECTOR, 'label')) # Text input element = form.find_element(By.CSS_SELECTOR, '[type="text"]') assert element.get_attribute('name') == f'formset-{i}-user' assert element.get_attribute('value') == f'{expected_text_values[i]}' # Delete flag if i < len(expected_delete_values): # only for the 2 initial forms element = form.find_element(By.CSS_SELECTOR, '[name$=DELETE]') assert element.get_attribute('name') == f'formset-{i}-DELETE' assert element.get_attribute('value') == f'{expected_delete_values[i]}' else: assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=DELETE]')) # Order index assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=ORDER]')) # Assert management form values total_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-TOTAL_FORMS"]') assert total_forms_input.get_attribute('value') == '4' initial_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-INITIAL_FORMS"]') assert initial_forms_input.get_attribute('value') == '2' min_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MIN_NUM_FORMS"]') assert min_num_forms_input.get_attribute('value') == '0' max_num_forms_input = selenium.find_element( By.CSS_SELECTOR, 'input[name="formset-MAX_NUM_FORMS"]') assert max_num_forms_input.get_attribute('value') == '5' # Assert that add form button does not have the `hidden` attribute set add_form_button = selenium.find_element( By.CSS_SELECTOR, '#formset #add-form-button') if is_legacy_edge(getattr(selenium, 'desired_capabilities', {})): assert add_form_button.get_attribute('hidden') == 'false' else: assert add_form_button.get_attribute('hidden') is None def test_ordering_forms1(live_server, selenium): """ Test behavior when reordering arbitrary forms. """ # Load webpage for test params = {'template_name': 'interaction/ordering_forms_all.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate click on move form up/down buttons of 1st, 3nd & 5th form forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') forms[0].find_element(By.CSS_SELECTOR, '#move-form-down-button').click() forms[2].find_element(By.CSS_SELECTOR, '#move-form-down-button').click() forms[4].find_element(By.CSS_SELECTOR, '#move-form-up-button').click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_form_indexes = ['1', '0', '3', '4', '2'] expected_text_values = ['user1', 'user0', 'user3', 'user4', 'user2'] expected_order_values = ['1', '2', '3', '4', '5'] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 5 for i, form in enumerate(forms): # Label assert not len(form.find_elements(By.CSS_SELECTOR, 'label')) # Text input element = form.find_element(By.CSS_SELECTOR, 'input[type="text"]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-user' ) assert element.get_attribute('value') == f'{expected_text_values[i]}' # Delete flag assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=DELETE]')) # Order index element = form.find_element(By.CSS_SELECTOR, 'input[name$="ORDER"]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-ORDER' ) assert element.get_attribute('value') == f'{expected_order_values[i]}' def test_ordering_forms2(live_server, selenium): """ Test behavior when attempting to move up the first form and move down the last form. """ # Load webpage for test params = {'template_name': 'interaction/ordering_forms_all.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate click on move form up button for first and on move form down # button for last form forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') forms[0].find_element(By.CSS_SELECTOR, '#move-form-up-button').click() forms[-1].find_element(By.CSS_SELECTOR, '#move-form-down-button').click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_form_indexes = ['0', '1', '2', '3', '4'] expected_text_values = ['user0', 'user1', 'user2', 'user3', 'user4'] expected_order_values = ['1', '2', '3', '4', '5'] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 5 for i, form in enumerate(forms): # Label assert not len(form.find_elements(By.CSS_SELECTOR, 'label')) # Text input element = form.find_element(By.CSS_SELECTOR, 'input[type="text"]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-user' ) assert element.get_attribute('value') == f'{expected_text_values[i]}' # Delete flag assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=DELETE]')) # Order index element = form.find_element(By.CSS_SELECTOR, 'input[name$="ORDER"]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-ORDER' ) assert element.get_attribute('value') == f'{expected_order_values[i]}' def test_combined_form_actions(live_server, selenium): """ Test behavior when combining adding, deleting and ordering multiple forms. """ # Load webpage for test params = {'template_name': 'interaction/combined_form_actions.html'} test_url = f'{live_server.url}?{urlencode(params)}' selenium.get(test_url) # Initiate click on delete form button of 2nd & 4th form and on move form # up/down buttons of 1st & 5th form forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') forms[1].find_element(By.CSS_SELECTOR, '#delete-form-button').click() forms[3].find_element(By.CSS_SELECTOR, '#delete-form-button').click() forms[0].find_element(By.CSS_SELECTOR, '#move-form-down-button').click() forms[4].find_element(By.CSS_SELECTOR, '#move-form-up-button').click() # Initiate click on add form button add_form_button = selenium.find_element( By.CSS_SELECTOR, '#formset #add-form-button') add_form_button.click() # Assert errors error_log = selenium.find_element(By.CSS_SELECTOR, '#error-log') error_messages = [ msg.strip() for msg in error_log.text.split('\n') if msg.strip() ] assert error_messages == [] # Assert attributes of form elements expected_form_indexes = ['2', '4', '0', '1', '3', '5'] expected_text_values = ['user2', 'user4', 'user0', 'user1', 'user3', ''] expected_delete_values = ['', '', '', 'on', 'on'] expected_order_values = ['1', '3', '5', '2', '4', '6'] forms = selenium.find_elements( By.CSS_SELECTOR, '#formset #forms-container .form') assert len(forms) == 6 for i, form in enumerate(forms): # Label assert not len(form.find_elements(By.CSS_SELECTOR, 'label')) # Text input element = form.find_element(By.CSS_SELECTOR, 'input[type="text"]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-user' ) assert element.get_attribute('value') == f'{expected_text_values[i]}' # Delete flag if i < len(expected_delete_values): # only for the 5 initial forms element = form.find_element(By.CSS_SELECTOR, '[name$=DELETE]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-DELETE' ) assert element.get_attribute('value') == f'{expected_delete_values[i]}' else: assert not len(form.find_elements(By.CSS_SELECTOR, '[name$=DELETE]')) # Order index element = form.find_element(By.CSS_SELECTOR, 'input[name$="ORDER"]') assert ( element.get_attribute('name') == f'formset-{expected_form_indexes[i]}-ORDER' ) assert element.get_attribute('value') == f'{expected_order_values[i]}'

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null

0

1

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1

0

null

0

7

8cc0b9125a70cac67690091e1fad2bf76147cba3

41,358

py

Python

data_process/evaluate.py

nju-websoft/SR3

bb602926f8a260887326a812e19e23f50e7eb273

[ "Apache-2.0" ]

null

data_process/evaluate.py

nju-websoft/SR3

bb602926f8a260887326a812e19e23f50e7eb273

[ "Apache-2.0" ]

null

data_process/evaluate.py

nju-websoft/SR3

bb602926f8a260887326a812e19e23f50e7eb273

[ "Apache-2.0" ]

null

from rouge import Rouge import numpy as np import re import jieba # import xlrd from nltk.translate.bleu_score import sentence_bleu,SmoothingFunction smooth = SmoothingFunction() rouge = Rouge() MAX_LEN = 60 stopwords = set([line.strip() for line in open('stopword', encoding='UTF-8')]) punctuation = [',', ';', '.', '，', '；', '。', '；', '？', '：', '、', '（', '）', '!', '！', '|'] punc = set(punctuation) def removeunuseful(text): text = str(text) text = text.replace('\n', '') text = text.replace('\\n', '') text = text.replace('(', '（') text = text.replace(')', '）') text = text.replace('<q>', '') text = re.sub('\xa0+', '', text) text = re.sub('\u3000+', '', text) text = re.sub('\\s+', '', text) text = re.sub(' +','',text) score_p = '[（][^（）]*\d+[^（）]*分[^）]*[）]' text = re.sub(score_p, '', text) return text def removeStopwords(text,replace=False): text = str(text) text = text.replace('\n', '') text = text.replace('\\n', '') text = text.replace('(', '（') text = text.replace(')', '）') text = text.replace('<q>', '') text = re.sub('\xa0+', '', text) text = re.sub('\u3000+', '', text) text = re.sub('\\s+', '', text) score_p = '[（][^（）]*\d+[^（）]*分[^）]*[）]' text = re.sub(score_p, '', text) sentence_depart = jieba.cut(text.strip()) outstr = [] for word in sentence_depart: if word in punc: # pass if replace: outstr.append(punctuation[0]) elif word not in stopwords: if word != '\t': outstr.append(word) text = ''.join(outstr) # punctuation_p = '[,;.，；。；？：、（）!！|]' # text = re.sub(punctuation_p, '', text) text = re.sub(' +', '', text) return text, outstr memory = {} def processText(rawText,replace=False): if rawText in memory: return memory[rawText] text, _ = removeStopwords(rawText,replace) text = ' '.join([w for w in text]) memory[rawText] = text return text def evalAnswer(answer, gold, metrics=(('rouge-1', 'f'), ('rouge-2', 'f'), ('rouge-l', 'f'),'BLEU'), max_len=MAX_LEN): zero_scores = {'rouge-1': {'r': 0, 'p': 0, 'f': 0}, 'rouge-2': {'r': 0, 'p': 0, 'f': 0}, 'rouge-l': {'r': 0, 'p': 0, 'f': 0}} if type(answer) is str: if len(answer) == 0: scores = zero_scores else: answer = ' '.join(processText(answer,replace=True).split()[:max_len]) gold = processText(gold,replace=True) if len(answer) == 0 or len(gold)==0: scores = zero_scores else: scores = rouge.get_scores(answer, gold)[0] if 'BLEU' in metrics: scores_bleu=sentence_bleu([gold], answer, smoothing_function=smooth.method1) #, weights=(0,0, 0,1) if metrics is None: return scores else: scores_list = [] for i, m in enumerate(metrics): # ('rouge-1','f') if m=='BLEU': scores_list.append(scores_bleu) else: scores_list.append(scores[m[0]][m[1]]) return scores_list else: scores = [evalAnswer(a, g, metrics=metrics, max_len=max_len) for a, g in zip(answer, gold)] if metrics is None: return scores else: return np.mean(scores, axis=0).tolist() def getScore(answerfile, goldfile): print(answerfile.split('/')[-2]) # material_index=[] # material_index=[33, 90, 112, 134, 139, 175, 259, 305, 328, 333, 351, 399, 413, 429, 449, 521, 596, 651, 663, 674, 696, 725, 740, 748, 768, 858, 889, 894, 899, 931, 980, 1010, 1035, 1105, 1119, 1133, 1149, 1236, 1276, 1287, 1306, 1311, 1316, 1329, 1331, 1385, 1420, 1423, 1437, 1441, 1443, 1453, 1470, 1505, 1533, 1580, 1691, 1701, 1817, 1820, 1876, 1880, 1911, 2079, 2093, 2102, 2140, 2297, 2355, 2363, 2422, 2489, 2525, 2565, 2572, 2584, 2613] # material_index=[11, 14, 33, 90, 112, 134, 139, 175, 191, 212, 238, 259, 305, 328, 333, 351, 399, 400, 413, 429, 449, 494, 521, 596, 651, 663, 674, 687, 696, 710, 725, 740, 748, 768, 783, 801, 858, 889, 894, 899, 931, 980, 1010, 1035, 1045, 1088, 1105, 1119, 1133, 1147, 1149, 1236, 1276, 1287, 1300, 1306, 1311, 1316, 1329, 1331, 1385, 1420, 1423, 1437, 1441, 1443, 1453, 1470, 1505, 1533, 1580, 1638, 1691, 1701, 1817, 1820, 1876, 1880, 1898, 1911, 2017, 2079, 2090, 2093, 2102, 2140, 2152, 2189, 2297, 2355, 2363, 2372, 2422, 2489, 2525, 2565, 2572, 2584, 2613, 2617, 2665] # material_index=[8, 11, 73, 75, 76, 90, 95, 97, 127, 134, 161, 168, 169, 237, 259, 261, 280, 326, 328, 349, 350, 399, 413, 421, 429, 479, 501, 521, 539, 576, 580, 597, 618, 651, 663, 664, 674, 710, 725, 727, 740, 748, 768, 783, 801, 889, 894, 898, 899, 931, 940, 959, 979, 980, 988, 1010, 1133, 1142, 1149, 1167, 1213, 1239, 1245, 1268, 1277, 1285, 1289, 1297, 1306, 1316, 1331, 1355, 1370, 1377, 1384, 1418, 1420, 1423, 1437, 1441, 1443, 1479, 1533, 1580, 1638, 1659, 1660, 1675, 1701, 1716, 1774, 1814, 1817, 1819, 1876, 1880, 1892, 1903, 1911, 1997, 2021, 2043, 2079, 2092, 2093, 2102, 2120, 2140, 2173, 2180, 2189, 2209, 2246, 2326, 2343, 2363, 2422, 2444, 2495, 2525, 2565, 2574, 2584, 2613, 2623, 2671, 2678] # material_index=[90, 112, 134, 175, 259, 305, 328, 399, 413, 429, 521, 596, 651, 663, 674, 696, 725, 740, 748, 768, 858, 889, 894, 899, 980, 1010, 1119, 1133, 1149, 1236, 1287, 1306, 1311, 1329, 1331, 1385, 1420, 1423, 1437, 1441, 1533, 1580, 1701, 1817, 1820, 1876, 1880, 1911, 2079, 2093, 2102, 2140, 2355, 2363, 2422, 2525, 2565, 2584, 2613] # material_index=[8, 11, 30, 53, 73, 75, 76, 90, 95, 97, 127, 134, 161, 168, 169, 230, 237, 245, 259, 261, 280, 319, 326, 328, 339, 349, 350, 351, 395, 399, 413, 421, 429, 479, 486, 493, 501, 521, 539, 566, 576, 580, 596, 597, 618, 651, 663, 664, 674, 710, 725, 727, 728, 740, 748, 768, 783, 801, 889, 894, 898, 899, 931, 940, 959, 979, 980, 988, 1010, 1133, 1142, 1149, 1167, 1213, 1218, 1239, 1245, 1268, 1277, 1285, 1289, 1297, 1300, 1306, 1316, 1331, 1355, 1370, 1377, 1384, 1395, 1418, 1420, 1423, 1437, 1441, 1443, 1453, 1479, 1505, 1533, 1580, 1599, 1600, 1638, 1659, 1660, 1675, 1701, 1716, 1774, 1807, 1814, 1817, 1819, 1854, 1876, 1880, 1892, 1903, 1911, 1997, 2021, 2043, 2079, 2092, 2093, 2095, 2102, 2120, 2140, 2173, 2180, 2189, 2209, 2246, 2259, 2306, 2326, 2335, 2343, 2355, 2362, 2363, 2422, 2444, 2495, 2499, 2516, 2525, 2565, 2574, 2584, 2605, 2613, 2623, 2671, 2678] # do_eval=[] # with open('resource/tagDataRaw/test_info','r',encoding='utf-8') as f: # for line in f: # search,qtype=line.strip().split('\t') # if search == 'False' and qtype=='generate': # do_eval.append(True) # else: # do_eval.append(False) # need_material=[] # with open('resource/tagDataKey/abs_bert_geo.1000.raw_src','r',encoding='utf-8') as f: # for line in f: # if '[SEP][CLS]' in line: # need_material.append(True) # else:need_material.append(False) metrics = [ # ("rouge-1", 'r'), # ("rouge-1", 'p'), ("rouge-1", 'f'), # ("rouge-2", 'r'), # ("rouge-2", 'p'), ("rouge-2", 'f'), # ("rouge-l", 'r'), # ("rouge-l", 'p'), ("rouge-l", 'f'), 'BLEU' ] scoreAll = [[] for _ in range(len(metrics))] with open(answerfile, "r", encoding='utf-8') as f_answer: answers = f_answer.readlines() with open(goldfile, "r", encoding='utf-8') as f_gold: golds = f_gold.readlines() # print() for idx, (answer, gold) in enumerate(zip(answers, golds)): # if not do_eval[idx] or not need_material[idx]: # continue # if idx not in material_index: # continue answer = answer.strip() gold = gold.strip() # print(answer) # print(gold) if len(answer) != 0: try: score = evalAnswer(answer, gold, metrics=metrics) # if score[5]>0.13: # material_index.append(idx) # print(score) for i in range(len(metrics)): scoreAll[i].append(score[i]*100) except: for i in range(len(scoreAll)): scoreAll[i].append(0) else: for i in range(len(scoreAll)): scoreAll[i].append(0) # print("nan score") # scoreAvg = np.nanmean(scoreAll, axis=1) # for metric, score in zip(metrics, scoreAvg): # print(metric, score) print("score") # print(material_index) # print(len(material_index)) scoreAvg = np.mean(scoreAll, axis=1) for metric, score in zip(metrics, scoreAvg): print(metric, round(score,2)) return scoreAll def getScoreForHuman(readfile): # material_index=[] # material_index=[33, 90, 112, 134, 139, 175, 259, 305, 328, 333, 351, 399, 413, 429, 449, 521, 596, 651, 663, 674, 696, 725, 740, 748, 768, 858, 889, 894, 899, 931, 980, 1010, 1035, 1105, 1119, 1133, 1149, 1236, 1276, 1287, 1306, 1311, 1316, 1329, 1331, 1385, 1420, 1423, 1437, 1441, 1443, 1453, 1470, 1505, 1533, 1580, 1691, 1701, 1817, 1820, 1876, 1880, 1911, 2079, 2093, 2102, 2140, 2297, 2355, 2363, 2422, 2489, 2525, 2565, 2572, 2584, 2613] # material_index=[11, 14, 33, 90, 112, 134, 139, 175, 191, 212, 238, 259, 305, 328, 333, 351, 399, 400, 413, 429, 449, 494, 521, 596, 651, 663, 674, 687, 696, 710, 725, 740, 748, 768, 783, 801, 858, 889, 894, 899, 931, 980, 1010, 1035, 1045, 1088, 1105, 1119, 1133, 1147, 1149, 1236, 1276, 1287, 1300, 1306, 1311, 1316, 1329, 1331, 1385, 1420, 1423, 1437, 1441, 1443, 1453, 1470, 1505, 1533, 1580, 1638, 1691, 1701, 1817, 1820, 1876, 1880, 1898, 1911, 2017, 2079, 2090, 2093, 2102, 2140, 2152, 2189, 2297, 2355, 2363, 2372, 2422, 2489, 2525, 2565, 2572, 2584, 2613, 2617, 2665] # material_index=[8, 11, 73, 75, 76, 90, 95, 97, 127, 134, 161, 168, 169, 237, 259, 261, 280, 326, 328, 349, 350, 399, 413, 421, 429, 479, 501, 521, 539, 576, 580, 597, 618, 651, 663, 664, 674, 710, 725, 727, 740, 748, 768, 783, 801, 889, 894, 898, 899, 931, 940, 959, 979, 980, 988, 1010, 1133, 1142, 1149, 1167, 1213, 1239, 1245, 1268, 1277, 1285, 1289, 1297, 1306, 1316, 1331, 1355, 1370, 1377, 1384, 1418, 1420, 1423, 1437, 1441, 1443, 1479, 1533, 1580, 1638, 1659, 1660, 1675, 1701, 1716, 1774, 1814, 1817, 1819, 1876, 1880, 1892, 1903, 1911, 1997, 2021, 2043, 2079, 2092, 2093, 2102, 2120, 2140, 2173, 2180, 2189, 2209, 2246, 2326, 2343, 2363, 2422, 2444, 2495, 2525, 2565, 2574, 2584, 2613, 2623, 2671, 2678] # material_index=[90, 112, 134, 175, 259, 305, 328, 399, 413, 429, 521, 596, 651, 663, 674, 696, 725, 740, 748, 768, 858, 889, 894, 899, 980, 1010, 1119, 1133, 1149, 1236, 1287, 1306, 1311, 1329, 1331, 1385, 1420, 1423, 1437, 1441, 1533, 1580, 1701, 1817, 1820, 1876, 1880, 1911, 2079, 2093, 2102, 2140, 2355, 2363, 2422, 2525, 2565, 2584, 2613] # material_index=[8, 11, 30, 53, 73, 75, 76, 90, 95, 97, 127, 134, 161, 168, 169, 230, 237, 245, 259, 261, 280, 319, 326, 328, 339, 349, 350, 351, 395, 399, 413, 421, 429, 479, 486, 493, 501, 521, 539, 566, 576, 580, 596, 597, 618, 651, 663, 664, 674, 710, 725, 727, 728, 740, 748, 768, 783, 801, 889, 894, 898, 899, 931, 940, 959, 979, 980, 988, 1010, 1133, 1142, 1149, 1167, 1213, 1218, 1239, 1245, 1268, 1277, 1285, 1289, 1297, 1300, 1306, 1316, 1331, 1355, 1370, 1377, 1384, 1395, 1418, 1420, 1423, 1437, 1441, 1443, 1453, 1479, 1505, 1533, 1580, 1599, 1600, 1638, 1659, 1660, 1675, 1701, 1716, 1774, 1807, 1814, 1817, 1819, 1854, 1876, 1880, 1892, 1903, 1911, 1997, 2021, 2043, 2079, 2092, 2093, 2095, 2102, 2120, 2140, 2173, 2180, 2189, 2209, 2246, 2259, 2306, 2326, 2335, 2343, 2355, 2362, 2363, 2422, 2444, 2495, 2499, 2516, 2525, 2565, 2574, 2584, 2605, 2613, 2623, 2671, 2678] # do_eval=[] # with open('resource/tagDataRaw/test_info','r',encoding='utf-8') as f: # for line in f: # search,qtype=line.strip().split('\t') # if search == 'False' and qtype=='generate': # do_eval.append(True) # else: # do_eval.append(False) # need_material=[] # with open('resource/tagDataKey/abs_bert_geo.1000.raw_src','r',encoding='utf-8') as f: # for line in f: # if '[SEP][CLS]' in line: # need_material.append(True) # else:need_material.append(False) metrics = [ # ("rouge-1", 'r'), # ("rouge-1", 'p'), ("rouge-1", 'f'), # ("rouge-2", 'r'), # ("rouge-2", 'p'), ("rouge-2", 'f'), # ("rouge-l", 'r'), # ("rouge-l", 'p'), ("rouge-l", 'f')] humandata = xlrd.open_workbook(readfile) humandata = humandata.sheet_by_index(0) nrows = humandata.nrows data = {} datas =[] for i in range(1, nrows): # temp=humandata.cell(i,0).value if humandata.cell(i, 0).value == "": data['answer'][humandata.cell(i, 3).value]= humandata.cell(i, 4).value else: if data: datas.append(data) data={} qid = humandata.cell(i, 0).value score = humandata.cell(i, 1).value gold = humandata.cell(i, 2).value data["qid"] = qid data["score"] = score data["gold"] = gold data['answer'] = {humandata.cell(i, 3).value: humandata.cell(i, 4).value} if data: datas.append(data) # print(datas[0]['answer'].keys()) for method in datas[0]['answer'].keys(): print(method+":") answers=[data['answer'][method] for data in datas] golds = [data['gold'] for data in datas] # print() scoreAll = [[] for _ in range(len(metrics))] for idx, (answer, gold) in enumerate(zip(answers, golds)): # if not do_eval[idx] or not need_material[idx]: # continue # if idx not in material_index: # continue answer = answer.strip() gold = gold.strip() # print(answer) # print(gold) if len(answer) != 0: try: score = evalAnswer(answer, gold, metrics=metrics) # if score[5]>0.13: # material_index.append(idx) # print(score) for i in range(len(metrics)): scoreAll[i].append(score[i]*100) except: for i in range(len(scoreAll)): scoreAll[i].append(0) else: for i in range(len(scoreAll)): scoreAll[i].append(0) print("score") scoreAvg = np.mean(scoreAll, axis=1) for metric, score in zip(metrics, scoreAvg): print(metric, score) def cmpScore(answerfile1,answerfile2, goldfile): metrics = [ # ("rouge-1", 'r'), # ("rouge-1", 'p'), ("rouge-1", 'f'), # ("rouge-2", 'r'), # ("rouge-2", 'p'), ("rouge-2", 'f'), # ("rouge-l", 'r'), # ("rouge-l", 'p'), ("rouge-l", 'f')] scoreAll = [[] for _ in range(len(metrics))] with open(answerfile1, "r", encoding='utf-8') as f_answer1: answers1 = f_answer1.readlines() with open(answerfile2, "r", encoding='utf-8') as f_answer2: answers2 = f_answer2.readlines() with open(goldfile, "r", encoding='utf-8') as f_gold: golds = f_gold.readlines() for idx, (answer1,answer2, gold) in enumerate(zip(answers1,answers2, golds)): # if not do_eval[idx] or not need_material[idx]: # continue # if idx not in material_index: # continue answer1 = answer1.strip() answer2 = answer2.strip() gold = gold.strip() if len(answer1) != 0 and len(answer2)!=0: try: score1 = evalAnswer(answer1, gold, metrics=metrics) score2 = evalAnswer(answer2, gold, metrics=metrics) if(score1[2]<score2[2] and score1[1]>score2[1]): print(answer1) print(answer2) print(gold) print(score1,score2) # if score[5]>0.13: # material_index.append(idx) except: for i in range(len(scoreAll)): scoreAll[i].append(0) import json def getScoreForHardEM(answerfile, goldfile): metrics = [ ("rouge-1", 'r'), ("rouge-1", 'p'), ("rouge-1", 'f'), ("rouge-2", 'r'), ("rouge-2", 'p'), ("rouge-2", 'f'), ("rouge-l", 'r'), ("rouge-l", 'p'), ("rouge-l", 'f')] scoreAll = [[] for _ in range(len(metrics))] with open(answerfile, "r", encoding='utf-8') as f_answer: data = json.load(f_answer) keys=data.keys() with open(goldfile, "r", encoding='utf-8') as f_gold: golds = f_gold.readlines() for idx, (key, gold) in enumerate(zip(keys, golds)): # print(idx) answer_list = [item["text"] for item in data[key]] gold = gold.strip() # print(answer) max_score=-1 max_score_all=[] for answer in answer_list: answer=answer.replace(" ","") if len(answer) != 0: try: score = evalAnswer(answer[:MAX_LEN], gold) if score[2]>max_score or max_score==-1: max_score=score[2] for i in range(len(metrics)): max_score_all.append(score[i]) except: for i in range(len(scoreAll)): max_score_all.append(0) else: for i in range(len(scoreAll)): scoreAll[i].append(0) for i in range(len(scoreAll)): scoreAll[i].append(max_score_all[i]) print("score") scoreAvg = np.mean(scoreAll, axis=1) for metric, score in zip(metrics, scoreAvg): print(metric, score) return scoreAvg def average_precision(gt, pred,maxnum=-1): """ Computes the average precision. This function computes the average prescision at k between two lists of items. Parameters ---------- gt: set A set of ground-truth elements (order doesn't matter) pred: list A list of predicted elements (order does matter) Returns ------- score: double The average precision over the input lists """ if not gt: return 0.0 score = 0.0 num_hits = 0.0 for i,p in enumerate(pred): if maxnum!=-1 and i >=maxnum: break if p in gt and p not in pred[:i]: num_hits += 1.0 score += num_hits / (i + 1.0) if maxnum==-1: maxnum=len(gt) return score / max(1.0, min(len(gt),maxnum)) def hit(gt,pred,maxnum=-1): num_hits = 0.0 for i, p in enumerate(pred): if maxnum!=-1 and i >=maxnum: break if p in gt and p not in pred[:i]: num_hits+=1.0 if maxnum==-1: maxnum=len(gt) return num_hits/max(1.0, min(len(gt),maxnum)) def NDCG(gt, pred,maxnum=-1, use_graded_scores=False): score = 0.0 for rank, item in enumerate(pred): if maxnum!=-1 and rank>=maxnum: break if item in gt: if use_graded_scores: grade = 1.0 / (gt.index(item) + 1) else: grade = 1.0 score += grade / np.log2(rank + 2) norm = 0.0 for rank in range(len(gt)): if maxnum!=-1 and rank>=maxnum: break if use_graded_scores: grade = 1.0 / (rank + 1) else: grade = 1.0 norm += grade / np.log2(rank + 2) return score / max(0.3, norm) def cal_metrics(gt, pred, metrics_map,maxnum=-1): ''' Returns a numpy array containing metrics specified by metrics_map. gt: set A set of ground-truth elements (order doesn't matter) pred: list A list of predicted elements (order does matter) ''' out = np.zeros((len(metrics_map),), np.float32) if ('MAP' in metrics_map): avg_precision = average_precision(gt=gt, pred=pred,maxnum=maxnum) out[metrics_map.index('MAP')] = avg_precision if ('RPrec' in metrics_map): intersec = len(gt & set(pred[:len(gt)])) out[metrics_map.index('RPrec')] = intersec / max(1., float(len(gt))) if 'MRR' in metrics_map: score = 0.0 for rank, item in enumerate(pred): if maxnum != -1 and rank >= maxnum: break if item in gt: score = 1.0 / (rank + 1.0) break out[metrics_map.index('MRR')] = score if 'MRR@10' in metrics_map: score = 0.0 for rank, item in enumerate(pred[:10]): if item in gt: score = 1.0 / (rank + 1.0) break out[metrics_map.index('MRR@10')] = score if ('NDCG' in metrics_map): out[metrics_map.index('NDCG')] = NDCG(gt, pred,maxnum=maxnum) if ('HIT' in metrics_map): out[metrics_map.index('HIT')] = hit(gt, pred,maxnum=maxnum) return out import scipy.stats as stats def ttest(scoreAll1,scoreAll2): # print(stats.levene(scoreAll1, scoreAll2)) # print(stats.ttest_ind(scoreAll1, scoreAll2)) print(stats.ttest_rel(scoreAll1, scoreAll2)) def ttestPair(readfile1,readfile2,goldfile): scoreAll1=getScore(readfile1, goldfile) scoreAll2=getScore(readfile2, goldfile) print(len(scoreAll1)) ttest(scoreAll1[0],scoreAll2[0]) ttest(scoreAll1[1], scoreAll2[1]) ttest(scoreAll1[2], scoreAll2[2]) ttest(scoreAll1[3],scoreAll2[3]) def eval_xunfei(readfile,writefile): import csv # import pandas as pd # data = pd.read_csv(readfile) # print(data) with open(readfile, 'r',encoding='gbk') as f: with open(writefile, 'w', encoding='utf-8',newline='') as f_w: f.readline() reader = csv.reader(f) writer = csv.writer(f_w) writer.writerow(["试卷","场景","问题","答案","系统答案","人工评分","rouge-1-f","rouge-2-f",'rouge-l-f',"rouge-1-r","rouge-2-r",'rouge-l-r']) for row in reader: row[-1]=float(row[-1]) gold=row[3] candidate=row[4] score = evalAnswer(candidate, gold, metrics=(('rouge-1', 'f'), ('rouge-2', 'f'), ('rouge-l', 'f'),('rouge-1', 'r'), ('rouge-2', 'r'), ('rouge-l', 'r')), max_len=10000) row.extend(score) # print(row) writer.writerow(row) # assert len(row)==9 def cal_pearson_spearman(readfile): import pandas as pd import matplotlib.pyplot as plt datas = pd.read_csv(readfile,encoding='utf-8') rouge1f = datas[['人工评分','rouge-1-f']] temp=rouge1f.corr() print("rouge1f: pearson: ",rouge1f.corr().iat[0,1] )#计算皮尔逊相关系数 print("rouge1f: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 rouge1f = datas[['人工评分','rouge-2-f']] print("rouge2f: pearson: ",rouge1f.corr().iat[0,1])#计算皮尔逊相关系数 print("rouge2f: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 rouge1f = datas[['人工评分','rouge-l-f']] print("rougelf: pearson: ",rouge1f.corr().iat[0,1])#计算皮尔逊相关系数 print("rougelf: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 rouge1f = datas[['人工评分','rouge-1-r']] print("rouge1r: pearson: ",rouge1f.corr().iat[0,1])#计算皮尔逊相关系数 print("rouge1r: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 rouge1f = datas[['人工评分','rouge-2-r']] print("rouge2r: pearson: ",rouge1f.corr().iat[0,1])#计算皮尔逊相关系数 print("rouge2r: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 rouge1f = datas[['人工评分','rouge-l-r']] print("rougelr: pearson: ",rouge1f.corr().iat[0,1])#计算皮尔逊相关系数 print("rougelr: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 rouge1f = datas[['人工评分','bert_dual']] print("bert_dual: pearson: ",rouge1f.corr().iat[0,1])#计算皮尔逊相关系数 print("bert_dual: spearman: ",rouge1f.corr('spearman').iat[0,1])#计算spearman相关系数 if __name__ == '__main__': getScore('resource_open_domain/image_data_20210118/baselines/MASS/dev_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/MASS/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/preSumm/dev.19500.candidate', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/preSumm/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/bert-nmt/dev.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/bert-nmt/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/json_data/val.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/json_data/test.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/BM25/val_rt_answer.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/BM25/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/smrs/dev_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/smrs/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/HardEM/dev_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/HardEM/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/learning_to_retrieve/dev.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/learning_to_retrieve/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') getScore('resource_open_domain/image_data_20210118/baselines/DPR/dev.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') getScore('resource_open_domain/image_data_20210118/baselines/DPR/test_result.txt', 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/ckgg_data/ernie_test.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/results_multidoc/.0.candidate', # 'resource_open_domain/image_data_20210118/results_multidoc/.0.gold') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent/.16500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copyword_graph/.14500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copyword/.20000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_multidoc/.19500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_noScenario/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_onlyScenario/.18500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copyword_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copyword/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_multidoc/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_noScenario/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_onlyScenario/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # cmpScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate','resource_open_domain/image_data_20210118/my_results/results_multidoc/.19500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_0.2/.15500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_0.4/.18500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_5/.14000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_10/.19000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_0.2/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_0.4/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_5/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # getScore('resource_open_domain/image_data_20210118/my_results/results_copysent_graph_10/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copyword/.20000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copyword_graph/.14500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copysent/.16500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_multidoc/.19500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_onlyScenario/.18500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copysent_graph_noScenario/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copyword/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copyword_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copysent/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_multidoc/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_onlyScenario/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/my_results/results_copysent_graph_noScenario/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val_rt_answer.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/smrs/dev_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/MASS/generate_15_dev.out', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/preSumm/dev.19500.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/HardEM/dev_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/json_data/val.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/DPR/dev.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/learning_to_retrieve/dev.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.17000.candidate', # 'resource_open_domain/image_data_20210118/baselines/bert-nmt/dev.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/val.tgt') # # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/BM25/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/smrs/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/MASS/generate_15_test.out', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/preSumm/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/HardEM/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/json_data/test.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/DPR/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/learning_to_retrieve/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt') # ttestPair('resource_open_domain/image_data_20210118/my_results/results_copysent_graph/.0.candidate', # 'resource_open_domain/image_data_20210118/baselines/bert-nmt/test_result.txt', # 'resource_open_domain/image_data_20210118/baselines/BM25/test.tgt')

56.269388

890

0.637289

5,435

41,358

4.645814

0.095676

0.084119

0.126178

0.160317

0.833584

0.818812

0.815366

0.806733

0.790337

0.782178

0

0.181008

0.221626

41,358

735

891

56.269388

0.603349

0.527516

0

0.455665

0

0.181317

0.128836

0

1

0.039409

false

0

0.024631

0

0.100985

0.068966

0

null

0

1

0

null

0

7

8ce56286dc167bf5dd30f3dbdc4e4fdda15c3962

83

py

Python

IA/Python/4/4.2/4.py

worthl3ss/random-small

ffb60781f57eb865acbd81aaa07056046bad32fe

[ "MIT" ]

1

2022-02-23T12:47:00.000Z

IA/Python/4/4.2/4.py

worthl3ss/random-small

ffb60781f57eb865acbd81aaa07056046bad32fe

[ "MIT" ]

null

IA/Python/4/4.2/4.py

worthl3ss/random-small

ffb60781f57eb865acbd81aaa07056046bad32fe

[ "MIT" ]

null

import re print(re.compile("[a-z]+\.[a-z]+[0-9]{0,2}@e-uvt\.ro").findall(input()))

27.666667

72

0.578313

18

83

2.666667

0.777778

0.083333

0

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8cf2c76767e2dd2d9de7cea3b6ff8a5a0f066aea

23,542

py

Python

app/test/unittest/test_sensor_type.py

michalkoziara/IoT-RESTful-Webservice

ecb0f3e09cded3190f3646e5cd6c913056d94981

[ "bzip2-1.0.6" ]

2

2021-09-24T02:45:32.000Z

2021-11-15T09:44:44.000Z

app/test/unittest/test_sensor_type.py

PKramek/IoT-RESTful-Webservice-1

ecb0f3e09cded3190f3646e5cd6c913056d94981

[ "bzip2-1.0.6" ]

null

app/test/unittest/test_sensor_type.py

PKramek/IoT-RESTful-Webservice-1

ecb0f3e09cded3190f3646e5cd6c913056d94981

[ "bzip2-1.0.6" ]

1

2021-09-11T11:47:32.000Z

from unittest.mock import patch import pytest from app.main.repository.admin_repository import AdminRepository from app.main.repository.device_group_repository import DeviceGroupRepository from app.main.repository.reading_enumerator_repository import ReadingEnumeratorRepository from app.main.repository.sensor_type_repository import SensorTypeRepository from app.main.repository.user_repository import UserRepository from app.main.service.sensor_type_service import SensorTypeService from app.main.util.constants import Constants def test_get_sensor_type_info_should_return_sensor_info_when_valid_request_and_reading_type_is_enum( get_sensor_type_default_values, create_sensor_type, get_sensor_reading_enumerator_default_values, create_sensor_reading_enumerator, create_device_group, create_user_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() test_user_id = '1' device_group = create_device_group() user_group = create_user_group() user = create_user() device_group.user_groups = [user_group] sensor_type = create_sensor_type() user_group.users = [user] first_enumerator_values = get_sensor_reading_enumerator_default_values() first_enumerator_values['number'] = 2 first_enumerator_values['text'] = 'text 2' first_enumerator = create_sensor_reading_enumerator(first_enumerator_values) second_enumerator_values = get_sensor_reading_enumerator_default_values() second_enumerator_values['number'] = 2 second_enumerator_values['text'] = 'text 2' second_enumerator = create_sensor_reading_enumerator(second_enumerator_values) expected_returned_values = { 'name': sensor_type.name, 'readingType': sensor_type.reading_type, 'rangeMin': sensor_type.range_min, 'rangeMax': sensor_type.range_max, 'enumerator': [ { 'number': first_enumerator.number, 'text': first_enumerator.text }, { 'number': second_enumerator.number, 'text': second_enumerator.text } ] } with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( UserRepository, 'get_user_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = user with patch.object( SensorTypeRepository, 'get_sensor_type_by_device_group_id_and_name' ) as get_sensor_type_by_device_group_id_and_name_mock: get_sensor_type_by_device_group_id_and_name_mock.return_value = sensor_type with patch.object( ReadingEnumeratorRepository, 'get_reading_enumerators_by_sensor_type_id' ) as get_reading_enumerators_by_sensor_type_id_mock: get_reading_enumerators_by_sensor_type_id_mock.return_value = [ first_enumerator, second_enumerator ] result, result_values = sensor_type_service_instance.get_sensor_type_info( device_group.product_key, user_group.name, test_user_id, False ) assert result == Constants.RESPONSE_MESSAGE_OK assert result_values == expected_returned_values @pytest.mark.parametrize("reading_type, range_min, range_max", [ ('Decimal', 0.1, 1.0), ('Boolean', True, False)]) def test_get_sensor_type_info_should_return_sensor_info_when_valid_request_and_reading_type_is_not_enum( reading_type, range_min, range_max, get_sensor_type_default_values, create_sensor_type, get_sensor_reading_enumerator_default_values, create_sensor_reading_enumerator, create_device_group, create_user_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() test_user_id = '1' device_group = create_device_group() user_group = create_user_group() user = create_user() device_group.user_groups = [user_group] sensor_type_values = get_sensor_type_default_values() sensor_type_values['reading_type'] = reading_type sensor_type_values['range_min'] = range_min sensor_type_values['range_max'] = range_max sensor_type = create_sensor_type(sensor_type_values) assert sensor_type.range_min == sensor_type_values['range_min'] assert sensor_type.reading_type == sensor_type_values['reading_type'] assert sensor_type.range_max == sensor_type_values['range_max'] user_group.users = [user] expected_returned_values = { 'name': sensor_type.name, 'readingType': sensor_type.reading_type, 'rangeMin': sensor_type.range_min, 'rangeMax': sensor_type.range_max, } with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( UserRepository, 'get_user_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = user with patch.object( SensorTypeRepository, 'get_sensor_type_by_device_group_id_and_name' ) as get_sensor_type_by_device_group_id_and_name_mock: get_sensor_type_by_device_group_id_and_name_mock.return_value = sensor_type result, result_values = sensor_type_service_instance.get_sensor_type_info( device_group.product_key, user_group.name, test_user_id, False ) assert result == Constants.RESPONSE_MESSAGE_OK assert result_values == expected_returned_values def test_get_sensor_type_info_should_return_error_message_when_sensor_type_not_in_device_group( get_sensor_type_default_values, create_sensor_type, get_sensor_reading_enumerator_default_values, create_sensor_reading_enumerator, create_device_group, create_user_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() test_user_id = '1' device_group = create_device_group() user_group = create_user_group() user = create_user() device_group.user_groups = [user_group] user_group.users = [user] with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( UserRepository, 'get_user_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = user with patch.object( SensorTypeRepository, 'get_sensor_type_by_device_group_id_and_name' ) as get_sensor_type_by_device_group_id_and_name_mock: get_sensor_type_by_device_group_id_and_name_mock.return_value = None result, result_values = sensor_type_service_instance.get_sensor_type_info( device_group.product_key, user_group.name, test_user_id, False ) assert result == Constants.RESPONSE_MESSAGE_SENSOR_TYPE_NOT_FOUND assert result_values is None def test_get_sensor_type_info_should_return_error_message_when_user_not_in_any_user_group_in_device_group( create_device_group, create_user_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() test_user_id = '1' device_group = create_device_group() user_group = create_user_group() user = create_user() device_group.user_groups = [user_group] user_group.users = [] with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( UserRepository, 'get_user_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = user result, result_values = sensor_type_service_instance.get_sensor_type_info( device_group.product_key, user_group.name, test_user_id, False ) assert result == Constants.RESPONSE_MESSAGE_USER_DOES_NOT_HAVE_PRIVILEGES assert result_values is None def test_get_sensor_type_info_should_return_error_message_when_user_not_found( create_device_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() test_user_id = '1' device_group = create_device_group() with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( UserRepository, 'get_user_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = None result, result_values = sensor_type_service_instance.get_sensor_type_info( device_group.product_key, 'user_group_name', test_user_id, False ) assert result == Constants.RESPONSE_MESSAGE_USER_NOT_DEFINED assert result_values is None def test_get_sensor_type_info_should_return_error_message_when_device_group_not_found(): sensor_type_service_instance = SensorTypeService.get_instance() test_user_id = '1' with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = None result, result_values = sensor_type_service_instance.get_sensor_type_info( 'device_group_product_key', 'user_group_name', test_user_id, False ) assert result == Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND assert result_values is None @pytest.mark.parametrize("product_key, type_name, user_id, expected_result", [ ('product_key', 'type_name', None, Constants.RESPONSE_MESSAGE_USER_NOT_DEFINED), ('product_key', None, 'user_id', Constants.RESPONSE_MESSAGE_SENSOR_TYPE_NAME_NOT_DEFINED), (None, 'type_name', 'user_id', Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND) ]) def test_get_sensor_type_info_should_return_error_message_when_one_of_parameters_is_None(product_key, type_name, user_id, expected_result): sensor_type_service_instance = SensorTypeService.get_instance() result, result_values = sensor_type_service_instance.get_sensor_type_info( product_key, type_name, user_id, False ) assert result == expected_result assert result_values is None def test_get_list_of_types_names_should_return_list_of_sensor_types_names_when_valid_request( create_sensor_type, create_device_group, create_admin): sensor_type_service_instance = SensorTypeService.get_instance() device_group = create_device_group() admin = create_admin() first_sensor_type = create_sensor_type() second_sensor_type = create_sensor_type() third_sensor_type = create_sensor_type() first_sensor_type.name = "sensor type 1" second_sensor_type.name = "sensor type 2" third_sensor_type.name = "sensor type 3" expected_returned_values = ["sensor type 1", "sensor type 2", "sensor type 3"] with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( AdminRepository, 'get_admin_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = admin with patch.object( SensorTypeRepository, 'get_sensor_types_by_device_group_id' ) as get_sensor_types_by_device_group_id_mock: get_sensor_types_by_device_group_id_mock.return_value = [first_sensor_type, second_sensor_type, third_sensor_type] result, result_values = sensor_type_service_instance.get_list_of_types_names( device_group.product_key, admin.id ) assert result == Constants.RESPONSE_MESSAGE_OK assert result_values == expected_returned_values def test_get_list_of_types_names_should_return_empty_list_when_valid_request_and_no_sensor_types_in_device_group( create_sensor_type, create_device_group, create_admin): sensor_type_service_instance = SensorTypeService.get_instance() device_group = create_device_group() admin = create_admin() expected_returned_values = [] with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( AdminRepository, 'get_admin_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = admin with patch.object( SensorTypeRepository, 'get_sensor_types_by_device_group_id' ) as get_sensor_types_by_device_group_id_mock: get_sensor_types_by_device_group_id_mock.return_value = [] result, result_values = sensor_type_service_instance.get_list_of_types_names( device_group.product_key, admin.id ) assert result == Constants.RESPONSE_MESSAGE_OK assert result_values == expected_returned_values def test_get_list_of_types_names_should_return_error_message_when_admin_not_found( create_device_group, create_user_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() device_group = create_device_group() with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group with patch.object( AdminRepository, 'get_admin_by_id' ) as get_user_by_id_mock: get_user_by_id_mock.return_value = None result, result_values = sensor_type_service_instance.get_list_of_types_names( device_group.product_key, device_group.admin_id ) assert result == Constants.RESPONSE_MESSAGE_ADMIN_NOT_DEFINED assert result_values is None def test_get_list_of_types_names_should_return_error_message_when_admin_not_assigned_to_device_group( create_device_group, create_user_group, create_user): sensor_type_service_instance = SensorTypeService.get_instance() device_group = create_device_group() with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = device_group result, result_values = sensor_type_service_instance.get_list_of_types_names( device_group.product_key, device_group.admin_id + 1 ) assert result == Constants.RESPONSE_MESSAGE_USER_DOES_NOT_HAVE_PRIVILEGES assert result_values is None def test_get_list_of_types_names_should_return_error_message_when_device_group_not_found(): sensor_type_service_instance = SensorTypeService.get_instance() with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = None result, result_values = sensor_type_service_instance.get_list_of_types_names( 'device_group.product_key', 'admin.id' ) assert result == Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND assert result_values is None @pytest.mark.parametrize("product_key, user_id, expected_result", [ ('product_key', None, Constants.RESPONSE_MESSAGE_USER_NOT_DEFINED), (None, 'user_id', Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND) ]) def test_get_list_of_types_names_should_return_error_message_when_one_of_parameters_is_none( product_key, user_id, expected_result): sensor_type_service_instance = SensorTypeService.get_instance() with patch.object( DeviceGroupRepository, 'get_device_group_by_product_key' ) as get_device_group_by_product_key_mock: get_device_group_by_product_key_mock.return_value = None result, result_values = sensor_type_service_instance.get_list_of_types_names( product_key, user_id ) assert result == expected_result assert result_values is None @pytest.mark.parametrize("reading_type", ['Decimal', 'Enum', 'Boolean']) def test_create_sensor_type_in_device_group_should_create_sensor_type_when_valid_parameters( create_device_group, reading_type ): sensor_type_service_instance = SensorTypeService.get_instance() device_group = create_device_group() with patch.object( DeviceGroupRepository, 'get_device_group_by_admin_id_and_product_key' ) as get_device_group_by_admin_id_and_product_key_mock: get_device_group_by_admin_id_and_product_key_mock.return_value = device_group with patch.object( SensorTypeRepository, 'get_sensor_type_by_device_group_id_and_name' ) as get_sensor_type_by_device_group_id_and_name_mock: get_sensor_type_by_device_group_id_and_name_mock.return_value = None with patch.object( SensorTypeRepository, 'save' ) as save_mock: save_mock.return_value = True with patch.object( ReadingEnumeratorRepository, 'save_but_do_not_commit' ): with patch.object( ReadingEnumeratorRepository, 'update_database' ) as update_database_mock: update_database_mock.return_value = True result = sensor_type_service_instance.create_sensor_type_in_device_group( device_group.product_key, 'type_name', reading_type, 0, 1, [ { 'number': 0, 'text': 'zero' }, { 'number': 1, 'text': 'one' } ], 'admin_id' ) assert result assert result == Constants.RESPONSE_MESSAGE_CREATED def test_create_sensor_type_in_device_group_should_return_error_message_when_no_device_group(): sensor_type_service_instance = SensorTypeService.get_instance() with patch.object( DeviceGroupRepository, 'get_device_group_by_admin_id_and_product_key' ) as get_device_group_by_admin_id_and_product_key_mock: get_device_group_by_admin_id_and_product_key_mock.return_value = None result = sensor_type_service_instance.create_sensor_type_in_device_group( 'product_key', 'type_name', 'Enum', 0, 1, [ { 'number': 0, 'text': 'zero' }, { 'number': 1, 'text': 'one' } ], 'admin_id' ) assert result assert result == Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND def test_create_sensor_type_in_device_group_should_return_sensor_type_already_exists_when_duplicated_name( create_device_group, create_sensor_type): sensor_type_service_instance = SensorTypeService.get_instance() device_group = create_device_group() sensor_type = create_sensor_type() with patch.object( DeviceGroupRepository, 'get_device_group_by_admin_id_and_product_key' ) as get_device_group_by_admin_id_and_product_key_mock: get_device_group_by_admin_id_and_product_key_mock.return_value = device_group with patch.object( SensorTypeRepository, 'get_sensor_type_by_device_group_id_and_name' ) as get_sensor_type_by_device_group_id_and_name_mock: get_sensor_type_by_device_group_id_and_name_mock.return_value = sensor_type result = sensor_type_service_instance.create_sensor_type_in_device_group( device_group.product_key, 'type_name', 'Enum', 0, 1, [ { 'number': 0, 'text': 'zero' }, { 'number': 1, 'text': 'one' } ], 'admin_id' ) assert result assert result == Constants.RESPONSE_MESSAGE_SENSOR_TYPE_ALREADY_EXISTS @pytest.mark.parametrize("product_key, type_name, admin_id, expected_result", [ ('product_key', 'type_name', None, Constants.RESPONSE_MESSAGE_USER_NOT_DEFINED), ('product_key', None, 'user_id', Constants.RESPONSE_MESSAGE_BAD_REQUEST), (None, 'type_name', 'user_id', Constants.RESPONSE_MESSAGE_PRODUCT_KEY_NOT_FOUND) ]) def test_create_sensor_type_in_device_group_should_return_bad_request_when_no_parameter_given( product_key, type_name, admin_id, expected_result ): sensor_type_service_instance = SensorTypeService.get_instance() result = sensor_type_service_instance.create_sensor_type_in_device_group( product_key, type_name, 'Enum', 0, 1, [ { 'number': 0, 'text': 'zero' }, { 'number': 1, 'text': 'one' } ], admin_id ) assert result assert result == expected_result

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7

509a280e9aecbc202b39f077a2602ce8af35b2dd

200,171

py

Python

src/genie/libs/parser/junos/tests/ShowInterfaces/cli/equal/golden_output_2_expected.py

sriram-chakka/genieparser

560e410b1dc99cd325452c035b6c6e42691f0e40

[ "Apache-2.0" ]

4

2020-08-20T12:23:12.000Z

2021-06-15T14:10:02.000Z

src/genie/libs/parser/junos/tests/ShowInterfaces/cli/equal/golden_output_2_expected.py

sriram-chakka/genieparser

560e410b1dc99cd325452c035b6c6e42691f0e40

[ "Apache-2.0" ]

119

2020-07-10T22:37:51.000Z

2021-03-18T02:40:05.000Z

src/genie/libs/parser/junos/tests/ShowInterfaces/cli/equal/golden_output_2_expected.py

sriram-chakka/genieparser

560e410b1dc99cd325452c035b6c6e42691f0e40

[ "Apache-2.0" ]

2

2020-07-10T15:33:42.000Z

2021-04-05T09:48:56.000Z

expected_output = { "interface-information": { "physical-interface": [ { "active-alarms": { "interface-alarms": { "alarm-not-present": True } }, "active-defects": { "interface-alarms": { "alarm-not-present": True } }, "admin-status": { "@junos:format": "Enabled" }, "bpdu-error": "None", "cos-information": { "cos-stream-information": { "cos-direction": "Output", "cos-queue-configuration": [ { "cos-queue-bandwidth": "95", "cos-queue-bandwidth-bps": "950000000", "cos-queue-buffer": "95", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "best-effort", "cos-queue-limit": "none", "cos-queue-number": "0", "cos-queue-priority": "low" }, { "cos-queue-bandwidth": "5", "cos-queue-bandwidth-bps": "50000000", "cos-queue-buffer": "5", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "00:50:56:ff:56:b6", "description": "none/100G/in/hktGCS002_ge-0/0/0", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", "fec_nccw_count": "0", "fec_nccw_error_rate": "0" }, "ethernet-filter-statistics": { "cam-destination-filter-count": "0", "cam-source-filter-count": "0", "input-packets": "133726908", "input-reject-count": "118", "input-reject-destination-address-count": "60", "input-reject-source-address-count": "0", "output-packet-error-count": "0", "output-packet-pad-count": "0", "output-packets": "129183361" }, "ethernet-mac-statistics": { "input-broadcasts": "0", "input-bytes": "21604601324", "input-code-violations": "0", "input-crc-errors": "0", "input-fifo-errors": "0", "input-fragment-frames": "0", "input-jabber-frames": "0", "input-mac-control-frames": "0", "input-mac-pause-frames": "0", "input-multicasts": "0", "input-oversized-frames": "0", "input-packets": "133726919", "input-total-errors": "0", "input-unicasts": "133726908", "input-vlan-tagged-frames": "0", "output-broadcasts": "0", "output-bytes": "16828244544", "output-crc-errors": "0", "output-fifo-errors": "0", "output-mac-control-frames": "0", "output-mac-pause-frames": "0", "output-multicasts": "0", "output-packets": "129183374", "output-total-errors": "0", "output-unicasts": "129183361" }, "ethernet-pcs-statistics": { "bit-error-seconds": "0", "errored-blocks-seconds": "0" }, "hardware-physical-address": "00:50:56:ff:56:b6", "if-auto-negotiation": "Enabled", "if-config-flags": { "iff-snmp-traps": True, "internal-flags": "0x4000" }, "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": 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"bit-error-seconds": "0", "errored-blocks-seconds": "0" }, "hardware-physical-address": "00:50:56:ff:93:cb", "if-auto-negotiation": "Enabled", "if-config-flags": { "iff-snmp-traps": True, "internal-flags": "0x4000" }, "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-10-25 08:50:18 UTC (21w5d 22:38 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", "link-level-type": "Ethernet", "local-index": "151", "logical-interface": [ { "address-family": [ { "address-family-name": "inet", "intf-curr-cnt": "1", "intf-dropcnt": "0", 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"cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "00:50:56:ff:3e:28", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", "fec_nccw_count": "0", "fec_nccw_error_rate": "0" }, "ethernet-filter-statistics": { "cam-destination-filter-count": "0", "cam-source-filter-count": "0", "input-packets": "0", "input-reject-count": "0", "input-reject-destination-address-count": "0", "input-reject-source-address-count": "0", "output-packet-error-count": "0", "output-packet-pad-count": "0", "output-packets": "0" }, "ethernet-mac-statistics": { "input-broadcasts": "0", "input-bytes": "0", "input-code-violations": "0", "input-crc-errors": "0", "input-fifo-errors": "0", "input-fragment-frames": "0", "input-jabber-frames": "0", "input-mac-control-frames": "0", "input-mac-pause-frames": "0", "input-multicasts": "0", "input-oversized-frames": "0", "input-packets": "0", "input-total-errors": "0", "input-unicasts": "0", "input-vlan-tagged-frames": "0", "output-broadcasts": "0", "output-bytes": "0", "output-crc-errors": "0", "output-fifo-errors": "0", "output-mac-control-frames": "0", "output-mac-pause-frames": "0", "output-multicasts": "0", "output-packets": "0", "output-total-errors": "0", "output-unicasts": "0" }, "ethernet-pcs-statistics": { "bit-error-seconds": "0", "errored-blocks-seconds": "0" }, "hardware-physical-address": "00:50:56:ff:3e:28", "if-auto-negotiation": "Enabled", "if-config-flags": { "iff-hardware-down": True, "iff-snmp-traps": True, "internal-flags": "0x4000" }, "if-device-flags": { "ifdf-down": True, "ifdf-present": True, "ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-08-29 09:09:20 UTC (29w6d 22:19 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", "link-level-type": "Ethernet", "local-index": "152", "loopback": "Disabled", "mru": "1522", "mtu": "1514", "name": "ge-0/0/4", "output-error-list": { "aged-packets": "0", "carrier-transitions": "2", "hs-link-crc-errors": "0", "mtu-errors": "0", "output-collisions": "0", "output-drops": "0", "output-errors": "0", "output-fifo-errors": "0", "output-resource-errors": "0" }, "pad-to-minimum-frame-size": "Disabled", "pfe-information": { "destination-mask": "(0x00)", "destination-slot": "0" }, "physical-interface-cos-information": { "physical-interface-cos-hw-max-queues": "8", "physical-interface-cos-use-max-queues": "8" }, 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"cos-queue-bandwidth-bps": "950000000", "cos-queue-buffer": "95", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "best-effort", "cos-queue-limit": "none", "cos-queue-number": "0", "cos-queue-priority": "low" }, { "cos-queue-bandwidth": "5", "cos-queue-bandwidth-bps": "50000000", "cos-queue-buffer": "5", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "2c:6b:f5:ff:01:1d", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", "fec_nccw_count": "0", "fec_nccw_error_rate": "0" }, "ethernet-filter-statistics": { "cam-destination-filter-count": "0", "cam-source-filter-count": "0", "input-packets": "0", "input-reject-count": "0", "input-reject-destination-address-count": "0", "input-reject-source-address-count": "0", "output-packet-error-count": "0", 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"iff-snmp-traps": True, "internal-flags": "0x4000" }, "if-device-flags": { "ifdf-down": True, "ifdf-present": True, "ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-08-29 09:09:20 UTC (29w6d 22:19 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", "link-level-type": "Ethernet", "local-index": "153", "loopback": "Disabled", "mru": "1522", "mtu": "1514", "name": "ge-0/0/5", "output-error-list": { "aged-packets": "0", "carrier-transitions": "2", "hs-link-crc-errors": "0", "mtu-errors": "0", "output-collisions": "0", "output-drops": "0", "output-errors": "0", 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"ethernet-alarm-link-down": True } }, "active-defects": { "interface-alarms": { "ethernet-alarm-link-down": True } }, "admin-status": { "@junos:format": "Enabled" }, "bpdu-error": "None", "cos-information": { "cos-stream-information": { "cos-direction": "Output", "cos-queue-configuration": [ { "cos-queue-bandwidth": "95", "cos-queue-bandwidth-bps": "950000000", "cos-queue-buffer": "95", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "best-effort", "cos-queue-limit": "none", "cos-queue-number": "0", "cos-queue-priority": "low" }, { "cos-queue-bandwidth": "5", "cos-queue-bandwidth-bps": "50000000", "cos-queue-buffer": "5", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "2c:6b:f5:ff:01:1e", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", 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"output-multicasts": "0", "output-packets": "0", "output-total-errors": "0", "output-unicasts": "0" }, "ethernet-pcs-statistics": { "bit-error-seconds": "0", "errored-blocks-seconds": "0" }, "hardware-physical-address": "2c:6b:f5:ff:01:1e", "if-auto-negotiation": "Enabled", "if-config-flags": { "iff-hardware-down": True, "iff-snmp-traps": True, "internal-flags": "0x4000" }, "if-device-flags": { "ifdf-down": True, "ifdf-present": True, "ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-08-29 09:09:20 UTC (29w6d 22:19 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", 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"network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "2c:6b:f5:ff:01:1f", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", "fec_nccw_count": "0", "fec_nccw_error_rate": "0" }, "ethernet-filter-statistics": { "cam-destination-filter-count": "0", "cam-source-filter-count": "0", "input-packets": "0", "input-reject-count": "0", "input-reject-destination-address-count": "0", "input-reject-source-address-count": "0", "output-packet-error-count": "0", "output-packet-pad-count": "0", "output-packets": "0" }, "ethernet-mac-statistics": { "input-broadcasts": "0", "input-bytes": "0", "input-code-violations": "0", "input-crc-errors": "0", "input-fifo-errors": "0", "input-fragment-frames": "0", "input-jabber-frames": "0", "input-mac-control-frames": "0", "input-mac-pause-frames": "0", "input-multicasts": "0", "input-oversized-frames": 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"0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-08-29 09:09:20 UTC (29w6d 22:19 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", "link-level-type": "Ethernet", "local-index": "155", "loopback": "Disabled", "mru": "1522", "mtu": "1514", "name": "ge-0/0/7", "output-error-list": { "aged-packets": "0", "carrier-transitions": "2", "hs-link-crc-errors": "0", "mtu-errors": "0", "output-collisions": "0", "output-drops": "0", "output-errors": "0", "output-fifo-errors": "0", "output-resource-errors": "0" }, "pad-to-minimum-frame-size": "Disabled", "pfe-information": { "destination-mask": "(0x00)", "destination-slot": "0" }, "physical-interface-cos-information": { "physical-interface-cos-hw-max-queues": "8", "physical-interface-cos-use-max-queues": "8" }, "snmp-index": "533", "sonet-mode": "LAN-PHY", "source-filtering": "Disabled", "speed": "1000mbps", 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"cos-queue-forwarding-class": "best-effort", "cos-queue-limit": "none", "cos-queue-number": "0", "cos-queue-priority": "low" }, { "cos-queue-bandwidth": "5", "cos-queue-bandwidth-bps": "50000000", "cos-queue-buffer": "5", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "2c:6b:f5:ff:01:20", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", "fec_nccw_count": "0", "fec_nccw_error_rate": "0" }, "ethernet-filter-statistics": { "cam-destination-filter-count": "0", "cam-source-filter-count": "0", "input-packets": "0", "input-reject-count": "0", "input-reject-destination-address-count": "0", "input-reject-source-address-count": "0", "output-packet-error-count": "0", "output-packet-pad-count": "0", "output-packets": "0" }, "ethernet-mac-statistics": { "input-broadcasts": 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"ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-08-29 09:09:20 UTC (29w6d 22:19 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", "link-level-type": "Ethernet", "local-index": "156", "loopback": "Disabled", "mru": "1522", "mtu": "1514", "name": "ge-0/0/8", "output-error-list": { "aged-packets": "0", "carrier-transitions": "2", "hs-link-crc-errors": "0", "mtu-errors": "0", "output-collisions": "0", "output-drops": "0", "output-errors": "0", "output-fifo-errors": "0", "output-resource-errors": "0" }, "pad-to-minimum-frame-size": "Disabled", "pfe-information": { 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"@junos:format": "Enabled" }, "bpdu-error": "None", "cos-information": { "cos-stream-information": { "cos-direction": "Output", "cos-queue-configuration": [ { "cos-queue-bandwidth": "95", "cos-queue-bandwidth-bps": "950000000", "cos-queue-buffer": "95", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "best-effort", "cos-queue-limit": "none", "cos-queue-number": "0", "cos-queue-priority": "low" }, { "cos-queue-bandwidth": "5", "cos-queue-bandwidth-bps": "50000000", "cos-queue-buffer": "5", "cos-queue-buffer-bytes": "0", "cos-queue-forwarding-class": "network-control", "cos-queue-limit": "none", "cos-queue-number": "3", "cos-queue-priority": "low" } ] } }, "current-physical-address": "2c:6b:f5:ff:01:21", "down-hold-time": "0", "eth-switch-error": "None", "ethernet-fec-statistics": { "fec_ccw_count": "0", "fec_ccw_error_rate": "0", "fec_nccw_count": "0", "fec_nccw_error_rate": "0" }, "ethernet-filter-statistics": { "cam-destination-filter-count": "0", 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"bit-error-seconds": "0", "errored-blocks-seconds": "0" }, "hardware-physical-address": "2c:6b:f5:ff:01:21", "if-auto-negotiation": "Enabled", "if-config-flags": { "iff-hardware-down": True, "iff-snmp-traps": True, "internal-flags": "0x4000" }, "if-device-flags": { "ifdf-down": True, "ifdf-present": True, "ifdf-running": True }, "if-flow-control": "Enabled", "if-media-flags": { "ifmf-none": True }, "if-remote-fault": "Online", "input-error-list": { "framing-errors": "0", "input-discards": "0", "input-drops": "0", "input-errors": "0", "input-fifo-errors": "0", "input-l2-channel-errors": "0", "input-l2-mismatch-timeouts": "0", "input-l3-incompletes": "0", "input-resource-errors": "0", "input-runts": "0" }, "interface-flapped": { "#text": "2019-08-29 09:09:20 UTC (29w6d 22:19 ago)" }, "interface-transmit-statistics": "Disabled", "ld-pdu-error": "None", "link-level-type": "Ethernet", "local-index": "157", "loopback": "Disabled", "mru": "1522", "mtu": "1514", "name": "ge-0/0/9", 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"output-packets": "0" }, "output-bps": "0", "output-bytes": "0", "output-packets": "0", "output-pps": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-config-flags": { "iff-snmp-traps": True }, "if-device-flags": { "ifdf-loopback": True, "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Unspecified", "local-index": "0", "logical-interface": [ { "address-family": [ { "address-family-name": "inet", "intf-curr-cnt": "0", "intf-dropcnt": "0", "intf-unresolved-cnt": "0", "max-local-cache": "0", "mtu": "Unlimited", "new-hold-limit": "0" }, { "address-family-flags": { "ifff-none": True }, "generation": "133", "interface-address": [ { "generation": "133", "ifa-broadcast": "Unspecified", "ifa-destination": "Unspecified", "ifa-flags": { 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"Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-config-flags": { "iff-snmp-traps": True }, "if-device-flags": { "ifdf-loopback": True, "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Unspecified", "local-index": "6", "logical-interface": [ { "address-family": [ { "address-family-name": "inet", "intf-curr-cnt": "0", "intf-dropcnt": "0", "intf-unresolved-cnt": "0", "max-local-cache": "0", "mtu": "Unlimited", "new-hold-limit": "0" }, { "address-family-flags": { "ifff-no-redirects": True, "ifff-sendbcast-pkt-to-re": True }, "generation": "143", "interface-address": { "generation": "135", "ifa-broadcast": "Unspecified", "ifa-destination": "Unspecified", "ifa-flags": { "ifaf-is-default": True, "ifaf-is-primary": True }, "ifa-local": "10.189.5.252" }, "route-table": "0" }, { "address-family-name": "inet6", "intf-curr-cnt": "0", "intf-dropcnt": "0", "intf-unresolved-cnt": "0", "max-local-cache": "0", "mtu": "Unlimited", "new-hold-limit": "0" }, { "generation": "144", "interface-address": { "ifa-destination": "Unspecified", "ifa-flags": { "ifaf-is-default": True, "ifaf-is-primary": True }, "ifa-local": "fe80::250:560f:fc8d:7c08" }, "route-table": "0" }, { "generation": "137" }, { "generation": "139" } ], "if-config-flags": { "iff-snmp-traps": True, "iff-up": True }, "local-index": "320", "name": "lo0.0", "snmp-index": "16", "traffic-statistics": { "input-bytes": "12188", "input-packets": "83", "ipv6-transit-statistics": { "input-bytes": "12188", "input-packets": "83", "output-bytes": "12188", "output-packets": "83" }, "output-bytes": "12188", "output-packets": "83" }, "transit-traffic-statistics": { "input-bps": "0", "input-bytes": "0", "input-packets": "0", "input-pps": "0", "ipv6-transit-statistics": { "input-bps": "0", "input-bytes": "0", "input-packets": "0", "input-pps": "0", "output-bps": "0", "output-bytes": "0", "output-packets": "0", "output-pps": "0" }, "output-bps": "0", "output-bytes": "0", "output-packets": "0", "output-pps": "0" } }, { "address-family": [ { "address-family-name": "inet", "intf-curr-cnt": "0", "intf-dropcnt": "0", "intf-unresolved-cnt": "0", "max-local-cache": "0", "mtu": "Unlimited", "new-hold-limit": "0" }, { "generation": "146", "interface-address": { "generation": "141", "ifa-broadcast": "Unspecified", "ifa-destination": "Unspecified", "ifa-flags": { "ifaf-none": True }, "ifa-local": "127.0.0.1" }, "route-table": "2" } ], "if-config-flags": { "iff-snmp-traps": True, "iff-up": True }, "local-index": "322", "name": "lo0.16384", "snmp-index": "21", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "transit-traffic-statistics": { "input-bps": "0", "input-bytes": "0", "input-packets": "0", "input-pps": "0", "output-bps": "0", "output-bytes": "0", "output-packets": "0", "output-pps": "0" } }, { "address-family": [ { "address-family-name": "inet", "intf-curr-cnt": "0", "intf-dropcnt": "0", "intf-unresolved-cnt": "0", "max-local-cache": "0", "mtu": "Unlimited", "new-hold-limit": "0" }, { "generation": "145", "route-table": "1" } ], "if-config-flags": { "iff-snmp-traps": True, "iff-up": True }, "local-index": "321", "name": "lo0.16385", "snmp-index": "22", "traffic-statistics": { "input-bytes": "38208797939", "input-packets": "33943317", "output-bytes": "38208797939", "output-packets": "33943317" }, "transit-traffic-statistics": { "input-bps": "0", "input-bytes": "0", "input-packets": "0", "input-pps": "0", "output-bps": "0", "output-bytes": "0", "output-packets": "0", "output-pps": "0" } } ], "name": "lo0", "output-error-list": {}, "snmp-index": "6", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "38208810127", "input-packets": "33943400", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "38208810127", "output-packets": "33943400" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Unspecified", "local-index": "4", "name": "lsi", "output-error-list": {}, "snmp-index": "4", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "down-hold-time": "0", "if-config-flags": { "iff-snmp-traps": True }, "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "link-level-type": "GRE", "local-index": "66", "mtu": "Unlimited", "name": "mtun", "snmp-index": "12", "speed": "Unlimited", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "down-hold-time": "0", "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "link-level-type": "PIM-Decapsulator", "local-index": "26", "mtu": "Unlimited", "name": "pimd", "snmp-index": "11", "speed": "Unlimited", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "down-hold-time": "0", "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "link-level-type": "PIM-Encapsulator", "local-index": "25", "mtu": "Unlimited", "name": "pime", "snmp-index": "10", "speed": "Unlimited", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "2c:6b:f5:ff:08:c8", "down-hold-time": "0", "hardware-physical-address": "2c:6b:f5:ff:08:c8", "if-config-flags": { "iff-snmp-traps": True }, "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Full-Duplex", "local-index": "130", "name": "pip0", "output-error-list": {}, "snmp-index": "515", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "link-level-type": "PPPoE", "link-type": "Full-Duplex", "local-index": "131", "mtu": "1532", "name": "pp0", "snmp-index": "516", "speed": "Unspecified", "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Full-Duplex", "local-index": "135", "name": "rbeb", "output-error-list": {}, "snmp-index": "517", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-config-flags": { "iff-snmp-traps": True }, "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Unspecified", "local-index": "12", "name": "tap", "output-error-list": {}, "snmp-index": "7", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" }, { "admin-status": { "@junos:format": "Enabled" }, "current-physical-address": "Unspecified", "down-hold-time": "0", "hardware-physical-address": "Unspecified", "if-device-flags": { "ifdf-present": True, "ifdf-running": True }, "if-media-flags": { "ifmf-none": True }, "input-error-list": {}, "interface-flapped": { "#text": "Never" }, "link-type": "Full-Duplex", "local-index": "133", "name": "vtep", "output-error-list": {}, "snmp-index": "518", "statistics-cleared": "Never", "traffic-statistics": { "input-bytes": "0", "input-packets": "0", "ipv6-transit-statistics": { "input-bytes": "0", "input-packets": "0", "output-bytes": "0", "output-packets": "0" }, "output-bytes": "0", "output-packets": "0" }, "up-hold-time": "0" } ] } }

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50a540e2f3c2a98ce3c69aa501a4773f8a3189e4

3,372

py

Python

scoreware/race/readers.py

bnorthan/raceresults

51453db113a9a3549a53a32965b78d3897eeb7da

[ "BSD-2-Clause" ]

null

scoreware/race/readers.py

bnorthan/raceresults

51453db113a9a3549a53a32965b78d3897eeb7da

[ "BSD-2-Clause" ]

null

scoreware/race/readers.py

bnorthan/raceresults

51453db113a9a3549a53a32965b78d3897eeb7da

[ "BSD-2-Clause" ]

null

import pandas as pd from scoreware.race.utils import get_last_name def parse_general(df, headers, id): newdf=pd.DataFrame() print((type(headers))) for key in headers: print((headers[key])) for column in df.columns: if column.lower() in headers[key]: print((column.lower()+' matches')) print(key) print(key=='name') #if (key=='time'): # df[column]=df[column].replace('nan', method='bfill') # df[column]=df[column].fillna(method='bfill') # print((df[column].loc[1:10])) # df[column]=df[column].astype(str) # print((df[column].loc[230:240])) # newdf['time']=df[column].apply(lambda x: '00:'+x.split(':')[0]+':'+x.split(':')[1]) if (key=='full_name'): df[column]=df[column].fillna(value='none none') df[column]=df[column].replace('nan', value='none none') df[column]=df[column].astype(str) newdf['first_name']=df[column].apply(lambda x: x.split()[0]) #newdf['last_name']=df[column].apply(lambda x: x.split()[-1]) newdf['last_name']=df[column].apply(lambda x: get_last_name(x)) print(newdf['last_name']) else: if (key=='age'): df[column]=df[column].fillna(value=-1) else: df[column]=df[column].fillna(value='none') newdf[key]=df[column] newdf['race_id']=id return newdf def parse_general2(df, headers, id): newdf=pd.DataFrame() print((type(headers))) for key in headers: print((headers[key])) for column in df.columns: if column.lower() in headers[key]: print((column.lower()+' matches')) print(key) print(key=='name') #if (key=='time'): # df[column]=df[column].replace('nan', method='bfill') # df[column]=df[column].fillna(method='bfill') # print((df[column].loc[1:10])) # df[column]=df[column].astype(str) # #newdf['time']=df[column].apply(lambda x: '00:'+x.split(':')[0]+':'+x.split(':')[1]) if (key=='full_name'): df[column]=df[column].fillna(value='none none') df[column]=df[column].replace('nan', value='none none') df[column]=df[column].astype(str) newdf['first_name']=df[column].apply(lambda x: x.split()[0]) #newdf['last_name']=df[column].apply(lambda x: x.split()[-1]) newdf['last_name']=df[column].apply(lambda x: raceutil.get_last_name(x)) print(newdf['last_name']) else: if (key=='age'): df[column]=df[column].fillna(value=-1) else: df[column]=df[column].fillna(value='none') newdf[key]=df[column] newdf['race_id']=id return newdf

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50b48bfd1256ca74c90c451fb9ebee378842be15

189

py

Python

gym_cryptotrading/envs/__init__.py

datafields-team/gym-cryptotrading

96cf28b07175fb2fbf2daa7060494db81ea8d58d

[ "MIT" ]

104

2018-04-26T06:30:45.000Z

2022-03-31T17:58:33.000Z

gym_cryptotrading/envs/__init__.py

datafields-team/gym-cryptotrading

96cf28b07175fb2fbf2daa7060494db81ea8d58d

[ "MIT" ]

1

2018-06-21T06:06:17.000Z

2019-02-09T20:23:17.000Z

gym_cryptotrading/envs/__init__.py

perara/gym-cryptotrading

96cf28b07175fb2fbf2daa7060494db81ea8d58d

[ "MIT" ]

42

2018-05-04T12:00:35.000Z

2022-03-30T18:33:08.000Z

from gym_cryptotrading.envs.realizedPnL import RealizedPnLEnv from gym_cryptotrading.envs.unrealizedPnL import UnRealizedPnLEnv from gym_cryptotrading.envs.weightedPnL import WeightedPnLEnv

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7

ba0d0da5a804219551a3e1d01cc6b920624515c2

1,386

py

Python

acpki/util/exceptions.py

sigurd120/acpki

5994fe50f97adaddece4f961fe4e888dd7d014f4

[ "BSD-Source-Code" ]

null

acpki/util/exceptions.py

sigurd120/acpki

5994fe50f97adaddece4f961fe4e888dd7d014f4

[ "BSD-Source-Code" ]

null

acpki/util/exceptions.py

sigurd120/acpki

5994fe50f97adaddece4f961fe4e888dd7d014f4

[ "BSD-Source-Code" ]

null

class ConfigError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class InvalidTokenError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class SessionError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class SubscriptionError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class RequestError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class NotFoundError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class ConnectionError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class IllegalStateError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value) class CertificateExpiredError(StandardError): def __init__(self, value): self.value = value def __str__(self): return repr(self.value)

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13

ba2244e73d49068f512b3098878b68b77b65d17e

24,658

py

Python

sdk/ml/azure-ai-ml/azure/ai/ml/automl/_automl_tabular.py

dubiety/azure-sdk-for-python

62ffa839f5d753594cf0fe63668f454a9d87a346

[ "MIT" ]

1

2022-02-01T18:50:12.000Z

sdk/ml/azure-ai-ml/azure/ai/ml/automl/_automl_tabular.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

sdk/ml/azure-ai-ml/azure/ai/ml/automl/_automl_tabular.py

ellhe-blaster/azure-sdk-for-python

82193ba5e81cc5e5e5a5239bba58abe62e86f469

[ "MIT" ]

null

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- """Entrypoints for creating AutoML tasks""" from typing import List, Union from azure.ai.ml.entities._inputs_outputs import Input from azure.ai.ml.entities._builders.base_node import pipeline_node_decorator from azure.ai.ml.entities._job.automl.tabular import ( ClassificationJob, ForecastingJob, ForecastingSettings, RegressionJob, ) @pipeline_node_decorator def classification( *, training_data: Input, target_column_name: str, primary_metric: str = None, enable_model_explainability: bool = True, weight_column_name: str = None, validation_data: Input = None, validation_data_size: float = None, n_cross_validations: Union[str, int] = None, cv_split_column_names: List[str] = None, test_data: Input = None, test_data_size: float = None, **kwargs, ) -> ClassificationJob: """ Function to create a ClassificationJob. A classification job is used to train a model that best predict the class of a data sample. Various models are trained using the training data. The model with the best performance on the validation data based on the primary metric is selected as the final model. :param training_data: The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). :type training_data: Input :param target_column_name: The name of the label column. This parameter is applicable to ``training_data``, ``validation_data`` and ``test_data`` parameters :type target_column_name: str :param primary_metric: The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric. Acceptable values: accuracy, AUC_weighted, norm_macro_recall, average_precision_score_weighted, and precision_score_weighted Defaults to accuracy :type primary_metric: str, optional :param enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. The default is True. For more information, see `Interpretability: model explanations in automated machine learning <https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__. Defaults to True :type enable_model_explainability: bool, optional :param weight_column_name: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers. This parameter is applicable to ``training_data`` and ``validation_data`` parameters :type weight_column_name: str, optional :param validation_data: The validation data to be used within the experiment. It should contain both training features and label column (optionally a sample weights column). Defaults to None :type validation_data: Input, optional :param validation_data_size: What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive. Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or ``validation_data_size`` to extract validation data out of the specified training data. For custom cross validation fold, use ``cv_split_column_names``. For more information, see `Configure data splits and cross-validation in automated machine learning <https://docs.microsoft.com /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__. Defaults to None :type validation_data_size: float, optional :param n_cross_validations: How many cross validations to perform when user validation data is not specified. Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or ``validation_data_size`` to extract validation data out of the specified training data. For custom cross validation fold, use ``cv_split_column_names``. For more information, see `Configure data splits and cross-validation in automated machine learning <https://docs.microsoft.com /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__. Defaults to None :type n_cross_validations: Union[str, int], optional :param cv_split_column_names: List of names of the columns that contain custom cross validation split. Each of the CV split columns represents one CV split where each row are either marked 1 for training or 0 for validation. Defaults to None :type cv_split_column_names: List[str], optional :param test_data: The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. The test data to be used for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions. If this parameter or the ``test_data_size`` parameter are not specified then no test run will be executed automatically after model training is completed. Test data should contain both features and label column. If ``test_data`` is specified then the ``target_column_name`` parameter must be specified. Defaults to None :type test_data: Input, optional :param test_data_size: The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. What fraction of the training data to hold out for test data for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions. This should be between 0.0 and 1.0 non-inclusive. If ``test_data_size`` is specified at the same time as ``validation_data_size``, then the test data is split from ``training_data`` before the validation data is split. For example, if ``validation_data_size=0.1``, ``test_data_size=0.1`` and the original training data has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the training data will have 810 rows. For regression based tasks, random sampling is used. For classification tasks, stratified sampling is used. Forecasting does not currently support specifying a test dataset using a train/test split. If this parameter or the ``test_data`` parameter are not specified then no test run will be executed automatically after model training is completed. Defaults to None :type test_data_size: float, optional :return: A job object that can be submitted to an Azure ML compute for execution. :rtype: ClassificationJob """ classification_job = ClassificationJob(primary_metric=primary_metric, **kwargs) classification_job.set_data( training_data=training_data, target_column_name=target_column_name, weight_column_name=weight_column_name, validation_data=validation_data, validation_data_size=validation_data_size, n_cross_validations=n_cross_validations, cv_split_column_names=cv_split_column_names, test_data=test_data, test_data_size=test_data_size, ) classification_job.set_training(enable_model_explainability=enable_model_explainability) return classification_job @pipeline_node_decorator def regression( *, training_data: Input, target_column_name: str, primary_metric: str = None, enable_model_explainability: bool = True, weight_column_name: str = None, validation_data: Input = None, validation_data_size: float = None, n_cross_validations: Union[str, int] = None, cv_split_column_names: List[str] = None, test_data: Input = None, test_data_size: float = None, **kwargs, ) -> RegressionJob: """Function to create a Regression Job. A regression job is used to train a model to predict continuous values of a target variable from a dataset. Various models are trained using the training data. The model with the best performance on the validation data based on the primary metric is selected as the final model. :param training_data: The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). :type training_data: Input :param target_column_name: The name of the label column. This parameter is applicable to ``training_data``, ``validation_data`` and ``test_data`` parameters :type target_column_name: str :param primary_metric: The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric. Acceptable values: spearman_correlation, r2_score, normalized_mean_absolute_error, normalized_root_mean_squared_error. Defaults to normalized_root_mean_squared_error :type primary_metric: str, optional :param enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. The default is True. For more information, see `Interpretability: model explanations in automated machine learning <https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__. Defaults to True :type enable_model_explainability: bool, optional :param weight_column_name: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers. This parameter is applicable to ``training_data`` and ``validation_data`` parameters :type weight_column_name: str, optional :param validation_data: The validation data to be used within the experiment. It should contain both training features and label column (optionally a sample weights column). Defaults to None :type validation_data: Input, optional :param validation_data_size: What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive. Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or ``validation_data_size`` to extract validation data out of the specified training data. For custom cross validation fold, use ``cv_split_column_names``. For more information, see `Configure data splits and cross-validation in automated machine learning <https://docs.microsoft.com /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__. Defaults to None :type validation_data_size: float, optional :param n_cross_validations: How many cross validations to perform when user validation data is not specified. Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or ``validation_data_size`` to extract validation data out of the specified training data. For custom cross validation fold, use ``cv_split_column_names``. For more information, see `Configure data splits and cross-validation in automated machine learning <https://docs.microsoft.com /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__. Defaults to None :type n_cross_validations: Union[str, int], optional :param cv_split_column_names: List of names of the columns that contain custom cross validation split. Each of the CV split columns represents one CV split where each row are either marked 1 for training or 0 for validation. Defaults to None :type cv_split_column_names: List[str], optional :param test_data: The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. The test data to be used for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions. If this parameter or the ``test_data_size`` parameter are not specified then no test run will be executed automatically after model training is completed. Test data should contain both features and label column. If ``test_data`` is specified then the ``target_column_name`` parameter must be specified. Defaults to None :type test_data: Input, optional :param test_data_size: The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. What fraction of the training data to hold out for test data for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions. This should be between 0.0 and 1.0 non-inclusive. If ``test_data_size`` is specified at the same time as ``validation_data_size``, then the test data is split from ``training_data`` before the validation data is split. For example, if ``validation_data_size=0.1``, ``test_data_size=0.1`` and the original training data has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the training data will have 810 rows. For regression based tasks, random sampling is used. For classification tasks, stratified sampling is used. Forecasting does not currently support specifying a test dataset using a train/test split. If this parameter or the ``test_data`` parameter are not specified then no test run will be executed automatically after model training is completed. Defaults to None :type test_data_size: float, optional :return: A job object that can be submitted to an Azure ML compute for execution. :rtype: RegressionJob """ regression_job = RegressionJob(primary_metric=primary_metric, **kwargs) regression_job.set_data( training_data=training_data, target_column_name=target_column_name, weight_column_name=weight_column_name, validation_data=validation_data, validation_data_size=validation_data_size, n_cross_validations=n_cross_validations, cv_split_column_names=cv_split_column_names, test_data=test_data, test_data_size=test_data_size, ) regression_job.set_training(enable_model_explainability=enable_model_explainability) return regression_job @pipeline_node_decorator def forecasting( *, training_data: Input, target_column_name: str, primary_metric: str = None, enable_model_explainability: bool = True, weight_column_name: str = None, validation_data: Input = None, validation_data_size: float = None, n_cross_validations: Union[str, int] = None, cv_split_column_names: List[str] = None, test_data: Input = None, test_data_size: float = None, forecasting_settings: ForecastingSettings = None, **kwargs, ) -> ForecastingJob: """Function to create a Forecasting job. A forecasting task is used to predict target values for a future time period based on the historical data. Various models are trained using the training data. The model with the best performance on the validation data based on the primary metric is selected as the final model. :param training_data: The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). :type training_data: Input :param target_column_name: The name of the label column. This parameter is applicable to ``training_data``, ``validation_data`` and ``test_data`` parameters :type target_column_name: str :param primary_metric: The metric that Automated Machine Learning will optimize for model selection. Automated Machine Learning collects more metrics than it can optimize. For more information on how metrics are calculated, see https://docs.microsoft.com/azure/machine-learning/how-to-configure-auto-train#primary-metric. Acceptable values: r2_score, normalized_mean_absolute_error, normalized_root_mean_squared_error Defaults to normalized_root_mean_squared_error :type primary_metric: str, optional :param enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all AutoML training iterations. The default is True. For more information, see `Interpretability: model explanations in automated machine learning <https://docs.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__. Defaults to True :type enable_model_explainability: bool, optional :param weight_column_name: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers. This parameter is applicable to ``training_data`` and ``validation_data`` parameters :type weight_column_name: str, optional :param validation_data: The validation data to be used within the experiment. It should contain both training features and label column (optionally a sample weights column). Defaults to None :type validation_data: Input, optional :param validation_data_size: What fraction of the data to hold out for validation when user validation data is not specified. This should be between 0.0 and 1.0 non-inclusive. Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or ``validation_data_size`` to extract validation data out of the specified training data. For custom cross validation fold, use ``cv_split_column_names``. For more information, see `Configure data splits and cross-validation in automated machine learning <https://docs.microsoft.com /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__. Defaults to None :type validation_data_size: float, optional :param n_cross_validations: How many cross validations to perform when user validation data is not specified. Specify ``validation_data`` to provide validation data, otherwise set ``n_cross_validations`` or ``validation_data_size`` to extract validation data out of the specified training data. For custom cross validation fold, use ``cv_split_column_names``. For more information, see `Configure data splits and cross-validation in automated machine learning <https://docs.microsoft.com /azure/machine-learning/how-to-configure-cross-validation-data-splits>`__. Defaults to None :type n_cross_validations: Union[str, int], optional :param cv_split_column_names: List of names of the columns that contain custom cross validation split. Each of the CV split columns represents one CV split where each row are either marked 1 for training or 0 for validation. Defaults to None :type cv_split_column_names: List[str], optional :param test_data: The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. The test data to be used for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions. If this parameter or the ``test_data_size`` parameter are not specified then no test run will be executed automatically after model training is completed. Test data should contain both features and label column. If ``test_data`` is specified then the ``target_column_name`` parameter must be specified. Defaults to None :type test_data: Input, optional :param test_data_size: The Model Test feature using test datasets or test data splits is a feature in Preview state and might change at any time. What fraction of the training data to hold out for test data for a test run that will automatically be started after model training is complete. The test run will get predictions using the best model and will compute metrics given these predictions. This should be between 0.0 and 1.0 non-inclusive. If ``test_data_size`` is specified at the same time as ``validation_data_size``, then the test data is split from ``training_data`` before the validation data is split. For example, if ``validation_data_size=0.1``, ``test_data_size=0.1`` and the original training data has 1000 rows, then the test data will have 100 rows, the validation data will contain 90 rows and the training data will have 810 rows. For regression based tasks, random sampling is used. For classification tasks, stratified sampling is used. Forecasting does not currently support specifying a test dataset using a train/test split. If this parameter or the ``test_data`` parameter are not specified then no test run will be executed automatically after model training is completed. Defaults to None :type test_data_size: float, optional :param forecasting_settings: The settings for the forecasting task :type forecasting_settings: ForecastingSettings, optional :return: A job object that can be submitted to an Azure ML compute for execution. :rtype: ForecastingJob """ forecast_job = ForecastingJob(primary_metric=primary_metric, forecasting_settings=forecasting_settings, **kwargs) forecast_job.set_data( training_data=training_data, target_column_name=target_column_name, weight_column_name=weight_column_name, validation_data=validation_data, validation_data_size=validation_data_size, n_cross_validations=n_cross_validations, cv_split_column_names=cv_split_column_names, test_data=test_data, test_data_size=test_data_size, ) forecast_job.set_training(enable_model_explainability=enable_model_explainability) return forecast_job

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7

e85ddd01a24b6d0a33f87c288ca34710dcf10758

93

py

Python

surreal/agent/__init__.py

PeihongYu/surreal

2556bd9c362a53e0a94da914ba59b5d4621c4081

[ "MIT" ]

471

2018-10-29T07:11:54.000Z

2022-03-31T11:56:00.000Z

surreal/agent/__init__.py

PeihongYu/surreal

2556bd9c362a53e0a94da914ba59b5d4621c4081

[ "MIT" ]

34

2018-10-30T12:02:56.000Z

2019-05-22T07:57:51.000Z

surreal/agent/__init__.py

PeihongYu/surreal

2556bd9c362a53e0a94da914ba59b5d4621c4081

[ "MIT" ]

77

2018-10-30T00:51:52.000Z

2022-03-01T08:26:35.000Z

from .base import * from .ddpg_agent import * from .q_agent import * from .ppo_agent import *

23.25

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e87f6879bd6bb86156d192f39276b5015e1d64c2

125

py

Python

tests/test_ttcal.py

datakortet/ttcal

0df8000d1b4e6d64d25a0fe1dcdbd328dcc5a8af

[ "MIT" ]

null

tests/test_ttcal.py

datakortet/ttcal

0df8000d1b4e6d64d25a0fe1dcdbd328dcc5a8af

[ "MIT" ]

3

2020-03-06T10:36:41.000Z

2022-01-19T16:21:20.000Z

tests/test_ttcal.py

datakortet/ttcal

0df8000d1b4e6d64d25a0fe1dcdbd328dcc5a8af

[ "MIT" ]

null

# -*- coding: utf-8 -*- import ttcal def test_from_idtag(): assert ttcal.from_idtag('m20124') == ttcal.Month(2012, 4)

15.625

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1

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7

e8a9167b310a9231e65f1eca690d0e3e8dcf3c8e

5,611

py

Python

tests/test_web_websocket_functional.py

mind1master/aiohttp

d59b0ef166e140dfffa1b4d30430c07a052f8086

[ "Apache-2.0" ]

1

2019-09-18T19:40:30.000Z

tests/test_web_websocket_functional.py

mind1master/aiohttp

d59b0ef166e140dfffa1b4d30430c07a052f8086

[ "Apache-2.0" ]

3

2019-09-19T19:00:38.000Z

2019-09-20T09:56:49.000Z

tests/test_web_websocket_functional.py

traff/aiohttp

fb2b9829480a64d94bf13b799d92e36a804556e1

[ "Apache-2.0" ]

null

"""HTTP websocket server functional tests""" import asyncio import pytest import aiohttp from aiohttp import helpers, web @pytest.mark.run_loop def test_websocket_json(create_app_and_client): @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) msg = yield from ws.receive() msg_json = msg.json() answer = msg_json['test'] ws.send_str(answer) yield from ws.close() return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') expected_value = 'value' payload = '{"test": "%s"}' % expected_value ws.send_str(payload) resp = yield from ws.receive() assert resp.data == expected_value @pytest.mark.run_loop def test_websocket_json_invalid_message(create_app_and_client): @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) try: yield from ws.receive_json() except ValueError: ws.send_str('ValueError was raised') else: raise Exception('No Exception') finally: yield from ws.close() return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') payload = 'NOT A VALID JSON STRING' ws.send_str(payload) data = yield from ws.receive_str() assert 'ValueError was raised' in data @pytest.mark.run_loop def test_websocket_send_json(create_app_and_client): @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) data = yield from ws.receive_json() ws.send_json(data) yield from ws.close() return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') expected_value = 'value' ws.send_json({'test': expected_value}) data = yield from ws.receive_json() assert data['test'] == expected_value @pytest.mark.run_loop def test_websocket_receive_json(create_app_and_client): @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) data = yield from ws.receive_json() answer = data['test'] ws.send_str(answer) yield from ws.close() return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') expected_value = 'value' payload = '{"test": "%s"}' % expected_value ws.send_str(payload) resp = yield from ws.receive() assert resp.data == expected_value @pytest.mark.run_loop def test_send_recv_text(create_app_and_client, loop): closed = helpers.create_future(loop) @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) msg = yield from ws.receive_str() ws.send_str(msg+'/answer') yield from ws.close() closed.set_result(1) return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') ws.send_str('ask') msg = yield from ws.receive() assert msg.tp == aiohttp.WSMsgType.TEXT assert 'ask/answer' == msg.data msg = yield from ws.receive() assert msg.tp == aiohttp.WSMsgType.CLOSE assert msg.data == 1000 assert msg.extra == '' assert ws.closed assert ws.close_code == 1000 yield from closed @pytest.mark.run_loop def test_send_recv_bytes(create_app_and_client, loop): closed = helpers.create_future(loop) @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) msg = yield from ws.receive_bytes() ws.send_bytes(msg+b'/answer') yield from ws.close() closed.set_result(1) return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') ws.send_bytes(b'ask') msg = yield from ws.receive() assert msg.tp == aiohttp.WSMsgType.BINARY assert b'ask/answer' == msg.data msg = yield from ws.receive() assert msg.tp == aiohttp.WSMsgType.CLOSE assert msg.data == 1000 assert msg.extra == '' assert ws.closed assert ws.close_code == 1000 yield from closed @pytest.mark.run_loop def test_send_recv_json(create_app_and_client, loop): closed = helpers.create_future(loop) @asyncio.coroutine def handler(request): ws = web.WebSocketResponse() yield from ws.prepare(request) data = yield from ws.receive_json() ws.send_json({'response': data['request']}) yield from ws.close() closed.set_result(1) return ws app, client = yield from create_app_and_client() app.router.add_route('GET', '/', handler) ws = yield from client.ws_connect('/') ws.send_str('{"request": "test"}') msg = yield from ws.receive() data = msg.json() assert msg.tp == aiohttp.WSMsgType.TEXT assert data['response'] == 'test' msg = yield from ws.receive() assert msg.tp == aiohttp.WSMsgType.CLOSE assert msg.data == 1000 assert msg.extra == '' yield from ws.close() yield from closed

25.621005

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0.644983

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4.728011

0.110961

0.126216

0.100744

0.087579

0.856325

0.84087

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0.802232

0.789353

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0.005402

0.241133

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218

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0.815171

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null

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1

0

1

0

null

0

7

e8ce308628ba58d87924d502deff5c95b0312999

1,457

py

Python

src/pipeline/model/model_metrics.py

guyfreund/data_drift_detection

80ca5eb7445b17e04f2aa98c5f6d9ac1fe6d5ac5

[ "MIT" ]

null

src/pipeline/model/model_metrics.py

guyfreund/data_drift_detection

80ca5eb7445b17e04f2aa98c5f6d9ac1fe6d5ac5

[ "MIT" ]

1

2021-12-12T22:13:58.000Z

2021-12-17T22:49:39.000Z

src/pipeline/model/model_metrics.py

guyfreund/data_drift_detection

80ca5eb7445b17e04f2aa98c5f6d9ac1fe6d5ac5

[ "MIT" ]

null

from src.pipeline.model.constants import ModelMetricType from src.pipeline.model.interfaces.imodel_metric import IModelMetric class Accuracy(IModelMetric): def __init__(self, value: float): self._value = value self._mtype = ModelMetricType.Accuracy @property def value(self): return self._value @property def mtype(self): return self._mtype class Precision(IModelMetric): def __init__(self, value: float): self._value = value self._mtype = ModelMetricType.Precision @property def value(self): return self._value @property def mtype(self): return self._mtype class Recall(IModelMetric): def __init__(self, value: float): self._value = value self._mtype = ModelMetricType.Recall @property def value(self): return self._value @property def mtype(self): return self._mtype class F1(IModelMetric): def __init__(self, value: float): self._value = value self._mtype = ModelMetricType.F1 @property def value(self): return self._value @property def mtype(self): return self._mtype class AUC(IModelMetric): def __init__(self, value: float): self._value = value self._mtype = ModelMetricType.AUC @property def value(self): return self._value @property def mtype(self): return self._mtype

19.958904

68

0.643102

161

1,457

5.565217

0.15528

0.15067

0.15625

0.128348

0.809152

0

0.001889

0.273164

1,457

72

69

20.236111

0.844193

0

0.769231

0

1

0.288462

false

0

0.038462

0.192308

0.615385

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null

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1

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null

0

1

0

1

0

10

fa7275fced1b5f5e17d8ec753a4923ca49ecb1ec

349

py

Python

PythonStuff/Assignment2/quick_python2.py

edhackney/mega12June2021

02909f9853edba4940b8d99a3c300844ce68ed1c

[ "BSL-1.0" ]

null

PythonStuff/Assignment2/quick_python2.py

edhackney/mega12June2021

02909f9853edba4940b8d99a3c300844ce68ed1c

[ "BSL-1.0" ]

null

PythonStuff/Assignment2/quick_python2.py

edhackney/mega12June2021

02909f9853edba4940b8d99a3c300844ce68ed1c

[ "BSL-1.0" ]

null

print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome") print("this is awesome")

24.928571

24

0.722063

56

349

4.5

0.071429

0.5

0.611111

1

0

0.117479

349

14

25

24.928571

0.818182

0

1

0

0.6

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1

0

true

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null

1

0

1

0

null

0

1

0

1

0

12

d715e954e973629b002a92b28a11953017b8271c

96

py

Python

instance/config.py

shemian/newsapp

3d93cb75854d1fb3a4f2c29c7c61fe2319ca5d00

[ "Unlicense" ]

null

instance/config.py

shemian/newsapp

3d93cb75854d1fb3a4f2c29c7c61fe2319ca5d00

[ "Unlicense" ]

null

instance/config.py

shemian/newsapp

3d93cb75854d1fb3a4f2c29c7c61fe2319ca5d00

[ "Unlicense" ]

null

NEWS_API_KEY = '3ef9be48386b4c529832364b81fbbc76' SECRET_KEY ='12345667899012345678901234898e2x'

48

49

0.895833

7

96

11.857143

0.857143

0

0.554348

0.041667

96

2

50

48

0.347826

0

0.659794

0

1

0

false

0

1

0

1

null

0

1

0

1

0

1

0

1

null

0

7

d7860a8e58287fdb8610467871fd6805bb158567

16,848

py

Python

docs/imu_extract.py

Justgo13/sensor_library

128d1ef6f9949e4566a5af80bf02ba201d5f8cae

[ "MIT" ]

null

docs/imu_extract.py

Justgo13/sensor_library

128d1ef6f9949e4566a5af80bf02ba201d5f8cae

[ "MIT" ]

null

docs/imu_extract.py

Justgo13/sensor_library

128d1ef6f9949e4566a5af80bf02ba201d5f8cae

[ "MIT" ]

null

def get_IMU_time(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the IMU timestamps for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the IMU timestamp for a single row specified by arg will be output. multiple_row: boolean If chosen the IMU timestamp for multiple rows specified by arg will be output. row_section: boolean If chosen the IMU timestamp for a row section by arg will be output. Return: An array of IMU timestamps. """ IMU_time = [] print("NOTE: All timestamps measured in seconds") if single_row is True: IMU_time_segment = list(data.values[arg, 1:9]) IMU_time_segment.reverse() IMU_time_segment = [int(elem) for index, elem in enumerate(IMU_time_segment)] del IMU_time_segment[0:3] IMU_time_segment[0] = float(IMU_time_segment[0]) IMU = float("".join(map(str, IMU_time_segment))) IMU_time.append(IMU) elif multiple_row is True: for index, elem in enumerate(arg): IMU_time.append('new row') IMU_time_segment = list(data.values[elem, 1:9]) IMU_time_segment.reverse() IMU_time_segment = [int(elem) for index, elem in enumerate(IMU_time_segment)] del IMU_time_segment[0:3] IMU_time_segment[0] = float(IMU_time_segment[0]) IMU = float("".join(map(str, IMU_time_segment))) IMU_time.append(IMU) elif row_section is True: for row in range(arg[0], arg[1]+1): IMU_time.append('new row') IMU_time_segment = list(data.values[row, 1:9]) IMU_time_segment.reverse() IMU_time_segment = [int(elem) for index, elem in enumerate(IMU_time_segment)] del IMU_time_segment[0:3] IMU_time_segment[0] = float(IMU_time_segment[0]) IMU = float("".join(map(str, IMU_time_segment))) IMU_time.append(IMU) return IMU_time def get_accel_time(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the accelerometer timestamps for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the accelerometer timestamp for a single row specified by arg will be output. multiple_row: boolean If chosen the accelerometer timestamp for multiple rows specified by arg will be output. row_section: boolean If chosen the accelerometer timestamp for a row section by arg will be output. Return: An array of accelerometer timestamps. """ accel_time = [] if single_row is True: accel_time_segment = list(data.values[arg, 9:17]) accel_time_segment.reverse() accel_time_segment = [int(elem) for index, elem in enumerate(accel_time_segment)] del accel_time_segment[0:3] accel_time_segment[0] = float(accel_time_segment[0]) accel = float("".join(map(str, accel_time_segment))) accel_time.append(accel) elif multiple_row is True: for index, elem in enumerate(arg): accel_time.append('new row') accel_time_segment = list(data.values[elem, 9:17]) accel_time_segment.reverse() accel_time_segment = [int(elem) for index, elem in enumerate(accel_time_segment)] del accel_time_segment[0:3] accel_time_segment[0] = float(accel_time_segment[0]) accel = float("".join(map(str, accel_time_segment))) accel_time.append(accel) elif row_section is True: for row in range(arg[0], arg[1]+1): accel_time.append('new row') accel_time_segment = list(data.values[row, 9:17]) accel_time_segment.reverse() accel_time_segment = [int(elem) for index, elem in enumerate(accel_time_segment)] del accel_time_segment[0:3] accel_time_segment[0] = float(accel_time_segment[0]) accel = float("".join(map(str, accel_time_segment))) accel_time.append(accel) return accel_time def get_gyro_time(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the gyroscope timestamps for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the gyroscope timestamp for a single row specified by arg will be output. multiple_row: boolean If chosen the gyroscope timestamp for multiple rows specified by arg will be output. row_section: boolean If chosen the gyroscope timestamp for a row section by arg will be output. Return: An array of gyroscope timestamps. """ gyro_time = [] if single_row is True: gyro_time_segment = list(data.values[arg, 17:25]) gyro_time_segment.reverse() gyro_time_segment = [int(elem) for index, elem in enumerate(gyro_time_segment)] del gyro_time_segment[0:3] gyro_time_segment[0] = float(gyro_time_segment[0]) gyro = float("".join(map(str, gyro_time_segment))) gyro_time.append(gyro) elif multiple_row is True: for index, elem in enumerate(arg): gyro_time.append('new row') gyro_time_segment = list(data.values[elem, 17:25]) gyro_time_segment.reverse() gyro_time_segment = [int(elem) for index, elem in enumerate(gyro_time_segment)] del gyro_time_segment[0:3] gyro_time_segment[0] = float(gyro_time_segment[0]) gyro = float("".join(map(str, gyro_time_segment))) gyro_time.append(gyro) elif row_section is True: for row in range(arg[0], arg[1]+1): gyro_time.append('new row') gyro_time_segment = list(data.values[row, 17:25]) gyro_time_segment.reverse() gyro_time_segment = [int(elem) for index, elem in enumerate(gyro_time_segment)] del gyro_time_segment[0:3] gyro_time_segment[0] = float(gyro_time_segment[0]) gyro = float("".join(map(str, gyro_time_segment))) gyro_time.append(gyro) return gyro_time def get_x_accel(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the x acceleration for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the x acceleration for a single row specified by arg will be output. multiple_row: boolean If chosen the x acceleration for multiple rows specified by arg will be output. row_section: boolean If chosen the x acceleration for a row section by arg will be output. Return: An array of x acceleration. """ x_accel = [] if single_row is True: x_accel_segment = list(data.values[arg, 25:29]) x_accel_segment.reverse() x = int("".join(map(str, x_accel_segment))) x_accel.append(x) elif multiple_row is True: for index, elem in enumerate(arg): x_accel.append('new row') x_accel_segment = list(data.values[elem, 25:29]) x_accel_segment.reverse() x = int("".join(map(str, x_accel_segment))) x_accel.append(x) elif row_section is True: for row in range(arg[0], arg[1]+1): x_accel.append('new row') x_accel_segment = list(data.values[row, 25:29]) x_accel_segment.reverse() x = int("".join(map(str, x_accel_segment))) x_accel.append(x) return x_accel def get_y_accel(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the y acceleration for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the y acceleration for a single row specified by arg will be output. multiple_row: boolean If chosen the y acceleration for multiple rows specified by arg will be output. row_section: boolean If chosen the y acceleration for a row section by arg will be output. Return: An array of y acceleration. """ y_accel = [] if single_row is True: y_accel_segment = list(data.values[arg, 29:33]) y_accel_segment.reverse() y = int("".join(map(str, y_accel_segment))) y_accel.append(y) elif multiple_row is True: for index, elem in enumerate(arg): y_accel.append('new row') y_accel_segment = list(data.values[elem, 29:33]) y_accel_segment.reverse() y = int("".join(map(str, y_accel_segment))) y_accel.append(y) elif row_section is True: for row in range(arg[0], arg[1]+1): y_accel.append('new row') y_accel_segment = list(data.values[row, 29:33]) y_accel_segment.reverse() y = int("".join(map(str, y_accel_segment))) y_accel.append(y) return y_accel def get_z_accel(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the z acceleration for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the z acceleration for a single row specified by arg will be output. multiple_row: boolean If chosen the z acceleration for multiple rows specified by arg will be output. row_section: boolean If chosen the z acceleration for a row section by arg will be output. Return: An array of z acceleration. """ z_accel = [] if single_row is True: z_accel_segment = list(data.values[arg, 33:37]) z_accel_segment.reverse() z = int("".join(map(str, z_accel_segment))) z_accel.append(z) elif multiple_row is True: for index, elem in enumerate(arg): z_accel.append('new row') z_accel_segment = list(data.values[elem, 33:37]) z_accel_segment.reverse() z = int("".join(map(str, z_accel_segment))) z_accel.append(z) elif row_section is True: for row in range(arg[0], arg[1]+1): z_accel.append('new row') z_accel_segment = list(data.values[row, 33:37]) z_accel_segment.reverse() z = int("".join(map(str, z_accel_segment))) z_accel.append(z) return z_accel def get_x_ang_vel(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the x angular velocity for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the x angular velocity for a single row specified by arg will be output. multiple_row: boolean If chosen the x angular velocity for multiple rows specified by arg will be output. row_section: boolean If chosen the x angular velocity for a row section by arg will be output. Return: An array of x angular velocity. """ x_ang_vel = [] if single_row is True: x_ang_vel_segment = list(data.values[arg, 37:41]) x_ang_vel_segment.reverse() x_ang = int("".join(map(str, x_ang_vel_segment))) x_ang_vel.append(x_ang) elif multiple_row is True: for index, elem in enumerate(arg): x_ang_vel.append('new row') x_ang_vel_segment = list(data.values[elem, 37:41]) x_ang_vel_segment.reverse() x_ang = int("".join(map(str, x_ang_vel_segment))) x_ang_vel.append(x_ang) elif row_section is True: for row in range(arg[0], arg[1]+1): x_ang_vel.append('new row') x_ang_vel_segment = list(data.values[row, 37:41]) x_ang_vel_segment.reverse() x_ang = int("".join(map(str, x_ang_vel_segment))) x_ang_vel.append(x_ang) return x_ang_vel def get_y_ang_vel(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the y angular velocity for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the y angular velocity for a single row specified by arg will be output. multiple_row: boolean If chosen the y angular velocity for multiple rows specified by arg will be output. row_section: boolean If chosen the y angular velocity for a row section by arg will be output. Return: An array of y angular velocity. """ y_ang_vel = [] if single_row is True: y_ang_vel_segment = list(data.values[arg, 41:45]) y_ang_vel_segment.reverse() y_ang = int("".join(map(str, y_ang_vel_segment))) y_ang_vel.append(y_ang) elif multiple_row is True: for index, elem in enumerate(arg): y_ang_vel.append('new row') y_ang_vel_segment = list(data.values[elem, 41:45]) y_ang_vel_segment.reverse() y_ang = int("".join(map(str, y_ang_vel_segment))) y_ang_vel.append(y_ang) elif row_section is True: for row in range(arg[0], arg[1]+1): y_ang_vel.append('new row') y_ang_vel_segment = list(data.values[row, 41:45]) y_ang_vel_segment.reverse() y_ang = int("".join(map(str, y_ang_vel_segment))) y_ang_vel.append(y_ang) return y_ang_vel def get_z_ang_vel(data, arg, single_row=False, multiple_row=False, row_section=False): """ Takes in a file and row arguement(s) along with a boolean that specifies the amount of rows to read the z angular velocity for. Parameter: data: file The csv file that is passed in to read. arg: int The row(s) that are to be read. single_row: boolean If chosen the z angular velocity for a single row specified by arg will be output. multiple_row: boolean If chosen the z angular velocity for multiple rows specified by arg will be output. row_section: boolean If chosen the z angular velocity for a row section by arg will be output. Return: An array of z angular velocity. """ z_ang_vel = [] if single_row is True: z_ang_vel_segment = list(data.values[arg, 45:49]) z_ang_vel_segment.reverse() z_ang = int("".join(map(str, z_ang_vel_segment))) z_ang_vel.append(z_ang) elif multiple_row is True: for index, elem in enumerate(arg): z_ang_vel.append('new row') z_ang_vel_segment = list(data.values[elem, 45:49]) z_ang_vel_segment.reverse() z_ang = int("".join(map(str, z_ang_vel_segment))) z_ang_vel.append(z_ang) elif row_section is True: for row in range(arg[0], arg[1]+1): z_ang_vel.append('new row') z_ang_vel_segment = list(data.values[row, 45:49]) z_ang_vel_segment.reverse() z_ang = int("".join(map(str, z_ang_vel_segment))) z_ang_vel.append(z_ang) return z_ang_vel

38.204082

137

0.619599

2,494

16,848

3.991981

0.03769

0.07955

0.040679

0.048815

0.958718

0.957011

0.923262

0.904881

0.897348

0

0.013673

0.296771

16,848

440

138

38.290909

0.826637

0.340515

0

0.756637

0

0.015799

0

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0.039823

false

0

0.079646

0.004425

0

null

0

1

0

1

0

null

0

7

d78cff50508c02a8485c7281b8461163af3579ac

192

py

Python

angelina/main.py

Ivan-Tuzhilkin/angelina

139bad534024833e7f2c5375384a9c18c7036757

[ "MIT" ]

null

angelina/main.py

Ivan-Tuzhilkin/angelina

139bad534024833e7f2c5375384a9c18c7036757

[ "MIT" ]

null

angelina/main.py

Ivan-Tuzhilkin/angelina

139bad534024833e7f2c5375384a9c18c7036757

[ "MIT" ]

null

from angelina.parser_processing import parser_processing from angelina.parser import create_parser def startapp(): '''Точка входа в приложение.''' parser_processing(create_parser())

24

56

0.786458

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192

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0.521739

0.328767

0.246575

0

0.130208

192

7

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27.428571

0.874252

0.130208

0

1

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true

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null

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ad11dcd0aab5fca0e112fb64e7782f544de1d61e

60,442

py

Python

h1/api/vmhost_project_instance_api.py

hyperonecom/h1-client-python

4ce355852ba3120ec1b8f509ab5894a5c08da730

[ "MIT" ]

null

h1/api/vmhost_project_instance_api.py

hyperonecom/h1-client-python

4ce355852ba3120ec1b8f509ab5894a5c08da730

[ "MIT" ]

null

h1/api/vmhost_project_instance_api.py

hyperonecom/h1-client-python

4ce355852ba3120ec1b8f509ab5894a5c08da730

[ "MIT" ]

null

""" HyperOne HyperOne API # noqa: E501 The version of the OpenAPI document: 0.1.0 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from h1.api_client import ApiClient, Endpoint as _Endpoint from h1.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types ) from h1.model.event import Event from h1.model.inline_response400 import InlineResponse400 from h1.model.resource_service import ResourceService from h1.model.tag import Tag from h1.model.tag_array import TagArray from h1.model.vmhost import Vmhost class VmhostProjectInstanceApi(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 __vmhost_project_instance_event_get( self, project_id, location_id, instance_id, event_id, **kwargs ): """Get vmhost/instance.event # noqa: E501 Get vmhost/instance.event # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_event_get(project_id, location_id, instance_id, event_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id event_id (str): eventId Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Event If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id kwargs['event_id'] = \ event_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_event_get = _Endpoint( settings={ 'response_type': (Event,), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/event/{eventId}', 'operation_id': 'vmhost_project_instance_event_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'event_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', 'event_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'event_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', 'event_id': 'eventId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'event_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_event_get ) def __vmhost_project_instance_event_list( self, project_id, location_id, instance_id, **kwargs ): """List vmhost/instance.event # noqa: E501 List vmhost/instance.event # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_event_list(project_id, location_id, instance_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id Keyword Args: limit (float): $limit. [optional] if omitted the server will use the default value of 100 skip (float): $skip. [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: [Event] If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_event_list = _Endpoint( settings={ 'response_type': ([Event],), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/event', 'operation_id': 'vmhost_project_instance_event_list', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'limit', 'skip', ], 'required': [ 'project_id', 'location_id', 'instance_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ 'limit', ] }, root_map={ 'validations': { ('limit',): { 'inclusive_maximum': 1000, 'inclusive_minimum': 1, }, }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'limit': (float,), 'skip': (float,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', 'limit': '$limit', 'skip': '$skip', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'limit': 'query', 'skip': 'query', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_event_list ) def __vmhost_project_instance_get( self, project_id, location_id, instance_id, **kwargs ): """Get vmhost/instance # noqa: E501 Returns a single instance # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_get(project_id, location_id, instance_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Vmhost If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_get = _Endpoint( settings={ 'response_type': (Vmhost,), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}', 'operation_id': 'vmhost_project_instance_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_get ) def __vmhost_project_instance_list( self, project_id, location_id, **kwargs ): """List vmhost/instance # noqa: E501 List instance # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_list(project_id, location_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id Keyword Args: enabled_services (str): Filter by enabledServices. [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: [Vmhost] If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_list = _Endpoint( settings={ 'response_type': ([Vmhost],), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance', 'operation_id': 'vmhost_project_instance_list', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'enabled_services', ], 'required': [ 'project_id', 'location_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'enabled_services': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'enabled_services': 'enabledServices', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'enabled_services': 'query', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_list ) def __vmhost_project_instance_service_get( self, project_id, location_id, instance_id, service_id, **kwargs ): """Get vmhost/instance.service # noqa: E501 Get vmhost/instance.service # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_service_get(project_id, location_id, instance_id, service_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id service_id (str): serviceId Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: ResourceService If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id kwargs['service_id'] = \ service_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_service_get = _Endpoint( settings={ 'response_type': (ResourceService,), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/service/{serviceId}', 'operation_id': 'vmhost_project_instance_service_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'service_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', 'service_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'service_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', 'service_id': 'serviceId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'service_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_service_get ) def __vmhost_project_instance_service_list( self, project_id, location_id, instance_id, **kwargs ): """List vmhost/instance.service # noqa: E501 List vmhost/instance.service # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_service_list(project_id, location_id, instance_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: [ResourceService] If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_service_list = _Endpoint( settings={ 'response_type': ([ResourceService],), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/service', 'operation_id': 'vmhost_project_instance_service_list', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_service_list ) def __vmhost_project_instance_tag_create( self, project_id, location_id, instance_id, tag, **kwargs ): """Create vmhost/instance.tag # noqa: E501 Create vmhost/instance.tag # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_tag_create(project_id, location_id, instance_id, tag, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id tag (Tag): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Tag If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id kwargs['tag'] = \ tag return self.call_with_http_info(**kwargs) self.vmhost_project_instance_tag_create = _Endpoint( settings={ 'response_type': (Tag,), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/tag', 'operation_id': 'vmhost_project_instance_tag_create', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'tag', ], 'required': [ 'project_id', 'location_id', 'instance_id', 'tag', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'tag': (Tag,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'tag': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client, callable=__vmhost_project_instance_tag_create ) def __vmhost_project_instance_tag_delete( self, project_id, location_id, instance_id, tag_id, **kwargs ): """Delete vmhost/instance.tag # noqa: E501 Delete vmhost/instance.tag # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_tag_delete(project_id, location_id, instance_id, tag_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id tag_id (str): tagId Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id kwargs['tag_id'] = \ tag_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_tag_delete = _Endpoint( settings={ 'response_type': None, 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/tag/{tagId}', 'operation_id': 'vmhost_project_instance_tag_delete', 'http_method': 'DELETE', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'tag_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', 'tag_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'tag_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', 'tag_id': 'tagId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'tag_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_tag_delete ) def __vmhost_project_instance_tag_get( self, project_id, location_id, instance_id, tag_id, **kwargs ): """Get vmhost/instance.tag # noqa: E501 Get vmhost/instance.tag # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_tag_get(project_id, location_id, instance_id, tag_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id tag_id (str): tagId Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: Tag If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id kwargs['tag_id'] = \ tag_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_tag_get = _Endpoint( settings={ 'response_type': (Tag,), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/tag/{tagId}', 'operation_id': 'vmhost_project_instance_tag_get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'tag_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', 'tag_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'tag_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', 'tag_id': 'tagId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'tag_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_tag_get ) def __vmhost_project_instance_tag_list( self, project_id, location_id, instance_id, **kwargs ): """List vmhost/instance.tag # noqa: E501 List vmhost/instance.tag # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_tag_list(project_id, location_id, instance_id, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: [Tag] If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id return self.call_with_http_info(**kwargs) self.vmhost_project_instance_tag_list = _Endpoint( settings={ 'response_type': ([Tag],), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/tag', 'operation_id': 'vmhost_project_instance_tag_list', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', ], 'required': [ 'project_id', 'location_id', 'instance_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client, callable=__vmhost_project_instance_tag_list ) def __vmhost_project_instance_tag_put( self, project_id, location_id, instance_id, tag_array, **kwargs ): """Replace vmhost/instance.tag # noqa: E501 Replace vmhost/instance.tag # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.vmhost_project_instance_tag_put(project_id, location_id, instance_id, tag_array, async_req=True) >>> result = thread.get() Args: project_id (str): Project Id location_id (str): Location Id instance_id (str): Instance Id tag_array (TagArray): Keyword Args: _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (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. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. async_req (bool): execute request asynchronously Returns: [Tag] If the method is called asynchronously, returns the request thread. """ kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) 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['_host_index'] = kwargs.get('_host_index') kwargs['project_id'] = \ project_id kwargs['location_id'] = \ location_id kwargs['instance_id'] = \ instance_id kwargs['tag_array'] = \ tag_array return self.call_with_http_info(**kwargs) self.vmhost_project_instance_tag_put = _Endpoint( settings={ 'response_type': ([Tag],), 'auth': [ 'BearerAuth' ], 'endpoint_path': '/vmhost/{locationId}/project/{projectId}/instance/{instanceId}/tag', 'operation_id': 'vmhost_project_instance_tag_put', 'http_method': 'PUT', 'servers': None, }, params_map={ 'all': [ 'project_id', 'location_id', 'instance_id', 'tag_array', ], 'required': [ 'project_id', 'location_id', 'instance_id', 'tag_array', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'project_id': (str,), 'location_id': (str,), 'instance_id': (str,), 'tag_array': (TagArray,), }, 'attribute_map': { 'project_id': 'projectId', 'location_id': 'locationId', 'instance_id': 'instanceId', }, 'location_map': { 'project_id': 'path', 'location_id': 'path', 'instance_id': 'path', 'tag_array': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client, callable=__vmhost_project_instance_tag_put )

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ad270642d41f69664319851de3fc6cf762da8665

27,800

py

Python

GAN_CNN_Visualization.py

JazzikPeng/Similarity-with-Deep-Ranking

e2a15f21ffc5fe67de201be66cd129387f440620

[ "MIT" ]

null

GAN_CNN_Visualization.py

JazzikPeng/Similarity-with-Deep-Ranking

e2a15f21ffc5fe67de201be66cd129387f440620

[ "MIT" ]

null

GAN_CNN_Visualization.py

JazzikPeng/Similarity-with-Deep-Ranking

e2a15f21ffc5fe67de201be66cd129387f440620

[ "MIT" ]

null

import os import torch import torchvision import torchvision.transforms as transforms import math import torch.optim as optim #import torch.optim.lr_scheduler as lr_scheduler import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import numpy as np from torch.autograd import Variable import torch.autograd as autograd import torch.nn as nn import torch.nn.functional as F import time import copy class discriminator(nn.Module): def __init__(self, AC = True): super(discriminator, self).__init__() self.conv = nn.Sequential( nn.Conv2d(3, 196, 3, stride=1, padding=1), nn.LayerNorm([32,32]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([4,4]), nn.LeakyReLU(), nn.MaxPool2d(4,4), ) self.fc1 = nn.Linear(196,1) self.fc10 = nn.Linear(196,10) def forward(self,x): x = self.conv(x) x = x.view(-1,196) return self.fc1(x), self.fc10(x) # Create Directory if os.path.exists('./visualization'): print('visualization exist') else: print('visualization dir does not exist, create one') os.mkdir('visualization') def plot(samples): fig = plt.figure(figsize=(10, 10)) gs = gridspec.GridSpec(10, 10) gs.update(wspace=0.02, hspace=0.02) for i, sample in enumerate(samples): ax = plt.subplot(gs[i]) plt.axis('off') ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(sample) return fig batch_size = 128 transform_test = transforms.Compose([ transforms.CenterCrop(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) testset = torchvision.datasets.CIFAR10(root='./', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=8) testloader = enumerate(testloader) model = torch.load('cifar10.model') model.cuda() model.eval() batch_idx, (X_batch, Y_batch) = next(testloader) X_batch = Variable(X_batch,requires_grad=True).cuda() Y_batch_alternate = (Y_batch + 1)%10 Y_batch_alternate = Variable(Y_batch_alternate).cuda() Y_batch = Variable(Y_batch).cuda() ## save real images samples = X_batch.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples[0:100]) plt.savefig('visualization/real_images.png', bbox_inches='tight') plt.close(fig) # Get the output from the fc10 layer and report the classification accuracy. _, output = model(X_batch) prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y_batch.data).sum() ) /float(batch_size))*100.0 print('Model Accuracy %f%%' %accuracy) ## slightly jitter all input images criterion = nn.CrossEntropyLoss(reduce=False) loss = criterion(output, Y_batch_alternate) gradients = torch.autograd.grad(outputs=loss, inputs=X_batch, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] # save gradient jitter gradient_image = gradients.data.cpu().numpy() gradient_image = (gradient_image - np.min(gradient_image))/(np.max(gradient_image)-np.min(gradient_image)) gradient_image = gradient_image.transpose(0,2,3,1) fig = plot(gradient_image[0:100]) plt.savefig('visualization/gradient_image.png', bbox_inches='tight') plt.close(fig) # jitter input image gradients[gradients>0.0] = 1.0 gradients[gradients<0.0] = -1.0 gain = 8.0 X_batch_modified = X_batch - gain*0.007843137*gradients X_batch_modified[X_batch_modified>1.0] = 1.0 X_batch_modified[X_batch_modified<-1.0] = -1.0 ## evaluate new fake images _, output = model(X_batch_modified) prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y_batch.data).sum() ) /float(batch_size))*100.0 print('Jittered Accuracy %f%%' %accuracy) ## save fake images samples = X_batch_modified.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples[0:100]) plt.savefig('visualization/jittered_images.png', bbox_inches='tight') plt.close(fig) #################################################################################### #################################################################################### #################################################################################### # Discriminator with out generator model = torch.load('cifar10.model') model.cuda() model.eval() X = X_batch.mean(dim=0) X = X.repeat(10,1,1,1) Y = torch.arange(10).type(torch.int64) Y = Variable(Y).cuda() lr = 0.1 weight_decay = 0.001 for i in range(200): _, output = model(X) loss = -output[torch.arange(10).type(torch.int64),torch.arange(10).type(torch.int64)] gradients = torch.autograd.grad(outputs=loss, inputs=X, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y.data).sum() ) /float(10.0))*100.0 print(i,accuracy,-loss) X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data) X[X>1.0] = 1.0 X[X<-1.0] = -1.0 ## save new images samples = X.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples) plt.savefig('visualization/max_class_no_G.png', bbox_inches='tight') plt.close(fig) # discriminator with Generator class discriminator(nn.Module): def __init__(self, AC = True): super(discriminator, self).__init__() self.conv = nn.Sequential( nn.Conv2d(3, 196, 3, stride=1, padding=1), nn.LayerNorm([32,32]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([4,4]), nn.LeakyReLU(), nn.MaxPool2d(4,4), ) self.fc1 = nn.Linear(196,1) self.fc10 = nn.Linear(196,10) def forward(self,x): x = self.conv(x) x = x.view(-1,196) return self.fc1(x), self.fc10(x) try: model = torch.load('discriminator.model') except: model = torch.load('tempD.model') model.cuda() model.eval() X = X_batch.mean(dim=0) X = X.repeat(10,1,1,1) Y = torch.arange(10).type(torch.int64) Y = Variable(Y).cuda() lr = 0.1 weight_decay = 0.001 for i in range(200): _, output = model(X) loss = -output[torch.arange(10).type(torch.int64),torch.arange(10).type(torch.int64)] gradients = torch.autograd.grad(outputs=loss, inputs=X, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y.data).sum() ) /float(10.0))*100.0 print(i,accuracy,-loss) X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data) X[X>1.0] = 1.0 X[X<-1.0] = -1.0 ## save new images samples = X.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples) plt.savefig('visualization/max_class_with_G.png', bbox_inches='tight') plt.close(fig) #################################################################################### #################################################################################### #################################################################################### # Synthetic Features Maximizing Features at Various Layers # Do 2 layers for distriminator model without Generator # Modifying the model # Since we trained on a sequential block, we have to load model and load the state of the parameters. # Load Model class discriminator(nn.Module): def __init__(self): super(discriminator, self).__init__() self.conv = nn.Sequential( nn.Conv2d(3, 196, 3, stride=1, padding=1), nn.LayerNorm([32,32]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([4,4]), nn.LeakyReLU(), nn.MaxPool2d(4,4), ) self.fc1 = nn.Linear(196,1) self.fc10 = nn.Linear(196,10) def forward(self, x): x = self.conv(x) x = x.view(-1,196) return self.fc1(x), self.fc10(x) class discriminator2(nn.Module): def __init__(self): super(discriminator2, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(3, 196, 3, stride=1, padding=1), nn.LayerNorm([32,32]), nn.LeakyReLU(), ) self.conv2 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), ) self.conv3 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), ) self.conv4 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv5 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv6 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv7 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv8 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([4,4]), nn.LeakyReLU(), nn.MaxPool2d(4,4), ) # self.max_pool2d = self.fc1 = nn.Linear(196,1) self.fc10 = nn.Linear(196,10) def forward(self, x, extract_features): x = self.conv1(x) if extract_features == 1: x = F.max_pool2d(x, 32, 32) x = x.view(x.size(0), -1) return x x = self.conv2(x) if extract_features == 2: # print('HERE') x = F.max_pool2d(x, 16, 16) x = x.view(x.size(0), -1) return x x = self.conv3(x) if extract_features == 3: x = F.max_pool2d(x, 16, 16) x = x.view(x.size(0), -1) return x x = self.conv4(x) if extract_features == 3: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv5(x) if extract_features == 5: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv6(x) if extract_features == 6: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv7(x) if extract_features == 7: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv8(x) x = x.view(x.size(0), -1) return x model = torch.load('cifar10.model', map_location='cpu') model_dic = copy.deepcopy(model.state_dict()) # The names doesn't match, we change the name keys = [] for i in range(1,9): w1 = 'conv'+str(i)+'.0.weight' b1 = 'conv'+str(i)+'.0.bias' w2 = 'conv'+str(i)+'.1.weight' b2 = 'conv'+str(i)+'.1.bias' keys.extend([w1,b1,w2,b2]) model_dic_keys = list(model_dic.keys()) for x in zip(keys, model_dic_keys): model_dic[x[0]] = model_dic.pop(x[1]) model2 = discriminator2() model2.load_state_dict(model_dic) # for param_tensor in model2.state_dict(): # print(param_tensor, "\t", model2.state_dict()[param_tensor].size()) for i in range(len(model.state_dict())): assert ((model.state_dict()[list(model.state_dict().keys())[i]] == model2.state_dict()[list(model2.state_dict().keys())[i]]).byte().all()) model2 model2.cuda() model2.eval() #################################################################################### # Do for layer 2 batch_size = 196 transform_test = transforms.Compose([ transforms.CenterCrop(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) testset = torchvision.datasets.CIFAR10(root='./', train=False, download=False, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=8) testloader = enumerate(testloader) batch_idx, (X_batch, Y_batch) = next(testloader) X_batch = Variable(X_batch,requires_grad=True).cuda() Y_batch_alternate = (Y_batch + 1)%10 Y_batch_alternate = Variable(Y_batch_alternate).cuda() Y_batch = Variable(Y_batch).cuda() X = X_batch.mean(dim=0) X = X.repeat(batch_size,1,1,1) Y = torch.arange(batch_size).type(torch.int64) Y = Variable(Y).cuda() lr = 0.1 weight_decay = 0.001 for i in range(200): # Extract_features == 2 output = model2(X, 2) loss = -output[torch.arange(batch_size).type(torch.int64),torch.arange(batch_size).type(torch.int64)] gradients = torch.autograd.grad(outputs=loss, inputs=X, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y.data).sum() ) /float(batch_size))*100.0 print(i,accuracy,-loss) X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data) X[X>1.0] = 1.0 X[X<-1.0] = -1.0 ## save new images samples = X.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples[0:100]) plt.savefig('visualization/max_features_without_G_Layer2.png', bbox_inches='tight') plt.close(fig) #################################################################################### #################################################################################### # Do for layer 8 batch_size = 196 transform_test = transforms.Compose([ transforms.CenterCrop(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) testset = torchvision.datasets.CIFAR10(root='./', train=False, download=False, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=8) testloader = enumerate(testloader) batch_idx, (X_batch, Y_batch) = next(testloader) X_batch = Variable(X_batch,requires_grad=True).cuda() Y_batch_alternate = (Y_batch + 1)%10 Y_batch_alternate = Variable(Y_batch_alternate).cuda() Y_batch = Variable(Y_batch).cuda() X = X_batch.mean(dim=0) X = X.repeat(batch_size,1,1,1) Y = torch.arange(batch_size).type(torch.int64) Y = Variable(Y).cuda() lr = 0.1 weight_decay = 0.001 for i in range(200): # Extract_features == 2 output = model2(X, 8) loss = -output[torch.arange(batch_size).type(torch.int64),torch.arange(batch_size).type(torch.int64)] gradients = torch.autograd.grad(outputs=loss, inputs=X, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y.data).sum() ) /float(batch_size))*100.0 print(i,accuracy,-loss) X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data) X[X>1.0] = 1.0 X[X<-1.0] = -1.0 ## save new images samples = X.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples[0:100]) plt.savefig('visualization/max_features_without_G_Layer8.png', bbox_inches='tight') plt.close(fig) #################################################################################### #################################################################################### #################################################################################### #################################################################################### #################################################################################### #################################################################################### #################################################################################### # Redo above for discriminator with Generator # Load Model class discriminator(nn.Module): def __init__(self): super(discriminator, self).__init__() self.conv = nn.Sequential( nn.Conv2d(3, 196, 3, stride=1, padding=1), nn.LayerNorm([32,32]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([4,4]), nn.LeakyReLU(), nn.MaxPool2d(4,4), ) self.fc1 = nn.Linear(196,1) self.fc10 = nn.Linear(196,10) def forward(self, x): x = self.conv(x) x = x.view(-1,196) return self.fc1(x), self.fc10(x) class discriminator2(nn.Module): def __init__(self): super(discriminator2, self).__init__() self.conv1 = nn.Sequential( nn.Conv2d(3, 196, 3, stride=1, padding=1), nn.LayerNorm([32,32]), nn.LeakyReLU(), ) self.conv2 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), ) self.conv3 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([16,16]), nn.LeakyReLU(), ) self.conv4 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv5 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv6 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv7 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=1, padding=1), nn.LayerNorm([8,8]), nn.LeakyReLU(), ) self.conv8 = nn.Sequential( nn.Conv2d(196, 196, 3, stride=2, padding=1), nn.LayerNorm([4,4]), nn.LeakyReLU(), nn.MaxPool2d(4,4), ) # self.max_pool2d = self.fc1 = nn.Linear(196,1) self.fc10 = nn.Linear(196,10) def forward(self, x, extract_features): x = self.conv1(x) if extract_features == 1: x = F.max_pool2d(x, 32, 32) x = x.view(x.size(0), -1) return x x = self.conv2(x) if extract_features == 2: # print('HERE') x = F.max_pool2d(x, 16, 16) x = x.view(x.size(0), -1) return x x = self.conv3(x) if extract_features == 3: x = F.max_pool2d(x, 16, 16) x = x.view(x.size(0), -1) return x x = self.conv4(x) if extract_features == 3: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv5(x) if extract_features == 5: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv6(x) if extract_features == 6: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv7(x) if extract_features == 7: x = F.max_pool2d(x, 8, 8) x = x.view(x.size(0), -1) return x x = self.conv8(x) x = x.view(x.size(0), -1) return x model = torch.load('discriminator.model', map_location='cpu') model_dic = copy.deepcopy(model.state_dict()) # The names doesn't match, we change the name keys = [] for i in range(1,9): w1 = 'conv'+str(i)+'.0.weight' b1 = 'conv'+str(i)+'.0.bias' w2 = 'conv'+str(i)+'.1.weight' b2 = 'conv'+str(i)+'.1.bias' keys.extend([w1,b1,w2,b2]) model_dic_keys = list(model_dic.keys()) for x in zip(keys, model_dic_keys): model_dic[x[0]] = model_dic.pop(x[1]) model2 = discriminator2() model2.load_state_dict(model_dic) # for param_tensor in model2.state_dict(): # print(param_tensor, "\t", model2.state_dict()[param_tensor].size()) for i in range(len(model.state_dict())): assert ((model.state_dict()[list(model.state_dict().keys())[i]] == model2.state_dict()[list(model2.state_dict().keys())[i]]).byte().all()) model2 model2.cuda() model2.eval() #################################################################################### # Do for layer 2 batch_size = 196 transform_test = transforms.Compose([ transforms.CenterCrop(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) testset = torchvision.datasets.CIFAR10(root='./', train=False, download=False, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=8) testloader = enumerate(testloader) batch_idx, (X_batch, Y_batch) = next(testloader) X_batch = Variable(X_batch,requires_grad=True).cuda() Y_batch_alternate = (Y_batch + 1)%10 Y_batch_alternate = Variable(Y_batch_alternate).cuda() Y_batch = Variable(Y_batch).cuda() X = X_batch.mean(dim=0) X = X.repeat(batch_size,1,1,1) Y = torch.arange(batch_size).type(torch.int64) Y = Variable(Y).cuda() lr = 0.1 weight_decay = 0.001 for i in range(200): # Extract_features == 2 output = model2(X, 2) loss = -output[torch.arange(batch_size).type(torch.int64),torch.arange(batch_size).type(torch.int64)] gradients = torch.autograd.grad(outputs=loss, inputs=X, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y.data).sum() ) /float(batch_size))*100.0 print(i,accuracy,-loss) X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data) X[X>1.0] = 1.0 X[X<-1.0] = -1.0 ## save new images samples = X.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples[0:100]) plt.savefig('visualization/max_features_without_G_Layer2.png', bbox_inches='tight') plt.close(fig) #################################################################################### #################################################################################### # Do for layer 8 batch_size = 196 transform_test = transforms.Compose([ transforms.CenterCrop(32), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) testset = torchvision.datasets.CIFAR10(root='./', train=False, download=False, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=8) testloader = enumerate(testloader) batch_idx, (X_batch, Y_batch) = next(testloader) X_batch = Variable(X_batch,requires_grad=True).cuda() Y_batch_alternate = (Y_batch + 1)%10 Y_batch_alternate = Variable(Y_batch_alternate).cuda() Y_batch = Variable(Y_batch).cuda() X = X_batch.mean(dim=0) X = X.repeat(batch_size,1,1,1) Y = torch.arange(batch_size).type(torch.int64) Y = Variable(Y).cuda() lr = 0.1 weight_decay = 0.001 for i in range(200): # Extract_features == 2 output = model2(X, 8) loss = -output[torch.arange(batch_size).type(torch.int64),torch.arange(batch_size).type(torch.int64)] gradients = torch.autograd.grad(outputs=loss, inputs=X, grad_outputs=torch.ones(loss.size()).cuda(), create_graph=True, retain_graph=False, only_inputs=True)[0] prediction = output.data.max(1)[1] # first column has actual prob. accuracy = ( float( prediction.eq(Y.data).sum() ) /float(batch_size))*100.0 print(i,accuracy,-loss) X = X - lr*gradients.data - weight_decay*X.data*torch.abs(X.data) X[X>1.0] = 1.0 X[X<-1.0] = -1.0 ## save new images samples = X.data.cpu().numpy() samples += 1.0 samples /= 2.0 samples = samples.transpose(0,2,3,1) fig = plot(samples[0:100]) plt.savefig('visualization/max_features_without_G_Layer8.png', bbox_inches='tight') plt.close(fig) ####################################################################################

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app/plugin/__init__.py

jonntd/JustList

a25ef56be8e9a5c990cc156478dd20e97b2ed57e

[ "MIT" ]

1

2020-12-24T08:48:17.000Z

app/plugin/__init__.py

jonntd/JustList

a25ef56be8e9a5c990cc156478dd20e97b2ed57e

[ "MIT" ]

null

app/plugin/__init__.py

jonntd/JustList

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MatchApp/migrations/0026_auto_20210319_1315.py

elizza19/django_local_library

f2dc053e44684b7a966d8bc0ff364f5251449f5b

[ "Apache-2.0" ]

null

MatchApp/migrations/0026_auto_20210319_1315.py

elizza19/django_local_library

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null

MatchApp/migrations/0026_auto_20210319_1315.py

elizza19/django_local_library

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[ "Apache-2.0" ]

null

# Generated by Django 3.1.7 on 2021-03-19 12:15 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('MatchApp', '0025_auto_20210319_1223'), ] operations = [ migrations.AlterField( model_name='mascotas', name='amigable', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='carinoso', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='curioso', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='deportista', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='dominante', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='educado', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='extrovertido', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='impulsivo', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='independiente', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='jugueton', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='temeroso', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), migrations.AlterField( model_name='mascotas', name='tranquilo', field=models.CharField(blank=True, choices=[('N/A', 'N/A'), ('0', '0'), ('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6'), ('7', '7'), ('8', '8'), ('9', '9'), ('10', '10')], max_length=3, null=True), ), ]

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0.85072

0.827089

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0.101409

0.243394

4,314

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59.09589

0.430147

0.010431

0

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ad718ec376d8fabbe6e22a1556b01477c2bbc6a4

65,097

py

Python

moody/m/b_send/__init__.py

tokenchain/moodyeth

e5a40ffe411fc4dc767ba8a279c1fd7c002dc975

[ "MIT" ]

null

moody/m/b_send/__init__.py

tokenchain/moodyeth

e5a40ffe411fc4dc767ba8a279c1fd7c002dc975

[ "MIT" ]

null

moody/m/b_send/__init__.py

tokenchain/moodyeth

e5a40ffe411fc4dc767ba8a279c1fd7c002dc975

[ "MIT" ]

null

"""Generated wrapper for BSend Solidity contract.""" # pylint: disable=too-many-arguments import json import time from typing import ( # pylint: disable=unused-import List, Optional, Tuple, Union, ) from eth_utils import to_checksum_address from hexbytes import HexBytes from web3.contract import ContractFunction from web3.datastructures import AttributeDict from web3.exceptions import ContractLogicError from moody import Bolors from moody.libeb import MiliDoS from moody.m.bases import ContractMethod, Validator, ContractBase, Signatures from moody.m.tx_params import TxParams # Try to import a custom validator class definition; if there isn't one, # declare one that we can instantiate for the default argument to the # constructor for BSend below. try: # both mypy and pylint complain about what we're doing here, but this # works just fine, so their messages have been disabled here. from . import ( # type: ignore # pylint: disable=import-self BSendValidator, ) except ImportError: class BSendValidator( # type: ignore Validator ): """No-op input validator.""" try: from .middleware import MIDDLEWARE # type: ignore except ImportError: pass class AddSignerMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the addSigner method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("addSigner") def validate_and_normalize_inputs(self, account: str) -> any: """Validate the inputs to the addSigner method.""" self.validator.assert_valid( method_name='addSigner', parameter_name='account', argument_value=account, ) account = self.validate_and_checksum_address(account) return (account) def block_send(self, account: str, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> None: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(account) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: add_signer") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, account: str, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (account) = self.validate_and_normalize_inputs(account) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(account).transact(tx_params.as_dict()) def build_transaction(self, account: str, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (account) = self.validate_and_normalize_inputs(account) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(account).buildTransaction(tx_params.as_dict()) def estimate_gas(self, account: str, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (account) = self.validate_and_normalize_inputs(account) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(account).estimateGas(tx_params.as_dict()) class BulkSendTokenMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the bulkSendToken method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("bulkSendToken") def validate_and_normalize_inputs(self, token_addr: str, addresses: List[str], amounts: List[int]) -> any: """Validate the inputs to the bulkSendToken method.""" self.validator.assert_valid( method_name='bulkSendToken', parameter_name='tokenAddr', argument_value=token_addr, ) token_addr = self.validate_and_checksum_address(token_addr) self.validator.assert_valid( method_name='bulkSendToken', parameter_name='addresses', argument_value=addresses, ) self.validator.assert_valid( method_name='bulkSendToken', parameter_name='amounts', argument_value=amounts, ) return (token_addr, addresses, amounts) def block_send(self, token_addr: str, addresses: List[str], amounts: List[int], _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(token_addr, addresses, amounts) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: bulk_send_token") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, token_addr: str, addresses: List[str], amounts: List[int], tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (token_addr, addresses, amounts) = self.validate_and_normalize_inputs(token_addr, addresses, amounts) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_addr, addresses, amounts).transact(tx_params.as_dict()) def build_transaction(self, token_addr: str, addresses: List[str], amounts: List[int], tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (token_addr, addresses, amounts) = self.validate_and_normalize_inputs(token_addr, addresses, amounts) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_addr, addresses, amounts).buildTransaction(tx_params.as_dict()) def estimate_gas(self, token_addr: str, addresses: List[str], amounts: List[int], tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (token_addr, addresses, amounts) = self.validate_and_normalize_inputs(token_addr, addresses, amounts) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_addr, addresses, amounts).estimateGas(tx_params.as_dict()) class BulkSendTrxMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the bulkSendTrx method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("bulkSendTrx") def validate_and_normalize_inputs(self, addresses: List[str], amounts: List[int]) -> any: """Validate the inputs to the bulkSendTrx method.""" self.validator.assert_valid( method_name='bulkSendTrx', parameter_name='addresses', argument_value=addresses, ) self.validator.assert_valid( method_name='bulkSendTrx', parameter_name='amounts', argument_value=amounts, ) return (addresses, amounts) def block_send(self, addresses: List[str], amounts: List[int], _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(addresses, amounts) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: bulk_send_trx") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, addresses: List[str], amounts: List[int], tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (addresses, amounts) = self.validate_and_normalize_inputs(addresses, amounts) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addresses, amounts).transact(tx_params.as_dict()) def build_transaction(self, addresses: List[str], amounts: List[int], tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (addresses, amounts) = self.validate_and_normalize_inputs(addresses, amounts) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addresses, amounts).buildTransaction(tx_params.as_dict()) def estimate_gas(self, addresses: List[str], amounts: List[int], tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (addresses, amounts) = self.validate_and_normalize_inputs(addresses, amounts) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addresses, amounts).estimateGas(tx_params.as_dict()) class ClaimInitMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the claimInit method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address) self._underlying_method = contract_function self.sign = validator.getSignature("claimInit") def block_send(self, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> None: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method() try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: claim_init") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().transact(tx_params.as_dict()) def build_transaction(self, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().buildTransaction(tx_params.as_dict()) def estimate_gas(self, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().estimateGas(tx_params.as_dict()) class DepositMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the deposit method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address) self._underlying_method = contract_function self.sign = validator.getSignature("deposit") def block_send(self, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method() try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: deposit") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().transact(tx_params.as_dict()) def build_transaction(self, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().buildTransaction(tx_params.as_dict()) def estimate_gas(self, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().estimateGas(tx_params.as_dict()) class EthSendFeeMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the ethSendFee method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address) self._underlying_method = contract_function self.sign = validator.getSignature("ethSendFee") def block_call(self, debug: bool = False) -> int: _fn = self._underlying_method() returned = _fn.call({ 'from': self._operate }) return int(returned) def estimate_gas(self, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().estimateGas(tx_params.as_dict()) class GetBalanceMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the getBalance method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("getBalance") def validate_and_normalize_inputs(self, addr: str) -> any: """Validate the inputs to the getBalance method.""" self.validator.assert_valid( method_name='getBalance', parameter_name='addr', argument_value=addr, ) addr = self.validate_and_checksum_address(addr) return (addr) def block_call(self, addr: str, debug: bool = False) -> int: _fn = self._underlying_method(addr) returned = _fn.call({ 'from': self._operate }) return int(returned) def estimate_gas(self, addr: str, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (addr) = self.validate_and_normalize_inputs(addr) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addr).estimateGas(tx_params.as_dict()) class IsSignerMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the isSigner method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("isSigner") def validate_and_normalize_inputs(self, account: str) -> any: """Validate the inputs to the isSigner method.""" self.validator.assert_valid( method_name='isSigner', parameter_name='account', argument_value=account, ) account = self.validate_and_checksum_address(account) return (account) def block_call(self, account: str, debug: bool = False) -> bool: _fn = self._underlying_method(account) returned = _fn.call({ 'from': self._operate }) return bool(returned) def estimate_gas(self, account: str, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (account) = self.validate_and_normalize_inputs(account) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(account).estimateGas(tx_params.as_dict()) class RenounceSignerMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the renounceSigner method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address) self._underlying_method = contract_function self.sign = validator.getSignature("renounceSigner") def block_send(self, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> None: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method() try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: renounce_signer") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().transact(tx_params.as_dict()) def build_transaction(self, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().buildTransaction(tx_params.as_dict()) def estimate_gas(self, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().estimateGas(tx_params.as_dict()) class SetEthFeeMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the setEthFee method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("setEthFee") def validate_and_normalize_inputs(self, eth_send_fee: int) -> any: """Validate the inputs to the setEthFee method.""" self.validator.assert_valid( method_name='setEthFee', parameter_name='_ethSendFee', argument_value=eth_send_fee, ) # safeguard against fractional inputs eth_send_fee = int(eth_send_fee) return (eth_send_fee) def block_send(self, eth_send_fee: int, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(eth_send_fee) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: set_eth_fee") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, eth_send_fee: int, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (eth_send_fee) = self.validate_and_normalize_inputs(eth_send_fee) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(eth_send_fee).transact(tx_params.as_dict()) def build_transaction(self, eth_send_fee: int, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (eth_send_fee) = self.validate_and_normalize_inputs(eth_send_fee) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(eth_send_fee).buildTransaction(tx_params.as_dict()) def estimate_gas(self, eth_send_fee: int, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (eth_send_fee) = self.validate_and_normalize_inputs(eth_send_fee) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(eth_send_fee).estimateGas(tx_params.as_dict()) class SetTokenFeeMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the setTokenFee method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("setTokenFee") def validate_and_normalize_inputs(self, token_send_fee: int) -> any: """Validate the inputs to the setTokenFee method.""" self.validator.assert_valid( method_name='setTokenFee', parameter_name='_tokenSendFee', argument_value=token_send_fee, ) # safeguard against fractional inputs token_send_fee = int(token_send_fee) return (token_send_fee) def block_send(self, token_send_fee: int, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(token_send_fee) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: set_token_fee") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, token_send_fee: int, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (token_send_fee) = self.validate_and_normalize_inputs(token_send_fee) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_send_fee).transact(tx_params.as_dict()) def build_transaction(self, token_send_fee: int, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (token_send_fee) = self.validate_and_normalize_inputs(token_send_fee) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_send_fee).buildTransaction(tx_params.as_dict()) def estimate_gas(self, token_send_fee: int, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (token_send_fee) = self.validate_and_normalize_inputs(token_send_fee) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_send_fee).estimateGas(tx_params.as_dict()) class TokenSendFeeMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the tokenSendFee method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address) self._underlying_method = contract_function self.sign = validator.getSignature("tokenSendFee") def block_call(self, debug: bool = False) -> int: _fn = self._underlying_method() returned = _fn.call({ 'from': self._operate }) return int(returned) def estimate_gas(self, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" tx_params = super().normalize_tx_params(tx_params) return self._underlying_method().estimateGas(tx_params.as_dict()) class WithdrawEtherMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the withdrawEther method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("withdrawEther") def validate_and_normalize_inputs(self, addr: str, amount: int) -> any: """Validate the inputs to the withdrawEther method.""" self.validator.assert_valid( method_name='withdrawEther', parameter_name='addr', argument_value=addr, ) addr = self.validate_and_checksum_address(addr) self.validator.assert_valid( method_name='withdrawEther', parameter_name='amount', argument_value=amount, ) # safeguard against fractional inputs amount = int(amount) return (addr, amount) def block_send(self, addr: str, amount: int, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(addr, amount) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: withdraw_ether") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, addr: str, amount: int, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (addr, amount) = self.validate_and_normalize_inputs(addr, amount) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addr, amount).transact(tx_params.as_dict()) def build_transaction(self, addr: str, amount: int, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (addr, amount) = self.validate_and_normalize_inputs(addr, amount) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addr, amount).buildTransaction(tx_params.as_dict()) def estimate_gas(self, addr: str, amount: int, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (addr, amount) = self.validate_and_normalize_inputs(addr, amount) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(addr, amount).estimateGas(tx_params.as_dict()) class WithdrawTokenMethod(ContractMethod): # pylint: disable=invalid-name """Various interfaces to the withdrawToken method.""" def __init__(self, elib: MiliDoS, contract_address: str, contract_function: ContractFunction, validator: Validator = None): """Persist instance data.""" super().__init__(elib, contract_address, validator) self._underlying_method = contract_function self.sign = validator.getSignature("withdrawToken") def validate_and_normalize_inputs(self, token_addr: str, to: str, amount: int) -> any: """Validate the inputs to the withdrawToken method.""" self.validator.assert_valid( method_name='withdrawToken', parameter_name='tokenAddr', argument_value=token_addr, ) token_addr = self.validate_and_checksum_address(token_addr) self.validator.assert_valid( method_name='withdrawToken', parameter_name='_to', argument_value=to, ) to = self.validate_and_checksum_address(to) self.validator.assert_valid( method_name='withdrawToken', parameter_name='_amount', argument_value=amount, ) # safeguard against fractional inputs amount = int(amount) return (token_addr, to, amount) def block_send(self, token_addr: str, to: str, amount: int, _gaswei: int, _pricewei: int, _valeth: int = 0, _debugtx: bool = False, _receipList: bool = False) -> bool: """Execute underlying contract method via eth_call. :param tx_params: transaction parameters :returns: the return value of the underlying method. """ _fn = self._underlying_method(token_addr, to, amount) try: _t = _fn.buildTransaction({ 'from': self._operate, 'gas': _gaswei, 'gasPrice': _pricewei }) _t['nonce'] = self._web3_eth.getTransactionCount(self._operate) if _valeth > 0: _t['value'] = _valeth if _debugtx: print(f"======== Signing ✅ by {self._operate}") print(f"======== Transaction ✅ check") print(_t) if 'data' in _t: signed = self._web3_eth.account.sign_transaction(_t) txHash = self._web3_eth.sendRawTransaction(signed.rawTransaction) tx_receipt = None if _receipList is True: print(f"======== awaiting Confirmation 🚸️ {self.sign}") tx_receipt = self._web3_eth.waitForTransactionReceipt(txHash) if _debugtx: print("======== TX Result ✅") print(tx_receipt) print(f"======== TX blockHash ✅") if tx_receipt is not None: print(f"{Bolors.OK}{tx_receipt.blockHash.hex()}{Bolors.RESET}") else: print(f"{Bolors.WARNING}{txHash.hex()}{Bolors.RESET} - broadcast hash") if _receipList is False: time.sleep(self._wait) except ContractLogicError as er: print(f"{Bolors.FAIL}Error {er} {Bolors.RESET}: withdraw_token") except ValueError as err: if "message" in err.args[0]: message = err.args[0]["message"] print(f"{Bolors.FAIL}Error Revert {Bolors.RESET} on set_asset_token: {message}") else: print(f"{Bolors.FAIL}Error Revert {Bolors.RESET}: set_asset_token") def send_transaction(self, token_addr: str, to: str, amount: int, tx_params: Optional[TxParams] = None) -> Union[HexBytes, bytes]: """Execute underlying contract method via eth_sendTransaction. :param tx_params: transaction parameters """ (token_addr, to, amount) = self.validate_and_normalize_inputs(token_addr, to, amount) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_addr, to, amount).transact(tx_params.as_dict()) def build_transaction(self, token_addr: str, to: str, amount: int, tx_params: Optional[TxParams] = None) -> dict: """Construct calldata to be used as input to the method.""" (token_addr, to, amount) = self.validate_and_normalize_inputs(token_addr, to, amount) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_addr, to, amount).buildTransaction(tx_params.as_dict()) def estimate_gas(self, token_addr: str, to: str, amount: int, tx_params: Optional[TxParams] = None) -> int: """Estimate gas consumption of method call.""" (token_addr, to, amount) = self.validate_and_normalize_inputs(token_addr, to, amount) tx_params = super().normalize_tx_params(tx_params) return self._underlying_method(token_addr, to, amount).estimateGas(tx_params.as_dict()) class SignatureGenerator(Signatures): """ The signature is generated for this and it is installed. """ def __init__(self, abi: any): super().__init__(abi) def add_signer(self) -> str: return self._function_signatures["addSigner"] def bulk_send_token(self) -> str: return self._function_signatures["bulkSendToken"] def bulk_send_trx(self) -> str: return self._function_signatures["bulkSendTrx"] def claim_init(self) -> str: return self._function_signatures["claimInit"] def deposit(self) -> str: return self._function_signatures["deposit"] def eth_send_fee(self) -> str: return self._function_signatures["ethSendFee"] def get_balance(self) -> str: return self._function_signatures["getBalance"] def is_signer(self) -> str: return self._function_signatures["isSigner"] def renounce_signer(self) -> str: return self._function_signatures["renounceSigner"] def set_eth_fee(self) -> str: return self._function_signatures["setEthFee"] def set_token_fee(self) -> str: return self._function_signatures["setTokenFee"] def token_send_fee(self) -> str: return self._function_signatures["tokenSendFee"] def withdraw_ether(self) -> str: return self._function_signatures["withdrawEther"] def withdraw_token(self) -> str: return self._function_signatures["withdrawToken"] # pylint: disable=too-many-public-methods,too-many-instance-attributes class BSend(ContractBase): """Wrapper class for BSend Solidity contract.""" _fn_add_signer: AddSignerMethod """Constructor-initialized instance of :class:`AddSignerMethod`. """ _fn_bulk_send_token: BulkSendTokenMethod """Constructor-initialized instance of :class:`BulkSendTokenMethod`. """ _fn_bulk_send_trx: BulkSendTrxMethod """Constructor-initialized instance of :class:`BulkSendTrxMethod`. """ _fn_claim_init: ClaimInitMethod """Constructor-initialized instance of :class:`ClaimInitMethod`. """ _fn_deposit: DepositMethod """Constructor-initialized instance of :class:`DepositMethod`. """ _fn_eth_send_fee: EthSendFeeMethod """Constructor-initialized instance of :class:`EthSendFeeMethod`. """ _fn_get_balance: GetBalanceMethod """Constructor-initialized instance of :class:`GetBalanceMethod`. """ _fn_is_signer: IsSignerMethod """Constructor-initialized instance of :class:`IsSignerMethod`. """ _fn_renounce_signer: RenounceSignerMethod """Constructor-initialized instance of :class:`RenounceSignerMethod`. """ _fn_set_eth_fee: SetEthFeeMethod """Constructor-initialized instance of :class:`SetEthFeeMethod`. """ _fn_set_token_fee: SetTokenFeeMethod """Constructor-initialized instance of :class:`SetTokenFeeMethod`. """ _fn_token_send_fee: TokenSendFeeMethod """Constructor-initialized instance of :class:`TokenSendFeeMethod`. """ _fn_withdraw_ether: WithdrawEtherMethod """Constructor-initialized instance of :class:`WithdrawEtherMethod`. """ _fn_withdraw_token: WithdrawTokenMethod """Constructor-initialized instance of :class:`WithdrawTokenMethod`. """ SIGNATURES: SignatureGenerator = None def __init__( self, core_lib: MiliDoS, contract_address: str, validator: BSendValidator = None, ): """Get an instance of wrapper for smart contract. """ # pylint: disable=too-many-statements super().__init__() self.contract_address = contract_address web3 = core_lib.w3 if not validator: validator = BSendValidator(web3, contract_address) # if any middleware was imported, inject it try: MIDDLEWARE except NameError: pass else: try: for middleware in MIDDLEWARE: web3.middleware_onion.inject( middleware['function'], layer=middleware['layer'], ) except ValueError as value_error: if value_error.args == ("You can't add the same un-named instance twice",): pass self._web3_eth = web3.eth functions = self._web3_eth.contract(address=to_checksum_address(contract_address), abi=BSend.abi()).functions signed = SignatureGenerator(BSend.abi()) validator.bindSignatures(signed) self.SIGNATURES = signed self._fn_add_signer = AddSignerMethod(core_lib, contract_address, functions.addSigner, validator) self._fn_bulk_send_token = BulkSendTokenMethod(core_lib, contract_address, functions.bulkSendToken, validator) self._fn_bulk_send_trx = BulkSendTrxMethod(core_lib, contract_address, functions.bulkSendTrx, validator) self._fn_claim_init = ClaimInitMethod(core_lib, contract_address, functions.claimInit, validator) self._fn_deposit = DepositMethod(core_lib, contract_address, functions.deposit, validator) self._fn_eth_send_fee = EthSendFeeMethod(core_lib, contract_address, functions.ethSendFee, validator) self._fn_get_balance = GetBalanceMethod(core_lib, contract_address, functions.getBalance, validator) self._fn_is_signer = IsSignerMethod(core_lib, contract_address, functions.isSigner, validator) self._fn_renounce_signer = RenounceSignerMethod(core_lib, contract_address, functions.renounceSigner, validator) self._fn_set_eth_fee = SetEthFeeMethod(core_lib, contract_address, functions.setEthFee, validator) self._fn_set_token_fee = SetTokenFeeMethod(core_lib, contract_address, functions.setTokenFee, validator) self._fn_token_send_fee = TokenSendFeeMethod(core_lib, contract_address, functions.tokenSendFee, validator) self._fn_withdraw_ether = WithdrawEtherMethod(core_lib, contract_address, functions.withdrawEther, validator) self._fn_withdraw_token = WithdrawTokenMethod(core_lib, contract_address, functions.withdrawToken, validator) def event_signer_added( self, tx_hash: Union[HexBytes, bytes] ) -> Tuple[AttributeDict]: """ Implementation of event signer_added in contract BSend Get log entry for SignerAdded event. :param tx_hash: hash of transaction emitting SignerAdded event """ tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash) return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=BSend.abi()).events.SignerAdded().processReceipt(tx_receipt) def event_signer_removed( self, tx_hash: Union[HexBytes, bytes] ) -> Tuple[AttributeDict]: """ Implementation of event signer_removed in contract BSend Get log entry for SignerRemoved event. :param tx_hash: hash of transaction emitting SignerRemoved event """ tx_receipt = self._web3_eth.getTransactionReceipt(tx_hash) return self._web3_eth.contract(address=to_checksum_address(self.contract_address), abi=BSend.abi()).events.SignerRemoved().processReceipt(tx_receipt) def add_signer(self, account: str) -> None: """ Implementation of add_signer in contract BSend Method of the function """ return self._fn_add_signer.block_send(account, self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def bulk_send_token(self, token_addr: str, addresses: List[str], amounts: List[int], wei: int = 0) -> bool: """ Implementation of bulk_send_token in contract BSend Method of the function """ return self._fn_bulk_send_token.block_send(token_addr, addresses, amounts, self.call_contract_fee_amount, self.call_contract_fee_price, wei, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def bulk_send_trx(self, addresses: List[str], amounts: List[int], wei: int = 0) -> bool: """ Implementation of bulk_send_trx in contract BSend Method of the function """ return self._fn_bulk_send_trx.block_send(addresses, amounts, self.call_contract_fee_amount, self.call_contract_fee_price, wei, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def claim_init(self) -> None: """ Implementation of claim_init in contract BSend Method of the function """ return self._fn_claim_init.block_send(self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def deposit(self, wei: int = 0) -> bool: """ Implementation of deposit in contract BSend Method of the function """ return self._fn_deposit.block_send(self.call_contract_fee_amount, self.call_contract_fee_price, wei, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def eth_send_fee(self) -> int: """ Implementation of eth_send_fee in contract BSend Method of the function """ return self._fn_eth_send_fee.block_call() def get_balance(self, addr: str) -> int: """ Implementation of get_balance in contract BSend Method of the function """ return self._fn_get_balance.block_call(addr) def is_signer(self, account: str) -> bool: """ Implementation of is_signer in contract BSend Method of the function """ return self._fn_is_signer.block_call(account) def renounce_signer(self) -> None: """ Implementation of renounce_signer in contract BSend Method of the function """ return self._fn_renounce_signer.block_send(self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def set_eth_fee(self, eth_send_fee: int) -> bool: """ Implementation of set_eth_fee in contract BSend Method of the function """ return self._fn_set_eth_fee.block_send(eth_send_fee, self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def set_token_fee(self, token_send_fee: int) -> bool: """ Implementation of set_token_fee in contract BSend Method of the function """ return self._fn_set_token_fee.block_send(token_send_fee, self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def token_send_fee(self) -> int: """ Implementation of token_send_fee in contract BSend Method of the function """ return self._fn_token_send_fee.block_call() def withdraw_ether(self, addr: str, amount: int) -> bool: """ Implementation of withdraw_ether in contract BSend Method of the function """ return self._fn_withdraw_ether.block_send(addr, amount, self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def withdraw_token(self, token_addr: str, to: str, amount: int) -> bool: """ Implementation of withdraw_token in contract BSend Method of the function """ return self._fn_withdraw_token.block_send(token_addr, to, amount, self.call_contract_fee_amount, self.call_contract_fee_price, 0, self.call_contract_debug_flag, self.call_contract_enforce_tx_receipt) def CallContractWait(self, t_long: int) -> "BSend": self._fn_add_signer.setWait(t_long) self._fn_bulk_send_token.setWait(t_long) self._fn_bulk_send_trx.setWait(t_long) self._fn_claim_init.setWait(t_long) self._fn_deposit.setWait(t_long) self._fn_eth_send_fee.setWait(t_long) self._fn_get_balance.setWait(t_long) self._fn_is_signer.setWait(t_long) self._fn_renounce_signer.setWait(t_long) self._fn_set_eth_fee.setWait(t_long) self._fn_set_token_fee.setWait(t_long) self._fn_token_send_fee.setWait(t_long) self._fn_withdraw_ether.setWait(t_long) self._fn_withdraw_token.setWait(t_long) return self @staticmethod def abi(): """Return the ABI to the underlying contract.""" return json.loads( '[{"inputs":[],"payable":true,"stateMutability":"payable","type":"constructor"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"account","type":"address"}],"name":"SignerAdded","type":"event"},{"anonymous":false,"inputs":[{"indexed":true,"internalType":"address","name":"account","type":"address"}],"name":"SignerRemoved","type":"event"},{"constant":false,"inputs":[{"internalType":"address","name":"account","type":"address"}],"name":"addSigner","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"internalType":"address","name":"tokenAddr","type":"address"},{"internalType":"address[]","name":"addresses","type":"address[]"},{"internalType":"uint256[]","name":"amounts","type":"uint256[]"}],"name":"bulkSendToken","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":true,"stateMutability":"payable","type":"function"},{"constant":false,"inputs":[{"internalType":"address[]","name":"addresses","type":"address[]"},{"internalType":"uint256[]","name":"amounts","type":"uint256[]"}],"name":"bulkSendTrx","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":true,"stateMutability":"payable","type":"function"},{"constant":false,"inputs":[],"name":"claimInit","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[],"name":"deposit","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":true,"stateMutability":"payable","type":"function"},{"constant":true,"inputs":[],"name":"ethSendFee","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"internalType":"address","name":"addr","type":"address"}],"name":"getBalance","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":true,"inputs":[{"internalType":"address","name":"account","type":"address"}],"name":"isSigner","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[],"name":"renounceSigner","outputs":[],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"internalType":"uint256","name":"_ethSendFee","type":"uint256"}],"name":"setEthFee","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"internalType":"uint256","name":"_tokenSendFee","type":"uint256"}],"name":"setTokenFee","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":true,"inputs":[],"name":"tokenSendFee","outputs":[{"internalType":"uint256","name":"","type":"uint256"}],"payable":false,"stateMutability":"view","type":"function"},{"constant":false,"inputs":[{"internalType":"address","name":"addr","type":"address"},{"internalType":"uint256","name":"amount","type":"uint256"}],"name":"withdrawEther","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":false,"stateMutability":"nonpayable","type":"function"},{"constant":false,"inputs":[{"internalType":"address","name":"tokenAddr","type":"address"},{"internalType":"address","name":"_to","type":"address"},{"internalType":"uint256","name":"_amount","type":"uint256"}],"name":"withdrawToken","outputs":[{"internalType":"bool","name":"","type":"bool"}],"payable":false,"stateMutability":"nonpayable","type":"function"}]' # noqa: E501 (line-too-long) ) # pylint: disable=too-many-lines

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ad75954730597d484088108d47b3a98ff1c03e5c

644

py

Python

examples/custom_op/code/python/my_layer.py

sophgo/tpu_compiler

6299ea0a3adae1e5c206bcb9bedf225d16e636db

[ "Apache-2.0" ]

3

2022-03-14T11:47:20.000Z

2022-03-16T01:45:37.000Z

examples/custom_op/code/python/my_layer.py

sophgo/tpu_compiler

6299ea0a3adae1e5c206bcb9bedf225d16e636db

[ "Apache-2.0" ]

null

examples/custom_op/code/python/my_layer.py

sophgo/tpu_compiler

6299ea0a3adae1e5c206bcb9bedf225d16e636db

[ "Apache-2.0" ]

null

import caffe class MyAdd(caffe.Layer): def setup(self, bottom, top): pass def reshape(self, bottom, top): top[0].reshape(*bottom[0].data.shape) def forward(self, bottom, top): top[0].data = bottom[0].data + bottom[1].data def backward(self, top, propagate_down, bottom): pass class MyMul(caffe.Layer): def setup(self, bottom, top): pass def reshape(self, bottom, top): top[0].reshape(*bottom[0].data.shape) def forward(self, bottom, top): top[0].data = bottom[0].data * bottom[1].data def backward(self, top, propagate_down, bottom): pass

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py

Python

chainlibpy/generated/cosmos/base/abci/v1beta1/abci_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

chainlibpy/generated/cosmos/base/abci/v1beta1/abci_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

chainlibpy/generated/cosmos/base/abci/v1beta1/abci_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

'Generated protocol buffer code.' from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from .....gogoproto import gogo_pb2 as gogoproto_dot_gogo__pb2 from .....tendermint.abci import types_pb2 as tendermint_dot_abci_dot_types__pb2 from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2 DESCRIPTOR = _descriptor.FileDescriptor(name='cosmos/base/abci/v1beta1/abci.proto', package='cosmos.base.abci.v1beta1', syntax='proto3', serialized_options=b'Z"github.com/cosmos/cosmos-sdk/types\xd8\xe1\x1e\x00', create_key=_descriptor._internal_create_key, serialized_pb=b'\n#cosmos/base/abci/v1beta1/abci.proto\x12\x18cosmos.base.abci.v1beta1\x1a\x14gogoproto/gogo.proto\x1a\x1btendermint/abci/types.proto\x1a\x19google/protobuf/any.proto"\xb8\x02\n\nTxResponse\x12\x0e\n\x06height\x18\x01 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\x01(\x04B%\xf2\xde\x1f\x12yaml:"total_count"\xea\xde\x1f\x0btotal_count\x12\r\n\x05count\x18\x02 \x01(\x04\x12:\n\x0bpage_number\x18\x03 \x01(\x04B%\xf2\xde\x1f\x12yaml:"page_number"\xea\xde\x1f\x0bpage_number\x127\n\npage_total\x18\x04 \x01(\x04B#\xf2\xde\x1f\x11yaml:"page_total"\xea\xde\x1f\npage_total\x12\r\n\x05limit\x18\x05 \x01(\x04\x121\n\x03txs\x18\x06 \x03(\x0b2$.cosmos.base.abci.v1beta1.TxResponse:\x04\x80\xdc \x01B(Z"github.com/cosmos/cosmos-sdk/types\xd8\xe1\x1e\x00b\x06proto3', dependencies=[gogoproto_dot_gogo__pb2.DESCRIPTOR, tendermint_dot_abci_dot_types__pb2.DESCRIPTOR, google_dot_protobuf_dot_any__pb2.DESCRIPTOR]) _TXRESPONSE = _descriptor.Descriptor(name='TxResponse', full_name='cosmos.base.abci.v1beta1.TxResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='height', full_name='cosmos.base.abci.v1beta1.TxResponse.height', index=0, number=1, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='txhash', full_name='cosmos.base.abci.v1beta1.TxResponse.txhash', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xe2\xde\x1f\x06TxHash', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='codespace', full_name='cosmos.base.abci.v1beta1.TxResponse.codespace', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='code', full_name='cosmos.base.abci.v1beta1.TxResponse.code', index=3, number=4, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='data', full_name='cosmos.base.abci.v1beta1.TxResponse.data', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='raw_log', full_name='cosmos.base.abci.v1beta1.TxResponse.raw_log', index=5, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='logs', full_name='cosmos.base.abci.v1beta1.TxResponse.logs', index=6, number=7, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xaa\xdf\x1f\x0fABCIMessageLogs\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='info', full_name='cosmos.base.abci.v1beta1.TxResponse.info', index=7, number=8, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='gas_wanted', full_name='cosmos.base.abci.v1beta1.TxResponse.gas_wanted', index=8, number=9, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='gas_used', full_name='cosmos.base.abci.v1beta1.TxResponse.gas_used', index=9, number=10, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='tx', full_name='cosmos.base.abci.v1beta1.TxResponse.tx', index=10, number=11, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='timestamp', full_name='cosmos.base.abci.v1beta1.TxResponse.timestamp', index=11, number=12, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x88\xa0\x1f\x00', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=144, serialized_end=456) _ABCIMESSAGELOG = _descriptor.Descriptor(name='ABCIMessageLog', full_name='cosmos.base.abci.v1beta1.ABCIMessageLog', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='msg_index', full_name='cosmos.base.abci.v1beta1.ABCIMessageLog.msg_index', index=0, number=1, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='log', full_name='cosmos.base.abci.v1beta1.ABCIMessageLog.log', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='events', full_name='cosmos.base.abci.v1beta1.ABCIMessageLog.events', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xaa\xdf\x1f\x0cStringEvents\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x80\xdc \x01', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=459, serialized_end=590) _STRINGEVENT = _descriptor.Descriptor(name='StringEvent', full_name='cosmos.base.abci.v1beta1.StringEvent', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='type', full_name='cosmos.base.abci.v1beta1.StringEvent.type', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='attributes', full_name='cosmos.base.abci.v1beta1.StringEvent.attributes', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x80\xdc \x01', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=592, serialized_end=688) _ATTRIBUTE = _descriptor.Descriptor(name='Attribute', full_name='cosmos.base.abci.v1beta1.Attribute', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='key', full_name='cosmos.base.abci.v1beta1.Attribute.key', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='value', full_name='cosmos.base.abci.v1beta1.Attribute.value', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=690, serialized_end=729) _GASINFO = _descriptor.Descriptor(name='GasInfo', full_name='cosmos.base.abci.v1beta1.GasInfo', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='gas_wanted', full_name='cosmos.base.abci.v1beta1.GasInfo.gas_wanted', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f\x11yaml:"gas_wanted"', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='gas_used', full_name='cosmos.base.abci.v1beta1.GasInfo.gas_used', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f\x0fyaml:"gas_used"', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=731, serialized_end=822) _RESULT = _descriptor.Descriptor(name='Result', full_name='cosmos.base.abci.v1beta1.Result', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='data', full_name='cosmos.base.abci.v1beta1.Result.data', index=0, number=1, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='log', full_name='cosmos.base.abci.v1beta1.Result.log', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='events', full_name='cosmos.base.abci.v1beta1.Result.events', index=2, number=3, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x88\xa0\x1f\x00', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=824, serialized_end=911) _SIMULATIONRESPONSE = _descriptor.Descriptor(name='SimulationResponse', full_name='cosmos.base.abci.v1beta1.SimulationResponse', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='gas_info', full_name='cosmos.base.abci.v1beta1.SimulationResponse.gas_info', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xd0\xde\x1f\x01\xc8\xde\x1f\x00', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='result', full_name='cosmos.base.abci.v1beta1.SimulationResponse.result', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=914, serialized_end=1047) _MSGDATA = _descriptor.Descriptor(name='MsgData', full_name='cosmos.base.abci.v1beta1.MsgData', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='msg_type', full_name='cosmos.base.abci.v1beta1.MsgData.msg_type', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='data', full_name='cosmos.base.abci.v1beta1.MsgData.data', index=1, number=2, type=12, cpp_type=9, label=1, has_default_value=False, default_value=b'', message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x80\xdc \x01', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1049, serialized_end=1096) _TXMSGDATA = _descriptor.Descriptor(name='TxMsgData', full_name='cosmos.base.abci.v1beta1.TxMsgData', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='data', full_name='cosmos.base.abci.v1beta1.TxMsgData.data', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x80\xdc \x01', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1098, serialized_end=1164) _SEARCHTXSRESULT = _descriptor.Descriptor(name='SearchTxsResult', full_name='cosmos.base.abci.v1beta1.SearchTxsResult', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='total_count', full_name='cosmos.base.abci.v1beta1.SearchTxsResult.total_count', index=0, number=1, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f\x12yaml:"total_count"\xea\xde\x1f\x0btotal_count', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='count', full_name='cosmos.base.abci.v1beta1.SearchTxsResult.count', index=1, number=2, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='page_number', full_name='cosmos.base.abci.v1beta1.SearchTxsResult.page_number', index=2, number=3, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f\x12yaml:"page_number"\xea\xde\x1f\x0bpage_number', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='page_total', full_name='cosmos.base.abci.v1beta1.SearchTxsResult.page_total', index=3, number=4, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f\x11yaml:"page_total"\xea\xde\x1f\npage_total', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='limit', full_name='cosmos.base.abci.v1beta1.SearchTxsResult.limit', index=4, number=5, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='txs', full_name='cosmos.base.abci.v1beta1.SearchTxsResult.txs', index=5, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x80\xdc \x01', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=1167, serialized_end=1448) _TXRESPONSE.fields_by_name['logs'].message_type = _ABCIMESSAGELOG _TXRESPONSE.fields_by_name['tx'].message_type = google_dot_protobuf_dot_any__pb2._ANY _ABCIMESSAGELOG.fields_by_name['events'].message_type = _STRINGEVENT _STRINGEVENT.fields_by_name['attributes'].message_type = _ATTRIBUTE _RESULT.fields_by_name['events'].message_type = tendermint_dot_abci_dot_types__pb2._EVENT _SIMULATIONRESPONSE.fields_by_name['gas_info'].message_type = _GASINFO _SIMULATIONRESPONSE.fields_by_name['result'].message_type = _RESULT _TXMSGDATA.fields_by_name['data'].message_type = _MSGDATA _SEARCHTXSRESULT.fields_by_name['txs'].message_type = _TXRESPONSE DESCRIPTOR.message_types_by_name['TxResponse'] = _TXRESPONSE DESCRIPTOR.message_types_by_name['ABCIMessageLog'] = _ABCIMESSAGELOG DESCRIPTOR.message_types_by_name['StringEvent'] = _STRINGEVENT DESCRIPTOR.message_types_by_name['Attribute'] = _ATTRIBUTE DESCRIPTOR.message_types_by_name['GasInfo'] = _GASINFO DESCRIPTOR.message_types_by_name['Result'] = _RESULT DESCRIPTOR.message_types_by_name['SimulationResponse'] = _SIMULATIONRESPONSE DESCRIPTOR.message_types_by_name['MsgData'] = _MSGDATA DESCRIPTOR.message_types_by_name['TxMsgData'] = _TXMSGDATA DESCRIPTOR.message_types_by_name['SearchTxsResult'] = _SEARCHTXSRESULT _sym_db.RegisterFileDescriptor(DESCRIPTOR) TxResponse = _reflection.GeneratedProtocolMessageType('TxResponse', (_message.Message,), {'DESCRIPTOR': _TXRESPONSE, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(TxResponse) ABCIMessageLog = _reflection.GeneratedProtocolMessageType('ABCIMessageLog', (_message.Message,), {'DESCRIPTOR': _ABCIMESSAGELOG, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(ABCIMessageLog) StringEvent = _reflection.GeneratedProtocolMessageType('StringEvent', (_message.Message,), {'DESCRIPTOR': _STRINGEVENT, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(StringEvent) Attribute = _reflection.GeneratedProtocolMessageType('Attribute', (_message.Message,), {'DESCRIPTOR': _ATTRIBUTE, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(Attribute) GasInfo = _reflection.GeneratedProtocolMessageType('GasInfo', (_message.Message,), {'DESCRIPTOR': _GASINFO, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(GasInfo) Result = _reflection.GeneratedProtocolMessageType('Result', (_message.Message,), {'DESCRIPTOR': _RESULT, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(Result) SimulationResponse = _reflection.GeneratedProtocolMessageType('SimulationResponse', (_message.Message,), {'DESCRIPTOR': _SIMULATIONRESPONSE, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(SimulationResponse) MsgData = _reflection.GeneratedProtocolMessageType('MsgData', (_message.Message,), {'DESCRIPTOR': _MSGDATA, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(MsgData) TxMsgData = _reflection.GeneratedProtocolMessageType('TxMsgData', (_message.Message,), {'DESCRIPTOR': _TXMSGDATA, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(TxMsgData) SearchTxsResult = _reflection.GeneratedProtocolMessageType('SearchTxsResult', (_message.Message,), {'DESCRIPTOR': _SEARCHTXSRESULT, '__module__': 'cosmos.base.abci.v1beta1.abci_pb2'}) _sym_db.RegisterMessage(SearchTxsResult) DESCRIPTOR._options = None _TXRESPONSE.fields_by_name['txhash']._options = None _TXRESPONSE.fields_by_name['logs']._options = None _TXRESPONSE._options = None _ABCIMESSAGELOG.fields_by_name['events']._options = None _ABCIMESSAGELOG._options = None _STRINGEVENT.fields_by_name['attributes']._options = None _STRINGEVENT._options = None _GASINFO.fields_by_name['gas_wanted']._options = None _GASINFO.fields_by_name['gas_used']._options = None _RESULT.fields_by_name['events']._options = None _RESULT._options = None _SIMULATIONRESPONSE.fields_by_name['gas_info']._options = None _MSGDATA._options = None _TXMSGDATA._options = None _SEARCHTXSRESULT.fields_by_name['total_count']._options = None _SEARCHTXSRESULT.fields_by_name['page_number']._options = None _SEARCHTXSRESULT.fields_by_name['page_total']._options = None _SEARCHTXSRESULT._options = None

317.518519

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1

0

null

0

7

d15e319f938623fa3e19c13d9a9c20df0c423f01

15,912

py

Python

devtest/test_Pawn.py

LLCoolDave/Chess3

076740e5d67b6aba0ae077c8e7a3bc24690803f2

[ "MIT" ]

null

devtest/test_Pawn.py

LLCoolDave/Chess3

076740e5d67b6aba0ae077c8e7a3bc24690803f2

[ "MIT" ]

null

devtest/test_Pawn.py

LLCoolDave/Chess3

076740e5d67b6aba0ae077c8e7a3bc24690803f2

[ "MIT" ]

null

from ChessboardUtils import MockBoard, ARMY_WHITE, ARMY_BLACK, MockPiece from Chess3 import Coordinate, MovementUtils, Move, MoveAction from Chess3.Pieces.PawnImpl import Pawn from collections import Counter # white movement def test_Pawn_movement_white_empty_board(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3), 'double move')]) def test_Pawn_movement_white_empty_board_moved(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3))) pawn = cb.get_piece(Coordinate(3, 3)) pawn.unmoved = False assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3))]) def test_Pawn_movement_white_obstructed_firendly_direct(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_WHITE, Coordinate(4, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([]) def test_Pawn_movement_white_obstructed_enemy_direct(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(4, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([]) def test_Pawn_movement_white_obstructed_firendly_distant(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_WHITE, Coordinate(5, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3))]) def test_Pawn_movement_white_obstructed_enemy_distant(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(5, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3))]) def test_Pawn_movement_white_end_of_board(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(7, 3))) pawn = cb.get_piece(Coordinate(7, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([]) def test_Pawn_movement_white_side_of_board(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 0)), (Pawn, ARMY_WHITE, Coordinate(3, 7))) pawn1 = cb.get_piece(Coordinate(3, 0)) pawn2 = cb.get_piece(Coordinate(3, 7)) assert Counter(pawn1.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 0), Coordinate(4, 0)), MovementUtils.move_from_to(Coordinate(3, 0), Coordinate(5, 0), 'double move')]) assert Counter(pawn2.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 7), Coordinate(4, 7)), MovementUtils.move_from_to(Coordinate(3, 7), Coordinate(5, 7), 'double move')]) def test_Pawn_movement_white_cant_capture_friendly(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_WHITE, Coordinate(4, 4)), (MockPiece, ARMY_WHITE, Coordinate(4, 2))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3), 'double move')]) def test_Pawn_movement_white_capture_enemy(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(4, 4)), (MockPiece, ARMY_BLACK, Coordinate(4, 2))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3), 'double move'), MovementUtils.move_and_capture(Coordinate(3, 3), Coordinate(4, 4)), MovementUtils.move_and_capture(Coordinate(3, 3), Coordinate(4, 2))]) def test_Pawn_movement_white_capture_enemy_pawn_en_passant(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (Pawn, ARMY_BLACK, Coordinate(3, 4)), (Pawn, ARMY_BLACK, Coordinate(3, 2))) pawn = cb.get_piece(Coordinate(3, 3)) enemypawn1 = cb.get_piece(Coordinate(3, 2)) enemypawn2 = cb.get_piece(Coordinate(3, 4)) enemypawn1.en_passant = True enemypawn2.en_passant = True assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3), 'double move'), Move('en passant', Coordinate(3, 3), Coordinate(4, 2), [MoveAction(Coordinate(3, 3), Coordinate(4, 2))], [Coordinate(3, 2)]), Move('en passant', Coordinate(3, 3), Coordinate(4, 4), [MoveAction(Coordinate(3, 3), Coordinate(4, 4))], [Coordinate(3, 4)])]) def test_Pawn_movement_white_cannot_capture_friendly_pawn_en_passant(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (Pawn, ARMY_WHITE, Coordinate(3, 4)), (Pawn, ARMY_WHITE, Coordinate(3, 2))) pawn = cb.get_piece(Coordinate(3, 3)) enemypawn1 = cb.get_piece(Coordinate(3, 2)) enemypawn2 = cb.get_piece(Coordinate(3, 4)) enemypawn1.en_passant = True enemypawn2.en_passant = True assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3), 'double move')]) def test_Pawn_movement_white_cannot_capture_enemy_non_pawn_en_passant(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(3, 4)), (MockPiece, ARMY_BLACK, Coordinate(3, 2))) pawn = cb.get_piece(Coordinate(3, 3)) enemypawn1 = cb.get_piece(Coordinate(3, 2)) enemypawn2 = cb.get_piece(Coordinate(3, 4)) enemypawn1.en_passant = True enemypawn2.en_passant = True assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3), 'double move')]) # black movement def test_Pawn_movement_black_empty_board(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(1, 3), 'double move')]) def test_Pawn_movement_black_empty_board_moved(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3))) pawn = cb.get_piece(Coordinate(3, 3)) pawn.unmoved = False assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3))]) def test_Pawn_movement_black_obstructed_firendly_direct(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(2, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([]) def test_Pawn_movement_black_obstructed_enemy_direct(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_WHITE, Coordinate(2, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([]) def test_Pawn_movement_black_obstructed_firendly_distant(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(1, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3))]) def test_Pawn_movement_black_obstructed_enemy_distant(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_WHITE, Coordinate(1, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3))]) def test_Pawn_movement_black_end_of_board(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(0, 3))) pawn = cb.get_piece(Coordinate(0, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([]) def test_Pawn_movement_black_side_of_board(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 0)), (Pawn, ARMY_BLACK, Coordinate(3, 7))) pawn1 = cb.get_piece(Coordinate(3, 0)) pawn2 = cb.get_piece(Coordinate(3, 7)) assert Counter(pawn1.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 0), Coordinate(2, 0)), MovementUtils.move_from_to(Coordinate(3, 0), Coordinate(1, 0), 'double move')]) assert Counter(pawn2.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 7), Coordinate(2, 7)), MovementUtils.move_from_to(Coordinate(3, 7), Coordinate(1, 7), 'double move')]) def test_Pawn_movement_black_cant_capture_friendly(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(2, 4)), (MockPiece, ARMY_BLACK, Coordinate(2, 2))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(1, 3), 'double move')]) def test_Pawn_movement_black_capture_enemy(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_WHITE, Coordinate(2, 4)), (MockPiece, ARMY_WHITE, Coordinate(2, 2))) pawn = cb.get_piece(Coordinate(3, 3)) assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(1, 3), 'double move'), MovementUtils.move_and_capture(Coordinate(3, 3), Coordinate(2, 4)), MovementUtils.move_and_capture(Coordinate(3, 3), Coordinate(2, 2))]) def test_Pawn_movement_black_capture_enemy_pawn_en_passant(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (Pawn, ARMY_WHITE, Coordinate(3, 4)), (Pawn, ARMY_WHITE, Coordinate(3, 2))) pawn = cb.get_piece(Coordinate(3, 3)) enemypawn1 = cb.get_piece(Coordinate(3, 2)) enemypawn2 = cb.get_piece(Coordinate(3, 4)) enemypawn1.en_passant = True enemypawn2.en_passant = True assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(1, 3), 'double move'), Move('en passant', Coordinate(3, 3), Coordinate(2, 2), [MoveAction(Coordinate(3, 3), Coordinate(2, 2))], [Coordinate(3, 2)]), Move('en passant', Coordinate(3, 3), Coordinate(2, 4), [MoveAction(Coordinate(3, 3), Coordinate(2, 4))], [Coordinate(3, 4)])]) def test_Pawn_movement_black_cannot_capture_friendly_pawn_en_passant(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (Pawn, ARMY_BLACK, Coordinate(3, 4)), (Pawn, ARMY_BLACK, Coordinate(3, 2))) pawn = cb.get_piece(Coordinate(3, 3)) enemypawn1 = cb.get_piece(Coordinate(3, 2)) enemypawn2 = cb.get_piece(Coordinate(3, 4)) enemypawn1.en_passant = True enemypawn2.en_passant = True assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(1, 3), 'double move')]) def test_Pawn_movement_black_cannot_capture_enemy_non_pawn_en_passant(): cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(3, 3)), (MockPiece, ARMY_BLACK, Coordinate(3, 4)), (MockPiece, ARMY_BLACK, Coordinate(3, 2))) pawn = cb.get_piece(Coordinate(3, 3)) enemypawn1 = cb.get_piece(Coordinate(3, 2)) enemypawn2 = cb.get_piece(Coordinate(3, 4)) enemypawn1.en_passant = True enemypawn2.en_passant = True assert Counter(pawn.get_all_possible_moves()) == Counter([MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(2, 3)), MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(1, 3), 'double move')]) # general stuff def test_Pawn_sets_en_passant_flag_correctly(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(3, 3))) pawn = cb.get_piece(Coordinate(3, 3)) assert not pawn.en_passant pawn.move_executed(MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3))) assert not pawn.en_passant pawn.move_executed(MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3))) assert not pawn.en_passant dbmove = MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(5, 3)) dbmove.name = 'double move' pawn.move_executed(dbmove) assert pawn.en_passant pawn.advance_tick() assert not pawn.en_passant dbmove = MovementUtils.move_from_to(Coordinate(3, 3), Coordinate(4, 3)) dbmove.name = 'double move' pawn.move_executed(dbmove) assert pawn.en_passant # test promotion def test_Pawn_offers_promotion_empty_board(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(6, 3))) pawn = cb.get_piece(Coordinate(6, 3)) pawn.army.promotions = {'promote to MockPiece': MockPiece} assert len(pawn.get_all_possible_moves()) == 2 pawn.army.promotions['promote to MockPiece 2'] = MockPiece assert len(pawn.get_all_possible_moves()) == 3 cb = MockBoard((Pawn, ARMY_BLACK, Coordinate(1, 3))) pawn = cb.get_piece(Coordinate(1, 3)) pawn.army.promotions = {'promote to MockPiece': MockPiece} assert len(pawn.get_all_possible_moves()) == 2 pawn.army.promotions['promote to MockPiece 2'] = MockPiece assert len(pawn.get_all_possible_moves()) == 3 def test_Pawn_offers_promotion_capture(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(6, 3)), (MockPiece, ARMY_BLACK, Coordinate(7, 4))) pawn = cb.get_piece(Coordinate(6, 3)) pawn.army.promotions = {'promote to MockPiece': MockPiece} assert len(pawn.get_all_possible_moves()) == 4 pawn.army.promotions['promote to MockPiece 2'] = MockPiece assert len(pawn.get_all_possible_moves()) == 6 # test promotion def test_Pawn_promotion_empty_board(): cb = MockBoard((Pawn, ARMY_WHITE, Coordinate(6, 3))) pawn = cb.get_piece(Coordinate(6, 3)) pawn.army.promotions = {'promote to MockPiece': MockPiece} promotionmove = [move for move in pawn.get_all_possible_moves() if move.name == 'promote to MockPiece'][0] cb.execute_move(promotionmove) assert pawn.field is None assert cb.get_piece(Coordinate(6, 3)) is None newpiece = cb.get_piece(Coordinate(7, 3)) assert newpiece is not None assert isinstance(newpiece, MockPiece)

56.828571

188

0.660068

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4.785235

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0.110599

0.09417

0.95542

0.930274

0.909738

0.870868

0.842316

0.81106

0

0.038492

0.206511

15,912

279

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0.752099

0.004588

0

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0

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0

0.208531

1

0.14218

false

0.137441

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0

0.161137

0

null

0

1

0

null

0

1

0

7

66ffbf618efb7e771c21d44dd9a169bf01eb4241

8,287

py

Python

venv/lib/python3.7/site-packages/tests/test_api.py

bfraz/python-flask-swagger-k8s

8381a172644d92c1fcbf0691340ea8545624b7e8

[ "MIT" ]

1

2021-03-06T05:07:42.000Z

tests/test_api.py

aaronbenz/flask-restplus

15d9e872f260658f0d2c7cb44ea4c3148e000b50

[ "MIT" ]

null

tests/test_api.py

aaronbenz/flask-restplus

15d9e872f260658f0d2c7cb44ea4c3148e000b50

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from flask import url_for, Blueprint import flask_restplus as restplus from . import TestCase class APITestCase(TestCase): def test_root_endpoint(self): api = restplus.Api(self.app, version='1.0') with self.context(): url = url_for('root') self.assertEqual(url, '/') self.assertEqual(api.base_url, 'http://localhost/') def test_root_endpoint_lazy(self): api = restplus.Api(version='1.0') api.init_app(self.app) with self.context(): url = url_for('root') self.assertEqual(url, '/') self.assertEqual(api.base_url, 'http://localhost/') def test_root_endpoint_with_blueprint(self): blueprint = Blueprint('api', __name__, url_prefix='/api') api = restplus.Api(blueprint, version='1.0') self.app.register_blueprint(blueprint) with self.context(): url = url_for('api.root') self.assertEqual(url, '/api/') self.assertEqual(api.base_url, 'http://localhost/api/') def test_root_endpoint_with_blueprint_with_subdomain(self): blueprint = Blueprint('api', __name__, subdomain='api', url_prefix='/api') api = restplus.Api(blueprint, version='1.0') self.app.register_blueprint(blueprint) with self.context(): url = url_for('api.root') self.assertEqual(url, 'http://api.localhost/api/') self.assertEqual(api.base_url, 'http://api.localhost/api/') def test_parser(self): api = restplus.Api() self.assertIsInstance(api.parser(), restplus.reqparse.RequestParser) def test_doc_decorator(self): api = restplus.Api(self.app, prefix='/api', version='1.0') params = {'q': {'description': 'some description'}} @api.doc(params=params) class TestResource(restplus.Resource): pass self.assertTrue(hasattr(TestResource, '__apidoc__')) self.assertEqual(TestResource.__apidoc__, {'params': params}) def test_doc_with_inheritance(self): api = restplus.Api(self.app, prefix='/api', version='1.0') base_params = {'q': {'description': 'some description', 'type': 'string', 'paramType': 'query'}} child_params = {'q': {'description': 'some new description'}, 'other': {'description': 'another param'}} @api.doc(params=base_params) class BaseResource(restplus.Resource): pass @api.doc(params=child_params) class TestResource(BaseResource): pass self.assertEqual(TestResource.__apidoc__, {'params': { 'q': { 'description': 'some new description', 'type': 'string', 'paramType': 'query' }, 'other': {'description': 'another param'}, }}) def test_specs_endpoint_not_added(self): api = restplus.Api() api.init_app(self.app, add_specs=False) self.assertNotIn('specs', api.endpoints) self.assertNotIn('specs', self.app.view_functions) def test_specs_endpoint_not_found_if_not_added(self): api = restplus.Api() api.init_app(self.app, add_specs=False) with self.app.test_client() as client: resp = client.get('/swagger.json') self.assertEqual(resp.status_code, 404) def test_default_endpoint(self): api = restplus.Api(self.app) @api.route('/test/') class TestResource(restplus.Resource): pass with self.context(): self.assertEqual(url_for('test_resource'), '/test/') def test_default_endpoint_lazy(self): api = restplus.Api() @api.route('/test/') class TestResource(restplus.Resource): pass api.init_app(self.app) with self.context(): self.assertEqual(url_for('test_resource'), '/test/') def test_default_endpoint_with_blueprint(self): blueprint = Blueprint('api', __name__, url_prefix='/api') api = restplus.Api(blueprint) self.app.register_blueprint(blueprint) @api.route('/test/') class TestResource(restplus.Resource): pass with self.context(): self.assertEqual(url_for('api.test_resource'), '/api/test/') def test_default_endpoint_with_blueprint_with_subdomain(self): blueprint = Blueprint('api', __name__, subdomain='api', url_prefix='/api') api = restplus.Api(blueprint) self.app.register_blueprint(blueprint) @api.route('/test/') class TestResource(restplus.Resource): pass with self.context(): self.assertEqual(url_for('api.test_resource'), 'http://api.localhost/api/test/') def test_default_endpoint_for_namespace(self): api = restplus.Api(self.app) ns = api.namespace('ns', 'Test namespace') @ns.route('/test/') class TestResource(restplus.Resource): pass with self.context(): self.assertEqual(url_for('ns_test_resource'), '/ns/test/') def test_default_endpoint_lazy_for_namespace(self): api = restplus.Api() ns = api.namespace('ns', 'Test namespace') @ns.route('/test/') class TestResource(restplus.Resource): pass api.init_app(self.app) with self.context(): self.assertEqual(url_for('ns_test_resource'), '/ns/test/') def test_default_endpoint_for_namespace_with_blueprint(self): blueprint = Blueprint('api', __name__, url_prefix='/api') api = restplus.Api(blueprint) ns = api.namespace('ns', 'Test namespace') @ns.route('/test/') class TestResource(restplus.Resource): pass self.app.register_blueprint(blueprint) with self.context(): self.assertEqual(url_for('api.ns_test_resource'), '/api/ns/test/') def test_multiple_default_endpoint(self): api = restplus.Api(self.app) @api.route('/test2/') @api.route('/test/') class TestResource(restplus.Resource): pass with self.context(): self.assertEqual(url_for('test_resource'), '/test/') self.assertEqual(url_for('test_resource_2'), '/test2/') def test_multiple_default_endpoint_lazy(self): api = restplus.Api() @api.route('/test2/') @api.route('/test/') class TestResource(restplus.Resource): pass api.init_app(self.app) with self.context(): self.assertEqual(url_for('test_resource'), '/test/') self.assertEqual(url_for('test_resource_2'), '/test2/') def test_multiple_default_endpoint_for_namespace(self): api = restplus.Api(self.app) ns = api.namespace('ns', 'Test namespace') @ns.route('/test2/') @ns.route('/test/') class TestResource(restplus.Resource): pass with self.context(): self.assertEqual(url_for('ns_test_resource'), '/ns/test/') self.assertEqual(url_for('ns_test_resource_2'), '/ns/test2/') def test_multiple_default_endpoint_lazy_for_namespace(self): api = restplus.Api() ns = api.namespace('ns', 'Test namespace') @ns.route('/test2/') @ns.route('/test/') class TestResource(restplus.Resource): pass api.init_app(self.app) with self.context(): self.assertEqual(url_for('ns_test_resource'), '/ns/test/') self.assertEqual(url_for('ns_test_resource_2'), '/ns/test2/') def test_multiple_default_endpoint_for_namespace_with_blueprint(self): blueprint = Blueprint('api', __name__, url_prefix='/api') api = restplus.Api(blueprint) ns = api.namespace('ns', 'Test namespace') @ns.route('/test2/') @ns.route('/test/') class TestResource(restplus.Resource): pass self.app.register_blueprint(blueprint) with self.context(): self.assertEqual(url_for('api.ns_test_resource'), '/api/ns/test/') self.assertEqual(url_for('api.ns_test_resource_2'), '/api/ns/test2/')

32.884921

112

0.608423

933

8,287

5.174705

0.098607

0.086993

0.060895

0.073944

0.850456

0.78894

0.757457

0.729495

0.718103

0.708989

0

0.004996

0.251237

8,287

251

113

33.015936

0.773086

0.002534

0

0.682796

0

0.137464

0.002662

0

0.172043

1

0.112903

false

0.080645

0.021505

0

0.22043

0.134409

0

null

0

1

0

null

0

1

0

7

0f0a766f3e690cc661e13d6501090ca032f2e5c5

12,469

py

Python

beanie/api/received_goods_api.py

altoyield/python-beanieclient

448b8dd328054eaf32dd7d0bdff700e603b5c27d

[ "Apache-2.0" ]

null

beanie/api/received_goods_api.py

altoyield/python-beanieclient

448b8dd328054eaf32dd7d0bdff700e603b5c27d

[ "Apache-2.0" ]

null

beanie/api/received_goods_api.py

altoyield/python-beanieclient

448b8dd328054eaf32dd7d0bdff700e603b5c27d

[ "Apache-2.0" ]

null

# coding: utf-8 """ Beanie ERP API An API specification for interacting with the Beanie ERP system # noqa: E501 OpenAPI spec version: 0.2 Contact: dev@bean.ie 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 beanie.api_client import ApiClient class ReceivedGoodsApi(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 add_received_goods(self, received_goods, **kwargs): # noqa: E501 """add_received_goods # noqa: E501 Creates a new received goods in the system # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.add_received_goods(received_goods, async=True) >>> result = thread.get() :param async bool :param ReceivedGoodsInput received_goods: Received goods to add to the system (required) :return: ReceivedGoods If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.add_received_goods_with_http_info(received_goods, **kwargs) # noqa: E501 else: (data) = self.add_received_goods_with_http_info(received_goods, **kwargs) # noqa: E501 return data def add_received_goods_with_http_info(self, received_goods, **kwargs): # noqa: E501 """add_received_goods # noqa: E501 Creates a new received goods in the system # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.add_received_goods_with_http_info(received_goods, async=True) >>> result = thread.get() :param async bool :param ReceivedGoodsInput received_goods: Received goods to add to the system (required) :return: ReceivedGoods If the method is called asynchronously, returns the request thread. """ all_params = ['received_goods'] # noqa: E501 all_params.append('async') 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 add_received_goods" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'received_goods' is set if ('received_goods' not in params or params['received_goods'] is None): raise ValueError("Missing the required parameter `received_goods` when calling `add_received_goods`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'received_goods' in params: body_params = params['received_goods'] # 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 = ['api_key'] # noqa: E501 return self.api_client.call_api( '/received_goods', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReceivedGoods', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _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 find_received_goods(self, **kwargs): # noqa: E501 """All received goods # noqa: E501 Returns all received goods from the system that the user has access to # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.find_received_goods(async=True) >>> result = thread.get() :param async bool :param list[str] tags: tags to filter by :param int limit: Maximum number of results to return :return: list[ReceivedGoods] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.find_received_goods_with_http_info(**kwargs) # noqa: E501 else: (data) = self.find_received_goods_with_http_info(**kwargs) # noqa: E501 return data def find_received_goods_with_http_info(self, **kwargs): # noqa: E501 """All received goods # noqa: E501 Returns all received goods from the system that the user has access to # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.find_received_goods_with_http_info(async=True) >>> result = thread.get() :param async bool :param list[str] tags: tags to filter by :param int limit: Maximum number of results to return :return: list[ReceivedGoods] If the method is called asynchronously, returns the request thread. """ all_params = ['tags', 'limit'] # noqa: E501 all_params.append('async') 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 find_received_goods" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'tags' in params: query_params.append(('tags', params['tags'])) # noqa: E501 collection_formats['tags'] = 'csv' # noqa: E501 if 'limit' in params: query_params.append(('limit', params['limit'])) # 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 # 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 = ['api_key'] # noqa: E501 return self.api_client.call_api( '/received_goods', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ReceivedGoods]', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _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 find_received_goods_by_id(self, id, **kwargs): # noqa: E501 """Find Received goods by ID # noqa: E501 Returns a single received goods if the user has access # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.find_received_goods_by_id(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of received goods to fetch (required) :return: ReceivedGoods If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.find_received_goods_by_id_with_http_info(id, **kwargs) # noqa: E501 else: (data) = self.find_received_goods_by_id_with_http_info(id, **kwargs) # noqa: E501 return data def find_received_goods_by_id_with_http_info(self, id, **kwargs): # noqa: E501 """Find Received goods by ID # noqa: E501 Returns a single received goods if the user has access # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.find_received_goods_by_id_with_http_info(id, async=True) >>> result = thread.get() :param async bool :param int id: ID of received goods to fetch (required) :return: ReceivedGoods If the method is called asynchronously, returns the request thread. """ all_params = ['id'] # noqa: E501 all_params.append('async') 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 find_received_goods_by_id" % 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 `find_received_goods_by_id`") # 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 # 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 = ['api_key'] # noqa: E501 return self.api_client.call_api( '/received_goods/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ReceivedGoods', # noqa: E501 auth_settings=auth_settings, async=params.get('async'), _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)

37.444444

127

0.610394

1,454

12,469

4.997937

0.116919

0.101968

0.039769

0.029723

0.89473

0.87629

0.851933

0.837485

0.820421

0.802394

0

0.017777

0.300746

12,469

332

128

37.557229

0.81569

0.061994

0

0.71345

1

0

0.165445

0.033329

0

null

0

0.023392

null

0

null

0

1

0

1

0

null

0

1

0

9

0f3a3138330a593219e44792d37244f77929e8f7

704

py

Python

bench_press/models/datasets/filter_fns.py

s-tian/bench-press

04240a8c9402c2b8639900567d6bfbee3cf0222e

[ "MIT" ]

9

2020-03-18T05:11:07.000Z

2020-11-21T03:17:30.000Z

bench_press/models/datasets/filter_fns.py

s-tian/bench-press

04240a8c9402c2b8639900567d6bfbee3cf0222e

[ "MIT" ]

6

2020-11-13T17:45:10.000Z

2022-02-09T23:36:40.000Z

bench_press/models/datasets/filter_fns.py

s-tian/bench-press

04240a8c9402c2b8639900567d6bfbee3cf0222e

[ "MIT" ]

4

2020-03-23T07:51:40.000Z

2020-11-10T03:15:14.000Z

from bench_press.utils.obs_to_np import denormalize class InsertFilter: def __call__(self, datapoint, conf): state = datapoint['state'] denormalized_state = denormalize(state, conf.norms.state_norm.mean, conf.norms.state_norm.scale) if denormalized_state[0] <= 5000 and denormalized_state[2] < 500: return True return False class PatternInsertFilter: def __call__(self, datapoint, conf): state = datapoint['state'] denormalized_state = denormalize(state, conf.norms.state_norm.mean, conf.norms.state_norm.scale) if denormalized_state[0] <= 5000 and denormalized_state[2] < 500: return True return False

33.52381

104

0.68892

85

704

5.458824

0.376471

0.219828

0.12069

0.155172

0.818966

0

0.032907

0.223011

704

20

105

35.2

0.815356

0

0.8

0

0.014205

0

1

0.133333

false

0

0.066667

0

0.6

0

null

1

0

1

0

1

0

null

0

1

0

9

0f58802b8adc95104f40ba3d865e282a9b409f3b

96

py

Python

glue_jupyter/ipyvolume/common/__init__.py

pkgw/glue-jupyter

f8fff629fa5859c69f9c71264042ff9c0e347076

[ "BSD-3-Clause" ]

null

glue_jupyter/ipyvolume/common/__init__.py

pkgw/glue-jupyter

f8fff629fa5859c69f9c71264042ff9c0e347076

[ "BSD-3-Clause" ]

null

glue_jupyter/ipyvolume/common/__init__.py

pkgw/glue-jupyter

f8fff629fa5859c69f9c71264042ff9c0e347076

[ "BSD-3-Clause" ]

null

from .viewer_options_widget import * # noqa from .viewer import * # noqa from .tools import *

24

44

0.729167

13

96

5.230769

0.538462

0.294118

0.411765

0

0.1875

96

3

45

32

0.871795

0.09375

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

0f6ff86c0916d4470c940dd1aab981598766a6ce

3,596

py

Python

test/test_mst.py

LoicGrobol/spanningtrees

10dc1bf37ee08c5a8a3b76e8f6eceb8e51399b89

[ "MIT" ]

null

test/test_mst.py

LoicGrobol/spanningtrees

10dc1bf37ee08c5a8a3b76e8f6eceb8e51399b89

[ "MIT" ]

null

test/test_mst.py

LoicGrobol/spanningtrees

10dc1bf37ee08c5a8a3b76e8f6eceb8e51399b89

[ "MIT" ]

null

from numpy.typing import NDArray import numpy as np from spanningtrees.mst import MST from spanningtrees.graph import Graph from spanningtrees.brute_force import all_best_trees, all_best_rc_trees from hypothesis import given, strategies as st from conftest import random_weights # We use `exclude_min` here because for now, `best_tree` use `0.0` to mark a forbidden edge @given( weights_matrix=random_weights( size=st.integers(min_value=3, max_value=6), weights_strategy=st.floats( 0.0, 1.0, allow_nan=False, allow_infinity=False, exclude_min=True ), ) ) def test_1b(weights_matrix: NDArray[np.number]): """ Test that MST decoding's tree and score matches the brute-force decoding's tree and score on randomly generated dense graphs. """ graph = Graph.build(weights_matrix) mst = MST(graph) tree = mst.mst().to_array() cost = graph.weight(tree) best_trees_bf, cost_bf = all_best_trees(weights_matrix) assert any(np.allclose(tree, tree_bf) for tree_bf in best_trees_bf) assert np.allclose(cost, cost_bf) # We use `exclude_min` here because for now, `best_tree` use `0.0` to mark a forbidden edge @given( weights_matrix=random_weights( size=st.integers(min_value=3, max_value=6), weights_strategy=st.floats( 0.0, 1.0, allow_nan=False, allow_infinity=False, exclude_min=True ), ) ) def test_1b_scc(weights_matrix: NDArray[np.number]): """ Test that MST (using SCC) decoding's tree and score matches the brute-force decoding's tree and score on randomly generated dense graphs. """ graph = Graph.build(weights_matrix) mst = MST(graph) tree = mst.mst_scc().to_array() cost = graph.weight(tree) best_trees_bf, cost_bf = all_best_trees(weights_matrix) assert any(np.allclose(tree, tree_bf) for tree_bf in best_trees_bf) assert np.allclose(cost, cost_bf) # We use `exclude_min` here because for now, `best_tree` use `0.0` to mark a forbidden edge @given( weights_matrix=random_weights( size=st.integers(min_value=3, max_value=6), weights_strategy=st.floats( 0.0, 1.0, allow_nan=False, allow_infinity=False, exclude_min=True ), ) ) def test_c1b(weights_matrix: NDArray[np.number]): """ Test that root-constrained MST decoding's tree and score matches the brute-force decoding's tree and score on randomly generated dense graphs. """ graph = Graph.build(weights_matrix) mst = MST(graph, True) tree = mst.mst().to_array() cost = graph.weight(tree) best_trees_bf, cost_bf = all_best_rc_trees(weights_matrix) any(np.allclose(tree, tree_bf) for tree_bf in best_trees_bf) assert np.allclose(cost, cost_bf) # We use `exclude_min` here because for now, `best_tree` use `0.0` to mark a forbidden edge @given( weights_matrix=random_weights( size=st.integers(min_value=3, max_value=6), weights_strategy=st.floats( 0.0, 1.0, allow_nan=False, allow_infinity=False, exclude_min=True ), ) ) def test_c1b_scc(weights_matrix): """ Test that root-constrained MST (using SCC) decoding's tree and score matches the brute-force decoding's tree and score on randomly generated dense graphs. """ graph = Graph.build(weights_matrix) mst = MST(graph, True) tree = mst.mst_scc().to_array() cost = graph.weight(tree) best_trees_bf, cost_bf = all_best_rc_trees(weights_matrix) any(np.allclose(tree, tree_bf) for tree_bf in best_trees_bf) assert np.allclose(cost, cost_bf)

35.60396

91

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558

3,596

4.327957

0.148746

0.086128

0.043064

0.053002

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0.879503

0.864596

0.834783

0

0.012491

0.198554

3,596

100

92

35.96

0.825468

0.256396

0

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pytorch-extension/pytorch_extension_official/tests.py

xdr940/utils

c4b7b1479956475a7feee90a723541904ec82306

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pytorch-extension/pytorch_extension_official/tests.py

xdr940/utils

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[ "MIT" ]

null

pytorch-extension/pytorch_extension_official/tests.py

xdr940/utils

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code/RP_Bilinear_Pooling.py

yz-cnsdqz/TemporalActionParsing-FineGrained

c5bb289b9d51a47d617a49d60f9111eba6460a80

[ "MIT" ]

15

2019-01-22T03:10:20.000Z

2021-09-09T13:50:08.000Z

code/RP_Bilinear_Pooling.py

samrat2825/TemporalActionParsing-FineGrained

c5bb289b9d51a47d617a49d60f9111eba6460a80

[ "MIT" ]

null

code/RP_Bilinear_Pooling.py

samrat2825/TemporalActionParsing-FineGrained

c5bb289b9d51a47d617a49d60f9111eba6460a80

[ "MIT" ]

5

2019-10-07T07:44:40.000Z

2021-08-10T03:52:15.000Z

import numpy as np import keras import sys from keras.models import Sequential, Model from keras.engine.topology import Layer from keras.layers import Input, Dense, TimeDistributed, merge, Lambda from keras.layers.core import * from keras.layers.convolutional import * from keras.layers.recurrent import * from tensorflow.python.framework import ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import math_ops import tensorflow as tf from keras import backend as K import scipy from keras.activations import relu from functools import partial clipped_relu = partial(relu, max_value=5) def max_filter(x): # Max over the best filter score (like ICRA paper) max_values = K.max(x, 2, keepdims=True) max_flag = tf.greater_equal(x, max_values) out = x * tf.cast(max_flag, tf.float32) return out def channel_normalization(x): # Normalize by the highest activation max_values = K.max(K.abs(x), 2, keepdims=True)+1e-5 out = x / max_values return out def WaveNet_activation(x): tanh_out = Activation('tanh')(x) sigm_out = Activation('sigmoid')(x) return keras.layers.Multiply()([tanh_out, sigm_out]) def lp_normalization(x,p=2): if p == 2: return K.l2_normalize(x, axis=-1) else: norm_x = tf.maximum(tf.norm(x, ord=p, axis=-1, keepdims=True), 1e-6) return x/norm_x def power_normalization(x): y = tf.sign(x) * tf.sqrt(tf.abs(x)) return y def is_power2(x): return x!=0 and ((x & (x-1))==0) class SqrtAcfun(Layer): def __init__(self, theta=1e-3,**kwargs): self.theta = theta super(SqrtAcfun, self).__init__(**kwargs) def build(self, input_shape): self.shape = input_shape self.gamma = self.add_weight(name='gamma',shape=[1], initializer=keras.initializers.Constant(value=self.theta), trainable=True) super(SqrtAcfun,self).build(input_shape) def call(self,x): x = K.sign(x)* (K.sqrt(K.abs(x)+self.gamma)-K.sqrt(self.gamma)) return x def compute_output_shape(self, input_shape): return (input_shape) class QRDecompose(Layer): def __init__(self, **kwargs): super(QRDecompose, self).__init__(**kwargs) def build(self, input_shape): self.shape = input_shape super(QRDecompose,self).build(input_shape) def call(self,x): q, r = tf.linalg.qr(x) return q def compute_output_shape(self, input_shape): return (input_shape) ### the output and the input have the same dimension class TensorChainDecomposePooling(Layer): def __init__(self, n_recur=1, same_mat=False, out_fusion_type='mean', act_fun_in='linear', act_fun_out='linear', stride=2, trainable=True, **kwargs): self.out_fusion_type=out_fusion_type self.n_recur = n_recur self.stride = stride self.trainable = trainable self.same_mat=same_mat if act_fun_in=='linear': self.act_fun_in = keras.activations.linear elif act_fun_in=='tanh': self.act_fun_in = K.tanh else: print('[ERROR]: no such activation function for input. Program terminates') sys.exit() if act_fun_out=='linear': self.act_fun_out = keras.activations.linear elif act_fun_out=='tanh': self.act_fun_out = K.tanh else: print('[ERROR]: no such activation function for output. Program terminates') sys.exit() super(TensorChainDecomposePooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] # self.out_dim = in_dim self.core_dim = in_dim if not self.same_mat: self.factorInMats = [] self.factorOutMats = [] self.factorInBias = [] self.factorOutBias = [] self.core_diag_list = [] for ii in range(self.n_recur): self.factorInMats.append(self.add_weight(name='mat_in{:d}'.format(ii), shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) ) self.factorInBias.append(self.add_weight(name='b_in{:d}'.format(ii), shape=[in_dim], initializer='glorot_normal', trainable=True) ) self.factorOutMats.append(self.add_weight(name='mat_out{:d}'.format(ii), shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) ) self.factorOutBias.append(self.add_weight(name='b_out{:d}'.format(ii), shape=[in_dim], initializer='glorot_normal', trainable=True) ) else: self.factorInMats = [] self.factorOutMats = [] self.factorInBias = [] self.factorOutBias = [] self.core_diag_list = [] W_in = self.add_weight(name='mat_in', shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) b_in = self.add_weight(name='b_in', shape=[in_dim], initializer='glorot_normal', trainable=True) W_out = self.add_weight(name='mat_out', shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) b_out = self.add_weight(name='b_out', shape=[in_dim], initializer='glorot_normal', trainable=True) for ii in range(self.n_recur): self.factorInMats.append(W_in) self.factorInBias.append(b_in) self.factorOutMats.append(W_out) self.factorOutBias.append(b_out) for ii in range(self.n_recur+1): if not self.trainable: self.core_diag_list.append(self.add_weight(name='core_diag_{}'.format(ii), shape=[self.core_dim], initializer=keras.initializers.Constant(value=1.0), trainable=False) ) else: self.core_diag_list.append(self.add_weight(name='core_diag_{}'.format(ii), shape=[self.core_dim], initializer='glorot_normal', trainable=True) ) super(TensorChainDecomposePooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] z_list = [] ## before iteration, we do the first bilinear fusion lambda_0 = K.reshape(K.abs(self.core_diag_list[0]), [1, 1, -1]) z = lambda_0 * X * X z = self.act_fun_out(z) z_list.append(z) for ii in range(self.n_recur): ## (1) obtain x_{t+1} = U_tx_t + b_t X = K.dot(X, self.factorInMats[ii]) + self.factorInBias[ii] X = self.act_fun_in(X) lambda_i = K.reshape(K.abs(self.core_diag_list[ii+1]), [1, 1, -1]) z = lambda_i * X * X zout = K.dot(z, self.factorOutMats[ii]) + self.factorOutBias[ii] zout = self.act_fun_out(zout) z_list.append(zout) if self.out_fusion_type == 'mean': ## compute the mean value z_list_tensor = K.stack(z_list, axis=-2) out = K.mean(z_list_tensor, axis=-2) elif self.out_fusion_type == 'concate': ## concatente features out = K.concatenate(z_list, axis=-1) else: print('[ERROR] no such fusion method. Program terminates') sys.exit() self.out_dim = out.get_shape().as_list()[-1] ### now out is [batch, T, out_dim], we do temporal local pooling #### zero padding # out = ZeroPadding1D((self.time_window_size//2))(out) # W = tf.reshape(self.conv_filter, [1, -1, 1]) # [1, |Nt|, 1] # out_pool_list = [ K.sum(out[:, i:i+self.time_window_size, :]*W, axis=1) # for i in range(0,n_frames, self.stride) ] # out_pool = K.stack(out_pool_list,axis=1) out_pool = AveragePooling1D(self.stride)(out) return out_pool def get_weights(self): return self.factorInMats+self.factorOutMats def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) ### since CP decomposition is not unique, we think of more than one types of decompositions. ### the output and the input have the same dimension class TensorStarDecomposePooling(Layer): def __init__(self, n_recur=1, same_mat=False, use_bias=True, out_fusion_type='sum', act_fun_in='linear', act_fun_out='linear', stride=2, trainable=True, **kwargs): self.out_fusion_type=out_fusion_type self.n_recur = n_recur self.stride = stride self.trainable = trainable self.same_mat=same_mat self.use_bias = use_bias if act_fun_in=='linear': self.act_fun_in = keras.activations.linear elif act_fun_in=='tanh': self.act_fun_in = K.tanh else: print('[ERROR]: no such activation function for input. Program terminates') sys.exit() if act_fun_out=='linear': self.act_fun_out = keras.activations.linear elif act_fun_out=='tanh': self.act_fun_out = K.tanh else: print('[ERROR]: no such activation function for output. Program terminates') sys.exit() super(TensorStarDecomposePooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] # self.out_dim = in_dim self.core_dim = in_dim # if self.out_dim != self.core_dim or in_dim != self.core_dim: # print('[ERROR]: when reduce_dim=True, keep in_dim == out_dim == core_dim!') # sys.exit() if not self.same_mat: self.factorInMats = [] self.factorOutMats = [] if self.use_bias: self.factorInBias = [] self.factorOutBias = [] self.core_diag_list = [] for ii in range(self.n_recur): self.factorInMats.append(self.add_weight(name='mat_in{:d}'.format(ii), shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) ) if self.use_bias: self.factorInBias.append(self.add_weight(name='b_in{:d}'.format(ii), shape=[in_dim], initializer='glorot_normal', trainable=True) ) self.factorOutMats.append(self.add_weight(name='mat_out{:d}'.format(ii), shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) ) if self.use_bias: self.factorOutBias.append(self.add_weight(name='b_out{:d}'.format(ii), shape=[in_dim], initializer='glorot_normal', trainable=True) ) else: self.factorInMats = [] self.factorOutMats = [] self.factorInBias = [] self.factorOutBias = [] self.core_diag_list = [] W_in = self.add_weight(name='mat_in', shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) if self.use_bias: b_in = self.add_weight(name='b_in', shape=[in_dim], initializer='glorot_normal', trainable=True) W_out = self.add_weight(name='mat_out', shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) if self.use_bias: b_out = self.add_weight(name='b_out', shape=[in_dim], initializer='glorot_normal', trainable=True) for ii in range(self.n_recur): self.factorInMats.append(W_in) self.factorOutMats.append(W_out) if self.use_bias: self.factorInBias.append(b_in) self.factorOutBias.append(b_out) for ii in range(self.n_recur+1): if not self.trainable: self.core_diag_list.append(self.add_weight(name='core_diag_{}'.format(ii), shape=[self.core_dim], initializer=keras.initializers.Constant(value=1.0), trainable=False) ) else: self.core_diag_list.append(self.add_weight(name='core_diag_{}'.format(ii), shape=[self.core_dim], initializer='glorot_normal', trainable=True) ) super(TensorStarDecomposePooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] z_list = [] ## before iteration, we do the first bilinear fusion lambda_0 = K.reshape(K.abs(self.core_diag_list[0]), [1, 1, -1]) z = lambda_0 * X * X z = self.act_fun_out(z) z_list.append(z) for ii in range(self.n_recur): ## (1) obtain x_{t+1} = U_tx_t + b_t # U = QRDecompose()(self.factorInMats[ii]) U = self.factorInMats[ii] if self.use_bias: X1 = K.dot(X, U) + self.factorInBias[ii] else: X1 = K.dot(X, U) X1 = self.act_fun_in(X1) lambda_i = K.reshape(K.abs(self.core_diag_list[ii+1]), [1, 1, -1]) z = lambda_i * X1 * X1 # W = QRDecompose()(self.factorOutMats[ii]) W = self.factorOutMats[ii] if self.use_bias: zout = K.dot(z, W) + self.factorOutBias[ii] else: zout = K.dot(z, W) zout = self.act_fun_out(zout) z_list.append(zout) if self.out_fusion_type == 'sum': # ## compute the mean value z_list_tensor = K.stack(z_list, axis=-1) out = K.sum(z_list_tensor, axis=-1) elif self.out_fusion_type == 'concate': ## concatente features out = K.concatenate(z_list, axis=-1) self.out_dim = out.get_shape().as_list()[-1] ## now out is [batch, T, out_dim], we do temporal local pooling ### zero padding # out = ZeroPadding1D((self.time_window_size//2))(out) # W = tf.reshape(self.conv_filter, [1, -1, 1]) # [1, |Nt|, 1] # out_pool_list = [ K.sum(out[:, i:i+self.time_window_size, :]*W, axis=1) # for i in range(0,n_frames, self.stride) ] # out_pool = K.stack(out_pool_list,axis=1) out_pool = AveragePooling1D(self.stride)(out) # out_pool = MaxPooling1D(self.stride)(out) return out_pool def get_weights(self): return self.factorInMats+self.factorOutMats def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) ### Recursive CP tensor decomposition class TensorDecomposePooling(Layer): def __init__(self, out_dim, core_dim, time_window_size, stride=2, trainable=False, reduce_dim=False, n_recur=1, **kwargs): self.out_dim = out_dim self.core_dim = core_dim self.reduce_dim = reduce_dim self.n_recur = n_recur self.time_window_size = time_window_size self.stride = stride self.trainable = trainable # self.act_fun = keras.activations.linear self.act_fun = K.tanh super(TensorDecomposePooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.reduce_dim: if is_power2(in_dim) and is_power2(self.out_dim) and is_power2(self.core_dim): self.in_recur = int(np.log2(in_dim // self.core_dim)) self.out_recur = int(np.log2(self.out_dim // self.core_dim) ) print('-- # inner recursion={}'.format(self.in_recur)) print('-- # outer recursion={}'.format(self.out_recur)) if self.core_dim < 8: print('[ERROR]: core_dim={}. Too small! Program terminates'.format(self.core_dim)) sys.exit() else: print('[ERROR]: the input feature dim is required to be power of 2.') sys.exit() else: self.in_recur=self.n_recur self.out_recur=self.n_recur if self.out_dim != self.core_dim or in_dim != self.core_dim: print('[ERROR]: when reduce_dim=True, keep in_dim == out_dim == core_dim!') sys.exit() if not self.trainable: self.core_diag = self.add_weight(name='core_diag', shape=[self.core_dim], initializer=keras.initializers.Constant(value=1.0), trainable=False) else: self.core_diag = self.add_weight(name='core_diag', shape=[self.core_dim], initializer='glorot_normal', trainable=True) # self.conv_filter = self.add_weight(name='conv_kernel', # shape=[self.time_window_size], # initializer=keras.initializers.Constant(value=1.0/self.time_window_size), # trainable=False) self.conv_filter = self.add_weight(name='conv_kernel', shape=[self.time_window_size], initializer='glorot_normal', trainable=True) self.factorInMats = [] self.factorOutMats = [] self.factorInBias = [] self.factorOutBias = [] if self.reduce_dim: for ii in range(self.in_recur): self.factorInMats.append(self.add_weight(name='mat_in{:d}'.format(ii), shape=[in_dim // 2**(ii), in_dim//2**(ii+1)], initializer='glorot_normal', trainable=True) ) self.factorInBias.append(self.add_weight(name='b_in{:d}'.format(ii), shape=[in_dim//2**(ii+1)], initializer=keras.initializers.Constant(value=0.0), trainable=True) ) for ii in range(self.out_recur): self.factorOutMats.append(self.add_weight(name='mat_out{:d}'.format(ii), shape=[self.out_dim // 2**(ii+1), self.out_dim//2**(ii)], initializer='glorot_normal', trainable=True) ) self.factorOutBias.append(self.add_weight(name='b_out{:d}'.format(ii), shape=[self.out_dim//2**(ii)], initializer=keras.initializers.Constant(value=0.0), trainable=True) ) else: for ii in range(self.in_recur): self.factorInMats.append(self.add_weight(name='mat_in{:d}'.format(ii), shape=[in_dim, in_dim], initializer='glorot_normal', trainable=True) ) self.factorInBias.append(self.add_weight(name='b_in{:d}'.format(ii), shape=[in_dim], initializer='glorot_normal', trainable=True) ) for ii in range(self.out_recur): self.factorOutMats.append(self.add_weight(name='mat_out{:d}'.format(ii), shape=[self.out_dim, self.out_dim], initializer='glorot_normal', trainable=True) ) self.factorOutBias.append(self.add_weight(name='b_out{:d}'.format(ii), shape=[self.out_dim], initializer='glorot_normal', trainable=True) ) super(TensorDecomposePooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] out = X for ii in range(self.in_recur): out = K.dot(out, self.factorInMats[ii]) + self.factorInBias[ii] out = self.act_fun(out) ### now we reach the core tensor ### do elementwise multiplication, self.core_diag is broadcasted. out = K.reshape(K.abs(self.core_diag), [1, 1, -1])*out*out ### now we go to the output dim for ii in range(self.out_recur): out = K.dot(out, self.factorOutMats[-ii-1]) + self.factorOutBias[-ii-1] out = self.act_fun(out) ### now out is [batch, T, out_dim], we do temporal local pooling #### zero padding out = ZeroPadding1D((self.time_window_size//2))(out) W = tf.reshape(self.conv_filter, [1, -1, 1]) # [1, |Nt|, 1] out_pool_list = [ K.sum(out[:, i:i+self.time_window_size, :]*W, axis=1) for i in range(0,n_frames, self.stride) ] out_pool = K.stack(out_pool_list,axis=1) return out_pool def get_weights(self): return self.factorInMats+self.factorOutMats def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) from keras.constraints import Constraint class non_neg_unit_norm (Constraint): def __init__(self, axis=0): self.axis=axis def __call__(self, w): w *= K.cast(K.greater_equal(w, 0.), K.floatx()) # non negative constraint #w = w / (K.epsilon() + K.sqrt(K.sum(K.square(w), # axis=self.axis, # keepdims=True))) w = w / (K.epsilon() + K.abs(w)) #w *= K.cast(K.greater_equal(w, 0.), K.floatx()) # non negative constraint return w class hard_binary_constraint (Constraint): def __init__(self, n_rows, n_cols): self.n_rows = n_rows self.n_cols = n_cols def __call__(self, w): # we assume that w is initialized by glorot_normal, sigma = np.sqrt(2 / (self.n_rows+self.n_cols)) w = w / (K.epsilon() + K.abs(w)) # the number of 2*sigma refers to https://www.tensorflow.org/api_docs/python/tf/random/truncated_normal # w = w * (2*sigma)/K.max(K.abs(w)) # w = K.tanh(2*w) # w = K.sign(w) * (K.abs(w)**0.5) # w = w * sigma*2e-3 return w class tanh_binary_constraint (Constraint): def __init__(self, tanh_k): self.tanh_k = tanh_k def __call__(self, w): # we assume that w is initialized by glorot_normal, w = K.tanh(self.tanh_k*w) return w class TensorRelaxationPooling(Layer): def __init__(self, n_basis, use_bias=False, use_normalization=False, constraint_type='tanh', tanh_k = 1.5, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis**2 self.out_fusion_type = out_fusion_type self.stride = stride self.use_bias = use_bias self.use_normalization = use_normalization self.time_window_size = time_window_size self.activation = 'linear' self.constraint_type = constraint_type self.tanh_k = tanh_k super(TensorRelaxationPooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() scalar_learnable=True if self.constraint_type == 'binary': weight_constraint = hard_binary_constraint(in_dim, self.n_basis) # scalar_learnable=False elif self.constraint_type == 'tanh_k2': weight_constraint = tanh_binary_constraint(2) elif self.constraint_type == 'tanh_k1': weight_constraint = tanh_binary_constraint(1.0+1e-2) elif self.constraint_type == 'tanh_k1.5': weight_constraint = tanh_binary_constraint(1.5) elif self.constraint_type == 'tanh_k2.5': weight_constraint = tanh_binary_constraint(2.5) elif self.constraint_type == 0: weight_constraint=None scalar_learnable=False else: print('[ERROR]: no such weight constraint.') sys.exit() ## define the two matrix with orthogonal columns stddev = np.sqrt(2 / (in_dim+self.n_basis)) weight_initializer = keras.initializers.TruncatedNormal(mean=0.0, stddev=stddev, seed=None) self.E = self.add_weight(name='E', shape=[in_dim, self.n_basis], # initializer=keras.initializers.Orthogonal(), # initializer='ones', initializer=weight_initializer, constraint=weight_constraint, # constraint=non_neg_unit_norm(axis=0), trainable=True) self.F = self.add_weight(name='F', shape=[in_dim, self.n_basis], # initializer=keras.initializers.Orthogonal(), # initializer='ones', initializer=weight_initializer, constraint=weight_constraint, trainable=True) self.G = self.add_weight(name='G', shape=[1], # initializer=keras.initializers.Orthogonal(), # initializer='ones', initializer=keras.initializers.Constant(1), constraint=keras.constraints.NonNeg(), trainable=scalar_learnable) if self.use_bias: self.bx = self.add_weight(name='bias_x', shape=[self.n_basis], initializer='glorot_normal', trainable=True) self.by = self.add_weight(name='bias_y', shape=[self.n_basis], initializer='glorot_normal', trainable=True) else: self.bx = self.add_weight(name='bias_x', shape=[self.n_basis], initializer='zeros', trainable=False) self.by = self.add_weight(name='bias_y', shape=[self.n_basis], initializer='zeros', trainable=False) if self.out_fusion_type == 'w-sum': self.conv_filter = self.add_weight(name='conv_kernel', shape=[self.time_window_size], initializer='glorot_normal', trainable=True) super(TensorRelaxationPooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] # self.E = K.l2_normalize(self.E, axis=0) # self.F = K.l2_normalize(self.F, axis=0) # self.E_softmax = K.softmax(self.E, axis=0) # self.F_softmax = K.softmax(self.F, axis=0) # apply uni-norm, non-negative constraint #self.E *= K.cast(K.greater_equal(self.E, 0), float) # non negative constraint # #self.E = self.E / (K.epsilon() + K.sqrt(K.sum(K.square(self.E), # axis=0, # keepdims=True))) #self.F *= K.cast(K.greater_equal(self.F, 0.), float) # non negative constraint # #self.F = self.F / (K.epsilon() + K.sqrt(K.sum(K.square(self.F), # axis=0, # keepdims=True))) z1 = K.dot(X, self.E )+K.reshape(self.bx, [1,1,-1]) z2 = K.dot(X, self.F )+K.reshape(self.by, [1,1,-1]) if self.activation == 'tanh': z1 = K.tanh(z1) z2 = K.tanh(z2) # outer product z1 = K.expand_dims(z1, axis=-1) z2 = K.expand_dims(z2, axis=-2) z = tf.matmul(z1, z2) z = self.G*K.reshape(z, [-1, n_frames, self.n_basis**2]) ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) elif self.out_fusion_type == 'w-sum': ### now out is [batch, T, out_dim], we do temporal local pooling #### zero padding out = ZeroPadding1D((self.time_window_size//2))(z) W = tf.reshape(self.conv_filter, [1, -1, 1]) # [1, |Nt|, 1] out_pool_list = [ K.sum(out[:, i:i+self.time_window_size, :]*W, axis=1) for i in range(0,n_frames, self.stride) ] out_pool = K.stack(out_pool_list,axis=1) elif self.out_fusion_type == 'linearproj': out_pool = Conv1D(self.out_dim, 1, strides=self.stride, padding='same')(z) return out_pool def get_mat(self): return [self.E, self.F] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class RPBinaryPooling(Layer): def __init__(self, n_basis=8, n_components=1, use_normalization=False, activation=None, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis**2 self.n_components=n_components self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size if activation == None: self.act_fun = tf.keras.activations.linear elif activation == 'tanh': self.act_fun = tf.keras.activations.tanh elif activation == 'relu': self.act_fun = tf.keras.activations.relu super(RPBinaryPooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() ## define the two matrix with orthogonal columns self.E_list = [] self.F_list = [] self.we_list = [] for n in range(self.n_components): E0 = np.sign(np.random.standard_normal([in_dim, in_dim])) # E0_signchange = np.sum(((np.roll(E0,1,axis=0)-E0) != 0).astype(int), axis=0) # E0 = E0[:, np.argsort(E0_signchange)] E0 = E0[:, :self.n_basis] self.E_list.append( self.add_weight(name='E_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Constant(E0), trainable=False) ) self.we_list.append(self.add_weight(name='we_{}'.format(n), shape=[1], initializer=keras.initializers.Constant(1.0/self.n_components), trainable=True)) F0 = np.sign(np.random.standard_normal([in_dim, in_dim])) # F0_signchange = np.sum(((np.roll(F0,1,axis=0)-F0) != 0).astype(int), axis=0) # F0 = F0[:, np.argsort(F0_signchange)] F0 = F0[:, :self.n_basis] self.F_list.append( self.add_weight(name='F_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Constant(F0), trainable=False) ) super(RPBinaryPooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] in_dim = float(X.get_shape().as_list()[-1]) z = 0.0 for ii in range(self.n_components): z1 = K.dot(X, self.E_list[ii] ) z2 = K.dot(X, self.F_list[ii] ) # outer product z1 = self.act_fun(K.expand_dims(z1, axis=-1)) z2 = self.act_fun(K.expand_dims(z2, axis=-2)) z12 = tf.matmul(z1, z2) z12 = K.reshape(z12, [-1, n_frames, self.n_basis**2]) / in_dim ## use power and l2 normalization if self.use_normalization: z12 = K.sign(z12) * K.sqrt(K.abs(z12)) z12 = K.l2_normalize(z12, axis=-1) z += z12 * self.we_list[ii] ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def get_mat(self): return [self.E_list, self.F_list] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class RPBinaryPooling2(Layer): def __init__(self, n_basis=8, n_components=1, use_normalization=False, activation=None, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis**2 self.n_components=n_components self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size if activation == None: self.act_fun = tf.keras.activations.linear elif activation == 'tanh': self.act_fun = tf.keras.activations.tanh elif activation == 'relu': self.act_fun = tf.keras.activations.relu super(RPBinaryPooling2, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() ## define the two matrix with orthogonal columns self.E_list = [] self.F_list = [] for n in range(self.n_components): E0 = np.sign(np.random.standard_normal([in_dim, in_dim])) # E0_signchange = np.sum(((np.roll(E0,1,axis=0)-E0) != 0).astype(int), axis=0) # E0 = E0[:, np.argsort(E0_signchange)] E0 = E0[:, :self.n_basis] self.E_list.append( self.add_weight(name='E_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Constant(E0), trainable=False) ) F0 = np.sign(np.random.standard_normal([in_dim, in_dim])) # F0_signchange = np.sum(((np.roll(F0,1,axis=0)-F0) != 0).astype(int), axis=0) # F0 = F0[:, np.argsort(F0_signchange)] F0 = F0[:, :self.n_basis] self.F_list.append( self.add_weight(name='F_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Constant(F0), trainable=False) ) super(RPBinaryPooling2, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] in_dim = float(X.get_shape().as_list()[-1]) z_list = [] z = 0 for ii in range(self.n_components): z1 = K.dot(X, self.E_list[ii] ) z2 = K.dot(X, self.F_list[ii] ) # outer product z1 = self.act_fun(K.expand_dims(z1, axis=-1)) z2 = self.act_fun(K.expand_dims(z2, axis=-2)) z12 = tf.matmul(z1, z2) z12 = K.reshape(z12, [-1, n_frames, self.n_basis**2]) / in_dim ## use power and l2 normalization if self.use_normalization: z12 = K.sign(z12) * K.sqrt(K.abs(z12)) z12 = K.l2_normalize(z12, axis=-1) z_list.append(z12) z += z12/self.n_components ## how to fuse the outputs from individual components # z = K.concatenate(z_list, axis=-1) ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def get_mat(self): return [self.E_list, self.F_list] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class RPGaussianPooling(Layer): def __init__(self, n_basis=8, n_components=1, init_sigma=None, use_normalization=False, activation=None, learnable_radius=True, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis**2 self.n_components=n_components self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size self.learnable_radius = learnable_radius self.init_sigma = init_sigma if activation == None: self.act_fun = tf.keras.activations.linear elif activation == 'tanh': self.act_fun = tf.keras.activations.tanh elif activation == 'relu': self.act_fun = tf.keras.activations.relu super(RPGaussianPooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() ## define the two matrix with orthogonal columns self.E_list = [] self.F_list = [] if not self.init_sigma: init_sigma = np.sqrt(in_dim) else: init_sigma = self.init_sigma for n in range(self.n_components): E0 = self.add_weight(name='E_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Orthogonal(), trainable=False) sigma_e = self.add_weight(name='sE_{}'.format(n), shape=[1], initializer=keras.initializers.Constant(init_sigma), constraint=keras.constraints.NonNeg(), trainable=self.learnable_radius) self.E_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_e) * E0 ) F0 = self.add_weight(name='F_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Orthogonal(), trainable=False) sigma_f = self.add_weight(name='sF_{}'.format(n), shape=[1], initializer=keras.initializers.Constant(init_sigma), constraint=keras.constraints.NonNeg(), trainable=self.learnable_radius) self.F_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_f) * F0 ) super(RPGaussianPooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] in_dim = float(X.get_shape().as_list()[-1]) z_list = [] for ii in range(self.n_components): z1 = K.dot(X, self.E_list[ii] ) z2 = K.dot(X, self.F_list[ii] ) # outer product z1 = self.act_fun(K.expand_dims(z1, axis=-1)) z2 = self.act_fun(K.expand_dims(z2, axis=-2)) z12 = tf.matmul(z1, z2) z12 = K.reshape(z12, [-1, n_frames, self.n_basis**2]) ## use power and l2 normalization if self.use_normalization: z12 = K.sign(z12) * K.sqrt(K.abs(z12)) z12 = K.l2_normalize(z12, axis=-1) z_list.append(z12) if len(z_list) > 1: z = keras.layers.Average()(z_list) else: z = z_list[0] ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def get_mat(self): return [self.E_list, self.F_list] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class RPGaussianPooling2(Layer): def __init__(self, n_basis=8, n_components=1, init_sigma=None, use_normalization=False, activation=None, learnable_radius=True, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.n_components=n_components self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size self.learnable_radius = True self.init_sigma = init_sigma self.out_dim = n_components*(n_basis)**2 # print('-----------init_sigma={}-------------'.format(init_sigma)) if activation == None: self.act_fun = tf.keras.activations.linear elif activation == 'tanh': self.act_fun = tf.keras.activations.tanh elif activation == 'relu': self.act_fun = tf.keras.activations.relu super(RPGaussianPooling2, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() ## define the two matrix with orthogonal columns self.E_list = [] self.F_list = [] if not self.init_sigma: init_sigma = np.sqrt(in_dim) else: init_sigma = self.init_sigma for n in range(self.n_components): E0 = self.add_weight(name='E_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Orthogonal(), trainable=False) sigma_e = self.add_weight(name='sE_{}'.format(n), shape=[1], initializer=keras.initializers.Constant(init_sigma), constraint=keras.constraints.NonNeg(), trainable=self.learnable_radius) self.E_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_e) * E0 ) F0 = self.add_weight(name='F_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Orthogonal(), trainable=False) sigma_f = self.add_weight(name='sF_{}'.format(n), shape=[1], initializer=keras.initializers.Constant(init_sigma), constraint=keras.constraints.NonNeg(), trainable=self.learnable_radius) self.F_list.append( np.sqrt(in_dim) / (K.epsilon() + sigma_f) * F0 ) super(RPGaussianPooling2, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] in_dim = float(X.get_shape().as_list()[-1]) z_list = [] for ii in range(self.n_components): z1 = K.dot(X, self.E_list[ii] ) z2 = K.dot(X, self.F_list[ii] ) # outer product z1 = K.expand_dims(z1, axis=-1) z2 = K.expand_dims(z2, axis=-2) z12 = tf.matmul(z1, z2) z12 = K.reshape(z12, [-1, n_frames, (self.n_basis)**2]) ## use power and l2 normalization if self.use_normalization: z12 = K.sign(z12) * K.sqrt(K.abs(z12)) z12 = K.l2_normalize(z12, axis=-1) z_list.append(z12) z = K.concatenate(z_list, axis=-1) ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def get_mat(self): return [self.E_list, self.F_list] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class RPLearnable(Layer): def __init__(self, n_basis=8, n_components=1, use_normalization=False, activation=None, learnable_radius=True, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.n_components=n_components self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size self.learnable_radius = learnable_radius self.out_dim = n_components*(n_basis)**2 if activation == None: self.act_fun = tf.keras.activations.linear elif activation == 'tanh': self.act_fun = tf.keras.activations.tanh elif activation == 'relu': self.act_fun = tf.keras.activations.relu super(RPLearnable, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() ## define the two matrix with orthogonal columns self.E_list = [] self.F_list = [] for n in range(self.n_components): E0 = self.add_weight(name='E_{}'.format(n), shape=[in_dim, self.n_basis], initializer='glorot_normal', trainable=True) self.E_list.append( E0 ) F0 = self.add_weight(name='F_{}'.format(n), shape=[in_dim, self.n_basis], initializer='glorot_normal', trainable=True) self.F_list.append( F0 ) super(RPLearnable, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] in_dim = float(X.get_shape().as_list()[-1]) z_list = [] for ii in range(self.n_components): z1 = K.dot(X, self.E_list[ii] ) z2 = K.dot(X, self.F_list[ii] ) # outer product z1 = K.expand_dims(z1, axis=-1) z2 = K.expand_dims(z2, axis=-2) z12 = tf.matmul(z1, z2) z12 = K.reshape(z12, [-1, n_frames, (self.n_basis)**2]) ## use power and l2 normalization if self.use_normalization: z12 = K.sign(z12) * K.sqrt(K.abs(z12)) z12 = K.l2_normalize(z12, axis=-1) z_list.append(z12) z = K.concatenate(z_list, axis=-1) ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class MultiModalLowRankPooling(Layer): def __init__(self, n_basis=8, n_components=1, use_normalization=False, activation=None, learnable_radius=True, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size self.learnable_radius = learnable_radius self.n_components = n_components if activation == None: self.act_fun = tf.keras.activations.linear elif activation == 'tanh': self.act_fun = tf.keras.activations.tanh elif activation == 'relu': self.act_fun = tf.keras.activations.relu super(MultiModalLowRankPooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] ## define the two matrix with orthogonal columns self.E = self.add_weight(name='E', shape=[in_dim, self.n_basis], initializer='glorot_normal', trainable=True) self.F = self.add_weight(name='F', shape=[in_dim, self.n_basis], initializer='glorot_normal', trainable=True) super(MultiModalLowRankPooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] z1 = K.dot(X, self.E ) z2 = K.dot(X, self.F ) z = z1*z2 ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class FBM(Layer): def __init__(self, in_dim, out_dim, n_factor=20, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.in_dim = in_dim self.out_dim = out_dim self.n_factor = n_factor self.out_fusion_type = out_fusion_type self.stride = stride self.act_fun = tf.keras.activations.linear super(FBM, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = self.in_dim ## define the two matrix with orthogonal columns self.E = self.add_weight(name='E', shape=[in_dim, self.n_factor, self.out_dim], initializer='glorot_normal', trainable=True) self.W = self.add_weight(name='W', shape=[in_dim, self.out_dim], initializer='glorot_normal', trainable=True) self.b = self.add_weight(name='b', shape=[1,self.out_dim], initializer='zeros', trainable=True) super(FBM, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] first_term = K.dot(X, self.W) second_term = tf.einsum('btd,dkD->btD', X, self.E) z = self.b + first_term + second_term ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) from scipy.linalg import hadamard def get_reverse_hadamard(in_dim, n_cols): E0 = hadamard(in_dim) E0_signchange = np.sum(((np.roll(E0,1,axis=0)-E0) != 0).astype(int), axis=0) E0 = E0[:, np.argsort(E0_signchange)[::-1]] return E0[:, :n_cols] class ReHadamardPooling(Layer): def __init__(self, n_basis=8, n_components=1, use_normalization=True, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis**2 self.n_components=n_components self.out_fusion_type = out_fusion_type self.stride = stride self.use_normalization = use_normalization self.time_window_size = time_window_size super(ReHadamardPooling, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() if (in_dim & (in_dim - 1)) != 0: print('[ERROR]: input feature dimension should be power of 2') sys.exit() ## define the two matrix with orthogonal columns self.E_list = [] self.F_list = [] for n in range(self.n_components): E0 = get_reverse_hadamard(in_dim, self.n_basis) self.E_list.append( self.add_weight(name='E_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Constant(E0), trainable=False) ) # F0 = np.sign(np.random.standard_normal([in_dim, self.n_basis])) self.F_list.append( self.add_weight(name='F_{}'.format(n), shape=[in_dim, self.n_basis], initializer=keras.initializers.Constant(E0), trainable=False) ) super(ReHadamardPooling, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] in_dim = float(X.get_shape().as_list()[-1]) z_list = [] for ii in range(self.n_components): z1 = K.dot(X, self.E_list[ii] ) z2 = K.dot(X, self.F_list[ii] ) # outer product z1 = K.expand_dims(z1, axis=-1) z2 = K.expand_dims(z2, axis=-2) z12 = tf.matmul(z1, z2) z12 = K.reshape(z12, [-1, n_frames, self.n_basis**2]) ## use power and l2 normalization if self.use_normalization: z12 = K.sign(z12) * K.sqrt(K.abs(z12)) z12 = K.l2_normalize(z12, axis=-1) z_list.append(z12) if len(z_list) > 1: z = keras.layers.Average()(z_list) else: z = z_list[0] ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) return out_pool def get_mat(self): return [self.E_list, self.F_list] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class TensorRelaxationPooling2(Layer): def __init__(self, n_basis, use_bias=False, use_normalization=False, out_activation='linear', out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.n_basis = n_basis self.out_dim = n_basis**2 self.out_fusion_type = out_fusion_type self.stride = stride self.use_bias = use_bias self.use_normalization = use_normalization self.time_window_size = time_window_size self.activation = out_activation super(TensorRelaxationPooling2, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] if self.n_basis > in_dim: print('[ERROR]: n_basis must not be larger than in_dim! Program terminates') sys.exit() self.E_layer = keras.layers.Conv1D(self.n_basis, kernel_size=1, activation=self.activation, use_bias=self.use_bias # kernel_constraint= non_neg_unit_norm(axis=1) # kernel_constraint=keras.constraints.NonNeg() # kernel_regularizer=regularizers.l1(100.0) ) self.F_layer = keras.layers.Conv1D(self.n_basis, kernel_size=1, activation=self.activation, use_bias=self.use_bias # kernel_constraint= non_neg_unit_norm(axis=1) # kernel_constraint=keras.constraints.NonNeg() # kernel_regularizer=regularizers.l1(100.0) ) super(TensorRelaxationPooling2, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] # self.E = K.l2_normalize(self.E, axis=0) # self.F = K.l2_normalize(self.F, axis=0) # self.E_softmax = K.softmax(self.E, axis=0) # self.F_softmax = K.softmax(self.F, axis=0) z1 = self.E_layer(X) z2 = self.F_layer(X) # if self.activation == 'tanh': # z1 = K.tanh(z1) # z2 = K.tanh(z2) # outer product z1 = K.expand_dims(z1, axis=-1) z2 = K.expand_dims(z2, axis=-2) z = tf.matmul(z1, z2) z = K.reshape(z, [-1, n_frames, self.n_basis**2]) ## use power and l2 normalization if self.use_normalization: z = SqrtAcfun(theta=1e-3)(z) z = K.l2_normalize(z, axis=-1) ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) elif self.out_fusion_type == 'w-sum': ### now out is [batch, T, out_dim], we do temporal local pooling #### zero padding out = ZeroPadding1D((self.time_window_size//2))(z) W = tf.reshape(self.conv_filter, [1, -1, 1]) # [1, |Nt|, 1] out_pool_list = [ K.sum(out[:, i:i+self.time_window_size, :]*W, axis=1) for i in range(0,n_frames, self.stride) ] out_pool = K.stack(out_pool_list,axis=1) elif self.out_fusion_type == 'linearproj': out_pool = Conv1D(self.out_dim, 1, strides=self.stride, padding='same')(z) return out_pool def get_weights(self): return [self.E_layer.get_weights()[0], self.F_layer.get_weights()[0]] def get_conv1d_layers(self): return [self.E_layer, self.F_layer] def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, self.out_dim) class FullCorrelationLayer(Layer): def __init__(self, out_fusion_type='avg', # or max or w-sum stride=2, time_window_size=5, **kwargs): self.out_fusion_type = out_fusion_type self.stride = stride super(FullCorrelationLayer, self).__init__(**kwargs) def build(self, input_shape): self.shape=input_shape in_dim = input_shape[-1] super(FullCorrelationLayer, self).build(input_shape) def call(self, X): ### here X is the entire mat with [batch, T, D] n_frames = X.get_shape().as_list()[1] # outer product z1 = K.expand_dims(X, axis=-1) z2 = K.expand_dims(X, axis=-2) z = tf.matmul(z1, z2) z = K.reshape(z, [-1, n_frames, self.shape[-1]**2]) ## pooling to reduce time sequence if self.out_fusion_type == 'avg': out_pool = AveragePooling1D(pool_size=self.stride, strides=self.stride, padding='same')(z) elif self.out_fusion_type == 'max': out_pool = MaxPooling1D(self.stride)(z) elif self.out_fusion_type == 'w-sum': ### now out is [batch, T, out_dim], we do temporal local pooling #### zero padding out = ZeroPadding1D((self.time_window_size//2))(z) W = tf.reshape(self.conv_filter, [1, -1, 1]) # [1, |Nt|, 1] out_pool_list = [ K.sum(out[:, i:i+self.time_window_size, :]*W, axis=1) for i in range(0,n_frames, self.stride) ] out_pool = K.stack(out_pool_list,axis=1) elif self.out_fusion_type == 'linearproj': out_pool = Conv1D(self.out_dim, 1, strides=self.stride, padding='same')(z) return out_pool def compute_output_shape(self, input_shape): return(input_shape[0], input_shape[1]//self.stride, input_shape[-1]**2) def constrained_loss(mat_list, weights=1.0, loss_type='orthogonal'): def loss(y_true, y_pred): constraint = 0 for i in range(len(mat_list)): if loss_type == 'orthogonal': constraint0 = (K.dot(K.transpose(mat_list[i]), mat_list[i] ) - K.eye(mat_list[i].get_shape().as_list()[1]) )**2 elif loss_type == 'softbinary': constraint0 = K.mean( K.abs(mat_list[i]**2 - 1.0)) constraint += K.sum(constraint0) return weights*(constraint) + K.categorical_crossentropy(y_true, y_pred) return loss

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7eb4d8d36eb6a0d03e196df0dbaf54a5df03225d

977,599

py

Python

commands/resources9.py

egigoka/commands

3431ccdb9b9e8b13957b6cfc10feb51c46188b48

[ "MIT" ]

1

2018-05-23T03:34:05.000Z

commands/resources9.py

egigoka/commands

3431ccdb9b9e8b13957b6cfc10feb51c46188b48

[ "MIT" ]

null

commands/resources9.py

egigoka/commands

3431ccdb9b9e8b13957b6cfc10feb51c46188b48

[ "MIT" ]

null

#! python3 # -*- coding: utf-8 -*- __version__ = "0.2.0" class Resources: @classmethod def to_file(cls, name, path): from.base64_9 import Base64 from .os9 import OS string = cls.resources_dict[f"{name.lower()}_{OS.name}_{OS.architecture}"] return Base64.to_file(string, path) resources_dict = { #"wget_windows_x32": "", #"wget_windows_x64": "" "psexec_windows_64bit": 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ItsJGBFhf9AD5XFi8VIg8QgQV9BXkFcX13DRTPAM9JIg8QgQV9BXkFcX13p/AQAAMzMzMzM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yMCAxMS5cdGFiXGZzMTkgRGlzY2xhaW1lciBvZiBXYXJyYW50eS5cY2FwczAgICAg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\nAGEAbQBhAGcAZQBzAC4ACgBQAGwAZQBhAHMAZQAgAG4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AAP8VaAwBAEiL8EiJRCRwSIXAD4TP\nAQAAQbgCAAAAQYvUSIvI/xXlCgEAhcAPhLUBAACDfCRMAA+EpAAAAIuEJJAAAACFwA+ElQAAAIvf\niVwkSEyLZCRAO9gPg4IAAABMjUQkUIvTSItMJGD/FY4KAQCFwA+EbgEAAEyLTCRQQQ+3QQKJRCQg\nQYPI/7oCAAAASIvO/xVuCgEAhcAPhEYBAABIi1QkUIA6AHUkgXoEAAAA8HUbSIPCCEmLzP8VPgkB\nAIXARA9F90SJtCSMAAAA/8OJXCRIi4QkkAAAAOl2////RYX2D4TDAAAATIt0JEBJi87/FR8KAQCL\n2P8VXwsBAEiLyEyNQwi6CAAAAP8VXQsBAEiL+EiJRCRYSIXAD4TEAAAAZscAAAtJi87/FeYJAQBI\ng8AIZolHAsdHBAAAAPBJi87/Fc4JAQCLyE2LxkiNVwj/FZ8IAQCFwA+EhwAAAA+3RwKJRCQgTIvP\nQYPI/7oCAAAASIvO/xWKCQEAhcB0ZsZHAQTHRwR/Aw8AD7dHAolEJCBMi89Bg8j/ugIAAABIi87/\nFV4JAQCFwHQ6RTPJTIvGuwEAAACL00mLz/8VHAgBAIXAdCBNi8dIjVQkOEiLTCR4/xWVDAEARItk\nJDCFwEQPRePrBUSLZCQwSIX/dBT/FWgKAQBIi8hMi8cz0v8VYgoBAEiF9nQU/xVPCgEASIvITIvG\nM9L/FUkKAQBNhe10FP8VNgoBAEiLyE2LxTPS/xUwCgEATYX/dBT/FR0KAQBIi8hNi8cz0v8VFwoB\nAEGLxEiLjCSgAAAASDPM6BQyAABMjZwksAAAAEmLW0BJi3NISYvjQV9BXkFdQVxfw8zMzMzMzMxI\niVwkEEiJdCQYSIl8JCBVQVRBVUFWQVdIjawkkLH//7hwTwAA6MZEAABIK+BIiwV8+AEASDPESImF\nYE4AADPbM8BIg87/TIvxSI1NiEiJXCRgTIvmTIvuTIv+SImdIEoAAEiJhShKAABIiYUwSgAASIlc\nJFhIiVwkaEiJRCRwSIlEJHiL++hlFAAAhMAPhEEEAABIjVQkcEmLzugA8P//x0WgyAAAAIXAD5RF\nrP8VPQkBAEyNTCRQRI1DEEiNVbBJi86JRahIiVwkIP8VLwkBAIXAD4T7AwAATI1MJFBEjUMQSI1V\noEmLzkiJXCQg/xU8CQEAhcAPhNgDAACLRaA5RbAPhcwDAACAfbQBdEY4Xax0IjhdvHQdTI1EJGhI\njVQkWEiNTCRw6HTt//+EwHUf6Z8DAABMjUQkaEiNVCRYSYvO6FkEAACEwA+EhQMAAEiNfCRoSI1E\nJFBMjUXAQblcSgAASYvWSIvPSIlEJCDonfD//4XAdRn/FdsIAQBIjQ2EcgEAi9Do9ez//+lEAwAA\nSI2VUEwAAEG4BAEAADPJ/xXLCAEASI2NUEwAALpcAAAA6EY+AAC6LgAAAEiDwAJIi8hIiUWA6DE+\nAABmiRg4neBFAAAPhW8CAACLRcRMi32ATI1NyEiNFVRyAQBIjY1ASgAATYvHiUQkIOiNLgAATI2N\nQEoAAEUzwDPSM8n/FWUHAQCLTcRMjU3IiUwkIEiNFTNyAQBIjY1ASgAATYvHSIlEJGDoUy4AAEiN\nRYhBuQEAAABIiUQkOMdEJDD/////SI2NQEoAAEWNQQW6AQAIAMdEJCgAAAEAx0QkIAAAAQD/FeoG\nAQBFM8BBjVABSIvITIvo/xUHBwEAi03ETI1NyIlMJCBIjRXtcQEASI2NQEoAAE2Lx+jiLQAASI1F\niEG5AQAAAEiJRCQ4x0QkMP////9IjY1ASgAARY1BBboCAAgAx0QkKAAAAQDHRCQgAAABAP8VeQYB\nAEUzwEGNUAFIi8hMi+D/FZYGAQCLTcRMjU3IiUwkIEiNFbRxAQBIjY1ASgAATYvH6HEtAABIjUWI\nQbkBAAAASIlEJDjHRCQw/////0iNjUBKAABFjUEFugIACADHRCQoAAABAMdEJCAAAAEA/xUIBgEA\nRTPAQY1QAUiLyEyL+P8VJQYBAEG5BAAAAEyNRCRQSYvWSIvP6M/v//+FwHUMSI0NbHEBAOjv6v//\nM9JJi83/FYQGAQCFwHUR/xWyBgEAPRcCAAAPhSMBAAAz0kmLzP8VZAYBAIXAdRH/FZIGAQA9FwIA\nAA+FAwEAADPSSYvP/xVEBgEAhcB1Ef8VcgYBAD0XAgAAD4XjAAAASI2FIEoAAEiNVcBNi81IiUQk\nOEiLRCRgTYvGSIlEJDBIi89MiXwkKEyJZCQg6H0PAABJi8z/FWwFAQBJi8z/FUsFAQBJi8//FVoF\nAQBJi8//FTkFAQDrXkiJXCRISIlcJEBIiVwkOEiJXCQwSI1VwEUzyU2LxjPJSIlcJChIiVwkIOiI\nAgAASIvI/xWnBQEATI1EJFBBuQQAAABJi9ZIi8/oue7//4XAdQxIjQ1WcAEA6Nnp///wgwVBCwIA\n/0yNRcBBuVxKAABJi9ZIi88PlIUUSgAA6IXu//9Ji87/FcQEAQBIi0wkWOiy6f//SIX/dAhIi8/o\nter//0mD/f90CUmLzf8VLgUBAEmD/P90CUmLzP8VHwUBAEmD//90CUmLz/8VEAUBAEiLRCRgSIXA\ndAlIi8j/Ff0EAQBIjbUgSgAASIs+SIX/dBWDyv9Ii8//FfoEAQBIi8//FdkEAQD/w0iDxghIY8NI\ng/gDctRJi87/FRkEAQBJi87/FbgEAQBIi41gTgAASDPM6HEsAABMjZwkcE8AAEmLWzhJi3NASYt7\nSEmL40FfQV5BXUFcXcNIiVwkCEiJdCQQVVdBVkiL7EiD7FBIi9lFM/ZIi8pJi/BIi/pMiXXoTIl1\n8Og/6///hcAPhA4BAABIiw9IjUU4TI1N8EiJRCQoSI1F6EWNRgEz0kiJRCQg6NPp//+FwA+E4gAA\nAEyNTeBFjUYESI1VOEiLy0yJdCQg/xURBAEAhcAPhLYAAABEi0U4SItV6EyNTeBIi8tMiXQkIP8V\n7wMBAIXAD4SUAAAATI1N4EWNRgRIjVU4SIvLTIl0JCD/FZ0DAQCFwHR2i1U4SItN6OidQAAARItF\nOEyNTeBIi9BIi8tIiUXoTIl0JCD/FXADAQCFwHUVSItN8P8VIgEBAEiLTejofScAAOs+RItNOEyL\nRehIi1XwSIsPSIl0JCDorun//4XAdM5Ii03o6FUnAABIi03w/xXnAAEAsAHrDEiLTfD/FdkAAQAy\nwEiLXCRwSIt0JHhIg8RQQV5fXcPMzMzMQFVTVldBVEFVQVdIjawkoLb//7hgSgAA6NM9AABIK+BI\niwWJ8QEASDPESImFUEkAAEiLheBJAABMi63QSQAATIul2EkAAEiJRcAzwEUy/0yJjZAAAABJi9hI\ni/pIiUWIi/BIiUUYSIlEJGiJRZRIiUWYSIlF0EiJReBIiUXYSIlF8EiJRaBIiU2oTIllgMdFAC4A\nAABNhe10BEmJRQBNheR0BEmJBCQz0kiNjaAAAABMibQkoEoAAESNQmjo3y8AAEyNhxACAABMjY8Y\nBAAASI0VamoBAEiNjUAHAADHhaAAAABoAAAA6KAoAABAOLchRgAAdFr/FWUCAQBMjUUQugsAAABI\ni8j/FSMAAQCFwA+ETwsAAEiLTRBIjUQkYEUzyUiJRCQoRTPAugAAAALHRCQgAQAAAP8V9P4AAEiL\nTRD/FeIBAQBMi3UI6YYCAABAOLc0RgAAD4TrAgAASI0NBWoBAP8VHwIBAEiNFeBpAQBIi8j/FS8C\nAQBIiQWYBwIASIXAdA9IjZc2RgAATI1FlDPJ/9BIjY82RgAAulwAAADoyzYAAEyL8EiFwHQSM8lI\njZ82RgAASYPGAmaJCOsLTI23NkYAAEiNXQBIiVwkcDl1lHQPSI0FtWkBAEG8BQAAAOsuSI0V/mkB\nAEiLy+jSHwAAhcB1DjPSi8JIiVXoRI1iBesTSI2HPkgAAEG8AgAAADPSSIlF6EA4tyJGAAAPhPoB\nAABIOTXlBgIAD4TtAQAAi48wRgAA6AQNAABIiUXISIXAdCK5KAAAAOgVJQAASIvwxwABAAAASItF\nyEiJRgjHRhAFAADASI0VmmkBAEiLy+hOHwAAhcAPhdAAAAAzyUiNRZBMjUwkeEiJRCQwSIlMJChM\njUWISI2XNkYAAEiJTCQgiUwkeP8Vk/0AAP8VpQABAIP4eg+FkgAAAItcJHj/FRIAAQBEi8NIi8i6\nCAAAAP8VEQABAEyNTCR4TI1FiEiJRYhIjUWQSI2XNkYAAEiJRCQwM8AzyUiJRCQoSIlEJCD/FTj9\nAACFwHQ9SIX2dAL/BosWSIvOSMHiBEiDwhjo6zwAAEiL8IsISItFiP/JSAPJSIlEzgiLDv/JSP/B\nSAPJxwTOBQAAwEiLXCRwTItF6DPJSI1EJGBIiUwkUEiJTCRISIlMJEBIiUwkOEiJRCQwSIl0JCiJ\nTCQgRYvMSIvTSYvO/xWGBQIAhcB1JEiF9nQISIvO6IkjAABIi03I6AwQAABIi02I6AMQAADpsggA\nAEiF9nQISIvO6GUjAABIi02I6OgPAABIi03I6N8PAABMi2WAgL8kRgAAAA+EBQEAAEiNDc9jAQD/\nFZn/AABIjRUqaAEASIvI/xWp/wAASIkF2gQCAEiFwA+FsAAAAMeHTEoAADIAAADpUggAAEiNTCRg\nRYvMTIvASIlMJCiJVCQgSIvTSYvO/xXi/AAAhcB1j+kdCAAASIvL/xU4/AAAhcAPhAYIAAD/FZr+\nAABMjU0Iuv8BDwBIi8hBuAEAAAD/FQr8AACFwA+E4AcAAP8VtPwAAEiLTQhIjUQkYEiJRCQoQbkC\nAAAARTPAuv8BDwDHRCQgAQAAAP8VgvsAAIXAD4SuBwAATIt1COkW////SItMJGDoJ9v//0iL2EiF\nwA+EjwcAAEiLTCRg/xVI/gAASIlcJGDrdoC/JUYAAAB0aEiLTCRgQbkEAAAASI1F/EGNUQ5MjUW4\nSIlEJCD/FZ78AACFwHRCg324A3U8SItMJGBBuQgAAABIjUX8QY1RC0yNRbBIiUQkIP8VcvwAAIXA\ndBZIi0wkYP8V2/0AAEiLXbBIiVwkYOsFSItcJGCAvyNGAAAAD4TdAAAAM8BIiUUoSMdFKDgAAABI\niUUwSIlFOEiJRUBIiUVISIlFUEiJRVhmOYc2RgAAdW1IjVQkcEiLy+jqDAAASItUJHBIjUWQSIlE\nJDBIjUX4TI1NvEiJRCQoSI2FMAUAAEyNhRABAAAzycdFvAgCAADHRfgIAgAASIlEJCD/FV76AABI\ni0wkcEiNhRABAABIiUUw6LENAABIi1wkYOsETIl1MEiNVShIi8v/FQH/AABEi/iEwHQpSItUJGBI\njUwkaEUzwOjIGgAASItMJGi7AAAAAIXASA9Ey0iJTCRo6wIz24C/IkYAAAAPhIgBAADoheD//4XA\ndEDHh0xKAAAyAAAAx4dYSgAAZQAAAEWE/3QPSItVWEiLTCRg/xV5/gAASItMJGhIhckPhNsFAADo\nZBoAAOnRBQAASItMJGBIjbcwRgAASIvW6I0RAACFwHQIiYdMSgAA67RIgz1JAgIAAHQdgz4AdBiA\nvyZGAAAAdQ9Ii12wSIldgDPb6QkBAAD/FfT9AABIjQ1FZQEAM9JBuAAABgBIiUWA/xWz/QAATIvw\nSIXAD4S8AAAASIvI/xW+/QAAM9JFM8BBuYEABgBIjQ0cZQEAOJcmRgAAdQdIjQ0lZQEA/xWH/QAA\nSIvYSIXAD4SAAAAASItMJGBIjVQkcOg0CwAAhcB0YEiLVCRwSYvO6EPt//+FwA+E7wQAAEiLVCRw\nSIvL6L7p//+FwA+E2gQAAIC/JkYAAABIjQ0AZQEASI0F0WQBAEgPRMFIi0wkcEiJhbAAAAC4BQAA\nAGaJheAAAADo8AsAAEiLy/8V9/wAADPb6yb/FYX7AACJh0xKAADplf7//0iLRbBIjbcwRgAAiR5I\niUWATIt1GEiNDd5kAQD/FYj7AABIjRWxZAEASIvI/xWY+wAASIkF4QACAEiFwHQJSI2NmAAAAP/Q\nuQEAAAD/FYD6AACAvyBGAAAAD4W1AQAAgL8iRgAAAA+FqAEAAEyNRXhIjVXgSI1N2EUzycdFeBgA\nAADHhYgAAAABAAAASImdgAAAAP8VEfoAAEiLTdhFM8BBjVAB/xUI+gAATI1FeEiNVZhIjU3QRTPJ\n/xXr+QAASItNmEUzwEGNUAH/FeL5AABMjUV4SI1V8EiNTaBFM8n/FcX5AABIi02gRTPAQY1QAf8V\nvPkAAEiLReC5IAAAAEiJhfgAAABIi0XQx4XcAAAAAQEAAEiJhfAAAABIi0XwZomd4AAAAEiJhQAB\nAADoVB4AAEiLTahIi53oSQAASIkITI0Fmw0AAEiJWBhIi03YTIvISIlICEiLjcBJAAAz0kiJSBAz\nyUiJTCQoiUwkIOg/MQAASItNwEiJAbkgAAAA6AIeAABIi02oTI0FYwwAAEiJCDPJSIlYGEiLVZhI\niUwkKEyLyEiJUAhIi5WQAAAAiUwkIEiJUBAz0uj0MAAASItNwEiJQQi5IAAAAOi2HQAASItNqEiJ\nCEiJWBhIi02gSIlICEiLjchJAABMjQUBCwAASIlIEDPJTIvISIlMJCgz0olMJCDoqDAAAEiLTcBI\niUEQ6waJndwAAACAvyBGAAAAD5TDhNt0GoM+AHQV/xXf+AAAD7bbuQAAAAA8BQ9E2esCM8lIi8GL\njyhGAABIjbcYRAAAZoM+AEiNVWAPtttIiVQkUEgPRcaByRQEAABIjZWgAAAATI2FQAcAAEUzyUiJ\nVCRISIlEJEBIi0QkaEiJRCQ4iUwkMEiLTCRgM8Az0olcJChIiUQkIP8Vq/UAAIXAD4XDAAAATI2H\nEAIAAEiNFVViAQBIjY0gAwAA6N0eAAAzyUiNVWBIiVQkUIvBi48oRgAAZjkGSI2VoAAAAEyNhUAH\nAABIiVQkSEgPRcaByRQEAABIiUQkQEiLRCRoSI2VIAMAAEiJRCQ4iUwkMEiLTCRgiVwkKDPbRTPJ\nSIlcJCD/FSP1AACFwHVB/xU5+AAAiYdMSgAARYT/dA9Ii0wkYEiNVSj/Fcf5AABIi0wkaEiFyXQF\n6LYVAABIi0wkYP8V1fcAAOkYAQAAM9tIg32YAHQeSItN0P8VvfcAAEiLTeD/FbP3AABIi03w/xWp\n9wAATYX2dBNJi87/FUP5AABIi02A/xVB+QAAgb8oRgAAAAAQAHUJSItNYOjU2f//i4csRgAAhcB0\nDEiLTWCL0P8VDvcAAItFcEiLTWiJn0xKAACJh0hKAAD/Fc32AABIi01o/xVD9wAARYT/dEKAvyBG\nAAAAdBxIi1VYSItMJGD/Ff74AABIi0wkYP8VG/cAAOs6TYXkdAhIi0VYSYkEJE2F7XQoSItEJGBJ\niUUA6x1Ii0wkYP8V8fYAAE2F7XQESYldAE2F5HQESYkcJEiLTCRoSIXJdAXophQAAEiLRWDrFP8V\n1PQAAP8V7vYAAImHTEoAADPATIu0JKBKAABIi41QSQAASDPM6GceAABIgcRgSgAAQV9BXUFcX15b\nXcPMzMzMzEiJXCQISIlsJBhWV0FWSIPscEmL2UUzyUyL8kmL+EiL8UGNUQFFM8Azyf8VqPUAAEyL\nlCTIAAAASIuMJLgAAABIiUQkSEyJVCRASIvoSI1EJFBMi8tMi8dIiUQkOEiNhCSYAAAASYvWSIlE\nJDBIiUwkKEiLjCSwAAAASIlMJCBIi87o2fL//0GDvkxKAAAASIvYdVlIi4QkwAAAAEiFwHQwRTPA\nSI1UJFhBg8n/QY1IAkiJXCRYSIlEJGD/FT/1AACD+AF1CzPSSIvL/xVX9QAAg8r/SIvL/xXD9QAA\nSY2WUEoAAEiLy/8VM/UAAEiLzf8VsvUAAEiLzf8VifUAAEiLVCRQSIu8JJgAAABIhdJ0CUiLz/8V\nRvcAAEiF/3QJSIvP/xVg9QAASIXbdAlIi8v/FVL1AABMjVwkcEmLWyBJi2swSYvjQV5fXsPMzMzM\nzEiJXCQQSIl8JBhVSI1sJKlIgeywAAAASIsFouMBAEgzxEiJRU8z20iNRQdIi/lIiUQkUIlcJEiJ\nXCRAiVwkOIlcJDBEjUMgSI1NR0G5IAIAALICiVwkKEiJXQdIiV0PiVwkIIldR2bHRUsABf8VPvMA\nAIXAdQcywOmmAAAAM8BFM8BMjU0PSIlFF0iJRR9IiUUvSIlFN0iJRT9IiUUnSItFB0iNVRdBjUgB\nx0UXAAAAEEjHRRsCAAAASIldL0iJRT/HRTcCAAAA/xVq8QAAhcB1pI1IKOiaGAAASIvYSIXAdJS6\nAQAAAEiLyP8VgPEAAIXAdIJMi0UPRTPJSIvLQY1RAf8VYPEAAIXAD4Rm////xwcYAAAASIlfCMdH\nEAEAAACwAUiLTU9IM8zo0RsAAEyNnCSwAAAASYtbGEmLeyBJi+Ndw8zMzMzMzMzMzMzMzEBTSIPs\nIEiDPbr5AQAAi9lIx0QkQAAAAAB0W0iNDb5bAQDomdb//4P7/3UUSIsFjfkBAEiFwHQG/9CL2OsC\nM9tIjVQkOIvL/xV7+QEAhcB0JUiLTCQ4SI1UJEDokAEAAEiLTCQ4/xV98wAASItEJEBIg8QgW8Mz\nwEiDxCBbw8zMQFdIgeyQBAAASIsF4OEBAEgzxEiJhCSABAAASI1MJEBAMv/oCP7//4TAdRJIiw31\n+AEA/xVP8wAA6RYBAABIjVQkYEG4BAEAADPJSImcJKAEAAD/FVfzAABIjUwkYLpcAAAA6NQoAAC6\nLgAAAEiNSAJIi9jowygAADPJTI1DAmaJCEiNjCRwAgAASI0V110BAOg2GQAASI1EJEC6AwAAAEiN\njCRwAgAASIlEJDjHRCQwECcAAESNQgNBuf8AAADHRCQoAAABAMdEJCAAAAEA/xXL8QAARTPAQY1Q\nAUiLyEiL2P8V6PEAAECE/3UQM9JFM8CNSgTotg0AAEC3ATPSSIvL/xVY8gAAhcB1Df8VhvIAAD0X\nAgAAdR3w/wUQ+AEASI0NGej//0yLwzPS6PMvAADpYP///0iLDev3AQD/FUXyAABIi8v/FRzyAABI\ni5wkoAQAAEiLjCSABAAASDPM6MwZAABIgcSQBAAAX8PMzMxMi9xJiVsISYlzEEmJeyBBVEFWQVdI\ng+xATIviSIv5RTP2RYlzGEGL9k2Jc+BJjUMYSYlDyEUzyUUzwEGNVgL/FTvwAACFwHU6/xXZ8QAA\ng/h6D4XDAAAAi1wkcP8VRvEAAEiLyESLw0GNVgj/FUbxAABIi/BIiUQkOEiFwA+EmAAAAEiNRCRw\nSIlEJCBEi0wkcEyLxroCAAAASIvP/xXd7wAAhcB0dEGL/kSJdCQwkDs+c2eLx0jB4ARMjTwwQfdH\nEAAAAMB0S0mLTwj/FY7vAACJRCRwi9j/FcrwAABIi8hEi8O6CAAAAP8VyfAAAEmJBCRNi0cISIvQ\ni0wkcP8VPO4AAIXAdBNBvgEAAABEiXQkNP/HiXwkMOuVSIX2dBT/FYLwAABIi8hMi8Yz0v8VfPAA\nAEGLxkiLXCRgSIt0JGhIi3wkeEiDxEBBX0FeQVzDzMzMzMzMzMzMzMzMzMzMSIlcJAhIiXQkGEiJ\nfCQgQVRBVkFXSIPsQEyL+kyL8TP2iXQkaIv+SIl0JDBIiTJIjUQkaEiJRCQgRTPJRTPAQbwBAAAA\nQYvU/xXS7gAAhcB1Nf8VcPAAAIP4eg+FhwAAAItcJGj/Fd3vAABIi8hEi8ONVgj/Fd7vAABIi/hI\niUQkMEiFwHRhSI1EJGhIiUQkIESLTCRoTIvHQYvUSYvO/xV77gAAhcB0P0iLD/8VTu4AAIlEJGiL\n2P8Viu8AAEiLyESLw7oIAAAA/xWJ7wAASYkHTIsHSIvQi0wkaP8V/uwAAIXAQQ9F9EiF/3QU/xVV\n7wAASIvITIvHM9L/FU/vAACLxkiLXCRgSIt0JHBIi3wkeEiDxEBBX0FeQVzDzMzMSIXJdCFTSIPs\nIEiL2f8VFe8AAEyLw0iLyDPS/xUP7wAASIPEIFvDzMzMzMzMzMzMSIPsKEiNDc1TAQD/FZfvAABI\njRVIWgEASIvI/xWn7wAASI0N0FgBAEiJBQH1AQD/FXPvAABIjRU0WgEASIvI/xWD7wAASI0NXFoB\nAEiJBc30AQD/FU/vAABIjRUwWgEASIvI/xVf7wAASIkFuPQBAP8Vou4AAIPK/0iLyP8VVu4AAEUz\nyUUzwEGNUQEzyf8VBO4AAEiNDUX7//9FM8Az0kiJBWH0AQDoWCwAAEiLDVX0AQCDyv9Ig8QoSP8l\nn+4AAMzMzMzMzMzMzMzMzMzMzEiLDTH0AQBIhcl0B0j/JYXuAADDzMzMzMzMzMzMzMzMSIlcJBBX\nuGAAAgDoECkAAEgr4EiLBcbcAQBIM8RIiYQkUAACAEiL2UiLSRgz0v8VPO4AAD0CAQAAdXEz/w8f\nAEiLSwhMjUwkMEiNVCRAQbgAAAEASIl8JCD/FdHtAACFwHUN/xUf7gAAPegAAAB1PESLTCQwQYvB\nSAPASD0CAAIAc2hIi1MQSIsLTI1EJEBmiXwEQOjn1v//SItLGDPS/xXL7QAAPQIBAAB0lEiLSwj/\nFfrsAABIi0sI/xWY7QAASIvL6IwRAAAzwEiLjCRQAAIASDPM6EYVAABIi5wkeAACAEiBxGAAAgBf\nw+igKQAAzMzMzMzMzMzMzMzMzMzMzEBTVldIg+wwSIvZSItJGDPS/xVZ7QAAPQIBAAAPhZQAAABI\njT0XCAIAM/YPH0QAAEiLUxBIiwtIjUQkWEyNRCRQQbkBAAAASIlEJCDo79T//4XAdGGLFcnyAQAP\nt0QkUGaJBFf/wovCiRW28gEASAPASD0AAAIAc2NEi0QkWGaJNDhIi0sITI1MJGBIjVQkUEiJdCQg\n/xXF7AAAhcB0F0iLSxgz0v8VxewAAD0CAQAAD4R6////SItLCP8V8OsAAEiLSwj/FY7sAABIi8vo\nghAAADPASIPEMF9eW8PorygAAMzMzMzMzMzMzMzMzMzMzEiJXCQQV7hwAAMA6CAnAABIK+BIiwXW\n2gEASDPESImEJGAAAwBIi9lIi0kYM9L/FUzsAAA9AgEAAA+F8gAAADP/SItLCEyNTCQwSI1UJEBB\nuAAAAQBIiXwkIP8V4OsAAIXAdRH/FS7sAAA96AAAAA+FvAAAAItEJDBIPQEAAQAPg+sAAABAiHwE\nQEiNhCRQAAEATI1EJEBBg8n/M9IzycdEJCgBAAEASIlEJCD/FZbrAABmOT2XBgIAdElIjYQkUAAB\nAEmDyP9mDx+EAAAAAABJ/8BmQjk8QHX2SI2UJFAAAQBIjQ1nBgIATQPA6O8sAACFwHUPZok9VAYC\nAIk9MvEBAOsWRItMJDBIi1MQSIsLTI1EJEDodtT//0iLSxgz0v8VWusAAD0CAQAAD4QQ////SItL\nCP8VheoAAEiLSwj/FSPrAABIi8voFw8AADPASIuMJGAAAwBIM8zo0RIAAEiLnCSIAAMASIHEcAAD\nAF/D6CsnAADMzMzMzMzMzMzMzEiJXCQIV0iD7CBIi/lIjQ3MUgEASIva6KTN//+DO/91HscDAAAA\nAEiLBZLwAQBIhcB0Bv/QiQPrBscDAAAAAEG5BAAAAEyLw0iLz0GNUQj/FRPpAACFwHURSItcJDBI\ng8QgX0j/Ja7qAAAzwEiLXCQwSIPEIF/DzMzMzMzMzMzMQFdIgexwAgAASIsF4NgBAEgzxEiJhCRg\nAgAAgz1C8AEAAEiL+Q+FowAAAEiJnCSAAgAA/xVf6gAASI0VqFUBADPJiQUY8AEA/xUK5wAARIsN\nC/ABAEyNBYxVAQBIjRU9VwEASI1MJGBIi9joXBAAAEiNRCRgSIl8JFhIiUQkUEiF23RCM8lBuQIA\nAABIjUQkUEiJTCRASIlEJDiJTCQwZkSJTCQoQY1R/0iJTCQgSIvLRTPARTPJ/xWT5gAASIvL/xWa\n5gAASIucJIACAABIi4wkYAIAAEgzzOhSEQAASIHEcAIAAF/DzMzMzMzMzMzMQFNIg+wg/xW86QAA\nSI1UJEBIi8j/Fd7qAABIjRWvVgEASI0NmFgBAEiL2OgADwAASI0NmQMAALoBAAAA/xVm6AAAi0wk\nQEiL0+ji+f//SIPEIFvDzMzMzMzMzMzMzMzMSIHsiAQAAEiLBZLXAQBIM8RIiYQkcAQAAEiNVCRw\nQbgAAgAAM8n/FTTpAACFwHU/SI0NiQECALoAAQAA6G8DAABIjRUoVgEASI0NQVYBAEyLwOh5DgAA\nSIuMJHAEAABIM8zoeRAAAEiBxIgEAADDM9IzyUG4PwAPAEiJnCSQBAAA/xVh5gAASIvYSIXAD4TC\nAAAAM8lIjQUsVgEATI0FzVUBAEiJTCRgSIlMJFhIiUQkUEiJTCRISIlMJEBIjUQkcEiJRCQ4x0Qk\nMAEAAABIjRXDUwEAQbn/AQ8ASIvLx0QkKAMAAADHRCQgEAAAAEiJvCSABAAA/xX85QAASIv4SIXA\ndB5IjRVlVQEASI0NvlUBAOi5DQAASIvP/xXo5QAA6yBIjQ2XAAIAugABAADofQIAAEiNDbZVAQBI\ni9Dojg0AAEiLy/8VveUAAEiLvCSABAAA6yBIjQ1kAAIAugABAADoSgIAAEiNDbtVAQBIi9DoWw0A\nAEiLnCSQBAAASIuMJHAEAABIM8zoUw8AAEiBxIgEAADDzMzMzMzMzMzMzMxAV0iD7CAz0jPJQbg/\nAA8A/xUy5QAASIv4SIXAD4SHAQAASI0Vz1IBAEG4/wEPAEiLyEiJXCQw/xUD5QAASIvYSIXAD4Qs\nAQAATI0F8AECALoBAAAASIvI/xUC5QAAhcAPhJUAAABIjRVjVAEASI0NTFUBAOi3DAAAuegDAAD/\nFQTnAABIjRW1AQIASIvL/xWk5AAAhcB0Pg8fhAAAAAAAiwWeAQIAg/gDdTFIjQ1ySwEA6HkMAAC5\n6AMAAP8VxuYAAEiNFXcBAgBIi8v/FWbkAACFwHXKiwVoAQIASI0V7VMBAEiNDfZUAQCD+AF0B0iN\nDQpVAQDoNQwAAEiLy/8VTOQAAIXAdDBIjRXBUwEASI0NGlUBAOgVDAAASIvL/xVE5AAASIvPSItc\nJDBIg8QgX0j/JTDkAABIjQ3h/gEAugABAADoxwAAAEiNDQBVAQBIi9Do2AsAAEiLy/8VB+QAAEiL\nz0iLXCQwSIPEIF9I/yXz4wAASI0NpP4BALoAAQAA6IoAAABIjQ37VAEASIvQ6JsLAABIi89Ii1wk\nMEiDxCBfSP8lv+MAAEiNDXD+AQC6AAEAAOhWAAAASI0Nx1MBAEiL0EiDxCBf6WILAADMzEiD7CiD\n+QF3IkiNFfBSAQBIjQ35VAEA6EQLAADoD/f//7gBAAAASIPEKMMzwEiDxCjDzMzMzMzMzMzMzMzM\nzMxIiVwkEEiJdCQYV0iD7EAz9ov6SIvZSIl0JFD/FXflAABIiXQkMESLwEiNRCRQRTPJM9K5ADEA\nAIl0JChIiUQkIP8VYeQAAIXAdD6DwA47+Hw3SItMJFD/FVPkAABIY8hIi0QkUGaJdEj8/xUo5QAA\nTItEJFBIjRV0VAEARIvISIvL6EULAADrA2aJM0iLTCRQSIXJdAb/FUTlAABIi3QkYEiLw0iLXCRY\nSIPEQF/DzEiD7CiDPanqAQAAD4WCAAAAM8CD+QJIiVwkIA+VwIkNYP8BAIkVYv8BAESJBWf/AQCJ\nBVH/AQCD+QR0G4P5AXQWiwXx0gEAiQVH/wEA/8CJBePSAQDrCscFM/8BAAAAAABIiw34/gEASI0V\nEf8BAP8VG+EAAIvYhcB1DkiNDUZRAQDoyfn//4vDSItcJCBIg8Qow7gBAAAASIPEKMPMzMxIiVwk\nCFdIg+xASI0Fh08BAEiL2ov5SIlEJCBIjQU+AQAASIlEJCgzwEiJRCQwSIlEJDiD+QF+YkiLUggP\ntgIsLaj9dVVIjQ1kTwEASP/C6EAmAACFwHUK6KP6///pxQAAAEiLUwhIjQ1LTwEASP/C6B8mAACF\nwHUK6CL8///ppAAAAEiLUwhIjQ0yTwEASP/C6P4lAACFwHR5SI0VJ08BAEiNDTBPAQDodwgAAEiN\nFRRPAQBIjQ1NTwEA6GQIAABIjRUBTwEASI0Nak8BAOhRCAAASI0Nnk8BAOhFCAAASI0Nwk8BAOg5\nCAAASI1MJCD/FfLfAACFwHUMSI0N308BAOiq+P//M8BIi1wkUEiDxEBfw0iL04vPxwXy6AEAAQAA\nAOh5+f//M8no2gYAAMzMSIPsKEUzwDPS/8l0D4sNof0BAEiDxCjpFP7//7kDAAAA6Ar+//9Ig8Qo\n6UH0///MSIlcJAhXSIPsIEiL2ov5SI0Vuv///0iNDRtOAQD/FXXfAABIiQU2/QEASIXAdE4z0kG4\nuAsAAMcFP/0BABAAAACNSgLHBUL9AQAAAAAA6K39//+FwHQKSIvTi8/oD/P//0iDPff8AQAAdBKL\nFT/oAQBFM8BBjUgB6IP9//9Ii1wkMEiDxCBfw/8lCuQAAP8lDOQAAP8lTuAAAP8lQOAAAP8lMuAA\nAP8lJOAAAEBTSIPsIEiL2cZBGABIhdIPhYIAAADonTcAAEiJQxBIi5DAAAAASIkTSIuIuAAAAEiJ\nSwhIOxWp3QEAdBaLgMgAAACFBQPfAQB1COh8KwAASIkDSIsFOtkBAEg5Qwh0G0iLQxCLiMgAAACF\nDdzeAQB1CehNLwAASIlDCEiLSxCLgcgAAACoAnUWg8gCiYHIAAAAxkMYAesHDxAC8w9/AUiLw0iD\nxCBbw0iD7ChFM8BMi8pMi9FEOQWM7QEAdWVIhcl1GuhwOQAAxwAWAAAA6NUnAAC4////f0iDxCjD\nSIXSdOFMK9JDD7cUCo1Cv2aD+Bl3BGaDwiBBD7cJjUG/ZoP4GXcEZoPBIEmDwQJmhdJ0BWY70XTP\nD7fJD7fCK8FIg8Qow0iDxCjpAAAAAEiLxEiJWAhIiWgQSIlwGFdIg+xASIvxSIv6SI1I2EmL0Oiu\n/v//M+1IhfZ0BUiF/3UX6NU4AADHABYAAADoOicAALj///9/63xIi0QkIEg5qDgBAAB1NEgr9w+3\nHD6NQ79mg/gZdwRmg8MgD7cPjUG/ZoP4GXcEZoPBIEiDxwJmhdt0OWY72XTR6zIPtw5IjVQkIOiw\nMwAAD7cPSI1UJCAPt9hIjXYC6JwzAABIjX8CD7fIZoXbdAVmO9h0zg+3yQ+3wyvBQDhsJDh0DEiL\nTCQwg6HIAAAA/UiLXCRQSItsJFhIi3QkYEiDxEBfw8zMzEBTSIPsIIvZTI1EJDhIjRV8TwEAM8n/\nFeTgAACFwHQbSItMJDhIjRV8TwEA/xU+4AAASIXAdASLy//QSIPEIFvDzMzMQFNIg+wgi9nor///\n/4vL/xWf4AAAzMzMQFNIg+wgi9no+0AAAIvL6GhBAABFM8C5/wAAAEGNUAHoxwEAAMzMzLoBAAAA\nM8lEi8LptQEAAMwz0jPJRI1CAemnAQAAzMzMQFNIg+wgSIM9ZqMBAACL2XQYSI0NW6MBAOjWQwAA\nhcB0CIvL/xVKowEA6C1HAABIjRVW4QAASI0NH+EAAOgOAQAAhcB1SkiNDRs4AADockUAAEiNFfvg\nAABIjQ3s4AAA6IsAAABIgz0nCwQAAHQfSI0NHgsEAOh5QwAAhcB0D0UzwDPJQY1QAv8VBgsEADPA\nSIPEIFvDzMxFM8BBjVAB6QABAABAU0iD7CAzyf8Vkt8AAEiLyEiL2OhPRwAASIvL6LMkAABIi8vo\nR0cAAEiLy+hXRwAASIvL6NtGAABIi8vom0kAAEiDxCBb6YU7AADMSIlcJAhIiWwkEEiJdCQYV0iD\n7CAz7UiL2kiL+Ugr2Yv1SIPDB0jB6wNIO8pID0fdSIXbdBZIiwdIhcB0Av/QSP/GSIPHCEg783Lq\nSItcJDBIi2wkOEiLdCRASIPEIF/DSIlcJAhXSIPsIDPASIv6SIvZSDvKcxeFwHUTSIsLSIXJdAL/\n0UiDwwhIO99y6UiLXCQwSIPEIF/DzMzMuQgAAADpCjcAAMzMuQgAAADp7jgAAMzMSIlcJAhIiXQk\nEESJRCQYV0FUQVVBVkFXSIPsQEWL8IvaRIvpuQgAAADozjYAAJCDPXLjAQABD4QHAQAAxwWi4wEA\nAQAAAESINZfjAQCF2w+F2gAAAEiLDaQJBAD/FUbeAABIi/BIiUQkMEiFwA+EqQAAAEiLDX4JBAD/\nFSjeAABIi/hIiUQkIEyL5kiJdCQoTIv4SIlEJDhIg+8ISIl8JCBIO/5ydjPJ/xXy3QAASDkHdQLr\n40g7/nJiSIsP/xXl3QAASIvYM8n/FdLdAABIiQf/00iLDSYJBAD/FcjdAABIi9hIiw0OCQQA/xW4\n3QAATDvjdQVMO/h0uUyL40iJXCQoSIvzSIlcJDBMi/hIiUQkOEiL+EiJRCQg65dIjRX13gAASI0N\nxt4AAOgd/v//SI0V8t4AAEiNDePeAADoCv7//5BFhfZ0D7kIAAAA6Jo3AABFhfZ1JscFR+IBAAEA\nAAC5CAAAAOiBNwAAQYvN6EX8//9Bi83/FTTdAADMSItcJHBIi3QkeEiDxEBBX0FeQV1BXF/DzMzM\nRTPAM9LpXv7//8zMSIXJdDdTSIPsIEyLwUiLDQDxAQAz0v8VoNsAAIXAdRfoCzQAAEiL2P8VBtwA\nAIvI6Bs0AACJA0iDxCBbw8zMzEiJXCQISIl0JBBXSIPsIEiL2UiD+eB3fL8BAAAASIXJSA9F+UiL\nDanwAQBIhcl1IOgDPQAAuR4AAADobT0AALn/AAAA6M/7//9Iiw2E8AEATIvHM9L/FSnbAABIi/BI\nhcB1LDkFc/ABAHQOSIvL6N1DAACFwHQN66vocjMAAMcADAAAAOhnMwAAxwAMAAAASIvG6xLot0MA\nAOhSMwAAxwAMAAAAM8BIi1wkMEiLdCQ4SIPEIF/DzMxIiw2NyQEAM8BIg8kBSDkNUOEBAA+UwMNI\ni8RIiUgISIlQEEyJQBhMiUggU1dIg+woM8BIhckPlcCFwHUV6PYyAADHABYAAADoWyEAAIPI/+tq\nSI18JEjoHAsAAEiNUDC5AQAAAOh+CwAAkOgICwAASI1IMOgHRgAAi9jo+AoAAEiNSDBMi89FM8BI\ni1QkQOjcRgAAi/jo3QoAAEiNUDCLy+iiRQAAkOjMCgAASI1QMLkBAAAA6LILAACLx0iDxChfW8PM\nSIvESIlICEiJUBBMiUAYTIlIIFNXSIPsKDPASIXJD5XAhcB1FehKMgAAxwAWAAAA6K8gAACDyP/r\nakiNfCRI6HAKAABIjVAwuQEAAADo0goAAJDoXAoAAEiNSDDoW0UAAIvY6EwKAABIjUgwTIvPRTPA\nSItUJEDocFEAAIv46DEKAABIjVAwi8vo9kQAAJDoIAoAAEiNUDC5AQAAAOgGCwAAi8dIg8QoX1vD\nzEiJVCQQTIlEJBhMiUwkIFVTV0iL7EiD7FBIg2XQAEiL+jPSSIvZSI1N2ESNQijo4QYAAEiF/3UV\n6IsxAADHABYAAADo8B8AAIPI/+tvSIXbdOZMjU0wSI1N0EUzwEiL18dF6EIAAABIiV3gSIld0MdF\n2P///3/oxVAAAP9N2IvYeBRIi03QxgEASItN0Ej/wUiJTdDrD0iNVdAzyehfXAAASItN0P9N2HgF\nxgEA6wtIjVXQM8noRlwAAIvDSIPEUF9bXcNIg+woTYtBOEiLykmL0egNAAAAuAEAAABIg8Qow8zM\nzEBTSIPsIEWLGEiL2kyLyUGD4/hB9gAETIvRdBNBi0AITWNQBPfYTAPRSGPITCPRSWPDSosUEEiL\nQxCLSAhIA0sI9kEDD3QMD7ZBA4Pg8EiYTAPITDPKSYvJSIPEIFvpHQAAAMzMzMzMzMzMzMzMzMzM\nzMzMzMxmZg8fhAAAAAAASDsNucYBAHURSMHBEGb3wf//dQLzw0jByRDpfRMAAMzMzMzMzMxmZg8f\nhAAAAAAATIvZTIvSSYP4EA+GuQAAAEgr0XMPSYvCSQPASDvID4yWAwAAD7ol+OwBAAFzE1dWSIv5\nSYvySYvI86ReX0mLw8MPuiXb7AEAAg+CVgIAAPbBB3Q29sEBdAuKBApJ/8iIAUj/wfbBAnQPZosE\nCkmD6AJmiQFIg8EC9sEEdA2LBApJg+gEiQFIg8EETYvIScHpBQ+F2QEAAE2LyEnB6QN0FEiLBApI\niQFIg8EISf/JdfBJg+AHTYXAdQdJi8PDDx8ASI0UCkyL0esDTYvTTI0N/aP//0OLhIEQXAAASQPB\n/+BUXAAAWFwAAGNcAABvXAAAhFwAAI1cAACfXAAAslwAAM5cAADYXAAA61wAAP9cAAAcXQAALV0A\nAEddAABiXQAAhl0AAEmLw8NID7YCQYgCSYvDw0gPtwJmQYkCSYvDw0gPtgJID7dKAUGIAmZBiUoB\nSYvDw4sCQYkCSYvDw0gPtgKLSgFBiAJBiUoBSYvDw0gPtwKLSgJmQYkCQYlKAkmLw8NID7YCSA+3\nSgGLUgNBiAJmQYlKAUGJUgNJi8PDSIsCSYkCSYvDw0gPtgJIi0oBQYgCSYlKAUmLw8NID7cCSItK\nAmZBiQJJiUoCSYvDw0gPtgJID7dKAUiLUgNBiAJmQYlKAUmJUgNJi8PDiwJIi0oEQYkCSYlKBEmL\nw8NID7YCi0oBSItSBUGIAkGJSgFJiVIFSYvDw0gPtwKLSgJIi1IGZkGJAkGJSgJJiVIGSYvDw0wP\ntgJID7dCAYtKA0iLUgdFiAJmQYlCAUGJSgNJiVIHSYvDw/MPbwLzQQ9/AkmLw8NmZmZmZg8fhAAA\nAAAASIsECkyLVAoISIPBIEiJQeBMiVHoSItECvBMi1QK+En/yUiJQfBMiVH4ddRJg+Af6fL9//9J\ng/ggD4bhAAAA9sEPdQ4PEAQKSIPBEEmD6BDrHQ8QDApIg8EggOHwDxBECvBBDxELSIvBSSvDTCvA\nTYvIScHpB3RmDylB8OsKZpAPKUHgDylJ8A8QBAoPEEwKEEiBwYAAAAAPKUGADylJkA8QRAqgDxBM\nCrBJ/8kPKUGgDylJsA8QRArADxBMCtAPKUHADylJ0A8QRArgDxBMCvB1rQ8pQeBJg+B/DyjBTYvI\nScHpBHQaZg8fhAAAAAAADylB8A8QBApIg8EQSf/Jde9Jg+APdA1JjQQIDxBMAvAPEUjwDylB8EmL\nw8MPH0AAQQ8QAkmNTAjwDxAMCkEPEQMPEQlJi8PDDx+EAAAAAABmZmaQZmZmkGaQD7olYukBAAIP\ngrkAAABJA8j2wQd0NvbBAXQLSP/JigQKSf/IiAH2wQJ0D0iD6QJmiwQKSYPoAmaJAfbBBHQNSIPp\nBIsECkmD6ASJAU2LyEnB6QV1QU2LyEnB6QN0FEiD6QhIiwQKSf/JSIkBdfBJg+AHTYXAdQ9Ji8PD\nZmZmDx+EAAAAAABJK8hMi9FIjRQK6X38//+QSItECvhMi1QK8EiD6SBIiUEYTIlREEiLRAoITIsU\nCkn/yUiJQQhMiRF11UmD4B/rjkmD+CAPhgX///9JA8j2wQ91DkiD6RAPEAQKSYPoEOsbSIPpEA8Q\nDApIi8GA4fAPEAQKDxEITIvBTSvDTYvIScHpB3RoDykB6w1mDx9EAAAPKUEQDykJDxBECvAPEEwK\n4EiB6YAAAAAPKUFwDylJYA8QRApQDxBMCkBJ/8kPKUFQDylJQA8QRAowDxBMCiAPKUEwDylJIA8Q\nRAoQDxAMCnWuDylBEEmD4H8PKMFNi8hJwekEdBpmZg8fhAAAAAAADykBSIPpEA8QBApJ/8l18EmD\n4A90CEEPEApBDxELDykBSYvDw8zMzMzMzMzMzMzMzMzMzMzMZmYPH4QAAAAAAEyL2Q+20kmD+BAP\nglwBAAAPuiWM5wEAAXMOV0iL+YvCSYvI86pf621JuQEBAQEBAQEBSQ+v0Q+6JWbnAQACD4KcAAAA\nSYP4QHIeSPfZg+EHdAZMK8FJiRNJA8tNi8hJg+A/ScHpBnU/TYvISYPgB0nB6QN0EWZmZpCQSIkR\nSIPBCEn/yXX0TYXAdAqIEUj/wUn/yHX2SYvDww8fgAAAAABmZmaQZmaQSIkRSIlRCEiJURBIg8FA\nSIlR2EiJUeBJ/8lIiVHoSIlR8EiJUfh12OuXZmZmZmZmZg8fhAAAAAAAZkgPbsJmD2DA9sEPdBYP\nEQFIi8FIg+APSIPBEEgryE6NRADwTYvIScHpB3Qy6wGQDykBDylBEEiBwYAAAAAPKUGgDylBsEn/\nyQ8pQcAPKUHQDylB4A8pQfB11UmD4H9Ni8hJwekEdBQPH4QAAAAAAA8pAUiDwRBJ/8l19EmD4A90\nBkEPEUQI8EmLw8NJuQEBAQEBAQEBSQ+v0UyNDc+d//9Di4SBRWIAAEwDyEkDyEmLw0H/4Z5iAACb\nYgAArGIAAJdiAADAYgAAtWIAAKliAACUYgAA1WIAAM1iAADEYgAAn2IAALxiAACxYgAApWIAAJBi\nAABmZmYPH4QAAAAAAEiJUfGJUflmiVH9iFH/w0iJUfXr8kiJUfKJUfpmiVH+w0iJUfOJUfuIUf/D\nSIlR9IlR/MNIiVH2ZolR/sNIiVH3iFH/w0iJUfjDzMxIiVwkCFdIg+wgiwV07AMAM9u/FAAAAIXA\ndQe4AAIAAOsFO8cPTMdIY8i6CAAAAIkFT+wDAOiyNgAASIkFO+wDAEiFwHUkjVAISIvPiT0y7AMA\n6JU2AABIiQUe7AMASIXAdQe4GgAAAOsjSI0Ng74BAEiJDANIg8EwSI1bCEj/z3QJSIsF8+sDAOvm\nM8BIi1wkMEiDxCBfw0iD7CjoAwIAAIA98NUBAAB0BehhVgAASIsNxusDAOid8///SIMluesDAABI\ng8Qow0iNBSW+AQDDQFNIg+wgSIvZSI0NFL4BAEg72XJASI0FmMEBAEg72Hc0SIvTSLirqqqqqqqq\nKkgr0Uj36kjB+gNIi8pIwek/SAPKg8EQ6JYoAAAPumsYD0iDxCBbw0iNSzBIg8QgW0j/JU/QAADM\nzMxAU0iD7CBIi9qD+RR9E4PBEOhiKAAAD7prGA9Ig8QgW8NIjUowSIPEIFtI/yUb0AAAzMzMSI0V\ngb0BAEg7ynI3SI0FBcEBAEg7yHcrD7pxGA9IK8pIuKuqqqqqqqoqSPfpSMH6A0iLykjB6T9IA8qD\nwRDp8SkAAEiDwTBI/yXSzwAAzMyD+RR9DQ+6chgPg8EQ6dIpAABIjUowSP8ls88AAMzMzEBTSIPs\nIEiL2UiFyXUKSIPEIFvpvAAAAOgvAAAAhcB0BYPI/+sg90MYAEAAAHQVSIvL6IUBAACLyOieVQAA\n99gbwOsCM8BIg8QgW8NIiVwkCEiJdCQQV0iD7CCLQRgz9kiL2SQDPAJ1P/dBGAgBAAB0Nos5K3kQ\nhf9+Leg8AQAASItTEESLx4vI6CZWAAA7x3UPi0MYhMB5D4Pg/YlDGOsHg0sYIIPO/0iLSxCDYwgA\ni8ZIi3QkOEiJC0iLXCQwSIPEIF/DzMzMuQEAAADpAgAAAMzMSIlcJAhIiXQkEEiJfCQYQVVBVkFX\nSIPsMESL8TP2M/+NTgHo2CYAAJAz20GDzf+JXCQgOx2b6QMAfX5MY/tIiwWH6QMASosU+EiF0nRk\n9kIYg3Rei8voLf7//5BIiwVp6QMASosM+PZBGIN0M0GD/gF1Eui0/v//QTvFdCP/xol0JCTrG0WF\n9nUW9kEYAnQQ6Jf+//9BO8VBD0T9iXwkKEiLFSXpAwBKixT6i8voWv7////D6Xb///+5AQAAAOgt\nKAAAQYP+AQ9E/ovHSItcJFBIi3QkWEiLfCRgSIPEMEFfQV5BXcPMzEiD7ChIhcl1FejiJAAAxwAW\nAAAA6EcTAACDyP/rA4tBHEiDxCjDzMxIiVwkCEiJdCQQSIl8JBhBV0iD7CBIY8FIi/BIwf4FTI09\nnuEBAIPgH0hr2FhJizz3g3w7DAB1NLkKAAAA6K4lAACQg3w7DAB1GEiNSxBIA89FM8C6oA8AAOju\nKAAA/0Q7DLkKAAAA6HQnAABJiwz3SIPBEEgDy/8VR80AALgBAAAASItcJDBIi3QkOEiLfCRASIPE\nIEFfw0iJXCQISIl8JBBBVkiD7CCFyXhvOw3W5gMAc2dIY8FMjTUG4QEASIv4g+AfSMH/BUhr2FhJ\niwT+9kQYCAF0REiDPBj/dD2DPS/aAQABdSeFyXQW/8l0C//JdRu59P///+sMufX////rBbn2////\nM9L/FcbMAABJiwT+SIMMA/8zwOsW6KgjAADHAAkAAADoLSMAAIMgAIPI/0iLXCQwSIt8JDhIg8Qg\nQV7DzMxIg+wog/n+dRXoBiMAAIMgAOhuIwAAxwAJAAAA602FyXgxOw0c5gMAcylIY8lMjQVM4AEA\nSIvBg+EfSMH4BUhr0VhJiwTA9kQQCAF0BkiLBBDrHOi8IgAAgyAA6CQjAADHAAkAAADoiREAAEiD\nyP9Ig8Qow0hj0UyNBQLgAQBIi8KD4h9IwfgFSGvKWEmLBMBIg8EQSAPISP8l6ssAAMzMSIPsGGYP\nbxQkD7fCTIvBZg9uwEUzyfIPcMgAZg9w2QBJi8Al/w8AAEg98A8AAHcr80EPbwhmD2/CZg/vwmYP\nb9BmD3XRZg91y2YP69FmD9fChcB1GEmDwBDrxWZBORB0I2ZFOQh0GUmDwALrsw+8yEwDwWZBORBN\nD0TISYvB6wczwOsDSYvASIPEGMODPTXKAQACRA+3ykyLwX0tSIvRM8lBD7cASYPAAmaFwHXzSYPo\nAkw7wnQGZkU5CHXxZkU5CEkPRMhIi8HDM8mL0esSZkU5CEkPRNBmQTkIdFpJg8ACQY1AAagOdeZm\nQTvJdSS4AQD//2YPbsjrBEmDwBDzQQ9vAGYPOmPIFXXvSGPBSY0EQMNBD7fBZg9uyPNBD28AZg86\nY8hBcwdIY8FJjRRAdAZJg8AQ6+RIi8LDzEiJXCQIV0iD7CCDz/9Ii9lIhcl1FOiOIQAAxwAWAAAA\n6PMPAAALx+tG9kEYg3Q66BT7//9Ii8uL+Oj6XgAASIvL6HL8//+LyOhrXQAAhcB5BYPP/+sTSItL\nKEiFyXQK6BTt//9Ig2MoAINjGACLx0iLXCQwSIPEIF/DzMxIiVwkEEiJTCQIV0iD7CBIi9mDz/8z\nwEiFyQ+VwIXAdRToBiEAAMcAFgAAAOhrDwAAi8frJvZBGEB0BoNhGADr8Ogu+f//kEiLy+g1////\ni/hIi8vot/n//+vWSItcJDhIg8QgX8PMzEiJXCQISIlsJBBIiXQkIFdBVkFXSIPsMDPbSYvpSYvw\nRIvyTIv5TYXAdRXolCAAAMcAFgAAAOj5DgAA6ZIAAAC6eAQAALkBAAAA6NkuAABIi/hIhcB0aOjo\nHQAASIvPSIuQwAAAAOiBHgAASItMJHhIg08I/4tEJHBIhclMjUQkYEwPRcFJi9ZMi89MiUQkKEyN\nBRUBAABJi89IibeQAAAASImvmAAAAIlEJCD/FSbJAABIhcB1Hf8VC8gAAIvYSIvP6M3r//+F23QH\ni8vooh8AADPASItcJFBIi2wkWEiLdCRoSIPEMEFfQV5fw8zMzEiD7CjoSx0AAJBIi4iYAAAA/5CQ\nAAAAi8joDgAAAJCLyOjO6P//kEiDxCjDSIlcJAhXSIPsIIv56DsdAABIi9hIhcB0boO4aAQAAAB0\nXYsVyM0BAIXSdURIjQ1pOgEAM9JBuAAIAAD/FSvHAABIjRVsOgEASIvI/xWrxwAASIXAdClIi8j/\nFRXIAABIiQWOzQEAxwWAzQEAAQAAAEiLDX3NAQD/Ff/HAAD/0EiLy+hlHAAAi8//FT3IAADMSIlc\nJAhIiXQkEFdIg+wgSIvx6AkbAACLyOhGIwAAM9tIi/hIhcB1L+jzGgAASIvWi8joSSMAAIXAdQ//\nFdvGAACLyP8V88cAAMz/FeTHAABIi/6JBussSIuGkAAAAEiLzkiJh5AAAABIi4aYAAAASImHmAAA\nAEiLRghIiUcI6KUaAADoQCMAAImHaAQAAIXAdGuLBcTMAQC+AQAAAIXAdUBIjQ1kOQEAM9JBuAAI\nAAD/FSbGAABIjRU/OQEASIvI/xWmxgAASIXAdCxIi8j/FRDHAABIiQV5zAEAiTV7zAEASIsNbMwB\nAP8V/sYAAIvO/9CFwA+Uw4mfaAQAAOhC/v//zMxIi8RIiVgISIloEEiJcBhIiXggQVZIg+wgTYtR\nOEiL8k2L8EGLGkiL6UmL0UjB4wRIi85Ji/lJA9pMjUME6O7s//9Ei1sERItVBEGLw0GD4wK6AQAA\nACPCQYDiZkQPRNhFhdt0E0yLz02LxkiL1kiLzej2BAAAi9BIi1wkMEiLbCQ4SIt0JEBIi3wkSIvC\nSIPEIEFew8zMzMzMzMzMzMzMzMzMzMzMZmYPH4QAAAAAAEiD7BBMiRQkTIlcJAhNM9tMjVQkGEwr\n0E0PQtNlTIscJRAAAABNO9NzFmZBgeIA8E2NmwDw//9BxgMATTvTdfBMixQkTItcJAhIg8QQw8zM\nQFNIg+wgSIvZ/xUxxgAAuQEAAACJBcbQAQDoAV0AAEiLy+gVJgAAgz2y0AEAAHUKuQEAAADo5lwA\nALkJBADASIPEIFvp0yUAAMzMzEiJTCQISIPsOLkXAAAA6C23AACFwHQHuQIAAADNKUiNDZ/LAQDo\nPiAAAEiLRCQ4SIkFhswBAEiNRCQ4SIPACEiJBRbMAQBIiwVvzAEASIkF4MoBAEiLRCRASIkF5MsB\nAMcFusoBAAkEAMDHBbTKAQABAAAAxwW+ygEAAQAAALgIAAAASGvAAEiNDbbKAQBIxwQBAgAAALgI\nAAAASGvAAEiLDX6yAQBIiUwEILgIAAAASGvAAUiLDXGyAQBIiUwEIEiNDR03AQDo6P7//0iDxDjD\nzMzMSIPsKLkIAAAA6AYAAABIg8Qow8yJTCQISIPsKLkXAAAA6Ea2AACFwHQIi0QkMIvIzSlIjQ23\nygEA6OYeAABIi0QkKEiJBZ7LAQBIjUQkKEiDwAhIiQUuywEASIsFh8sBAEiJBfjJAQDHBd7JAQAJ\nBADAxwXYyQEAAQAAAMcF4skBAAEAAAC4CAAAAEhrwABIjQ3ayQEAi1QkMEiJFAFIjQ1rNgEA6Db+\n//9Ig8Qow8xIiVwkCEiJdCQQV0iD7CBIi9pIi/lIhcl1CkiLyugy5///62pIhdJ1B+jm5v//61xI\ng/rgd0NIiw3r1wEAuAEAAABIhdtID0TYTIvHM9JMi8v/FSnEAABIi/BIhcB1bzkFy9cBAHRQSIvL\n6DUrAACFwHQrSIP74Ha9SIvL6CMrAADovhoAAMcADAAAADPASItcJDBIi3QkOEiDxCBfw+ihGgAA\nSIvY/xWcwgAAi8josRoAAIkD69XoiBoAAEiL2P8Vg8IAAIvI6JgaAACJA0iLxuu7zEiLxEiJWAhI\niWgQSIlwGEiJeCBBVkiD7DAz20mL6ESL8kiL8UiFyXUV6EIaAADHABYAAADopwgAAOmCAAAAungE\nAAC5AQAAAOiHKAAASIv4SIXAdFjolhcAAEiLz0iLkMAAAADoLxgAAEyNBeQAAABJi9ZMi88zyUiJ\nfCQoSIm3kAAAAEiJr5gAAADHRCQgBAAAAP8V68IAAEiL2EiJRwhIhcB1Ov8VycEAAIvYSIvP6Ivl\n//+F23QHi8voYBkAAEiDyP9Ii1wkQEiLbCRISIt0JFBIi3wkWEiDxDBBXsNIi8j/FdzAAACD+P90\nuEiLw+vSzMxIg+wo6PMWAACQSIuImAAAAP+QkAAAAOgQAAAAkIvI6Hji//+QSIPEKMPMzEBTSIPs\nIOjpFgAASIvYSIXAdBhIi0gISIP5/3QG/xUBwQAASIvL6GkWAAAzyf8VQcIAAMxAU0iD7CBIi9no\nFhUAAIvI6FMdAABIi8hIhcB1IugCFQAASIvTi8joWB0AAIXAdTv/FerAAACLyP8VAsIAAMxIi4OQ\nAAAASImBkAAAAEiLg5gAAABIiYGYAAAASItDCEiJQQhIi8vowRQAAOgw////zMzMzEiLxEiJWAhI\niWgQSIlwGFdBVEFVQVZBV0iD7EBNi2EITYs5SYtZOE0r/PZBBGZNi/FMi+pIi+kPhd4AAABBi3FI\nSIlIyEyJQNA7Mw+DbQEAAIv+SAP/i0T7BEw7+A+CqgAAAItE+whMO/gPg50AAACDfPsQAA+EkgAA\nAIN8+wwBdBeLRPsMSI1MJDBJi9VJA8T/0IXAeH1+dIF9AGNzbeB1KEiDPerbAwAAdB5IjQ3h2wMA\n6GwkAACFwHQOugEAAABIi83/FcrbAwCLTPsQQbgBAAAASYvVSQPM6NVVAABJi0ZAi1T7EESLTQBI\niUQkKEmLRihJA9RMi8VJi81IiUQkIP8V5MAAAOjXVQAA/8bpNf///zPA6agAAABJi3EgQYt5SEkr\n9OmJAAAAi89IA8mLRMsETDv4cnmLRMsITDv4c3D2RQQgdERFM8mF0nQ4RYvBTQPAQotEwwRIO/By\nIEKLRMMISDvwcxaLRMsQQjlEwxB1C4tEywxCOUTDDHQIQf/BRDvKcshEO8p1MotEyxCFwHQHSDvw\ndCXrF41HAUmL1UGJRkhEi0TLDLEBTQPEQf/Q/8eLEzv6D4Jt////uAEAAABMjVwkQEmLWzBJi2s4\nSYtzQEmL40FfQV5BXUFcX8PMzMzMzMzMzMxmZg8fhAAAAAAASCvRSYP4CHIi9sEHdBRmkIoBOgQK\ndSxI/8FJ/8j2wQd17k2LyEnB6QN1H02FwHQPigE6BAp1DEj/wUn/yHXxSDPAwxvAg9j/w5BJwekC\ndDdIiwFIOwQKdVtIi0EISDtECgh1TEiLQRBIO0QKEHU9SItBGEg7RAoYdS5Ig8EgSf/Jdc1Jg+Af\nTYvIScHpA3SbSIsBSDsECnUbSIPBCEn/yXXuSYPgB+uDSIPBCEiDwQhIg8EISIsMEUgPyEgPyUg7\nwRvAg9j/w8xMi8lFD7YBSf/BQY1Av4P4GXcEQYPAIA+2Ckj/wo1Bv4P4GXcDg8EgRYXAdAVEO8F0\n0UQrwUGLwMPMzMxIg+wogz21yQEAAHUtSIXJdRromRUAAMcAFgAAAOj+AwAAuP///39Ig8Qow0iF\n0nThSIPEKOmK////RTPASIPEKOkCAAAAzMxIiVwkCEiJdCQQV0iD7EBIi/FIi/pIjUwkIEmL0OgO\n2///SIX2dAVIhf91F+g3FQAAxwAWAAAA6JwDAAC7////f+tLSItEJCBIg7g4AQAAAHUPSIvXSIvO\n6B////+L2OstSCv3D7YMPkiNVCQg6ApVAAAPtg9IjVQkIIvY6PtUAABI/8eF23QEO9h02CvYgHwk\nOAB0DEiLTCQwg6HIAAAA/UiLdCRYi8NIi1wkUEiDxEBfw8xIiVwkEFdIg+wwvwEAAACLz+hmWgAA\nuE1aAABmOQUuif//dAQz2+s4SGMFXYn//0iNDRqJ//9IA8GBOFBFAAB147kLAgAAZjlIGHXYM9uD\nuIQAAAAOdgk5mPgAAAAPlcOJXCRA6GMoAACFwHUigz1U1AEAAnQF6IEdAAC5HAAAAOjrHQAAuf8A\nAADoTdz//+gMEwAAhcB1IoM9KdQBAAJ0BehWHQAAuRAAAADowB0AALn/AAAA6CLc///orRQAAJDo\nz0wAAIXAeQq5GwAAAOitAAAA/xWfvAAASIkFuNcDAOiLWgAASIkFnMcBAOibVQAAhcB5CrkIAAAA\n6PXb///oRFgAAIXAeQq5CQAAAOji2///i8/oI9z//4XAdAeLyOjQ2///TIsFdcEBAEyJBabBAQBI\nixVXwQEAiw1NwQEA6CzX//+L+IlEJCCF23UHi8joI9///+jS2///6xeL+IN8JEAAdQiLyOho3P//\nzOiq2///kIvHSItcJEhIg8QwX8NAU0iD7CCDPTvTAQACi9l0BehmHAAAi8vo0xwAALn/AAAASIPE\nIFvpMNv//0iD7CjoD1kAAEiDxCjpQv7//8zMSIvESIlYEEiJcBhIiXggVUiNqEj7//9IgeywBQAA\nSIsFK6kBAEgzxEiJhaAEAABBi/iL8ovZg/n/dAXowFIAAINkJDAASI1MJDQz0kG4lAAAAOjp5///\nSI1EJDBIjU3QSIlEJCBIjUXQSIlEJCjosRUAAEiLhbgEAABIiYXIAAAASI2FuAQAAIl0JDBIg8AI\niXwkNEiJRWhIi4W4BAAASIlEJED/FXa7AABIjUwkIIv46GYbAACFwHUQhf91DIP7/3QHi8voNlIA\nAEiLjaAEAABIM8zot+H//0yNnCSwBQAASYtbGEmLcyBJi3soSYvjXcPMzEiJDd3FAQDDSIlcJAhI\niWwkEEiJdCQYV0iD7DBIi+lIiw2+xQEAQYvZSYv4SIvy/xWfugAARIvLTIvHSIvWSIvNSIXAdBdI\ni1wkQEiLbCRISIt0JFBIg8QwX0j/4EiLRCRgSIlEJCDoJAAAAMzMzMxIg+w4SINkJCAARTPJRTPA\nM9Izyeh/////SIPEOMPMzEiD7Ci5FwAAAOjaqwAAhcB0B7kFAAAAzSlBuAEAAAC6FwQAwEGNSAHo\nT/7//7kXBADASIPEKOk9GgAAzPD/AUiLgdgAAABIhcB0A/D/AEiLgegAAABIhcB0A/D/AEiLgeAA\nAABIhcB0A/D/AEiLgfgAAABIhcB0A/D/AEiNQShBuAYAAABIjRXEsQEASDlQ8HQLSIsQSIXSdAPw\n/wJIg3joAHQMSItQ+EiF0nQD8P8CSIPAIEn/yHXMSIuBIAEAAPD/gFwBAADDSIlcJAhIiWwkEEiJ\ndCQYV0iD7CBIi4HwAAAASIvZSIXAdHlIjQ3KuAEASDvBdG1Ii4PYAAAASIXAdGGDOAB1XEiLi+gA\nAABIhcl0FoM5AHUR6CLc//9Ii4vwAAAA6PZXAABIi4vgAAAASIXJdBaDOQB1EegA3P//SIuL8AAA\nAOjgWAAASIuL2AAAAOjo2///SIuL8AAAAOjc2///SIuD+AAAAEiFwHRHgzgAdUJIi4sAAQAASIHp\n/gAAAOi42///SIuLEAEAAL+AAAAASCvP6KTb//9Ii4sYAQAASCvP6JXb//9Ii4v4AAAA6Inb//9I\ni4sgAQAASI0Fl7ABAEg7yHQag7lcAQAAAHUR6MBYAABIi4sgAQAA6Fzb//9IjbMoAQAASI17KL0G\nAAAASI0FVbABAEg5R/B0GkiLD0iFyXQSgzkAdQ3oLdv//0iLDugl2///SIN/6AB0E0iLT/hIhcl0\nCoM5AHUF6Avb//9Ig8YISIPHIEj/zXWySIvLSItcJDBIi2wkOEiLdCRASIPEIF/p4tr//8zMSIXJ\nD4SXAAAAQYPJ//BEAQlIi4HYAAAASIXAdATwRAEISIuB6AAAAEiFwHQE8EQBCEiLgeAAAABIhcB0\nBPBEAQhIi4H4AAAASIXAdATwRAEISI1BKEG4BgAAAEiNFY6vAQBIOVDwdAxIixBIhdJ0BPBEAQpI\ng3joAHQNSItQ+EiF0nQE8EQBCkiDwCBJ/8h1ykiLgSABAADwRAGIXAEAAEiLwcNAU0iD7CDo4QsA\nAEiL2IsNbLMBAIWIyAAAAHQYSIO4wAAAAAB0DujBCwAASIuYwAAAAOsruQwAAADoVg8AAJBIjYvA\nAAAASIsVy7EBAOgmAAAASIvYuQwAAADoJREAAEiF23UIjUsg6EDW//9Ii8NIg8QgW8PMzMxIiVwk\nCFdIg+wgSIv6SIXSdENIhcl0PkiLGUg72nQxSIkRSIvK6Jb8//9Ihdt0IUiLy+it/v//gzsAdRRI\njQVtsQEASDvYdAhIi8vo/Pz//0iLx+sCM8BIi1wkMEiDxCBfw8zMSIPsKIM9ueEDAAB1FLn9////\n6MEDAADHBaPhAwABAAAAM8BIg8Qow0BTSIPsQIvZSI1MJCAz0ugc0///gyVZwQEAAIP7/nUSxwVK\nwQEAAQAAAP8VHLQAAOsVg/v9dRTHBTPBAQABAAAA/xUNtAAAi9jrF4P7/HUSSItEJCDHBRXBAQAB\nAAAAi1gEgHwkOAB0DEiLTCQwg6HIAAAA/YvDSIPEQFvDzMzMSIlcJAhIiWwkEEiJdCQYV0iD7CBI\njVkYSIvxvQEBAABIi8tEi8Uz0ugH4v//M8BIjX4MSIlGBEiJhiACAAC5BgAAAA+3wGbzq0iNPdSo\nAQBIK/6KBB+IA0j/w0j/zXXzSI2OGQEAALoAAQAAigQ5iAFI/8FI/8p180iLXCQwSItsJDhIi3Qk\nQEiDxCBfw8zMSIlcJBBIiXwkGFVIjawkgPv//0iB7IAFAABIiwWTogEASDPESImFcAQAAEiL+YtJ\nBEiNVCRQ/xX4sgAAuwABAACFwA+ENQEAADPASI1MJHCIAf/ASP/BO8Ny9YpEJFbGRCRwIEiNVCRW\n6yJED7ZCAQ+2yOsNO8tzDovBxkQMcCD/wUE7yHbuSIPCAooChMB12otHBINkJDAATI1EJHCJRCQo\nSI2FcAIAAESLy7oBAAAAM8lIiUQkIOi7XQAAg2QkQACLRwRIi5cgAgAAiUQkOEiNRXCJXCQwSIlE\nJChMjUwkcESLwzPJiVwkIOh4WwAAg2QkQACLRwRIi5cgAgAAiUQkOEiNhXABAACJXCQwSIlEJChM\njUwkcEG4AAIAADPJiVwkIOg/WwAATI1FcEyNjXABAABMK8dIjZVwAgAASI1PGUwrz/YCAXQKgAkQ\nQYpECOfrDfYCAnQQgAkgQYpECeeIgQABAADrB8aBAAEAAABI/8FIg8ICSP/LdcnrPzPSSI1PGUSN\nQp9BjUAgg/gZdwiACRCNQiDrDEGD+Bl3DoAJII1C4IiBAAEAAOsHxoEAAQAAAP/CSP/BO9Nyx0iL\njXAEAABIM8zoKNr//0yNnCSABQAASYtbGEmLeyBJi+Ndw8zMzEiJXCQQV0iD7CDo5QcAAEiL+IsN\ncK8BAIWIyAAAAHQTSIO4wAAAAAB0CUiLmLgAAADrbLkNAAAA6F8LAACQSIufuAAAAEiJXCQwSDsd\nf6kBAHRCSIXbdBvw/wt1FkiNBUymAQBIi0wkMEg7yHQF6NHV//9IiwVWqQEASImHuAAAAEiLBUip\nAQBIiUQkMPD/AEiLXCQwuQ0AAADo7QwAAEiF23UIjUsg6AjS//9Ii8NIi1wkOEiDxCBfw8zMSIvE\nSIlYCEiJcBBIiXgYTIlwIEFXSIPsMIv5QYPP/+gUBwAASIvw6Bj///9Ii564AAAAi8/oFvz//0SL\n8DtDBA+E2wEAALkoAgAA6EwYAABIi9gz/0iFwA+EyAEAAEiLhrgAAABIi8uNVwREjUJ8DxAADxEB\nDxBIEA8RSRAPEEAgDxFBIA8QSDAPEUkwDxBAQA8RQUAPEEhQDxFJUA8QQGAPEUFgSQPIDxBIcA8R\nSfBJA8BI/8p1tw8QAA8RAQ8QSBAPEUkQSItAIEiJQSCJO0iL00GLzuhpAQAARIv4hcAPhRUBAABI\ni464AAAATI01AKUBAPD/CXURSIuOuAAAAEk7znQF6H7U//9IiZ64AAAA8P8D9obIAAAAAg+FBQEA\nAPYFpK0BAAEPhfgAAAC+DQAAAIvO6KYJAACQi0MEiQVgvAEAi0MIiQVbvAEASIuDIAIAAEiJBWG8\nAQCL10yNBfB8//+JVCQgg/oFfRVIY8oPt0RLDGZBiYRIWD8CAP/C6+KL14lUJCCB+gEBAAB9E0hj\nyopEGRhCiIQBkCUCAP/C6+GJfCQggf8AAQAAfRZIY8+KhBkZAQAAQoiEAaAmAgD/x+veSIsNSKcB\nAIPI//APwQH/yHURSIsNNqcBAEk7znQF6KDT//9IiR0lpwEA8P8Di87o1woAAOsrg/j/dSZMjTXt\nowEASTvedAhIi8vodNP//+ifBwAAxwAWAAAA6wUz/0SL/0GLx0iLXCRASIt0JEhIi3wkUEyLdCRY\nSIPEMEFfw0iJXCQYSIlsJCBWV0FUQVZBV0iD7EBIiwWznQEASDPESIlEJDhIi9ro3/n//zP2i/iF\nwHUNSIvL6E/6///pRAIAAEyNJZelAQCL7kG/AQAAAEmLxDk4D4Q4AQAAQQPvSIPAMIP9BXLsjYcY\nAv//QTvHD4YVAQAAD7fP/xWwrwAAhcAPhAQBAABIjVQkIIvP/xW7rQAAhcAPhOMAAABIjUsYM9JB\nuAEBAADoEtz//4l7BEiJsyACAABEOXwkIA+GpgAAAEiNVCQmQDh0JCZ0OUA4cgF0Mw+2egFED7YC\nRDvHdx1BjUgBSI1DGEgDwUEr+EGNDD+ACARJA8dJK8919UiDwgJAODJ1x0iNQxq5/gAAAIAICEkD\nx0krz3X1i0sEgemkAwAAdC6D6QR0IIPpDXQS/8l0BUiLxusiSIsFdyEBAOsZSIsFZiEBAOsQSIsF\nVSEBAOsHSIsFRCEBAEiJgyACAABEiXsI6wOJcwhIjXsMD7fGuQYAAABm86vp/gAAADk1+rkBAA+F\nqf7//4PI/+n0AAAASI1LGDPSQbgBAQAA6Bvb//+LxU2NTCQQTI0cQEyNNSGkAQC9BAAAAEnB4wRN\nA8tJi9FBODF0QEA4cgF0OkQPtgIPtkIBRDvAdyRFjVABQYH6AQEAAHMXQYoGRQPHQQhEGhgPtkIB\nRQPXRDvAduBIg8ICQDgydcBJg8EITQP3SSvvdayJewREiXsIge+kAwAAdCmD7wR0G4PvDXQN/891\nIkiLNX0gAQDrGUiLNWwgAQDrEEiLNVsgAQDrB0iLNUogAQBMK9tIibMgAgAASI1LDEuNPCO6BgAA\nAA+3RA/4ZokBSI1JAkkr13XvSIvL6Jb4//8zwEiLTCQ4SDPM6HvU//9MjVwkQEmLW0BJi2tISYvj\nQV9BXkFcX17DzMxIiVwkEGaJTCQIVUiL7EiD7FC4//8AAGY7yA+EnwAAAEiNTeDoY8r//0iLXeBI\ni4M4AQAASIXAdRMPt1UQjUK/ZoP4GXdlZoPCIOtfD7dNELoAAQAAZjvKcyW6AQAAAOj8VgAAhcB1\nBg+3VRDrPQ+3TRBIi4MQAQAAD7YUCOssSI1NIEG5AQAAAEyNRRBEiUwkKEiJTCQgSIvI6ClXAAAP\nt1UQhcB0BA+3VSCAffgAdAtIi03wg6HIAAAA/Q+3wkiLXCRoSIPEUF3DzMyLBVajAQDDzEiFyQ+E\nKQEAAEiJXCQQV0iD7CBIi9lIi0k4SIXJdAXonM///0iLS0hIhcl0BeiOz///SItLWEiFyXQF6IDP\n//9Ii0toSIXJdAXocs///0iLS3BIhcl0Behkz///SItLeEiFyXQF6FbP//9Ii4uAAAAASIXJdAXo\nRc///0iLi6AAAABIjQWTMgEASDvIdAXoLc///78NAAAAi8/oeQQAAJBIi4u4AAAASIlMJDBIhcl0\nHPD/CXUXSI0Fb58BAEiLTCQwSDvIdAbo9M7//5CLz+g0BgAAuQwAAADoOgQAAJBIi7vAAAAASIX/\ndCtIi8/o7fP//0g7PaKmAQB0GkiNBammAQBIO/h0DoM/AHUJSIvP6DPy//+QuQwAAADo6AUAAEiL\ny+iYzv//SItcJDhIg8QgX8PMQFNIg+wgSIvZiw0RogEAg/n/dCJIhdt1DujqBgAAiw38oQEASIvY\nM9Lo9gYAAEiLy+iW/v//SIPEIFvDQFNIg+wg6BkAAABIi9hIhcB1CI1IEOilyv//SIvDSIPEIFvD\nSIlcJAhXSIPsIP8VUKoAAIsNqqEBAIv46IsGAABIi9hIhcB1R41IAbp4BAAA6I4QAABIi9hIhcB0\nMosNgKEBAEiL0Oh8BgAASIvLhcB0FjPS6C4AAAD/FRyrAABIg0sI/4kD6wfows3//zPbi8//FeSp\nAABIi8NIi1wkMEiDxCBfw8zMSIlcJAhXSIPsIEiL+kiL2UiNBe0wAQBIiYGgAAAAg2EQAMdBHAEA\nAADHgcgAAAABAAAAuEMAAABmiYFkAQAAZomBagIAAEiNBcedAQBIiYG4AAAASIOhcAQAAAC5DQAA\nAOiaAgAAkEiLg7gAAADw/wC5DQAAAOh1BAAAuQwAAADoewIAAJBIibvAAAAASIX/dQ5IiwXrpAEA\nSImDwAAAAEiLi8AAAADo+O///5C5DAAAAOg5BAAASItcJDBIg8QgX8PMzEBTSIPsIOg1yv//6LgD\nAACFwHReSI0NCf3//+gIBQAAiQVSoAEAg/j/dEe6eAQAALkBAAAA6D4PAABIi9hIhcB0MIsNMKAB\nAEiL0OgsBQAAhcB0HjPSSIvL6N7+////FcypAABIg0sI/4kDuAEAAADrB+gJAAAAM8BIg8QgW8PM\nSIPsKIsN7p8BAIP5/3QM6LAEAACDDd2fAQD/SIPEKOncAQAASIPsKOgL/v//SIXAdQlIjQUzoQEA\n6wRIg8AUSIPEKMNIiVwkCFdIg+wgi/no4/3//0iFwHUJSI0FC6EBAOsESIPAFIk46Mr9//9IjR3z\noAEASIXAdARIjVgQi8/oLwAAAIkDSItcJDBIg8QgX8PMzEiD7Cjom/3//0iFwHUJSI0Fv6ABAOsE\nSIPAEEiDxCjDTI0VRZ8BADPSTYvCRI1KCEE7CHQv/8JNA8FIY8JIg/gtcu2NQe2D+BF3BrgNAAAA\nw4HBRP///7gWAAAAg/kOQQ9GwcNIY8JBi0TCBMPMzMxAV0iD7CBIjT0/owEASDk9KKMBAHQruQwA\nAADomAAAAJBIi9dIjQ0RowEA6Gzx//9IiQUFowEAuQwAAADoZwIAAEiDxCBfw8xIiVwkCFdIg+wg\nSI0d62sBAEiNPeRrAQDrDkiLA0iFwHQC/9BIg8MISDvfcu1Ii1wkMEiDxCBfw0iJXCQIV0iD7CBI\njR3DawEASI09vGsBAOsOSIsDSIXAdAL/0EiDwwhIO99y7UiLXCQwSIPEIF/DSIlcJAhXSIPsIEhj\n2UiNPfCjAQBIA9tIgzzfAHUR6KkAAACFwHUIjUgR6OnG//9IiwzfSItcJDBIg8QgX0j/JZCnAABI\niVwkCEiJbCQQSIl0JBhXSIPsIL8kAAAASI0doKMBAIvvSIszSIX2dBuDewgBdBVIi87/FS+lAABI\ni87oI8r//0iDIwBIg8MQSP/NddRIjR1zowEASItL+EiFyXQLgzsBdQb/Ff+kAABIg8MQSP/PdeNI\ni1wkMEiLbCQ4SIt0JEBIg8QgX8PMSIlcJAhIiXwkEEFWSIPsIEhj2UiDPdW6AQAAdRnoMgcAALke\nAAAA6JwHAAC5/wAAAOj+xf//SAPbTI01+KIBAEmDPN4AdAe4AQAAAOteuSgAAADomAwAAEiL+EiF\nwHUP6Kf9///HAAwAAAAzwOs9uQoAAADou/7//5BIi89JgzzeAHUTRTPAuqAPAADo/wEAAEmJPN7r\nBuhAyf//kEiLDTSjAQD/FWamAADrm0iLXCQwSIt8JDhIg8QgQV7DzMzMSIlcJAhIiXQkEFdIg+wg\nM/ZIjR1gogEAjX4kg3sIAXUkSGPGSI0VPbEBAEUzwEiNDID/xkiNDMq6oA8AAEiJC+iLAQAASIPD\nEEj/z3XNSItcJDBIi3QkOI1HAUiDxCBfw8zMzEhjyUiNBQqiAQBIA8lIiwzISP8l1KUAAEiJXCQg\nV0iD7EBIi9n/FYmjAABIi7v4AAAASI1UJFBFM8BIi8//FWmjAABIhcB0MkiDZCQ4AEiLVCRQSI1M\nJFhIiUwkMEiNTCRgTIvISIlMJCgzyUyLx0iJXCQg/xUipQAASItcJGhIg8RAX8PMzMxAU1ZXSIPs\nQEiL2f8VG6MAAEiLs/gAAAAz/0iNVCRgRTPASIvO/xX5ogAASIXAdDlIg2QkOABIi1QkYEiNTCRo\nSIlMJDBIjUwkcEyLyEiJTCQoM8lMi8ZIiVwkIP8VsqQAAP/Hg/8CfLFIg8RAX15bw8zMzEiLBcG+\nAwBIMwU6kgEAdANI/+BI/yVuogAAzMxIiwWtvgMASDMFHpIBAHQDSP/gSP8lOqIAAMzMSIsFmb4D\nAEgzBQKSAQB0A0j/4Ej/JS6iAADMzEiLBYW+AwBIMwXmkQEAdANI/+BI/yUKogAAzMxIg+woSIsF\nbb4DAEgzBcaRAQB0B0iDxChI/+D/Ff+hAAC4AQAAAEiDxCjDzEBTSIPsIIsFsKIBADPbhcB5L0iL\nBfu+AwCJXCQwSDMFiJEBAHQRSI1MJDAz0v/Qg/h6jUMBdAKLw4kFfaIBAIXAD5/Di8NIg8QgW8NA\nU0iD7CBIjQ3DGgEA/xUdowAASI0V1hoBAEiLyEiL2P8VMqMAAEiNFdMaAQBIi8tIMwUpkQEASIkF\nor0DAP8VFKMAAEiNFb0aAQBIMwUOkQEASIvLSIkFjL0DAP8V9qIAAEiNFa8aAQBIMwXwkAEASIvL\nSIkFdr0DAP8V2KIAAEiNFaEaAQBIMwXSkAEASIvLSIkFYL0DAP8VuqIAAEiNFaMaAQBIMwW0kAEA\nSIvLSIkFSr0DAP8VnKIAAEiNFZUaAQBIMwWWkAEASIvLSIkFNL0DAP8VfqIAAEiNFY8aAQBIMwV4\nkAEASIvLSIkFHr0DAP8VYKIAAEiNFYkaAQBIMwVakAEASIvLSIkFCL0DAP8VQqIAAEiNFYMaAQBI\nMwU8kAEASIvLSIkF8rwDAP8VJKIAAEiNFX0aAQBIMwUekAEASIvLSIkF3LwDAP8VBqIAAEiNFX8a\nAQBIMwUAkAEASIvLSIkFxrwDAP8V6KEAAEiNFXkaAQBIMwXijwEASIvLSIkFsLwDAP8VyqEAAEiN\nFXMaAQBIMwXEjwEASIvLSIkFmrwDAP8VrKEAAEiNFW0aAQBIMwWmjwEASIvLSIkFhLwDAP8VjqEA\nAEiNFWcaAQBIMwWIjwEASIvLSIkFbrwDAP8VcKEAAEgzBXGPAQBIjRViGgEASIvLSIkFWLwDAP8V\nUqEAAEiNFWsaAQBIMwVMjwEASIvLSIkFQrwDAP8VNKEAAEiNFW0aAQBIMwUujwEASIvLSIkFLLwD\nAP8VFqEAAEiNFW8aAQBIMwUQjwEASIvLSIkFFrwDAP8V+KAAAEiNFWkaAQBIMwXyjgEASIvLSIkF\nALwDAP8V2qAAAEiNFWsaAQBIMwXUjgEASIvLSIkF6rsDAP8VvKAAAEiNFWUaAQBIMwW2jgEASIvL\nSIkF3LsDAP8VnqAAAEiNFVcaAQBIMwWYjgEASIvLSIkFtrsDAP8VgKAAAEiNFUkaAQBIMwV6jgEA\nSIvLSIkFqLsDAP8VYqAAAEiNFTsaAQBIMwVcjgEASIvLSIkFkrsDAP8VRKAAAEiNFS0aAQBIMwU+\njgEASIvLSIkFfLsDAP8VJqAAAEiNFS8aAQBIMwUgjgEASIvLSIkFZrsDAP8VCKAAAEiNFSkaAQBI\nMwUCjgEASIvLSIkFULsDAP8V6p8AAEiNFRsaAQBIMwXkjQEASIvLSIkFOrsDAP8VzJ8AAEiNFRUa\nAQBIMwXGjQEASIvLSIkFJLsDAP8Vrp8AAEiNFQcaAQBIMwWojQEASIvLSIkFDrsDAP8VkJ8AAEgz\nBZGNAQBIjRUCGgEASIvLSIkF+LoDAP8Vcp8AAEgzBXONAQBIiQXsugMASIPEIFvDzMxI/yWtnQAA\nzEj/Jd2eAADMQFNIg+wgi9n/FfaeAACL00iLyEiDxCBbSP8lTZ4AAMxAU0iD7CBIi9kzyf8Vc50A\nAEiLy0iDxCBbSP8lbJ0AAEiD7Ci5AwAAAOh6PAAAg/gBdBe5AwAAAOhrPAAAhcB1HYM93KwBAAF1\nFLn8AAAA6EAAAAC5/wAAAOg2AAAASIPEKMPMTI0NaRkBADPSTYvBQTsIdBL/wkmDwBBIY8JIg/gX\ncuwzwMNIY8JIA8BJi0TBCMPMSIlcJBBIiWwkGEiJdCQgV0FWQVdIgexQAgAASIsFfowBAEgzxEiJ\nhCRAAgAAi/nonP///zP2SIvYSIXAD4SZAQAAjU4D6Mo7AACD+AEPhB0BAACNTgPouTsAAIXAdQ2D\nPSqsAQABD4QEAQAAgf/8AAAAD4RjAQAASI0tIawBAEG/FAMAAEyNBVQjAQBIi81Bi9fohUkAADPJ\nhcAPhbsBAABMjTUqrAEAQbgEAQAAZok1Ja4BAEmL1v8Vop0AAEGNf+eFwHUZTI0FSyMBAIvXSYvO\n6EVJAACFwA+FKQEAAEmLzuihSQAASP/ASIP4PHY5SYvO6JBJAABIjU28TI0FRSMBAEiNDEFBuQMA\nAABIi8FJK8ZI0fhIK/hIi9foo0kAAIXAD4X0AAAATI0FICMBAEmL10iLzehZSAAAhcAPhQQBAABM\ni8NJi9dIi83oQ0gAAIXAD4XZAAAASI0VACMBAEG4ECABAEiLzeiKTAAA62u59P////8VBZ0AAEiL\n+EiNSP9Ig/n9d1NEi8ZIjVQkQIoLiApmOTN0FUH/wEj/wkiDwwJJY8BIPfQBAABy4kiNTCRAQIi0\nJDMCAADokEsAAEyNTCQwSI1UJEBIi89Mi8BIiXQkIP8VRZwAAEiLjCRAAgAASDPM6PXD//9MjZwk\nUAIAAEmLWyhJi2swSYtzOEmL40FfQV5fw0UzyUUzwDPSM8lIiXQkIOi44v//zEUzyUUzwDPSM8lI\niXQkIOij4v//zEUzyUUzwDPSM8lIiXQkIOiO4v//zEUzyUUzwDPSM8lIiXQkIOh54v//zEUzyUUz\nwDPSSIl0JCDoZuL//8zMTGNBPEUzyUyL0kwDwUEPt0AURQ+3WAZIg8AYSQPARYXbdB6LUAxMO9Jy\nCotICAPKTDvRcg5B/8FIg8AoRTvLcuIzwMPMzMzMzMzMzMzMzMxIiVwkCFdIg+wgSIvZSI09DGj/\n/0iLz+g0AAAAhcB0Ikgr30iL00iLz+iC////SIXAdA+LQCTB6B/30IPgAesCM8BIi1wkMEiDxCBf\nw8zMzEiLwblNWgAAZjkIdAMzwMNIY0g8SAPIM8CBOVBFAAB1DLoLAgAAZjlRGA+UwMPMzEBTSIPs\nILoIAAAAjUoY6FUBAABIi8hIi9j/FbGbAABIiQUKxwMASIkF+8YDAEiF23UFjUMY6wZIgyMAM8BI\ng8QgW8PMSIlcJAhIiXQkEEiJfCQYQVRBVkFXSIPsIEyL4eizvP//kEiLDcPGAwD/FWWbAABMi/BI\niw2rxgMA/xVVmwAASIvYSTvGD4KbAAAASIv4SSv+TI1/CEmD/wgPgocAAABJi87ogUwAAEiL8Ek7\nx3NVugAQAABIO8JID0LQSAPQSDvQchFJi87olQEAADPbSIXAdRrrAjPbSI1WIEg71nJJSYvO6HkB\nAABIhcB0PEjB/wNIjRz4SIvI/xXPmgAASIkFKMYDAEmLzP8Vv5oAAEiJA0iNSwj/FbKaAABIiQUD\nxgMASYvc6wIz2+jzu///SIvDSItcJEBIi3QkSEiLfCRQSIPEIEFfQV5BXMPMzEiD7Cjo6/7//0j3\n2BvA99j/yEiDxCjDzEiLxEiJWAhIiWgQSIlwGEiJeCBBVkiD7CAz20iL8kiL6UGDzv9FM8BIi9ZI\ni83ozUsAAEiL+EiFwHUmOQXfrQEAdh6Ly+hS+v//jYvoAwAAOw3KrQEAi9lBD0feQTvedcRIi1wk\nMEiLbCQ4SIt0JEBIi8dIi3wkSEiDxCBBXsPMSIvESIlYCEiJaBBIiXAYSIl4IEFWSIPsIIs1ga0B\nADPbSIvpQYPO/0iLzej8vP//SIv4SIXAdSSF9nQgi8vo2fn//4s1V60BAI2L6AMAADvOi9lBD0fe\nQTvedcxIi1wkMEiLbCQ4SIt0JEBIi8dIi3wkSEiDxCBBXsPMzEiLxEiJWAhIiWgQSIlwGEiJeCBB\nVkiD7CAz20iL8kiL6UGDzv9Ii9ZIi83oLNX//0iL+EiFwHUrSIX2dCY5BeGsAQB2HovL6FT5//+N\ni+gDAAA7DcysAQCL2UEPR95BO951wkiLXCQwSItsJDhIi3QkQEiLx0iLfCRISIPEIEFew8zMzEiJ\nXCQIV0iD7CAz/0iNHYWXAQBIiwv/FcyYAAD/x0iJA0hjx0iNWwhIg/gKcuVIi1wkMEiDxCBfw8zM\nzEiD7CjoV+3//0iLiNAAAABIhcl0BP/R6wDozkoAAJDMSIPsKEiNDdX/////FXuYAABIiQU0rAEA\nSIPEKMPMzMxAU0iD7CBIi9lIiw0krAEA/xVemAAASIXAdBBIi8v/0IXAdAe4AQAAAOsCM8BIg8Qg\nW8PMSIkN+asBAMNIiQ35qwEAw0iLDQmsAQBI/yUimAAAzMxIiQ3pqwEASIkN6qsBAEiJDeurAQBI\niQ3sqwEAw8zMzEiJXCQYSIl0JCBXQVRBVUFWQVdIg+wwi9lFM+1EIWwkaDP/iXwkYDP2i9GD6gIP\nhMQAAACD6gJ0YoPqAnRNg+oCdFiD6gN0U4PqBHQug+oGdBb/ynQ16NHu///HABYAAADoNt3//+tA\nTI01aasBAEiLDWKrAQDpiwAAAEyNNWarAQBIiw1fqwEA63tMjTVOqwEASIsNR6sBAOtr6DDs//9I\ni/BIhcB1CIPI/+lrAQAASIuQoAAAAEiLykxjBVceAQA5WQR0E0iDwRBJi8BIweAESAPCSDvIcuhJ\ni8BIweAESAPCSDvIcwU5WQR0AjPJTI1xCE2LPusgTI010aoBAEiLDcqqAQC/AQAAAIl8JGD/FeuW\nAABMi/hJg/8BdQczwOn2AAAATYX/dQpBjU8D6Bm3///Mhf90CDPJ6BXv//+QQbwQCQAAg/sLdzNB\nD6Pccy1Mi66oAAAATIlsJChIg6aoAAAAAIP7CHVSi4awAAAAiUQkaMeGsAAAAIwAAACD+wh1OYsN\nlx0BAIvRiUwkIIsFjx0BAAPIO9F9LEhjykgDyUiLhqAAAABIg2TICAD/wolUJCCLDWYdAQDr0zPJ\n/xU0lgAASYkGhf90BzPJ6HLw//+D+wh1DYuWsAAAAIvLQf/X6wWLy0H/14P7Cw+HLP///0EPo9wP\ngyL///9Mia6oAAAAg/sID4US////i0QkaImGsAAAAOkD////SItcJHBIi3QkeEiDxDBBX0FeQV1B\nXF/DzEiJDb2pAQDDhcl0MlNIg+wg90IYABAAAEiL2nQcSIvK6HPG//+BYxj/7v//g2MkAEiDIwBI\ng2MQAEiDxCBbw8xIiVwkCEiJfCQQQVZIg+wgSIvZ6LDH//+LyOj9RwAAhcAPhJUAAADozMT//0iD\nwDBIO9h1BDPA6xPousT//0iDwGBIO9h1dbgBAAAA/wWamgEA90MYDAEAAHVhTI01KqkBAEhj+EmL\nBP5IhcB1K7kAEAAA6CT7//9JiQT+SIXAdRhIjUMgSIlDEEiJA7gCAAAAiUMkiUMI6xVIiUMQSIkD\nx0MkABAAAMdDCAAQAACBSxgCEQAAuAEAAADrAjPASItcJDBIi3wkOEiDxCBBXsPMSIPsKP8VYpMA\nADPJSIXASIkFtqgBAA+VwYvBSIPEKMNIiVwkGFVWV0FUQVVBVkFXSI2sJCD+//9IgezgAgAASIsF\n/oEBAEgzxEiJhdgBAAAzwEiL8UiJTCRoSIv6SI1NqEmL0E2L6YlEJHBEi/CJRCRURIvgiUQkSIlE\nJGCJRCRYi9iJRCRQ6CSx///oV+v//0GDyP9FM9JIiUWASIX2D4Q2CQAA9kYYQEyNDdRf//8PhYYA\nAABIi87oOsb//0yNBQ+TAQBMY9BBjUoCg/kBdiJJi9JJi8pIjQWmX///g+IfSMH5BUxrylhMA4zI\nYEgCAOsDTYvIQfZBOH8PhdoIAABBjUICTI0NeF///4P4AXYZSYvKSYvCg+EfSMH4BUxrwVhNA4TB\nYEgCAEH2QDiAD4WmCAAAQYPI/0Uz0kiF/w+ElggAAESKP0GL8kSJVCRARIlUJERBi9JMiVWIRYT/\nD4SOCAAAQbsAAgAASP/HSIl9mIX2D4h5CAAAQY1H4DxYdxJJD77HQg++jAjQuQEAg+EP6wNBi8pI\nY8JIY8lIjRTIQg++lArwuQEAwfoEiVQkXIvKhdIPhOIGAAD/yQ+E9AcAAP/JD4ScBwAA/8kPhFgH\nAAD/yQ+ESAcAAP/JD4QLBwAA/8kPhCgGAAD/yQ+FCwYAAEEPvs+D+WQPj2kBAAAPhFsCAACD+UEP\nhC8BAACD+UMPhMwAAACNQbup/f///w+EGAEAAIP5U3Rtg/lYD4TGAQAAg/ladBeD+WEPhAgBAACD\n+WMPhKcAAADpHAQAAEmLRQBJg8UISIXAdC9Ii1gISIXbdCYPvwBBD7rmC3MSmcdEJFABAAAAK8LR\n+OnmAwAARIlUJFDp3AMAAEiLHSGRAQDpxQMAAEH3xjAIAAB1BUEPuu4LSYtdAEU74EGLxLn///9/\nD0TBSYPFCEH3xhAIAAAPhP0AAABIhdvHRCRQAQAAAEgPRB3gkAEASIvL6dYAAABB98YwCAAAdQVB\nD7ruC0mDxQhB98YQCAAAdCdFD7dN+EiNVdBIjUwkRE2Lw+ivRgAARTPShcB0GcdEJFgBAAAA6w9B\nikX4x0QkRAEAAACIRdBIjV3Q6S4DAADHRCRgAQAAAEGAxyBBg85ASI1d0EGL80WF5A+JIQIAAEG8\nBgAAAOlcAgAAg/lnftyD+WkPhOoAAACD+W4PhK8AAACD+W8PhJYAAACD+XB0YYP5cw+ED////4P5\ndQ+ExQAAAIP5eA+FwwIAAI1Br+tR/8hmRDkRdAhIg8EChcB18Egry0jR+esgSIXbSA9EHeOPAQBI\ni8vrCv/IRDgRdAdI/8GFwHXyK8uJTCRE6X0CAABBvBAAAABBD7ruD7gHAAAAiUQkcEG5EAAAAEWE\n9nldBFHGRCRMMEGNUfKIRCRN61BBuQgAAABFhPZ5QUUL8+s8SYt9AEmDxQjodLT//0Uz0oXAD4SU\nBQAAQfbGIHQFZok36wKJN8dEJFgBAAAA6WwDAABBg85AQbkKAAAAi1QkSLgAgAAARIXwdApNi0UA\nSYPFCOs6QQ+65gxy70mDxQhB9sYgdBlMiWwkeEH2xkB0B00Pv0X46xxFD7dF+OsVQfbGQHQGTWNF\n+OsERYtF+EyJbCR4QfbGQHQNTYXAeQhJ99hBD7ruCESF8HUKQQ+65gxyA0WLwEWF5HkIQbwBAAAA\n6wtBg+b3RTvjRQ9P40SLbCRwSYvASI2dzwEAAEj32BvJI8qJTCRIQYvMQf/Mhcl/BU2FwHQgM9JJ\ni8BJY8lI9/FMi8CNQjCD+Dl+A0EDxYgDSP/L69FMi2wkeEiNhc8BAAArw0j/w4lEJERFhfMPhAkB\nAACFwHQJgDswD4T8AAAASP/L/0QkRMYDMOntAAAAdQ5BgP9ndT5BvAEAAADrNkU740UPT+NBgfyj\nAAAAfiZBjbwkXQEAAEhjz+gd9f//SIlFiEiFwHQHSIvYi/frBkG8owAAAEmLRQBIiw3EjQEASYPF\nCEEPvv9IY/ZIiUWg/xXXjgAASI1NqESLz0iJTCQwi0wkYEyLxolMJChIjU2gSIvTRIlkJCD/0EGL\n/oHngAAAAHQbRYXkdRZIiw2LjQEA/xWVjgAASI1VqEiLy//QQYD/Z3Uahf91FkiLDWONAQD/FXWO\nAABIjVWoSIvL/9CAOy11CEEPuu4ISP/DSIvL6I88AABFM9KJRCRERDlUJFgPhVYBAABB9sZAdDFB\nD7rmCHMHxkQkTC3rC0H2xgF0EMZEJEwrvwEAAACJfCRI6xFB9sYCdAfGRCRMIOvoi3wkSIt0JFRM\ni3wkaCt0JEQr90H2xgx1EUyNTCRATYvHi9axIOigAwAASItFgEyNTCRASI1MJExNi8eL10iJRCQg\n6NcDAABB9sYIdBdB9sYEdRFMjUwkQE2Lx4vWsTDoZgMAAIN8JFAAi3wkRHRwhf9+bEyL+0UPtw9I\njZXQAQAASI1NkEG4BgAAAP/PTY1/AuiAQgAARTPShcB1NItVkIXSdC1Ii0WATItEJGhMjUwkQEiN\njdABAABIiUQkIOhbAwAARTPShf91rEyLfCRo6yxMi3wkaIPI/4lEJEDrIkiLRYBMjUwkQE2Lx4vX\nSIvLSIlEJCDoJAMAAEUz0otEJECFwHgaQfbGBHQUTI1MJEBNi8eL1rEg6K4CAABFM9JIi0WISIXA\ndA9Ii8jo2q///0Uz0kyJVYhIi32Yi3QkQItUJFxBuwACAABMjQ2GWP//RIo/RYT/D4TpAQAAQYPI\n/+lY+f//QYD/SXQ0QYD/aHQoQYD/bHQNQYD/d3XTQQ+67gvrzIA/bHUKSP/HQQ+67gzrvUGDzhDr\nt0GDziDrsYoHQQ+67g88NnURgH8BNHULSIPHAkEPuu4P65U8M3URgH8BMnULSIPHAkEPuvYP64As\nWDwgdxRIuQEQgiABAAAASA+jwQ+CZv///0SJVCRcSI1VqEEPts9EiVQkUOj9PgAAhcB0IUiLVCRo\nTI1EJEBBis/oawEAAESKP0j/x0WE/w+EBwEAAEiLVCRoTI1EJEBBis/oSgEAAEUz0un7/v//QYD/\nKnUZRYtlAEmDxQhFheQPifn+//9Fi+Dp8f7//0eNJKRBD77HRY1kJOhGjSRg6dv+//9Fi+Lp0/7/\n/0GA/yp1HEGLRQBJg8UIiUQkVIXAD4m5/v//QYPOBPfY6xGLRCRUjQyAQQ++x40ESIPA0IlEJFTp\nl/7//0GA/yB0QUGA/yN0MUGA/yt0IkGA/y10E0GA/zAPhXX+//9Bg84I6Wz+//9Bg84E6WP+//9B\ng84B6Vr+//9BD7ruB+lQ/v//QYPOAulH/v//RIlUJGBEiVQkWESJVCRURIlUJEhFi/JFi+BEiVQk\nUOkj/v//6Aji///HABYAAADobdD//4PI/0Uz0usCi8ZEOFXAdAtIi024g6HIAAAA/UiLjdgBAABI\nM8zob7H//0iLnCQwAwAASIHE4AIAAEFfQV5BXUFcX15dw0BTSIPsIPZCGEBJi9h0DEiDehAAdQVB\n/wDrJf9KCHgNSIsCiAhI/wIPtsHrCA++yei7DAAAg/j/dQQJA+sC/wNIg8QgW8PMzIXSfkxIiVwk\nCEiJbCQQSIl0JBhXSIPsIEmL+UmL8IvaQIrpTIvHSIvWQIrN/8vohf///4M//3QEhdt/50iLXCQw\nSItsJDhIi3QkQEiDxCBfw8zMzEiJXCQISIlsJBBIiXQkGFdBVkFXSIPsIEH2QBhASItcJGBJi/lE\niztJi+iL8kyL8XQMSYN4EAB1BUEBEes9gyMAhdJ+M0GKDkyLx0iL1f/O6A////9J/8aDP/91EoM7\nKnURTIvHSIvVsT/o9f7//4X2f9KDOwB1A0SJO0iLXCRASItsJEhIi3QkUEiDxCBBX0FeX8NIiVwk\nGFVWV0FUQVVBVkFXSI2sJCD8//9IgezgBAAASIsFvnYBAEgzxEiJhdADAAAzwEiL8UiJTCRwSIlV\niEiNTZBJi9BNi+FMiUwkUIlFgESL8IlEJFiL+IlEJESJRCRIiUQkfIlEJHiL2IlEJEzo3KX//+gP\n4P//RTPSSIlFuEiF9nUq6P7f///HABYAAADoY87//zPJOE2odAtIi0Wgg6DIAAAA/YPI/+ncBwAA\nTItFiE2FwHTNRQ+3OEGL8kSJVCRARYvqQYvSTIlVsGZFhf8PhKAHAABBuyAAAABBuQACAABJg8AC\nTIlFiIX2D4iEBwAAQQ+3x7lYAAAAZkErw2Y7wXcVSI0NBw4BAEEPt8cPvkwI4IPhD+sDQYvKSGPC\nSGPJSI0UyEiNBeUNAQAPvhQCwfoEiVQkaIvKhdIPhBoIAAD/yQ+EIgkAAP/JD4S/CAAA/8kPhHUI\nAAD/yQ+EYAgAAP/JD4QdCAAA/8kPhEEHAAD/yQ+F7gYAAEEPt8+D+WQPjwwCAAAPhA8DAACD+UEP\nhMkBAACD+UMPhEoBAACNQbup/f///w+EsgEAAIP5Uw+EjQAAALhYAAAAO8gPhFkCAACD+Vp0F4P5\nYQ+EmgEAAIP5Yw+EGwEAAOnSAAAASYsEJEmDxAhMiWQkUEiFwHQ7SItYCEiF23Qyvy0AAABBD7rm\nC3MYD78Ax0QkTAEAAACZK8LR+ESL6OmYAAAARA+/KESJVCRM6YoAAABIix0fhgEASIvL6Fc1AABF\nM9JMi+jrbkH3xjAIAAB1A0UL84N8JET/SYscJLj///9/D0T4SYPECEyJZCRQRYTzD4RqAQAASIXb\nRYvqSA9EHdKFAQBIi/OF/34mRDgWdCEPtg5IjVWQ6LI5AABFM9KFwHQDSP/GQf/FSP/GRDvvfNqL\ndCRAvy0AAABEOVQkeA+FcwUAAEH2xkAPhDQEAABBD7rmCA+D+wMAAGaJfCRcvwEAAACJfCRI6RoE\nAABB98YwCAAAdQNFC/NBD7cEJEmDxAjHRCRMAQAAAEyJZCRQZolEJGBFhPN0N4hEJGRIi0WQRIhU\nJGVMY4DUAAAATI1NkEiNVCRkSI1N0OgvPQAARTPShcB5DsdEJHgBAAAA6wRmiUXQSI1d0EG9AQAA\nAOlS////x0QkfAEAAABmRQP7uGcAAABBg85ASI1d0EGL8YX/D4k9AgAAQb0GAAAARIlsJETpgAIA\nALhnAAAAO8h+1IP5aQ+E9wAAAIP5bg+EtAAAAIP5bw+ElQAAAIP5cHRWg/lzD4SK/v//g/l1D4TS\nAAAAg/l4D4Xa/v//jUGv60VIhdvHRCRMAQAAAEgPRB1rhAEASIvD6wz/z2ZEORB0CEiDwAKF/3Xw\nSCvDSNH4RIvo6Z/+//+/EAAAAEEPuu4PuAcAAACJRYBBuRAAAABBvwACAABFhPZ5d0GNSSBmg8BR\njVHSZolMJFxmiUQkXutkQbkIAAAARYT2eU9BvwACAABFC/frSkmLPCRJg8QITIlkJFDo2qj//0Uz\n0oXAD4QE/P//RY1aIEWE83QFZok36wKJN8dEJHgBAAAA6Z4DAABBg85AQbkKAAAAQb8AAgAAi1Qk\nSLgAgAAARIXwdApNiwQkSYPECOs9QQ+65gxy70mDxAhFhPN0G0yJZCRQQfbGQHQITQ+/RCT46x9F\nD7dEJPjrF0H2xkB0B01jRCT46wVFi0Qk+EyJZCRQQfbGQHQNTYXAeQhJ99hBD7ruCESF8HUKQQ+6\n5gxyA0WLwIX/eQe/AQAAAOsLQYPm90E7/0EPT/+LdYBJi8BIjZ3PAQAASPfYG8kjyolMJEiLz//P\nhcl/BU2FwHQfM9JJi8BJY8lI9/FMi8CNQjCD+Dl+AgPGiANI/8vr1It0JEBIjYXPAQAAiXwkRCvD\nSP/DRIvoRYX3D4QP/f//hcC4MAAAAHQIOAMPhP78//9I/8tB/8WIA+nx/P//dRFmRDv4dUFBvQEA\nAADptv3//0E7+UG9owAAAEEPT/mJfCREQTv9fieBx10BAABIY8/oc+n//0iJRbBIhcAPhIX9//9I\ni9iL90SLbCRE6wNEi+9JiwQkSIsNFIIBAEmDxAhMiWQkUEEPvv9IY/ZIiUXA/xUigwAASI1NkEiJ\nTCQwi0wkfESLz4lMJChIjU3ATIvGSIvTRIlsJCD/0EGL/oHngAAAAHQbRYXtdRZIiw3WgQEA/xXg\nggAASI1VkEiLy//QuWcAAABmRDv5dRqF/3UWSIsNqYEBAP8Vu4IAAEiNVZBIi8v/0L8tAAAAQDg7\ndQhBD7ruCEj/w0iLy+jQMAAAi3QkQEUz0kSL6Onl+///QfbGAXQPuCsAAABmiUQkXOn1+///QfbG\nAnQTuCAAAABmiUQkXI144Yl8JEjrCYt8JEi4IAAAAESLfCRYSIt0JHBFK/1EK/9B9sYMdRJMjUwk\nQIvITIvGQYvX6J4DAABIi0W4TI1MJEBIjUwkXEyLxovXSIlEJCDo1QMAAEiLfCRwQfbGCHQbQfbG\nBHUVTI1MJEC5MAAAAEyLx0GL1+hbAwAAM8A5RCRMdXBFhe1+a0iL+0GL9UiLRZBMjU2QSI1MJGBM\nY4DUAAAASIvX/87oxjgAAEUz0kxj4IXAfipIi1QkcA+3TCRgTI1EJEDo1AIAAEkD/EUz0oX2f7pM\ni2QkUEiLfCRw6zJMi2QkUEiLfCRwg87/iXQkQOsjSItFuEyNTCRATIvHQYvVSIvLSIlEJCDoGwMA\nAEUz0ot0JECF9ngiQfbGBHQcTI1MJEC5IAAAAEyLx0GL1+ihAgAAi3QkQEUz0kG7IAAAAEiLRbBI\nhcB0E0iLyOgLpP//RTPSRY1aIEyJVbCLfCRETItFiItUJGhBuQACAABFD7c4ZkWF/w+FbPj//0Q4\nVah0C0iLTaCDocgAAAD9i8ZIi43QAwAASDPM6Iqn//9Ii5wkMAUAAEiBxOAEAABBX0FeQV1BXF9e\nXcNBD7fHg/hJdDyD+Gh0L7lsAAAAO8F0DIP4d3WZQQ+67gvrkmZBOQh1C0mDwAJBD7ruDOuBQYPO\nEOl4////RQvz6XD///9BD7cAQQ+67g9mg/g2dRZmQYN4AjR1DkmDwARBD7ruD+lL////ZoP4M3UW\nZkGDeAIydQ5Jg8AEQQ+69g/pL////2aD6FhmQTvDdxRIuQEQgiABAAAASA+jwQ+CEf///0SJVCRo\nSItUJHBMjUQkQEEPt8/HRCRMAQAAAOgfAQAAi3QkQEUz0kWNWiDp0/7//2ZBg/8qdR5BizwkSYPE\nCEyJZCRQiXwkRIX/D4nB/v//g8//6w2NPL9BD7fHjX/ojTx4iXwkROmm/v//QYv6RIlUJETpmf7/\n/2ZBg/8qdSFBiwQkSYPECEyJZCRQiUQkWIXAD4l5/v//QYPOBPfY6xGLRCRYjQyAQQ+3x40ESIPA\n0IlEJFjpV/7//0EPt8dBO8N0SYP4I3Q6uSsAAAA7wXQouS0AAAA7wXQWuTAAAAA7wQ+FKv7//0GD\nzgjpIf7//0GDzgTpGP7//0GDzgHpD/7//0EPuu4H6QX+//9Bg84C6fz9//+Dz/9EiVQkfESJVCR4\nRIlUJFhEiVQkSEWL8ol8JEREiVQkTOnU/f//zMxAU0iD7CD2QhhASYvYdAxIg3oQAHUFQf8A6xbo\nwDMAALn//wAAZjvBdQWDC//rAv8DSIPEIFvDzIXSfkxIiVwkCEiJbCQQSIl0JBhXSIPsIEmL+UmL\n8IvaD7fpTIvHSIvWD7fN/8volf///4M//3QEhdt/50iLXCQwSItsJDhIi3QkQEiDxCBfw8zMzEiJ\nXCQISIlsJBBIiXQkGFdBVkFXSIPsIEH2QBhASItcJGBJi/lEiztJi+iL8kyL8XQMSYN4EAB1BUEB\nEetCgyMAhdJ+OEEPtw5Mi8dIi9X/zuge////gz//TY12AnUVgzsqdRS5PwAAAEyLx0iL1egA////\nhfZ/zYM7AHUDRIk7SItcJEBIi2wkSEiLdCRQSIPEIEFfQV5fw8zMzEiLxEiJWBBIiWgYSIlwIIlI\nCFdIg+wgSIvKSIva6Lav//+LSxhIY/D2wYJ1F+iW1P//xwAJAAAAg0sYIIPI/+kyAQAA9sFAdA3o\netT//8cAIgAAAOviM//2wQF0GYl7CPbBEA+EiQAAAEiLQxCD4f5IiQOJSxiLQxiJewiD4O+DyAKJ\nQxipDAEAAHUv6Hes//9Ig8AwSDvYdA7oaaz//0iDwGBIO9h1C4vO6H0vAACFwHUISIvL6Mk2AAD3\nQxgIAQAAD4SLAAAAiytIi1MQK2sQSI1CAUiJA4tDJP/IiUMIhe1+GUSLxYvO6OIDAACL+OtVg8kg\niUsY6T////+NRgKD+AF2HkiLzkiLxkyNBbKQAQCD4R9IwfgFSGvRWEkDFMDrB0iNFYp7AQD2Qggg\ndBcz0ovORI1CAujLNAAASIP4/w+E8f7//0iLSxCKRCQwiAHrFr0BAAAASI1UJDCLzkSLxehpAwAA\ni/g7/Q+Fx/7//w+2RCQwSItcJDhIi2wkQEiLdCRISIPEIF/DzEiD7ChIiw0NewEASI1BAkiD+AF2\nBv8V9XoAAEiDxCjDSIlcJAhIiWwkEEiJdCQYV0iD7BAzyTPAM/8PoscF3noBAAIAAADHBdB6AQAB\nAAAARIvbi9lEi8KB8250ZWxEi8pBi9NBgfBpbmVJgfJHZW51i+hEC8ONRwFEC8JBD5TCQYHzQXV0\naEGB8WVudGlFC9mB8WNBTUREC9lAD5TGM8kPokSL2USLyIlcJASJVCQMRYTSdE+L0IHi8D//D4H6\nwAYBAHQrgfpgBgIAdCOB+nAGAgB0G4HCsPn8/4P6IHckSLkBAAEAAQAAAEgPo9FzFESLBTWPAQBB\ng8gBRIkFKo8BAOsHRIsFIY8BAECE9nQbQYHhAA/wD0GB+QAPYAB8C0GDyAREiQUBjwEAuAcAAAA7\n6HwiM8kPoov7iQQkiUwkCIlUJAwPuuMJcwtBg8gCRIkF1o4BAEEPuuMUc1DHBbl5AQACAAAAxwWz\neQEABgAAAEEPuuMbczVBD7rjHHMuxwWXeQEAAwAAAMcFkXkBAA4AAABA9scgdBTHBX15AQAFAAAA\nxwV3eQEALgAAAEiLXCQgSItsJChIi3QkMDPASIPEEF/DSIlcJAhIiXQkEFdIg+wwM/+NTwHoh9L/\n/5CNXwOJXCQgOx1NlQMAfWNIY/NIiwU5lQMASIsM8EiFyXRM9kEYg3QQ6AWw//+D+P90Bv/HiXwk\nJIP7FHwxSIsFDpUDAEiLDPBIg8Ew/xXgdwAASIsN+ZQDAEiLDPHozJz//0iLBemUAwBIgyTwAP/D\n65G5AQAAAOj60///i8dIi1wkQEiLdCRISIPEMF/DSIlcJBiJTCQIVldBVkiD7CBIY/mD//51EOiy\n0P//xwAJAAAA6Z0AAACFyQ+IhQAAADs9WZMDAHN9SIvHSIvfSMH7BUyNNYKNAQCD4B9Ia/BYSYsE\n3g++TDAIg+EBdFeLz+iiq///kEmLBN72RDAIAXQri8/o06z//0iLyP8VkncAAIXAdQr/FUh4AACL\n2OsCM9uF23QV6MXP//+JGOgu0P//xwAJAAAAg8v/i8/oDq3//4vD6xPoFdD//8cACQAAAOh6vv//\ng8j/SItcJFBIg8QgQV5fXsPMSIlcJBCJTCQIVldBVEFWQVdIg+wgQYvwTIvySGPZg/v+dRjoYM//\n/4MgAOjIz///xwAJAAAA6ZEAAACFyXh1Ox1zkgMAc21Ii8NIi/tIwf8FTI0lnIwBAIPgH0xr+FhJ\niwT8Qg++TDgIg+EBdEaLy+i7qv//kEmLBPxC9kQ4CAF0EUSLxkmL1ovL6FUAAACL+OsW6GDP///H\nAAkAAADo5c7//4MgAIPP/4vL6Dis//+Lx+sb6M/O//+DIADoN8///8cACQAAAOicvf//g8j/SItc\nJFhIg8QgQV9BXkFcX17DzMzMSIlcJCBVVldBVEFVQVZBV0iNrCTA5f//uEAbAADojrH//0gr4EiL\nBURlAQBIM8RIiYUwGgAARTPkRYv4TIvySGP5RIlkJEBBi9xBi/RFhcB1BzPA6W4HAABIhdJ1IOhB\nzv//RIkg6KnO///HABYAAADoDr3//4PI/+lJBwAASIvHSIvPSI0VhYsBAEjB+QWD4B9IiUwkSEiL\nDMpMa+hYRYpkDThMiWwkWEUC5EHQ/EGNRCT/PAF3FEGLx/fQqAF1C+jezf//M8mJCOuaQfZEDQgg\ndA0z0ovPRI1CAuhbMAAAi8/okCkAAEiLfCRIhcAPhEADAABIjQUUiwEASIsE+EH2RAUIgA+EKQMA\nAOiDy///SI1UJGRIi4jAAAAAM8BIOYE4AQAAi/hIi0QkSEiNDdyKAQBAD5THSIsMwUmLTA0A/xXB\ndgAAM8mFwA+E3wIAADPAhf90CUWE5A+EyQIAAP8VonQAAEmL/olEJGgzwA+3yGaJRCREiUQkYEWF\n/w+EBgYAAESL6EWE5A+FowEAAIoPTItsJFhIjRVyigEAgPkKD5TARTPAiUQkZEiLRCRISIsUwkU5\nRBVQdB9BikQVTIhMJG2IRCRsRYlEFVBBuAIAAABIjVQkbOtJD77J6DopAACFwHQ0SYvHSCvHSQPG\nSIP4AQ+OswEAAEiNTCREQbgCAAAASIvX6EAuAACD+P8PhNkBAABI/8frHEG4AQAAAEiL10iNTCRE\n6B8uAACD+P8PhLgBAACLTCRoM8BMjUQkREiJRCQ4SIlEJDBIjUQkbEG5AQAAADPSx0QkKAUAAABI\niUQkIEj/x/8VknUAAESL6IXAD4RwAQAASItEJEhIjQ2LiQEATI1MJGBIiwzBM8BIjVQkbEiJRCQg\nSItEJFhFi8VIiwwI/xVEdAAAhcAPhC0BAACLRCRAi99BK94D2EQ5bCRgD4ylBAAARTPtRDlsJGR0\nWEiLRCRIRY1FAcZEJGwNSI0NJ4kBAEyJbCQgTItsJFhIiwzBTI1MJGBIjVQkbEmLTA0A/xXkcwAA\nhcAPhMMAAACDfCRgAQ+MzwAAAP9EJEAPt0wkRP/D628Pt0wkROtjQY1EJP88AXcZD7cPM8Bmg/kK\nRIvoZolMJERBD5TFSIPHAkGNRCT/PAF3OOjBLgAAD7dMJERmO8F1dIPDAkWF7XQhuA0AAACLyGaJ\nRCRE6J4uAAAPt0wkRGY7wXVR/8P/RCRATItsJFiLx0ErxkE7x3NJM8Dp2P3//4oHTIt8JEhMjSVW\niAEAS4sM/P/DSYv/QYhEDUxLiwT8QcdEBVABAAAA6xz/FTtzAACL8OsN/xUxcwAAi/BMi2wkWEiL\nfCRIi0QkQIXbD4XEAwAAM9uF9g+EhgMAAIP+BQ+FbAMAAOj9yv//xwAJAAAA6ILK//+JMOlN/P//\nSIt8JEjrB0iLfCRIM8BMjQ3ShwEASYsM+UH2RA0IgA+E6AIAAIvwRYTkD4XYAAAATYvmRYX/D4Qq\nAwAAug0AAADrAjPARItsJEBIjb0wBgAASIvIQYvEQSvGQTvHcydBigQkSf/EPAp1C4gXQf/FSP/H\nSP/BSP/BiAdI/8dIgfn/EwAAcs5IjYUwBgAARIvHRIlsJEBMi2wkWEQrwEiLRCRISYsMwTPATI1M\nJFBJi0wNAEiNlTAGAABIiUQkIP8VA3IAAIXAD4Ti/v//A1wkUEiNhTAGAABIK/hIY0QkUEg7xw+M\n3f7//0GLxLoNAAAATI0N8IYBAEErxkE7xw+CQP///+m9/v//QYD8Ak2L5g+F4AAAAEWF/w+ESAIA\nALoNAAAA6wIzwESLbCRASI29MAYAAEiLyEGLxEErxkE7x3MyQQ+3BCRJg8QCZoP4CnUPZokXQYPF\nAkiDxwJIg8ECSIPBAmaJB0iDxwJIgfn+EwAAcsNIjYUwBgAARIvHRIlsJEBMi2wkWEQrwEiLRCRI\nSYsMwTPATI1MJFBJi0wNAEiNlTAGAABIiUQkIP8VFnEAAIXAD4T1/f//A1wkUEiNhTAGAABIK/hI\nY0QkUEg7xw+M8P3//0GLxLoNAAAATI0NA4YBAEErxkE7xw+CNf///+nQ/f//RYX/D4RoAQAAQbgN\nAAAA6wIzwEiNTYBIi9BBi8RBK8ZBO8dzL0EPtwQkSYPEAmaD+Ap1DGZEiQFIg8ECSIPCAkiDwgJm\niQFIg8ECSIH6qAYAAHLGSI1FgDP/TI1FgCvISIl8JDhIiXwkMIvBuen9AADHRCQoVQ0AAJkrwjPS\n0fhEi8hIjYUwBgAASIlEJCD/FU1xAABEi+iFwA+EI/3//0hjx0WLxUiNlTAGAABIA9BIi0QkSEiN\nDTaFAQBIiwzBM8BMjUwkUEiJRCQgSItEJFhEK8dIiwwI/xX0bwAAhcB0CwN8JFBEO+9/tesI/xUH\ncAAAi/BEO+8Pj838//9Bi9xBuA0AAABBK95BO98Pgv7+///ps/z//0mLTA0ATI1MJFBFi8dJi9ZI\niUQkIP8Vn28AAIXAdAuLXCRQi8bpl/z///8Vsm8AAIvwi8PpiPz//0yLbCRYSIt8JEjpefz//4vO\n6D/H///p7Pj//0iLfCRISI0FeoQBAEiLBPhB9kQFCEB0CkGAPhoPhKb4///oY8f//8cAHAAAAOjo\nxv//iRjps/j//yvYi8NIi40wGgAASDPM6NqW//9Ii5wkmBsAAEiBxEAbAABBX0FeQV1BXF9eXcPM\nzMxIi8RIiVgISIlwEEiJeBhMiWAgQVVBVkFXSIHswAAAAEiJZCRIuQsAAADoGcj//5C/WAAAAIvX\nRI1vyEGLzehB1f//SIvISIlEJChFM+RIhcB1GUiNFQoAAABIi8zomgQAAJCQg8j/6Z8CAABIiQWp\ngwEARIktZokDAEgFAAsAAEg7yHM5ZsdBCAAKSIMJ/0SJYQyAYTiAikE4JH+IQThmx0E5CgpEiWFQ\nRIhhTEgDz0iJTCQoSIsFYIMBAOu8SI1MJFD/FRNtAABmRDmkJJIAAAAPhEIBAABIi4QkmAAAAEiF\nwA+EMQEAAEyNcARMiXQkOEhjMEkD9kiJdCRAQb8ACAAARDk4RA9MOLsBAAAAiVwkMEQ5PcaIAwB9\nc0iL10mLzehd1P//SIvISIlEJChIhcB1CUSLPaWIAwDrUkhj00yNBdWCAQBJiQTQRAEtjogDAEmL\nBNBIBQALAABIO8hzKmbHQQgACkiDCf9EiWEMgGE4gGbHQTkKCkSJYVBEiGFMSAPPSIlMJCjrx//D\n64BBi/xEiWQkIEyNLX6CAQBBO/99d0iLDkiNQQJIg/gBdlFB9gYBdEtB9gYIdQr/FaJtAACFwHQ7\nSGPPSIvBSMH4BYPhH0hr2VhJA1zFAEiJXCQoSIsGSIkDQYoGiEMISI1LEEUzwLqgDwAA6KbJ////\nQwz/x4l8JCBJ/8ZMiXQkOEiDxghIiXQkQOuEQYv8RIlkJCBJx8f+////g/8DD43NAAAASGP3SGve\nWEgDHdyBAQBIiVwkKEiLA0iDwAJIg/gBdhAPvkMID7roB4hDCOmSAAAAxkMIgY1H//fYG8mDwfW4\n9v///4X/D0TI/xXcbAAATIvwSI1IAUiD+QF2RkiLyP8VzmwAAIXAdDlMiTMPtsCD+AJ1CQ++QwiD\nyEDrDIP4A3UKD75DCIPICIhDCEiNSxBFM8C6oA8AAOjWyP///0MM6yEPvkMIg8hAiEMITIk7SIsF\nLYgDAEiFwHQISIsE8ESJeBz/x4l8JCDpKv///7kLAAAA6C/H//8zwEyNnCTAAAAASYtbIEmLcyhJ\ni3swTYtjOEmL40FfQV5BXcPMzMxIiVwkGIlMJAhWV0FWSIPsIEhj2YP7/nUY6GLD//+DIADoysP/\n/8cACQAAAOmBAAAAhcl4ZTsddYYDAHNdSIvDSIv7SMH/BUyNNZ6AAQCD4B9Ia/BYSYsE/g++TDAI\ng+EBdDeLy+i+nv//kEmLBP72RDAIAXQLi8voRwAAAIv46w7oasP//8cACQAAAIPP/4vL6Eqg//+L\nx+sb6OHC//+DIADoScP//8cACQAAAOiusf//g8j/SItcJFBIg8QgQV5fXsPMSIlcJAhXSIPsIEhj\n+YvP6JSf//9Ig/j/dFlIiwUHgAEAuQIAAACD/wF1CUCEuLgAAAB1Cjv5dR32QGABdBfoZZ///7kB\nAAAASIvY6Fif//9IO8N0HovP6Eyf//9Ii8j/FZtqAACFwHUK/xXBagAAi9jrAjPbi8/ogJ7//0iL\n10iLz0jB+QWD4h9MjQWYfwEASYsMyEhr0ljGRBEIAIXbdAyLy+g0wv//g8j/6wIzwEiLXCQwSIPE\nIF/DzMxAU0iD7CD2QRiDSIvZdCL2QRgIdBxIi0kQ6CKO//+BYxj3+///M8BIiQNIiUMQiUMISIPE\nIFvDzMzMzMzMzMzMzMxmZg8fhAAAAAAASIHs2AQAAE0zwE0zyUiJZCQgTIlEJCjohlwAAEiBxNgE\nAADDzMzMzMzMZg8fRAAASIlMJAhIiVQkGESJRCQQScfBIAWTGesIzMzMzMzMZpDDzMzMzMzMZg8f\nhAAAAAAAw8zMzEiJXCQISIlsJBBIiXQkGFdIg+wgSIvyi/noRr///0UzyUiL2EiFwA+EiAEAAEiL\nkKAAAABIi8o5OXQQSI2CwAAAAEiDwRBIO8hy7EiNgsAAAABIO8hzBDk5dANJi8lIhckPhE4BAABM\ni0EITYXAD4RBAQAASYP4BXUNTIlJCEGNQPzpMAEAAEmD+AF1CIPI/+kiAQAASIurqAAAAEiJs6gA\nAACDeQQID4XyAAAAujAAAABIi4OgAAAASIPCEEyJTAL4SIH6wAAAAHzngTmOAADAi7uwAAAAdQ/H\ng7AAAACDAAAA6aEAAACBOZAAAMB1D8eDsAAAAIEAAADpigAAAIE5kQAAwHUMx4OwAAAAhAAAAOt2\ngTmTAADAdQzHg7AAAACFAAAA62KBOY0AAMB1DMeDsAAAAIIAAADrToE5jwAAwHUMx4OwAAAAhgAA\nAOs6gTmSAADAdQzHg7AAAACKAAAA6yaBObUCAMB1DMeDsAAAAI0AAADrEoE5tAIAwHUKx4OwAAAA\njgAAAIuTsAAAALkIAAAAQf/QibuwAAAA6wpMiUkIi0kEQf/QSImrqAAAAOnY/v//M8BIi1wkMEiL\nbCQ4SIt0JEBIg8QgX8ODJamCAwAAw0iJfCQQTIl0JCBVSIvsSIPscEhj+UiNTeDoloX//4H/AAEA\nAHNdSItV4IO61AAAAAF+FkyNReC6AQAAAIvP6BUjAABIi1Xg6w5Ii4IIAQAAD7cEeIPgAYXAdBBI\ni4IQAQAAD7YEOOnEAAAAgH34AHQLSItF8IOgyAAAAP2Lx+m9AAAASItF4IO41AAAAAF+K0SL90iN\nVeBBwf4IQQ+2zugMGwAAhcB0E0SIdRBAiH0RxkUSALkCAAAA6xjoLL///7kBAAAAxwAqAAAAQIh9\nEMZFEQBIi1Xgx0QkQAEAAABMjU0Qi0IESIuSOAEAAEG4AAEAAIlEJDhIjUUgx0QkMAMAAABIiUQk\nKIlMJCBIjU3g6OcOAACFwA+ETv///4P4AQ+2RSB0CQ+2TSHB4AgLwYB9+AB0C0iLTfCDocgAAAD9\nTI1cJHBJi3sYTYtzKEmL413DzMyDPaFyAQAAdQ6NQb+D+Bl3A4PBIIvBwzPS6Y7+///MzEiD7ChI\niwGBOGNzbeB1HIN4GAR1FotIII2B4Pps5oP4AnYPgfkAQJkBdAczwEiDxCjD6GHO///MSIPsKEiN\nDb3////oKMf//zPASIPEKMPMSIlcJBhIiXQkIFdIg+wwgz1GkgMAAHUF6Huw//9IjT0QfQEAQbgE\nAQAAM8lIi9fGBQJ+AQAA/xXoZAAASIsdyYEDAEiJPdprAQBIhdt0BYA7AHUDSIvfSI1EJEhMjUwk\nQEUzwDPSSIvLSIlEJCDogQAAAEhjdCRASLn/////////H0g78XNZSGNMJEhIg/n/c05IjRTxSDvR\nckVIi8roacz//0iL+EiFwHQ1TI0E8EiNRCRITI1MJEBIi9dIi8tIiUQkIOgrAAAAi0QkQEiJPTBr\nAQD/yIkFJGsBADPA6wODyP9Ii1wkUEiLdCRYSIPEMF/DzEiLxEiJWAhIiWgQSIlwGEiJeCBBVEFW\nQVdIg+wgTIt0JGBNi+FJi/hBgyYATIv6SIvZQccBAQAAAEiF0nQHTIkCSYPHCDPtgDsidREzwIXt\nQLYiD5TASP/Di+jrN0H/BkiF/3QHigOIB0j/xw+2M0j/w4vO6H8hAACFwHQSQf8GSIX/dAeKA4gH\nSP/HSP/DQIT2dBuF7XWvQID+IHQGQID+CXWjSIX/dAnGR/8A6wNI/8sz9oA7AA+E3gAAAIA7IHQF\ngDsJdQVI/8Pr8YA7AA+ExgAAAE2F/3QHSYk/SYPHCEH/BCS6AQAAADPJ6wVI/8P/wYA7XHT2gDsi\ndTWEynUdhfZ0DkiNQwGAOCJ1BUiL2OsLM8Az0oX2D5TAi/DR6esQ/8lIhf90BsYHXEj/x0H/BoXJ\ndeyKA4TAdEyF9nUIPCB0RDwJdECF0nQ0D77I6KQgAABIhf90GoXAdA2KA0j/w4gHSP/HQf8GigOI\nB0j/x+sKhcB0Bkj/w0H/BkH/Bkj/w+ld////SIX/dAbGBwBI/8dB/wbpGf///02F/3QESYMnAEH/\nBCRIi1wkQEiLbCRISIt0JFBIi3wkWEiDxCBBX0FeQVzDzEiJXCQISIlsJBBIiXQkGFdIg+wwgz2F\njwMAAHUF6Lqt//9Iix0XbwEAM/9Ihdt1HIPI/+m1AAAAPD10Av/HSIvL6DISAABI/8NIA9iKA4TA\ndeaNRwG6CAAAAEhjyOhuyf//SIv4SIkF7GgBAEiFwHS/SIsdyG4BAIA7AHRQSIvL6PMRAACAOz2N\ncAF0Lkhj7roBAAAASIvN6DPJ//9IiQdIhcB0XUyLw0iL1UiLyOiRHwAAhcB1ZEiDxwhIY8ZIA9iA\nOwB1t0iLHXNuAQBIi8voZ4b//0iDJWNuAQAASIMnAMcFuY4DAAEAAAAzwEiLXCRASItsJEhIi3Qk\nUEiDxDBfw0iLDU9oAQDoLob//0iDJUJoAQAA6RX///9Ig2QkIABFM8lFM8Az0jPJ6Myo///MzMzM\niQ1+cAEAw8xIg+wohcl4IIP5An4Ng/kDdRaLBSx6AQDrIYsFJHoBAIkNHnoBAOsT6AO6///HABYA\nAADoaKj//4PI/0iDxCjDSIlcJCBVSIvsSIPsIEiLBThQAQBIg2UYAEi7MqLfLZkrAABIO8N1b0iN\nTRj/FSpiAABIi0UYSIlFEP8VzGIAAIvASDFFEP8VCGIAAEiNTSCLwEgxRRD/FfBhAACLRSBIweAg\nSI1NEEgzRSBIM0UQSDPBSLn///////8AAEgjwUi5M6LfLZkrAABIO8NID0TBSIkFtU8BAEiLXCRI\nSPfQSIkFrk8BAEiDxCBdw0iLxEiJWAhIiWgQSIlwGEiJeCBBVkiD7ED/FZlhAABFM/ZIi/hIhcAP\nhKkAAABIi9hmRDkwdBRIg8MCZkQ5M3X2SIPDAmZEOTN17EyJdCQ4SCvYTIl0JDBI0ftMi8Az0kSN\nSwEzyUSJdCQoTIl0JCD/FbphAABIY+iFwHRRSIvN6KPH//9Ii/BIhcB0QUyJdCQ4TIl0JDBEjUsB\nTIvHM9IzyYlsJChIiUQkIP8Vf2EAAIXAdQtIi87oV4T//0mL9kiLz/8V92AAAEiLxusLSIvP/xXp\nYAAAM8BIi1wkUEiLbCRYSIt0JGBIi3wkaEiDxEBBXsNIhckPhAABAABTSIPsIEiL2UiLSRhIOw2Y\nYAEAdAXo/YP//0iLSyBIOw2OYAEAdAXo64P//0iLSyhIOw2EYAEAdAXo2YP//0iLSzBIOw16YAEA\ndAXox4P//0iLSzhIOw1wYAEAdAXotYP//0iLS0BIOw1mYAEAdAXoo4P//0iLS0hIOw1cYAEAdAXo\nkYP//0iLS2hIOw1qYAEAdAXof4P//0iLS3BIOw1gYAEAdAXobYP//0iLS3hIOw1WYAEAdAXoW4P/\n/0iLi4AAAABIOw1JYAEAdAXoRoP//0iLi4gAAABIOw08YAEAdAXoMYP//0iLi5AAAABIOw0vYAEA\ndAXoHIP//0iDxCBbw8zMSIXJdGZTSIPsIEiL2UiLCUg7DXlfAQB0Bej2gv//SItLCEg7DW9fAQB0\nBejkgv//SItLEEg7DWVfAQB0BejSgv//SItLWEg7DZtfAQB0BejAgv//SItLYEg7DZFfAQB0Beiu\ngv//SIPEIFvDSIXJD4TwAwAAU0iD7CBIi9lIi0kI6I6C//9Ii0sQ6IWC//9Ii0sY6HyC//9Ii0sg\n6HOC//9Ii0so6GqC//9Ii0sw6GGC//9IiwvoWYL//0iLS0DoUIL//0iLS0joR4L//0iLS1DoPoL/\n/0iLS1joNYL//0iLS2DoLIL//0iLS2joI4L//0iLSzjoGoL//0iLS3DoEYL//0iLS3joCIL//0iL\ni4AAAADo/IH//0iLi4gAAADo8IH//0iLi5AAAADo5IH//0iLi5gAAADo2IH//0iLi6AAAADozIH/\n/0iLi6gAAADowIH//0iLi7AAAADotIH//0iLi7gAAADoqIH//0iLi8AAAADonIH//0iLi8gAAADo\nkIH//0iLi9AAAADohIH//0iLi9gAAADoeIH//0iLi+AAAADobIH//0iLi+gAAADoYIH//0iLi/AA\nAADoVIH//0iLi/gAAADoSIH//0iLiwABAADoPIH//0iLiwgBAADoMIH//0iLixABAADoJIH//0iL\nixgBAADoGIH//0iLiyABAADoDIH//0iLiygBAADoAIH//0iLizABAADo9ID//0iLizgBAADo6ID/\n/0iLi0ABAADo3ID//0iLi0gBAADo0ID//0iLi1ABAADoxID//0iLi2gBAADouID//0iLi3ABAADo\nrID//0iLi3gBAADooID//0iLi4ABAADolID//0iLi4gBAADoiID//0iLi5ABAADofID//0iLi2AB\nAADocID//0iLi6ABAADoZID//0iLi6gBAADoWID//0iLi7ABAADoTID//0iLi7gBAADoQID//0iL\ni8ABAADoNID//0iLi8gBAADoKID//0iLi5gBAADoHID//0iLi9ABAADoEID//0iLi9gBAADoBID/\n/0iLi+ABAADo+H///0iLi+gBAADo7H///0iLi/ABAADo4H///0iLi/gBAADo1H///0iLiwACAADo\nyH///0iLiwgCAADovH///0iLixACAADosH///0iLixgCAADopH///0iLiyACAADomH///0iLiygC\nAADojH///0iLizACAADogH///0iLizgCAADodH///0iLi0ACAADoaH///0iLi0gCAADoXH///0iL\ni1ACAADoUH///0iLi1gCAADoRH///0iLi2ACAADoOH///0iLi2gCAADoLH///0iLi3ACAADoIH//\n/0iLi3gCAADoFH///0iLi4ACAADoCH///0iLi4gCAADo/H7//0iLi5ACAADo8H7//0iLi5gCAADo\n5H7//0iLi6ACAADo2H7//0iLi6gCAADozH7//0iLi7ACAADowH7//0iLi7gCAADotH7//0iDxCBb\nw8zMQFVBVEFVQVZBV0iD7FBIjWwkQEiJXUBIiXVISIl9UEiLBRJJAQBIM8VIiUUIi11gM/9Ni+FF\ni+hIiVUAhdt+KkSL00mLwUH/ykA4OHQMSP/ARYXSdfBBg8r/i8NBK8L/yDvDjVgBfAKL2ESLdXiL\n90WF9nUHSIsBRItwBPedgAAAAESLy02LxBvSQYvOiXwkKIPiCEiJfCQg/8L/Fe9ZAABMY/iFwHUH\nM8DpFwIAAEm58P///////w+FwH5uM9JIjULgSff3SIP4AnJfS40MP0iNQRBIO8F2UkqNDH0QAAAA\nSIH5AAQAAHcqSI1BD0g7wXcDSYvBSIPg8Oh5lP//SCvgSI18JEBIhf90nMcHzMwAAOsT6NN9//9I\ni/hIhcB0CscA3d0AAEiDxxBIhf8PhHT///9Ei8tNi8S6AQAAAEGLzkSJfCQoSIl8JCD/FT5ZAACF\nwA+EWQEAAEyLZQAhdCQoSCF0JCBJi8xFi89Mi8dBi9XocAcAAEhj8IXAD4QwAQAAQbkABAAARYXp\ndDaLTXCFyQ+EGgEAADvxD48SAQAASItFaIlMJChFi89Mi8dBi9VJi8xIiUQkIOgpBwAA6e8AAACF\nwH53M9JIjULgSPf2SIP4AnJoSI0MNkiNQRBIO8F2W0iNDHUQAAAASTvJdzVIjUEPSDvBdwpIuPD/\n//////8PSIPg8Ohrk///SCvgSI1cJEBIhdsPhJUAAADHA8zMAADrE+jBfP//SIvYSIXAdA7HAN3d\nAABIg8MQ6wIz20iF23RtRYvPTIvHQYvVSYvMiXQkKEiJXCQg6IgGAAAzyYXAdDyLRXAz0kiJTCQ4\nRIvOTIvDSIlMJDCFwHULiUwkKEiJTCQg6w2JRCQoSItFaEiJRCQgQYvO/xUoWQAAi/BIjUvwgTnd\n3QAAdQXo+Xv//0iNT/CBOd3dAAB1Bejoe///i8ZIi00ISDPN6KZ///9Ii11ASIt1SEiLfVBIjWUQ\nQV9BXkFdQVxdw0iJXCQISIl0JBBXSIPscEiL8kiL0UiNTCRQSYvZQYv46JN1//+LhCTAAAAASI1M\nJFBMi8uJRCRAi4QkuAAAAESLx4lEJDiLhCSwAAAASIvWiUQkMEiLhCSoAAAASIlEJCiLhCSgAAAA\niUQkIOij/P//gHwkaAB0DEiLTCRgg6HIAAAA/UyNXCRwSYtbEEmLcxhJi+Nfw8zMQFVBVEFVQVZB\nV0iD7EBIjWwkMEiJXUBIiXVISIl9UEiLBY5FAQBIM8VIiUUARIt1aDP/RYv5TYvgRIvqRYX2dQdI\niwFEi3AE911wQYvOiXwkKBvSSIl8JCCD4gj/wv8VqFYAAEhj8IXAdQczwOneAAAAfndIuPD/////\n//9/SDvwd2hIjQw2SI1BEEg7wXZbSI0MdRAAAABIgfkABAAAdzFIjUEPSDvBdwpIuPD///////8P\nSIPg8Og3kf//SCvgSI1cJDBIhdt0occDzMwAAOsT6JF6//9Ii9hIhcB0D8cA3d0AAEiDwxDrA0iL\n30iF2w+EdP///0yLxjPSSIvLTQPA6J2D//9Fi89Ni8S6AQAAAEGLzol0JChIiVwkIP8V6FUAAIXA\ndBVMi01gRIvASIvTQYvN/xWhVgAAi/hIjUvwgTnd3QAAdQXo2nn//4vHSItNAEgzzeiYff//SItd\nQEiLdUhIi31QSI1lEEFfQV5BXUFcXcPMzEiJXCQISIl0JBBXSIPsYIvySIvRSI1MJEBBi9lJi/jo\nhHP//4uEJKAAAABIjUwkQESLy4lEJDCLhCSYAAAATIvHiUQkKEiLhCSQAAAAi9ZIiUQkIOgv/v//\ngHwkWAB0DEiLTCRQg6HIAAAA/UiLXCRwSIt0JHhIg8RgX8NmiUwkCFNIg+wguP//AAAPt9pmO8h1\nBDPA60W4AAEAAGY7yHMQSIsFNFYBAA+3yQ+3BEjrJrkBAAAATI1MJEBIjVQkMESLwf8Vm1UAADPJ\nhcB0BQ+3TCRAD7fBD7fLI8FIg8QgW8PMzEiJXCQISIl0JBBXSIPsMEljwUmL2Iv6SIvxRYXJfgtI\ni9BIi8voMgEAAEyLw4vXRIvISIvOSItcJEBIi3QkSEiDxDBf6b8CAADMzMxAU0iD7CBFM9JMi8lI\nhcl0DkiF0nQJTYXAdR1mRIkR6Iys//+7FgAAAIkY6PCa//+Lw0iDxCBbw2ZEORF0CUiDwQJI/8p1\n8UiF0nUGZkWJEevNSSvIQQ+3AGZCiQQBTY1AAmaFwHQFSP/KdelIhdJ1EGZFiRHoNqz//7siAAAA\n66gzwOutzMzMQFNIg+wgRTPSSIXJdA5IhdJ0CU2FwHUdZkSJEegHrP//uxYAAACJGOhrmv//i8NI\ng8QgW8NMi8lNK8hBD7cAZkOJBAFNjUACZoXAdAVI/8p16UiF0nUQZkSJEejIq///uyIAAADrvzPA\n68TMSIvBD7cQSIPAAmaF0nX0SCvBSNH4SP/Iw8zMzEUzwEGLwEiF0nQSZkQ5AXQMSP/ASIPBAkg7\nwnLuw8zMQFNIg+wgM9tNhcl1DkiFyXUOSIXSdSAzwOsvSIXJdBdIhdJ0Ek2FyXUFZokZ6+hNhcB1\nHGaJGehEq///uxYAAACJGOiomf//i8NIg8QgW8NMi9lMi9JJg/n/dRxNK9hBD7cAZkOJBANNjUAC\nZoXAdC9J/8p16esoTCvBQw+3BBhmQYkDTY1bAmaFwHQKSf/KdAVJ/8l15E2FyXUEZkGJG02F0g+F\nbv///0mD+f91C2aJXFH+QY1CUOuQZokZ6L6q//+7IgAAAOl1////SIvESIlYCEiJaBBIiXAYSIl4\nIEFWSIPsIEiL6TP/vuMAAABMjTXm8AAAjQQ+QbhVAAAASIvNmSvC0fhIY9hIi9NIA9JJixTW6AMB\nAACFwHQTeQWNc//rA417ATv+fsuDyP/rC0iLw0gDwEGLRMYISItcJDBIi2wkOEiLdCRASIt8JEhI\ng8QgQV7DzMxIg+woSIXJdCLoZv///4XAeBlImEg95AAAAHMPSI0NIeIAAEgDwIsEwesCM8BIg8Qo\nw8zMTIvcSYlbCEmJcxBXSIPsUEyLFZltAwBBi9lJi/hMMxUsQAEAi/J0KjPASYlD6EmJQ+BJiUPY\ni4QkiAAAAIlEJChIi4QkgAAAAEmJQ8hB/9LrLeh1////RIvLTIvHi8iLhCSIAAAAi9aJRCQoSIuE\nJIAAAABIiUQkIP8VAVIAAEiLXCRgSIt0JGhIg8RQX8PMRTPJTIvSTIvZTYXAdENMK9pDD7cME41B\nv2aD+Bl3BGaDwSBBD7cSjUK/ZoP4GXcEZoPCIEmDwgJJ/8h0CmaFyXQFZjvKdMoPt8JED7fJRCvI\nQYvBw8zMzMzMzMzMzMzMzMzMzMzMZmYPH4QAAAAAAEiLwUj32UipBwAAAHQPZpCKEEj/wITSdF+o\nB3XzSbj//v7+/v7+fkm7AAEBAQEBAYFIixBNi8hIg8AITAPKSPfSSTPRSSPTdOhIi1D4hNJ0UYT2\ndEdIweoQhNJ0OYT2dC9IweoQhNJ0IYT2dBfB6hCE0nQKhPZ1uUiNRAH/w0iNRAH+w0iNRAH9w0iN\nRAH8w0iNRAH7w0iNRAH6w0iNRAH5w0iNRAH4w0BTVVZXQVRBVkFXSIPsUEiLBZI+AQBIM8RIiUQk\nSEyL+TPJQYvoTIvi/xXpUAAAM/9Ii/Dow6z//0g5PThoAQBEi/APhfgAAABIjQ0AEwEAM9JBuAAI\nAAD/FbJPAABIi9hIhcB1Lf8V5E8AAIP4Vw+F4AEAAEiNDdQSAQBFM8Az0v8ViU8AAEiL2EiFwA+E\nwgEAAEiNFc4SAQBIi8v/Ff1PAABIhcAPhKkBAABIi8j/FWNQAABIjRW8EgEASIvLSIkFsmcBAP8V\n1E8AAEiLyP8VQ1AAAEiNFawSAQBIi8tIiQWaZwEA/xW0TwAASIvI/xUjUAAASI0VpBIBAEiLy0iJ\nBYJnAQD/FZRPAABIi8j/FQNQAABIiQV8ZwEASIXAdCBIjRWYEgEASIvL/xVvTwAASIvI/xXeTwAA\nSIkFT2cBAP8VMVAAAIXAdB1Nhf90CUmLz/8Vh08AAEWF9nQmuAQAAADp7wAAAEWF9nQXSIsNBGcB\nAP8Vpk8AALgDAAAA6dMAAABIiw0FZwEASDvOdGNIOTUBZwEAdFr/FYFPAABIiw3yZgEASIvY/xVx\nTwAATIvwSIXbdDxIhcB0N//TSIXAdCpIjUwkMEG5DAAAAEyNRCQ4SIlMJCBBjVH1SIvIQf/WhcB0\nB/ZEJEABdQYPuu0V60BIiw2GZgEASDvOdDT/FRtPAABIhcB0Kf/QSIv4SIXAdB9Iiw1tZgEASDvO\ndBP/FfpOAABIhcB0CEiLz//QSIv4SIsNPmYBAP8V4E4AAEiFwHQQRIvNTYvESYvXSIvP/9DrAjPA\nSItMJEhIM8zohHX//0iDxFBBX0FeQVxfXl1bw8xIg+woSIXJdRnoyqX//8cAFgAAAOgvlP//SIPI\n/0iDxCjDTIvBSIsNkGIBADPSSIPEKEj/JTtOAADMzMxIiVwkCFdIg+wgSYv4SIvaSIXJdB0z0kiN\nQuBI9/FIO8NzD+h0pf//xwAMAAAAM8DrXUgPr9m4AQAAAEiF20gPRNgzwEiD++B3GEiLDS9iAQCN\nUAhMi8P/FdNMAABIhcB1LYM9H2IBAAB0GUiLy+iJtf//hcB1y0iF/3SyxwcMAAAA66pIhf90BscH\nDAAAAEiLXCQwSIPEIF/DzMy5AgAAAOk2bf//zMxIg+wo6I+1//9IhcB0CrkWAAAA6LC1///2BeFN\nAQACdCm5FwAAAOhBPwAAhcB0B7kHAAAAzSlBuAEAAAC6FQAAQEGNSALotpH//7kDAAAA6MBt///M\nzMzMSIPsKIP5/nUN6I6k///HAAkAAADrQoXJeC47DTxnAwBzJkhjyUiNFWxhAQBIi8GD4R9IwfgF\nSGvJWEiLBMIPvkQICIPgQOsS6E+k///HAAkAAADotJL//zPASIPEKMPMQFNIg+xAi9lIjUwkIOjy\naf//SItEJCAPttNIi4gIAQAAD7cEUSUAgAAAgHwkOAB0DEiLTCQwg6HIAAAA/UiDxEBbw8xAU0iD\n7ECL2UiNTCQgM9LorGn//0iLRCQgD7bTSIuICAEAAA+3BFElAIAAAIB8JDgAdAxIi0wkMIOhyAAA\nAP1Ig8RAW8PMzMxIiVwkCEiJdCQYZkSJTCQgV0iD7GBJi/hIi/JIi9lIhdJ1E02FwHQOSIXJdAIh\nETPA6ZUAAABIhcl0A4MJ/0mB+P///392E+hko///uxYAAACJGOjIkf//629Ii5QkkAAAAEiNTCRA\n6Axp//9Ii0QkQEiDuDgBAAAAdX8Pt4QkiAAAALn/AAAAZjvBdlBIhfZ0EkiF/3QNTIvHM9JIi87o\nWHj//+gHo///xwAqAAAA6Pyi//+LGIB8JFgAdAxIi0wkUIOhyAAAAP2Lw0yNXCRgSYtbEEmLcyBJ\ni+Nfw0iF9nQLSIX/D4SJAAAAiAZIhdt0VccDAQAAAOtNg2QkeABIjUwkeEyNhCSIAAAASIlMJDhI\ng2QkMACLSARBuQEAAAAz0ol8JChIiXQkIP8Va0sAAIXAdBmDfCR4AA+FZP///0iF23QCiQMz2+lo\n/////xVgSgAAg/h6D4VH////SIX2dBJIhf90DUyLxzPSSIvO6Ih3///oN6L//7siAAAAiRjom5D/\n/+ks////zMxIg+w4SINkJCAA6GX+//9Ig8Q4w0iJXCQYSIlsJCBWV0FWSIPsQEiLBVM4AQBIM8RI\niUQkMPZCGEBIi/oPt/EPhXkBAABIi8ro53z//0iNLbxJAQBMjTXFXgEAg/j/dDFIi8/ozHz//4P4\n/nQkSIvP6L98//9Ii89IY9hIwfsF6LB8//+D4B9Ia8hYSQMM3usDSIvNikE4JH88Ag+EBgEAAEiL\nz+iLfP//g/j/dDFIi8/ofnz//4P4/nQkSIvP6HF8//9Ii89IY9hIwfsF6GJ8//+D4B9Ia8hYSQMM\n3usDSIvNikE4JH88AQ+EuAAAAEiLz+g9fP//g/j/dC9Ii8/oMHz//4P4/nQiSIvP6CN8//9Ii89I\nY9hIwfsF6BR8//+D4B9Ia+hYSQMs3vZFCIAPhIkAAABIjVQkJEiNTCQgRA+3zkG4BQAAAOiy/v//\nM9uFwHQKuP//AADpiQAAADlcJCB+PkyNdCQk/08IeBZIiw9BigaIAUiLBw+2CEj/wEiJB+sOQQ++\nDkiL1+jMy///i8iD+f90vf/DSf/GO1wkIHzHD7fG60BIY08ISIPB/olPCIXJeCZIiw9miTHrFUhj\nRwhIg8D+iUcIhcB4D0iLB2aJMEiDBwIPt8brC0iL1w+3zuh5BQAASItMJDBIM8zozG///0iLXCRw\nSItsJHhIg8RAQV5fXsPMSIvESIlYCEiJaBBIiXAYSIl4IEFWSIPsUEUz9kmL6EiL8kiL+UiF0nQT\nTYXAdA5EODJ1JkiFyXQEZkSJMTPASItcJGBIi2wkaEiLdCRwSIt8JHhIg8RQQV7DSI1MJDBJi9Ho\nfWX//0iLRCQwTDmwOAEAAHUVSIX/dAYPtgZmiQe7AQAAAOmtAAAAD7YOSI1UJDDoSfv//7sBAAAA\nhcB0WkiLTCQwRIuJ1AAAAEQ7y34vQTvpfCqLSQRBi8ZIhf8PlcCNUwhMi8aJRCQoSIl8JCD/FflG\nAABIi0wkMIXAdRJIY4HUAAAASDvocj1EOHYBdDeLmdQAAADrPUGLxkiF/0SLyw+VwEyLxroJAAAA\niUQkKEiLRCQwSIl8JCCLSAT/FatGAACFwHUO6O6e//+Dy//HACoAAABEOHQkSHQMSItMJECDocgA\nAAD9i8Pp7v7//8zMzEUzyemk/v//SIlcJBCJTCQIVldBVEFWQVdIg+wgQYvwTIvySGPZg/v+dRjo\nKJ7//4MgAOiQnv//xwAJAAAA6ZQAAACFyXh4Ox07YQMAc3BIi8NIi/tIwf8FTI0lZFsBAIPgH0xr\n+FhJiwT8Qg++TDgIg+EBdEmLy+iDef//kEmLBPxC9kQ4CAF0EkSLxkmL1ovL6FkAAABIi/jrF+gn\nnv//xwAJAAAA6Kyd//+DIABIg8//i8vo/nr//0iLx+sc6JSd//+DIADo/J3//8cACQAAAOhhjP//\nSIPI/0iLXCRYSIPEIEFfQV5BXF9ew8zMzEiJXCQISIl0JBBXSIPsIEhj2UGL+EiL8ovL6DV6//9I\ng/j/dRHorp3//8cACQAAAEiDyP/rTUyNRCRIRIvPSIvWSIvI/xUyRgAAhcB1D/8ViEUAAIvI6C2d\n///r00iLy0iLw0iNFWpaAQBIwfgFg+EfSIsEwkhryViAZAgI/UiLRCRISItcJDBIi3QkOEiDxCBf\nw8xAU0iD7CD/BWxLAQBIi9m5ABAAAOgPrP//SIlDEEiFwHQNg0sYCMdDJAAQAADrE4NLGARIjUMg\nx0MkAgAAAEiJQxBIi0MQg2MIAEiJA0iDxCBbw8xmiUwkCEiD7DhIiw38RgEASIP5/nUM6MEDAABI\niw3qRgEASIP5/3UHuP//AADrJUiDZCQgAEyNTCRISI1UJEBBuAEAAAD/FVFFAACFwHTZD7dEJEBI\ng8Q4w8zMzEiJdCQQVVdBVkiL7EiD7GBIY/lEi/JIjU3gSYvQ6Dpi//+NRwE9AAEAAHcRSItF4EiL\niAgBAAAPtwR563mL90iNVeDB/ghAD7bO6An4//+6AQAAAIXAdBJAiHU4QIh9OcZFOgBEjUoB6wtA\niH04xkU5AESLykiLReCJVCQwTI1FOItIBEiNRSCJTCQoSI1N4EiJRCQg6Bru//+FwHUUOEX4dAtI\ni0Xwg6DIAAAA/TPA6xgPt0UgQSPGgH34AHQLSItN8IOhyAAAAP1Ii7QkiAAAAEiDxGBBXl9dw8xI\niVwkCEiJdCQQV0iD7ECL2kiL0UiNTCQgQYv5QYvw6Fxh//9Ii0QkKA+200CEfAIZdR6F9nQUSItE\nJCBIi4gIAQAAD7cEUSPG6wIzwIXAdAW4AQAAAIB8JDgAdAxIi0wkMIOhyAAAAP1Ii1wkUEiLdCRY\nSIPEQF/DzMzMi9FBuQQAAABFM8Azyely////zMxAU0iD7CBIhcl0DUiF0nQITYXAdRxEiAHoC5v/\n/7sWAAAAiRjob4n//4vDSIPEIFvDTIvJTSvIQYoAQ4gEAUn/wITAdAVI/8p17UiF0nUOiBHo0pr/\n/7siAAAA68UzwOvKzMzMSIlcJAhIiWwkGFZXQVZIg+wgRIvxSIvKSIva6LR1//+LUxhIY/D2woJ1\nGeiUmv//xwAJAAAAg0sYILj//wAA6TYBAAD2wkB0Deh2mv//xwAiAAAA6+Az//bCAXQZiXsI9sIQ\nD4SKAAAASItDEIPi/kiJA4lTGItDGIl7CIPg74PIAolDGKkMAQAAdS/oc3L//0iDwDBIO9h0Duhl\ncv//SIPAYEg72HULi87oefX//4XAdQhIi8voxfz///dDGAgBAAAPhIoAAACLK0iLUxAraxBIjUIC\nSIkDi0Mkg+gCiUMIhe1+GUSLxYvO6N3J//+L+OtVg8ogiVMY6Tz///+NRgKD+AF2HkiLzkiLxkyN\nBa1WAQCD4R9IwfgFSGvRWEkDFMDrB0iNFYVBAQD2QgggdBcz0ovORI1CAujG+v//SIP4/w+E7v7/\n/0iLQxBmRIkw6xy9AgAAAEiNVCRIi85Ei8VmRIl0JEjoYMn//4v4O/0PhcD+//9BD7fGSItcJEBI\ni2wkUEiDxCBBXl9ew8zMzEiD7ChIiw1FQwEASI1BAkiD+AF2Bv8V7UAAAEiDxCjDSIPsSEiDZCQw\nAINkJCgAQbgDAAAASI0N5AQBAEUzyboAAABARIlEJCD/FaFAAABIiQX6QgEASIPESMPMQFNWV0iB\n7IAAAABIiwUiLwEASDPESIlEJHhIi/FIi9pIjUwkSEmL0EmL+ehwXv//SI1EJEhIjVQkQEiJRCQ4\ng2QkMACDZCQoAINkJCAASI1MJGhFM8lMi8PoRg0AAIvYSIX/dAhIi0wkQEiJD0iNTCRoSIvW6HIH\nAACLyLgDAAAAhNh1DIP5AXQag/kCdRPrBfbDAXQHuAQAAADrB/bDAnUCM8CAfCRgAHQMSItMJFiD\nocgAAAD9SItMJHhIM8zosGf//0iBxIAAAABfXlvDzEiJXCQYV0iB7IAAAABIiwVQLgEASDPESIlE\nJHhIi/lIi9pIjUwkQEmL0OihXf//SI1EJEBIjVQkYEiJRCQ4g2QkMACDZCQoAINkJCAASI1MJGhF\nM8lMi8PodwwAAEiNTCRoSIvXi9jo+AAAAIvIuAMAAACE2HUMg/kBdBqD+QJ1E+sF9sMBdAe4BAAA\nAOsH9sMCdQIzwIB8JFgAdAxIi0wkUIOhyAAAAP1Ii0wkeEgzzOjuZv//SIucJKAAAABIgcSAAAAA\nX8PMRTPJ6WD+///pAwAAAMzMzEiNBQ0fAABIjQ1SFAAASIkFnz4BAEiNBZgfAABIiQ2JPgEASIkF\nkj4BAEiNBcsfAABIiQ2cPgEASIkFhT4BAEiNBT4gAABIiQV/PgEASI0FMBQAAEiJBYE+AQBIjQVa\nHwAASIkFez4BAEiNBaweAABIiQV1PgEASI0Fhh8AAEiJBW8+AQDDzMzMzMzMSIlcJAhIiXQkGEiJ\nfCQgVUFUQVVBVkFXSIvsSIPsYEiLBdosAQBIM8RIiUX4D7dBCkQPtwkz24v4JQCAAABBweEQiUXE\ni0EGgef/fwAAiUXoi0ECge//PwAAQbwfAAAASIlV0ESJTdiJRexEiU3wjXMBRY10JOSB/wHA//91\nKUSLw4vDOVyF6HUNSAPGSTvGfPLptwQAAEiJXeiJXfC7AgAAAOmmBAAASItF6EWLxEGDz/9IiUXg\niwUjQAEAiX3A/8hEi+uJRcj/wJlBI9QDwkSL0EEjxEHB+gUrwkQrwE1j2kKLTJ3oRIlF3EQPo8EP\ng54AAABBi8hBi8dJY9LT4PfQhUSV6HUZQY1CAUhjyOsJOVyN6HUKSAPOSTvOfPLrcotFyEGLzJlB\nI9QDwkSLwEEjxCvCQcH4BYvWK8hNY9hCi0Sd6NPijQwQO8hyBDvKcwNEi+5BjUD/QolMnehIY9CF\nwHgnRYXtdCKLRJXoRIvrRI1AAUQ7wHIFRDvGcwNEi+5EiUSV6Egr1nnZRItF3E1j2kGLyEGLx9Pg\nQiFEnehBjUIBSGPQSTvWfR1IjU3oTYvGTCvCSI0MkTPSScHgAugrav//RItN2EWF7XQCA/6LDQY/\nAQCLwSsFAj8BADv4fRRIiV3oiV3wRIvDuwIAAADpVAMAADv5D48xAgAAK03ASItF4EWL10iJReiL\nwUSJTfCZTYveRIvLQSPUTI1F6APCRIvoQSPEK8JBwf0Fi8iL+LggAAAAQdPiK8FEi/BB99JBiwCL\nz4vQ0+hBi85BC8FBI9JEi8pBiQBNjUAEQdPhTCveddxNY9VBjXsCRY1zA02LykSLx0n32U07wnwV\nSYvQSMHiAkqNBIqLTAXoiUwV6OsFQolchehMK8Z53ESLRchFi9xBjUABmUEj1APCRIvIQSPEK8JB\nwfkFRCvYSWPBi0yF6EQPo9kPg5gAAABBi8tBi8dJY9HT4PfQhUSV6HUZQY1BAUhjyOsJOVyN6HUK\nSAPOSTvOfPLrbEGLwEGLzJlBI9QDwkSL0EEjxCvCQcH6BYvWK8hNY+pCi0St6NPii8tEjQQQRDvA\ncgVEO8JzAovOQY1C/0aJRK3oSGPQhcB4JIXJdCCLRJXoi8tEjUABRDvAcgVEO8ZzAovORIlElehI\nK9Z53EGLy0GLx9PgSWPJIUSN6EGNQQFIY9BJO9Z9GUiNTehNi8ZMK8JIjQyRM9JJweAC6FVo//+L\nBUM9AQBBvSAAAABEi8v/wEyNReiZQSPUA8JEi9BBI8QrwkHB+gWLyESL2EHT50Qr6EH310GLAEGL\ny4vQ0+hBi81BC8FBI9dEi8pBiQBNjUAEQdPhTCv2ddtNY9JMi8dNi8pJ99lNO8J8FUmL0EjB4gJK\njQSKi0wF6IlMFejrBUKJXIXoTCvGedxEi8OL3+kbAQAAiwWvPAEARIsVnDwBAEG9IAAAAJlBI9QD\nwkSL2EEjxCvCQcH7BYvIQdPnQffXQTv6fHpIiV3oD7pt6B+JXfBEK+iL+ESLy0yNRehBiwCLz0GL\n1yPQ0+hBi81BC8FEi8pB0+FBiQBNjUAETCv2ddxNY8tBjX4CTYvBSffYSTv5fBVIi9dIweICSo0E\ngotMBeiJTBXo6wSJXL3oSCv+ed1EiwUYPAEAi95FA8Lrb0SLBQo8AQAPunXoH0SL00QDx4v4RCvo\nTI1N6EGLAYvPi9DT6EGLzUELwkEj10SL0kGJAU2NSQRB0+JMK/Z13E1j00GNfgJNi8pJ99lJO/p8\nFUiL10jB4gJKjQSKi0wF6IlMFejrBIlcvehIK/553UiLVdBEKyWPOwEAQYrMQdPg913EG8AlAAAA\ngEQLwIsFejsBAEQLReiD+EB1C4tF7ESJQgSJAusIg/ggdQNEiQKLw0iLTfhIM8zoqGD//0yNXCRg\nSYtbMEmLc0BJi3tISYvjQV9BXkFdQVxdw8zMSIlcJAhIiXQkGEiJfCQgVUFUQVVBVkFXSIvsSIPs\nYEiLBSInAQBIM8RIiUX4D7dBCkQPtwkz24v4JQCAAABBweEQiUXEi0EGgef/fwAAiUXoi0ECge//\nPwAAQbwfAAAASIlV0ESJTdiJRexEiU3wjXMBRY10JOSB/wHA//91KUSLw4vDOVyF6HUNSAPGSTvG\nfPLptwQAAEiJXeiJXfC7AgAAAOmmBAAASItF6EWLxEGDz/9IiUXgiwWDOgEAiX3A/8hEi+uJRcj/\nwJlBI9QDwkSL0EEjxEHB+gUrwkQrwE1j2kKLTJ3oRIlF3EQPo8EPg54AAABBi8hBi8dJY9LT4PfQ\nhUSV6HUZQY1CAUhjyOsJOVyN6HUKSAPOSTvOfPLrcotFyEGLzJlBI9QDwkSLwEEjxCvCQcH4BYvW\nK8hNY9hCi0Sd6NPijQwQO8hyBDvKcwNEi+5BjUD/QolMnehIY9CFwHgnRYXtdCKLRJXoRIvrRI1A\nAUQ7wHIFRDvGcwNEi+5EiUSV6Egr1nnZRItF3E1j2kGLyEGLx9PgQiFEnehBjUIBSGPQSTvWfR1I\njU3oTYvGTCvCSI0MkTPSScHgAuhzZP//RItN2EWF7XQCA/6LDWY5AQCLwSsFYjkBADv4fRRIiV3o\niV3wRIvDuwIAAADpVAMAADv5D48xAgAAK03ASItF4EWL10iJReiLwUSJTfCZTYveRIvLQSPUTI1F\n6APCRIvoQSPEK8JBwf0Fi8iL+LggAAAAQdPiK8FEi/BB99JBiwCLz4vQ0+hBi85BC8FBI9JEi8pB\niQBNjUAEQdPhTCveddxNY9VBjXsCRY1zA02LykSLx0n32U07wnwVSYvQSMHiAkqNBIqLTAXoiUwV\n6OsFQolchehMK8Z53ESLRchFi9xBjUABmUEj1APCRIvIQSPEK8JBwfkFRCvYSWPBi0yF6EQPo9kP\ng5gAAABBi8tBi8dJY9HT4PfQhUSV6HUZQY1BAUhjyOsJOVyN6HUKSAPOSTvOfPLrbEGLwEGLzJlB\nI9QDwkSL0EEjxCvCQcH6BYvWK8hNY+pCi0St6NPii8tEjQQQRDvAcgVEO8JzAovOQY1C/0aJRK3o\nSGPQhcB4JIXJdCCLRJXoi8tEjUABRDvAcgVEO8ZzAovORIlElehIK9Z53EGLy0GLx9PgSWPJIUSN\n6EGNQQFIY9BJO9Z9GUiNTehNi8ZMK8JIjQyRM9JJweAC6J1i//+LBaM3AQBBvSAAAABEi8v/wEyN\nReiZQSPUA8JEi9BBI8QrwkHB+gWLyESL2EHT50Qr6EH310GLAEGLy4vQ0+hBi81BC8FBI9dEi8pB\niQBNjUAEQdPhTCv2ddtNY9JMi8dNi8pJ99lNO8J8FUmL0EjB4gJKjQSKi0wF6IlMFejrBUKJXIXo\nTCvGedxEi8OL3+kbAQAAiwUPNwEARIsV/DYBAEG9IAAAAJlBI9QDwkSL2EEjxCvCQcH7BYvIQdPn\nQffXQTv6fHpIiV3oD7pt6B+JXfBEK+iL+ESLy0yNRehBiwCLz0GL1yPQ0+hBi81BC8FEi8pB0+FB\niQBNjUAETCv2ddxNY8tBjX4CTYvBSffYSTv5fBVIi9dIweICSo0EgotMBeiJTBXo6wSJXL3oSCv+\ned1EiwV4NgEAi95FA8Lrb0SLBWo2AQAPunXoH0SL00QDx4v4RCvoTI1N6EGLAYvPi9DT6EGLzUEL\nwkEj10SL0kGJAU2NSQRB0+JMK/Z13E1j00GNfgJNi8pJ99lJO/p8FUiL10jB4gJKjQSKi0wF6IlM\nFejrBIlcvehIK/553UiLVdBEKyXvNQEAQYrMQdPg913EG8AlAAAAgEQLwIsF2jUBAEQLReiD+EB1\nC4tF7ESJQgSJAusIg/ggdQNEiQKLw0iLTfhIM8zo8Fr//0yNXCRgSYtbMEmLc0BJi3tISYvjQV9B\nXkFdQVxdw8zMSIlcJBhVVldBVEFVQVZBV0iNbCT5SIHsoAAAAEiLBW0hAQBIM8RIiUX/TIt1fzPb\nRIlNk0SNSwFIiU2nSIlVl0yNVd9miV2PRIvbRIlNi0SL+4ldh0SL40SL64vzi8tNhfZ1F+jLiv//\nxwAWAAAA6DB5//8zwOm/BwAASYv4QYA4IHcZSQ++AEi6ACYAAAEAAABID6PCcwVNA8Hr4UGKEE0D\nwYP5BQ+PCgIAAA+E6gEAAESLyYXJD4SDAQAAQf/JD4Q6AQAAQf/JD4TfAAAAQf/JD4SJAAAAQf/J\nD4WaAgAAQbkBAAAAsDBFi/lEiU2HRYXbdTDrCUGKEEEr8U0DwTrQdPPrH4D6OX8eQYP7GXMOKtBF\nA9lBiBJNA9FBK/FBihBNA8E60H3djULVqP10JID6Qw+OPAEAAID6RX4MgOpkQTrRD4crAQAAuQYA\nAADpSf///00rwbkLAAAA6Tz///9BuQEAAACwMEWL+eshgPo5fyBBg/sZcw0q0EUD2UGIEk0D0esD\nQQPxQYoQTQPBOtB920mLBkiLiPAAAABIiwE6EHWFuQQAAADp7/7//41CzzwIdxO5AwAAAEG5AQAA\nAE0rwenV/v//SYsGSIuI8AAAAEiLAToQdRC5BQAAAEG5AQAAAOm0/v//gPowD4XyAQAAQbkBAAAA\nQYvJ6Z3+//+NQs9BuQEAAABFi/k8CHcGQY1JAuuqSYsGSIuI8AAAAEiLAToQD4R5////jULVqP0P\nhB7///+A+jB0venw/v//jULPPAgPhmr///9JiwZIi4jwAAAASIsBOhAPhHn///+A+it0KYD6LXQT\ngPowdINBuQEAAABNK8HpcAEAALkCAAAAx0WPAIAAAOlQ////uQIAAABmiV2P6UL///+A6jBEiU2H\ngPoJD4fZAAAAuQQAAADpCv///0SLyUGD6QYPhJwAAABB/8l0c0H/yXRCQf/JD4S0AAAAQYP5Ag+F\nmwAAADldd3SKSY14/4D6K3QXgPotD4XtAAAAg02L/7kHAAAA6dn+//+5BwAAAOnP/v//QbkBAAAA\nRYvh6wZBihBNA8GA+jB09YDqMYD6CA+HRP///7kJAAAA6YX+//+NQs88CHcKuQkAAADpbv7//4D6\nMA+FjwAAALkIAAAA6X/+//+NQs9JjXj+PAh22ID6K3QHgPotdIPr1rkHAAAAg/kKdGfpWf7//0yL\nx+tjQbkBAAAAQLcwRYvh6ySA+jl/PUeNbK0AD77CRY1t6EaNLGhBgf1QFAAAfw1BihBNA8FAOtd9\n1+sXQb1RFAAA6w+A+jkPj6H+//9BihBNA8FAOtd97OmR/v//TIvHQbkBAAAASItFl0yJAEWF/w+E\nEwQAAEGD+xh2GYpF9jwFfAZBAsGIRfZNK9FBuxgAAABBA/FFhdt1FQ+30w+3w4v7i8vp7wMAAEH/\ny0ED8U0r0UE4GnTyTI1Fv0iNTd9Bi9PoThAAADldi30DQffdRAPuRYXkdQREA21nOV2HdQREK21v\nQYH9UBQAAA+PggMAAEGB/bDr//8PjGUDAABIjTUUMQEASIPuYEWF7Q+EPwMAAHkOSI01XjIBAEH3\n3UiD7mA5XZN1BGaJXb9Fhe0PhB0DAAC/AAAAgEG5/38AAEGLxUiDxlRBwf0DSIl1n4PgBw+E8QIA\nAEiYQbsAgAAAQb4BAAAASI0MQEiNFI5IiVWXZkQ5GnIli0II8g8QAkiNVc+JRdfyDxFFz0iLRc9I\nwegQSIlVl0ErxolF0Q+3QgoPt03JSIldr0QPt+BmQSPBiV23ZkQz4WZBI8lmRSPjRI0EAWZBO8kP\ng2cCAABmQTvBD4NdAgAAQbr9vwAAZkU7wg+HTQIAAEG6vz8AAGZFO8J3DEiJXcOJXb/pSQIAAGaF\nyXUgZkUDxvdFx////391Ezldw3UOOV2/dQlmiV3J6SQCAABmhcB1FmZFA8b3Qgj///9/dQk5WgR1\nBDkadLREi/tMjU2vQboFAAAARIlVh0WF0n5sQ40EP0iNfb9IjXIISGPIQYvHQSPGSAP5i9APtwcP\ntw5Ei9sPr8hBiwFEjTQIRDvwcgVEO/FzBkG7AQAAAEWJMUG+AQAAAEWF23QFZkUBcQREi12HSIPH\nAkiD7gJFK95EiV2HRYXbf7JIi1WXRSvWSYPBAkUD/kWF0g+PeP///0SLVbdEi02vuALAAABmRAPA\nvwAAAIBBv///AABmRYXAfj9Ehdd1NESLXbNBi9FFA9LB6h9FA8lBi8vB6R9DjQQbZkUDxwvCRAvR\nRIlNr4lFs0SJVbdmRYXAf8dmRYXAf2pmRQPHeWRBD7fAi/tm99gPt9BmRAPCRIR1r3QDQQP+RItd\ns0GLwkHR6UGLy8HgH0HR68HhH0QL2EHR6kQLyUSJXbNEiU2vSSvWdcuF/0SJVbe/AAAAgHQSQQ+3\nwWZBC8ZmiUWvRItNr+sED7dFr0iLdZ9BuwCAAABmQTvDdxBBgeH//wEAQYH5AIABAHVIi0Wxg8n/\nO8F1OItFtYldsTvBdSIPt0W5iV21ZkE7x3ULZkSJXblmRQPG6xBmQQPGZolFuesGQQPGiUW1RItV\nt+sGQQPGiUWxQbn/fwAAZkU7wXMdD7dFsWZFC8REiVXFZolFv4tFs2ZEiUXJiUXB6xRmQffcSIld\nvxvAI8cFAID/f4lFx0WF7Q+F7vz//4tFxw+3Vb+LTcGLfcXB6BDrNYvTD7fDi/uLy7sBAAAA6yWL\nyw+307j/fwAAuwIAAAC/AAAAgOsPD7fTD7fDi/uLy7sEAAAATItFp2YLRY9mQYlACovDZkGJEEGJ\nSAJBiXgGSItN/0gzzOiKUv//SIucJPAAAABIgcSgAAAAQV9BXkFdQVxfXl3DzMzMSIPsSItEJHhI\ng2QkMACJRCQoi0QkcIlEJCDoBQAAAEiDxEjDSIPsOEGNQbtBut////9BhcJ0SkGD+WZ1FkiLRCRw\nRItMJGBIiUQkIOhbCAAA60pBjUG/RItMJGBBhcJIi0QkcEiJRCQoi0QkaIlEJCB0B+gICQAA6yPo\nJQAAAOscSItEJHBEi0wkYEiJRCQoi0QkaIlEJCDoswUAAEiDxDjDzMxIi8RIiVgISIloEEiJcBhX\nQVRBVUFWQVdIg+xQSIv6SIuUJKgAAABMi/FIjUi4Qb8wAAAAQYvZSYvwQbz/AwAAQQ+37+irR///\nRTPJhdtBD0jZSIX/dQzo0IH//7sWAAAA6x1IhfZ0741DC0SID0hjyEg78XcZ6LGB//+7IgAAAIkY\n6BVw//9FM8np7gIAAEmLBrn/BwAASMHoNEgjwUg7wQ+FkgAAAEyJTCQoRIlMJCBMjUb+SIP+/0iN\nVwJEi8tMD0TGSYvO6OAEAABFM8mL2IXAdAhEiA/poAIAAIB/Ai2+AQAAAHUGxgctSAP+i5wkoAAA\nAESIP7plAAAAi8P32BrJgOHggMF4iAw3SI1OAUgDz+gcDQAARTPJSIXAD4RWAgAA99sayYDh4IDB\ncIgIRIhIA+lBAgAASLgAAAAAAAAAgL4BAAAASYUGdAbGBy1IA/5Ei6wkoAAAAEWL10m7////////\nDwBEiBdIA/5Bi8X32EGLxRrJgOHggMF4iA9IA/732BvSSLgAAAAAAADwf4Pi4IPq2UmFBnUbRIgX\nSYsGSAP+SSPDSPfYTRvkQYHk/gMAAOsGxgcxSAP+TIv/SAP+hdt1BUWID+sUSItEJDBIi4jwAAAA\nSIsBighBiA9NhR4PhogAAABJuAAAAAAAAA8Ahdt+LUmLBkCKzUkjwEkjw0jT6GZBA8Jmg/g5dgNm\nA8KIB0nB6AQr3kgD/maDxfx5z2aF7XhISYsGQIrNSSPASSPDSNPoZoP4CHYzSI1P/4oBLEao33UI\nRIgRSCvO6/BJO890FIoBPDl1B4DCOogR6w1AAsaIAesGSCvOQAAxhdt+GEyLw0GK0kiLz+j1VP//\nSAP7RTPJRY1RMEU4D0kPRP9B990awCTgBHCIB0mLDkgD/kjB6TSB4f8HAABJK8x4CMYHK0gD/usJ\nxgctSAP+SPfZTIvHRIgXSIH56AMAAHwzSLjP91PjpZvEIEj36UjB+gdIi8JIweg/SAPQQY0EEogH\nSAP+SGnCGPz//0gDyEk7+HUGSIP5ZHwuSLgL16NwPQrXo0j36UgD0UjB+gZIi8JIweg/SAPQQY0E\nEogHSAP+SGvCnEgDyEk7+HUGSIP5CnwrSLhnZmZmZmZmZkj36UjB+gJIi8JIweg/SAPQQY0EEogH\nSAP+SGvC9kgDyEECyogPRIhPAUGL2UQ4TCRIdAxIi0wkQIOhyAAAAP1MjVwkUIvDSYtbMEmLazhJ\ni3NASYvjQV9BXkFdQVxfw0iLxEiJWAhIiWgQSIlwGEiJeCBBVUFWQVdIg+xQTIvySIuUJKAAAABI\ni/lIjUjIRYvpSWPw6ApE//9Ihf90BU2F9nUM6DN+//+7FgAAAOsbM8CF9g9PxoPACUiYTDvwdxbo\nFn7//7siAAAAiRjoemz//+k4AQAAgLwkmAAAAABIi6wkkAAAAHQ0M9uDfQAtD5TDRTP/SAPfhfZB\nD5/HRYX/dBpIi8vo3dT//0ljz0iL00yNQAFIA8voi03//4N9AC1Ii9d1B8YHLUiNVwGF9n4bikIB\niAJIi0QkMEj/wkiLiPAAAABIiwGKCIgKM8lIjRwyTI0Fh+kAADiMJJgAAAAPlMFIA9lIK/tJg/7/\nSIvLSY0UPkkPRNboN+L//4XAD4W+AAAASI1LAkWF7XQDxgNFSItFEIA4MHRWRItFBEH/yHkHQffY\nxkMBLUGD+GR8G7gfhetRQffowfoFi8LB6B8D0ABTAmvCnEQDwEGD+Ap8G7hnZmZmQffowfoCi8LB\n6B8D0ABTA2vC9kQDwEQAQwT2BUE9AQABdBSAOTB1D0iNUQFBuAMAAADom0z//zPbgHwkSAB0DEiL\nTCRAg6HIAAAA/UyNXCRQi8NJi1sgSYtrKEmLczBJi3s4SYvjQV9BXkFdw0iDZCQgAEUzyUUzwDPS\nM8noFGv//8zMzMxAU1VWV0iB7IgAAABIiwXJEgEASDPESIlEJHBIiwlJi9hIi/pBi/G9FgAAAEyN\nRCRYSI1UJEBEi83oKgsAAEiF/3UT6Dh8//+JKOihav//i8XpiAAAAEiF23ToSIPK/0g72nQaM8CD\nfCRALUiL0w+UwEgr0DPAhfYPn8BIK9AzwIN8JEAtRI1GAQ+UwDPJhfYPn8FIA8dMjUwkQEgDyOgp\nCQAAhcB0BcYHAOsySIuEJNgAAABEi4wk0AAAAESLxkiJRCQwSI1EJEBIi9NIi8/GRCQoAEiJRCQg\n6Cb9//9Ii0wkcEgzzOgxS///SIHEiAAAAF9eXVvDzEiLxEiJWAhIiWgQSIlwGEiJeCBBVkiD7EBB\ni1kESIvySItUJHhIi/lIjUjYSYvp/8tFi/DoF0H//0iF/3QFSIX2dRboQHv//7sWAAAAiRjopGn/\n/+nYAAAAgHwkcAB0GkE73nUVM8CDfQAtSGPLD5TASAPHZscEATAAg30ALXUGxgctSP/Hg30EAH8g\nSIvP6ADS//9IjU8BSIvXTI1AAeiwSv//xgcwSP/H6wdIY0UESAP4RYX2fndIi89IjXcB6NDR//9I\ni9dIi85MjUAB6IFK//9Ii0QkIEiLiPAAAABIiwGKCIgPi10Ehdt5QvfbgHwkcAB1C4vDQYveRDvw\nD03Yhdt0GkiLzuiH0f//SGPLSIvWTI1AAUgDzug1Sv//TGPDujAAAABIi87opU///zPbgHwkOAB0\nDEiLTCQwg6HIAAAA/UiLbCRYSIt0JGBIi3wkaIvDSItcJFBIg8RAQV7DzMzMQFNVVldIg+x4SIsF\ncBABAEgzxEiJRCRgSIsJSYvYSIv6QYvxvRYAAABMjUQkSEiNVCQwRIvN6NEIAABIhf91EOjfef//\niSjoSGj//4vF62tIhdt060iDyv9IO9p0EDPAg3wkMC1Ii9MPlMBIK9BEi0QkNDPJTI1MJDBEA8aD\nfCQwLQ+UwUgDz+jjBgAAhcB0BcYHAOslSIuEJMAAAABMjUwkMESLxkiJRCQoSIvTSIvPxkQkIADo\n4f3//0iLTCRgSDPM6PhI//9Ig8R4X15dW8PMzMxAU1VWV0FWSIHsgAAAAEiLBZcPAQBIM8RIiUQk\ncEiLCUmL+EiL8kGL6bsWAAAATI1EJFhIjVQkQESLy+j4BwAASIX2dRPoBnn//4kY6G9n//+Lw+nB\nAAAASIX/dOhEi3QkRDPAQf/Og3wkQC0PlMBIg8r/SI0cMEg7+nQGSIvXSCvQTI1MJEBEi8VIi8vo\nCgYAAIXAdAXGBgDrfotEJET/yEQ78A+cwYP4/Hw7O8V9N4TJdAyKA0j/w4TAdfeIQ/5Ii4Qk2AAA\nAEyNTCRARIvFSIlEJChIi9dIi87GRCQgAejj/P//6zJIi4Qk2AAAAESLjCTQAAAARIvFSIlEJDBI\njUQkQEiL10iLzsZEJCgBSIlEJCDou/n//0iLTCRwSDPM6MZH//9IgcSAAAAAQV5fXl1bwzPS6QEA\nAADMQFNIg+xASIvZSI1MJCDoyT3//4oLTItEJCCEyXQZSYuA8AAAAEiLEIoCOsh0CUj/w4oLhMl1\n84oDSP/DhMB0PesJLEWo33QJSP/DigOEwHXxSIvTSP/LgDswdPhJi4DwAAAASIsIigE4A3UDSP/L\nigJI/8NI/8KIA4TAdfKAfCQ4AHQMSItEJDCDoMgAAAD9SIPEQFvDzMxFM8npAAAAAEBTSIPsMEmL\nwEiL2k2LwUiL0IXJdBRIjUwkIOhM3///SItEJCBIiQPrEEiNTCRA6ADg//+LRCRAiQNIg8QwW8Mz\n0ukBAAAAzEBTSIPsQEiL2UiNTCQg6OE8//8Pvgvoebj//4P4ZXQPSP/DD7YL6I0CAACFwHXxD74L\n6F24//+D+Hh1BEiDwwJIi0QkIIoTSIuI8AAAAEiLAYoIiAtI/8OKA4gTitCKA0j/w4TAdfE4RCQ4\ndAxIi0QkMIOgyAAAAP1Ig8RAW8PM8g8QATPAZg8vBariAAAPk8DDzMxIiVwkCEiJbCQQSIl0JBhX\nQVRBVkiD7BBBgyAAQYNgBABBg2AIAE2L0Iv6SIvpu05AAACF0g+EQQEAAEUz20UzwEUzyUWNYwHy\nQQ8QAkWLcghBi8jB6R9FA8BFA8nyDxEEJEQLyUONFBtBi8PB6B9FA8lEC8CLwgPSQYvIwegfRQPA\nwekfRAvAM8BEC8mLDCRBiRKNNApFiUIERYlKCDvycgQ78XMDQYvEQYkyhcB0JEGLwEH/wDPJRDvA\ncgVFO8RzA0GLzEWJQgSFyXQHQf/BRYlKCEiLBCQzyUjB6CBFjRwARTvYcgVEO9hzA0GLzEWJWgSF\nyXQHRQPMRYlKCEUDzo0UNkGLy8HpH0eNBBtFA8lEC8mLxkGJEsHoH0WJSghEC8AzwEWJQgQPvk0A\nRI0cCkQ72nIFRDvZcwNBi8RFiRqFwHQkQYvAQf/AM8lEO8ByBUU7xHMDQYvMRYlCBIXJdAdB/8FF\niUoISQPsRYlCBEWJSgj/zw+FzP7//0GDeggAdTpFi0IEQYsSQYvARYvIweAQi8rB4hDB6RBBwekQ\nQYkSRIvBRAvAuPD/AABmA9hFhcl00kWJQgRFiUoIQYtSCEG7AIAAAEGF03U4RYsKRYtCBEGLyEGL\nwUUDwMHoHwPSwekfRAvAuP//AAAL0WYD2EUDyUGF03TaRYkKRYlCBEGJUghIi2wkOEiLdCRAZkGJ\nWgpIi1wkMEiDxBBBXkFcX8PMzEBTSIPsQIM9dygBAABIY9l1EEiLBR8ZAQAPtwRYg+AE61JIjUwk\nIDPS6A46//9Ii0QkIIO41AAAAAF+FUyNRCQgugQAAACLy+iT1///i8jrDkiLgAgBAAAPtwxYg+EE\ngHwkOAB0DEiLRCQwg6DIAAAA/YvBSIPEQFvDzMxIg+wYRTPATIvJhdJ1SEGD4Q9Ii9EPV8lIg+Lw\nQYvJQYPJ/0HT4WYPbwJmD3TBZg/XwEEjwXUUSIPCEGYPbwJmD3TBZg/XwIXAdOwPvMBIA8LppgAA\nAIM9fxsBAAIPjZ4AAABMi9EPtsJBg+EPSYPi8IvID1fSweEIC8hmD27BQYvJQYPJ/0HT4fIPcMgA\nZg9vwmZBD3QCZg9w2QBmD9fIZg9vw2ZBD3QCZg/X0EEj0UEjyXUuD73KZg9vymYPb8NJA8qF0kwP\nRcFJg8IQZkEPdApmQQ90AmYP18lmD9fQhcl00ovB99gjwf/II9APvcpJA8qF0kwPRcFJi8BIg8QY\nw/bBD3QZQQ++ATvCTQ9EwUGAOQB040n/wUH2wQ915w+2wmYPbsBmQQ86YwFAcw1MY8FNA8FmQQ86\nYwFAdLtJg8EQ6+JIiVwkCFdIg+wgSIvZSYtJEEUz0kiF23UY6JZy//+7FgAAAIkY6Ppg//+Lw+mP\nAAAASIXSdONBi8JFhcBEiBNBD0/A/8BImEg70HcM6GNy//+7IgAAAOvLSI17AcYDMEiLx+saRDgR\ndAgPvhFI/8HrBbowAAAAiBBI/8B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pZXJzIG91IGRhbnMg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\naQBuA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Db3Jwb3JhdGlvbjEjMCEGA1UEAxMa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QkFMQ8XDTE2MDYyODE4NDEwM1owIwYJKoZIhvcNAQkEMRYEFCvRwBh/3cGz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\nA4N8sqr39pD/wimSpMkPFIUJLk0ybdWXx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Ai9iDxASJMzP2OXX8diBXjXsEi/+LRfhX/3TwBGoA/xVsoEEARoPHDDt1/HLn\nX/91+Oj6QgAAXv919Oj3QgAAi8Nbi+Vdw8zMzMzMzMzMzMzMzMzMVYvsg+wIVot1CFdqQFb/FWih\nQQBoRAlCAGhcCUIA/xV4oUEAUP8VjKFBAIv4hf90JGoEjUX8x0X8AAAAAFBqIVb/12oEjUX4x0X4\nAQAAAFBqJ1b/119ei+Vdw8zMzMzMZoM9ULNCAAB1FGr1/xV8oUEAaFCzQgBQ/xUMoUEAVmjUCUIA\n6AFWAACDwEBQ6D1ZAAAPvwVQs0IAg8QISDP2hcB+I2jYCUIA6N1VAACDwEBQ6BlZAAAPvwVQs0IA\nRkiDxAg78HzdaNQJQgDoulUAAIPAQFDo9lgAAIPECF7DzMxVi+yB7BACAAChMKFCADPFiUX8i0UQ\nU4tdDFZXaDgOQgBTiYXw/f//vwEAAADoEmUAAIvwg8QIhfZ1KFNoPA5CAOhkVQAAg8BAUOigWAAA\ng8QM/xVYoUEAUOiiBAAA6aIAAABWjYX0/f//aAgCAABQ6BNkAACDxAyFwA+EfwAAAIpdCI2F9P3/\n/2oKUOjTXgAAg8QIhcB0Fo2F9P3//2oKUOi+XgAAg8QIM8lmiQhmg730/f//AHQuhNt0FI2F9P3/\n/1BoBA5CAOjfSQAAg8QIjYX0/f//UP+V8P3//4PEBPfYG8Aj+FaNhfT9//9oCAIAAFDokGMAAIPE\nDIXAdYRW6B9gAACLTfyDxASLxzPNX15b6AVNAACL5V3DzMzMzMxVi+xTil0IVot1DFe/AQAAAGos\nVugrXgAAg8QIhcB0EGosVugcXgAAg8QIM8lmiQiE23QOVmgEDkIA6E1JAACDxAhW/1UQg8QEi873\n2BvAI/iNUQJmiwGDwQJmhcB19SvK0fmNNE6DxgJmOQZ1o4vHX15bXcPMzMzMzMzMzMzMzFWL7IHs\nIAIAAKEwoUIAM8WJRfyLRQxWV2jUDUIAiYXk/f//vwEAAADHhfD9//8AAAAAx4Xs/f//AAAAAMeF\n4P3//wAAAADHhej9//8AAAAA6LVTAACDwEBQ6PFWAACDxAiNhej9//9QagBqA42F4P3//1CNhez9\n//9Qav+NhfD9//9QamVqAOjePgAAhcB0Jj3qAAAAdB9QaBQOQgDoaFMAAIPAQFDoUF8AAIu18P3/\n/4PEDOt9i7Xw/f//hfZ0fVMz2zmd7P3//3Zn6wONSQCF9nRYi1YEjYX0/f//jUkAD7cKjVICZokI\njUACZoXJde84TQh0FI2F9P3//1BoBA5CAOgFSAAAg8QIjYX0/f//UP+V5P3//4PEBPfYG8BDI/iD\nxhg7nez9//9ypIu18P3//1uF9nQGVugzPgAAi038i8dfM81e6DFLAACL5V3DzFWL7FaLdQxmgz5A\ndRX/dRCNRgJQ/3UI6PT8//+DxAxeXcO5ZA5CAIvGjaQkAAAAAGaLEGY7EXUeZoXSdBVmi1ACZjtR\nAnUPg8AEg8EEZoXSdd4zwOsFG8CDyAGFwHUR/3UQ/3UI6Eb+//+DxAheXcNqLFbo/FsAAIPECIXA\ndBL/dRBW/3UI6KX9//+DxAxeXcNW/1UQg8QEXl3DzMzMzMxVi+xXaNwJQgD/dQhqAP8VJKFBAIv4\nhf91BTLAX13DU1ZXagD/FUChQQBXagCL8P8VPKFBAFaL+P8VZKFBAGjsCUIA/3UMi9joZ2EAAIvw\ng8QIhfZ1B15bMsBfXcNWV2oBU+gPYAAAVugnXQAAg8QUsAFeW19dw8zMzMzMzMzMzFWL7FNoPwAP\nAGoA/3UI/xVAoEEAi9iF23UDW13DVleLPVihQQC5EAEAAI2bAAAAAIB9GAC4EAAAAGoAagBqAGoA\nagD/dRQPRcFqAGoDUGj/AQ8A/3UM/3UQU/8VSKBBAIXAdSr/1zPJPTEEAAAPlMGFyXUh/9eL8IP+\nNHQYgf4xBAAAdBBW/xVUoUEA6zhQ/xVQoEEA/3UQU+gSCQAAg8QIhMB1H//Xi/C5EAEAAIP+IA+E\nef///4H+5QMAAHUH6Wz///8z9lP/FVCgQQBW/xVUoUEAM8CF9l9eD5TAW13Dw8zMzMzMzMzMzMzM\nzMzMzFWL7FFTi10IVjP2V42DzPf//78AEwAAPYMDAAB3GmoCVmi8CUIA/xUooUEAi/C4ABsAAIX2\nD0X4agBqAI1FCFBoAAQAAFNWV/8VLKFBAIXAdCJqAI1N/FFQ/3UIavT/FXyhQQBQ/xU4oUEA/3UI\n/xWEoUEAhfZ0B1b/FWChQQBfXluL5V3DzMzMzMzMzMzMzFWL7IHs3AgAAKEwoUIAM8WJRfyLRRiJ\nhTz3//+LRRxTi10UiYUw9///i0UgVot1DImFLPf//4tFJFeLfRCJhTT3//+NhST3//9QjYXg+P//\nx4Uk9///BAEAAFD/FRShQQCNheD4//9WUOiwPgAAg8QIhcAPhb8BAABoeAxCAMaFOPf//wHokE8A\nAIPAQFDozFIAAIPECIA9SbNCAAB1GI2FUPf//1BoAgIAAOjoOgAAxgVJs0IAAWgEAQAAjYXw/P//\nUOjKOgAAjYXw/P//UOi4OgAAD79ICotADFH/MI2FRPf//1DotkcAAIPEDI2F9P3//2gEAQAAUGr/\n/7VE9///6IA6AABQagBqAP8VEKFBAItNMI2F9P3//1GLjTT3//9Ri40s9///UYuNMPf//1GLjTz3\n//9Ri00IU1f/tTj3//9QVlHoZgQAAIPELITAD4TKAQAA6Jb4//+AvTj3//8AuAwNQgAPRMZQV2go\nDUIA6KVOAACDwEBQ6OFRAAD/tTz3//+Nhej6//9oZA1CAFDoTEQAAP91KI2F6Pr//1BTV1bo2fz/\n/4PEMIXAdXeL//8VWKFBAImFKPf//+gv+P//g70o9///Ag+FZAEAAP91MI2F9P3///+1NPf///+1\nLPf///+1MPf///+1PPf//1NX/7U49///UFb/dQjorwMAAIPELITAD4QTAQAA/3UojYXo+v//UFNX\nVuhi/P//g8QUhcB0i19esAFbi038M83oWEYAAIvlXcONlfT9///GhTj3//8Ai84r1o2kJAAAAAAP\ntwGNSQJmiUQK/maFwHXwVmi0DEIA6K9NAACDwEBQ6OtQAACDxAyDfSz/D4SQ/v//aizoPkEAAItN\nCGoAagCICI2N9P3//4lICIuNPPf//4lIGIuNMPf//1CJSByLjSz3//+JSCCLjTT3//9oMDdAAIlI\nJItNMGoAagCJcATGQAwAiXgQiVgUiEgo6LNdAABpTSzoAwAAg8QcUVD/FUihQQA9AgEAAA+FWP7/\n/1Zo3AxCAOgSTQAAg8BAUOhOUAAAg8QMaLQFAAD/FVShQQBfXjLAW4tN/DPN6F5FAACL5V3DgH0w\nAHQjVldoiA1CAOjWTAAAg8BAUOgSUAAA/7Uo9///6Bj8//+DxBT/tTz3//9TVv91COglAAAAi038\ng8QQM80ywF9eW+gPRQAAi+Vdw8zMzMzMzMzMzMzMzMzMzFWL7IHsLAgAAKEwoUIAM8WJRfyLRRBT\ni10MVjP2iZ3Y9///gH0IAFeLfRQPhMkAAABQU+hIBgAAg8QIx4XU9///BAEAAI2F1Pf//1CNhdz3\n//9Q/xUUoUEAjYXc9///U1DoKDsAAIPECIXAdUNoBAEAAI2F9P3//1D/FSChQQCNhfT9//9qXFDo\nZVYAADPJV2aJCI2F9P3//1CNhez7//9oCAdCAFDopkEAAIPEGOsWV1ONhez7//9oCApCAFDojkEA\nAIPEEIsdWKFBAIs9RKFBAI2F7Pv//1D/FRihQQCFwHUR/9OD+AV1Cmpk/9dGg/4Kct6Lndj3//+A\nPUizQgAAdCVTjYXk+f//aPQJQgBQ6DxBAACDxAyNheT5//9qAWoAUOjsNgAAi038X14zzVvox0MA\nAIvlXcPMzMzMzMzMVYvsgewwAgAAoTChQgAzxYlF/ItFCDPJVot1DFeLfRBmiY3w/f//ZjkOdRJf\nMsBei038M83ogkMAAIvlXcNQjYX0/f//aPQJQgBQ6MFAAACDxAyNhfD9//8PV8DzD3+F4P3//4mF\n4P3//42F9P3//2oAVomF5P3//42F0P3//1fzD3+F0P3//1DHhdT9//8AAAAAx4Xc/f//AwAAAMeF\n7P3//wAAAADoIjYAAA+2DUizQgCL0IXSuAEAAABfD0TIM8CF0ogNSLNCAItN/A+UwDPNXujkQgAA\ni+Vdw8zMzMxVi+yB7DAGAAChMKFCADPFiUX8i00Yi1Uki0UQU4pdFImN1Pn//4tNIImV4Pn//4tV\nLImF3Pn//4mN0Pn//4mV2Pn//1aLdShXi30MhNt0TmaDPgB1SGgEAQAAjYXs+///UP8VIKFBAI2F\n7Pv//2pcUOhcVAAA/7XQ+f//M8lmiQiNhez7//9QjYX0/f//aAgHQgBQ6Jg/AACDxBjrKFFQjYX0\n/f//aAgKQgBQ6IA/AAD/tdj5//9W/7Xc+f//6F3+//+DxByNhfT9//9Q/7Xg+f//6Hj3//+DxAiE\nwA+FNwEAAIs1WKFBADhFCHUL/9aD+CAPhCEBAAD/1oP4BXQTPS4FAAB0DD3nAwAAdAWD+DV1HY2F\n9P3//1D/teD5///oK/f//4PECITAD4XqAAAA6Avz//+AfTAAD4SVAAAAhNt0Df+11Pn//2goCkIA\n6wZXaGgKQgDoD0kAAIPAQFDoS0wAAIPEDP/Wi/BW6E/4//+DxASD/kN0KIP+NXQjgf7QBAAAdAiB\n/s8EAAB1RoTbdAdocAtCAOsRV2jwC0IA6yKE23QYaJgKQgDou0gAAIPAQFDo90sAAIPECOsXV2j4\nCkIA6KJIAACDwEBQ6N5LAACDxAyAPUizQgAAdCr/tdz5//+NheT5//9o9AlCAFDoPT4AAIPEDI2F\n5Pn//2oBagBQ6O0zAABfXjLAW4tN/DPN6MZAAACL5V3Di038sAFfXjPNW+izQAAAi+Vdw8zMzFWL\n7FNWi3UID7ZGKFD/diQPtkYM/3Yg/3Yc/3YY/3YU/3YQUP92CA+2Bv92BFDonf3//1aK2OhzOwAA\ng8QwD7bDXltdwgQAzMzMzMzMzMzMVYvsg+wkoTChQgAzxYlF/FOLHTChQQBWi3UIV4t9DP/Tiw1M\ns0IAiUXchcl0B1H/FVCgQQBo/wEPAFdW/xU8oEEAo0yzQgCFwHURX15bi038M83oBEAAAIvlXcNq\nAGoAUP8VNKBBAIXAdQ3/FVihQQA9IAQAAHVMizU4oEEAjUXgUP81TLNCAL8BAAAA/9aFwHQxi0Xk\ng/gEdCuD+AF0JP/TK0XcPWDqAAB3VGpk/xVEoUEAjUXgUP81TLNCAP/WhcB1zzP/ix1UoUEA/xVY\noUEA/zVMs0IAi/D/FVCgQQBWxwVMs0IAAAAAAP/Ti038i8dfXjPNW+hgPwAAi+Vdw4sdVKFBAGgd\nBAAA/9Mz/+u7zMzMzMzMzMzMzMzMzMzMVYvsg+wkoTChQgAzxYlF/FaLdQxXi30I/xUwoUEAaP8B\nDwBWV4lF3P8VPKBBAIvwhfZ1EF9ei038M83o/D4AAIvlXcONReBQagFW/xVMoEEAi/iF/3RKU4sd\nOKBBAI1F4FBW/9OFwHQ1jaQkAAAAAIN95AF0Kv8VMKFBACtF3D1g6gAAdw2NReBQVv/ThcB13+sL\naB0EAAD/FVShQQAz/1tW/xVQoEEAi038i8dfM81e6IY+AACL5V3DzMzMzMzMVYvsU1ZoPwAPAGoA\n/3UI/xVAoEEAi3UMi9hWU+gf////g8QIaP8BDwBWU/8VPKBBAIvwhfZ0Dlb/FUSgQQBW/xVQoEEA\nU/8VUKBBAF64AQAAAFtdw8zMzMzMzMzMzMzMVYvsi0UIhcB0CWoAUP8VLKBBAF3DzMzMzMzMzMzM\nzMxVi+yD7AxTVmiUEEIA6L70//+LXQxT6OUBAACDxAiFwA+ElgAAAFeLfQiLN41GE1CJRfTo/jgA\nAFb/dwiJRfhQ6Ms9AACLD4PEEIt1+PMPbwW8EEIA8w9/BDFmocwQQgBmiUQxEKDOEEIAiEQxEo1F\n/FBqAGoAaASAAAD/M/8VDKBBAF+FwHQ0agD/dfRW/3X8/xUIoEEAhcB0If91EGoA/3X8aBBmAAD/\nM/8VJKBBAIXAdAhesAFbi+Vdw14ywFuL5V3DzMzMzMzMzMzMzMzMVYvsVot1CIsGhcB0B1D/FSCg\nQQCLRgxehcB0CYlFCF3pCTgAAF3DzMzMzMzMzMzMVYvsUVaLdRRXi30QVmoBV/91CP8VKKBBAIXA\ndQZfXovlXcNTi10cuAEAAABTagA7+LkGAAAAi30MagAPRMFQV/82izUcoEEAiUX8/9b/M+jiNwAA\ni00Yg8QEU1BqAP91/IkBi0UUV/8w/9ZbX16L5V3DzMzMzMzMzMzMzMzMzMxVi+yD7BShMKFCADPF\niUX8i0UIjU3sVleLfQxRUOjuAgAAi/CDxAiF9nUlahDohDcAAPMPb0XsiUcIg8QE8w9/AMcHEAAA\nAIvGx0cEEAAAAItN/F8zzV7oIDwAAIvlXcNVi+z/dRhqAP91DP91FP91EP91CP8VGKBBAF3DzMzM\nzFWL7FaLdQhXgz4AD4WQAAAAiz0woEEAagBqGGoAagBW/9eFwHV7aGgOQgBo9A5CAOib8v//g8QI\naghqGGoAagBW/9eFwHVaaAAPQgBo9A5CAOh68v//g8QIaiBqGGoAagBW/9eFwHU5aHgPQgBo9A5C\nAOhZ8v//g8QIaihqGGoAagBW/9eFwHUYaAgQQgBo9A5CAOg48v//g8QIM8BfXl3DX7gBAAAAXl3D\nzMzMzMzMVYvsUYN9CABTVldqAHUb/3UY/3UU/3UQ/3UM/xUIoUEAX4vwXluL5V3Di10YjUX8U2oE\nUP91DP8VCKFBAIXAdR3/FVihQQBQaFgQQgDozvH//4PECDPAX15bi+Vdw4t1/FboHzYAAIPEBIv4\nagBTVlf/dQz/FQihQQCFwHUj/xVYoUEAUGhsEEIA6JHx//9X6Lk1AACDxAwzwF9eW4vlXcOLRQhT\nV2oAagFqAP8w/xUQoEEAi/CF9nQg/zNX/3UQ6Js6AACDxAxX6IA1AACDxASLxl9eW4vlXcP/FVih\nQQBQaIAQQgDoNfH//4PECFfoWjUAAIPEBIvGX15bi+Vdw1WL7ItFCIPsDFNWV2oAhcB1HI1F+FD/\ndRT/dRD/dQz/FTihQQCL+F9eW4vlXcOLfRSNTfyLHRSgQQBRagBqAGoBagD/MIl9/P/Ti3X8Vugz\nNQAAVv91EIvwVugBOgAAg8QQiX34jUX4/3X8UItFCFZqAGoBagD/MP/ThcB1ElboyjQAAIPEBDPA\nX15bi+Vdw4t9DI1F+IsdOKFBAGoAUGoEjUX8UFf/04XAdNJqAI1F+FD/dfxWV//TVov46I00AACD\nxASLx19eW4vlXcPMzMzMzMzMzMzMzMzMzMzMzMzMVYvsgeycCAAAoTChQgAzxYlF/ItFCItNDFNW\nV1CNhfz3//+JjYz3//9oMBFCAFDojTYAAKFss0IAg8QMizWMoUEAiz10oUEAhcB1FGj8EEIAaOgQ\nQgD/11D/1qNss0IAjY389///UY2NZPf//1H/0I2FZPf//8eFbPf//xgAAACJhXT3//+hcLNCAMeF\ncPf//wAAAADHhXj3//9AAAAAx4V89///AAAAAMeFgPf//wAAAACFwHUUaBQRQgBo6BBCAP/XUP/W\no3CzQgBokAAAAGoBjY2E9///UY2NbPf//1FoAQAQAI2NkPf//1H/0IvYhdsPhe8AAAChdLNCAIud\nkPf//8eF3Pf//wYAAADHheD3//8CAAAAhcB1FGggEUIAaOgQQgD/11D/1qN0s0IAakSNjZT3//9R\naiSNjdj3//9RaKMBFACNjYT3//9RagBqAGoAU//Qi9iF23USi4WM9///8w9vhcD3///zD38Ai4WQ\n9///iYWM9///oXSzQgDHhdz3//8GAAAAx4Xg9///AQAAAIXAdRRoIBFCAGjoEEIA/9dQ/9ajdLNC\nAGoAagBqJI2N2Pf//1ForAEUAI2NhPf//1FqAGoAagD/tYz3////0D0DAQAAdQ5q//+1kPf///8V\nSKFBAP+1kPf///8VNKFBAKFos0IAhcB1FGjQEEIAaOgQQgD/11D/1qNos0IAU//Qi038X14zzVvo\nTDcAAIvlXcPMzMzMzMzMzMzMzMxVi+xTi10IVleDz/8z9ldT/xXwoEEAg/4FcyZqAGoAagNqAGoA\n/3UMU/8VHKFBAIv4g///dQtqZP8VRKFBAEbr1YvHX15bXcPMzMzMzMzMzFWL7IN9CAAPhYoAAACA\nPZIHQwAAdEFogLNCAGjAWkUA6FktAACDxAiFwHQrgz2oB0MAAHUWaHQRQgDoOz4AAIPAQFDoI0oA\nAIPECMcFqAdDAAEAAADrImisEUIA6Bk+AACDwEBQ6AFKAACDxAj/NWAWQwD/FVChQQCAPZEHQwAA\ndQz/NVwWQwD/FVChQQC4AQAAAF3CBAAzwF3CBADMzMxVi+yD7DBTi10UM8APV8CJRfxWV2YPE0Xg\nZg8TReiIA+iL5///i3UIVmiAFkUAaLQRQgDooj0AAIPAQFDo3kAAAGpcaIAWRQDo9UcAAItNDIPE\nGIkBhcB1CMcBgBZFAOsFg8ACiQH/MYt9EFZoCApCAFfoKjMAAIPEEIA9lAdDAAAPhP0AAACNRfTG\nBX+zQgABUFfokCgAAIvwVujmMAAAg8QEi9iJXfBTVmoAV+h8KAAAhcB0S4tLMDPAC0M0iUUMjUX0\nUGiAFkUAiU346FYoAACL8FborDAAAIPEBIvYiV38U1ZqAGiAFkUA6D4oAACFwHQZi0X8M/aLWzAL\ncDTrEotF5IlF+ItF4IlFDIt16Itd7ItF8IXAdAlQ6C4wAACDxASLRfyFwHQJUOgeMAAAg8QEi00M\ni8GLVfgLwnQKO9NyBndWO85zUo1F2FBX6O0GAACDxAiEwHQnjUXQUGiAFkUA6NgGAACDxAiEwHQS\ni0XcO0XUcgp3IotF2DtF0HMai10UizXsoEEAagFXaIAWRQD/1oXAdAzGAwFfXrABW4vlXcOAPX+z\nQgAAdBpogAAAAFf/FeSgQQBqAFdogBZFAP/WhcB11P8VWKFBAIvwgf63AAAAdMTo1OX//2iAFkUA\naOARQgDo7zsAAIPAQFDoKz8AAFboNuv//4PEEDLAX15bi+Vdw8zMzMzMzMzMzMxVi+yD7BBTi10M\nVldTx0XwAAAAAMdF/AAAAADoIPj//4PEBIXAdQ/odOX//2gsEkIA6fsAAACLfQiNRfRqAFBqBI1F\n+FBX/xUIoUEAhcB1D+hL5f//aGgSQgDp0gAAAIt1+FboDi8AAIPEBIlF/I1N9GoAUVZQV/8VCKFB\nAIXAdQ/oGuX//2jYEkIA6aEAAACNRfBQ/3X4/3X8agD/M+h99///g8QUhcB1DOjx5P//aDgTQgDr\ne/91/OiALgAAjUX4x0X8AAAAAFCNRfxQ/3UQaBBmAAD/dfD/M+hh9v//g8QchcB1DOi15P//aHgT\nQgDrP4s1OKFBAI1F9GoAUGoEjUX4UFf/1oXAdBxqAI1F9FD/dfj/dfxX/9aFwHQJX16wAVuL5V3D\n6HTk//9ouBNCAOiUOgAAg8BAUOjQPQAAg8QI/xVYoUEAUOjS6f//i0Xwg8QEhcB0B1D/FSCgQQCL\nRfyFwHQJUOjSLQAAg8QEX14ywFuL5V3DzMzMzMzMzMxVi+y4aEIAAOgTVwAAoTChQgAzxYlF/FNW\nV2pAjYWcvf//x4WYvf//AAAAAGoAUOjsOAAAD1fAalxogMNCAPMPf4Xcvf//6LlDAACL8IPEFIX2\ndA8zwL+Aw0IAZokGg8YC6wq+gMNCAL/AWkUAaIjFQgBogBZFAI2F9L3//2iIGUIAUOiJLwAAix00\noUEAg8QQgD2WB0MAAA+FngAAAGaDPgAPhJQAAAAzyY2F3L3//2Y5DYgFQwBQjYWYvf//ULiIBUMA\nD0TBUKGYB0MAUQ0UBAAAUI2F9L3//1AzwDgFCqFCAFEPlcBQaKAYRQBXVv8VpAdDAIXAD4VSAQAA\naIAWRQBo+BlCAOg+OQAAg8BAUOh6PAAAizVYoUEAg8QM/9ZQ6Hro//+DxAT/1l9eW4tN/DPN6IQx\nAACL5V3DjYXwvf//UGj/AQ8A/xVcoUEAUP8VVKBBAIA9lgdDAAB0ZP+18L3//+gX3v//i/iDxASF\n/3VGaJgZQgDozjgAAIPAQFDoCjwAAIs1WKFBAIPECP/WUOgK6P//g8QE/7Xwvf///xU0oUEA/9Zf\nXluLTfwzzegIMQAAi+Vdw/+18L3////T6waLvfC9//8zyY2F3L3//2Y5DYgFQwBQjYWYvf//ULiI\nBUMAD0TBUKGYB0MAUQ0UBAAAUFFRUY2F9L3//1BRV/8VBKBBAIXAdUJogBZFAGj4GUIA6DE4AACD\nwEBQ6G07AACLNVihQQCDxAz/1lDobef//4PEBFf/0//WX15bi038M83odDAAAIvlXcNX/9OBPZgH\nQwAAABAAdQ7/tdy9///oWuH//4PEBKGcB0MAhcB0DVD/tdy9////FQShQQD/teC9////FbygQQCA\nPZEHQwAAdUFq//+13L3///8VSKFBAI2F7L3//1D/tdy9////FbigQQD/tey9//9ogBZFAGg0GkIA\n6IA3AACDwEBQ6Lw6AADrKv+15L3//2iAFkUAaHgaQgDoYDcAAIPAQFDonDoAAIuF5L3//4mF7L3/\n/4PEEP+14L3////T/7Xcvf///9OLTfyLhey9//8zzV9eW+iXLwAAi+Vdw8zMzMzMzMxVi+xRVot1\nCFb/FeigQQCD+P90I4tNFCvOjaQkAAAAAA+3Bo12AmaJRDH+ZoXAdfCwAV6L5V3Di86NUQKNZCQA\nZosBg8ECZoXAdfWLRRArytH5A8FTV40ERQQAAABQ6GMqAACLXQyDxASL+Il9/IXbD4TAAAAAZoM7\nAA+EtgAAAIvXi8Mr0+sIjaQkAAAAAJAPtwiNQAJmiUwC/maFyXXwajtX6CxAAACDxAiFwHQFM8lm\niQiDx/7rB42kJAAAAABmi0cCjX8CZoXAdfShsBFCAIvWiQdmiwKDwgJmhcB19Yt9/CvWg8f+6waN\nmwAAAABmi0cCg8cCZoXAdfSLysHpAvOli8qD4QPzpIt9/Ff/FeigQQCD+P91L2o7U+izPwAAi9iD\nxAiF23QMi3UIg8MCD4VA////V+hSKQAAg8QEMsBfW16L5V3Di1UUi88r15APtwGNSQJmiUQK/maF\nwHXwV+goKQAAg8QEsAFfW16L5V3DzMzMzMzMzMzMzMzMzMxVi+xRVmoAagBqA2oAagNoAAAAgP91\nCMZF/wH/FRyhQQCL8IP+/3UHMsBei+Vdw1P/dQxqAGoAVv8VxKBBAIvY99tWGtsiXf//FTShQQCK\nw1tei+Vdw8zMzMzMzMzMzMxVi+yB7LACAAChMKFCADPFiUX8i00Ii0UQUzLbiYVU/f//xoVy/f//\nAIidc/3//4idYP3//4idYf3//4idZ/3//4idZP3//4idZf3//4idZv3//4idYv3//4idX/3//4id\nY/3//1eLfQyJvWj9//+D+QF/Gv836DUZAACDxARfW4tN/DPN6CItAACL5V3DxwAAAAAAM8Bmo6AY\nRQC4AQAAAImFbP3//1Y7yA+OqQgAAIsMh40ch4mdWP3//w+3EYvCg+gtdEaD6AJ0QYPoEYqFc/3/\n/w+FNQYAAITAD4UtBgAAuoCzQgAPtwGNUgKDwQJmiUL+ZoXAde6LhWz9//+xAYiNcv3//+kMCAAA\nioVz/f//hMAPhfUFAACNQQJoFCxCAFDoFiMAAIPECIXAD4SbAAAAiwODwAJoLCxCAFDo+yIAAIPE\nCIXAD4SAAAAAgz2YB0MAAHVZM9u+4JBCADP/i4VY/f//VosAg8ACUOjMIgAAg8QIhcB0HIHHBAIA\nAEOBxgQCAACB/xwOAABy0qGYB0MA6xFpwwQCAACLgOCSQgCjmAdDAIXAdSSLnVj9//+LA4PAAmhA\nLEIAUOh+IgAAg8QIhcB1EsYFlwdDAAGLhWz9///pOQcAAIsDvgEAAACJtVj9//9mg3gCAHThuQIA\nAAAPtwQBUOgASwAAg8C/g8QEg/gXD4cKCAAA/ySFxFRAAIC9YP3//wAPhfYHAADGBX6zQgABxoVg\n/f//AenLAQAAgL1h/f//AA+F1gcAAMYFf7NCAAHGhWH9//8B6asBAACAvWP9//8AD4W2BwAAxgWV\nB0MAAcaFY/3//wHpiwEAAIC9Zv3//wAPhZYHAADGBZQHQwABxoVm/f//AelrAQAAgL1k/f//AA+F\ndgcAAMYFkQdDAAHGhWT9//8B6UsBAACAPQqhQgAAD4RWBwAAxgUKoUIAAOkyAQAAgL1l/f//AA+F\nPQcAAIuFbP3//0DGBZIHQwABxoVl/f//ATtFCA+NBwEAAItTBA+3AoP4LQ+E+AAAAIP4Lw+E7wAA\nAIvKvgEAAACNeQLrBo2bAAAAAGaLAYPBAmaFwHX1K8/R+YP5AX4vD7cEcmoEUOjoSQAAg8QIhcB0\nHItTBEaLwo14AmaLCIPAAmaFyXX1K8fR+DvwfNGLSwSNUQKNpCQAAAAAZosBg8ECZoXAdfUrytH5\nO/EPhJgAAACLtVj9///rboC9X/3//wAPhXkGAACDPXizQgAAD4SMBQAAxgWWB0MAAcaFX/3//wHr\nRIA9kwdDAAAPhU8GAADGBZMHQwABxgWSB0MAAesbgL1n/f//AA+FMgYAAMYFkAdDAAHGhWf9//8B\ni4VU/f//xwABAAAAiwNGibVY/f//jQw2ZoM8AQAPhd79//+LhWz9///p8wQAAGj8nkIAaAwtQgD/\ncwTo4EsAAIPEDIXAD4TZBQAAi41U/f//i4Vs/f//xgUIoUIAAEDHAQEAAADptwQAAIM9nAdDAAAP\nha0FAACNhfD9//9QjYXs/f//UI2F6P3//1CNheT9//9QjYXg/f//UI2F3P3//1CNhdj9//9QjYXU\n/f//UI2F0P3//1CNhcz9//9QjYXI/f//UI2FxP3//1CNhcD9//9QjYW8/f//UI2FuP3//1CNhbT9\n//9QjYWw/f//UI2FrP3//1CNhaj9//9QjYWk/f//UI2FoP3//1CNhZz9//9QjYWY/f//UI2FlP3/\n/1CNhZD9//9QjYWM/f//UI2FiP3//1CNhYT9//9QjYWA/f//UI2FfP3//1CNhXj9//9QjYV0/f//\nUIsDaEgsQgCNBHCDwARQ6LZKAACBxIgAAAAz9oXAfiSLPZwHQwCNSQCLjLV0/f//ugEAAADT4kYL\n+jvwfOuJPZwHQwCLhWz9//9A6XsDAACLhWz9//9AiYVs/f//O0UID49oBAAAgz0MoUIA/w+FWwQA\nAGgMoUIAaAwtQgD/cwToRUoAAIPEDIXAD4Q+BAAAi4Vs/f//6TADAACLhWz9//9AiYVs/f//O0UI\nD40dBAAAgD19s0IAAA+FEAQAAItLBLqgGEUAjUkAD7cBjVICg8ECZolC/maFwHXui4Vs/f//xgV9\ns0IAAeneAgAAi4Vs/f//QImFbP3//ztFCA+NywMAAIA9fLNCAAAPhb4DAACLSwS6gMNCAJAPtwGN\nUgKDwQJmiUL+ZoXAde6LhWz9///GBXyzQgAB6Y4CAACLhWz9//9AiYVs/f//O0UID417AwAAgL1i\n/f//AA+FbgMAAItLBLqIBUMAkA+3AY1SAoPBAmaJQv5mhcB17ouFbP3//8aFYv3//wHpPgIAAIuF\nbP3//0CJhWz9//87RQgPjSsDAACLSwS6AJ9CAA+3AY1SAoPBAmaJQv5mhcB17ouFbP3//+kDAgAA\ng/pcdUFmOVECdTuAvXL9//8AdTKDwQS6gLNCAOsDjUkAD7cBjVICg8ECZolC/maFwHXui4Vs/f//\nsQGIjXL9///pwwEAAITAD4UzAQAAg/oidTBmg3kCAHQejaQkAAAAAA+3QQKD+CJ0DmaJhfT9//9m\ng3kCAHXpM8BmiYX0/f//6x6NlfT9//8r0Y2bAAAAAA+3AY1JAmaJRBH+ZoXAdfCAPX6zQgAAD4Sc\nAAAAagBqAGiULUIA/xWsoUEAi/CNDDZR6A8hAACDxASL+FZXaJQtQgD/FayhQQBogBZFAFaNhfT9\n//9XUOgk9v//g8QQhMAPhXAAAACNjfT9//+Dwf5mi0ECg8ECZoXAdfShoC1CAIkBoaQtQgCJQQRm\noagtQgBogBZFAFZmiUEIjYX0/f//V1Do2PX//4PEEITAdShorC1CAOlwAQAAM8nrA41JAA+3hA30\n/f//ZomBgBZFAIPBAmaFwHXpi4Vs/f//swGKjXL9//+InXP9///pjgAAAP8VcKFBAIudbP3//zP2\ni8iF2345/zS3UejRRAAAi9CDxAiF0nQriwy3jXkCjWQkAGaLAYPBAmaFwHX1K89Gi71o/f//0fmN\nDEo783zHhcl1BItMt/xmgzkidQODwQJmgzkgdQODwQK6iMVCACvRi/8PtwFmiQQRg8ECZoXAdfGL\nRQiKjXL9//+KnXP9//9AiYVs/f//O0UIfSuLvWj9///pe/f//2gYLUIA6P0gAACDxAQywF5fW4tN\n/DPN6FskAACL5V3DhMl1WY2FUP3//8eFUP3//wAQAABQaICzQgD/FRShQQCAvWX9//8AdDSDPfye\nQgD/dSto3C1CAGj0LUIA/xV4oUEAUP8VjKFBAIvwhfZ0Dmj8nkIA/xXcoEEAUP/WgD2WB0MAAHQy\ngD2VB0MAAHQpaBAuQgDobCsAAIPAQFDoVDcAAIPECDLAXl9bi038M83owyMAAIvlXcOE23QkgD19\ns0IAAHQJgD18s0IAAHQSgL1m/f//AHQqgD1/s0IAAHQqi4Vo/f///zDojg8AAIPEBF5fW4tN/DPN\n6HojAACL5V3DgD1/s0IAAHQJgD1+s0IAAHTNaICzQgBowFpFAOjnGQAAg8QIhcB0CYA9kwdDAAB1\nrg+2BQqhQgAzyTiNZ/3//14PRcGLTfxfogqhQgAzzbABW+gfIwAAi+Vdw41JAItOQABFVEAAQkxA\nAMJMQADiTEAAYkxAAEVUQACCTEAA+0xAAEVUQABFVEAAv01AAEVUQADHT0AARVRAABJQQABFVEAA\nBFFAAAZOQABFVEAAZFBAAKJMQAC0UEAA6U1AAMzMzMzMzMzMzMzMzFWL7LgMnQAA6ANHAAChMKFC\nADPFiUX8U1YzwMaFR2P//wBXi30ID1fAV2jAWkUAib0kY///ZomFLGP//2aJhShj//+JhTBj//+J\nhRRj//9mD9aFGGP//4mFIGP//4mFQGP//+jWGAAAg8QIhcAPlIU/Y///agBqAGoBagD/FdigQQBo\nXEoAAKNgFkMAjYVIY///agBQ6HYoAACDxAzHhUhj//9cSgAAx4WYrf//AAAAAGoBaPBAQAD/FfSg\nQQCNhQxj///HhQxj//8EAQAAUI2F3Pf//1D/FRShQQCNhdz3//9ogLNCAFDoURgAAA+2jbit//+F\nwGgAn0IAugEAAACNhez7//8PRMpoGBRCAFCIjbit///o5x4AAIA9lwdDAAG4RBRCAGoB/zUMoUIA\nvigUQgBqAGigGEUAaIDDQgAPRfCNhez7//9WUGgAn0IAaACfQgBXagDoxNj//4sdWKFBAIPEQITA\ndVX/0z20BQAAdDlqAP81DKFCAI2FKGP//2oAUI2FLGP//1BWjYXs+///UGgAn0IAaACfQgBXagDo\nfNj//4PELITAdRP/019eW4tN/DPN6PIgAACL5V3D6E3S//9XaFgUQgDobCgAAIPAQFDoqCsAAGgA\nn0IAV42F9P3//2isFEIAUOgTHgAAizUcoUEAg8QcagBqAGoDagBqAGgAAADAjYX0/f//UP/Woxyh\nQgCD+P91F//TPecAAAAPhQsBAAChHKFCAIP4/3THagBqAI2NEGP//8eFEGP//wIAAABRUP8V0KBB\nAI2FGGP//1BX6Nfj//+DxAjHhThj//8AAAAAhcDHhbSt///IAAAAD5SFwK3///8VAKFBAGoAiYW8\nrf//jYU4Y///UGoQjYWkrf//UGoQjYW0rf//UP81HKFCAP8V1KBBAIXAD4RVBwAAi4W0rf//O4Wk\nrf//D4VDBwAAgL24rf//AQ+EngAAAIC9wK3//wB0ZYC9sK3//wB0XI2FFGP//1CNhTBj//9QjYUY\nY///UOix4f//g8QMhMB1X+gF0f//aHwWQgDoJScAAIPAQFDoYSoAAIPECP/TUOhn1v//g8QE6WoH\nAADo2tD//1doyBRCAOk2BwAAjYUUY///UI2FMGP//1D/NRyhQgDoFuv//4PEDITAD4Q2BwAAjYUU\nY///iYVAY///gD1+s0IAAHQ9jYVHY///UI2F5Pn//1CNhTRj//9QV+jL6P//g8QQhMB1E//Ti7VA\nY///iYWUrf//6e4GAACLtTRj///rBb6AFkUA/xXcoEEAUFdoAJ9CAI2F9P3//2i4FkIAUOgnHAAA\ng8QUjYX0/f//UGoAagBqAP8V2KBBAKNcFkMA6BbQ//9XaIAWRQBo2BZCAOgwJgAAg8BAUOhsKQAA\ng8QQM8nrBo2bAAAAAA+3gcBaRQCNSQJmiYQNTmP//2aFwHXpM8mNpCQAAAAAD7eBiAVDAI1JAmaJ\nhA1ep///ZoXAdemh/J5CAMeFlK3//wEAAACJhXip////FdygQQCKDXyzQgCJhUxj//+gkQdDAIiF\naKn//6CQB0MAiIVpqf//oJIHQwCIhWqp//+glgdDAIiFbKn//6CVB0MAiIVtqf//oZgHQwCJhXCp\n//+gkwdDAIiFbqn//6GcB0MAiYV0qf//oAqhQgCIjXyp//+IhWup//+EyXQ8M8mNSQAPt4GAw0IA\njUkCZomEDXyp//9mhcB16TPJjaQkAAAAAA+3gaAYRQCNSQJmiYQNhKv//2aFwHXpjY1YZf//K86Q\nD7cGjXYCZolEDv5mhcB18DPJD7eBiMVCAI1JAmaJhA1eZ///ZoXAdemLtUBj//+NhUhj//9oXEoA\nAFD/NRyhQgBW6Oni//+DxBCFwA+EEgMAAI2FOGP//1BqBFD/NRyhQgBW6Mjh//+DxBSFwA+E8QIA\nAOhozv//aAQXQgDoiCQAAIPAQFDocDAAAIPECIA9kQdDAAAPhaMCAAAz9rjQDUIAgL0/Y///AA9E\nx4mFNGP///8V3KBBAFBowFpFAGgAn0IA/7U0Y///jYX0/f//aAgXQgBQ6PcZAACNhfT9//9oAAAA\nQFDoVeX//4PEIKMQoUIAg/j/dQT/04vw/xXcoEEAUGjAWkUAaACfQgD/tTRj//+NhfT9//9oPBdC\nAFDorRkAAI2F9P3//2gAAACAUOgL5f//g8QgoxShQgCD+P91BP/Ti/D/FdygQQBQaMBaRQBoAJ9C\nAP+1NGP//42F9P3//2hwF0IAUOhjGQAAjYX0/f//aAAAAIBQ6MHk//+DxCCjGKFCAIP4/3UIhfZ1\nBP/Ti/BopBdCAOiy0v//g8QEgz0QoUIA/w+E6gEAAIM9GKFCAP8PhN0BAACLDRShQgCD+f8PhM4B\nAAChYBZDAIs9XKFBAGoAiYXErf//i4VAY///agCJhfRi//+Nhcit//9oAAAAEFCJjfhi////11CN\nhfRi//9QagBoQGJAAOhlPgAAg8QMUP/XizXIoEEAUP/Wi4VAY///agCJhQRj//+hGKFCAGoAiYUI\nY///jYXMrf//aAAAABBQ/9dQjYUEY///UGoAaJBgQADoGT4AAIPEDFD/11D/1ouFQGP//2oAiYX8\nYv//oRChQgBqAImFAGP//42F0K3//2gAAAAQUP/XUI2F/GL//1BqAGiAYUAA6NM9AACDxAxQ/9dQ\n/9aLvSRj//+NhWBn//9QjYVYZf//UFeNhdS1//9o2BdCAFDo/RcAAIPEFI2F1LX//1D/FfigQQBq\n/4s1wKBBAI2FxK3//2oAUGoE/9b/NWAWQwD/FVChQQBq/2oBjYXIrf//UGoC/9b/tcit////FTSh\nQQD/tcyt////FTShQQD/tdCt////FTShQQCLtUBj//+NhThj//9QaFxKAACNhUhj//9Q/zUcoUIA\nVujT3v//g8QUhcB1Z+h3y///V2gYFkIA6JYhAACDwEBQ6NIkAACDxAz/01Do2ND//4PEBP/TiYWU\nrf//6dUBAADoQ8v//1doyBRCAOhiIQAAg8BAUOieJAAAVuip0P//ibWUrf//g8QQi7VAY///6aIB\nAACDvZSt//8AD4SgAAAA6APL//9XjYVYZf//UGjwF0IA6BshAACDwEBQ6FckAACLhZSt//+DxBA9\nZgYAAHUbaDgYQgDo+CAAAIPAQFDo4CwAAIPECOlKAQAAUOg30P//i4Wgrf//g8QEhcAPhDMBAABo\nAAQAAI2N1K3//1FQagD/FXShQQBQ/xUUs0IAjYXUrf//UGioGEIA6KMgAACDwEBQ6HMkAACDxAzp\n9QAAAIA9kQdDAACNhVhl//91I/+1mK3//1dQaLAYQgDocSAAAIPAQFDorSMAAIPEFOnDAAAA/7WQ\nrf//V1BoABlCAOhOIAAAg8BAUOiKIwAAi4WQrf//g8QUiYWUrf//6ZQAAADoAsr//4O9OGP//wBX\ndFpoSBVCAOgYIAAAg8BAUOhUIwAAg8QM/7W0rf//aMgVQgDo/B8AAIPAQFDoOCMAAIPEDP+1pK3/\n/2jwFUIA6OAfAACDwEBQ6BwjAACDxAzHhZSt//9+BAAA6yloGBZCAOi+HwAAg8BAUOj6IgAAg8QM\n/9NQ6ADP//+DxAT/04mFlK3//zP2oRyhQgCD+P90B1D/FTShQQCAvUdj//8AdEOAPZEHQwAAdTqN\nheT5//9Q/xUYoUEAhcB1KVeNhVhl//9QaEwZQgDoVx8AAIPAQFDokyIAAIPEEP/TUOiZzv//g8QE\n/7UwY///6IvZ//+DxASF9nQJVuhu2v//g8QEjYXs+///UGgAn0IAV/+1nK3//+iD0v//i42Urf//\ng8QQi4WYrf//hckPRcGLTfxfXjPNW+heFwAAi+Vdw8zMzMzMzMzMzMzMzMzMVYvsuAwAAgDoozsA\nAKEwoUIAM8WJRfxWV4t9CGoA/zVgFkMAi3cE/xVIoUEAPQIBAAAPhYMAAABTix1YoUEAkI2F9P/9\n/1BoAAABAI2F+P/9/1BW/zfopdv//4PEFIXAdQn/0z3oAAAAdU+LhfT//f+NDACB+QIAAgBzXzPS\nUmaJlA34//3/jY30//3/UVCNhfj//f9QavT/FXyhQQBQ/xU4oUEAagD/NWAWQwD/FUihQQA9AgEA\nAHSGW1b/FcygQQBW/xU0oUEAi038M8BfM81e6HUWAACL5V3D6JwzAADMzMzMzMzMzMzMzMzMzMzM\nVYvsg+wMVot1CFdq9ot2BP8VfKFBAIs9SKFBAGoA/zVgFkMAiUX4/9c9AgEAAHVzU4sd/KBBAGoA\njUX0UGoBjUX8UP91+P/ThcB0VosNoAdDAGaLRfxmiQRNgBZDAEGJDaAHQwADyYH5AAACAHNK/3X0\nM8BmiYGAFkMAjUX8UItFCFb/MOiG2///g8QQhcB0EWoA/zVgFkMA/9c9AgEAAHSVW1b/FcygQQBW\n/xU0oUEAXzPAXovlXcPozjIAAMzMVYvsuBQAAwDo8zkAAKEwoUIAM8WJRfxTix1IoUEAVot1CFdq\nAP81YBZDAIt+BIm17P/8///TPQIBAAAPhTYBAACNhfD//P9QaAAAAQCNhfj//v9QV/826PTZ//+D\nxBSFwHUR/xVYoUEAPegAAAAPhQMBAACLhfD//P89AQABAA+DEwEAAGgBAAEAxoQF+P/+/wCNhfT/\n/P9Qav+Nhfj//v9QagBqAP8VEKFBAGaDPYAWQwAAD4R9AAAAjY30//z/jVECZosBg8ECZoXAdfUr\nyo2F9P/8/9H5uoAWQwCNNAmD7gRyEYsKOwh1EIPCBIPABIPuBHPvg/78dC2KCjoIdTaD/v10IopK\nATpIAXUpg/7+dBWKSgI6SAJ1HIP+/3QIikoDOkgDdQ8zwGajgBZDAKOgB0MA6yVqAI2F8P/8/1D/\ntfD//P+Nhfj//v9QavX/FXyhQQBQ/xU4oUEAagD/NWAWQwD/04u17P/8/z0CAQAAD4TK/v//V/8V\nzKBBAFf/FTShQQCLTfwzwF9eM81b6AgUAACL5V3D6C8xAADMzMxVi+xouBpCAOjQDwAAaAgbQgDo\nxg8AAP91CGgwG0IA6LkPAABo+BtCAOivDwAAaEgcQgDopQ8AAGiYHEIA6JsPAABo3BxCAOiRDwAA\naPgcQgDohw8AAGjIHUIA6H0PAABoEB5CAOhzDwAAaFAeQgDoaQ8AAGjIHkIA6F8PAABoqB9CAOhV\nDwAAaPgfQgDoSw8AAGhAIEIA6EEPAACDxEBokCBCAOg0DwAAaOAgQgDoKg8AAGgoIUIA6CAPAABo\neCFCAOgWDwAAaAgiQgDoDA8AAGhcIkIA6AIPAABodCJCAOj4DgAAaLgiQgDo7g4AAGgYI0IA6OQO\nAABosCNCAOjaDgAAaPgjQgDo0A4AAGggJEIA6MYOAABoiCRCAOi8DgAAaNAkQgDosg4AAGgYJUIA\n6KgOAABoaCVCAOieDgAAg8RAaLglQgDokQ4AAGgAJkIA6IcOAABoUCZCAOh9DgAAaJgmQgDocw4A\nAGjgJkIA6GkOAABoKCdCAOhfDgAAaHwnQgDoVQ4AAGicJ0IA6EsOAABo0CdCAOhBDgAAaBQoQgDo\nNw4AAGhQKEIA6C0OAABooChCAOgjDgAAaPAoQgDoGQ4AAGg0KUIA6A8OAABoeClCAOgFDgAAaLwp\nQgDo+w0AAIPEQGj4KUIA6O4NAABoQCpCAOjkDQAAaIgqQgDo2g0AAGjQKkIA6NANAABoGCtCAOjG\nDQAAaGArQgDovA0AAGiwK0IA6LINAABo+CtCAOioDQAAaBAsQgDong0AAIPEJDLAXcPMzMzMzMzM\nzMzMVYvsuBiAAADoAzYAAKEwoUIAM8WJRfxTVot1CFeLfQyJtfR///+NBLUAAAAAib3wf///UMeF\n+H///wAAAADoowwAAIPEBIvYhfZ0DovOi/eL+/Oli73wf///V42F9H///1BoTC5CAOgEu///V42F\n9H///1BoTC5CAOgCsf//g8QYhcB1Fl9euAEAAABbi038M83oFREAAIvlXcNT6A0MAACDxATHheh/\n//8QAAAAjYXof///UGjAWkUA/xUUoUEA/xUAoUEAPQAAAIByKmhcLkIA6GMYAACDwEBQ6EskAACD\nxAiDyP9fXluLTfwzzei5EAAAi+VdwzP2M/85tfR///92U4uF8H///4sMsIvRjVoCZosCg8ICZoXA\ndfUr09H6gfoDAQAAD4fWAAAAjVECkGaLAYPBAmaFwHX1K8rR+QP5gf//HwAAD4eLAAAARju19H//\n/3KtizVwoUEA/9aNUAJmiwiDwAJmhcl19SvC0fg9AEAAAHdh/9aNlfx///8r0A+3CI1AAmaJTAL+\nZoXJdfBmg738f///Io21/H///w+FkAAAAGaLjf5///+Ntf5///+LxmaFyQ+EmAAAAA+3yWaD+SIP\nhHwAAAAPt0gCg8ACZoXJderrbmi0LkIA6FgXAACDwEBQ6EAjAACDxAiDyP9fXluLTfwzzeiuDwAA\ni+Vdw4uF8H////80sGiELkIA6CUXAACDwEBQ6GEaAACDxAyDyP9fXluLTfwzzeh7DwAAi+Vdw42F\n/H///2ogUOixIAAAg8QIhcB0C2aDOAB0BTPJZokIjYXsf///UGjAGkUAaAAgAABW/xXgoEEAi4Xs\nf///hcB0BTPJZokIix14oUEAaMwuQgBooAlCAP/TizWMoUEAUP/Wo3izQgCNhfh///9Q/7Xwf///\n/7X0f///6Ezh//+DxAyEwA+EnwEAAGjkLkIAaPguQgD/01D/1oXAdBmDvfh///8AdRCNjfh///9R\naIDDQgBqAP/QgD18s0IAAA+E6QAAAIA9fbNCAAAPhdwAAABqXGiAw0IA6OMfAACL+IPECIX/dAUz\nwGaJB2gUL0IAaIDDQgDoEAUAAIPECIXAD4SKAAAAaDAvQgBogMNCAOj2BAAAg8QIhcB0dGhIL0IA\n6DQKAADo3BUAAIPAIFDoYhcAAIPECDP2jWQkAOhpDAAAD7fAg/gNdBqD+AMPhN8AAABmiQR1oBhF\nAEaB/gcCAABy2QP2gf4IAgAAD4PdAAAAM8BoECxCAGaJhqAYRQDo1QkAAIs1jKFBAIPEBOsSM8DH\nhfh///8BAAAAZqOgGEUAhf90CLhcAAAAZokHaFQvQgBooAlCAP/TUP/WaICzQgBowFpFAKOkB0MA\n6DoEAACDxAiFwHUuOAWVB0MAdSY5hfh///91HjkFpAdDAHQW6Mja//9fXluLTfwzzeh3DQAAi+Vd\nw2gwVUAAaICzQgBqAeg2wv//i038g8QMM81fXlvoUg0AAIvlXcNq/+g1CAAAaKwRQgDoyxQAAIPA\nQFDosyAAAIPECGoA6BgIAADoVSoAAMz/JXSiQQD/JXiiQQD/JXyiQQD/JWiiQQD/JWyiQQD/JYyi\nQQD/JZCiQQD/JYSiQQD/JYiiQQD/JWCiQQD/JVyiQQC4LLNCAOkAAAAAUVJQaJx1QgDowwAAAFpZ\n/+C4JLNCAOnl////uCCzQgDp2////7gcs0IA6dH///+4GLNCAOnH////uAyzQgDpvf///7gIs0IA\n6bP///+4KLNCAOmp////uBCzQgDpn////7gUs0IA6ZX///+4/LJCAOkAAAAAUVJQaLx1QgDoTgAA\nAFpZ/+C4+LJCAOnl////uPSyQgDp2////7jwskIA6dH///+4ALNCAOnH////uOyyQgDpvf////8l\nYKBBAP8lAKBBAP8lXKBBAP8lWKBBAIv/VYvsg+wwU4tdCFZXvwAAQADHRdAkAAAAi0MEi3MIA8eL\nSwwD94tTEAPPA9eJdQiLexSBxwAAQACJXdSJffSLexyJffyLfQyJfdgz//cDAQAAAIlF3Il94Il9\n5Il96Il97Il98HUfjUXQiUUMjUUMUGoBV2hXAG3A/xWwoUEAM8DpggEAAItFDIs+K8HB+AKJRfiL\nDIKLwcHoH/fQg+ABiUXgjYECAEAAdQMPt8GJReQz9qG0B0MAhcB0Fo1N0FFW/9CL8IX2D4UeAQAA\nobQHQwCF/3V9hcB0Do1N0FFqAf/Qi/iF/3VUagBqAP913P8VtKFBAIv4hf91Qf8VWKFBAIlF8KG4\nB0MAhcB0Do1N0FFqA//Qi/iF/3UhjUXQiUUMjUUMUGoBagBofgBtwP8VsKFBAItF7OnMAAAAi00I\ni8eHATvHdQdX/xVgoUEAobQHQwCJfeiFwHQKjU3QUWoC/9CL8IX2dX45cxR0LTlzHHQoi0c8gTw4\nUEUAAHUci038OUw4CHUTO3w4NHUNi3X4i0X0izSwhfZ1TP915Ff/FYyhQQCL8IX2dTz/FVihQQCJ\nRfChuAdDAIXAdA6NTdBRagT/0IvwhfZ1HI1F0IlFCI1FCFBqAWoAaH8AbcD/FbChQQCLdeyLRQyJ\nMKG0B0MAhcB0EoNl8ACNTdBRagWJfeiJdez/0IvGX15bi+VdwggAVYvsVovxi00IxkYMAIXJdWZX\n6Pc9AACL+Il+CItXbIkWi09oiU4EOxXEq0IAdBGhgKxCAIVHcHUH6PMzAACJBotGBF87BeynQgB0\nFYtOCKGArEIAhUFwdQjoVTcAAIlGBItOCItBcKgCdRaDyAKJQXDGRgwB6wqLAYkGi0EEiUYEi8Ze\nXcIEAFWL7IM9UAtDAAB1dYtVCIXSdRfobz8AAMcAFgAAAOi8MAAAuP///39dw4tNDIXJdOJTVldq\nQV9qWivRWw+3BApmO8dyDWY7w3cIg8AgD7fw6wKL8A+3AWY7x3ILZjvDdwaDwCAPt8CDwQJmhfZ0\nBWY78HTID7fIXw+3xl4rwVtdw2oA/3UM/3UI6AUAAACDxAxdw1WL7IPsEI1N8FNW/3UQ6NL+//+L\nXQiF23QHi3UMhfZ1GujOPgAAxwAWAAAA6BswAAC6////f+mLAAAAi0XwV4O4qAAAAAB1QmpBWWpa\nK95aD7cEM2Y7wXINZjvCdwiDwCAPt/jrAov4D7cGZjvBcgtmO8J3BoPAIA+3wIPGAmaF/3Q6Zjv4\ndMjrMw+3A41N8FFQ6Do6AACNTfAPt/gPtwaNWwJRUOgnOgAAg8QQD7fAjXYCZoX/dAVmO/h0zQ+3\n1w+3wCvQX4B9/ABeW3QHi034g2Fw/YvCi+Vdw8xVi+xRjUX8UGiML0IAagD/FcShQQCFwHQXaKQv\nQgD/dfz/FYyhQQCFwHQF/3UI/9CL5V3DVYvs/3UI6MH///9Z/3UI/xXAoUEAzFWL7OjpQwAA/3UI\n6D5EAABZaP8AAADoowAAAMxqAWoBagDoTQEAAIPEDMNqAWoAagDoPgEAAIPEDMNVi+yDPYBvQgAA\ndBlogG9CAOgZRgAAWYXAdAr/dQj/FYBvQgBZ6F1JAABouKJBAGigokEA6M0AAABZWYXAdUNou65A\nAOgCSAAAxwQknKJBAGiYokEA6HYAAACDPYhtRQAAWVl0G2iIbUUA6MBFAABZhcB0DGoAagJqAP8V\niG1FADPAXcNVi+xqAGoB/3UI6KcAAACDxAxdw1ZqAP8VuKFBAIvwVuhoSQAAVugFLgAAVuhpSQAA\nVuh9SQAAVugZSQAAVuiCSwAAg8QYXukVQAAAVYvsi0UMU1aLdQgz2yvGg8ADwegCOXUMVxv/99cj\n+HYQiwaFwHQC/9CDxgRDO99y8F9eW13DVYvsVot1CDPA6w+FwHUQiw6FyXQC/9GDxgQ7dQxy7F5d\nw2oI6Es9AABZw2oI6Kw+AABZw2ocaCBwQgDoGUsAAGoI6C09AABZg2X8AIM9vAdDAAEPhMkAAADH\nBeQHQwABAAAAikUQouAHQwCDfQwAD4WcAAAA/zWQbUUAizW8oUEA/9aL2Ild1IXbdHT/NYxtRQD/\n1ov4iV3kiX3giX3cg+8EiX3cO/tyV2oA/xW4oUEAOQd06jv7ckf/N//Wi/BqAP8VuKFBAIkH/9b/\nNZBtRQCLNbyhQQD/1olF2P81jG1FAP/Wi03YOU3kdQU5ReB0rolN5IvZiV3UiUXgi/jrnGjQokEA\naLyiQQDou/7//1lZaNiiQQBo1KJBAOiq/v//WVnHRfz+////6CAAAACDfRAAdSnHBbwHQwABAAAA\nagjomT0AAFn/dQjoXP3//4N9EAB0CGoI6IM9AABZw+g8SgAAw1WL7GoAagD/dQjowv7//4PEDF3D\nVYvsg30IAHQt/3UIagD/NQgTQwD/FcyhQQCFwHUYVujzOgAAi/D/FVihQQBQ6Pg6AABZiQZeXcNV\ni+xWi3UIg/7gd29TV6EIE0MAhcB1HejmQAAAah7oPEEAAGj/AAAA6M78//+hCBNDAFlZhfZ0BIvO\n6wMzyUFRagBQ/xXQoUEAi/iF/3UmagxbOQUME0MAdA1W6NZGAABZhcB1qesH6HM6AACJGOhsOgAA\niRiLx19b6xRW6LVGAABZ6Fg6AADHAAwAAAAzwF5dw4sNMKFCADPAg8kBOQ3wB0MAD5TAw2oMaEBw\nQgDo90gAADP/iX3kM8A5RQgPlcCFwHUV6BY6AADHABYAAADoYysAAIPI/+th6HYLAACDwCBQagHo\nsAsAAFlZiX386GELAACDwCBQ6DhJAABZi/CNRQxQV/91COhICwAAg8AgUOjaSQAAi/iJfeToNQsA\nAIPAIFBW6NpIAACDxBjHRfz+////6AsAAACLx+iwSAAAw4t95OgMCwAAg8AgUGoB6LALAABZWcNq\nDGhgcEIA6EhIAAAz/4l95DPAOUUID5XAhcB1FehnOQAAxwAWAAAA6LQqAACDyP/rYejHCgAAg8Ag\nUGoB6AELAABZWYl9/OiyCgAAg8AgUOiJSAAAWYvwjUUMUFf/dQjomQoAAIPAIFDoP1YAAIv4iX3k\n6IYKAACDwCBQVugrSAAAg8QYx0X8/v///+gLAAAAi8foAUgAAMOLfeToXQoAAIPAIFBqAegBCwAA\nWVnDVYvsjUUMUGoA/3UI6EVVAACDxAxdw1WL7IPsIFNXM9uNfeRqBzPAiV3gWfOrOUUMdRXoozgA\nAMcAFgAAAOjwKQAAg8j/62yLRQiFwHTkVolF6IlF4I1FEFBT/3UMjUXgx0XsQgAAAFDHReT///9/\n6IpVAACDxBCL8P9N5HgOi03giBmLTeBBiU3g6w+NReBQU+hHYgAAWVmLTeD/TeR4BIgZ6wyNReBQ\nU+gvYgAAWVmLxl5fW4vlXcNVi+yNRRRQagD/dRD/dQz/dQjoV2QAAIPEFF3DVYvsi0UIhcB0EoPo\nCIE43d0AAHUHUOjT/P//WV3DagxogHBCAOimRgAAg2XkAGoD6LY4AABZg2X8AOglAAAAi/CJdeTH\nRfz+////6AsAAACLxui+RgAAw4t15GoD6PM5AABZw1WL7IPsIKEwoUIAM8WJRfyhIKFCAFODy/87\nw3QOD7bAiR0goUIA6ZUAAACLDeitQgCD+f51C+hBZAAAiw3orUIAO8t1BIvD63dWjUXgUFH/FdSh\nQQBqAP816K1CAP8V3KFBAIsd2KFBAOsog33kAHQ6Zjl16HUfg33sAHQZD7Z19oX2dSmNRexQ6E8A\nAABZhcB1PDP2Ro1F5FBWjUXoUP816K1CAP/ThcB1wIPO//914P816K1CAP8V3KFBAIvGXotN/DPN\nW+i9AAAAi+Vdww+2MA+2QAGjIKFCAOvRVYvsi1UIVleLSgz3wQABAAB0TA+3eggzwIvwi9BmObq4\nL0IAdAuDwgpGg/p4cu7rdmvGCvbBA3QHBcAvQgDrZ/bBDHQHBb4vQgDrW/bBEHQHBbwvQgDrTwW6\nL0IA60gPt0II9sEDdAmNBMU2MEIA6yP2wQx0CY0ExTQwQgDrFfbBEHQJjQTFMjBCAOsHjQTFMDBC\nAIoIhMl0BYD54HUGgHgBAHUCM8BfXl3DOw0woUIAdQLzw+kmHAAAzMzMzMxXVot0JBCLTCQUi3wk\nDIvBi9EDxjv+dgg7+A+CaAMAAA+6JRQTQwABcwfzpOkXAwAAgfmAAAAAD4LOAQAAi8czxqkPAAAA\ndQ4PuiXsrUIAAQ+C2gQAAA+6JRQTQwAAD4OnAQAA98cDAAAAD4W4AQAA98YDAAAAD4WXAQAAD7rn\nAnMNiwaD6QSNdgSJB41/BA+65wNzEfMPfg6D6QiNdghmD9YPjX8I98YHAAAAdGMPuuYDD4OyAAAA\nZg9vTvSNdvRmD29eEIPpMGYPb0YgZg9vbjCNdjCD+TBmD2/TZg86D9kMZg9/H2YPb+BmDzoPwgxm\nD39HEGYPb81mDzoP7AxmD39vII1/MH23jXYM6a8AAABmD29O+I12+I1JAGYPb14Qg+kwZg9vRiBm\nD29uMI12MIP5MGYPb9NmDzoP2QhmD38fZg9v4GYPOg/CCGYPf0cQZg9vzWYPOg/sCGYPf28gjX8w\nfbeNdgjrVmYPb078jXb8i/9mD29eEIPpMGYPb0YgZg9vbjCNdjCD+TBmD2/TZg86D9kEZg9/H2YP\nb+BmDzoPwgRmD39HEGYPb81mDzoP7ARmD39vII1/MH23jXYEg/kQfBPzD28Og+kQjXYQZg9/D41/\nEOvoD7rhAnMNiwaD6QSNdgSJB41/BA+64QNzEfMPfg6D6QiNdghmD9YPjX8IiwSNKHtAAP/g98cD\nAAAAdRXB6QKD4gOD+QhyKvOl/ySVKHtAAJCLx7oDAAAAg+kEcgyD4AMDyP8khTx6QAD/JI04e0AA\nkP8kjbx6QACQTHpAAHh6QACcekAAI9GKBogHikYBiEcBikYCwekCiEcCg8YDg8cDg/kIcszzpf8k\nlSh7QACNSQAj0YoGiAeKRgHB6QKIRwGDxgKDxwKD+QhypvOl/ySVKHtAAJAj0YoGiAeDxgHB6QKD\nxwGD+QhyiPOl/ySVKHtAAI1JAB97QAAMe0AABHtAAPx6QAD0ekAA7HpAAOR6QADcekAAi0SO5IlE\nj+SLRI7oiUSP6ItEjuyJRI/si0SO8IlEj/CLRI70iUSP9ItEjviJRI/4i0SO/IlEj/yNBI0AAAAA\nA/AD+P8klSh7QACL/zh7QABAe0AATHtAAGB7QACLRCQMXl/DkIoGiAeLRCQMXl/DkIoGiAeKRgGI\nRwGLRCQMXl/DjUkAigaIB4pGAYhHAYpGAohHAotEJAxeX8OQjXQx/I18Ofz3xwMAAAB1JMHpAoPi\nA4P5CHIN/fOl/P8klcR8QACL//fZ/ySNdHxAAI1JAIvHugMAAACD+QRyDIPgAyvI/ySFyHtAAP8k\njcR8QACQ2HtAAPx7QAAkfEAAikYDI9GIRwOD7gHB6QKD7wGD+Qhysv3zpfz/JJXEfEAAjUkAikYD\nI9GIRwOKRgLB6QKIRwKD7gKD7wKD+QhyiP3zpfz/JJXEfEAAkIpGAyPRiEcDikYCiEcCikYBwekC\niEcBg+4Dg+8Dg/kID4JW/////fOl/P8klcR8QACNSQB4fEAAgHxAAIh8QACQfEAAmHxAAKB8QACo\nfEAAu3xAAItEjhyJRI8ci0SOGIlEjxiLRI4UiUSPFItEjhCJRI8Qi0SODIlEjwyLRI4IiUSPCItE\njgSJRI8EjQSNAAAAAAPwA/j/JJXEfEAAi//UfEAA3HxAAOx8QAAAfUAAi0QkDF5fw5CKRgOIRwOL\nRCQMXl/DjUkAikYDiEcDikYCiEcCi0QkDF5fw5CKRgOIRwOKRgKIRwKKRgGIRwGLRCQMXl/DjaQk\nAAAAAFeLxoPgD4XAD4XSAAAAi9GD4X/B6gd0ZY2kJAAAAACQZg9vBmYPb04QZg9vViBmD29eMGYP\nfwdmD39PEGYPf1cgZg9/XzBmD29mQGYPb25QZg9vdmBmD29+cGYPf2dAZg9/b1BmD393YGYPf39w\njbaAAAAAjb+AAAAASnWjhcl0T4vRweoEhdJ0F42bAAAAAGYPbwZmD38HjXYQjX8QSnXvg+EPdCqL\nwcHpAnQNixaJF412BI1/BEl184vIg+EDdA+KBogHRkdJdfeNmwAAAABYXl/DjaQkAAAAAOsDzMzM\nuhAAAAAr0CvKUYvCi8iD4QN0CYoWiBdGR0l198HoAnQNixaJF412BI1/BEh181np+v7//8zMzMzM\nzMzMzMzMzItUJAyLTCQEhdJ0fw+2RCQID7olFBNDAAFzDYtMJAxXi3wkCPOq612LVCQMgfqAAAAA\nfA4PuiXsrUIAAQ+C5l0AAFeL+YP6BHIx99mD4QN0DCvRiAeDxwGD6QF19ovIweAIA8GLyMHgEAPB\ni8qD4gPB6QJ0BvOrhdJ0CogHg8cBg+oBdfaLRCQIX8OLRCQEw6GEbUUAVmoUXoXAdQe4AAIAAOsG\nO8Z9B4vGo4RtRQBqBFDoyjkAAKOAbUUAWVmFwHUeagRWiTWEbUUA6LE5AACjgG1FAFlZhcB1BWoa\nWF7DM9K5OKFCAIkMAoPBII1SBIH5uKNCAH0HoYBtRQDr6DPAXsPoqAEAAIA94AdDAAB0BejaXQAA\n/zWAbUUA6H3z//+DJYBtRQAAWcO4OKFCAMNVi+xWi3UIuTihQgA78XIigf6Yo0IAdxqLxivBwfgF\ng8AQUOhDLwAAgU4MAIAAAFnrCo1GIFD/FeChQQBeXcNVi+yLRQiD+BR9FoPAEFDoGC8AAItFDFmB\nSAwAgAAAXcOLRQyDwCBQ/xXgoUEAXcNVi+yLRQi5OKFCADvBch89mKNCAHcYgWAM/3///yvBwfgF\ng8AQUOg7MAAAWV3Dg8AgUP8V5KFBAF3DVYvsi00Ii0UMg/kUfROBYAz/f///jUEQUOgOMAAAWV3D\ng8AgUP8V5KFBAF3DVYvsVot1CIX2dQlW6PsAAABZ6y9W6CwAAABZhcB0BYPI/+sf90YMAEAAAHQU\nVugSAgAAUOhOXQAA99hZWRvA6wIzwF5dw1WL7FNWi3UIM9uLRgwkAzwCdUL3RgwIAQAAdDlXiz4r\nfgiF/34uV/92CFbozwEAAFlQ6PNdAACDxAw7x3UPi0YMhMB5D4Pg/YlGDOsHg04MIIPL/1+LTgiL\nw4NmBACJDl5bXcNqAehbAAAAWcNqDGigcEIA6Ls7AAAz/4l95It1CIX2dQlX6DsAAABZ6yRW6FD+\n//9ZiX38Vugc////WYv4iX3kx0X8/v///+gOAAAAi8fowjsAAMOLdQiLfeRW6I/+//9Zw2oUaMBw\nQgDoYjsAADP/iX3kIX3cagHobi0AAFkhffwz9otdCIl14Ds1hG1FAA+NhgAAAKGAbUUAiwSwhcB0\nXfZADIN0V1BW6BH+//9ZWcdF/AEAAAChgG1FAIsEsPZADIN0MIP7AXUSUOiG/v//WYP4/3QfR4l9\n5OsZhdt1FfZADAJ0D1Doav7//1mD+P91AwlF3INl/ADoDAAAAEbrhYtdCIt95It14KGAbUUA/zSw\nVugR/v//WVnDx0X8/v///+gWAAAAg/sBi8d0A4tF3OjfOgAAw4tdCIt95GoB6BEuAABZw1WL7IN9\nCAB1FeixKwAAxwAWAAAA6P4cAACDyP9dw1aLdQyF9nUW6JQrAADHABYAAADo4RwAAIPI/15dw/91\nCOiQZQAAiQYjwolWBFmD+P905TPA6+RVi+yLRQiFwHUV6FsrAADHABYAAADoqBwAAIPI/13Di0AQ\nXcNqDGjocEIA6AU6AAAz24ld5DPAi30Ihf8PlcCFwHUV6CIrAADHABYAAADobxwAAIPI/+tMM8A5\nRQwPlcCFwHTfiX0IV+h4/P//WYld/FfoSDoAAIvwjUUQUFP/dQxX6AdIAACL2Ild5FdW6Ps5AACD\nxBzHRfz+////6A4AAACLw+jROQAAw4td5It9CFfonvz//1nDVYvsjUUQUGoA/3UM/3UI6ANpAACD\nxBBdw1WL7ItNCIXJdRXoiioAAMcAFgAAAOjXGwAAahZYXcOhGBRDAIkBM8Bdw2oQaAhxQgDoLjkA\nAINl5ACLfQyB/wBAAAB0MIH/AIAAAHQogf8AAAEAdCCB/wAABAB0GIH/AAACAHQQ6C8qAADHABYA\nAADprAAAAIt1CIP+/nUQ6BcqAADHAAkAAADpmQAAAIX2D4iBAAAAOzXQXEUAc3mLxsH4BYlFDIve\ng+MfweMGiwSFGBNDAA++RBgEg+ABdFhW6DgBAABZg2X8AItFDIsEhRgTQwD2RBgEAXQNV1boTgAA\nAFlZi/jrDuitKQAAxwAJAAAAg8//iX3kx0X8/v///+gKAAAAi8frIYt1CIt95FbojgQAAFnD6H4p\nAADHAAkAAADoyxoAAIPI/+h7OAAAw1WL7FNWi3UIi97B+wWD5h/B5gZXizydGBNDAIpMNwQPtsEl\ngAAAAIlFCIpENyQCwA++0ItFDNH6PQBAAAB0WT0AgAAAdEk9AAABAHQqPQAAAgB0Iz0AAAQAdVCA\nyYCITDcEiwydGBNDAIpEMSQkgQwBiEQxJOs0gMmAiEw3BIsMnRgTQwCKRDEkJIIMAuvigOF/iEw3\nBOsTgMmAiEw3BIsEnRgTQwCAZDAkgIN9CABfXlt1B7gAgAAAXcP32hvSgeIAwAAAjYIAQAAAXcNq\nCGhQcUIA6Fw3AACLfQiLx8H4BYv3g+YfweYGAzSFGBNDADPbOV4IdTFqCuhSKQAAWYld/DleCHUV\nU2igDwAAjUYMUOg3KwAAg8QM/0YIx0X8/v///+gqAAAAi8fB+AWD5x/B5waLBIUYE0MAg8AMA8dQ\n/xXgoUEAM8BA6Cw3AADDi30IagroYSoAAFnDahhoKHFCAOjONgAAg87/iXXkg2XcAGoL6GApAABZ\nhcB1BwvG6W4BAABqC+jFKAAAWYNl/AAz24ld2IP7QA+NRgEAAIs8nRgTQwCF/w+EvgAAAIl94IsE\nnRgTQwAFAAgAADv4D4OcAAAA9kcEAXVbg38IAHU0agroeSgAAFnHRfwBAAAAg38IAHUWagBooA8A\nAI1HDFDoWCoAAIPEDP9HCINl/ADoLwAAAItF3IXAdRqNRwxQ/xXgoUEA9kcEAXQhjUcMUP8V5KFB\nAIPHQOuDi3Xki13Yi33gagrogCkAAFnDi0XchcB14sZHBAGDD/+L9ys0nRgTQwDB/gaLw8HgBQPw\niXXkg/7/dX9D6Sf///9qQGog6MIxAABZWYvIiU3ghcl0ZYkMnRgTQwCDBdBcRQAgiwSdGBNDAAUA\nCAAAO8hzFWbHQQQACoMJ/4NhCACDwUCJTeDr24vzweYFiXXki8bB+AWLzoPhH8HhBosEhRgTQwDG\nRAgEAVbo9P3//1mFwHUGg87/iXXkx0X8/v///+gLAAAAi8bogzUAAMOLdeRqC+i4KAAAWcNVi+yL\nRQhWV4XAeGA7BdBcRQBzWIv4i/DB/wWD5h/B5gaLDL0YE0MA9kQOBAF0PYM8Dv90N4M9qAxDAAF1\nHzPJK8F0EEh0CEh1E1Fq9OsIUWr16wNRavb/FeihQQCLBL0YE0MAgwwG/zPA6xbo9SUAAMcACQAA\nAOi2JQAAgyAAg8j/X15dw1WL7ItNCIP5/nUV6JwlAACDIADoyCUAAMcACQAAAOtChcl4JjsN0FxF\nAHMei8GD4R/B+AXB4QaLBIUYE0MA9kQIBAF0BYsECF3D6F0lAACDIADoiSUAAMcACQAAAOjWFgAA\ng8j/XcNVi+yLTQhTV4XJeFw7DdBcRQBzVIvZi/nB+wWD5x/B5waLBJ0YE0MAgzwH/3U6gz2oDEMA\nAVaLdQx1HoPpAHQQSXQISXUTVmr06whWavXrA1Zq9v8V6KFBAIsEnRgTQwCJNAczwF7rFugMJQAA\nxwAJAAAA6M0kAACDIACDyP9fW13DVYvsi00Ii8HB+AWD4R/B4QaDwQyLBIUYE0MAA8FQ/xXkoUEA\nXcNTi9xRUYPk8IPEBFWLawSJbCQEi+yLSwiD7CCDPRATQwABZotTDHxID7fCZg9uwPIPcMAAZg9w\n0ACLwSX/DwAAPfAPAAB3PvMPbwFmD+/JZg91yGYPdcJmD+vIZg/XwYXAdTSDwRDr0WY7wnQLg8EC\nD7cBZoXAdfAzwGY5EQ+VwEgjwesiD7cBZjvCdBhmhcB0D4PBAuuiD7zAiUX8A8jr1zPA6wKLwYvl\nXYvjW8NVi+xWM/aDPRATQwACfS2LTQiL0WaLAYPBAmaFwHX1ZotFDIPpAjvKdAVmOQF19GY5AXUE\ni8HrazPA62eLVQhmi00M6xIPtwJmO8F1AovyZoXAdE2DwgKNQgGoDnXnM8BmO8F1H7gBAP//Zg9u\nyOsDg8IQ8w9vAmYPOmPIFXXxjQRK6x8Pt8FmD27A8w9vCmYPOmPBQXMDjTRKdAWDwhDr6ovGXl3D\nVYvsVot1CFeDz/+F9nUU6GkjAADHABYAAADothQAAAvH60X2RgyDdDlW6OX1//9Wi/johWYAAFbo\n1vf//1DoBGUAAIPEEIXAeQWDz//rE4N+HAB0Df92HOgJ6P//g2YcAFmDZgwAi8dfXl3DagxocHFC\nAOjQMQAAg8//iX3kM8CLdQiF9g+VwIXAdRjo7CIAAMcAFgAAAOg5FAAAi8fo6jEAAMP2RgxAdAaD\nZgwA6+xW6EL0//9Zg2X8AFboP////1mL+Il95MdF/P7////oCAAAAOvHi3UIi33kVuiG9P//WcNq\nDGiQcUIA6FkxAAAz24ld5DPAi30Ihf8PlcCFwHUY6HYiAADHABYAAADowxMAAIPI/+nHAAAAM8A5\nRQwPlcCFwHTciX0IV+jJ8///WSFd/PZHDEB1cVfo1Pb//1mL0IP6/3QZg/r+dBSLysH5BYPgH8Hg\nBgMEjRgTQwDrBbjwrUIA9kAkf3Upg/r/dBmD+v50FIvCwfgFg+IfweIGAxSFGBNDAOsFuvCtQgD2\nQiSAdBbo5CEAAMcAFgAAAOgxEwAAg8v/iV3khdt1JVfoHjEAAIvwjUUQUFP/dQxX6MkxAACL2Ild\n5FdW6NEwAACDxBzHRfz+////6A4AAACLw+inMAAAw4td5It9CFfodPP//1nDVYvsUVGLTQxTVleF\nyXQei10Qhdt0F4t1FIX2dRnoZiEAAMcAFgAAAOizEgAAM8BfXluL5V3Dg30IAHThg8j/M9L38TvY\nd9aL+Q+v+/dGDAwBAACL33QFi04Y6wW5ABAAAIlN/IX/D4TTAAAAi1YMgeIIAQAAdDaLRgSJRfiF\nwHQsD4jAAAAAO9hzBYvDiV34UP91CP826Ojq//+LRfiDxAwpRgQr2AEGAUUI61072XJehdJ0Elbo\nZPP//1mFwA+FhwAAAItN/IvDhcl0DTPS9/GLwyvCiUX46wOJXfhQ/3UIVug39f//WVDoW1EAAIPE\nDIP4/3RRi034i9E7wXcCi9ABVQgr2jvBcj2LTfzrKItFCFYPvgBQ6G9KAABZWYP4/3Qo/0UIS4tO\nGIlN/IXJfwYzyUGJTfyF2w+FLf///4tFEOno/v//g04MICv7M9KLx/d1DOnW/v//agxosHFCAOju\nLgAAg2XkAIN9DAB0JIN9EAB0HjPAi3UUhfYPlcCFwHUY6AAgAADHABYAAADoTREAADPA6P4uAADD\nVuhi8f//WYNl/ABW/3UQ/3UM/3UI6Ev+//+DxBCL+Il95MdF/P7////oCgAAAIvH68iLdRSLfeRW\n6Jnx//9Zw2oUaNBxQgDobC4AAIt1CIveiXXkhfZ1GTl1DHQU6IsfAADHABYAAADo2BAAADPA63cz\nwDlFDA+dwIXAdOAzwIt9EIX/D5XAhcB00oN9DAB03Il9CFfozvD//1mDZfwAhfZ0N/9NDHQtV+iu\nYgAAWQ+3wIlF4D3//wAAdQs73nUVM/aJdeTrE2aJA4PDAold3IP4CnXOM8BmiQPHRfz+////6A4A\nAACLxugQLgAAw4t15It9CFfo3fD//1nDVYvsakD/dQz/dQjoBQAAAIPEDF3Dagxo8HFCAOibLQAA\nM8mJTeQzwIt9CIX/D5XAhcB1F+i4HgAAxwAWAAAA6AUQAAAzwOmAAAAAM8CLXQyF2w+VwIXAdNsz\nwGY5Cw+VwIXAdM/or2MAAIvwiXUIhfZ1Deh5HgAAxwAYAAAA68QzwIlF/GY5B3Ug6GIeAADHABYA\nAABq/o1F8FBoMKFCAOg3ZwAAg8QM65pW/3UQU1fog2QAAIPEEIv4iX3kx0X8/v///+gOAAAAi8fo\nMS0AAMOLdQiLfeRW6P7v//9Zw1WL7FOLXRBXM/+F23UU6P8dAADHABYAAADoTA8AADPA63NWaLwD\nAABqAeivKAAAi/BZWYX2dEno1RsAAP9wbFboUxwAAItFFINOBP+JRliLRRyJXlRZWYXAdQONRRBQ\n/3UYVmhDkUAA/3UM/3UI/xXsoUEAhcB1HP8VWKFBAIv4Vuh14v//WYX/dAdX6GAdAABZM8BeX1td\nw2oMaBByQgDoOCwAAOhnGwAAg2X8AP9wWP9QVFDoIAAAAItN7IsBiwCJReRRUOiEZwAAWVnDi2Xo\n/3Xk6Cng///MVYvsVuhGGwAAi/CF9nQVg760AwAAAHQF6OYAAABW6N4aAABZ/3UI/xX0oUEAzIM9\n/AdDAAB1OWj4MkIAaAAIAABqAGgIM0IA/xUooUEAUP8VjKFBAIXAdQHDUP8VuKFBAKP4B0MAxwX8\nB0MAAQAAAGoB/zX4B0MA/xW8oUEA/9D32BvAQMNVi+xW6DcZAABQ6EUfAACL8FmF9nUsi3UIVugh\nGQAAUOhOHwAAWVmFwHUN/xVYoUEAUP8V9KFBAP8V8KFBAIkG6xuLTQhRi0FUiUZUi0FYiUZYi0EE\niUYE6OkYAADoYR8AAImGtAMAAIXAdAvoN////4mGtAMAAOi9/v//zIM9BAhDAAB1OGggM0IAaAAI\nAABqAGgIM0IA/xUooUEAUP8VjKFBAIXAdCRQ/xW4oUEAowAIQwDHBQQIQwABAAAA/zUACEMA/xW8\noUEA/+DDUY1MJAgryIPhDwPBG8kLwVnpGgoAAFGNTCQIK8iD4QcDwRvJC8FZ6QQKAADMzMzMVYvs\ng+wYU4tdDFZXxkX/AIt7CI1zEDM9MKFCAMdF9AEAAACLB4P4/nQNi08EA84zDDDoZeX//4tHCItP\nDAPOMwww6FXl//+LRQj2QARmD4XPAAAAiUXoi0UQiUXsjUXoiUP8i0MMiUX4g/j+D4TtAAAAjQRA\njUAEi0yHBI0Eh4sYiUXwhcl0e4vW6ANlAACxAYhN/4XAD4h+AAAAfmiLRQiBOGNzbeB1KIM9aF1F\nAAB0H2hoXUUA6JQjAACDxASFwHQOagH/dQj/FWhdRQCDxAiLVQiLTQzo5mQAAItFDItV+DlQDHQQ\naDChQgBWi8jo52QAAItFDIlYDIsHg/j+dHXrZopN/4ld+IvDg/v+D4Ve////hMl0R+shx0X0AAAA\nAOsYg3sM/nQ2aDChQgBWi8u6/v///+igZAAAiweD+P50DYtPBAPOMwww6E3k//+LVwiLTwwDzjMM\nMug95P//i0X0X15bi+Vdw4tPBAPOMwww6Cbk//+LRwiLTwwDzjMMMOgW5P//i03wi9aLSQjoFmQA\nAMxVi+z/FfihQQBqAaMkC0MA6KRlAAD/dQjoESAAAIM9JAtDAABZWXUIagHoimUAAFloCQQAwOjf\nHwAAWV3DVYvsgewkAwAAahfolwABAIXAdAVqAlnNKaMICUMAiQ0ECUMAiRUACUMAiR38CEMAiTX4\nCEMAiT30CEMAZowVIAlDAGaMDRQJQwBmjB3wCEMAZowF7AhDAGaMJegIQwBmjC3kCEMAnI8FGAlD\nAItFAKMMCUMAi0UEoxAJQwCNRQijHAlDAIuF3Pz//8cFWAhDAAEAAQChEAlDAKMUCEMAxwUICEMA\nCQQAwMcFDAhDAAEAAADHBRgIQwABAAAAagRYa8AAx4AcCEMAAgAAAGoEWGvAAIsNMKFCAIlMBfhq\nBFjB4ACLDTShQgCJTAX4aDAzQgDozP7//4vlXcNVi+xqCOgCAAAAXcNVi+yB7BwDAABqF+iQ/wAA\nhcB0BYtNCM0powgJQwCJDQQJQwCJFQAJQwCJHfwIQwCJNfgIQwCJPfQIQwBmjBUgCUMAZowNFAlD\nAGaMHfAIQwBmjAXsCEMAZowl6AhDAGaMLeQIQwCcjwUYCUMAi0UAowwJQwCLRQSjEAlDAI1FCKMc\nCUMAi4Xk/P//oRAJQwCjFAhDAMcFCAhDAAkEAMDHBQwIQwABAAAAxwUYCEMAAQAAAGoEWGvAAItN\nCImIHAhDAGgwM0IA6PD9//+L5V3DVYvsg+wYjU3oU/91DOjs1///i10IgfsAAQAAc2CLTeiDeXQB\nfhSNRehQagJT6HtjAACLTeiDxAzrDYuBkAAAAA+3BFiD4AKFwHQegH30AIuBmAAAAA+2DBh0B4tF\n8INgcP2LwenWAAAAgH30AHQHi03wg2Fw/YvD6cIAAACLReiDeHQBfi2Lw41N6MH4CIlFCFEPtsBQ\n6MNjAABZWYXAdBKLRQhqAohF/Ihd/cZF/gBZ6xXoVRcAADPJQccAKgAAAIhd/MZF/QCLReiNVfhq\nAf9wBGoDUlGNTfxRaAACAAD/sKgAAACNRehQ6L5lAACDxCSFwHUVOEX0D4R3////i0Xwg2Bw/elr\n////g/gBdROAffQAD7ZF+HQli03wg2Fw/escD7ZV+A+2RfnB4ggL0IB99AB0B4tN8INhcP2LwluL\n5V3DVYvsgz1QC0MAAHUSi00IjUGfg/gZdwODweCLwV3DagD/dQjolP7//1lZXcNVi+xRZotFCLn/\n/wAAZjvBdQQzwOs8uQABAABmO8FzDg+3yKE0rkIAD7cESOscjUX8UGoBjUUIUGoB/xUAokEA99gb\nwCNF/A+3wItNDA+3ySPBi+Vdw1OL3FFRg+Twg8QEVYtrBIlsJASL7IPsKFZXi3sMM8APtw+JTfhm\nO8F1CItDCOlYAgAAoRATQwCD+AEPjvUAAACLxyX/DwAAagJePfAPAAB3BvMPbwfrKWoIZg/vwA+3\nyVoPt8FmD3PYAmYPxMAHM8BmO8F0BQP+D7cPSnXki034i1MIi8Il/w8AAD3wDwAAd2TzD28KZg86\nY8ENdgiDwhCLTfjr3g+D3QEAAGYPOmPBDY0USotLDIv6i8eJTfwl/w8AAD3wDwAAd0WLwSX/DwAA\nPfAPAAB3N/MPbw/zD28RZg86Y9ENcT94RItN/IPHEIPBEOvED7cCM/9mO/gPhIUBAABmO8F0rAPW\n6XT///8PtwGDZfwAZjlF/HQSZjkHdQYD/gPO65ID1uly////i8LpVQEAAA+F6AAAAA+3wWYP79uL\nSwhqAl5mD27A8g9wwABmD3DgAIvBJf8PAAA98A8AAHcs8w9vCWYPb8NmD3XBZg91zGYP68FmD9fA\nhcB1BYPBEOvRD7zAiUX40eiNDEEPtwEz0mY70A+E6gAAAA+3F2Y70HVyi9GLxyX/DwAAPfAPAAB3\nR4vCJf8PAAA98A8AAHc58w9vF/MPbwpmD2/DZg91ymYPdcJmD3XLZg/ryGYP18GFwHUIg8IQg8cQ\n67gPvMCJRfiD4P4D0AP4D7cHg2X4AGY5Rfh0FWY5AnUGA9YD/uuTi3sMA87pN////4vB62eLUwgP\ntwJmhcB0WoNl+ACLyIvCK8eJRfxqAl5mhcl0Jw+3D2aFyQ+E3/7//w+3DDgPtwcryItF/ItN+HUN\nA/5mOQw4ddvrA4tN+GY5Dw+EuP7//4t7DAPWA8aJRfwPtwpmhcl1uzPAX16L5V2L41vDVYvsjUUQ\nUGoA/3UM/3UIaNL9QADoBQAAAIPEFF3DVYvsg+wgg2XgADPAVot1DFeNfeRqB1nzq4X2dRXodBMA\nAMcAFgAAAOjBBAAAg8j/60E5RRB05lboP2IAAMdF7EkAAACJdeiJdeBZPf///z92CcdF5P///3/r\nBQPAiUXk/3UYjUXg/3UU/3UQUP9VCIPEEF9ei+Vdw1WL7FOLXQhWM/aF23UV6AoTAADHABYAAADo\nVwQAAIPI/+toV2i8AwAAagHouR0AAIv4WVmF/3Q96N8QAAD/cGxX6F0RAACLRRBZWVdqBFdo45tA\nAP91DIlfVFaJR1j/FeyhQQCL8Il3BIX2dSL/FVihQQCL8Ffoi9f//1mF9nQHVuh2EgAAWYPI/19e\nW13DVv8VvKBBAIP4/3TSi8br62oMaDByQgDoPSEAAOhsEAAAg2X8AP9wWP9QVFnoIAAAAItN7IsB\niwCJReRRUOiJXAAAWVnDi2Xo/3Xk6C7V///MVuhOEAAAi/CF9nQWg34E/3QJ/3YE/xU0oUEAVujl\nDwAAWWoA/xX0oUEAzFWL7OiYDgAAUOimFAAAi9BZhdJ1If91COiDDgAAUOiwFAAAWVmFwHUo/xVY\noUEAUP8V9KFBAItNCFGLQVSJQlSLQViJQliLQQSJQgToVQ4AAOg+////zMzMzMzMzFGNTCQEK8gb\nwPfQI8iLxCUA8P//O8hyCovBWZSLAIkEJMMtABAAAIUA6+lqFGhQckIA6EkgAABqAeg1eAAAWbhN\nWgAAZjkFAABAAHQEM9vrM6E8AEAAgbgAAEAAUEUAAHXruQsBAABmOYgYAEAAdd0z24O4dABAAA52\nCTmY6ABAAA+Vw4ld5OgkIQAAhcB1CGoc6OgAAABZ6FEQAACFwHUIahDo1wAAAFnorREAAINl/ADo\nFFAAAIXAeQhqG+i9AAAAWf8VcKFBAKNkXUUA6Il4AACjLAtDAOh3dAAAhcB5CGoI6P/S//9Z6KN2\nAACFwHkIagno7tL//1lqAegg0///WYXAdAdQ6NvS//9ZodAHQwCj6AdDAFD/NcgHQwD/NcAHQwDo\nx8j//4PEDIvwiXXchdt1BlboT9X//+jS0v//6y6LTeyLAYsAiUXgUVDon1oAAFlZw4tl6It14Il1\n3IN95AB1BlboOtP//+iT0v//x0X8/v///4vG6EcfAADDVYvsgz0sFkMAAnQF6EgWAAD/dQjonRYA\nAGj/AAAA6C/S//9ZWV3D6BV3AADpe/7//1WL7IHsKAMAAKEwoUIAM8WJRfyDfQj/V3QJ/3UI6Htb\nAABZg6Xg/P//AI2F5Pz//2pMagBQ6Bbg//+NheD8//+DxAyJhdj8//+NhTD9//+Jhdz8//+JheD9\n//+Jjdz9//+Jldj9//+JndT9//+JtdD9//+Jvcz9//9mjJX4/f//ZoyN7P3//2aMncj9//9mjIXE\n/f//ZoylwP3//2aMrbz9//+cj4Xw/f//i0UEiYXo/f//jUUEiYX0/f//x4Uw/f//AQABAItA/ImF\n5P3//4tFDImF4Pz//4tFEImF5Pz//4tFBImF7Pz///8V+KFBAIv4jYXY/P//UOgUFQAAWYXAdROF\n/3UPg30I/3QJ/3UI6IhaAABZi038M81f6MvY//+L5V3DVYvsi0UIozALQwBdw1WL7P81MAtDAP8V\nvKFBAIXAdANd/+D/dRj/dRT/dRD/dQz/dQjoEQAAAMwzwFBQUFBQ6Mn///+DxBTDahfoVPUAAIXA\ndAVqBVnNKVZqAb4XBADAVmoC6HP+//9W6GoUAACDxBBew1WL7ItVCDPJU1ZBV4vB8A/BAotyeIX2\ndAaLwfAPwQaLsoAAAACF9nQGi8HwD8EGi3J8hfZ0BovB8A/BBouyiAAAAIX2dAaLwfAPwQZqBo1y\nHFuBfvhcqkIAdAyLPoX/dAaLwfAPwQeDfvQAdA2LfvyF/3QGi8HwD8EHg8YQS3XSi4KcAAAABbAA\nAADwD8EIQV9eW13DVYvsU1aLdQgz21eLhoQAAACFwHRmPUCuQgB0X4tGeIXAdFg5GHVUi4aAAAAA\nhcB0FzkYdRNQ6IbS////toQAAADolnUAAFlZi0Z8hcB0FzkYdRNQ6GjS////toQAAADodHYAAFlZ\n/3Z46FPS////toQAAADoSNL//1lZi4aIAAAAhcB0RDkYdUCLhowAAAAt/gAAAFDoJ9L//4uGlAAA\nAL+AAAAAK8dQ6BTS//+LhpgAAAArx1DoBtL///+2iAAAAOj70f//g8QQi4acAAAAPWCqQgB0GzmY\nsAAAAHUTUOhbdgAA/7acAAAA6NLR//9ZWWoGWI2eoAAAAIlFCI1+HIF/+FyqQgB0HYsHhcB0FIM4\nAHUPUOin0f///zPooNH//1lZi0UIg3/0AHQWi0f8hcB0DIM4AHUHUOiD0f//WYtFCIPDBIPHEEiJ\nRQh1slbobdH//1lfXltdw1WL7ItVCIXSD4SOAAAAU1aDzv9Xi8bwD8ECi0p4hcl0BovG8A/BAYuK\ngAAAAIXJdAaLxvAPwQGLSnyFyXQGi8bwD8EBi4qIAAAAhcl0BovG8A/BAWoGjUocW4F5+FyqQgB0\nDIs5hf90BovG8A/BB4N59AB0DYt5/IX/dAaLxvAPwQeDwRBLddKLipwAAACBwbAAAADwD8ExTl9e\nW4vCXcNqDGhwckIA6J0aAACDZeQA6MgJAACL8IsNgKxCAIVOcHQig35sAHQc6LAJAACLcGyF9nUI\naiDo1c3//1mLxuirGgAAw2oM6HkMAABZg2X8AP81xKtCAI1GbFDoIQAAAFlZi/CJdeTHRfz+////\n6AUAAADrvIt15GoM6LANAABZw1WL7FeLfQyF/3Q7i0UIhcB0NFaLMDv3dChXiTjo0Pz//1mF9nQb\nVui0/v//gz4AWXUPgf7Iq0IAdAdW6Eb9//9Zi8de6wIzwF9dw4M9lG1FAAB1Emr96E0DAABZxwWU\nbUUAAQAAADPAw1WL7ItFCC2kAwAAdCaD6AR0GoPoDXQOSHQEM8Bdw6FEM0IAXcOhQDNCAF3DoTwz\nQgBdw6E4M0IAXcNVi+yD7BCNTfBqAOicyv//gyVMC0MAAItFCIP4/nUSxwVMC0MAAQAAAP8VDKJB\nAOssg/j9dRLHBUwLQwABAAAA/xUIokEA6xWD+Px1EItF8McFTAtDAAEAAACLQASAffwAdAeLTfiD\nYXD9i+Vdw1WL7FOLXQhWV2gBAQAAM/+NcxhXVuh+2v//iXsEM8CJewiDxAyJuxwCAAC5AQEAAI17\nDKurq7/IpUIAK/uKBDeIBkZJdfeNixkBAAC6AAEAAIoEOYgBQUp1919eW13DVYvsgewgBQAAoTCh\nQgAzxYlF/FNWi3UIjYXo+v//V1D/dgT/FRCiQQAz278AAQAAhcAPhPAAAACLw4iEBfz+//9AO8dy\n9IqF7vr//42N7vr//8aF/P7//yDrHw+2UQEPtsDrDTvHcw3GhAX8/v//IEA7wnbvg8ECigGEwHXd\nU/92BI2F/Pr//1BXjYX8/v//UGoBU+g0dwAAU/92BI2F/P3//1dQV42F/P7//1BX/7YcAgAAU+jU\nVwAAg8RAjYX8/P//U/92BFdQV42F/P7//1BoAAIAAP+2HAIAAFPorFcAAIPEJIvLD7eETfz6//+o\nAXQOgEwOGRCKhA38/f//6xCoAnQVgEwOGSCKhA38/P//iIQOGQEAAOsHiJwOGQEAAEE7z3LB61lq\nn42WGQEAAIvLWCvCiYXg+v//A9EDwomF5Pr//4PAIIP4GXcKgEwOGRCNQSDrE4O95Pr//xl3Do0E\nDoBIGSCNQeCIAusCiBqLheD6//+NlhkBAABBO89yuotN/F9eM81b6EfS//+L5V3DagxokHJCAOgb\nFwAAM/aJdeToRQYAAIv4iw2ArEIAhU9wdBw5d2x0F4t3aIX2dQhqIOhYyv//WYvG6C4XAADDag3o\n/AgAAFmJdfyLd2iJdeQ7NeynQgB0NIX2dBiDyP/wD8EGdQ+B/silQgB0B1bo0sz//1mh7KdCAIlH\naIs17KdCAIl15DPAQPAPwQbHRfz+////6AUAAADrkYt15GoN6AgKAABZw2oQaLByQgDodRYAAIPP\n/+ihBQAAi9iJXeDoPP///4tzaP91COjS/P//WYlFCDtGBA+EaAEAAGggAgAA6IoSAABZi9iF2w+E\nVQEAALmIAAAAi0Xgi3Boi/vzpTP2iTNT/3UI6EEBAABZWYv4iX0Ihf8PhQcBAACLReCLSGiDyv/w\nD8ERdRWLSGiB+cilQgB0ClHoCcz//1mLReCJWGgzwEDwD8EDi0Xg9kBwAg+F7wAAAPYFgKxCAAEP\nheIAAABqDejXBwAAWYl1/ItDBKM0C0MAi0MIozgLQwCLgxwCAACjSAtDAIvOiU3kg/kFfRBmi0RL\nDGaJBE08C0MAQevoi86JTeSB+QEBAAB9DYpEGRiIgcCjQgBB6+iJdeSB/gABAAB9EIqEHhkBAACI\nhsikQgBG6+Wh7KdCAIPJ//APwQh1E6Hsp0IAPcilQgB0B1DoTMv//1mJHeynQgAzwEDwD8EDx0X8\n/v///+gFAAAA6zGLfQhqDeiNCAAAWcPrI4P//3UegfvIpUIAdAdT6A/L//9Z6CAGAADHABYAAADr\nAjP/i8foHxUAAMNVi+yD7CChMKFCADPFiUX8U1b/dQiLdQzoNvv//4vYWYXbdQ5W6Jf7//9ZM8Dp\nqQEAAFcz/4vPi8eJTeQ5mPCnQgAPhOgAAABBg8AwiU3kPfAAAABy5oH76P0AAA+ExgAAAIH76f0A\nAA+EugAAAA+3w1D/FQSiQQCFwA+EqAAAAI1F6FBT/xUQokEAhcAPhIIAAABoAQEAAI1GGFdQ6LfV\n//+JXgSDxAwz24m+HAIAAEM5Xeh2T4B97gCNRe50IYpIAYTJdBoPttEPtgjrBoBMDhkEQTvKdvaD\nwAKAOAB1341GGrn+AAAAgAgIQEl1+f92BOgi+v//g8QEiYYcAgAAiV4I6wOJfggzwI1+DKurq+m8\nAAAAOT1MC0MAdAtW6J76///prwAAAIPI/+mqAAAAaAEBAACNRhhXUOga1f//g8QMa0XkMIlF4I2A\nAKhCAIlF5IA4AIvIdDWKQQGEwHQrD7YRD7bA6xeB+gABAABzE4qH6KdCAAhEFhlCD7ZBATvQduWD\nwQKAOQB1zotF5EeDwAiJReSD/wRyuFOJXgTHRggBAAAA6G/5//+DxASJhhwCAACLReCNTgxqBo2Q\n9KdCAF9miwKNUgJmiQGNSQJPdfFW6En6//9ZM8Bfi038XjPNW+gOzv//i+Vdw1WL7Lj//wAAg+wU\nZjlFCA+EmAAAAFb/dQyNTezo88P//4t17IuGqAAAAIXAdRWLTQiNQb9mg/gZdwRmg8EgD7fB6x66\nAAEAAGoBZjlVCHMo/3UI6C7t//9ZhcCLRQhZdQgPt8APt8DrKg+3yIuGlAAAAA+2BAjrG41N/FFq\nAY1NCFFSUOi5cQAAg8QYhcB1C4tFCA+3wA+3wOsED7dF/IB9+ABedAeLTfSDYXD9i+Vdw6HgqEIA\nw2oIaNByQgDoKxIAAIt1CIX2D4T+AAAAg34kAHQJ/3Yk6DDI//9Zg34sAHQJ/3Ys6CHI//9Zg340\nAHQJ/3Y06BLI//9Zg348AHQJ/3Y86API//9Zg35AAHQJ/3ZA6PTH//9Zg35EAHQJ/3ZE6OXH//9Z\ng35IAHQJ/3ZI6NbH//9ZgX5cYEVCAHQJ/3Zc6MTH//9Zag3ouQMAAFmDZfwAi05ohcl0GIPI//AP\nwQF1D4H5yKVCAHQHUeiZx///WcdF/P7////oVwAAAGoM6IIDAABZx0X8AQAAAIt+bIX/dCNX6An2\n//9ZOz3Eq0IAdBSB/8irQgB0DIM/AHUHV+iT9P//WcdF/P7////oHgAAAFboQcf//1noYhEAAMIE\nAIt1CGoN6JUEAABZw4t1CGoM6IkEAABZw1WL7KHgqEIAg/j/dCdWi3UIhfZ1DlDovgQAAIvwoeCo\nQgBZagBQ6M0EAABZWVbomP7//15dw1boEgAAAIvwhfZ1CGoQ6CDE//9Zi8Zew1ZX/xVYoUEA/zXg\nqEIAi/jodgQAAIvwWYX2dUdovAMAAGoB6I0MAACL8FlZhfZ0M1b/NeCoQgDobgQAAFlZhcB0GGoA\nVuglAAAAWVn/FfChQQCDTgT/iQbrCVbocsb//1kz9lf/FVShQQBfi8Zew2oIaPhyQgDoORAAAIt1\nCMdGXGBFQgCDZggAM/9HiX4UiX5wakNYZomGuAAAAGaJhr4BAADHRmjIpUIAg6a4AwAAAGoN6BcC\nAABZg2X8AItGaIvP8A/BCMdF/P7////oPgAAAGoM6PYBAABZiX38i0UMiUZshcB1CKHEq0IAiUZs\n/3Zs6IXy//9Zx0X8/v///+gVAAAA6PAPAADDM/9Hi3UIag3oIgMAAFnDagzoGQMAAFnD6MbD///o\n1AIAAIXAdQjoYwAAADPAw2iJqkAA6AwDAACj4KhCAFmD+P9041ZovAMAAGoB6FsLAACL8FlZhfZ0\nLVb/NeCoQgDoPAMAAFlZhcB0G2oAVujz/v//WVn/FfChQQCDTgT/iQYzwEBew+gEAAAAM8Bew6Hg\nqEIAg/j/dA5Q6MQCAACDDeCoQgD/WelOAQAA6Eb+//+FwHUGuFSqQgDDg8AMw1WL7Fbo5P///4tN\nCFGJCOggAAAAWYvw6AUAAACJMF5dw+gS/v//hcB1BrhQqkIAw4PACMNVi+yLTQgzwDsMxeioQgB0\nJ0CD+C1y8Y1B7YP4EXcFag1YXcONgUT///9qDlk7yBvAI8GDwAhdw4sExeyoQgBdw2oIaCBzQgDo\naw4AAL7Iq0IAOTXEq0IAdCpqDOhyAAAAWYNl/ABWaMSrQgDoHvT//1lZo8SrQgDHRfz+////6AYA\nAADodA4AAMNqDOisAQAAWcNWV74QcEIAvxBwQgDrC4sGhcB0Av/Qg8YEO/dy8V9ew1ZXvhhwQgC/\nGHBCAOsLiwaFwHQC/9CDxgQ793LxX17DVYvsVot1CIM89YisQgAAdRNW6HEAAABZhcB1CGoR6CnB\n//9Z/zT1iKxCAP8V4KFBAF5dw1ZXvoisQgCL/lOLH4XbdBeDfwQBdBFT/xUUokEAU+isw///gycA\nWYPHCIH/qK1CAHzYW4M+AHQOg34EAXUI/zb/FRSiQQCDxgiB/qitQgB84l9ew2oIaEBzQgDoUQ0A\nAIM9CBNDAAB1GOibBAAAah7o8QQAAGj/AAAA6IPA//9ZWYt9CDPbORz9iKxCAHVcahjoZAkAAFmL\n8IX2dQ/oRP7//8cADAAAADPA60JqCugZ////WYld/Dkc/YisQgB1GFNooA8AAFbo/QAAAIPEDIk0\n/YisQgDrB1bo8cL//1nHRfz+////6AkAAAAzwEDoAw0AAMNqCug7AAAAWcNWV76IrEIAv1gLQwCD\nfgQBdRZqAIk+g8cYaKAPAAD/NuinAAAAg8QMg8YIgf6orUIAfNkzwF9AXsNVi+yLRQj/NMWIrEIA\n/xXkoUEAXcNVi+yh4FxFADMFMKFCAHQH/3UI/9Bdw13/JSiiQQBVi+yh5FxFADMFMKFCAP91CHQE\n/9Bdw/8VNKJBAF3DVYvsoehcRQAzBTChQgD/dQh0BP/QXcP/FSyiQQBdw1WL7KHsXEUAMwUwoUIA\n/3UM/3UIdAT/0F3D/xUwokEAXcNVi+yh8FxFADMFMKFCAHQN/3UQ/3UM/3UI/9Bdw/91DP91CP8V\nIKJBADPAQF3DVYvsUVaLNaitQgCF9nkloVRdRQAz9jMFMKFCAIl1/HQNVo1N/FH/0IP4enUBRok1\nqK1CADPAhfZeD5/Ai+Vdw1ZXaFg3QgD/FXShQQCLNYyhQQCL+Gh0N0IAV//WMwUwoUIAaIA3QgBX\no+BcRQD/1jMFMKFCAGiIN0IAV6PkXEUA/9YzBTChQgBolDdCAFej6FxFAP/WMwUwoUIAaKA3QgBX\no+xcRQD/1jMFMKFCAGi8N0IAV6PwXEUA/9YzBTChQgBozDdCAFej9FxFAP/WMwUwoUIAaOA3QgBX\no/hcRQD/1jMFMKFCAGj4N0IAV6P8XEUA/9YzBTChQgBoEDhCAFejAF1FAP/WMwUwoUIAaCQ4QgBX\nowRdRQD/1jMFMKFCAGhEOEIAV6MIXUUA/9YzBTChQgBoXDhCAFejDF1FAP/WMwUwoUIAaHQ4QgBX\noxBdRQD/1jMFMKFCAGiIOEIAV6MUXUUA/9YzBTChQgCjGF1FAGicOEIAV//WMwUwoUIAaLg4QgBX\noxxdRQD/1jMFMKFCAGjYOEIAV6MgXUUA/9YzBTChQgBo9DhCAFejJF1FAP/WMwUwoUIAaBQ5QgBX\noyhdRQD/1jMFMKFCAGgoOUIAV6MsXUUA/9YzBTChQgBoRDlCAFejMF1FAP/WMwUwoUIAaFg5QgBX\nozhdRQD/1jMFMKFCAGhoOUIAV6M0XUUA/9YzBTChQgBoeDlCAFejPF1FAP/WMwUwoUIAaIg5QgBX\no0BdRQD/1jMFMKFCAGiYOUIAV6NEXUUA/9YzBTChQgBotDlCAFejSF1FAP/WMwUwoUIAaMg5QgBX\no0xdRQD/1jMFMKFCAGjYOUIAV6NQXUUA/9YzBTChQgBo7DlCAFejVF1FAP/WMwUwoUIAo1hdRQBo\n/DlCAFf/1jMFMKFCAGgcOkIAV6NcXUUA/9YzBTChQgBfo2BdRQBew1WL7P91CP8VHKJBAF3DVYvs\n/3UI/xVEoUEAXcNVi+z/dQj/FVyhQQBQ/xUkokEAXcNVi+xqAP8VHKJBAP91CP8VGKJBAF3DagPo\noWAAAFmD+AF0FWoD6JRgAABZhcB1H4M9qAxDAAF1Fmj8AAAA6DEAAABo/wAAAOgnAAAAWVnDVYvs\ni00IM8A7DMU4OkIAdApAg/gXcvEzwF3DiwTFPDpCAF3DVYvsgez8AQAAoTChQgAzxYlF/FaLdQhX\nVui+////i/hZhf8PhHkBAABTagPoGmAAAFmD+AEPhA8BAABqA+gJYAAAWYXAdQ2DPagMQwABD4T2\nAAAAgf78AAAAD4RBAQAAaNhDQgBoFAMAAGiwDEMA6MZnAACDxAwz24XAD4UxAQAAaAQBAABo4gxD\nAFNmo+oOQwD/FWyhQQC++wIAAIXAdRtoDERCAFZo4gxDAOiJZwAAg8QMhcAPhfYAAABo4gxDAOig\nRwAAQFmD+Dx2NWjiDEMA6I9HAABqA2g8REIAjQxFbAxDAIvBLeIMQwDR+CvwVlHo9GcAAIPEFIXA\nD4WwAAAAaEREQgBoFAMAAL6wDEMAVui3ZgAAg8QMhcAPhZAAAABXaBQDAABW6KBmAACDxAyFwHV9\naBAgAQBoUERCAFboIWoAAIPEDOtXavT/FXyhQQCL8IX2dEmD/v90RDPbi8uKBE+IhA0I/v//Zjkc\nT3QJQYH59AEAAHLnU42FBP7//4hd+1CNhQj+//9Q6EppAABZUI2FCP7//1BW/xU4oUEAW4tN/F8z\nzV7oucH//4vlXcNTU1NTU+gs6f//zMzMzMzMzMzMzMzMzMzMVYvsi0UIM9JTVleLSDwDyA+3QRQP\nt1kGg8AYA8GF23Qbi30Mi3AMO/5yCYtICAPOO/lyCkKDwCg703LoM8BfXltdw8zMzMzMzMzMzMzM\nzMxVi+xq/mhgc0IAaECSQABkoQAAAABQg+wIU1ZXoTChQgAxRfgzxVCNRfBkowAAAACJZejHRfwA\nAAAAaAAAQADofAAAAIPEBIXAdFSLRQgtAABAAFBoAABAAOhS////g8QIhcB0OotAJMHoH/fQg+AB\nx0X8/v///4tN8GSJDQAAAABZX15bi+Vdw4tF7IsAM8mBOAUAAMAPlMGLwcOLZejHRfz+////M8CL\nTfBkiQ0AAAAAWV9eW4vlXcPMzMzMzMxVi+yLRQi5TVoAAGY5CHQEM8Bdw4tIPAPIM8CBOVBFAAB1\nDLoLAQAAZjlRGA+UwF3DVmoEaiDoNgEAAFlZi/BW/xW4oUEAo5BtRQCjjG1FAIX2dQVqGFhew4Mm\nADPAXsNqDGiAc0IA6AQFAACDZeQA6MS5//+DZfwA/3UI6CMAAABZi/CJdeTHRfz+////6AsAAACL\nxugbBQAAw4t15Oifuf//w1WL7FFTVos1vKFBAFf/NZBtRQD/1v81jG1FAIlF/P/Wi9iLRfw72A+C\nggAAAIv7K/iNTwSD+QRydlDojmkAAIvwjUcEWTvwc0e4AAgAADvwcwKLxotd/APGO8ZyDVBT6PgA\nAABZWYXAdRSNRhA7xnI+UFPo5AAAAFlZhcB0McH/AlCNHLj/FbihQQCjkG1FAP91CP8VuKFBAI1L\nBIkDUf8VuKFBAKOMbUUAi0UI6wIzwF9eW4vlXcNVi+z/dQjo+f7///fYWRvA99hIXcNVi+xWVzP2\nagD/dQz/dQjoMWoAAIv4g8QMhf91JTkF2BJDAHYdVujz+v//gcboAwAAWTs12BJDAHYDg87/g/7/\ndcWLx19eXcNVi+xTVleLPdgSQwAz9v91COj3uf//i9hZhdt1I4X/dB9W6K/6//+LPdgSQwCBxugD\nAABZO/d2A4PO/4P+/3XOX16Lw1tdw1WL7FZXM/b/dQz/dQjokGgAAIv4WVmF/3UqOUUMdCU5BdgS\nQwB2HVboYvr//4HG6AMAAFk7NdgSQwB2A4PO/4P+/3XDi8dfXl3DVYvsVlcz9v91EP91DP91COju\naAAAi/iDxAyF/3UqOUUQdCU5BdgSQwB2HVboFPr//4HG6AMAAFk7NdgSQwB2A4PO/4P+/3W/i8df\nXl3DVjP2/7asrUIA/xW4oUEAiYasrUIAg8YEg/4ocuZew2oIaKBzQgDorQIAAOjc8f//i0B4hcB0\nFoNl/AD/0OsHM8BAw4tl6MdF/P7////oT2kAAMxoB7pAAP8VuKFBAKPcEkMAw1WL7P814BJDAP8V\nvKFBAIXAdA//dQj/0FmFwHQFM8BAXcMzwF3DVYvsi0UIo+ASQwBdw1WL7ItFCKPkEkMAXcP/NfAS\nQwD/FbyhQQDDVYvsi0UIo+gSQwCj7BJDAKPwEkMAo/QSQwBdw2okaMBzQgDo/wEAAINl1ACDZdAA\nM9uJXeAz/4l92It1CIP+C39QdBWLxmoCWSvBdCIrwXQIK8F0XivBdUjoFfH//4v4iX3Yhf91FoPI\n/+liAQAAx0Xk6BJDAKHoEkMA617/d1xW6FEBAABZWYPACIlF5IsA61aLxoPoD3Q2g+gGdCNIdBLo\ntvL//8cAFgAAAOgD5P//67THReTwEkMAofASQwDrGsdF5OwSQwCh7BJDAOsMx0Xk9BJDAKH0EkMA\nM9tDiV3gUP8VvKFBAIlF3IP4AQ+E2wAAAIXAdQdqA+hNtf//hdt0CGoA6Djz//9Zg2X8AIP+CHQK\ng/4LdAWD/gR1HItHYIlF1INnYACD/gh1P4tHZIlF0MdHZIwAAACD/gh1LYsN+EVCAIvRiVXMofxF\nQgADwTvQfSRrygyLR1yDZAgIAEKJVcyLDfhFQgDr3moA/xW4oUEAi03kiQHHRfz+////6BgAAACD\n/gh1IP93ZFb/VdxZ6xqLdQiLXeCLfdiF23QIagDoBPT//1nDVv9V3FmD/gh0CoP+C3QFg/4EdRGL\nRdSJR2CD/gh1BotF0IlHZDPA6JYAAADDVYvsi1UMiw3wRUIAVot1CDlyBHQNa8EMg8IMA0UMO9By\n7mvJDANNDDvRcwk5cgR1BIvC6wIzwF5dw1WL7ItFCKP8EkMAXcPMzMzMzMzMzMxoQJJAAGT/NQAA\nAACLRCQQiWwkEI1sJBAr4FNWV6EwoUIAMUX8M8VQiWXo/3X4i0X8x0X8/v///4lF+I1F8GSjAAAA\nAMOLTfBkiQ0AAAAAWV9fXluL5V1Rw1WL7IN9CAB0JlaLdQz3RgwAEAAAdBhW6FfD//+BZgz/7v//\nM8BZiUYYiQaJRgheXcNVi+xWi3UIVug0xf//UOhvZgAAWVmFwA+EhgAAAFfo/cH//4PAIDvwdQQz\n/+sP6O3B//+DwEA78HVmM/9H/wX0B0MA90YMDAEAAHVUgzy9ABNDAABTuwAQAAB1JVPoXfv//4kE\nvQATQwBZhcB1E41GFGoCiUYIiQZYiUYYiUYE6xKLDL0AE0MAiU4IiQ6JXhiJXgSBTgwCEQAAM8BA\nW+sCM8BfXl3D/xU8okEAM8mjCBNDAIXAD5XBi8HDVYvsgeyAAgAAoTChQgAzxYlF/ItFCI2NkP3/\n/1NWiYXQ/f//i0UMV/91EIt9FImF8P3//zPAi9iJveT9//+Jhaz9//+L8Imd6P3//4mFwP3//4mF\n2P3//4mFzP3//4mFpP3//4mFtP3//4mFyP3//+hsr///6Hbv//+Jhaj9//+LhdD9//+FwA+EvQoA\nAPZADEB1Y1Do7MP//1mLyIP5/3QZg/n+dBSL0cH4BYPiH8HiBgMUhRgTQwDrBbrwrUIA9kIkfw+F\ngQoAAIP5/3QZg/n+dBSLwYPhH8H4BcHhBgMMhRgTQwDrBbnwrUIA9kEkgA+FVAoAAIuV8P3//4XS\nD4RGCgAAihIzwImF3P3//4vIiY3g/f//iYW8/f//iYWw/f//iJXv/f//iJW4/f//hNIPhO4JAACL\nhfD9//9AiYXw/f//hckPiNkJAACNQuA8WHcPD77CD76AmERCAIPgD+sCM8CLvbz9//8PvrzHuERC\nAIvHib28/f//i73k/f//wfgEiYW8/f//g/gHD4d3CQAA/ySFZclAADPAg43Y/f///4vYiYWk/f//\niYW0/f//iYXA/f//iYXM/f//iZ3o/f//iYXI/f//6TwJAAAPvsKD6CB0RoPoA3Q5g+gIdC9ISHQd\ng+gDi4Xw/f//D4UdCQAAg8sIiZ3o/f//6Q8JAACDywSJnej9///p+wgAAIPLAevwgcuAAAAA6+iD\nywLr44D6KnUviweDxwSJveT9//+JhcD9//+FwA+JywgAAIPLBPfYiZ3o/f//iYXA/f//6bUIAABr\njcD9//8KD77Cg8HQA8GJhcD9///plQgAADPAiYXY/f//6Y4IAACA+ip1K4sHg8cEiYXY/f//hcCL\nhfD9//+JveT9//8PiXAIAACDjdj9////6WQIAABrjdj9//8KD77Cg8HQA8GJhdj9///pPggAAID6\nSXRFgPpodDiLhfD9//+A+mx0FID6dw+FLAgAAIHLAAgAAOkH////gDhsdQxAgcsAEAAA6fb+//+D\nyxDp7v7//4PLIOn0/v//i4Xw/f//igA8NnUci73w/f//gH8BNHUQi8eDwAKBywCAAADpvv7//zwz\ndRyLvfD9//+AfwEydRCLx4PAAoHj/3///+me/v//PGQPhKoHAAA8aQ+EogcAADxvD4SaBwAAPHUP\nhJIHAAA8eA+EigcAADxYD4SCBwAAM8CJhbz9///rAjPAiYXI/f//jYWQ/f//UA+2wlDomDgAAFlZ\nhcB0OI2F4P3//1D/tdD9////tbj9///ouQcAAIuN8P3//4PEDIoBQYiFuP3//4mN8P3//4TAD4Rk\nBwAAjYXg/f//UP+10P3///+1uP3//+iBBwAAg8QM6fwGAAAPvsKD+GQPj80BAAAPhFECAACD+FMP\nj+0AAAB0fIPoQXQQSEh0VkhIdAhISA+FGAUAAIDCIMeFpP3//wEAAACIle/9//+Lhdj9//+NtfT9\n//+Dy0C5AAIAAImd6P3//4mNxP3//4XAD4kyAgAAx4XY/f//BgAAAOmAAgAA98MwCAAAD4WeAAAA\ngcsACAAAiZ3o/f//6Y0AAAD3wzAIAAB1DIHLAAgAAImd6P3//4uV2P3//7n///9/g/r/dAKLyos3\ng8cEib3k/f//98MQCAAAD4RTBAAAhfZ1Bos15K1CAMeFyP3//wEAAACLxoXJdA8z0klmORB0B4PA\nAoXJdfMrxtH46TwEAACD6FgPhLACAABISHRwg+gHD4Qn////SEgPhSQEAACDxwSJveT9///3wxAI\nAAB0MA+3R/xQaAACAACNhfT9//9QjYXc/f//UOjkYQAAg8QQhcB0H8eFtP3//wEAAADrE4pH/IiF\n9P3//8eF3P3//wEAAACNtfT9///pxQMAAIsHg8cEib3k/f//hcB0M4twBIX2dCwPvwD3wwAIAAB0\nFJkrwseFyP3//wEAAADR+OmKAwAAM8mJjcj9///pfQMAAIs14K1CAFbocVsAAFnpawMAAIP4cA+P\n4wEAAA+EzwEAAIP4ZQ+MWQMAAIP4Zw+OS/7//4P4aXRkg/hudCWD+G8PhT0DAADHhdz9//8IAAAA\nhNt5W4HLAAIAAImd6P3//+tNg8cEib3k/f//i3/86GKv//+FwA+EAgUAAIuF4P3///bDIHQFZokH\n6wKJB8eFtP3//wEAAADpegQAAIPLQImd6P3//8eF3P3//woAAAD3wwCAAAB1DPfDABAAAA+EjgEA\nAIsPg8cIib3k/f//M/aLf/zprgEAAHURgPpndVbHhdj9//8BAAAA60o7wX4Ii8GJhdj9//89owAA\nAH43jbhdAQAAV+gs9P//ipXv/f//iYWw/f//WYXAdAqL8Im9xP3//+sKx4XY/f//owAAAIu95P3/\n/4sHg8cIiYWI/f//ib3k/f//i0f8iYWM/f//jYWQ/f//UP+1pP3//w++wv+12P3//1D/tcT9//+N\nhYj9//9WUP81xK1CAP8VvKFBAP/Qi/uDxByB54AAAAB0IYO92P3//wB1GI2FkP3//1BW/zXQrUIA\n/xW8oUEA/9BZWYC97/3//2d1HIX/dRiNhZD9//9QVv81zK1CAP8VvKFBAP/QWVmAPi0PhSj+//+B\nywABAABGiZ3o/f//6Rb+///Hhdj9//8IAAAAagfrHIPocw+E3/z//0hID4SW/v//g+gDD4VrAQAA\naifHhdz9//8QAAAAWImFrP3//4TbD4l8/v//BFHGhdT9//8wiIXV/f//x4XM/f//AgAAAOle/v//\ng8cEM/aJveT9///2wyB0EfbDQHQGD79H/OsOD7dH/OsI9sNAdAqLR/yZi8iL+usFi0/8i/72w0B0\nHDv+fxh8BDvOcxL32RP+99+BywABAACJnej9///3wwCQAAB1Aov+i5XY/f//hdJ5BTPSQusUg+P3\nuAACAACJnej9//870H4Ci9CLwQvHdQaJtcz9//+NdfOLwkqJldj9//+FwH8Gi8ELx3Q9i4Xc/f//\nmVJQV1Hoh14AAIPBMImdhP3//4mFxP3//4v6g/k5fgYDjaz9//+Lldj9//+IDk6LjcT9///rsIud\n6P3//41F8yvGRomF3P3///fDAAIAAHQ2hcB0BYA+MHQtTv+F3P3//8YGMOshhfZ1Bos14K1CAIvG\n6wdJgDgAdAVAhcl19SvGiYXc/f//g720/f//AA+FhgEAAPbDQHQ198MAAQAAdAnGhdT9//8t6xr2\nwwF0CcaF1P3//yvrDPbDAnQRxoXU/f//IMeFzP3//wEAAACLvcD9//8rvdz9//+Lhcz9//8r+PbD\nDHUejYXg/f//UP+10P3//1dqIOgCAgAAi4XM/f//g8QQ/7Wo/f//jY3g/f//Uf+10P3//1CNhdT9\n//9Q6AUCAACDxBT2wwh0HfbDBHUYjYXg/f//UP+10P3//1dqMOi3AQAAg8QQg73I/f//AIuF3P3/\n/3R9hcB+eYvOSImFxP3//w+3AYPBAlBqBo1F9ImNhP3//1CNhaD9//9Q6PBcAACDxBCFwHU/OYWg\n/f//dDf/taj9//+NheD9//9Q/7XQ/f//jUX0/7Wg/f//UOh0AQAAi4XE/f//g8QUi42E/f//hcB1\nlusog8n/iY3g/f//6yP/taj9//+NjeD9//9R/7XQ/f//UFboOgEAAIPEFIuN4P3//4XJeCP2wwR0\nHo2F4P3//1D/tdD9//9XaiDo5wAAAIPEEIuN4P3//4uFsP3//4XAdBVQ6N+p//8zwFmJhbD9//+L\njeD9//+LhfD9//+KEIiV7/3//4iVuP3//4TSD4UY9v//i8GAvZz9//8AX15bdAqLjZj9//+DYXD9\ni038M83okK7//4vlXcPooOT//8cAFgAAAOjt1f//g8j/68eWwUAAnr9AANK/QAAlwEAAc8BAAIDA\nQADKwEAADMJAAFWL7ItVDPZCDEB0BoN6CAB0L/9KBHgOiwKKTQiICP8CD7bB6w+LRQhSD77AUOhB\nDgAAWVmD+P91CItFEIMI/13Di0UQ/wBdw1WL7FaLdQyF9n4eV4t9FFf/dRBO/3UI6Jz///+DxAyD\nP/90BIX2f+dfXl3DVYvsVot1GFeLfRCLBolFGPZHDEB0EIN/CAB1CotNFItFDAEB606DJgBTi10M\nhdt+QItFFFCLRQhLVw+2AFDoSf///4tFFIPEDP9FCIM4/3UUgz4qdRNQV2o/6C3///+LRRSDxAyF\n23/Lgz4AdQWLRRiJBltfXl3DVYvs/3UQ/3UM/3UIaPUlQQDoBQAAAIPEEF3DahBo4HNCAOgo8v//\n6M20//+NeCCJfeCDZeQAM8k5TQwPlcGFyXUV6D3j///HABYAAADoitT//4PI/+s9V+iitP//WYNl\n/ABX6HHy//+L8P91FP91EP91DFf/VQiL2Ild5FdW6CXy//+DxBzHRfz+////6A4AAACLw+j78f//\nw4t94Itd5FfoyLT//1nDVYvsgeyIBAAAoTChQgAzxYlF/ItFCI2NsPv//1NWiYXY+///i0UMV/91\nEIt9FImF+Pv//zPAi9iJvfD7//+JhaT7//+L8Imd7Pv//4mF0Pv//4mF6Pv//4mF3Pv//4mFqPv/\n/4mFwPv//4mF1Pv//+hYov//6GLi//+JhZz7//85ndj7//91KuhP4v//xwAWAAAA6JzT//84nbz7\n//90CouFuPv//4NgcP2DyP/p9QoAAIuV+Pv//4XSdMwPtxIzyYmN9Pv//4vBiYXg+///iY3M+///\niY2s+///iZXk+///ZoXSD4SqCgAAx4WQ+///WAAAAMeFjPv//2QAAADHhYj7//9pAAAAx4WY+///\nbwAAAIOF+Pv//wKFwA+IcwoAAGpYjULgX2Y7x3cPD7fCD76AmERCAIPgD+sCM8CLvcz7//8PvrzH\nuERCAIvHib3M+///i73w+///wfgEiYXM+///g/gHD4cLCgAA/ySF/dZAADPAg43o+////4vYiYWo\n+///iYXA+///iYXQ+///iYXc+///iZ3s+///iYXU+///6dAJAAAPt8JqIForwnRGg+gDdDmD6Ah0\nL0hIdB2D6AOLhfj7//8Pha8JAACDywiJnez7///poQkAAIPLBImd7Pv//+mNCQAAg8sB6/CBy4AA\nAADr6IPLAuvjaipYZjvQdS+LB4PHBIm98Pv//4mF0Pv//4XAD4laCQAAg8sE99iJnez7//+JhdD7\n///pRAkAAGuN0Pv//woPt8KDwdADwYmF0Pv//+kkCQAAM8CJhej7///pHQkAAGoqWGY70HUriweD\nxwSJhej7//+FwIuF+Pv//4m98Pv//w+J/AgAAION6Pv////p8AgAAGuN6Pv//woPt8KDwdADwYmF\n6Pv//+nKCAAAD7fCg/hJdFeD+Gh0SGpsWjvCdBqD+HeLhfj7//8PhbMIAACBywAIAADp/P7//4uF\n+Pv//2Y5EHUUg8ACgcsAEAAAiYX4+///6d3+//+DyxDp1f7//2ogWAvY6dn+//+Lhfj7//8PtwCD\n+DZ1I4u9+Pv//2aDfwI0dRaLx4PABIHLAIAAAImF+Pv//+ma/v//g/gzdSOLvfj7//9mg38CMnUW\ni8eDwASB4/9///+Jhfj7///pcv7//2Y7hYz7//8PhAsIAABmO4WI+///D4T+BwAAZjuFmPv//w+E\n8QcAAIP4dQ+E6AcAAIP4eA+E3wcAAGY7hZD7//8PhNIHAAAzwImFzPv//42F4Pv//8eF1Pv//wEA\nAABQ/7XY+///Uug7CAAAg8QM6Z8HAAAPt8KD+GQPjykCAAAPhLECAACD+FMPjyUBAAB0fYPoQXQQ\nSEh0WEhIdAhISA+FmgUAAGogWAPQx4Wo+///AQAAAImV5Pv//4uF6Pv//421/Pv//4PLQLkAAgAA\niZ3s+///iY30+///hcAPiY4CAADHhej7//8GAAAA6d8CAAD3wzAIAAAPhdgAAABqIFgL2Imd7Pv/\n/+nIAAAA98MwCAAAdQtqIFgL2Imd7Pv//4uV6Pv//7////9/g/r/dAKL+ou18Pv//4PGBIm18Pv/\n/4t2/PbDIA+EvwQAAIX2dQaLNeCtQgAzyYvGiYXk+///iY30+///hf8PjtAEAACKAITAD4TGBAAA\njY2w+///D7bAUVDoRioAAFmFwIuF5Pv//1l0AUCLjfT7//9AQYmF5Pv//4mN9Pv//zvPfMHpjAQA\nAIPoWA+E3AIAAEhID4SLAAAAg+gHD4Tt/v//SEgPhWoEAAAPtweDxwTHhdT7//8BAAAAib3w+///\niYWg+///9sMgdESIhcT7//8zwIiFxfv//42FsPv//1CLhbD7////cHSNhcT7//9QjYX8+///UOgZ\nYwAAg8QQhcB5E8eFwPv//wEAAADrB2aJhfz7//8zyY21/Pv//0Hp6gMAAIsHg8cEib3w+///hcB0\nNotwBIX2dC/3wwAIAAB0Fw+/AJkrwseF1Pv//wEAAACLyOmzAwAAM8mJjdT7//8PvwjppQMAAIs1\n4K1CAFboOU4AAFmLyOmRAwAAg/hwD4/rAQAAD4TXAQAAg/hlD4x/AwAAg/hnD47x/f//amlaO8J0\nZoP4bnQnam9aO8IPhV8DAADHheT7//8IAAAAhNt5W4HLAAIAAImd7Pv//+tNg8cEib3w+///i3/8\n6CSi//+FwA+ERQUAAIuF4Pv///bDIHQFZokH6wKJB8eFwPv//wEAAADpwwQAAIPLQImd7Pv//8eF\n5Pv//woAAAD3wwCAAAB1DPfDABAAAA+ElwEAAIsPg8cIib3w+///i3/86bABAAB1FGpnWGY70HVW\nx4Xo+///AQAAAOtKO8F+CIvBiYXo+///PaMAAAB+N424XQEAAFfo7eb//4uV5Pv//4mFrPv//1mF\nwHQKi/CJvfT7///rCseF6Pv//6MAAACLvfD7//+LB4PHCImFePv//4m98Pv//4tH/ImFfPv//42F\nsPv//1D/taj7//8PvsL/tej7//9Q/7X0+///jYV4+///VlD/NcStQgD/FbyhQQD/0Iv7g8QcgeeA\nAAAAdCGDvej7//8AdRiNhbD7//9QVv810K1CAP8VvKFBAP/QWVlqZ1hmOYXk+///dRyF/3UYjYWw\n+///UFb/NcytQgD/FbyhQQD/0FlZgD4tD4Ue/v//gcsAAQAARomd7Pv//+kM/v//x4Xo+///CAAA\nAGoH6xyD6HMPhHv8//9ISA+Ekv7//4PoAw+FiQEAAGonx4Xk+///EAAAAFiJhaT7//+E2w+JeP7/\n/2owWYPAUWaJjcj7//9miYXK+///x4Xc+///AgAAAOlV/v//g8cEib3w+///9sMgdBH2w0B0Bg+/\nR/zrDg+3R/zrCPbDQHQMi0f8mYvIi/ozwOsHi0/8M8CL+PbDQHQcO/h/GHwEO8hzEvfZE/j334HL\nAAEAAImd7Pv///fDAJAAAHUCi/iLlej7//+F0nkFM9JC6xaD4/eJnez7//+B+gACAAB+BboAAgAA\ni8ELx3UGiYXc+///jbX7/f//i8JKiZXo+///hcB/BovBC8d0PYuF5Pv//5lSUFdR6DlRAACDwTCJ\nnYT7//+JhfT7//+L+oP5OX4GA42k+///i5Xo+///iA5Oi430+///67CLnez7//+Njfv9//8rzkaJ\njfT7///3wwACAAB0RYXJdAWAPjB0PE5BajBYiAbrLYX2dQaLNeStQgDHhdT7//8BAAAAi86F/3QP\nM8BPZjkBdAeDwQKF/3XzK87R+YmN9Pv//4O9wPv//wAPha0BAAD2w0B0IPfDAAEAAA+EHQEAAGot\nWGaJhcj7///Hhdz7//8BAAAAaiBai73Q+///i4Xc+///K/kr+PbDDHUdjYXg+///UP+12Pv//1dS\n6D8CAACLhdz7//+DxBD/tZz7//+NjeD7//9R/7XY+///UI2FyPv//1DoQgIAAIPEFPbDCHQf9sME\ndRqNheD7//9Q/7XY+///V2owWFDo8gEAAIPEEIO91Pv//wCLhfT7//8PhbMAAACFwA+OqwAAAIvO\nibXk+///SImFhPv//42FsPv//1CLhbD7////cHSNhaD7//9RUOgMXgAAg8QQiYWU+///hcB+Z42F\n4Pv//1D/tdj7////taD7///oTQEAAIuN5Pv//4PEDAONlPv//4uFhPv//4mN5Pv//4XAf5jrVvbD\nAXQHaivp2f7///bDAg+E4v7//2ogWmaJlcj7///Hhdz7//8BAAAA6cz+//+DyP+JheD7///rI/+1\nnPv//42N4Pv//1H/tdj7//9QVug7AQAAg8QUi4Xg+///hcB4H/bDBHQajYXg+///UP+12Pv//1dq\nIFhQ6OYAAACDxBCLhaz7//+FwHQPUOhenP//M8BZiYWs+///i430+///i4X4+///D7cQi4Xg+///\niZXk+///ZoXSD4V+9f//gL28+///AHQKi424+///g2Fw/YtN/F9eM81b6A+h//+L5V3D6B/X///H\nABYAAADobMj//4C9vPv//wAPhNX0//+Ljbj7//+DYXD96cb0///FzkAAi8xAAL/MQAAUzUAAZc1A\nAHLNQAC/zUAA6s5AAFWL7ItFDPZADEB0BoN4CAB0HVD/dQjo+VoAAFlZuf//AABmO8F1CItFEIMI\n/13Di0UQ/wBdw1WL7FaLdQyF9n4eV4t9FFf/dRBO/3UI6K7///+DxAyDP/90BIX2f+dfXl3DVYvs\nVot1GFeLfRCLBolFGPZHDEB0EIN/CAB1CotNFItFDAEB60+DJgBTi10Mhdt+QYtFFFCLRQhLVw+3\nAFDoW////4tFFIPEDINFCAKDOP91FIM+KnUTUFdqP+g+////i0UUg8QMhdt/yoM+AHUFi0UYiQZb\nX15dw1WL7FaLdQxXVuiFqv//WYtODIv49sGCdRfo39X//8cACQAAAINODCCDyP/pGwEAAPbBQHQN\n6MPV///HACIAAADr4lMz2/bBAXQTiV4E9sEQdH2LRgiD4f6JBolODItGDIPg74leBIPIAolGDKkM\nAQAAdSro+qb//4PAIDvwdAzo7qb//4PAQDvwdQtX6ENLAABZhcB1B1bon10AAFn3RgwIAQAAdHqL\nVgiLDivKiU0MjUIBiQaLRhhIiUYEhcl+F1FSV+j2BQAAg8QMi9jrR4PJIIlODOtog///dBuD//50\nFovHi8/B+AWD4R/B4QYDDIUYE0MA6wW58K1CAPZBBCB0FGoCU1NX6MBbAAAjwoPEEIP4/3Qli04I\nikUIiAHrFjPAQFCJRQyNRQhQV+iNBQAAg8QMi9g7XQx0CYNODCCDyP/rBotFCA+2wFtfXl3DVYvs\ng+wgg2XgADPAV4195GoHWfOrOUUUdRjoj9T//8cAFgAAAOjcxf//g8j/6cQAAACLfRBWi3UMhf90\nHIX2dRjoaNT//8cAFgAAAOi1xf//g8j/6ZwAAADHRexCAAAAiXXoiXXggf////8/dgnHReT///9/\n6waNBD+JReRT/3UcjUXg/3UY/3UUUP9VCIPEEIvYhfZ0XYXbeEn/TeR4D4tF4MYAAItF4ECJReDr\nFY1F4FBqAOj3/f//WVmD+P90I4tF4P9N5HgFxgAA6xKNReBQagDo2P3//1lZg/j/dASLw+sQM8A5\nReRmiUR+/g+dwIPoAlteX4vlXcNVi+yDfRAAdRXopdP//8cAFgAAAOjyxP//g8j/XcNWi3UIhfZ0\nO4N9DAB2Nf91GP91FP91EP91DFZo9SVBAOjC/v//g8QYhcB5BTPJZokOg/j+dSDoWtP//8cAIgAA\nAOsL6E3T///HABYAAADomsT//4PI/15dw6HorUIAg/j/dAyD+P50B1D/FTShQQDDM8BQUGoDUGoD\naAAAAMBoFEVCAP8VHKFBAKPorUIAw1WL7IMlEBNDAACD7BxTM9tDCR3srUIAagroq7kAAIXAD4RM\nAQAAM8mJHRATQwAzwA+iVos17K1CAFeNfeSDzgKJB4lfBIlPCIlXDItF5ItN8IlF9IHxaW5lSYtF\n7DVudGVsiTXsrUIAC8iLReg1R2VudQvI99lqARrJWP7BagBZD6KJB4lfBIlPCIlXDItN7IlN+HRD\ni0XkJfA//w89wAYBAHQjPWAGAgB0HD1wBgIAdBU9UAYDAHQOPWAGAwB0Bz1wBgMAdRGLPRQTQwCD\nzwGJPRQTQwDrBos9FBNDAIN99Ad8NWoHM8mNdeRYD6KJBovGizXsrUIAiVgEiUgIi034iVAMi0Xo\nqQACAAB0DYPPAok9FBNDAOsCM8D3wQAAEAB0TYPOBMcFEBNDAAIAAACJNeytQgD3wQAAAAh0MvfB\nAAAAEHQqg84IxwUQE0MAAwAAAIk17K1CAKggdBODziDHBRATQwAFAAAAiTXsrUIAX14zwFuL5V3D\nhcB1BmYP78DrEWYPbsBmD2DAZg9hwGYPcMAAU1GL2YPjD4XbdXiL2oPif8HrB3QwZg9/AWYPf0EQ\nZg9/QSBmD39BMGYPf0FAZg9/QVBmD39BYGYPf0FwjYmAAAAAS3XQhdJ0N4vawesEdA/rA41JAGYP\nfwGNSRBLdfaD4g90HIvaweoCdApmD34BjUkESnX2g+MDdAaIAUFLdfpYW8P324PDECvTUovTg+ID\ndAaIAUFKdfrB6wJ0CmYPfgGNSQRLdfZa6V7///9qEGgAdEIA6ITf//8z/4l95GoB6JPR//9ZIX38\nagNeiXXgOzWEbUUAfVOhgG1FAIsEsIXAdET2QAyDdBBQ6HKt//9Zg/j/dARHiX3kg/4UfCmhgG1F\nAIsEsIPAIFD/FRSiQQChgG1FAP80sOg+lf//WaGAbUUAgySwAEbrosdF/P7////oCwAAAIvH6EXf\n///Di33kagHoetL//1nDahRoIHRCAOjn3v//M/aJdeSLfQiD//51EOgK0P//xwAJAAAA6bcAAACF\n/w+InwAAADs90FxFAA+DkwAAAIvHwfgFiUXgi9+D4x/B4waLBIUYE0MAD75EAwSD4AF0clfoJ6f/\n/1mJdfyLReCLBIUYE0MA9kQDBAF0KFfoyan//1lQ/xVAokEAhcB1CP8VWKFBAIvwiXXkhfZ0GOhV\nz///iTDogs///8cACQAAAIPO/4l15MdF/P7////oCgAAAIvG6yGLfQiLdeRX6GOq//9Zw+hTz///\nxwAJAAAA6KDA//+DyP/oUN7//8NqEGhAdEIA6P7d//8z24ld5It1CIP+/nUX6O3O//+JGOgaz///\nxwAJAAAA6bYAAACF9g+IlwAAADs10FxFAA+DiwAAAIvewfsFi/6D5x/B5waLBJ0YE0MAD75EOASD\n4AF1Cuikzv//gyAA62pW6DCm//9Zg2X8AIsEnRgTQwD2RDgEAXQT/3UQ/3UMVuheAAAAg8QMi/jr\nFuiizv//xwAJAAAA6GPO//+DIACDz/+JfeTHRfz+////6AoAAACLx+soi3UIi33kVuh7qf//WcPo\nN87//4kY6GTO///HAAkAAADosb///4PI/+hh3f//w1WL7LjwGgAA6I68//+hMKFCADPFiUX8g6VE\n5f//AItFCItNDFYz9omFOOX//1cz/4mNMOX//4m1QOX//zl1EHUHM8DpDQgAAIXJdR/oy83//yEw\n6PjN///HABYAAADoRb///4PI/+nqBwAAi9CLyMH6BYPhH8HhBomVKOX//1OLFJUYE0MAiY0k5f//\nilwRJALb0PuA+wJ0BYD7AXUri0UQ99CoAXUc6HDN//8hMOidzf//xwAWAAAA6Oq+///piAcAAIuF\nOOX///ZEEQQgdA9qAmoAagBQ6D9VAACDxBD/tTjl///oPUMAAFmFwA+EUAMAAIuFKOX//4uNJOX/\n/4sEhRgTQwD2RAEEgA+EMgMAAOg5y///M8mLQGw5iKgAAACNhRjl//9Qi4Uo5f//D5TBiY085f//\ni40k5f//iwSFGBNDAP80Af8V1KFBAIXAD4TuAgAAObU85f//dAiE2w+E3gIAAP8VRKJBAIuVMOX/\n/zPJIY045f//iYUQ5f//iY005f//iZUs5f//OU0QD4aBBgAAi4Us5f//M9KJlUDl///HhRTl//8K\nAAAAIb085f//hNsPha4BAACKEDPAi40k5f//gPoKD5TAiYUY5f//i4Uo5f//iwSFGBNDAImFPOX/\n/zl8ATh0HIpEATSIRfSLhTzl//+IVfVqAiF8ATiNRfRQ61oPvsJQ6M0YAABZhcB0RIuFMOX//4uV\nLOX//yvCA0UQg/gBD4bbAQAAagJSjYU05f//UOjLUgAAg8QMg/j/D4QFAwAAi4Us5f//QP+FQOX/\n/+smagH/tSzl//+NhTTl//9Q6JxSAACDxAyD+P8PhNYCAACLhSzl//8zyUD/hUDl//9RUWoFiYUs\n5f//jUX0UGoBjYU05f//UFH/tRDl////FcihQQCJhTzl//+FwA+ElQIAAGoAjY045f//UYuNJOX/\n/1CNRfRQi4Uo5f//iwSFGBNDAP80Af8VOKFBAIXAD4RMAQAAi7VA5f//i41E5f//A/GLhTzl//85\nhTjl//8PjEkCAAA5vRjl//90S4uNJOX//42FOOX//2oAUGoBjUX0xkX0DVCLhSjl//+LBIUYE0MA\n/zQB/xU4oUEAhcAPhO0AAACDvTjl//8BD4z3AQAA/4VE5f//RouNNOX//+mGAAAAgPsBdAWA+wJ1\nMw+3CDPSZjuNFOX//4mNNOX//w+UwoPAAomVPOX//4uVQOX//4PCAomFLOX//4mVQOX//4D7AXQF\ngPsCdUtR6B9TAABZi4005f//ZjvBdXWDxgI5vTzl//90ImoNWFCJhTTl///o+VIAAFmLjTTl//9m\nO8F1T0b/hUTl//+LlUDl//+LhSzl//87VRAPgqn9///pRQEAAIudKOX//0aKAouVJOX//4sMnRgT\nQwCIRAo0iwSdGBNDAMdEAjgBAAAA6RcBAAD/FVihQQCL+OkKAQAAi4Uo5f//iwyFGBNDAIuFJOX/\n//ZECASAD4R1AwAAi5Uw5f//M/+JvTTl//+E2w+FDgEAAItdEImVOOX//4XbD4SNAwAAM8mNvfTr\n//+LwomNPOX//yuFMOX//zvDc0SKCkJAiI0f5f//gPkKiZU45f//i4085f//dQv/hUTl///GBw1H\nQYqVH+X//4gXR4uVOOX//0GJjTzl//+B+f8TAAByuIuNJOX//42F9Ov//yv4jYUg5f//agBQV42F\n9Ov//1CLhSjl//+LBIUYE0MA/zQB/xU4oUEAhcAPhBP///8DtSDl//85vSDl//98FouVOOX//4vC\nK4Uw5f//O8MPgkH///+LvTTl//+LjUTl//+F9g+F9QIAAIX/D4SsAgAAagVbO/sPhZgCAADo3cj/\n/8cACQAAAOieyP//iRjpxgIAAIvKgPsCD4XqAAAAOXUQD4Z8AgAAx4UU5f//CgAAAIOlGOX//wCN\nnfTr//+LwWoNK8KLlRjl//9eO0UQczMPtzmDwAKDwQJmO70U5f//dRCDhUTl//8CZokzg8MCg8IC\nZok7g8ICg8MCgfr+EwAAcsiNhfTr//+JjTzl//+LjSTl//8r2GoAjYUg5f//UFONhfTr//9Qi4Uo\n5f//iwSFGBNDAP80Af8VOKFBAIu1QOX//4u9NOX//4XAD4Ty/f//A7Ug5f//ibVA5f//OZ0g5f//\nD4zx/v//i4085f//i8GLlTDl//8rwjtFEA+CLv///+nT/v//i10QiY045f//hdsPhIoBAADHhRTl\n//8KAAAAg6UY5f//AI2FSOX//4u9OOX//yvKi5UY5f//O8tzOw+3N4PBAoPHAom9OOX//2Y7tRTl\n//91EmoNX2aJOIPAAou9OOX//4PCAmaJMIPCAoPAAoH6qAYAAHLBM/aNjZzy//9WVmhVDQAAUY2N\nSOX//yvBmSvC0fhQi8FQVmjp/QAA/xXIoUEAi7VA5f//i7005f//iYU85f//hcAPhAD9//8zyYmN\nQOX//2oAK8GNlSDl//9SUI2FnPL//wPBi40k5f//UIuFKOX//4sEhRgTQwD/NAH/FTihQQCFwHQe\ni41A5f//A40g5f//i4U85f//iY1A5f//O8F/r+sa/xVYoUEAi41A5f//i/iLhTzl//+JvTTl//87\nwQ+Pmv3//4uNOOX//4vxi5Uw5f//K/KJtUDl//878w+CxP7//+l3/f//agCNlSDl//9S/3UQ/7Uw\n5f///zQI/xU4oUEAhcAPhD38//+LtSDl//8z/+lH/f//V+gjxv//Wes8i5Uw5f//i4Uo5f//i40k\n5f//iwSFGBNDAPZEAQRAdAmAOhp1BDPA6xzoE8b//8cAHAAAAOjUxf//gyAAg8j/6wQr8YvGW4tN\n/F8zzV7o1o///4vlXcNqEGhgdEIA6KrU//8z/4l94Il95DPAi3UIhfYPlcCFwHUX6MTF///HABYA\nAADoEbf//4PK/4vC6ytW6CeX//9ZiX38VugyAAAAWYv4iX3gi9qJXeTHRfz+////6BMAAACLx4vT\n6JLU///Di3UIi13ki33gVuhcl///WcNVi+y4JBAAAOius///oTChQgAzxYlF/FOLXQhWV1Po45n/\n/zP/i/BZibXo7///OXsEfQOJewRqAVdXVuj/SwAAg8QQiYXw7///iZX07///O9d/EHwEO8dzCoPI\n/wvQ6WEDAACLxovOwfgFg+EfweEGiYXs7///iY3c7///izSFGBNDAIpEDiQCwND490MMCAEAAIiF\n++///4uF8O///3Udi0MEi43w7///i7307///mSvIi8Eb+ovX6QcDAACLO4v3K3MI9kMMAw+EkAEA\nAIC9++///wEPhUQBAACLhezv//+LFIUYE0MAi4Xw7///g3wKMACLlfTv//8PhCABAADR7oN7BAAP\nhLkCAACLlezv//+Lnejv//9qAIsElRgTQwD/dAgs/3QIKFPoEUsAAIv4iZXk7///i4Xs7///g8QQ\nib3g7///iwyFGBNDAIuF3O///zt8ASgPhf3+//87VAEsD4Xz/v//agCNlejv//9SaAAQAACNlfzv\n//9S/zQB/xUIoUEAhcAPhM3+//+LvfTv//+LjfDv//9qAFdRU+idSgAAg8QQhdJ/Dg+Mqv7//4XA\nD4Ki/v//i4Xo7///O/APh5T+//+Njfzv//+F9nQzi9ED0E47ynMqigE8DXUTjUL/O8hzGI1BAYA4\nCnUQi8jrDA+2wA++gLCuQgADyEGF9nXRjYX87///M9IryAON4O///4vBE5Xk7///6aUBAACLlezv\n//+LBJUYE0MAi5X07///9kQIBICLhfDv//90EItLCOsHgDkKdQFGQTvPcvWLyAvKdR+LxjPS6WYB\nAAD2QwyAdevoB8P//8cAFgAAAOnl/f//9kMMAQ+EMQEAAIN7BAB1BzP26SQBAACLlezv//+Ljdzv\n//8rewgDewSLBJUYE0MAi5X07///9kQIBICLhfDv//8PhN4AAABqAmoAagD/tejv///oc0kAAIuN\n8O///4PEEImV5O///4uV9O///zvBdUw5leTv//91RItDCIOl5O///wADx4vIK0sIO0MIG8D30CPB\niYXg7///dhmLQwiLjeTv//+AOAp1AUdAQTuN4O///3Lw90MMACAAAOtfagBSUf+16O///+j/SAAA\ng8QQhdJ/Dg+MDP3//4XAD4IE/f//uAACAAA7+HcR9kMMCHQL90MMAAQAAIv4dAOLexiLhezv//+L\njdzv//+LlfTv//+LBIUYE0MA9kQIBASLhfDv//90AUeAvfvv//8BdQLR7yvHg9oAiZX07///gL37\n7///AXUC0e4z0gPGE5X07///i038X14zzVvohov//4vlXcNVi+z/dRT/dRD/dQz/dQho9SVBAOgF\nAAAAg8QUXcNqDGiAdEIA6DzQ//+DZeQAM8CLfQyF/w+VwIXAdRXoWsH//8cAFgAAAOinsv//g8j/\n60wzwDlFEA+VwIXAdN+JfQxX6LCS//9Zg2X8AFfof9D//4vw/3UY/3UU/3UQV/9VCIvYiV3kV1bo\nM9D//4PEHMdF/P7////oDgAAAIvD6AnQ///Di13ki30MV+jWkv//WcNqZGigdEIA6KnP//9qC+i9\nwf//WTPbiV38akBqIF9X6JLL//9ZWYvIiU3chcl1G2r+jUXwUGgwoUIA6JYJAACDxAyDyP/pWwIA\nAKMYE0MAiT3QXEUABQAIAAA7yHMxZsdBBAAKgwn/iVkIgGEkgIpBJCR/iEEkZsdBJQoKiVk4iFk0\ng8FAiU3coRgTQwDrxo1FjFD/FTiiQQBmg32+AA+ELwEAAItFwIXAD4QkAQAAiwiJTeSDwASJRdgD\nwYlF4LgACAAAO8h8BYvIiU3kM/ZGiXXQOQ3QXEUAfSBqQFfo08r//1lZi8iJTdyFyQ+FlAAAAIsN\n0FxFAIlN5Iv7iX3Uav5bi0XYi1XgO/kPjcUAAACLMoP+/3RbO/N0V4oAqAF0UagIdQ5W/xWAoUEA\ni1XghcB0PIvHwfgFi/eD5h/B5gYDNIUYE0MAiXXciwKJBotF2IoAiEYEagBooA8AAI1GDFDoZsL/\n/4PEDP9GCItV4ItN5EeJfdSLRdhAiUXYg8IEiVXg64OJDLUYE0MAAT3QXEUAiwS1GBNDAAUACAAA\nO8hzJGbHQQQACoMJ/4lZCIBhJIBmx0ElCgqJWTiIWTSDwUCJTdzrzEaJddCLTeTpAP///2r+WzP/\niX3Ug/8DD423AAAAi/fB5gYDNRgTQwCJddyDPv90EjkedA4PvkYEDICIRgTpjAAAAMZGBIGF/3UF\navZY6wqNR//32BvAg8D1UP8VfKFBAIlF5IP4/3RMhcB0SFD/FYChQQCFwHQ9i03kiQ4l/wAAAIP4\nAnUID75GBAxA6wuD+AN1CQ++RgQMCIhGBGoAaKAPAACNRgxQ6FrB//+DxAz/RgjrGg++RgQMQIhG\nBIkeoYBtRQCFwHQGiwS4iVgQR+k9////iV386AgAAAAzwOhQzf//w2oL6IjA//9Zw2oQaMB0QgDo\n9cz//zPbiV3ki3UIg/7+dRfo5L3//4kY6BG+///HAAkAAADpogAAAIX2D4iDAAAAOzXQXEUAc3uL\n3sH7BYv+g+cfwecGiwSdGBNDAA++RDgEg+ABdQron73//4MgAOtaVugrlf//WYNl/ACLBJ0YE0MA\n9kQ4BAF0C1boVAAAAFmL+OsO6KW9///HAAkAAACDz/+JfeTHRfz+////6AoAAACLx+soi3UIi33k\nVuiGmP//WcPoQr3//4kY6G+9///HAAkAAADovK7//4PI/+hszP//w1WL7FZXi30IV+htl///WYP4\n/3RQoRgTQwCD/wF1CfaAhAAAAAF1C4P/AnUc9kBEAXQWagLoQpf//2oBi/DoOZf//1lZO8Z0HFfo\nLZf//1lQ/xU0oUEAhcB1Cv8VWKFBAIvw6wIz9lfoiZb//1mLz4PnH8H5BcHnBosMjRgTQwDGRDkE\nAIX2dAxW6Ky8//9Zg8j/6wIzwF9eXcNVi+xWi3UI9kYMg3Qg9kYMCHQa/3YI6I+B//+BZgz3+///\nM8BZiQaJRgiJRgReXcNVi+xRU1ZXi30Iu/CtQgD2RwxAD4V4AQAAV+gIkf//WYP4/3QuV+j8kP//\nWYP4/nQiV+jwkP//i/BXwf4F6OWQ//+D4B9ZweAGAwS1GBNDAFnrAovD9kAkf3RT/08EeAqLBw+2\nCECJB+sJV+iNTQAAWYvIg/n/dQq4//8AAOkwAQAA/08EiE0IeAqLBw+2CECJB+sJV+hjTQAAWYvI\ng/n/dNaITQlmi0UI6QQBAAD2RwxAD4XZAAAAV+hpkP//WYP4/3QuV+hdkP//WYP4/nQiV+hRkP//\ni/BXwf4F6EaQ//+L2IPjH1nB4wYDHLUYE0MAWfZDBIAPhJUAAAAz9kb/TwR4CosHD7YIQIkH6wlX\n6OlMAABZi8iD+f8PhFj///8PtsFQiE386PAHAABZhcB0Nv9PBHgKiwcPtghAiQfrCVfot0wAAFmL\nyIP5/3UTD75F/FdQ6KVOAABZuP//AADrUGoCiE39XlaNRfxQjUUIUOjTQQAAg8QMg/j/D4Uk////\n6Au7///HACoAAADp5/7//4tHBIP4AnwSiw+DwP6JRwQPtwGDwQKJD+sHV+i9TgAAWV9eW4vlXcNq\nEGjgdEIA6JfJ//8z24vziXXkagHopLv//1mJXfyL+4l94Ds9hG1FAA+NtgAAAKGAbUUAiwS4hcB0\nWvZADIN1SvdADACAAAB1QY1H/YP4EHcSjUcQUOjru///WYXAD4SBAAAAoYBtRQD/NLhX6CCM//9Z\nWaGAbUUAiwS49kAMg3QMUFfoc4z//1lZR+uSi/CJdeTrUGo46FDF//9Zi8ihgG1FAIkMuIXJdDpT\naKAPAAChgG1FAIsEuIPAIFDo+bz//4PEDKGAbUUAiwS4g8AgUP8V4KFBAKGAbUUAizS4iXXkiV4M\nhfZ0FoFmDACAAACJXgSJXgiJHoleHINOEP/HRfz+////6AsAAACLxujWyP//w4t15GoB6Au8//9Z\nw1WL7FFRU1aLdQwzyVeLPVAWQwCL0WogiU38iU34WOsDg8YCZjkGdPgPtwaD+GF0L4P4cnQjg/h3\ndBfoern//8cAFgAAAOjHqv//M8DpRgIAALsBAwAA6wyL2YPPAesIuwkBAACDzwKDxgIzyUEPtwZm\nhcAPhMcBAADHRQwAEAAAhckPhAcBAAAPt8CD+FMPj4oAAAB0doPoIA+E4QAAAIPoC3RSSHRDg+gY\ndC2D6Ap0HYPoBHWEOUX8D4W7AAAAx0X8AQAAAIPLEOmyAAAAgcuAAAAA6acAAAD2w0APhZgAAACD\ny0DplgAAAMdF+AEAAADphAAAAPbDAnV/g+P+g+f8g8sCgc+AAAAA63SDffwAdWjHRfwBAAAAg8sg\n62KD6FR0UIPoDnQ7SHQrg+gLdBmD6AYPhQH////3wwDAAAB1OYHLAEAAAOs3hdJ1LUKB5/+////r\nKoXSdSBCgc8AQAAA6x33wwDAAAB1D4HLAIAAAOsNi0UMhdh0BDPJ6wIL2IPGAg+3BmaFwA+F8f7/\n/4tF+IXAD4SmAAAAaiBY6wODxgJmOQZ0+GoDVmgkRUIA6DonAACDxAyFwA+Fe/7//2ogg8YGWOsD\ng8YCZjkGdPhmgz49D4Vh/v//g8YCZjkGdPhqBWgsRUIAVujXVQAAg8QMhcB1C4PGCoHLAAAEAOtA\naghoOEVCAFbouFUAAIPEDIXAdQuDxhCBywAAAgDrIWoHaExFQgBW6JlVAACDxAyFwA+FA/7//4PG\nDoHLAAABAGogWOsDg8YCZjkGdPgzwGY5Bg+F4v3//2iAAQAA/3UQjUUMU/91CFDoOlUAAIPEFIXA\nD4XS/f//i0UU/wX0B0MAM8mJSASJCIlICIlIHItNDIl4DIlIEF9eW4vlXcPMzMzMzMxTVleLVCQQ\ni0QkFItMJBhVUlBRUWhw90AAZP81AAAAAKEwoUIAM8SJRCQIZIklAAAAAItEJDCLWAiLTCQsMxmL\ncAyD/v50O4tUJDSD+v50BDvydi6NNHaNXLMQiwuJSAyDewQAdcxoAQEAAItDCOhyVwAAuQEAAACL\nQwjohFcAAOuwZI8FAAAAAIPEGF9eW8OLTCQE90EEBgAAALgBAAAAdDOLRCQIi0gIM8joTID//1WL\naBj/cAz/cBD/cBToPv///4PEDF2LRCQIi1QkEIkCuAMAAADDVYtMJAiLKf9xHP9xGP9xKOgV////\ng8QMXcIEAFVWV1OL6jPAM9sz0jP2M///0VtfXl3Di+qL8YvBagHoz1YAADPAM9szyTPSM///5lWL\n7FNWV2oAUmgW+EAAUeionAAAX15bXcNVi2wkCFJR/3QkFOi1/v//g8QMXcIIAFWL7Fbo0bP//4vw\nhfYPhEUBAACLVlyLyleLfQg5OXQNg8EMjYKQAAAAO8hy742CkAAAADvIcwQ5OXQCM8mFyQ+EEAEA\nAItRCIXSD4QFAQAAg/oFdQyDYQgAM8BA6fYAAACD+gF1CIPI/+npAAAAi0UMU4teYIlGYIN5BAgP\nhcAAAABqJF+LRlyDZAcIAIPHDIH/kAAAAHztgTmOAADAi35kdQzHRmSDAAAA6YYAAACBOZAAAMB1\nCcdGZIEAAADrdYE5kQAAwHUJx0ZkhAAAAOtkgTmTAADAdQnHRmSFAAAA61OBOY0AAMB1CcdGZIIA\nAADrQoE5jwAAwHUJx0ZkhgAAAOsxgTmSAADAdQnHRmSKAAAA6yCBObUCAMB1CcdGZI0AAADrD4E5\ntAIAwHUHx0ZkjgAAAP92ZGoI/9JZiX5k6wn/cQSDYQgA/9JZiV5gg8j/W+sCM8BfXl3DgyXMXEUA\nAMNVi+yD7BiNTehT/3UQ6D90//+LXQiNQwE9AAEAAHcPi0Xoi4CQAAAAD7cEWOtui8ONTejB+AiJ\nRQhRD7bAUOhuAAAAWVmFwHQSi0UIagKIRfiIXfnGRfoAWesKM8mIXfjGRfkAQYtF6GoB/3AEjUX8\nUFGNRfhQjUXoagFQ6L4hAACDxByFwHUQOEX0dAeLRfCDYHD9M8DrFA+3RfwjRQyAffQAdAeLTfCD\nYXD9W4vlXcNVi+yD7BD/dQyNTfDoj3P//4tFCA+2yItF8IuAkAAAAA+3BEglAIAAAIB9/AB0B4tN\n+INhcP2L5V3DVYvsagD/dQjouf///1lZXcNVi+xRUaEwoUIAM8WJRfxTVot1GFeF9n4hi0UUi85J\ngDgAdAhAhcl19YPJ/4vGK8FIO8aNcAF8Aovwi00kM/+FyXUNi0UIiwCLQASLyIlFJDPAOUUoagBq\nAFb/dRQPlcCNBMUBAAAAUFH/FRChQQCLyIlN+IXJdQczwOlxAQAAfldq4DPSWPfxg/gCcksDyY1B\nCDvBdj+LRfiNBEUIAAAAPQAEAAB3E+jTlv//i9yF23QexwPMzAAA6xNQ6MV3//+L2FmF23QJxwPd\n3QAAg8MIi0346wWLTfgz24XbdJpRU1b/dRRqAf91JP8VEKFBAIXAD4TwAAAAi3X4agBqAFZT/3UQ\n/3UM6PoiAACL+IPEGIX/D4TPAAAA90UQAAQAAHQsi00ghckPhLsAAAA7+Q+PswAAAFH/dRxWU/91\nEP91DOjAIgAAg8QY6ZoAAACF/35PauAz0lj394P4AnJDjQw/jUEIO8F2OY0EfQgAAAA9AAQAAHcT\n6AWW//+L9IX2dGfHBszMAADrE1Do93b//4vwWYX2dFLHBt3dAACDxgjrAjP2hfZ0QYtF+FdWUFP/\ndRD/dQzoTSIAAIPEGIXAdCEzwFBQOUUgdQRQUOsG/3Ug/3UcV1ZQ/3Uk/xXIoUEAi/hW6Hl5//9Z\nU+hyef//WYvHjWXsX15bi038M83oS3v//4vlXcNVi+yD7BD/dQiNTfDoQHH///91KI1F8P91JP91\nIP91HP91GP91FP91EP91DFDoyv3//4PEJIB9/AB0B4tN+INhcP2L5V3DVYvsi0UIZosIg8ACZoXJ\ndfUrRQjR+Ehdw1WL7FaLdQxXiwY5RQh1TIt9EGoEUIsPO00UdS7orLv//4kHWVmFwHUEM8DrMYtF\nGMcAAQAAAIsGA8BQ/3UU/zfot3r//4PEDOsPUehWvP//g8QMhcB00okH0SYzwEBfXl3DVYvsU1aL\ndQj3xgD/AAB1EQ+33g+2w1Do0FMAAFmFwHUJD7feg+Pfg+sHXmaLw1tdw1WL7Lj//wAAZjtFCHQG\nXelMUQAAXcNVi+xWV4t9CP91DP8H6LXz//8Pt/C4//8AAFlmO/B0DmoIVuiTmf//WVmFwHXaX2aL\nxl5dw1WL7IHsRAMAAKEwoUIAM8WJRfyLVRSNjUD9//+LRQhTi10MiZXY/P//M9KJhST9//+JnTT9\n//+JjRD9///HheD8//9eAQAAiZXk/P//iZXc/P//iZXU/P//iZX0/P//iZUA/f//iZXQ/P//Vovy\nibU4/f//hdt1GOimr///xwAWAAAA6POg//+DyP/pxhIAAIXAdORX/3UQjY28/P//6HFv//8zwIv4\niIU9/f//i8iJvTD9//8PtwOJvRj9//+Jjej8//9mhcAPhHMSAABqCFDosZj//1lZhcB0U4uFJP3/\n/09QUI2FGP3//4m9GP3//1Do1f7//1lZD7fAUOi0/v//WVmDwwJqCA+3A1DodZj//1lZhcB17Iu9\nGP3//4mdNP3//4m9MP3//+ncEQAAD7cDaiVZZjvID4VkEQAAZjtLAg+ERxEAADPAtgGL8ImFzPz/\n/4rQibXs/P//i8iJhQj9//+Jtfj8//+L+IiF8/z//4iFF/3//4iFP/3//4iVD/3//4iFH/3//4iF\nPv3//4i1K/3//4mNBP3//+sGi500/f//g8MCiZ00/f//D7cb98MA/wAAdTQPtsNQ6LBRAACKlQ/9\n//9Zi40E/f//hcB0FGv/CkaJtfj8//+Dx9AD++nXAAAAirUr/f//g/tOD487AQAAD4TCAAAAg/sq\nD4QWAQAAg/tGD4SwAAAAg/tJdBaD+0wPhSYBAAD+xoi1K/3//+mVAAAAi400/f//D7dBAoP4NnUz\nZoN5BDR1Jo1BBIuNBP3//0GJhTT9//8z24mNBP3//4md9Pz//4mdAP3//+tfi400/f//g/gzdRhm\ng3kEMnURjUEEi40E/f//iYU0/f//6zxqZFlmO8F0KGppWWY7wQ+EBwEAAGpvWWY7wXQUanhZZjvB\ndAxqWFlmO8EPhYoAAACLjQT9//+LhTT9//+KnT/9//+KvT79//+KtSv9//+E0g+Ewf7//4u17Pz/\n/4m9LP3//4u9MP3//4TbD4XIAAAAi43Y/P//iY3Q/P//g8EEiY3Y/P//i0n8iY38/P//6awAAACK\nnT/9//+LhTT9///+w4idP/3//+uZg/todFVqbFg72HQSg/t3dCr+woiVD/3//+lp////i4U0/f//\ng8ACamxbZjkYD4Tk/v///saItSv9//+KvT79//+LhTT9///+x4qdP/3//4i9Pv3//+lF////ir0+\n/f///s7+z4i1K/3//4i9Pv3//+sMir0+/f//i40E/f//ip0//f//i4U0/f//6RD///+Dpfz8//8A\nMtuE/3UUD7cAg/hTdAmD+EN0BP7H6wOAz/+LhTT9//9qIA+3EFgL0GpuWImVIP3//zvQD4TzAAAA\ng/pjdCaD+nt0If+1JP3//42FGP3//1Dosfv//4u9GP3//1mJvTD9///rGP+1JP3//0eJvTD9//+J\nvRj9///oUe///1kPt8i4//8AAImNOP3//2Y7wQ+EfgMAAIuVIP3//4O9+Pz//wCLhSz9//90CIXA\nD4SlDgAAg/pvD49XBgAAD4SyCgAAg/pjD4QbBQAAg/pkD4SgCgAAD45jBgAAg/pnfmpqaVg70HRT\nam5YO9APhUwGAACAvT/9//8Ai/cPhHsNAACLnTj9//+KjT39//+LhTT9//+LvTD9///+wYPAAoiN\nPf3//4mFNP3//+nqDQAAi404/f//6WP///9qZFiL+Im9IP3//+l+BwAAi504/f//M/9qLVpmO9N1\nDIuNEP3//0dmiRHrCGorWWY7y3Uri7Uw/f//SP+1JP3//0aJhSz9//+JtTD9///oQ+7//w+32FmJ\nnTj9///rBou1MP3//4O9+Pz//wAPhYgAAACDjSz9////638PtsNQ6PBNAABZhcB0fYuNLP3//4vB\nSYmNLP3//4XAdGqLjRD9////hQj9//9mD77DZokEeY2F5Pz//1CNhUD9//9HUI2FEP3//1CNheD8\n//9QV+hT+f//g8QUhcAPhP0BAAD/tST9//9GibUw/f//6Kbt//8Pt9hZiZ04/f//98MA/wAAD4R1\n////i4W8/P//i4CEAAAAi0AwD7cwi84PvsM7yIuNLP3//w+F7gAAAIvBSYmNLP3//4XAD4TdAAAA\n/7Uk/f///4Uw/f//6Ent//8Pt9iLhRD9//+JnTj9//9miTR4jYXk/P//UI2FQP3//0dQjYUQ/f//\nUI2F4Pz//1BX6KP4//+DxBiFwA+ETQEAAL4A/wAAhd51fA+2w1Do1EwAAFmLjSz9//+FwHRui8FJ\niY0s/f//hcB0YYuFEP3///+FCP3//2aJHHiNheT8//9QjYVA/f//R1CNhRD9//9QjYXg/P//UFfo\nO/j//4PEFIXAD4TlAAAA/7Uk/f///4Uw/f//6I/s//8Pt9hZiZ04/f//64CLjSz9//+DvQj9//8A\nD4S7AQAAamVaZjvTdAxqRVhmO8MPhacBAACLwUmJjSz9//+FwA+ElgEAAIuFEP3//2aJFHiNheT8\n//9QjYVA/f//R1CNhRD9//9QjYXg/P//UFforvf//4PEFIXAdFyLtTD9////tST9//9G6AXs//9Z\nai0Pt9hZiZ04/f//ZjvLD4WUAAAAi4UQ/f//ZokMeI2F5Pz//1CNhUD9//9HUI2FEP3//1CNheD8\n//9QV+hS9///g8QUhcB1bIu1OP3//4O91Pz//wF1DP+13Pz//+gbbf//WYO95Pz//wF1DP+1EP3/\n/+gGbf//Wbj//wAAZjvGD4UlCwAAi4Xo/P//hcAPhR8LAAA4hT39//8PhRMLAACDyP/pCwsAAGor\nWGY7ww+FkAAAAIuNLP3//4vBSYmNLP3//4XAdWeJhSz9///rdQ+2w1DoCUsAAFmFwHR3i40s/f//\ni8FJiY0s/f//hcB0ZIuFEP3///+FCP3//2aJHHiNheT8//9QjYVA/f//R1CNhRD9//9QjYXg/P//\nUFfocPb//4PEFIXAD4Qa/////7Uk/f//RujJ6v//D7fYWYmdOP3///fDAP8AAHSD6waLtTD9//9O\nibUw/f//ibUY/f//i7Uk/f//VlPouPb//4O9CP3//wBZWQ+Ey/7//4C9P/3//wAPhbD7//+LhRD9\n//8zyf+F6Pz//2aJDHiLheD8//+NPEUCAAAAV+j5sf//i/BZhfYPhI7+//+NT/8zwFH/tRD9//9X\nVlDonU0AAIPEFIXAdBKD+BYPhPkJAACD+CIPhPAJAACNhbz8//9QD76FK/3//1b/tfz8//9IUP81\nyK1CAP8VvKFBAP/QVuhra///g8QU6SD7//+Dvfj8//8AdRFAx4X4/P//AQAAAImFLP3//4T/fgfG\nhR/9//8Bi4Uw/f///7Uk/f//i504/f//SIu1/Pz//1OJhTD9//+JhRj9///ovPX//4u9IP3//1lZ\ng734/P//AHQXi40s/f//i8FJiY0s/f//hcAPhMwEAACLhTD9////tST9//9AiYUw/f//iYUY/f//\n6FPp//8Pt9i4//8AAImdOP3//1lmO8MPhHUEAACD/2N0T4P/c3UWg/sJcgmD+w0Phl0EAABqIFhm\nO9h1NIP/ew+FTAQAAA+3wzPSi8hCg+EHwegD0+KLjdz8//8PvgwID76F8/z//zPIhdEPhCEEAACA\nvT/9//8AD4UMBAAAgL0f/f//AA+ErQMAAIuF/Pz//2aJGIPAAunjAwAAi8KD6HAPhE8EAACD6AMP\nhNn+//9ISA+ERQQAAIPoAw+EiQEAAIPoA3Q7i4U0/f//i504/f//ZjkYD4UPCAAAio09/f///smA\nvT/9//8AD4Wr+f//i5XQ/P//iZXY/P//6Zr5//+E/34HxoUf/f//AYu9NP3//4PHAmpeWGY7B3UK\ng8cCxoXz/P///4u13Pz//4X2dSVoACAAAOjCr///i/CJtdz8//9ZhfYPhFH8///HhdT8//8BAAAA\naAAgAAAzwFBW6NB0//+DxAxqXVpmOxd1EGogWIvaiEYLg8cC6akAAACLncz8///pngAAAA+3yIPH\nAmotWGY7wXV8ZoXbdHcPtwdmO9B0b4PHAom9NP3//2Y72HMEi/DrBQ+384vYibXs/P//Zjvecy8r\n8w+3+w+33ou13Pz//4vXi8/B6gOD4QewAdLgCAQyR0t164u17Pz//4u9NP3//w+3zrABi7Xc/P//\ni9HB6gOD4QfS4AgEMjPAi9jrEYvRi9nB6gOD4QewAdLgCAQyal1aD7cHZjvQD4VW////M8BmOQcP\nhF77//+JvTT9///pSv3//4u9IP3//2otWGY7wXUJxoUX/f//AesIaitYZjvBdT3/jSz9//91DYO9\n+Pz//wB0BLMB6yiLhTD9////tST9//9AiYUw/f//iYUY/f//6Lzm//9ZD7fIiY04/f//ajBYZjvB\nD4WVAAAAi4Uw/f///7Uk/f//QImFMP3//4mFGP3//+iI5v//WQ+3yGp4WImNOP3//2Y7wQ+E+wAA\nAGpYWmY70Q+E7wAAAMeFCP3//wEAAAA7+HQbg734/P//AHQK/40s/f//dQL+w2pvWukSAQAAi70w\n/f///7Uk/f//T1GJvTD9//+JvRj9///oQfL//1lZajBYi8iJjTj9//+LlSD9//9qeFiDvQT9//8A\nD4RVAwAAhNsPhT8DAACLnTD9//+LtST9//870A+EAgIAAIP6cA+E+QEAAPfBAP8AAA+FuQIAAA+2\nwVDon0UAAIu9IP3//1mFwA+E9gIAAGpvWDv4D4WiAQAAi404/f//ajhYZjvBD4aKAgAAi4UA/f//\ni7X0/P//D6TwA4mFAP3//8HmA+n5AQAAi70w/f///7Uk/f//R4m9MP3//4m9GP3//+hZ5f//g734\n/P//AFkPt8iJjTj9//90FouFLP3//4PoAomFLP3//4P4AX0C/sNqeFiL0ImVIP3//+kY////UzPA\nagX/tfz8//+Jhez8//+Nhez8//9Q6OMYAACDxBCFwHQXg/gWD4THBAAAg/giD4VZ+///6bkEAACL\nhfz8//8Dhez8//+Jhfz8///pPfv//4PGAuk1+///i4Uw/f///7Uk/f//SFOJhTD9//+JhRj9///o\nyvD//1lZi4X8/P//O/APhNz4//+AvT/9//8AD4XB9f///4Xo/P//g/9jD4Sy9f//M8k4jR/9//90\nCGaJCOmg9f//iAjpmfX//8aFK/3//wFqLVhmO8F1CcaFF/3//wHrDGorWGY7wQ+FM/7///+NLP3/\n/3UQg734/P//AHQHswHpG/7///+1JP3//0eJvTD9//+JvRj9///oDuT//1kPt8jp7v3//4uNAP3/\n/zPAUGoKUYuN9Pz//1HoeUcAAIuNOP3//4vwiZUA/f//61/3wQD/AAAPhcAAAAAPtsFQ6NFDAABZ\nhcAPhKgAAACLhfT8//+LjQD9//+LtTj9//8PpMEEVsHgBImNAP3//4mF9Pz//+iG7///i70g/f//\ni7X0/P//WQ+3yImNOP3///+FCP3//w+3wWowWSvBi40A/f//mQPwibX0/P//E8qDvfj8//8AiY0A\n/f//dAj/jSz9//90WYu1JP3//0NWiZ0w/f//iZ0Y/f//6C/j//+LlSD9//9ZD7fIaniJjTj9//9Y\n6TD9//+LjTj9//+LvSD9//9LVlGJnTD9//+JnRj9///oGu///1lZi40A/f//i7Xs/P//gL0X/f//\nAA+EYgEAAIuF9Pz///fYagCJhfT8//9YE8j32YmNAP3//+lCAQAAi404/f//66mLvSD9//+LjQD9\n///rvYTbD4UTAQAAi50w/f//O9B0TIP6cHRH98EA/wAAD4XVAAAAD7bBUOhYQgAAi70g/f//WYuN\nOP3//4XAD4S9AAAAam9YO/h1EWo4WGY7wQ+GqgAAAMHmA+tKa/YK60X3wQD/AAAPhY4AAAAPtsFQ\n6DxCAABZhcB0esHmBIm17Pz//4u1OP3//1boC+7//4u9IP3//4u17Pz//1kPt8iJjTj9////hQj9\n//+DxtAPt8ED8IO9+Pz//wB0CP+NLP3//3Rf/7Uk/f//Q4mdMP3//4mdGP3//+jM4f//i5Ug/f//\nWQ+3yGp4iY04/f//WOkc////i404/f//i70g/f///7Uk/f//S1GJnTD9//+JnRj9///osu3//1lZ\n6waLvSD9//+AvRf9//8AdAL33oPvRvffG/8jvQj9//8PhKz1//+AvT/9//8AD4WL8v///4Xo/P//\ng70E/f//AHQci4X8/P//i430/P//iQiLjQD9//+JSATpYPL//4qFK/3//4udOP3//4TAio09/f//\ni4X8/P//dAeJMOlJ8v//Zokw6UHy//9mO8h1Do1DAmY7CHUGiYU0/f//i7Uk/f//R1aJvTD9//+J\nvRj9///o2OD//1mLjTT9//8Pt9iJnTj9//9miwGDwQKJjTT9//9mO8N1XIu1OP3//7j//wAAZjvD\ni500/f//dRlqJVlmOQsPheD0//9qblhmOUMCD4XT9P//D7cDZoXAD4W37f//6cL0////tST9//9R\n6wf/tST9//9T6IXs//9ZWemg9P//Vuvwi43o/P//i8GAvcj8//8AX3QKi43E/P//g2Fw/YtN/F4z\nzVvoqGb//4vlXcMzwFBQUFBQ6BmO///MVYvsi0UIiwCBOGNzbeB1JYN4EAN1H4tAFD0gBZMZdBs9\nIQWTGXQUPSIFkxl0DT0AQJkBdAYzwF3CBADogqj//8xoRRFBAOhBov//WTPAw1WL7FFRU1ZXaAQB\nAAC7IBRDADPAUzP/ZqMoFkMAV/8VbKFBAIs1ZF1FAIkd2AdDAIX2dAVmOT51AovzjUX4UI1F/FBX\nV1boYwAAAItd/IPEFIH7////P3NLi0X4Pf///39zQY0MWAPAA8k7yHI2UegBp///i/hZhf90KY1F\n+FCNRfxQjQSfUFdW6CAAAACLRfyDxBRIiT3IB0MAo8AHQwAzwOsDg8j/X15bi+Vdw1WL7FFRi0UU\ni00Ii1UQU4tdGFaLdQxXM/+JO8cAAQAAAIX2dAiJFoPGBIl1DMdFCCAAAADHRfwJAAAAaiJYZjkB\ndREzwIX/aiIPlMCDwQKL+FjrGv8DhdJ0CWaLAWaJAoPCAg+3AYPBAmaFwHQchf91yWY7RQh0BmY7\nRfx1vYXSdAszwGaJQv7rA4PpAot9GDPbiV34ZjkZD4TfAAAAD7cBZjtFCHQGZjtF/HUFg8EC6+xm\nORkPhMIAAACF9nQIiRaDxgSJdQyLRRSLdfj/AGpcx0X4AQAAAFjrBIPBAkNmOQF092oiWGY5AWpc\nWHU79sMBdSWF9nQRaiJfZjl5Aot9GHUFg8EC6w2DZfgAM8CF9g+UwIvwalxY0evrDUuF0nQGZokC\ng8IC/weF23XvD7cBZoXAdCyF9nUMZjtFCHQiZjtF/HQcg334AHQMhdJ0BmaJAoPCAv8Hg8ECM9vp\nbf///4l1+It1DIXSdAgzwGaJAoPCAv8HM9vpGP///4X2dAKJHotFFF9eW/8Ai+Vdw1NWizUsC0MA\nM9tXi/uF9nUbg8j/6aEAAABmO8F0AUdW6OXo//9ZjTRGg8YCD7cGaj1ZZoXAdeKNRwFqBFDorqT/\n/4v4iT3QB0MAWVmF/3TBizUsC0MAZjkedERW6Kno//9Zaj2NWAFYZjkGdCJqAlPoe6T//4kHWVmF\nwHRBVlNQ6FkIAACDxAyFwHVJg8cEjTReM9tmOR51wos1LAtDAFboaF7//4kdLAtDADPAiR/HBZht\nRQABAAAAWV9eW8P/NdAHQwDoRF7//4Ml0AdDAACDyP/r5DPAUFBQUFDoq4r//8xVi+yLRQijqAxD\nAF3DVYvsi0UIhcB4IYP4An4Ng/gDdReLDSwWQwDrC4sNLBZDAKMsFkMAi8Fdw+gFmf//xwAWAAAA\n6FKK//+DyP9dw1WL7IPsFINl9ACDZfgAoTChQgBWV79O5kC7vgAA//87x3QNhcZ0CffQozShQgDr\nZo1F9FD/FVCiQQCLRfgzRfSJRfz/FfChQQAxRfz/FdygQQAxRfyNRexQ/xVMokEAi03wjUX8M03s\nM038M8g7z3UHuU/mQLvrEIXOdQyLwQ0RRwAAweAQC8iJDTChQgD30YkNNKFCAF9ei+Vdw1WL7FFX\n/xVUokEAi/iF/3RHUzPbVov3ZjkfdBCDxgJmOR51+IPGAmY5HnXwK/eDxgJW6Daj//+JRfxZhcB0\nDlZXUOgNYv//i138g8QMV/8VkKFBAF6Lw1tfi+Vdw1WL7FaLdQiF9g+E6gAAAItGDDsFTK5CAHQH\nUOjFXP//WYtGEDsFUK5CAHQHUOizXP//WYtGFDsFVK5CAHQHUOihXP//WYtGGDsFWK5CAHQHUOiP\nXP//WYtGHDsFXK5CAHQHUOh9XP//WYtGIDsFYK5CAHQHUOhrXP//WYtGJDsFZK5CAHQHUOhZXP//\nWYtGODsFeK5CAHQHUOhHXP//WYtGPDsFfK5CAHQHUOg1XP//WYtGQDsFgK5CAHQHUOgjXP//WYtG\nRDsFhK5CAHQHUOgRXP//WYtGSDsFiK5CAHQHUOj/W///WYtGTDsFjK5CAHQHUOjtW///WV5dw1WL\n7FaLdQiF9nRZiwY7BUCuQgB0B1Dozlv//1mLRgQ7BUSuQgB0B1DovFv//1mLRgg7BUiuQgB0B1Do\nqlv//1mLRjA7BXCuQgB0B1DomFv//1mLRjQ7BXSuQgB0B1Dohlv//1leXcNVi+xWi3UIhfYPhG4D\nAAD/dgToa1v///92COhjW////3YM6Ftb////dhDoU1v///92FOhLW////3YY6ENb////Nug8W///\n/3Yg6DRb////diToLFv///92KOgkW////3Ys6Bxb////djDoFFv///92NOgMW////3Yc6ARb////\ndjjo/Fr///92POj0Wv//g8RA/3ZA6Ola////dkTo4Vr///92SOjZWv///3ZM6NFa////dlDoyVr/\n//92VOjBWv///3ZY6Lla////dlzosVr///92YOipWv///3Zk6KFa////dmjomVr///92bOiRWv//\n/3Zw6Ila////dnTogVr///92eOh5Wv///3Z86HFa//+DxED/toAAAADoY1r///+2hAAAAOhYWv//\n/7aIAAAA6E1a////towAAADoQlr///+2kAAAAOg3Wv///7aUAAAA6Cxa////tpgAAADoIVr///+2\nnAAAAOgWWv///7agAAAA6Ata////tqQAAADoAFr///+2qAAAAOj1Wf///7a4AAAA6OpZ////trwA\nAADo31n///+2wAAAAOjUWf///7bEAAAA6MlZ////tsgAAADovln//4PEQP+2zAAAAOiwWf///7a0\nAAAA6KVZ////ttQAAADomln///+22AAAAOiPWf///7bcAAAA6IRZ////tuAAAADoeVn///+25AAA\nAOhuWf///7boAAAA6GNZ////ttAAAADoWFn///+27AAAAOhNWf///7bwAAAA6EJZ////tvQAAADo\nN1n///+2+AAAAOgsWf///7b8AAAA6CFZ////tgABAADoFln///+2BAEAAOgLWf//g8RA/7YIAQAA\n6P1Y////tgwBAADo8lj///+2EAEAAOjnWP///7YUAQAA6NxY////thgBAADo0Vj///+2HAEAAOjG\nWP///7YgAQAA6LtY////tiQBAADosFj///+2KAEAAOilWP///7YsAQAA6JpY////tjABAADoj1j/\n//+2NAEAAOiEWP///7Y4AQAA6HlY////tjwBAADoblj///+2QAEAAOhjWP///7ZEAQAA6FhY//+D\nxED/tkgBAADoSlj///+2TAEAAOg/WP///7ZQAQAA6DRY////tlQBAADoKVj///+2WAEAAOgeWP//\n/7ZcAQAA6BNY////tmABAADoCFj//4PEHF5dw1WL7FGhMKFCADPFiUX8i00cU1ZXM/+FyXUNi0UI\niwCLQASLyIlFHFczwDlFIFf/dRQPlcD/dRCNBMUBAAAAUFH/FRChQQCL2IXbdQczwOmRAAAAfkuB\n+/D//393Q40MG41BCDvBdjmNBF0IAAAAPQAEAAB3E+i6dv//i/SF9nTMxwbMzAAA6xNQ6KxX//+L\n8FmF9nS3xwbd3QAAg8YI6wKL94X2dKaNBBtQV1bos2L//4PEDFNW/3UU/3UQagH/dRz/FRChQQCF\nwHQQ/3UYUFb/dQz/FQCiQQCL+FboM1r//1mLx41l8F9eW4tN/DPN6Axc//+L5V3DVYvsg+wQ/3UI\njU3w6AFS////dSCNRfD/dRz/dRj/dRT/dRD/dQxQ6Nz+//+DxByAffwAdAeLTfiDYXD9i+Vdw1WL\n7ItFFIXAfgtQ/3UQ6OQAAABZWf91HP91GFD/dRD/dQz/dQjoWAIAAIPEGF3DVYvsVleLfQiF/3QT\ni00Mhcl0DItVEIXSdRozwGaJB+iOkf//ahZeiTDo3IL//4vGX15dw4v3ZoM+AHQGg8YCSXX0hcl0\n1CvyD7cCZokEFo1SAmaFwHQDSXXuM8CFyXXQZokH6EqR//9qIuu6VYvsVot1CIX2dBOLVQyF0nQM\ni00Qhcl1GTPAZokG6COR//9qFl6JMOhxgv//i8ZeXcNXi/4r+Q+3AWaJBA+NSQJmhcB0A0p17jPA\nX4XSdd9miQbo7pD//2oi68lVi+wz0ovCOUUMdhGLTQhmORF0CUCDwQI7RQxy8l3DVYvsi0UQhcB1\nAl3Di00Mi1UIVkh0FQ+3MmaF9nQNZjsxdQiDwgKDwQLr6A+3Ag+3CSvBXl3DVYvsi1UUi00IVoXS\ndQ2FyXUNOU0MdSYzwOszhcl0HotFDIXAdBeF0nUHM8BmiQHr5ot1EIX2dRkzwGaJAehTkP//ahZe\niTDooYH//4vGXl3DU4vZV4v4g/r/dRYr3g+3BmaJBDONdgJmhcB0JU917usgK/EPtwQeZokDjVsC\nZoXAdAZPdANKdeuF0nUFM8BmiQOF/19bD4V7////g/r/dQ+LRQwz0mpQZolUQf5Y654zwGaJAejb\nj///aiLrhlWL7FNWVzP/u+MAAACNBDuZK8KL8NH+alX/NPU4VUIA/3UI6JwAAACDxAyFwHQTeQWN\nXv/rA41+ATv7ftCDyP/rB4sE9TxVQgBfXltdw1WL7IN9CAB0Hf91COih////WYXAeBA95AAAAHMJ\niwTFGE5CAF3DM8Bdw1WL7KFQXUUAMwUwoUIAdBszyVFRUf91HP91GP91FP91EP91DP91CP/QXcP/\ndRz/dRj/dRT/dRD/dQz/dQjolP///1lQ/xWUoUEAXcNVi+xWi3UQM8CF9nRei00MU1eLfQhqQVtq\nWlor+YlVEOsDalpaD7cED2Y7w3INZjvCdwiDwCAPt9DrAovQD7cBZjvDcgxmO0UQdwaDwCAPt8CD\nwQJOdApmhdJ0BWY70HTBD7fID7fCXyvBW15dw4tMJAT3wQMAAAB0JIoBg8EBhMB0TvfBAwAAAHXv\nBQAAAACNpCQAAAAAjaQkAAAAAIsBuv/+/n4D0IPw/zPCg8EEqQABAYF06ItB/ITAdDKE5HQkqQAA\n/wB0E6kAAAD/dALrzY1B/4tMJAQrwcONQf6LTCQEK8HDjUH9i0wkBCvBw41B/ItMJAQrwcNVi+yD\n7CShMKFCADPFiUX8i0UIU4sduKFBAFZXiUXkM/aLRQxWiUXg/9OL+Il96Oj3kP//iUXsOTU8FkMA\nD4WwAAAAaAAIAABWaIhuQgD/FSihQQCL+IX/dSb/FVihQQCD+FcPhWoBAABWVmiIbkIA/xUooUEA\ni/iF/w+EUwEAAGigbkIAV/8VjKFBAIXAD4Q/AQAAUP/TaKxuQgBXozwWQwD/FYyhQQBQ/9NovG5C\nAFejQBZDAP8VjKFBAFD/02jQbkIAV6NEFkMA/xWMoUEAUP/To0wWQwCFwHQUaOxuQgBX/xWMoUEA\nUP/To0gWQwCLfej/FfihQQCFwHQbi0XkhcB0B1D/FZihQQA5dex0HWoEWOm9AAAAOXXsdBD/NTwW\nQwD/FbyhQQBqA+vloUgWQwCLHbyhQQA7x3RPOT1MFkMAdEdQ/9P/NUwWQwCJRez/04tN7IlF6IXJ\ndC+FwHQr/9GFwHQajU3cUWoMjU3wUWoBUP9V6IXAdAb2RfgBdQuLfRCBzwAAIADrMKFAFkMAO8d0\nJFD/04XAdB3/0IvwhfZ0FaFEFkMAO8d0DFD/04XAdAVW/9CL8It9EP81PBZDAP/ThcB0DFf/deD/\ndeRW/9DrAjPAi038X14zzVvoH1b//4vlXcNVi+yDfQgAdRXoJoz//8cAFgAAAOhzff//g8j/XcP/\ndQhqAP81CBNDAP8VnKFBAF3DVYvsg30IAHUL/3UM6BNR//9ZXcNWi3UMhfZ1Df91COjIUP//WTPA\n601T6zCF9nUBRlb/dQhqAP81CBNDAP8VoKFBAIvYhdt1XjkFDBNDAHRAVugDmP//WYXAdB2D/uB2\ny1bo85f//1nolov//8cADAAAADPAW15dw+iFi///i/D/FVihQQBQ6IqL//9ZiQbr4uhti///i/D/\nFVihQQBQ6HKL//9ZiQaLw+vKVYvsVot1DFcz/4X2dBtq4DPSWPf2O0UQcw/oOYv//8cADAAAADPA\n6zwPr3UQU4tdCIXbdAlT6On+//9Zi/hWU+gQ////i9hZWYXbdBU7/nMRK/eNBB9WagBQ6ERb//+D\nxAyLw1tfXl3DVYvsVot1CIX2dBtq4DPSWPf2O0UMcw/o04r//8cADAAAADPA61EPr3UMhfZ1AUYz\nyYP+4HcVVmoI/zUIE0MA/xXQoUEAi8iFyXUqgz0ME0MAAHQUVujslv//WYXAddCLRRCFwHS867SL\nRRCFwHQGxwAMAAAAi8FeXcNqAuigTP//WcPo/pb//4XAdAhqFugcl///WfYFoK5CAAJ0IWoX6A5x\nAACFwHQFagdZzSlqAWgVAABAagPoL3r//4PEDGoD6BpN///MVYvsi00Ig/n+dQ3oGYr//8cACQAA\nAOs4hcl4JDsN0FxFAHMci8GD4R/B+AXB4QaLBIUYE0MAD75ECASD4EBdw+jkif//xwAJAAAA6DF7\n//8zwF3DVYvsg+wQU4tdDFeLfRCF23UShf90DotFCIXAdAODIAAzwOt/i0UIhcB0A4MI/1aB////\n/392EeiZif//ahZeiTDo53r//+tY/3UYjU3w6HNJ//+LRfAz9jmwqAAAAHViZotFFLn/AAAAZjvB\ndjuF23QPhf90C1dWU+iiWf//g8QM6E+J///HACoAAADoRIn//4swgH38AHQHi034g2Fw/YvGXl9b\ni+Vdw4XbdAaF/3RfiAOLRQiFwHTZxwABAAAA69GNTQyJdQxRVldTagGNTRRRVv9wBP8VyKFBAIvI\nhcl0EDl1DHWai0UIhcB0pYkI66H/FVihQQCD+Hp1hIXbdA+F/3QLV1ZT6BNZ//+DxAzowIj//2oi\nXokw6A56///pb////1WL7GoA/3UU/3UQ/3UM/3UI6Mb+//+DxBRdw8xWi0QkFAvAdSiLTCQQi0Qk\nDDPS9/GL2ItEJAj38Yvwi8P3ZCQQi8iLxvdkJBAD0etHi8iLXCQQi1QkDItEJAjR6dHb0erR2AvJ\ndfT384vw92QkFIvIi0QkEPfmA9FyDjtUJAx3CHIPO0QkCHYJTitEJBAbVCQUM9srRCQIG1QkDPfa\n99iD2gCLyovTi9mLyIvGXsIQAFWL7IHsiAQAAKEwoUIAM8WJRfyLRQiNjbT7//9TVomF2Pv//4tF\nDFf/dRCLfRSJhfj7//8zwIvYib3w+///iYWs+///i/CJnez7//+JhdD7//+Jhej7//+Jhdz7//+J\nhaj7//+JhbD7//+JhdT7///ofEf//+iGh///iYWg+///OZ3Y+///dSroc4f//8cAFgAAAOjAeP//\nOJ3A+///dAqLhbz7//+DYHD9g8j/6RALAACLlfj7//+F0nTMD7cSM8mJjfT7//+LwYmF4Pv//4mN\nzPv//4mNpPv//4mV5Pv//2aF0g+ExQoAAMeFkPv//1gAAADHhYz7//9kAAAAx4WI+///aQAAAMeF\nmPv//28AAACDhfj7//8ChcAPiH8KAABqWI1C4F9mO8d3Dw+3wg+2gOhuQgCD4A/rAjPAi73M+///\na8AJD7a8OAhvQgCLx4m9zPv//4u98Pv//8HoBImFzPv//4P4CA+EkQoAAIP4Bw+HCwoAAP8khREy\nQQAzwION6Pv///+L2ImFqPv//4mFsPv//4mF0Pv//4mF3Pv//4md7Pv//4mF1Pv//+nQCQAAD7fC\naiBaK8J0RoPoA3Q5g+gIdC9ISHQdg+gDi4X4+///D4WvCQAAg8sIiZ3s+///6aEJAACDywSJnez7\n///pjQkAAIPLAevwgcuAAAAA6+iDywLr42oqWGY70HUviweDxwSJvfD7//+JhdD7//+FwA+JWgkA\nAIPLBPfYiZ3s+///iYXQ+///6UQJAABrjdD7//8KD7fCg8HQA8GJhdD7///pJAkAADPAiYXo+///\n6R0JAABqKlhmO9B1K4sHg8cEiYXo+///hcCLhfj7//+JvfD7//8PifwIAACDjej7////6fAIAABr\njej7//8KD7fCg8HQA8GJhej7///pyggAAA+3woP4SXRXg/hodEhqbFo7wnQag/h3i4X4+///D4Wz\nCAAAgcsACAAA6fz+//+Lhfj7//9mORB1FIPAAoHLABAAAImF+Pv//+nd/v//g8sQ6dX+//9qIFgL\n2OnZ/v//i4X4+///D7cAg/g2dSOLvfj7//9mg38CNHUWi8eDwASBywCAAACJhfj7///pmv7//4P4\nM3Uji734+///ZoN/AjJ1FovHg8AEgeP/f///iYX4+///6XL+//9mO4WM+///D4QLCAAAZjuFiPv/\n/w+E/gcAAGY7hZj7//8PhPEHAACD+HUPhOgHAACD+HgPhN8HAABmO4WQ+///D4TSBwAAM8CJhcz7\n//+NheD7///HhdT7//8BAAAAUP+12Pv//1LoU63//4PEDOmfBwAAD7fCg/hkD48pAgAAD4SxAgAA\ng/hTD48lAQAAdH2D6EF0EEhIdFhISHQISEgPhZoFAABqIFgD0MeFqPv//wEAAACJleT7//+Lhej7\n//+Ntfz7//+Dy0C5AAIAAImd7Pv//4mN9Pv//4XAD4mOAgAAx4Xo+///BgAAAOnfAgAA98MwCAAA\nD4XYAAAAaiBYC9iJnez7///pyAAAAPfDMAgAAHULaiBYC9iJnez7//+Llej7//+/////f4P6/3QC\ni/qLtfD7//+DxgSJtfD7//+Ldvz2wyAPhL8EAACF9nUGizXgrUIAM8mLxomF5Pv//4mN9Pv//4X/\nD47QBAAAigCEwA+ExgQAAI2NtPv//w+2wFFQ6F7P//9ZhcCLheT7//9ZdAFAi430+///QEGJheT7\n//+JjfT7//87z3zB6YwEAACD6FgPhNwCAABISA+EiwAAAIPoBw+E7f7//0hID4VqBAAAD7cHg8cE\nx4XU+///AQAAAIm98Pv//4mFnPv///bDIHREiIXE+///M8CIhcX7//+NhbT7//9Qi4W0+////3B0\njYXE+///UI2F/Pv//1DoMQgAAIPEEIXAeRPHhbD7//8BAAAA6wdmiYX8+///M8mNtfz7//9B6eoD\nAACLB4PHBIm98Pv//4XAdDaLcASF9nQv98MACAAAdBcPvwCZK8LHhdT7//8BAAAAi8jpswMAADPJ\niY3U+///D78I6aUDAACLNeCtQgBW6FHz//9Zi8jpkQMAAIP4cA+P6wEAAA+E1wEAAIP4ZQ+MfwMA\nAIP4Zw+O8f3//2ppWjvCdGaD+G50J2pvWjvCD4VfAwAAx4Xk+///CAAAAITbeVuBywACAACJnez7\n///rTYPHBIm98Pv//4t//Og8R///hcAPhFQFAACLheD7///2wyB0BWaJB+sCiQfHhbD7//8BAAAA\n6cMEAACDy0CJnez7///HheT7//8KAAAA98MAgAAAdQz3wwAQAAAPhJcBAACLD4PHCIm98Pv//4t/\n/OmwAQAAdRRqZ1hmO9B1VseF6Pv//wEAAADrSjvBfgiLwYmF6Pv//z2jAAAAfjeNuF0BAABX6AWM\n//+LleT7//+JhaT7//9ZhcB0Covwib30+///6wrHhej7//+jAAAAi73w+///iweDxwiJhXj7//+J\nvfD7//+LR/yJhXz7//+NhbT7//9Q/7Wo+///D77C/7Xo+///UP+19Pv//42FePv//1ZQ/zXErUIA\n/xW8oUEA/9CL+4PEHIHngAAAAHQhg73o+///AHUYjYW0+///UFb/NdCtQgD/FbyhQQD/0FlZamdY\nZjmF5Pv//3Uchf91GI2FtPv//1BW/zXMrUIA/xW8oUEA/9BZWYA+LQ+FHv7//4HLAAEAAEaJnez7\n///pDP7//8eF6Pv//wgAAABqB+scg+hzD4R7/P//SEgPhJL+//+D6AMPhYkBAABqJ8eF5Pv//xAA\nAABYiYWs+///hNsPiXj+//9qMFmDwFFmiY3I+///ZomFyvv//8eF3Pv//wIAAADpVf7//4PHBIm9\n8Pv///bDIHQR9sNAdAYPv0f86w4Pt0f86wj2w0B0DItH/JmLyIv6M8DrB4tP/DPAi/j2w0B0HDv4\nfxh8BDvIcxL32RP499+BywABAACJnez7///3wwCQAAB1Aov4i5Xo+///hdJ5BTPSQusWg+P3iZ3s\n+///gfoAAgAAfgW6AAIAAIvBC8d1BomF3Pv//421+/3//4vCSomV6Pv//4XAfwaLwQvHdD2LheT7\n//+ZUlBXUehR9v//g8EwiZ2E+///iYX0+///i/qD+Tl+BgONrPv//4uV6Pv//4gOTouN9Pv//+uw\ni53s+///jY37/f//K85GiY30+///98MAAgAAdEWFyXQFgD4wdDxOQWowWIgG6y2F9nUGizXkrUIA\nx4XU+///AQAAAIvOhf90DzPAT2Y5AXQHg8EChf918yvO0fmJjfT7//+DvbD7//8AD4WtAQAA9sNA\ndCD3wwABAAAPhB0BAABqLVhmiYXI+///x4Xc+///AQAAAGogWou90Pv//4uF3Pv//yv5K/j2wwx1\nHY2F4Pv//1D/tdj7//9XUuhXp///i4Xc+///g8QQ/7Wg+///jY3g+///Uf+12Pv//1CNhcj7//9Q\n6Fqn//+DxBT2wwh0H/bDBHUajYXg+///UP+12Pv//1dqMFhQ6Aqn//+DxBCDvdT7//8Ai4X0+///\nD4WzAAAAhcAPjqsAAACLzom15Pv//0iJhYT7//+NhbT7//9Qi4W0+////3B0jYWc+///UVDoJAMA\nAIPEEImFlPv//4XAfmeNheD7//9Q/7XY+////7Wc+///6GWm//+LjeT7//+DxAwDjZT7//+LhYT7\n//+JjeT7//+FwH+Y61b2wwF0B2or6dn+///2wwIPhOL+//9qIFpmiZXI+///x4Xc+///AQAAAOnM\n/v//g8j/iYXg+///6yP/taD7//+NjeD7//9R/7XY+///UFboU6b//4PEFIuF4Pv//4XAeB/2wwR0\nGo2F4Pv//1D/tdj7//9XaiBYUOj+pf//g8QQi4Wk+///hcB0D1DodkH//zPAWYmFpPv//4uN9Pv/\n/4uF+Pv//w+3EIuF4Pv//4mV5Pv//2aF0g+FcvX//4uNzPv//4XJdAWD+Qd1UIC9wPv//wB0CouN\nvPv//4NhcP2LTfxfXjPNW+gYRv//i+Vdw+gofP//xwAWAAAA6HVt//+AvcD7//8AD4S69P//i428\n+///g2Fw/emr9P//6Px7///HABYAAADoSW3//4C9wPv//wDpg/T//5CtKUEAcydBAKcnQQD8J0EA\nTShBAFooQQCnKEEA0ilBAFWL7IPsEKEwoUIAM8WJRfxTVleLfQz2RwxAD4U2AQAAV+g0UP//u/Ct\nQgBZg/j/dC5X6CNQ//9Zg/j+dCJX6BdQ//+L8FfB/gXoDFD//4PgH1nB4AYDBLUYE0MAWesCi8OK\nQCQkfzwCD4ToAAAAV+jmT///WYP4/3QuV+jaT///WYP4/nQiV+jOT///i/BXwf4F6MNP//+D4B9Z\nweAGAwS1GBNDAFnrAovDikAkJH88AQ+EnwAAAFfonU///1mD+P90LlfokU///1mD+P50IlfohU//\n/4vwV8H+Beh6T///i9iD4x9ZweMGAxy1GBNDAFn2QwSAdF//dQiNRfRqBVCNRfBQ6Ary//+DxBCF\nwHQHuP//AADrXjP2OXXwfjL/TwR4EosPikQ19IgBiwcPtghAiQfrEA++RDX0V1DohqT//1lZi8iD\n+f90xkY7dfB8zmaLRQjrH4NHBP6LRQh4CosPZokBgwcC6wwPt8BXUOiAIQAAWVmLTfxfXjPNW+gp\nRP//i+Vdw1WL7IPsEFNWi3UMhfZ0GItdEIXbdBGAPgB1FItFCIXAdAUzyWaJCDPAXluL5V3DV/91\nFI1N8Oj0Of//i0Xwg7ioAAAAAHUVi00Ihcl0Bg+2BmaJATP/R+mEAAAAjUXwUA+2BlDoJsb//1lZ\nhcB0QIt98IN/dAF+JztfdHwlM8A5RQgPlcBQ/3UI/3d0VmoJ/3cE/xUQoUEAi33whcB1CztfdHIu\ngH4BAHQoi3906zEzwDlFCA+VwDP/UP91CItF8EdXVmoJ/3AE/xUQoUEAhcB1Duhnef//g8//xwAq\nAAAAgH38AHQHi034g2Fw/YvHX+k0////VYvsagD/dRD/dQz/dQjo+P7//4PEEF3DahhoAHVCAOjw\nh///g87/iXXYiXXci30Ig//+dRjo23j//4MgAOgHef//xwAJAAAA6b0AAACF/w+InQAAADs90FxF\nAA+DkQAAAIvHwfgFiUXki9+D4x/B4waLBIUYE0MAD75EGASD4AF0cFfoJFD//1mDZfwAi0XkiwSF\nGBNDAPZEGAQBdBj/dRT/dRD/dQxX6GcAAACDxBCL8Iva6xXojnj//8cACQAAAOhPeP//gyAAi96J\nddiJXdzHRfz+////6A0AAACL0+sri30Ii13ci3XYV+hiU///WcPoHnj//4MgAOhKeP//xwAJAAAA\n6Jdp//+L1ovG6EaH///DVYvsUVFWi3UIV1boRVL//4PP/1k7x3UR6Bh4///HAAkAAACLx4vX60T/\ndRSNTfhR/3UQ/3UMUP8VpKFBAIXAdQ//FVihQQBQ6Md3//9Z69OLxoPmH8H4BcHmBosEhRgTQwCA\nZDAE/YtF+ItV/F9ei+Vdw1WL7P8F9AdDAFa+ABAAAFbowYL//1mLTQiJQQiFwHQJg0kMCIlxGOsR\ng0kMBI1BFIlBCMdBGAIAAACLQQiDYQQAiQFeXcNVi+xRocCvQgCD+P51CugRIAAAocCvQgCD+P91\nB7j//wAA6xtqAI1N/FFqAY1NCFFQ/xWooUEAhcB04maLRQiL5V3DahBoIHVCAOj4hf//M9uJXeSL\ndQiD/v51F+jndv//iRjoFHf//8cACQAAAOnbAAAAhfYPiLwAAAA7NdBcRQAPg7AAAACL3sH7BYv+\ng+cfwecGiwSdGBNDAA++RDgEg+ABdQ3onnb//4MgAOmMAAAAuP///387RRAbwEB1FeiEdv//gyAA\n6LB2///HABYAAADrdVboBU7//1mDZfwAiwSdGBNDAPZEOAQBdBP/dRD/dQxW6F4AAACDxAyL+OsW\n6Hd2///HAAkAAADoOHb//4MgAIPP/4l95MdF/P7////oCgAAAIvH6yiLdQiLfeRW6FBR//9Zw+gM\ndv//iRjoOXb//8cACQAAAOiGZ///g8j/6DaF///DVYvsg+woi0UIU1aLdRBXav5ZM/+JTeiJfeCJ\nddg7wXUX6Mt1//+JOOj4df//xwAJAAAA6U8HAACFwA+IMAcAADsF0FxFAA+DJAcAAIvIi9jB+QWD\n4x/B4waJTfSJXdyLFI0YE0MAikQaBKgBD4T+BgAAgf7///9/dhfocnX//4k46J91///HABYAAADp\n8QYAAIX2D4TTBgAAqAIPhcsGAAA5fQx1CehGdf//ITjr0opEGiQCwND4iEUTD77ASHQWSHULi8b3\n0KgBdNuD5v6LRQyJRfDrZIvG99CoAXTI0e6D/gRzA2oEXlboS4D//4lF8FmFwHUb6Cp1///HAAwA\nAADo63T//8cACAAAAOl2BgAAagFqAGoA/3UI6NH8//+LTfSDxBCLDI0YE0MAiUQZKItF8IlUGSyL\nTfSLFI0YE0MAi030iUXk9kQaBEgPhIUAAACKVBoFgPoKdHyF9nR4iBAz/41QAUeLBI0YE0MAToB9\nEwCJVeTGRBgFCnRaiwSNGBNDAIpEGCU8CnRLhfZ0R4gCQosEjRgTQwBOgH0TAWoCiVXkX8ZEGCUK\ndSuLBI0YE0MAikQYJjwKdByF9nQYagOIAkKLBI0YE0MATl9qClmJVeSITBgm/3UI6B3q//+FwItF\n9Fl0cYsMhRgTQwD2RBkEgHRjjUXgUP80Gf8V1KFBAIlF4IXAdEyAfRMCdUZqAI1F7NHuUItF9Fb/\ndeSLBIUYE0MA/zQY/xX8oEEAhcB1GP8VWKFBAFDoxnP//1mDy/+LRfDpuAIAAItF7I0MAIlN7Os4\ni0X0iwSFGBNDAI1N7GoAUVb/deT/NBj/FQihQQCFwA+EvgQAAItN7IXJD4izBAAAO84Ph6sEAACL\nVfQD+Ys0lRgTQwCKRB4EhMAPiVcCAACAfRMCD4RtAgAAhcl0DItN8IA5CnUEDATrAiT7iEQeBItF\n8IvwiUXgjQw4iU3kO8EPgw4BAABqDVmL+IoHPBoPhOMAAAA6wXQJiAZGR+mcAAAAi0XkSDv4cxyN\nRwGAOAp1DWoKWIPHAogG6asAAACIDov4Rut4agCNRexHUGoBjUX/UIsElRgTQwD/NBj/FQihQQCF\nwHUK/xVYoUEAhcB1fIN97AB0dotV9IsElRgTQwD2RBgESHQjagpYag04Rf91BYgGRusmxgYNRosM\nlRgTQwCKRf+IRBkF6xI7dfB1GWoKWDhF/3URiAZGag1ZO33kD4JE////60tqAWr/av//dQjoRPr/\n/4tV9IPEEGoKWDhF/3TWag1ZiA5G69GLVfRqDVmIDkbrxosMlRgTQwCKRBkEqEB1CAwCiEQZBOsF\nigeIBkaLRfCL/iv4gH0TAQ+FBQEAAIX/D4T9AAAATooGhMB4BkbprwAAAA+2wDPSQoC4sK5CAAB1\nHYtd8IP6BH8SO/NyDk5CD7YGgLiwrkIAAHTpi13cD7YGD76AsK5CAIXAdRDo13H//8cAKgAAAOnm\n/f//QDvCdQQD8utci0X0iwyFGBNDAPZEGQRIdDeKBkaIRBkFi0X0g/oCfBGLDIUYE0MAigZGiEQZ\nJYtF9IP6A3UOiwyFGBNDAIoGRohEGSYr8usU99qLwmoBmVJQ/3UI6DX5//+DxBCLRdiLffAr99Ho\nUP91DFZXagBo6f0AAP8VEKFBAIv4hf8PhE39//+LRfQzyTv+D5XBA/+LBIUYE0MAiUwYMItF8Itd\n6DtFDHQHUOgBNv//WYP7/nQCi/uLx+luAgAAg33gAHR5i3Xwi8eZi/4rwovO0fiNFEc7+nNVag3H\nRRAaAAAAXw+3AWY7RRB0M2Y7x3QLZokGg8YCg8EC6x2NQv47yHMWg8ECagpYZjkBdAVqDVhQX2aJ\nBoPGAjvKcsbrD4tF9IsEhRgTQwCATBgEAotF8Iv+K/iD5/7pZP///4XJdBKLTfBqClpmORGLVfR1\nBAwE6wIk+4hEHgSLRfCL8IlF2I0MOIlN4DvBD4NfAQAAag1Zx0UQGgAAAIv4D7cHZjtFEA+EJgEA\nAGY7wXQFZokG6yeLReCDwP47+HMoagpaZjlXAotV9HUOagpYg8cEZokG6eUAAABmiQ6DxgKDxwLp\nkwAAAGoAjUXsg8cCUGoCjUX4UIsElRgTQwD/NBj/FQihQQCFwHUO/xVYoUEAhcAPhasAAACDfewA\nD4ShAAAAi1X0iwSVGBNDAPZEGARIdFJqClhqDWY5Rfh1CGaJBoPGAuszWGaJBoPGAosMlRgTQwCK\nRfhqCohEGQWLDJUYE0MAikX5iEQZJYsElRgTQwBZiEwYJmoNWTt94A+CG////+tsO3XwdRFqClhm\nOUX4dQhmiQaDxgLr3GoBav9q/v91COgM9///i1X0g8QQagpYZjlF+HS/ag1ZZokOg8YC67eLVfRq\nDVlmiQ6DxgLrqYsMlRgTQwCKRBkEqEB1CAwCiEQZBOsJZosHZokGg8YCi0Xwi/4r+OnH/f///xVY\noUEAagVeO8Z1F+jbbv//xwAJAAAA6Jxu//+JMOnj+v//g/htD4XT+v//M9vp1vr//zPA6xrofG7/\n/4k46Klu///HAAkAAADo9l///4PI/19eW4vlXcNVi+xXi30Ihf91FeiEbv//xwAWAAAA6NFf///p\n+wAAAItHDKiDD4TwAAAAqEAPhegAAACoAnQLg8ggiUcM6dkAAACDyAGJRwypDAEAAHUJV+h99v//\nWesFi0cIiQf/dxj/dwhX6MFC//9ZUOjr9v//g8QMiUcEhcAPhIwAAACD+P8PhIMAAAD2RwyCdVFX\n6JdC//9Zg/j/dDBX6ItC//9Zg/j+dCRWV+h+Qv//i/BXwf4F6HNC//+D4B9ZweAGAwS1GBNDAFle\n6wW48K1CAIpABCSCPIJ1B4FPDAAgAACBfxgAAgAAdRb2RwwIdBD3RwwABAAAdQfHRxgAEAAAiw//\nTwQPtgFBiQ/rFPfYG8CD4BCDwBAJRwyDZwQAg8j/X13DVYvsU1aLdQxXg8//9kYMQHVvVujxQf//\nWYvIu/CtQgA7z3QZg/n+dBSL0cH4BYPiH8HiBgMUhRgTQwDrAovT9kIkf3UlO890GYP5/nQUi8GD\n4R/B+AXB4QYDDIUYE0MA6wKLy/ZBJIB0F+gEbf//xwAWAAAA6FFe//+Lx19eW13Di10IO9908vZG\nDAF1DPZGDIB05vZGDAJ14IN+CAB1B1boC/X//1mLBjtGCHUJg34EAHXGQIkG/w72RgxAiwZ0CTgY\ndAdAiQbrsIgYi0YM/0YEg+Dvg8gBiUYMD7bD65xqDGhAdUIA6FJ7//+DZeQAM8CLdQyF9g+VwIXA\ndRXocGz//8cAFgAAAOi9Xf//g8j/6ylW6NU9//9Zg2X8AFb/dQjo2f7//1lZi/iJfeTHRfz+////\n6A4AAACLx+hCe///w4t1DIt95FboDz7//1nDVYvsUVNWV4t9CDPbiF3/hf91FegLbP//xwAWAAAA\n6Fhd///pOQEAAItHDKiDD4QuAQAAqEAPhSYBAACoAnQLg8ggiUcM6RcBAAAz9kYLxolHDKkMAQAA\ndQlX6AL0//9Z6xM5dwR1CYsHi96KAIhF/4tHCIkH/3cY/3cIV+g4QP//WVDoYvT//4PEDIlHBIXA\nD4S6AAAAO8YPhLIAAACD+P8PhKkAAAD2RwyCdU9X6AZA//9Zg/j/dC5X6Po///9Zg/j+dCJX6O4/\n//+L8FfB/gXo4z///4PgH1nB4AYDBLUYE0MAWesFuPCtQgCKQAQkgjyCdQeBTwwAIAAAgX8YAAIA\nAHUW9kcMCHQQ90cMAAQAAHUHx0cYABAAAIXbdCGLF4pF/w+2wA+2CmbB4QhmC8j/TwSNQgEPt8mJ\nB4vB6yaLD4NHBP4PtwGDwQKJD+sW99gbwIPgEIPAEAlHDINnBAC4//8AAF9eW4vlXcNVi+yD7Bjo\nvG3//4XAdFdoZG9CAGhYN0IA/xV0oUEAUP8VjKFBAIvIhcl1BYPI/+tTi0Ucg2X0AINl/ACJReyL\nRSCJRfCLRRSJRfiNRehQ/3UYx0XoGAAAAP91EP91DP91CP/R6x6LRRwLRSBqAFD/dRj/dRT/dRD/\ndQz/dQj/FRyhQQCL5V3DahRoYHVCAOjteP//M8mJTeSJTeAzwIt1GIX2D5XAhcB1E+gHav//ahZe\niTDoVVv//4vG61yDDv8zwDlFCA+VwIXAdN45TRx0D4tFFCV//v//99gbwEB0yolN/P91HP91FP91\nEP91DP91CFaNReBQ6FUAAACDxByL+Il95MdF/P7////oFQAAAIX/dAODDv+Lx+ioeP//w4t1GIt9\n5IN94AB0JYX/dBmLDovBwfgFg+EfweEGiwSFGBNDAIBkCAT+/zbocET//1nDVYvsg+w4UzPbx0XI\nDAAAAPZFFIBXiV3giF3+iV3MdAmJXdDGRf8Q6wrHRdABAAAAiF3/jUXgUOiQPv//WYXAD4UDBwAA\ni00UuACAAACFyHUR98EAQAcAdQU5ReB0BIBN/4CLwboAAADAg+ADK8N0SEh0Kkh0I+i2aP//iRiL\nRQyDCP/o3Wj//2oWW4kY6Cta//+Lw+mRAQAAi/rrIPbBCHQP98EAAAcAdAeL+olV+OsPvwAAAEDr\nBb8AAACAiX34i0UYVmoQXmoCWolV2CvGdDYrxnQpK8Z0ICvGdBOD6EB1dYH/AAAAgA+UwIlF9Osa\nx0X0AwAAAOsRiVX06wzHRfQBAAAA6wOJXfSLwb4ABAAAJQAHAAA7xn8odCGFwHQdPQABAAB0Ej0A\nAgAAdEo9AAMAAHUii/LrRmoE6wJqA17rPT0ABQAAdDM9AAYAAHQoPQAHAAB0JejVZ///iRiLRQyD\nCP/o/Gf//2oWW4kY6EpZ///pyQUAAGoF68Mz9ka6gAAAAIvDiVXoiUXw98EAAQAAdBah3AdDAPfQ\nI0UchMCLw3gGM9JCiVXo9sFAdBWBzwAAAQC4AAAABINN9ASJRfCJffj3wQAQAAB0CYHKAAEAAIlV\n6PfBACAAAHQIDQAAAAKJRfD2wSB0Bw0AAAAI6wr2wRB0CA0AAAAQiUXw6FM///+LfQyJB4P4/3Uj\n6B9n//+JGIMP/+hJZ///xwAYAAAA6D5n//+LAF5fW4vlXcP/dfCLRQj/dehWxwABAAAAjUXIUP91\n9P91+P91EOhd/P//g8QciUXkg/j/dXOLTfi6AAAAwIvBI8I7wnUx9kUUAXQr/3XwjUXIgeH///9/\n/3XoiU34VlD/dfRR/3UQ6Bz8//+DxByJReSD+P91MosPi8HB+AWD4R/B4QaLBIUYE0MAgGQIBP7/\nFVihQQBQ6IFm//9Z6Jxm//+LGOlxBAAAUP8VgKFBAIXAdUKLD4vBwfgFg+EfweEGiwSFGBNDAIBk\nCAT+/xVYoUEAi/BW6EJm//9Z/3Xk/xU0oUEAhfZ1tOhQZv//xwANAAAA66eD+AJ1BoBN/0DrCYP4\nA3UEgE3/CP915P836LFA//+KVf9ZWYsPgMoBi8GIVf/B+AWD4R/B4QaLBIUYE0MAiFQIBIsPi8HB\n+AWD4R/B4QaLBIUYE0MAgGQIJICKwotNFCRIiEX9D4WWAAAAhNIPibICAAD2wQIPhIUAAABqAmr/\nav//N+iL7f//i8iJRdQjyolV7IPEEIP5/3UZ6HVl//+BOIMAAAB0U/836D+o///p8/7//2oBjUXc\niV3cUP836GDv//+DxAyFwHUcZoN93Bp1Ff917P911P836DEOAACDxAyD+P90wVNTU/836CPt//8j\nwoPEEIP4/3StilX/i00UhNIPiRwCAAD3wQBABwB1F4tF4CUAQAcAdQiByQBAAADrAgvIiU0Ui8El\nAEAHAD0AQAAAdEQ9AAABAHQpPQBAAQB0Ij0AAAIAdCk9AEACAHQiPQAABAB0Bz0AQAQAdR3GRf4B\n6xeLwSUBAwAAPQEDAAB1CcZF/gLrA4hd/vfBAAAHAA+EnAEAAIld7PbCQA+FkAEAAItF+LkAAADA\nI8E9AAAAQA+E7QAAAD0AAACAdFs7wQ+FbAEAAIX2D4RkAQAAagJZO/EPhhABAACD/gR2DoP+BQ+E\nAgEAAOlGAQAAUVNT/zfoK+z//4PEEAvCD4TlAAAAU1NT/zfoFuz//yPCg8QQg/j/D4Sc/v//agON\nRexQ/zfoC+7//4PEDIP4/w+Eg/7//4tN7GoCWjvCdBqD+AMPhYgAAACB+e+7vwB1CcZF/gHp3AAA\nAIHh//8AAIH5/v8AAHUX/zfokKb//1no5WP//2oWW4kY6bcBAACB+f/+AAB1SlNTUv836JTr//8j\nwoPEEIP4/w+EGv7//8ZF/gLpjgAAAIX2D4SGAAAAagJZO/F2NoP+BA+HIv///1FTU/836Fvr//+D\nxBALwnQZU1NT/zfoSuv//yPCg8QQg/j/dVHpz/3//2oCWQ++Rf6L80h0DEh1PcdF7P/+AADrDWoD\nWcdF7O+7vwCJTdiLwSvGUI1F7APGUP836PqT//+DxAyD+P8PhIz9//+LTdgD8DvOf9mLF4vCwfgF\ng+IfweIGiwyFGBNDAIpEESQyRf4kfzBEESSLN4vGi00Ug+YfwfgFweYGwekQwOEHixSFGBNDAIpE\nMiQkfwrIiEwyJItNFDhd/XUh9sEIdByLD4vBwfgFg+EfweEGiwSFGBNDAIBMCAQgi00Ui3X4ugAA\nAMCLxiPCO8J1ffbBAXR4/3Xk/xU0oUEA/3XwjUXIgeb///9//3XoagNQ/3X0Vv91EOi/9///g8Qc\ng/j/dTL/FVihQQBQ6EBi//+LD4vBwfgFg+EfweEGiwSFGBNDAIBkCAT+/zfo2zv//1npmfv//4sX\ni8rB+QWD4h/B4gaLDI0YE0MAiQQKi8Pp4fr//1NTU1NT6HtT///MVYvsagH/dQj/dRj/dRT/dRD/\ndQzoy/f//4PEGF3DVYvsUYM9UAtDAABXD4WMAAAAi30QM8CF/w+EkgAAAItVCIXSdRfoyWH//8cA\nFgAAAOgWU///uP///3/rdItNDIXJdOJTVmpBW2paXivRiXX86wNqWl4PtwQKZjvDcg1mO8Z3CIPA\nIA+38OsCi/APtwFmO8NyDGY7Rfx3BoPAIA+3wIPBAk90CmaF9nQFZjvwdMEPt8gPt8ZeK8Fb6xNq\nAP91EP91DP91COgIAAAAg8QQX4vlXcNVi+yD7BRTVjP2Vzl1EA+E3AAAAItdCIXbdRroHmH//8cA\nFgAAAOhrUv//uP///3/pvQAAAIt9DIX/dN//dRSNTezo6CD//4tF7DmwqAAAAHVRi00QK99qQVpq\nWl6JdfzrA2paXg+3BDtmO8JyDWY7xncIg8AgD7fw6wKL8A+3B2Y7wnIMZjtF/HcGg8AgD7fAg8cC\nSXREZoX2dD9mO/B0wes4jUXsUA+3A1Doa1z//w+38I1F7FAPtwdQ6Ftc//+DxBAPt8D/TRCNWwKN\nfwJ0CmaF9nQFZjvwdMgPt8APt/Yr8IB9+AB0B4tN9INhcP2Lxl9eW4vlXcNVi+xTVldVagBqAGjI\nTUEA/3UI6PZGAABdX15bi+Vdw4tMJAT3QQQGAAAAuAEAAAB0MotEJBSLSPwzyOjsKf//VYtoEItQ\nKFKLUCRS6BQAAACDxAhdi0QkCItUJBCJArgDAAAAw1NWV4tEJBBVUGr+aNBNQQBk/zUAAAAAoTCh\nQgAzxFCNRCQEZKMAAAAAi0QkKItYCItwDIP+/3Q6g3wkLP90Bjt0JCx2LY00dosMs4lMJAyJSAyD\nfLMEAHUXaAEBAACLRLMI6EkAAACLRLMI6F8AAADrt4tMJARkiQ0AAAAAg8QYX15bwzPAZIsNAAAA\nAIF5BNBNQQB1EItRDItSDDlRCHUFuAEAAADDU1G7sK9CAOsLU1G7sK9CAItMJAyJSwiJQwSJawxV\nUVBYWV1ZW8IEAP/Qw1WL7IPsEKEwoUIAM8WJRfxTi10IVr7//wAAV4t9DGY73g+EYQEAAPZHDAF1\nFPZHDIAPhFEBAAD2RwwCD4VHAQAAg38IAHUHV+j/5v//WfZHDEAPhQMBAABX6EYz//9Zg/j/dDNX\n6Doz//9Zg/j+dCdX6C4z//+L8FfB/gXoIzP//4PgH8HgBgMEtRgTQwC+//8AAFlZ6wW48K1CAPZA\nBIAPhLUAAABX6Pgy//9Zg/j/dC5X6Owy//9Zg/j+dCJX6OAy//+L8FfB/gXo1TL//4PgH1nB4AYD\nBLUYE0MAWesFuPCtQgD2QCR/dA5qAlmIXfSIffWJTfDrIVNqBY1F9FCNRfBQ6FTV//+DxBCFwHQH\nuP//AADrdItN8ItHCAPBOQdzDYN/BAB15ztPGH/iiQeNUf+F0ngQ/w+LD4pEFfRKiAF584tN8AFP\nBItPDIPh74PJAYlPDGaLw+swi0cIg8ACOQdzDoN/BAB1HYN/GAJyF4kHgwf+9kcMQIsHdB5mORh0\nHIPAAokHZovGi038X14zzVvoXSf//4vlXcNmiRiLRwyDRwQCg+Dvg8gBiUcM66VVi+yD7BD/dQyN\nTfDoPR3//4tN8IN5dAF+FY1F8FBqBP91COjVqP//g8QMi8jrEIuJkAAAAItFCA+3DEGD4QSAffwA\ndAeLRfiDYHD9i8GL5V3DVYvsg+wQ/3UMjU3w6Osc//+LTfCDeXQBfhiNRfBQaIAAAAD/dQjogKj/\n/4PEDIvI6xOLiZAAAACLRQgPtwxBgeGAAAAAgH38AHQHi0X4g2Bw/YvBi+Vdw1WL7IM9UAtDAAB1\nEYtNCKFYrEIAD7cESIPgBF3DagD/dQjoL////1lZXcNVi+yDPVALQwAAdROLTQihWKxCAA+3BEgl\ngAAAAF3DagD/dQjoVP///1lZXcNVi+yD7CyhMKFCADPFiUX8i0UIU4tdEFYz9olF6Il18FeLfQyJ\nfeyFwHQLhdt1BzPA6SACAACF/3UY6Bxc///HABYAAADoaU3//4PI/+kEAgAA/3UUjU3U6O8b//+L\nTeiLRdSFyQ+EggEAADmwqAAAAHU0hdsPhJgBAAC6/wAAAGY5Fw+HVAEAAIoHiAQxZosHg8cCZoXA\nD4R2AQAARjvzct7pbAEAAIN4dAF1YYXbdCKLx4vTZjkwdAaDwAJKdfWF0nQMZjkwdQeL2Cvf0ftD\ni0XUjVXwUlZTUVNXVv9wBP8VyKFBAIvwhfYPhO0AAACDffAAD4XjAAAAi0XogHww/wAPhQsBAABO\n6QUBAACNVfBSVlNRav9XVv9wBP8VyKFBAIv4hf90ETl18A+FrAAAAI13/+naAAAAOXXwD4WbAAAA\n/xVYoUEAg/h6D4WMAAAAhdsPhO4AAACLReyNTfBRi03UjVX0Vv9xdFJqAVBW/3EE/xXIoUEAi9CF\n0g+EtwAAADl18A+FrgAAAIXSD4imAAAAg/oFD4edAAAAjQQ6O8MPh6AAAACLxolF5IXSfh6LTeiK\nRAX0iAQ5hMAPhIUAAACLReRAR4lF5DvCfOWLReyDwAKJRew7+3KB62roeVr//4PO/8cAKgAAAOsm\nObCoAAAAdSIPtwdmhcB0Frr/AAAAZjvCdzKDxwJGD7cHZoXAde+L/uswjU3wUVZWVmr/V1b/cAT/\nFcihQQCFwHQKOXXwdQWNeP/rDugdWv//g8//xwAqAAAAgH3gAHQHi03cg2Fw/YvHi038X14zzVvo\n3yP//4vlXcNVi+xRVot1DDPAiUX8V4t9EIX2dC6F/3QuhfZ0AogGU4tdCIXbdAKJA4vHOX0YdwOL\nRRg9////f3Yg6LVZ//9qFutZhf900uioWf//ahZeiTDo9kr//4vG62v/dRxQ/3UUVugy/f//g8QQ\ng/j/dRCF9nQDxgYA6HlZ//+LAOtFQIX2dDU7x3Yng30Y/3QaxgYAO/h3E+haWf//aiJeiTDoqEr/\n/4vG6xxqUIvHWesDi038xkQw/wDrA4tN/IXbdAKJA4vBW19ei+Vdw1WL7GoA/3UY/3UU/3UQ/3UM\n/3UI6Bz///+DxBhdw8zMzMzMzItEJAiLTCQQC8iLTCQMdQmLRCQE9+HCEABT9+GL2ItEJAj3ZCQU\nA9iLRCQI9+ED01vCEABVi+xRVot1DFdW6Fgt//9Zi04Mi/j2wYJ1GeiyWP//xwAJAAAAg04MILj/\n/wAA6SkBAAD2wUB0DeiUWP//xwAiAAAA6+BTM9v2wQF0E4leBPbBEHR/i0YIg+H+iQaJTgyLRgyD\n4O+JXgSDyAKJRgypDAEAAHUq6Msp//+DwCA78HQM6L8p//+DwEA78HULV+gUzv//WYXAdQdW6HDg\n//9Z90YMCAEAAHR9i1YIiw4ryolNDI1CAokGi0YYg+gCiUYEhcl+F1FSV+jFiP//g8QMi9jrR4PJ\nIIlODOt1g///dBuD//50FovHi8/B+AWD4R/B4QYDDIUYE0MA6wW58K1CAPZBBCB0FGoCU1NX6I/e\n//8jwoPEEIP4/3Qyi0YIi00IZokI6yKLRQhmiUX8jUX8agJQV8dFDAIAAADoUoj//4tNCIPEDIvY\nO10MdAuDTgwguP//AADrAw+3wVtfXovlXcOhwK9CAIP4/3QMg/j+dAdQ/xU0oUEAwzPAUFBqA1Bq\nA2gAAABAaHBvQgD/FRyhQQCjwK9CAMNVi+yD7BhTVleLfQgz9moBVlZXi96Jdfjo597//4vKiUXo\nI8GJTeyDxBCD+P90WWoCVlZX6Mve//+LyIPEECPCg/j/dEOLdQyLRRAr8RvCiUX8D4jJAAAAfwiF\n9g+EvwAAAGgAEAAAagj/FTyiQQBQ/xXQoUEAiUX0hcB1Gei3Vv//xwAMAAAA6KxW//+LAF9eW4vl\nXcNoAIAAAFfoLi3//1mJRfCLRfxZhcB8EX8Igf4AEAAAcge4ABAAAOsCi8ZQ/3X0V+gfiP//g8QM\ng/j/dBWZK/CLRfwbwolF/Hgjf8iF9nQd68LoGVb//4M4BXUL6ENW///HAA0AAACDy/8JXfj/dfBX\n6MQs//9ZWf919GoA/xU8okEAUP8VzKFBAOtmhcB/bnwEhfZzaGoA/3UQ/3UMV+jL3f//I8KDxBCD\n+P8PhEH///9X6Aww//9ZUP8VTKFBAPfYG8D32EiZi9iLwovLiUX4I8iD+f91JujHVf//xwANAAAA\n6IhV//+L8P8VWKFBAIkGI134g/v/D4T1/v//agD/dez/dehX6GPd//8jwoPEEIP4/w+E2f7//zPA\n6dn+//9Vi+yD7CyhMKFCADPFiUX8i0UIjU3UU1aLdQxX/3UQiUXsi0UUiUXk6EcV//+NRdQz/1BX\nV1dXVo1F6FCNRfBQ6G0MAACL2IPEIItF5IXAdAWLTeiJCP917I1F8FDo3gYAAFlZ9sMDdQ6D+AF0\nE4P4AnURagTrDPbDAXX39sMCdANqA1+AfeAAdAeLTdyDYXD9i038i8dfXjPNW+jKHv//i+Vdw1WL\n7IPsKKEwoUIAM8WJRfxTVot1DI1N2Ff/dRCLfQjorBT//41F2DPbUFNTU1NWjUXoUI1F8FDo0gsA\nAIlF7I1F8FdQ6OEAAACLyIPEKItF7KgDdQ6D+QF0EYP5AnUPagTrCqgBdfioAnQDagNbgH3kAHQH\ni03gg2Fw/YtN/IvDX14zzVvoPB7//4vlXcNVi+xqAP91EP91DP91COi7/v//g8QQXcNVi+zoDwAA\nAIN9CAB0BeibHQAA2+Jdw7jMbEEAxwWwrUIAuHVBAKOsrUIAxwW0rUIASXZBAMcFuK1CAKN2QQDH\nBbytQgAod0EAo8CtQgDHBcStQgDtbEEAxwXIrUIAYXZBAMcFzK1CAMl1QQDHBdCtQgC0dkEAw8zM\nzMzMzMzMzMzMzMxVi+yD7EShMKFCADPFiUX8i00IU1ZXD7dBCjPbi30Mi9AlAIAAAIl9wIlFvIHi\n/38AAItBBoHq/z8AAIlF8ItBAolF9A+3AcHgEIlV4IlF+IH6AcD//3Uli/OLwzlchfB1C0CD+AN8\n9Om5BAAAM8CNffCrq6tqAlvppgQAAKHgr0IAjXXwjX3kiVXcpUiJRcxqH4ld1KWNSAGLwZmlXiPW\nA9DB+gWJVcSB4R8AAIB5BUmDyeBBK/EzwECJddCLzoPP/9PgagNehUSV8A+EpAAAAIvH0+D30IVE\nlfDrBDlclfB1CkI71nz16YUAAACLRcyZah9ZI9ED0ItFzMH6BSUfAACAeQVIg8jgQCvIiV3UM8BA\n0+CJRciLRJXwi03IA8iJTdg7yItF2IvLav9fcgU7RchzBjPJQYlN1IlElfBKeC6FyXQni0SV8IvL\niV3UjXgBO/iJfdiLx3IFg/gBcwYzyUGJTdSJRJXwSnnVg8//i03Qi1XEi8fT4CFElfCNQgE7xn0R\njX3wi86NPIcryDPA86uDz/+LTeA5XdR0AUGLFdyvQgCLwisF4K9CADvIfQ8zwI198Kurq4vz6bb+\n//87yg+PGQIAACtV3I115IlV0I198IvCpZmD4h8DwsH4BaWJRcSLRdClJR8AAIB5BUiDyOBAiUXQ\ng8//i8eJXeCLfdCLz9Pg99BqIIlF2Fgrx2oDiUXIXotUnfCLz4vC0+oLVeAjRdiLTcjT4IlUnfBD\niUXgO95834tFxI1V+MHgAjPbagIr0IPP/4tFxFk7yHwLiwKJRI3wi0XE6wSJXI3wg+oESXnni03M\nQYvBmYPiHwPQwfoFiVXUgeEfAACAeQVJg8ngQWofWCvBiUXQM8CLTdBA0+CFRJXwD4SSAAAAi8fT\n4PfQhUSV8OsEOVyV8HUHQjvWfPXrdot9zIvHah+ZWSPRA9DB+gWB5x8AAIB5BU+Dz+BHi0SV8CvP\nM/9H0+eLy4l93AP4iX3gO/iLReBq/19yBTtF3HMDM8lBiUSV8Ep4KIXJdCGLRJXwi8uNeAE7+Il9\n4IvHcgWD+AFzAzPJQYlElfBKeduDz/+LTdCLVdSLx9PgIUSV8EI71n0RjX3wi86NPJcryjPA86uD\nz/+LDeSvQgBBi8GZg+IfA8LB+AWJRdiB4R8AAIB5BUmDyeBBiU3ci8PT52ogiV3g99eLXdxZK8uJ\nRcyJTdyLVIXwi8uLwtPqi03MI8cLVeCJVI3wi03c0+CJReCLRcxAiUXMO8Z814t12I1V+IvGweAC\nagIr0DPbWTvOfAiLAolEjfDrBIlcjfCD6gRJeerp2P3//zsN2K9CAA+MogAAAIsN5K9CAI198DPA\nq6uri8GBTfAAAACAmYPiHwPCwfgFiUXMgeEfAACAeQVJg8ngQYPP/4lNyGog0+dYK8GJXeD314lF\n2ItUnfCLwtPqI8cLVeCLTdjT4ItNyIlUnfBDiUXgO95834t1zI1V+IvGweACagIr0DPbWTvOfAiL\nAolEjfDrBIlcjfCD6gRJeeqLNeyvQgAz2wM12K9CAEPplQAAAIs17K9CAIFl8P///38D8YsN5K9C\nAIvBmYPiH4l1yAPCwfgFiUXYgeEfAACAeQVJg8ngQWogiV3gi/PT54vZWCvDiU3c99eJRdyLVLXw\ni8uLwtPqC1XgI8eLTdzT4IlUtfBGiUXgg/4DfN+LfdiNVfiLdciLx8HgAmoCK9Az21k7z3wIiwKJ\nRI3w6wSJXI3wg+oESXnqi33Aah9YKwXkr0IAi8iLRbzT5vfYG8AlAAAAgAvwoeivQgALdfCD+EB1\nCotF9Il3BIkH6weD+CB1Aok3i038i8NfXjPNW+guGP//i+Vdw1WL7IPsRKEwoUIAM8WJRfyLTQhT\nVlcPt0EKM9uLfQyL0CUAgAAAiX3AiUW8geL/fwAAi0EGger/PwAAiUXwi0ECiUX0D7cBweAQiVXg\niUX4gfoBwP//dSWL84vDOVyF8HULQIP4A3z06bkEAAAzwI198Kurq2oCW+mmBAAAofivQgCNdfCN\nfeSJVdylSIlFzGofiV3UpY1IAYvBmaVeI9YD0MH6BYlVxIHhHwAAgHkFSYPJ4EEr8TPAQIl10IvO\ng8//0+BqA16FRJXwD4SkAAAAi8fT4PfQhUSV8OsEOVyV8HUKQjvWfPXphQAAAItFzJlqH1kj0QPQ\ni0XMwfoFJR8AAIB5BUiDyOBAK8iJXdQzwEDT4IlFyItElfCLTcgDyIlN2DvIi0XYi8tq/19yBTtF\nyHMGM8lBiU3UiUSV8Ep4LoXJdCeLRJXwi8uJXdSNeAE7+Il92IvHcgWD+AFzBjPJQYlN1IlElfBK\nedWDz/+LTdCLVcSLx9PgIUSV8I1CATvGfRGNffCLzo08hyvIM8Dzq4PP/4tN4Dld1HQBQYsV9K9C\nAIvCKwX4r0IAO8h9DzPAjX3wq6uri/Pptv7//zvKD48ZAgAAK1XcjXXkiVXQjX3wi8KlmYPiHwPC\nwfgFpYlFxItF0KUlHwAAgHkFSIPI4ECJRdCDz/+Lx4ld4It90IvP0+D30GogiUXYWCvHagOJRche\ni1Sd8IvPi8LT6gtV4CNF2ItNyNPgiVSd8EOJReA73nzfi0XEjVX4weACM9tqAivQg8//i0XEWTvI\nfAuLAolEjfCLRcTrBIlcjfCD6gRJeeeLTcxBi8GZg+IfA9DB+gWJVdSB4R8AAIB5BUmDyeBBah9Y\nK8GJRdAzwItN0EDT4IVElfAPhJIAAACLx9Pg99CFRJXw6wQ5XJXwdQdCO9Z89et2i33Mi8dqH5lZ\nI9ED0MH6BYHnHwAAgHkFT4PP4EeLRJXwK88z/0fT54vLiX3cA/iJfeA7+ItF4Gr/X3IFO0XccwMz\nyUGJRJXwSngohcl0IYtElfCLy414ATv4iX3gi8dyBYP4AXMDM8lBiUSV8Ep524PP/4tN0ItV1IvH\n0+AhRJXwQjvWfRGNffCLzo08lyvKM8Dzq4PP/4sN/K9CAEGLwZmD4h8DwsH4BYlF2IHhHwAAgHkF\nSYPJ4EGJTdyLw9PnaiCJXeD314td3Fkry4lFzIlN3ItUhfCLy4vC0+qLTcwjxwtV4IlUjfCLTdzT\n4IlF4ItFzECJRcw7xnzXi3XYjVX4i8bB4AJqAivQM9tZO858CIsCiUSN8OsEiVyN8IPqBEl56unY\n/f//Ow3wr0IAD4yiAAAAiw38r0IAjX3wM8Crq6uLwYFN8AAAAICZg+IfA8LB+AWJRcyB4R8AAIB5\nBUmDyeBBg8//iU3IaiDT51grwYld4PfXiUXYi1Sd8IvC0+ojxwtV4ItN2NPgi03IiVSd8EOJReA7\n3nzfi3XMjVX4i8bB4AJqAivQM9tZO858CIsCiUSN8OsEiVyN8IPqBEl56os1BLBCADPbAzXwr0IA\nQ+mVAAAAizUEsEIAgWXw////fwPxiw38r0IAi8GZg+IfiXXIA8LB+AWJRdiB4R8AAIB5BUmDyeBB\naiCJXeCL89Pni9lYK8OJTdz314lF3ItUtfCLy4vC0+oLVeAjx4tN3NPgiVS18EaJReCD/gN834t9\n2I1V+It1yIvHweACagIr0DPbWTvPfAiLAolEjfDrBIlcjfCD6gRJeeqLfcBqH1grBfyvQgCLyItF\nvNPm99gbwCUAAACAC/ChALBCAAt18IP4QHUKi0X0iXcEiQfrB4P4IHUCiTeLTfyLw19eM81b6LwS\n//+L5V3DVYvsgeyAAAAAoTChQgAzxYlF/ItFCIlFgItFDIlFmDPAUzPbQFaJRZSL84vDiV2QV419\n4IldtIldoIldpIldnIldrDlFJHUX6IRI///HABYAAADo0Tn//zPA6QgHAACLVRCLyolNsIoKgPkg\ndA+A+Ql0CoD5CnQFgPkNdQNC6+eKCkKITauD+AsPh3sCAAD/JIWcbEEAjUHPPAh3BmoDWErr3YtF\nJIsAi4CEAAAAiwA6CHUFagVY68cPvsGD6Ct0H0hIdA6D6AMPhY4CAAAzwEDrrWoCuQCAAABYiU2Q\n66BqAliJXZDrmDPAQIlFoI1BzzwIdqiLRSSLAIuAhAAAAIsAOgh1BGoE66yA+St0K4D5LXQmgPkw\ndLWA+UMPjjoCAACA+UV+DIDpZID5AQ+HKQIAAGoG6Xz///9KagvpdP///41BzzwID4ZQ////i0Uk\niwCLgIQAAACLADoID4RS////gPkwD4Rj////i1Ww6eoBAAAzwECJRaCA+TB8KotFtIt1rID5OX8X\ng/gZcwmA6TBAiA9H6wFGigpCgPkwfeSJdayL84lFtItFJIsAi4CEAAAAiwA6CA+ESf///4D5Kw+E\ndP///4D5LQ+Ea////+lF////M8BAiUWgiUWki0W0hcB1F4D5MHUVi0WsigpIQoD5MHT3iUWsi0W0\ngPkwfCWLdayA+Tl/FYP4GXMIgOkwQIgPR06KCkKA+TB95ol1rIvziUW0gPkrD4QM////gPktD4QD\n////gPlDfhWA+UUPju7+//+A6WSA+QEPhuL+//9K6QkBAAAzwIDpMECJRaSA+QkPhwL///9qBOkv\n/v//jUL+iUWwjUHPPAh3B2oJ6Rv+//8PvsGD6Ct0IkhIdBCD6AMPhdL+//9qCOkW/v//ageDyf9Y\niU2U6dL9//9qB+kB/v//M8BAiUWc6wOKCkKA+TB0+IDpMYD5CA+HiwAAAOuqjUHPPAh2o4D5MOu0\nOV0gdCKNQv+JRbAPvsGD6Ct0vEhID4Vx/v//g02U/2oHWOl6/f//agpYSoP4Cg+Fbf3//+tIM8CL\n80CJRZzrH4D5OX8za84KD751q4PG0APxgf5QFAAAfw2KCkKITauA+TB93OsSik2rvlEUAADrCID5\nOX8IigpCgPkwffNKi0W0i02YiRGLTaCFyQ+E1wMAAIP4GHYZikX3PAV8Bf7AiEX3i02sT2oYQViJ\nTazrA4tNrIXAD4SkAwAATzgfdQpIQU84H3T5iU2sjU3EUVCNReBQ6LcOAACLTZSDxAyFyXkC994D\ndayLRZyFwHUDA3UYi0WkhcB1Ayt1HIH+UBQAAA+PSgMAAIH+sOv//w+MLwMAALoIsEIAg+pghfYP\nhA0DAAB5CrposUIA996D6mA5XRQPhfACAAAzwGaJRcTp5QIAAIvGg8JUwf4DiVWsiXW0g+AHD4TO\nAgAAa8gMuACAAAADyolNsGY5AXIRi/GNfbiNTbiJTbClpaX/TboPt3kKi1XOi8czwoldhCUAgAAA\niV3UiUWguP9/AAAj0Ild2CP4iV3cjQQXD7fwuP9/AACJdZRmO9APg0kCAABmO/gPg0ACAAC4/b8A\nAGY78A+HMgIAALi/PwAAZjvwdwiJXczpNwIAAGaF0nUkRvdFzP///3+JdZR1F4N9yAB1EYN9xAB1\nCzPAZolFzukUAgAAZoX/dRZG90EI////f4l1lHUJOVkEdQQ5GXS0agWLw41V2F+JRYyJfZiJfaSF\n/35YjXXEjTRGjUEIiUWcD7cGiUWki0Wci02kiV2ID7cAD6/IiU2kA0r8O0r8cgU7TaRzBTPAQOsD\ni0WIiUr8hcB0A2b/AoNtnAKDxgJPhf9/vYtNsIt9mItFjIPCAkBPiUWMiX2Yhf9/kot1lItV3IHG\nAsAAAIt91IlVsGaF9n47hdJ4MotF2IvXweofi8gDwMHpHwvCA/+LVbCJRdgD0rj//wAAiX3UC9ED\n8IlVsIlV3GaF9n/KZoX2f2m4//8AAAPwZoX2eV2LXYSLxvfYD7fAiUWYA/D2RdQBdAFDi03Yi8LB\n4B+JTbDRbbAJRbCLRbDB4R/R79HqC/n/TZiJVdyJRdiJfdR1zmoAhduJVbBbdBJmi8cz/0dmC8dm\niUXUi33U6wRmi0XUugCAAABmO8J3DoHn//8BAIH/AIABAHVAi0XWg/j/dTSLRdqJXdaD+P91IGaL\nRd65//8AAIld2mY7wXUHZolV3kbrDGZAZolF3usEQIlF2otN3OsHQIlF1otNsItVrLj/fwAAZjvw\nch8zwIldyGY5RaCJXcQPlMBIJQAAAIAFAID/f4lFzOs6ZotF1gt1oGaJRcSLRdiJRcaJTcpmiXXO\n6yAzwGY5RaAPlMBIJQAAAIAFAID/f4lFzIldyIldxItVrIt1tIX2D4UT/f//i0XMD7dNxItVxot1\nysHoEOsyM/+Ly4vDi/OL041fAesjuP9/AAC+AAAAgGoC6xCLy4vDi/OL0+sLi8OL82oEi8uL01uL\nfYALRZBmiUcKi8NmiQ+JVwKJdwaLTfxfXjPNW+hEC///i+Vdw7plQQAMZkEAZmZBAJdmQQD4ZkEA\ne2dBAJRnQQD3Z0EA2WdBADloQQAuaEEAA2hBAFWL7GoA/3Uc/3UY/3UU/3UQ/3UM/3UI6AUAAACD\nxBxdw1WL7ItFFIP4ZXRfg/hFdFqD+GZ1Gf91IP91GP91EP91DP91COjiBgAAg8QUXcOD+GF0HoP4\nQXQZ/3Ug/3Uc/3UY/3UQ/3UM/3UI6H0HAADrMP91IP91HP91GP91EP91DP91COgeAAAA6xf/dSD/\ndRz/dRj/dRD/dQz/dQjo0AQAAIPEGF3DVYvsg+wsU1ZXajBY/3Uci8jHRfj/AwAAiU38M9uNTdTo\nTgD//4t9FIX/eQKL+4t1DIX2dAeLTRCFyXUJ6EFA//9qFusQjUcLiB47yHcU6C9A//9qIl+JOOh9\nMf//6eQCAACLVQiLAotaBIlF7IvDwegUJf8HAAA9/wcAAHV5M8A7wHV1g8j/O8h0A41B/moAV1CN\nXgJTUujAAgAAi/iDxBSF/3QIxgYA6ZkCAACAOy11BMYGLUaLfRiF/2owWIgGD5TA/sgk4AR4iEYB\njUYCamVQ6EYTAABZWYXAdBOF/w+Uwf7JgOHggMFwiAjGQAMAM//pTwIAADPAgeMAAACAC8N0BMYG\nLUaDfRgAi10YajBYiAYPlMD+yCTgBHj324hGAYtKBBvbg+PggeEAAPB/g8MnM8ALwYld8HUnajBY\niEYCg8YDi0IEiwol//8PAAvIdQczwIlF+OsQx0X4/gMAAOsHxkYCMYPGA4vORolN9IX/dQXGAQDr\nD4tF1IuAhAAAAIsAigCIAYtCBCX//w8AiUXodwmDOgAPhsIAAACDZRQAuQAADwCLRfyJTQyF/35T\niwKLUgQjRRQj0YtN/IHi//8PAA+/yehyFgAAajBZZgPBD7fAg/g5dgIDw4tNDItVCIgGRotFFA+s\nyASJRRSLRfzB6QSD6ARPiU0MiUX8ZoXAealmhcB4V4sCi1IEI0UUI9GLTfyB4v//DwAPv8noGhYA\nAGaD+Ah2NmowjUb/W4oIgPlmdAWA+UZ1BYgYSOvvi13wO0X0dBSKCID5OXUHgMM6iBjrCf7BiAjr\nA/5A/4X/fhBXajBYUFboXw7//4PEDAP3i0X0gDgAdQKL8IN9GACxNItVCA+UwP7IJOAEcIgGiwKL\nUgToohUAAIvIi9ozwIHh/wcAACPYK034G9h4D38EO8hyCcZGASuDxgLrDcZGAS2DxgL32RPY99vG\nBjCL/jvYfEG66AMAAH8EO8pyF1BSU1HodBQAAAQwiVXoiAZGM8A793ULO9h8G38Fg/lkchRQamRT\nUehRFAAABDCJVeiIBkYzwDv3dQs72HwefwWD+QpyF1BqClNR6C4UAAAEMIlV6IgGRold6DPAgMEw\ni/iIDohGAYB94AB0B4tN3INhcP2Lx19eW4vlXcNVi+xqAP91GP91FP91EP91DP91COhWAQAAg8QY\nXcNVi+yD7BCNTfBTV/91IOjm/P7/i10Ihdt0BoN9DAB3CejjPP//ahbrHItVEDP/i8KF0n8Ci8eD\nwAk5RQx3FOjFPP//aiJfiTjoEy7//+nfAAAAgH0cAHQgi00YM8CF0g+fwFAzwIM5LQ+UwAPDUOji\nBQAAi1UQWVmLRRhWi/ODOC11BsYDLY1zAYXSfhWKRgGIBkaLRfCLgIQAAACLAIoAiAYzwDhFHA+U\nwAPCA/CDyP85RQx0B4vDK8YDRQxohG9CAFBW6CoJAACDxAyFwHV2jU4COX0UdAPGBkWLVRiLQgyA\nODB0LYtSBEp5BvfaxkYBLWpkWzvTfAiLwpn3+wBGAmoKWzvTfAiLwpn3+wBGAwBWBPYFWBZDAAFe\ndBSAOTB1D2oDjUEBUFHoJQkAAIPEDIB9/AB0B4tN+INhcP2Lx19bi+Vdw1dXV1dX6CAt///MVYvs\ng+wsoTChQgAzxYlF/ItFCI1N5FOLXRRWV4t9DGoWXlZRjU3UUf9wBP8w6M8RAACDxBSF/3UQ6Hk7\n//+JMOjKLP//i8brdIt1EIX2dQroYjv//2oWXuvkg8n/O/F0FjPAi86DfdQtD5TAK8gzwIXbD5/A\nK8iNRdRQjUMBUFEzyYN91C0PlMEzwIXbD5/AA88DwVDo7w8AAIPEEIXAdAXGBwDrF/91HI1F1GoA\nUP91GFNWV+j1/f//g8Qci038X14zzVvo1AT//4vlXcNVi+yD7BSLRRSNTexTVv91HItABEiJRfzo\nvfr+/4t1CIX2dAaDfQwAdxToujr//2oWW4kY6Ags///pmQAAADPbV4t9EDhdGHQai038O891E4tV\nFDPAgzotD5TAA8FmxwQwMACLRRSDOC11BMYGLUaLQASFwH8QagFW6LgDAABZxgYwRlnrAgPwhf9+\nSmoBVuiiAwAAi0XsWVmLgIQAAACLAIoAiAZGi0UUi0AEhcB5JjhdGHQGi/j33+sI99g7+HwCi/hX\nVuhsAwAAV2owVuhhCv//g8QUX4B9+AB0B4tN9INhcP1ei8Nbi+Vdw1WL7IPsLKEwoUIAM8WJRfyL\nRQiNTeRTV4t9DGoWW1NRjU3UUf9wBP8w6BkQAACDxBSF/3UQ6MM5//+JGOgUK///i8PrbFaLdRCF\n9nUQ6Ks5//+JGOj8Kv//i8PrU4PJ/zvxdA0zwIvOg33ULQ+UwCvIi10UjUXUUItF2APDUDPAg33U\nLVEPlMADx1DoPw4AAIPEEIXAdAXGBwDrFP91GI1F1GoAUFNWV+hn/v//g8QYXotN/F8zzVvoJwP/\n/4vlXcNVi+yD7DChMKFCADPFiUX8i0UIjU3kU1eLfQxqFltTUY1N0FH/cAT/MOhYDwAAg8QUhf91\nE+gCOf//iRjoUyr//4vD6acAAABWi3UQhfZ1E+jnOP//iRjoOCr//4vD6YsAAACLRdQzyUiDfdAt\niUXgD5TBg8j/jRw5O/B0BIvGK8GNTdBR/3UUUFPofw0AAIPEEIXAdAXGBwDrU4tF1Eg5ReAPnMGD\n+Px8KztFFH0mhMl0CooDQ4TAdfmIQ/7/dRyNRdBqAVD/dRRWV+iD/f//g8QY6xn/dRyNRdBqAVD/\ndRj/dRRWV+hJ+///g8QcXotN/F8zzVvoKAL//4vlXcNVi+xqAP91COgEAAAAWVldw1WL7IPsEFf/\ndQyNTfDoC/j+/4tVCIt98IoKhMl0FYuHhAAAAIsAigA6yHQHQooKhMl19YoCQoTAdDTrCTxldAs8\nRXQHQooChMB18VaL8kqAOjB0+ouHhAAAAIsIigI6AXUBSooGQkaIAoTAdfZegH38AF90B4tF+INg\ncP2L5V3DVYvsagD/dRD/dQz/dQjoBQAAAIPEEF3DVYvsUVGDfQgA/3UU/3UQdBmNRfhQ6Jvi//+L\nTQyLRfiJAYtF/IlBBOsRjUUIUOgQ4///i00Mi0UIiQGDxAyL5V3DVYvsagD/dQjoBAAAAFlZXcNV\ni+yD7BCNTfBW/3UM6CD3/v+LdQgPvgZQ6OIDAACD+GXrDEYPtgZQ6GHa//+FwFl18Q++BlDoxQMA\nAFmD+Hh1A4PGAotF8IoOi4CEAAAAiwCKAIgGRooGiA6KyIoGRoTAdfNeOEX8dAeLRfiDYHD9i+Vd\nw1WL7ItFCNnu3Bjf4PbEQXoFM8BAXcMzwF3DVYvsV4t9DIX/dBpWi3UIVuj5p///QFCNBD5WUOjd\nAwAAg8QQXl9dw1ZoAAADAGgAAAEAM/ZW6FIOAACDxAyFwHUCXsNWVlZWVujNJ///zFWL7IPsHFOL\nXRAz0rhOQAAAVleJRfyJE4lTBIlTCDlVDA+GPAEAAIvKiVUQiU30iVX4i1X0jX3ki/OLwcHoHwPS\npaWli3UQi86LffgD9gvwwekfA/+Lwgv5wegfi84D0gP2wekfC/CJE4tF5AP/C/mJcwQDwol7CDPJ\niUUQO8JyBTtF5HMDM8lBiQOFyXQei8YzyY1wATvwcgWD/gFzAzPJQYlzBIXJdARHiXsIi1XoM8CN\nDBaJTfQ7znIEO8pzAzPAQIlLBIXAdARHiXsIi1UQi8KLdfQD0gN97AP2g2XwAAP/wegfC/DB6R+L\nRQgL+YkTiXMEiXsID74AiXUQiX34iUXkjQwCiU30O8pyBDvIcwUzwEDrA4tF8IkLhcB0JIvGM9KN\ncAGJdRA78HIFg/4BcwMz0kKJcwSF0nQHR4l9+Il7CItFDEiJcwT/RQiJewiJRQyFwA+F1v7//7hO\nQAAAM9I5Uwh1LotTBIsLi/KLwcHiEMHoEAvQwe4Qi0X8weEQBfD/AACJC4lF/IX2dNuJUwSJcwiL\nUwj3wgCAAAB1NIs7i3MEi8eLzsHoHwP2C/DB6R+LRfwD0gvRBf//AAAD/4lF/PfCAIAAAHTZiTuJ\ncwSJUwhfXmaJQwpbi+Vdw1WL7IPsGI1N6FNX/3UM6GL0/v+LXQi/AAEAADvfc2CLTeiDeXQBfhSN\nRehQagFT6PB///+LTeiDxAzrDYuBkAAAAA+3BFiD4AGFwHQegH30AIuBlAAAAA+2DBh0B4tF8INg\ncP2LwenSAAAAgH30AHQHi03wg2Fw/YvD6b4AAACLReiDeHQBfi2Lw41N6MH4CIlFCFEPtsBQ6DiA\n//9ZWYXAdBKLRQhqAohF/Ihd/cZF/gBZ6xXoyjP//zPJQccAKgAAAIhd/MZF/QCLReiNVfhqAf9w\nBGoDUlGNTfxRV/+wqAAAAI1F6FDoN4L//4PEJIXAdRU4RfQPhHv///+LRfCDYHD96W////+D+AF1\nE4B99AAPtkX4dCWLTfCDYXD96xwPtlX4D7ZF+cHiCAvQgH30AHQHi03wg2Fw/YvCX1uL5V3DVYvs\ngz1QC0MAAHUSi00IjUG/g/gZdwODwSCLwV3DagD/dQjolf7//1lZXcNVi+xWi3UIhfZ0EItVDIXS\ndAmLTRCFyXUWiA7o9TL//2oWXokw6EMk//+Lxl5dw1eL/iv5igGIBA9BhMB0A0p181+F0nULiBbo\nyDL//2oi69EzwOvXzMzMzMzMzMzMzMxXVot0JBCLTCQUi3wkDIvBi9EDxjv+dgg7+A+CaAMAAA+6\nJRQTQwABcwfzpOkXAwAAgfmAAAAAD4LOAQAAi8czxqkPAAAAdQ4PuiXsrUIAAQ+C2gQAAA+6JRQT\nQwAAD4OnAQAA98cDAAAAD4W4AQAA98YDAAAAD4WXAQAAD7rnAnMNiwaD6QSNdgSJB41/BA+65wNz\nEfMPfg6D6QiNdghmD9YPjX8I98YHAAAAdGMPuuYDD4OyAAAAZg9vTvSNdvRmD29eEIPpMGYPb0Yg\nZg9vbjCNdjCD+TBmD2/TZg86D9kMZg9/H2YPb+BmDzoPwgxmD39HEGYPb81mDzoP7AxmD39vII1/\nMH23jXYM6a8AAABmD29O+I12+I1JAGYPb14Qg+kwZg9vRiBmD29uMI12MIP5MGYPb9NmDzoP2Qhm\nD38fZg9v4GYPOg/CCGYPf0cQZg9vzWYPOg/sCGYPf28gjX8wfbeNdgjrVmYPb078jXb8i/9mD29e\nEIPpMGYPb0YgZg9vbjCNdjCD+TBmD2/TZg86D9kEZg9/H2YPb+BmDzoPwgRmD39HEGYPb81mDzoP\n7ARmD39vII1/MH23jXYEg/kQfBPzD28Og+kQjXYQZg9/D41/EOvoD7rhAnMNiwaD6QSNdgSJB41/\nBA+64QNzEfMPfg6D6QiNdghmD9YPjX8IiwSNeH5BAP/g98cDAAAAdRXB6QKD4gOD+QhyKvOl/ySV\neH5BAJCLx7oDAAAAg+kEcgyD4AMDyP8khYx9QQD/JI2IfkEAkP8kjQx+QQCQnH1BAMh9QQDsfUEA\nI9GKBogHikYBiEcBikYCwekCiEcCg8YDg8cDg/kIcszzpf8klXh+QQCNSQAj0YoGiAeKRgHB6QKI\nRwGDxgKDxwKD+QhypvOl/ySVeH5BAJAj0YoGiAeDxgHB6QKDxwGD+QhyiPOl/ySVeH5BAI1JAG9+\nQQBcfkEAVH5BAEx+QQBEfkEAPH5BADR+QQAsfkEAi0SO5IlEj+SLRI7oiUSP6ItEjuyJRI/si0SO\n8IlEj/CLRI70iUSP9ItEjviJRI/4i0SO/IlEj/yNBI0AAAAAA/AD+P8klXh+QQCL/4h+QQCQfkEA\nnH5BALB+QQCLRCQMXl/DkIoGiAeLRCQMXl/DkIoGiAeKRgGIRwGLRCQMXl/DjUkAigaIB4pGAYhH\nAYpGAohHAotEJAxeX8OQjXQx/I18Ofz3xwMAAAB1JMHpAoPiA4P5CHIN/fOl/P8klRSAQQCL//fZ\n/ySNxH9BAI1JAIvHugMAAACD+QRyDIPgAyvI/ySFGH9BAP8kjRS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TdvbiBzb250IGV4Y2x1ZXMuXHBhcgAAAABccGFyZFxrZWVwblxzYjEyMFxzYTEyMFxiIExJ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\nAGkAYwBpAGUAbAAsACAAYQB1AHgAIABzAGUAc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EggAA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\nzMxXVlUz/zPti0QkFAvAfRVHRYtUJBD32Pfag9gAiUQ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0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\nACcAYQB1AHQAcgBlAHMAIABkAHIAb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yKRAoIsc3Tj2gMj2QzaE4BoVcTlaCKCoFMrdL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\nIqF4ETIheu9BCrE/+6jMpF3BoYibV3FWTkhFwELJm3ZbCoBIa/15n8G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WNkMDU5ODQwHwYDVR0jBBgwFoAUyxHoytK0FlgByTcuMxYWuUyaCh8wVgYDVR0fBE8w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\nggHpMBAGCSsGAQQBgjcVAQQDA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7d07421ae7bc380a1edc85f120fdfb8aaf04f10d

66,942

py

Python

app/adm/tests/test_view_ato.py

rogeriopaulos/gep

e56fd0450bdb8f572e2e35cc59a74ab0f0b372e2

[ "MIT" ]

null

app/adm/tests/test_view_ato.py

rogeriopaulos/gep

e56fd0450bdb8f572e2e35cc59a74ab0f0b372e2

[ "MIT" ]

2

2021-09-02T04:22:45.000Z

2021-09-02T04:52:26.000Z

app/adm/tests/test_view_ato.py

rogeriopaulos/gep

e56fd0450bdb8f572e2e35cc59a74ab0f0b372e2

[ "MIT" ]

1

2021-09-15T02:16:38.000Z

from adm.tests.factories import ( AdministrativoFactory, DespachoAdmFactory, DocumentoAdmFactory, LocalizacaoStatusAdmFactory, MidiaAdmFactory, OficioExternoAdmFactory, OficioInternoAdmFactory, StatusAdmFactory ) from adm.tests.utils import SetUpTestViewAtoData from core.tests.factories import ConteudoOficioExternoFactory, ConteudoOficioInternoFactory, MotivoVinculoFactory from django.test import TestCase from django.urls import reverse from guardian.shortcuts import assign_perm class AtoAdmCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_ofinterno_adm' model_factory = OficioInternoAdmFactory permission = 'adm.add_administrativo' response_arquivado_GET = 'componentes/singles/core/Arquivado.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.superior, arquivar=True) cls.ato = cls.model_factory.build( processo=cls.processo, autor=cls.superior, autoridade=cls.superior.get_full_name()) cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) assign_perm('adm.view_administrativo', cls.group_superior) assign_perm(cls.permission, cls.group_superior) def test_processo_arquivado_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_arquivado_GET[1]) self.assertTemplateUsed(response, self.response_arquivado_GET[0]) class AtoAdmUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_ofinterno_adm' model_factory = OficioInternoAdmFactory permission = 'adm.change_administrativo' response_arquivado_GET = 'componentes/singles/core/Arquivado.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.superior, arquivar=True) cls.ato = cls.model_factory(processo=cls.processo, autor=cls.superior, autoridade=cls.superior.get_full_name()) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) assign_perm(cls.permission, cls.group_superior, cls.processo) assign_perm(cls.permission, cls.group_superior, cls.processo) def test_processo_arquivado_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_arquivado_GET[1]) self.assertTemplateUsed(response, self.response_arquivado_GET[0]) class OfInternoCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_ofinterno_adm' model_factory = OficioInternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory.build(processo=cls.processo) cls.conteudo = ConteudoOficioInternoFactory() cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) cls.context = { 'destino': cls.ato.destino, 'autoridade': cls.superior.profile.pk, 'conteudo': cls.conteudo.pk } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmCreateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmCreateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class OfInternoUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_ofinterno_adm' model_factory = OficioInternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory( processo=cls.processo, autor=cls.normal_user, autoridade=cls.superior.get_full_name() ) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'destino': cls.ato.destino, 'autoridade': cls.superior.profile.pk, 'conteudo': cls.ato.conteudo.pk } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class OfInternoArquivoUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_ofinterno_arq_adm' model_factory = OficioInternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/DadosPopup.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/DadosPopup.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory( processo=cls.processo, autor=cls.normal_user, autoridade=cls.superior.get_full_name() ) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'arquivo': cls.ato.arquivo, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class OfInternoAdmConfirmViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_confirmacao_adm' model_factory = OficioInternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/ConfirmacaoPopup.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/ConfirmacaoPopup.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory( processo=cls.processo, autor=cls.normal_user, autoridade=cls.superior.get_full_name() ) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'destino': cls.ato.destino, 'autoridade': cls.superior.profile.pk, 'conteudo': cls.ato.conteudo.pk } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class OfInternoAdmDataEnvioViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_dataenvio_adm' model_factory = OficioInternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/DataEnvioPopup.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/DataEnvioPopup.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory( processo=cls.processo, autor=cls.normal_user, autoridade=cls.superior.get_full_name() ) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'destino': cls.ato.destino, 'autoridade': cls.superior.profile.pk, 'conteudo': cls.ato.conteudo.pk } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class OfExternoAdmCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_ofexterno_adm' model_factory = OficioExternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory(processo=cls.processo) cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) cls.context = { 'origem': cls.ato.origem, 'arquivo': cls.ato.arquivo, 'num_oficio': cls.ato.num_oficio, 'conteudo': cls.ato.conteudo.pk, 'data_recebimento': cls.ato.data_recebimento, 'nome_recebimento': cls.ato.nome_recebimento } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmCreateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmCreateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class OfExternoAdmUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_ofexterno_adm' model_factory = OficioExternoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory(processo=cls.processo) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) conteudo_externo = ConteudoOficioExternoFactory() cls.context = { 'num_oficio': cls.ato.num_oficio, 'origem': cls.ato.origem, 'conteudo': conteudo_externo.pk, 'outros': cls.ato.outros, 'data_recebimento': cls.ato.data_recebimento, 'nome_recebimento': cls.ato.nome_recebimento, 'arquivo': cls.ato.arquivo, 'descricao': cls.ato.descricao, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class DespachoAdmCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_despacho_adm' model_factory = DespachoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = '403.html', 403 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/CKEditorConteudo.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory.build(processo=cls.processo) cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) cls.context = { 'conteudo': cls.ato.conteudo, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmCreateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmCreateViewTestCase.permission, cls.normal_user) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class DespachoAdmUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_despacho_adm' model_factory = DespachoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '404.html', 404 response_normal_GET = 'componentes/shares/CKEditorConteudo.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = '404.html', 404 response_superior_POST = '404.html', 404 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory(processo=cls.processo, autor=cls.normal_user) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'conteudo': cls.ato.conteudo, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class StatusAdmCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_status_adm' model_factory = StatusAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory.build(processo=cls.processo, autor=cls.normal_user) cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) localizacao_adm = LocalizacaoStatusAdmFactory() cls.context = { 'localizacao': localizacao_adm.pk, 'situacao': cls.ato.situacao, 'outros': cls.ato.outros, 'descricao': cls.ato.descricao, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmCreateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmCreateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class StatusAdmUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_status_adm' model_factory = StatusAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory(processo=cls.processo, autor=cls.normal_user) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) localizacao_adm = LocalizacaoStatusAdmFactory() cls.context = { 'localizacao': localizacao_adm.pk, 'situacao': cls.ato.situacao, 'outros': cls.ato.outros, 'descricao': cls.ato.descricao, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class GravacaoAdmCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_gravacao_adm' model_factory = MidiaAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = MidiaAdmFactory.build(processo=cls.processo) cls.tipo_gravacao_link = MidiaAdmFactory(processo=cls.processo).tipo_gravacao_link cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) cls.context = { 'destino': cls.ato.destino, 'solicitante': cls.ato.solicitante, 'tipo_gravacao_link': cls.tipo_gravacao_link.pk, 'autor': cls.normal_user.pk } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmCreateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmCreateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class DocumentoCreateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:add_documento_adm' model_factory = DocumentoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = 'componentes/shares/FormGeral.html', 200 response_superior_POST = 'componentes/shares/NotifyForm.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory.build(processo=cls.processo, autor=cls.normal_user) cls.url = reverse(cls.view_name, kwargs={'pk': cls.processo.pk, 'tipo_ato': cls.ato.tipo_ato}) cls.context = { 'nome_doc': cls.ato.nome_doc, 'motivo_anulacao': cls.ato.motivo_anulacao, 'documento': cls.ato.documento, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmCreateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmCreateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class DocumentoUpdateViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:editar_documento_adm' model_factory = DocumentoAdmFactory response_unlogged = 'componentes/singles/core/Home.html', 200 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '404.html', 404 response_normal_GET = 'componentes/shares/FormGeral.html', 200 response_normal_POST = 'componentes/shares/NotifyForm.html', 200 response_superior_GET = '404.html', 404 response_superior_POST = '404.html', 404 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.normal_user, arquivar=False) cls.ato = cls.model_factory(processo=cls.processo, autor=cls.normal_user) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'nome_doc': cls.ato.nome_doc, 'motivo_anulacao': cls.ato.motivo_anulacao, 'documento': cls.ato.documento, } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_normal_user_permission_GET(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_normal_GET[1]) self.assertTemplateUsed(response, self.response_normal_GET[0]) def test_logged_with_normal_user_permission_POST(self): self.client.login(username=self.normal_user.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_normal_POST[1]) self.assertTemplateUsed(response, self.response_normal_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) class AnularAtoViewTestCase(SetUpTestViewAtoData, TestCase): view_name = 'adm:anular_ato' model_factory = MidiaAdmFactory response_unlogged = '403.html', 403 response_without_perm_GET = '403.html', 403 response_without_perm_POST = '403.html', 403 response_normal_GET = '', 000 response_normal_POST = '', 000 response_superior_GET = 'componentes/shares/ModalAnulacao.html', 200 response_superior_POST = 'componentes/singles/processos/adm/detalhes/_DetalheGeral.html', 200 def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.superior, arquivar=False) cls.ato = cls.model_factory(autor=cls.superior, processo=cls.processo, anulado=False) cls.url = reverse(cls.view_name, kwargs={'pk': cls.ato.pk}) cls.context = { 'motivo_anulacao': cls.ato.motivo_anulacao, 'tipo_ato': cls.ato.tipo_ato } assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.group_superior) assign_perm(AtoAdmUpdateViewTestCase.permission, cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) url = reverse('adm:detalhe_processo_adm', kwargs={'pk': self.processo.pk}) response = self.client.get(url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) class VincularProcessosCreateViewTestCase(SetUpTestViewAtoData, TestCase): def setUp(cls): cls.processo = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.superior, arquivar=False) cls.processo_b = AdministrativoFactory(orgao_processo=cls.orgao, autor=cls.superior, arquivar=False) cls.url = reverse('adm:vincular_processos', kwargs={'pk': cls.processo_b.pk}) cls.motivo = MotivoVinculoFactory(motivo='Um motivo qualquer.') cls.context = { "processo_b": cls.processo_b.pk, "motivo_vinculo": cls.motivo.pk } cls.response_unlogged = '403.html', 403 cls.response_without_perm_GET = '403.html', 403 cls.response_without_perm_POST = '403.html', 403 cls.response_normal_GET = '', 000 cls.response_normal_POST = '', 000 cls.response_superior_GET = 'componentes/shares/VincularProcessos.html', 200 cls.response_superior_POST = 'componentes/singles/processos/adm/detalhes/_DetalheGeral.html', 200 assign_perm('adm.view_administrativo', cls.group_superior) assign_perm('adm.view_administrativo', cls.normal_user, cls.processo) assign_perm('adm.add_administrativo', cls.group_superior) assign_perm('adm.add_administrativo', cls.normal_user, cls.processo) def test_unlogged_GET(self): response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_unlogged_POST(self): response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_unlogged[1]) self.assertTemplateUsed(response, self.response_unlogged[0]) def test_logged_without_permission_GET(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_without_perm_GET[1]) self.assertTemplateUsed(response, self.response_without_perm_GET[0]) def test_logged_without_permission_POST(self): self.client.login(username=self.usuario.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_without_perm_POST[1]) self.assertTemplateUsed(response, self.response_without_perm_POST[0]) def test_logged_with_superior_permission_POST(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.post(self.url, self.context, follow=True) self.assertEqual(response.status_code, self.response_superior_POST[1]) self.assertTemplateUsed(response, self.response_superior_POST[0]) def test_logged_with_superior_permission_GET(self): self.client.login(username=self.superior.username, password=self.password) response = self.client.get(self.url, follow=True) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0]) def test_logged_with_superior_permission_POST_invalid(self): self.client.login(username=self.superior.username, password=self.password) processo_b = AdministrativoFactory(arquivar=False) response = self.client.post(self.url, {"processo_b": processo_b.pk, "motivo_vinculo": self.motivo.pk}) self.assertEqual( response.context['form'].errors, {'processo_b': ['Faça uma escolha válida. Sua escolha não é uma das disponíveis.']} ) self.assertEqual(response.status_code, self.response_superior_GET[1]) self.assertTemplateUsed(response, self.response_superior_GET[0])

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119

0.738161

8,142

66,942

5.843527

0.020511

0.072134

0.061877

0.07741

0.95872

0.952058

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0.929064

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51.061785

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false

0.090735

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0

null

0

1

0

1

0

null

0

1

0

8

7d3d9779d9d21a7c482a0f14ef1fed331a7545d0

4,383

py

Python

datalad_osf/osfclient/osfclient/tests/test_cloning.py

adswa/datalad-osf-2

25988f898ffc6f489c0855933136f39f79cf8c65

[ "BSD-3-Clause" ]

80

2017-10-24T05:06:00.000Z

2022-02-20T18:44:17.000Z

datalad_osf/osfclient/osfclient/tests/test_cloning.py

adswa/datalad-osf-2

25988f898ffc6f489c0855933136f39f79cf8c65

[ "BSD-3-Clause" ]

133

2017-03-07T22:36:54.000Z

2017-10-24T04:43:57.000Z

datalad_osf/osfclient/osfclient/tests/test_cloning.py

adswa/datalad-osf-2

25988f898ffc6f489c0855933136f39f79cf8c65

[ "BSD-3-Clause" ]

37

2017-10-26T03:34:17.000Z

2021-11-17T05:24:10.000Z

"""Test `osf clone` command.""" import os from mock import patch, mock_open, call from osfclient import OSF from osfclient.cli import clone from osfclient.tests.mocks import MockProject from osfclient.tests.mocks import MockArgs @patch.object(OSF, 'project', return_value=MockProject('1234')) def test_clone_project(OSF_project): # check that `osf clone` opens files with the right names and modes args = MockArgs(project='1234') mock_open_func = mock_open() with patch('osfclient.cli.open', mock_open_func): with patch('osfclient.cli.makedirs'): with patch('osfclient.cli.os.getenv', side_effect='SECRET'): clone(args) OSF_project.assert_called_once_with('1234') # check that the project and the files have been accessed for store in OSF_project.return_value.storages: assert store._name_mock.called for f in store.files: assert f._path_mock.called fname = f._path_mock.return_value if fname.startswith('/'): fname = fname[1:] full_path = os.path.join('1234', store._name_mock.return_value, fname) assert call(full_path, 'wb') in mock_open_func.mock_calls @patch('osfclient.cli.checksum', return_value = '0' * 32) @patch.object(OSF, 'project', return_value=MockProject('1234')) def test_clone_project_update_file_exists_and_matches(OSF_project, checksum): # check that `osf clone --update` downloads all files except for any that # already exist locally and match the corresponding remote file args = MockArgs(project='1234', update=True) mock_open_func = mock_open() def exists(file_path): if file_path == '1234/osfstorage/a/a/a': return True else: return False with patch('osfclient.cli.open', mock_open_func): with patch('osfclient.cli.makedirs'): with patch('osfclient.cli.os.getenv', side_effect='SECRET'): with patch('osfclient.cli.os.path.exists', side_effect=exists): clone(args) OSF_project.assert_called_once_with('1234') # check that the project and the files have been accessed for store in OSF_project.return_value.storages: assert store._name_mock.called for f in store.files: assert f._path_mock.called fname = f._path_mock.return_value if fname.startswith('/'): fname = fname[1:] full_path = os.path.join('1234', store._name_mock.return_value, fname) if full_path == '1234/osfstorage/a/a/a': assert call(full_path, 'wb') not in mock_open_func.mock_calls else: assert call(full_path, 'wb') in mock_open_func.mock_calls @patch('osfclient.cli.checksum', return_value = '1' * 32) @patch.object(OSF, 'project', return_value=MockProject('1234')) def test_clone_project_update_file_exists_and_differs(OSF_project, checksum): # check that `osf clone --update` downloads all files and overwrites # existing files if they differ from the remote args = MockArgs(project='1234', update=True) mock_open_func = mock_open() def exists(file_path): if file_path == '1234/osfstorage/a/a/a': return True else: return False with patch('osfclient.cli.open', mock_open_func): with patch('osfclient.cli.makedirs'): with patch('osfclient.cli.os.getenv', side_effect='SECRET'): with patch('osfclient.cli.os.path.exists', side_effect=exists): clone(args) OSF_project.assert_called_once_with('1234') # check that the project and the files have been accessed for store in OSF_project.return_value.storages: assert store._name_mock.called for f in store.files: assert f._path_mock.called fname = f._path_mock.return_value if fname.startswith('/'): fname = fname[1:] full_path = os.path.join('1234', store._name_mock.return_value, fname) assert call(full_path, 'wb') in mock_open_func.mock_calls

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adb7505596632e2d64df8677877c0cb84efa73c6

17,317

py

Python

web2py-appliances-master/HotelManagementExample/models/db_functions.py

wantsomechocolate/WantsomeBeanstalk

8c8a0a80490d04ea52661a3114fd3db8de65a01e

[ "BSD-3-Clause" ]

null

web2py-appliances-master/HotelManagementExample/models/db_functions.py

wantsomechocolate/WantsomeBeanstalk

8c8a0a80490d04ea52661a3114fd3db8de65a01e

[ "BSD-3-Clause" ]

null

web2py-appliances-master/HotelManagementExample/models/db_functions.py

wantsomechocolate/WantsomeBeanstalk

8c8a0a80490d04ea52661a3114fd3db8de65a01e

[ "BSD-3-Clause" ]

null

# coding: utf8 def __index_0(table): title = T('Manage') grid = SQLFORM.grid(table, user_signature = False) return dict(title = title, grid = grid) def __index_1(table, add_link, search_link, show_link, manage_link, active): menu = ('[ ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ]') title = T('List') if len(request.args): page = int(request.args[0]) else: page = 0 items_per_page = 10 limitby = (page * items_per_page, (page + 1) * items_per_page + 1) rows = db(active == True).select(limitby = limitby, orderby = ~table.id, cache = (cache.ram, 10)) return dict(menu = menu, title = title, show_link = show_link, rows = rows, page = page, items_per_page = items_per_page) def __index_2(table, draft_link, add_link, search_link, show_link, manage_link, status, active): menu = ('[ ', A(T('Draft'), _href = URL(draft_link), _title = T('Draft')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ]') title = T('List') if len(request.args): page = int(request.args[0]) else: page = 0 items_per_page = 10 limitby = (page * items_per_page, (page + 1) * items_per_page + 1) rows = db((status == 'Published') & (active == True)).select(limitby = limitby, orderby = ~table.id, cache = (cache.ram, 10)) return dict(menu = menu, title = title, show_link = show_link, rows = rows, page = page, items_per_page = items_per_page) @auth.requires(auth.has_membership(role = 'Admin') or auth.has_membership(role = 'Manager')) def __draft_0(table, index_link, add_link, search_link, show_link, manage_link, status, active): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ]') title = T('List') if len(request.args): page = int(request.args[0]) else: page = 0 items_per_page = 10 limitby = (page * items_per_page, (page + 1) * items_per_page + 1) rows = db((status == 'Draft') & (active == True)).select(limitby = limitby, orderby = ~table.id, cache = (cache.ram, 10)) return dict(menu = menu, title = title, show_link = show_link, rows = rows, page = page, items_per_page = items_per_page) @auth.requires(auth.has_membership(role = 'Admin') or auth.has_membership(role = 'Manager')) def __add_0(table, index_link, search_link, manage_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ]') title = T('Add') form = crud.create(table, next = URL(index_link)) return dict(menu = menu, title = title, form = form) @auth.requires(auth.has_membership(role = 'Admin') or auth.has_membership(role = 'Manager')) def __add_1(table, index_link, search_link, manage_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ]') title = T('Add') form = crud.create(table, onvalidation = __date_comparation, onaccept = __update_table_2, next = URL(index_link) ) return dict(menu = menu, title = title, form = form) @auth.requires(auth.has_membership(role = 'Admin') or auth.has_membership(role = 'Manager')) def __add_2(table, index_link, draft_link, search_link, manage_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Draft'), _href = URL(draft_link), _title = T('Draft')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ]') title = T('Add') form = crud.create(table) return dict(menu = menu, title = title, form = form) @auth.requires_login() def __add_3(table_0, index_link, table_1, field): page = table_0(request.args(0)) or redirect(URL(index_link)) field.default = page.id form = crud.create(table_1, message = T('Record Inserted'), next = URL(args = page.id)) return dict(page = page, form = form) @auth.requires(auth.has_membership(role = 'Manager')) def __edit_0(table, index_link, add_link, search_link, show_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Show'), _href = URL(show_link, args = request.args), _title = T('Show')), ' ]') title = T('Edit') page = table(request.args(0)) or redirect(URL(index_link)) form = crud.update(table, page, next = URL(show_link, args = request.args), onaccept = crud.archive) return dict(menu = menu, title = title, form = form) @auth.requires(auth.has_membership(role = 'Manager')) def __edit_1(table, index_link, draft_link, add_link, search_link, show_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Draft'), _href = URL(draft_link), _title = T('Draft')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Show'), _href = URL(show_link, args = request.args), _title = T('Show')), ' ]') title = T('Edit') page = table(request.args(0)) or redirect(URL(index_link)) form = crud.update(table, page, next = URL(show_link, args = request.args), onaccept = crud.archive) return dict(menu = menu, title = title, form = form) def __show_0(table, index_link, add_link, search_link, edit_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Edit'), _href = URL(edit_link, args = request.args), _title = T('Edit')), ' ]') page = table(request.args(0)) or redirect(URL(index_link)) return dict(menu = menu, page = page) @auth.requires_login() def __show_1(table, index_link, draft_link, add_link, search_link, edit_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Draft'), _href = URL(draft_link), _title = T('Draft')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' | ', A(T('Edit'), _href = URL(edit_link, args = request.args), _title = T('Edit')), ' ]') page = table(request.args(0)) or redirect(URL(index_link)) return dict(menu = menu, page = page) @auth.requires_login() def __show_2(table_0, index_link, table_1, field, active): page = table_0(request.args(0)) or redirect(URL(index_link)) results = db((field == page.id) & (active == True)).select(orderby = ~table_1.id, cache = (cache.ram, 10)) return dict(page = page, results = results) def __search_0(table, index_link, add_link, manage_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ] ') title = T('Search') search, rows = crud.search(table) return dict(menu = menu, title = title, table = table, search = search, rows = rows) def __search_1(table, index_link, draft_link, add_link, manage_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Draft'), _href = URL(draft_link), _title = T('Draft')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Manage'), _href = URL(manage_link), _title = T('Manage')), ' ] ') title = T('Search') search, rows = crud.search(table) return dict(menu = menu, title = title, table = table, search = search, rows = rows) @auth.requires(auth.has_membership(role = 'Manager')) def __manage_0(table, index_link, add_link, search_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' ] ') title_1 = T('Manage') title_2 = T('Search') form = crud.update(table, request.args(1), message = T("Succeed"), onaccept = crud.archive) table.id.represent = lambda id: A('Edit:', id, _href = URL(args = (request.args(0), id))) search, rows = crud.search(table) return dict(menu = menu, title_1 = title_1, title_2 = title_2, form = form, table = table, search = search, rows = rows) @auth.requires(auth.has_membership(role = 'Manager')) def __manage_1(table, index_link, draft_link, add_link, search_link): menu = ('[ ', A(T('Index'), _href = URL(index_link), _title = T('Index')), ' | ', A(T('Draft'), _href = URL(draft_link), _title = T('Draft')), ' | ', A(T('Add'), _href = URL(add_link), _title = T('Add')), ' | ', A(T('Search'), _href = URL(search_link), _title = T('Search')), ' ] ') title_1 = T('Manage') title_2 = T('Search') form = crud.update(table, request.args(1), message = T("Succeed"), onaccept = crud.archive) table.id.represent = lambda id: A('Edit:', id, _href = URL(args = (request.args(0), id))) search, rows = crud.search(table) return dict(menu = menu, title_1 = title_1, title_2 = title_2, form = form, table = table, search = search, rows = rows) def __update_table_2(form): if request.function == 'check_out_add': db.room(db.room.id == form.vars.room_id).update_record(status_id = 1) elif request.function == 'cleaning_add': db.room(db.room.id == form.vars.room_id).update_record(status_id = 2) elif request.function == 'booking_add': db.room(db.room.id == form.vars.room_id).update_record(status_id = 3) elif request.function == 'check_in_add': db.room(db.room.id == form.vars.room_id).update_record(status_id = 4) def __date_comparation(form): if request.function == 'booking_add': if form.vars.from_date > form.vars.to_date: form.errors.to_date = 'To Date must Greater than From Date' def __like(table): page = table[request.vars.id] new_like = page.like + 1 page.update_record(like = new_like) return str(new_like)

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null

0

1

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1

0

null

0

7

add84f3e57d4fde421abdd1b4b28309c19b48dba

4,563

py

Python

pyaz/ts/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/ts/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

null

pyaz/ts/__init__.py

py-az-cli/py-az-cli

9a7dc44e360c096a5a2f15595353e9dad88a9792

[ "MIT" ]

1

2022-02-03T09:12:01.000Z

''' Manage template specs at subscription or resource group scope. ''' from .. pyaz_utils import _call_az def create(name, resource_group, description=None, display_name=None, location=None, tags=None, template_file=None, ui_form_definition=None, version=None, version_description=None, yes=None): ''' Create a template spec and or template spec version. Required Parameters: - name -- The name of the template spec. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` Optional Parameters: - description -- The description of the parent template spec. - display_name -- The display name of the template spec - location -- The location to store the template-spec and template-spec version(s). Cannot be changed after creation. - tags -- space-separated tags: key[=value] [key[=value] ...]. Use '' to clear existing tags. - template_file -- a path to a template file or Bicep file in the file system - ui_form_definition -- The uiFormDefinition file path in the file system for the template spec version. - version -- The template spec version. - version_description -- The description of the template spec version. - yes -- Do not prompt for confirmation ''' return _call_az("az ts create", locals()) def update(description=None, display_name=None, name=None, resource_group=None, tags=None, template_file=None, template_spec=None, ui_form_definition=None, version=None, version_description=None, yes=None): ''' Update a template spec version. Optional Parameters: - description -- The description of the parent template spec. - display_name -- The display name of the template spec - name -- The name of the template spec. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - tags -- space-separated tags: key[=value] [key[=value] ...]. Use '' to clear existing tags. - template_file -- a path to a template file or Bicep file in the file system - template_spec -- The template spec resource id. - ui_form_definition -- The uiFormDefinition file path in the file system for the template spec version. - version -- The template spec version. - version_description -- The description of the template spec version. - yes -- Do not prompt for confirmation. ''' return _call_az("az ts update", locals()) def export(output_folder, name=None, resource_group=None, template_spec=None, version=None): ''' Export the specified template spec version and artifacts (if any) to the specified output folder. Required Parameters: - output_folder -- Existing folder to output export(s). Optional Parameters: - name -- The name of the template spec. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - template_spec -- The template spec resource id. - version -- The template spec version. ''' return _call_az("az ts export", locals()) def show(name=None, resource_group=None, template_spec=None, version=None): ''' Get the specified template spec or template spec version. Optional Parameters: - name -- The name of the template spec. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - template_spec -- The template spec resource id. - version -- The template spec version. ''' return _call_az("az ts show", locals()) def list(name=None, resource_group=None): ''' List template specs or template spec versions. Optional Parameters: - name -- The name of the template spec. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` ''' return _call_az("az ts list", locals()) def delete(name=None, resource_group=None, template_spec=None, version=None, yes=None): ''' Delete a specified template spec or template spec version by name or resource ID.. Optional Parameters: - name -- The name of the template spec. - resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>` - template_spec -- The template spec resource id. - version -- The template spec version. - yes -- Do not prompt for confirmation. ''' return _call_az("az ts delete", locals())

45.178218

206

0.70984

625

4,563

5.0912

0.1392

0.158391

0.103708

0.053426

0.807982

0.763671

0.747329

0.709302

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0.198773

4,563

100

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0.87035

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0.064947

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1

0

null

0

1

0

1

0

8

adfec6553ba0d76b0648e2987c8257e1d3e7c12b

107

py

Python

src/onqg/models/modules/__init__.py

WING-NUS/RL-for-Question-Generation

745b8f823df9bcf4cc422c97a83ce096ac9c5e35

[ "MIT" ]

1

2021-01-05T05:30:00.000Z

src/onqg/models/modules/__init__.py

MrSchnappi/RL-for-Question-Generation

d1966a47ef28c076902189469508194f659c5270

[ "MIT" ]

null

src/onqg/models/modules/__init__.py

MrSchnappi/RL-for-Question-Generation

d1966a47ef28c076902189469508194f659c5270

[ "MIT" ]

1

2021-03-23T16:34:06.000Z

from onqg.models.modules.Attention import GatedSelfAttention from onqg.models.modules.MaxOut import MaxOut

53.5

61

0.869159

14

107

6.642857

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0.172043

0.301075

0.451613

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null

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0

7

bc08cedb2510d3bc9102a52eb412814dee086376

4,686

py

Python

src/genie/libs/parser/nxos/tests/ShowBgpVrfAllNeighborsRoutes/cli/equal/golden_output_3_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

204

2018-06-27T00:55:27.000Z

2022-03-06T21:12:18.000Z

src/genie/libs/parser/nxos/tests/ShowBgpVrfAllNeighborsRoutes/cli/equal/golden_output_3_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

468

2018-06-19T00:33:18.000Z

2022-03-31T23:23:35.000Z

src/genie/libs/parser/nxos/tests/ShowBgpVrfAllNeighborsRoutes/cli/equal/golden_output_3_expected.py

balmasea/genieparser

d1e71a96dfb081e0a8591707b9d4872decd5d9d3

[ "Apache-2.0" ]

309

2019-01-16T20:21:07.000Z

2022-03-30T12:56:41.000Z

expected_output = { 'vrf': {'default': {'neighbor': {'10.4.6.6': {'address_family': {'ipv4 unicast': {'bgp_table_version': 773961, 'local_router_id': '10.94.44.44', 'routes': {'10.169.1.0/24': {'index': {1: {'metric': 2219, 'next_hop': '10.4.6.6', 'origin_codes': 'e', 'path': '300 ' '33299 ' '51178 ' '47751 ' '{27016}', 'path_type': 'e', 'status_codes': '*>', 'weight': 0}}}, '10.169.2.0/24': {'index': {1: {'metric': 2219, 'next_hop': '10.4.6.6', 'origin_codes': 'e', 'path': '300 ' '33299 ' '51178 ' '47751 ' '{27016}', 'path_type': 'e', 'status_codes': '*>', 'weight': 0}}}, '10.169.3.0/24': {'index': {1: {'metric': 2219, 'next_hop': '10.4.6.6', 'origin_codes': 'e', 'path': '300 ' '33299 ' '51178 ' '47751 ' '{27016}', 'path_type': 'e', 'status_codes': '*>', 'weight': 0}}}, '10.169.4.0/24': {'index': {1: {'metric': 2219, 'next_hop': '10.4.6.6', 'origin_codes': 'e', 'path': '300 ' '33299 ' '51178 ' '47751 ' '{27016}', 'path_type': 'e', 'status_codes': '*>', 'weight': 0}}}, '10.169.5.0/24': {'index': {1: {'metric': 2219, 'next_hop': '10.4.6.6', 'origin_codes': 'e', 'path': '300 ' '33299 ' '51178 ' '47751 ' '{27016}', 'path_type': 'e', 'status_codes': '*>', 'weight': 0}}}}}, 'ipv6 unicast': {'bgp_table_version': 256033, 'local_router_id': '10.94.44.44', 'routes': {}}}}}}}}

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0.152155

194

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0.035139

0.043924

0.818448

0.739385

0

0.219298

0.756722

4,686

86

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54.488372

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false

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null

0

1

0

1

0

1

0

1

0

null

0

10

bc0f8e11fb3ff759853d25b449ebf1b7ef253403

5,806

py

Python

examples/physics.py

ziyangli/gym-pybullet-drones

5593ec16a53c299f5300c62f6dff14b15247fcf5

[ "MIT" ]

null

examples/physics.py

ziyangli/gym-pybullet-drones

5593ec16a53c299f5300c62f6dff14b15247fcf5

[ "MIT" ]

null

examples/physics.py

ziyangli/gym-pybullet-drones

5593ec16a53c299f5300c62f6dff14b15247fcf5

[ "MIT" ]

1

2020-10-12T20:30:45.000Z

import os import time import pdb import math import numpy as np import pybullet as p from utils import * from gym_pybullet_drones.envs.BaseAviary import DroneModel from gym_pybullet_drones.envs.CtrlAviary import CtrlAviary DURATION_SEC = 30 NUM_RESETS = 2 if __name__ == "__main__": #### Initialize the simulation ##################################################################### env = CtrlAviary(drone_model=DroneModel.CF2X, initial_xyzs=np.array([-.7, -.5, .3]).reshape(1,3), \ initial_rpys=np.array([0, -30*(np.pi/180), 0]).reshape(1,3), gui=True, obstacles=True) #### Get PyBullet's and drone's ids ################################################################ PYB_CLIENT = env.getPyBulletClient(); DRONE_IDS = env.getDroneIds() #### Make the drone weightless ##################################################################### p.setGravity(0, 0, 0, physicsClientId=PYB_CLIENT) #### Run the simulation ############################################################################ START = time.time() for i in range(DURATION_SEC*env.SIM_FREQ): #### Apply x-axis force to the base ################################################################ # p.applyExternalForce(DRONE_IDS[0], linkIndex=-1, forceObj=[1e-4,0.,0,], posObj=[0.,0.,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalForce(DRONE_IDS[0], linkIndex=-1, forceObj=[1e-4,0.,0,], posObj=[0.,0.,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### Apply y-axis force to the base ################################################################ # p.applyExternalForce(DRONE_IDS[0], linkIndex=-1, forceObj=[0.,1e-4,0.], posObj=[0.,0.,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalForce(DRONE_IDS[0], linkIndex=-1, forceObj=[0.,1e-4,0.], posObj=[0.,0.,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### Apply z-axis force to the base ################################################################ # p.applyExternalForce(DRONE_IDS[0], linkIndex=-1, forceObj=[0.,0.,1e-4], posObj=[0.,0.,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalForce(DRONE_IDS[0], linkIndex=-1, forceObj=[0.,0.,1e-4], posObj=[0.,0.,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### To propeller 0 ################################################################################ # p.applyExternalForce(DRONE_IDS[0], linkIndex=0, forceObj=[0.,0.,1e-4], posObj=[0.,0.,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) p.applyExternalForce(DRONE_IDS[0], linkIndex=0, forceObj=[0.,0.,1e-4], posObj=[0.,0.,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### To the center of mass ######################################################################### # p.applyExternalForce(DRONE_IDS[0], linkIndex=4, forceObj=[0.,0.,1e-4], posObj=[0.,0.,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalForce(DRONE_IDS[0], linkIndex=4, forceObj=[0.,0.,1e-4], posObj=[0.,0.,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### Apply x-axis torque to the base ############################################################### # p.applyExternalTorque(DRONE_IDS[0], linkIndex=-1, torqueObj=[5e-6,0.,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalTorque(DRONE_IDS[0], linkIndex=-1, torqueObj=[5e-6,0.,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### Apply y-axis torque to the base ############################################################### # p.applyExternalTorque(DRONE_IDS[0], linkIndex=-1, torqueObj=[0.,5e-6,0.], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalTorque(DRONE_IDS[0], linkIndex=-1, torqueObj=[0.,5e-6,0.], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### Apply z-axis torque to the base ############################################################### # p.applyExternalTorque(DRONE_IDS[0], linkIndex=-1, torqueObj=[0.,0.,5e-6], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalTorque(DRONE_IDS[0], linkIndex=-1, torqueObj=[0.,0.,5e-6], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### To propeller 0 ################################################################################ # p.applyExternalTorque(DRONE_IDS[0], linkIndex=0, torqueObj=[0.,0.,5e-6], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) # p.applyExternalTorque(DRONE_IDS[0], linkIndex=0, torqueObj=[0.,0.,5e-6], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### To the center of mass ######################################################################### # p.applyExternalTorque(DRONE_IDS[0], linkIndex=4, torqueObj=[0.,0.,5e-6], flags=p.WORLD_FRAME, physicsClientId=PYB_CLIENT) p.applyExternalTorque(DRONE_IDS[0], linkIndex=4, torqueObj=[0.,0.,5e-6], flags=p.LINK_FRAME, physicsClientId=PYB_CLIENT) #### Draw base frame ############################################################################### env._showDroneFrame(0) #### Step, sync the simulation, printout the state ################################################# p.stepSimulation(physicsClientId=PYB_CLIENT); sync(i, START, env.TIMESTEP) if i%env.SIM_FREQ==0: env.render() #### Reset ######################################################################################### if i>1 and i%((DURATION_SEC/(NUM_RESETS+1))*env.SIM_FREQ)==0: env.reset(); p.setGravity(0, 0, 0, physicsClientId=PYB_CLIENT) #### Close the environment ######################################################################### env.close()

72.575

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4.473525

0.163389

0.027054

0.186676

0.121745

0.768346

0.741968

0.716266

0

0.035792

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5,806

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1

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1

0

null

0

1

0

7

cb03593d2e2b9a68b7d4d4f8676aab83feb4f69b

254

py

Python

tests/bytecode/mp-tests/assign2.py

LabAixBidouille/micropython

11aa6ba456287d6c80598a7ebbebd2887ce8f5a2

[ "MIT" ]

303

2015-07-11T17:12:55.000Z

2018-01-08T03:02:37.000Z

tests/bytecode/mp-tests/assign2.py

LabAixBidouille/micropython

11aa6ba456287d6c80598a7ebbebd2887ce8f5a2

[ "MIT" ]

13

2016-05-12T16:51:22.000Z

2018-01-10T22:33:25.000Z

tests/bytecode/mp-tests/assign2.py

LabAixBidouille/micropython

11aa6ba456287d6c80598a7ebbebd2887ce8f5a2

[ "MIT" ]

26

2018-01-18T09:15:33.000Z

2022-02-07T13:09:14.000Z

*a, = b a, *b = c a, *b, = c a, *b, c = d [*a] = b [*a,] = b [a, *b] = c [a, *b,] = c [a, *b, c] = d (*a,) = x (*a, b) = x (a, *b) = x (*a, b, c) = x (a, *b, c) = x (a, b, *c) = x (*a, b, c, d) = x (a, *b, c, d) = x (a, b, *c, d) = x (a, b, c, *d) = x

11.545455

17

0.259843

66

254

1

0.075758

0.545455

0.590909

0.424242

0.984848

0.893939

0

0.34252

254

21

18

12.095238

0.39521

0

1

0

true

0

1

null

1

0

1

0

null

0

1

0

12

cb20e0e6c0132540885af08c1821e94e3775a417

8,489

py

Python

service_pro/doc_events/sales_invoice.py

leaftechnology/servicepro

e204a97452d0bf128dd3dc206d3242854bc56b3f

[ "MIT" ]

1

2021-09-17T12:08:04.000Z

service_pro/doc_events/sales_invoice.py

leaftechnology/servicepro

e204a97452d0bf128dd3dc206d3242854bc56b3f

[ "MIT" ]

null

service_pro/doc_events/sales_invoice.py

leaftechnology/servicepro

e204a97452d0bf128dd3dc206d3242854bc56b3f

[ "MIT" ]

1

2021-09-17T12:08:05.000Z

import frappe # def generate_jv(doc): # if doc.paid: # doc_jv = { # "doctype": "Journal Entry", # "voucher_type": "Journal Entry", # "posting_date": doc.posting_date, # "accounts": jv_accounts_paid(doc), # } # # jv = frappe.get_doc(doc_jv) # jv.insert(ignore_permissions=1) # jv.submit() # frappe.db.sql(""" UPDATE `tabSales Invoice` SET journal_entry=%s WHERE name=%s""", (jv.name, doc.name)) # frappe.db.commit() # # elif doc.unpaid: # doc_jv = { # "doctype": "Journal Entry", # "voucher_type": "Journal Entry", # "posting_date": doc.posting_date, # "accounts": jv_accounts_unpaid(doc), # } # # jv = frappe.get_doc(doc_jv) # jv.insert(ignore_permissions=1) # jv.submit() # doc.journal_entry = jv.name # frappe.db.sql(""" UPDATE `tabSales Invoice` SET journal_entry=%s WHERE name=%s""", (jv.name, doc.name)) # frappe.db.commit() def jv_accounts_unpaid(doc): accounts = [] accounts.append({ 'account': doc.expense_account, 'debit_in_account_currency': doc.incentive, 'credit_in_account_currency': 0, 'cost_center': doc.expense_cost_center, }) accounts.append({ 'account': doc.liabilities_account, 'debit_in_account_currency': 0, 'credit_in_account_currency': doc.incentive }) return accounts def jv_accounts_paid(doc): accounts = [] accounts.append({ 'account': doc.expense_account, 'debit_in_account_currency': doc.incentive, 'credit_in_account_currency': 0, 'cost_center': doc.expense_cost_center, }) if doc.cash: mop_cash = frappe.db.sql(""" SELECT * FROM `tabMode of Payment Account` WHERE parent=%s """, (doc.showroom_cash), as_dict=1) if len(mop_cash) > 0: accounts.append({ 'account': mop_cash[0].default_account, 'debit_in_account_currency': 0, 'credit_in_account_currency': doc.incentive }) return accounts @frappe.whitelist() def on_submit_si(doc, method): # if len(doc.sales_team) > 0 and not doc.paid and not doc.unpaid: # frappe.throw("Please select Paid or Unpaid for Sales Person") # generate_jv(doc) for prod in doc.production: production = frappe.db.sql(""" SELECT * FROM `tabProduction` WHERE name=%s """, prod.reference, as_dict=1) if len(production) > 0: if doc.update_stock and get_dn_si_qty("", production[0].qty, prod.reference) > 0 : frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("Partially Delivered", prod.reference)) frappe.db.commit() elif get_dn_si_qty("", production[0].qty, prod.reference) == 0 and get_lengths(prod.reference)[0] == get_lengths(prod.reference)[1] : frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("Completed", prod.reference)) frappe.db.commit() get_service_records(prod.reference) elif get_dn_qty(prod.reference) >= 0 and \ ((get_dn_si_qty("", production[0].qty, prod.reference) >= 0 and get_lengths(prod.reference)[0] != get_lengths(prod.reference)[1])): frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("To Deliver", prod.reference)) frappe.db.commit() elif get_dn_si_qty("", production[0].qty, prod.reference) > 0: frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("Partially Delivered", prod.reference)) frappe.db.commit() def get_service_records(reference): estimation_ = "" estimation = frappe.db.sql(""" SELECT * FROM `tabProduction` WHERE name= %s""", reference, as_dict=1) if len(estimation) > 0: estimation_ = estimation[0].estimation frappe.db.sql(""" UPDATE `tabEstimation` SET status=%s WHERE name=%s""", ("Completed", estimation_)) inspections = frappe.db.sql(""" SELECT * FROM `tabInspection Table` WHERE parent=%s """, estimation_, as_dict=1) for i in inspections: frappe.db.sql(""" UPDATE `tabInspection` SET status=%s WHERE name=%s""", ("Completed", i.inspection)) srn = frappe.db.sql(""" SELECT * FROM `tabEstimation` WHERE name=%s """, estimation_, as_dict=1) if len(srn) > 0: srn_ = srn[0].service_receipt_note frappe.db.sql(""" UPDATE `tabService Receipt Note` SET status=%s WHERE name=%s""", ("Completed", srn_)) frappe.db.commit() def get_service_records_cancel(reference): estimation_ = "" estimation = frappe.db.sql(""" SELECT * FROM `tabProduction` WHERE name= %s""", reference, as_dict=1) if len(estimation) > 0: estimation_ = estimation[0].estimation frappe.db.sql(""" UPDATE `tabEstimation` SET status=%s WHERE name=%s""", ("To Production", estimation_)) inspections = frappe.db.sql(""" SELECT * FROM `tabInspection Table` WHERE parent=%s """, estimation_, as_dict=1) for i in inspections: frappe.db.sql(""" UPDATE `tabInspection` SET status=%s WHERE name=%s""", ("To Production", i.inspection)) srn = frappe.db.sql(""" SELECT * FROM `tabEstimation` WHERE name=%s """, estimation_, as_dict=1) if len(srn) > 0: srn_ = srn[0].receipt_note frappe.db.sql(""" UPDATE `tabService Receipt Note` SET status=%s WHERE name=%s""", ("To Production", srn_)) frappe.db.commit() @frappe.whitelist() def on_cancel_si(doc, method): for prod in doc.production: production = frappe.db.sql(""" SELECT * FROM `tabProduction` WHERE name=%s """, prod.reference, as_dict=1) if len(production) > 0: if doc.update_stock: frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("To Deliver and Bill", prod.reference)) frappe.db.commit() get_service_records(prod.reference) elif get_lengths(prod.reference)[0] == 0 and get_lengths(prod.reference)[1] == 0: frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("To Deliver and Bill", prod.reference)) frappe.db.commit() get_service_records(prod.reference) elif get_dn_qty(prod.reference) >= 0 and \ ((get_dn_si_qty("", production[0].qty, prod.reference) >= 0 and get_lengths(prod.reference)[0] != get_lengths(prod.reference)[1])): frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("To Bill", prod.reference)) frappe.db.commit() elif get_dn_si_qty("", production[0].qty, prod.reference) > 0: frappe.db.sql(""" UPDATE `tabProduction` SET status=%s WHERE name=%s""", ("Partially Delivered", prod.reference)) frappe.db.commit() def get_lengths(name): si_query = """ SELECT SIP.qty as qty, SI.status FROM `tabSales Invoice` AS SI INNER JOIN `tabSales Invoice Production` AS SIP ON SI.name = SIP.parent WHERE SIP.reference=%s and SIP.parenttype=%s and SI.docstatus = 1 and SI.status!='Cancelled' and SI.update_stock = 0 """ si = frappe.db.sql(si_query, (name, "Sales Invoice"), as_dict=1) dn_query = """ SELECT SIP.qty as qty, DN.status FROM `tabDelivery Note` AS DN INNER JOIN `tabSales Invoice Production` AS SIP ON DN.name = SIP.parent WHERE SIP.reference=%s and SIP.parenttype=%s and DN.docstatus = 1 and DN.status!='Cancelled' """ dn = frappe.db.sql(dn_query, (name, "Delivery Note"), as_dict=1) return len(dn), len(si) def get_dn_si_qty(item_code, qty, name): si_query = """ SELECT SIP.qty as qty, SI.status FROM `tabSales Invoice` AS SI INNER JOIN `tabSales Invoice Production` AS SIP ON SI.name = SIP.parent WHERE SIP.reference=%s and SIP.parenttype=%s and SI.docstatus = 1 and SI.status!='Cancelled' """ si = frappe.db.sql(si_query,(name,"Sales Invoice"), as_dict=1) dn_query = """ SELECT SIP.qty as qty, DN.status FROM `tabDelivery Note` AS DN INNER JOIN `tabSales Invoice Production` AS SIP ON DN.name = SIP.parent WHERE SIP.reference=%s and SIP.parenttype=%s and DN.docstatus = 1 and DN.status!='Cancelled' """ dn = frappe.db.sql(dn_query,(name, "Delivery Note"), as_dict=1) total_qty = 0 if len(si) > len(dn): for i in si: total_qty += i.qty elif len(dn) > len(si): for d in dn: total_qty += d.qty elif len(dn) == len(si): for d in dn: total_qty += d.qty return float(qty) - float(total_qty) def get_dn_qty(name): dn_query = """ SELECT SIP.qty as qty, DN.status FROM `tabDelivery Note` AS DN INNER JOIN `tabSales Invoice Production` AS SIP ON DN.name = SIP.parent WHERE SIP.reference=%s and SIP.parenttype=%s and DN.docstatus = 1 and DN.status!='Cancelled' """ dn = frappe.db.sql(dn_query, (name, "Delivery Note"), as_dict=1) total_qty = 0 if len(dn) > 0: for d in dn: total_qty += d.qty return float(total_qty)

38.067265

136

0.669455

1,235

8,489

4.463968

0.093927

0.060947

0.059859

0.049338

0.889897

0.87992

0.874297

0.85906

0.85253

0

0.009407

0.173519

8,489

223

137

38.067265

0.776368

0.112734

0

0.732919

1

0.031056

0.397867

0.041867

0

1

0.055901

false

0

0.006211

0

0.093168

0

null

0

1

0

1

0

null

0

8

cbb6384c897e391cdee9396b112639fd7381bf6d

118

py

Python

rlapy/comps/__init__.py

Tianyu-Liang/rlapy

225c90f28de46f1f2fa8a7e93937de996c1f958e

[ "BSD-3-Clause" ]

null

rlapy/comps/__init__.py

Tianyu-Liang/rlapy

225c90f28de46f1f2fa8a7e93937de996c1f958e

[ "BSD-3-Clause" ]

null

rlapy/comps/__init__.py

Tianyu-Liang/rlapy

225c90f28de46f1f2fa8a7e93937de996c1f958e

[ "BSD-3-Clause" ]

null

import rlapy.comps.sketchers as sketchers import rlapy.comps.qb as qb import rlapy.comps.rangefinders as rangefinders

29.5

47

0.847458

18

118

5.555556

0.388889

0.33

0.48

0

0.101695

118

3

48

39.333333

0.943396

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

cbe4bbf250cacfa976bc7b0497570b7b76b1ee7d

8,345

py

Python

testAppium/unit/testcase/publish_process_activity/publish_units.py

moulage/appium-android

082e4018673fecd260552d758f8a8ba154838b9a

[ "Apache-2.0" ]

null

testAppium/unit/testcase/publish_process_activity/publish_units.py

moulage/appium-android

082e4018673fecd260552d758f8a8ba154838b9a

[ "Apache-2.0" ]

null

testAppium/unit/testcase/publish_process_activity/publish_units.py

moulage/appium-android

082e4018673fecd260552d758f8a8ba154838b9a

[ "Apache-2.0" ]

null

# !/usr/bin/env python # -*- coding = utf-8 -*- # @Author:wanghui # @Time: # @File:publish_units.py import os from time import sleep from testAppium.unit.common import toolUnits PATH = lambda p: os.path.abspath( os.path.join(os.path.dirname(__file__), p) ) """ 注册登录测试用例集合 """ def input_publish_activity(driver): """进入发布页面并且选择新地址""" # 进入选择地址页面 driver.find_element_id_and_click_wait(driver.ele.FBXT_actionbarwidget_moreTextView) driver.find_element_class_name_and_click('位于新地址') driver.find_element_id_and_click_wait(driver.ele.FBXT_actionbarwidget_moreTextView) # 选择国家/城市/地区 driver.find_element_id_and_click_wait(driver.ele.FBTX_view_nationAddress_addressCreate) driver.find_element_id_and_click_wait(driver.ele.FBXT_regions_name) driver.find_element_id_and_click_wait(driver.ele.FBXT_regions_name) driver.find_element_id_and_click_wait(driver.ele.FBXT_regions_name) driver.find_element_class_name_and_click('海淀区') # 填写地址详情 driver.find_element_id_and_click_wait(driver.ele.FBXT_view_addressAddress_addressCreate) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '紫竹院街道北京理工大学2号楼小猪短租') driver.find_element_id_and_click_wait(driver.ele.FBXT_actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_view_numberAddress_addressCreate) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '201') driver.find_element_id_and_click_wait(driver.ele.FBXT_actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_view_locationAddress_addressCreate) driver.swipeDown(0.5, 0.6, 0.5) driver.find_element_id_and_click_wait(driver.ele.FBXT_actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_btn_submit_publishLuAddressCreate) def select_house_type(driver): """选择房源类型""" driver.find_element_id_and_click_wait(driver.ele.FBXT_iv_lu_category_item_radio) driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_lu_category_list_commit) def select_rent_type(driver): """选择出租类型""" driver.find_element_id_and_click_wait(driver.ele.FBXT_me_Publish_Change_Rentaltype_TypeAllIcon) driver.find_element_id_and_click_wait(driver.ele.FBXT_me_Publish_Change_Rentaltype_Submit) def input_basis_message(driver): """输入基本信息""" driver.find_element_class_name_and_click('基本信息') driver.find_element_id_and_click_wait(driver.ele.FBXT_phi_housetype) driver.find_element_id_and_click_wait(driver.ele.FBXT_actv_plus) # driver.find_element_id_and_click_wait(driver.ele.FBXT_actv_plus, 1) # driver.find_element_id_and_click_wait(driver.ele.FBXT_actv_plus, 2) # driver.find_element_id_and_click_wait(driver.ele.FBXT_actv_plus, 3) # driver.find_element_id_and_click_wait(driver.ele.FBXT_actv_plus, 4) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_phi_area) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_lease_area, 100) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_phi_wc) driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_pop_window_item_name) driver.find_element_id_and_click_wait(driver.ele.FBXT_phi_mannum) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_livable_number, 3) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_back) def input_bed_message(driver): """输入床铺信息""" driver.find_element_class_name_and_click('床铺信息') driver.find_element_class_name_and_click('添加床铺') driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_title_publish_lu_bed_add_item) driver.find_element_id_and_click_wait(driver.ele.FBXT_btn_submit_publish_lu_bed_add) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_back) def input_house_describe(driver): """输入房源描述""" driver.find_element_class_name_and_click('房源描述') driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_caption_publishLuDescListItem) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '北京理工大学小猪短租房源测试') driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_caption_publishLuDescListItem, 1) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '你将结实一个热情、爱分享、爱读书、喜欢音乐和下厨的IT男生') driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_caption_publishLuDescListItem, 2) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '现在装饰的房间,给你不一样的感觉,让你有一种在家的温馨感受') driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_caption_publishLuDescListItem, 3) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '位于地铁口,出门就是公交站牌,方便快捷') driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_caption_publishLuDescListItem, 4) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_inputBox_publishLuDescEdit, '想玩、想吃、想喝、想蹦迪,都没有~ 哈哈哈') driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_back) def select_facility(driver): """选择配套设施""" driver.find_element_class_name_and_click('配套设施') driver.find_element_id_and_click_wait(driver.ele.FBXT_cb_status) driver.find_element_id_and_click_wait(driver.ele.FBXT_cb_status, 1) driver.find_element_id_and_click_wait(driver.ele.FBXT_cb_status, 2) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) def input_money(driver): """输入价格规则""" driver.find_element_class_name_and_click('价格规则') driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_title) driver.find_element_id_and_send_keys(driver.ele.FBXT_et_day_price, 10) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_moreTextView) driver.swipeDown(0.5, 0.9, 0.1) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_back) def add_house_photo(driver): """添加房源照片""" driver.find_element_id_and_click_wait(driver.ele.FBXT_btn_addLuPic_publishLuMainPicItem) driver.find_element_id_and_click_wait(driver.ele.FBXT_lu_image_add) toolUnits.upload_picture(driver, 1) driver.swipeDown(0.5, 0.9, 0.1, 5) # driver.find_element_id_and_click_wait(driver.ele.FBXT_lu_image_add, -3) # toolUnits.upload_picture(driver, 1) # driver.swipeDown(0.5, 0.9, 0.1, 5) # driver.find_element_id_and_click_wait(driver.ele.FBXT_lu_image_add, -1) # toolUnits.upload_picture(driver, 5) # driver.swipeDown(0.5, 0.9, 0.1, 5) driver.find_element_id_and_click_wait(driver.ele.actionbarwidget_back) if driver.hasElement(driver.ele.FBXT_standard_dialog_two_btn_left): driver.find_element_id_and_click_wait(driver.ele.FBXT_standard_dialog_two_btn_left) def select_home_type(driver): """发布页面修改房源类型""" driver.swipeDown(0.5, 0.7, 0.3) def select_rent_out_type(driver): """发布页面修改出租类型""" driver.swipeDown(0.5, 0.7, 0.3) def select_house_address(driver): """发布页面修改地址""" driver.swipeDown(0.5, 0.7, 0.3) def select_publish_sure(driver): """同意发布房源""" driver.swipeDown(0.5, 0.7, 0.3) driver.find_element_id_and_click_wait(driver.ele.FBXT_switch_publishLuMainItem) # driver.find_element_id_and_click_wait(driver.ele.FBXT_btn_submit_publish_lu_main) def delete_house_message(driver): """删除房源信息""" driver.swipeDown(t=1) driver.swipeDown() driver.find_element_id_and_click_wait(driver.ele.FBXT_tv_deleteLu_publishLuMainItem) driver.find_element_id_and_click_wait(driver.ele.FBXT_iv_delete_house_reason) driver.find_element_id_and_click_wait(driver.ele.FBXT_actionbarwidget_moreTextView) driver.find_element_id_and_click_wait(driver.ele.FBXT_bt_delete)

44.388298

120

0.820491

1,264

8,345

4.910601

0.147152

0.132109

0.224585

0.226518

0.812792

0.809892

0.806992

0.768648

0.753343

0.746899

0

0.011058

0.089754

8,345

187

121

44.625668

0.806082

0.104494

0

0.264151

0

0.022403

0.007875

0

1

0.132075

false

0

0.028302

0

0.160377

0

null

0

1

0

null

0

8

1dcf5fde610de029bd12e20df8e40821a16810e3

15,410

py

Python

accelbyte_py_sdk/api/iam/wrappers/_o_auth2_0.py

AccelByte/accelbyte-python-sdk

dcd311fad111c59da828278975340fb92e0f26f7

[ "MIT" ]

null

accelbyte_py_sdk/api/iam/wrappers/_o_auth2_0.py

AccelByte/accelbyte-python-sdk

dcd311fad111c59da828278975340fb92e0f26f7

[ "MIT" ]

1

2021-10-13T03:46:58.000Z

accelbyte_py_sdk/api/iam/wrappers/_o_auth2_0.py

AccelByte/accelbyte-python-sdk

dcd311fad111c59da828278975340fb92e0f26f7

[ "MIT" ]

null

# Copyright (c) 2021 AccelByte Inc. All Rights Reserved. # This is licensed software from AccelByte Inc, for limitations # and restrictions contact your company contract manager. # # Code generated. DO NOT EDIT! # template file: justice_py_sdk_codegen/__main__.py # pylint: disable=duplicate-code # pylint: disable=line-too-long # pylint: disable=missing-function-docstring # pylint: disable=missing-function-docstring # pylint: disable=missing-module-docstring # pylint: disable=too-many-arguments # pylint: disable=too-many-branches # pylint: disable=too-many-instance-attributes # pylint: disable=too-many-lines # pylint: disable=too-many-locals # pylint: disable=too-many-public-methods # pylint: disable=too-many-return-statements # pylint: disable=too-many-statements # pylint: disable=unused-import from typing import Any, Dict, List, Optional, Tuple, Union from ....core import HeaderStr from ....core import get_namespace as get_services_namespace from ....core import run_request from ....core import run_request_async from ....core import same_doc_as from ..models import OauthapiRevocationList from ..models import OauthcommonJWKSet from ..models import OauthmodelErrorResponse from ..models import OauthmodelTokenIntrospectResponse from ..models import OauthmodelTokenResponse from ..models import OauthmodelTokenResponseV3 from ..models import OauthmodelTokenThirdPartyResponse from ..models import RestErrorResponse from ..operations.o_auth2_0 import AdminRetrieveUserThirdPartyPlatformTokenV3 from ..operations.o_auth2_0 import AuthCodeRequestV3 from ..operations.o_auth2_0 import AuthorizeV3 from ..operations.o_auth2_0 import AuthorizeV3CodeChallengeMethodEnum, AuthorizeV3ResponseTypeEnum from ..operations.o_auth2_0 import Change2faMethod from ..operations.o_auth2_0 import GetJWKSV3 from ..operations.o_auth2_0 import GetRevocationListV3 from ..operations.o_auth2_0 import PlatformTokenGrantV3 from ..operations.o_auth2_0 import RetrieveUserThirdPartyPlatformTokenV3 from ..operations.o_auth2_0 import RevokeUserV3 from ..operations.o_auth2_0 import TokenGrantV3 from ..operations.o_auth2_0 import TokenGrantV3GrantTypeEnum from ..operations.o_auth2_0 import TokenIntrospectionV3 from ..operations.o_auth2_0 import TokenRevocationV3 from ..operations.o_auth2_0 import Verify2faCode @same_doc_as(AdminRetrieveUserThirdPartyPlatformTokenV3) def admin_retrieve_user_third_party_platform_token_v3(platform_id: str, user_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): if namespace is None: namespace, error = get_services_namespace() if error: return None, error request = AdminRetrieveUserThirdPartyPlatformTokenV3.create( platform_id=platform_id, user_id=user_id, namespace=namespace, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(AdminRetrieveUserThirdPartyPlatformTokenV3) async def admin_retrieve_user_third_party_platform_token_v3_async(platform_id: str, user_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): if namespace is None: namespace, error = get_services_namespace() if error: return None, error request = AdminRetrieveUserThirdPartyPlatformTokenV3.create( platform_id=platform_id, user_id=user_id, namespace=namespace, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(AuthCodeRequestV3) def auth_code_request_v3(platform_id: str, request_id: str, client_id: Optional[str] = None, redirect_uri: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = AuthCodeRequestV3.create( platform_id=platform_id, request_id=request_id, client_id=client_id, redirect_uri=redirect_uri, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(AuthCodeRequestV3) async def auth_code_request_v3_async(platform_id: str, request_id: str, client_id: Optional[str] = None, redirect_uri: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = AuthCodeRequestV3.create( platform_id=platform_id, request_id=request_id, client_id=client_id, redirect_uri=redirect_uri, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(AuthorizeV3) def authorize_v3(client_id: str, response_type: Union[str, AuthorizeV3ResponseTypeEnum], code_challenge: Optional[str] = None, code_challenge_method: Optional[Union[str, AuthorizeV3CodeChallengeMethodEnum]] = None, create_headless: Optional[bool] = None, redirect_uri: Optional[str] = None, scope: Optional[str] = None, state: Optional[str] = None, target_auth_page: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = AuthorizeV3.create( client_id=client_id, response_type=response_type, code_challenge=code_challenge, code_challenge_method=code_challenge_method, create_headless=create_headless, redirect_uri=redirect_uri, scope=scope, state=state, target_auth_page=target_auth_page, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(AuthorizeV3) async def authorize_v3_async(client_id: str, response_type: Union[str, AuthorizeV3ResponseTypeEnum], code_challenge: Optional[str] = None, code_challenge_method: Optional[Union[str, AuthorizeV3CodeChallengeMethodEnum]] = None, create_headless: Optional[bool] = None, redirect_uri: Optional[str] = None, scope: Optional[str] = None, state: Optional[str] = None, target_auth_page: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = AuthorizeV3.create( client_id=client_id, response_type=response_type, code_challenge=code_challenge, code_challenge_method=code_challenge_method, create_headless=create_headless, redirect_uri=redirect_uri, scope=scope, state=state, target_auth_page=target_auth_page, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(Change2faMethod) def change2fa_method(factor: str, mfa_token: str, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = Change2faMethod.create( factor=factor, mfa_token=mfa_token, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(Change2faMethod) async def change2fa_method_async(factor: str, mfa_token: str, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = Change2faMethod.create( factor=factor, mfa_token=mfa_token, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(GetJWKSV3) def get_jwksv3(x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = GetJWKSV3.create() return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(GetJWKSV3) async def get_jwksv3_async(x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = GetJWKSV3.create() return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(GetRevocationListV3) def get_revocation_list_v3(x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = GetRevocationListV3.create() return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(GetRevocationListV3) async def get_revocation_list_v3_async(x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = GetRevocationListV3.create() return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(PlatformTokenGrantV3) def platform_token_grant_v3(platform_id: str, client_id: Optional[str] = None, create_headless: Optional[bool] = None, device_id: Optional[str] = None, platform_token: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = PlatformTokenGrantV3.create( platform_id=platform_id, client_id=client_id, create_headless=create_headless, device_id=device_id, platform_token=platform_token, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(PlatformTokenGrantV3) async def platform_token_grant_v3_async(platform_id: str, client_id: Optional[str] = None, create_headless: Optional[bool] = None, device_id: Optional[str] = None, platform_token: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = PlatformTokenGrantV3.create( platform_id=platform_id, client_id=client_id, create_headless=create_headless, device_id=device_id, platform_token=platform_token, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(RetrieveUserThirdPartyPlatformTokenV3) def retrieve_user_third_party_platform_token_v3(platform_id: str, user_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): if namespace is None: namespace, error = get_services_namespace() if error: return None, error request = RetrieveUserThirdPartyPlatformTokenV3.create( platform_id=platform_id, user_id=user_id, namespace=namespace, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(RetrieveUserThirdPartyPlatformTokenV3) async def retrieve_user_third_party_platform_token_v3_async(platform_id: str, user_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): if namespace is None: namespace, error = get_services_namespace() if error: return None, error request = RetrieveUserThirdPartyPlatformTokenV3.create( platform_id=platform_id, user_id=user_id, namespace=namespace, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(RevokeUserV3) def revoke_user_v3(user_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): if namespace is None: namespace, error = get_services_namespace() if error: return None, error request = RevokeUserV3.create( user_id=user_id, namespace=namespace, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(RevokeUserV3) async def revoke_user_v3_async(user_id: str, namespace: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): if namespace is None: namespace, error = get_services_namespace() if error: return None, error request = RevokeUserV3.create( user_id=user_id, namespace=namespace, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(TokenGrantV3) def token_grant_v3(grant_type: Union[str, TokenGrantV3GrantTypeEnum], client_id: Optional[str] = None, code: Optional[str] = None, code_verifier: Optional[str] = None, device_id: Optional[Union[str, HeaderStr]] = None, extend_exp: Optional[bool] = None, password: Optional[str] = None, redirect_uri: Optional[str] = None, refresh_token: Optional[str] = None, username: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = TokenGrantV3.create( grant_type=grant_type, client_id=client_id, code=code, code_verifier=code_verifier, device_id=device_id, extend_exp=extend_exp, password=password, redirect_uri=redirect_uri, refresh_token=refresh_token, username=username, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(TokenGrantV3) async def token_grant_v3_async(grant_type: Union[str, TokenGrantV3GrantTypeEnum], client_id: Optional[str] = None, code: Optional[str] = None, code_verifier: Optional[str] = None, device_id: Optional[Union[str, HeaderStr]] = None, extend_exp: Optional[bool] = None, password: Optional[str] = None, redirect_uri: Optional[str] = None, refresh_token: Optional[str] = None, username: Optional[str] = None, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = TokenGrantV3.create( grant_type=grant_type, client_id=client_id, code=code, code_verifier=code_verifier, device_id=device_id, extend_exp=extend_exp, password=password, redirect_uri=redirect_uri, refresh_token=refresh_token, username=username, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(TokenIntrospectionV3) def token_introspection_v3(token: str, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = TokenIntrospectionV3.create( token=token, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(TokenIntrospectionV3) async def token_introspection_v3_async(token: str, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = TokenIntrospectionV3.create( token=token, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(TokenRevocationV3) def token_revocation_v3(token: str, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = TokenRevocationV3.create( token=token, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(TokenRevocationV3) async def token_revocation_v3_async(token: str, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = TokenRevocationV3.create( token=token, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(Verify2faCode) def verify2fa_code(code: str, factor: str, mfa_token: str, remember_device: bool, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = Verify2faCode.create( code=code, factor=factor, mfa_token=mfa_token, remember_device=remember_device, ) return run_request(request, additional_headers=x_additional_headers, **kwargs) @same_doc_as(Verify2faCode) async def verify2fa_code_async(code: str, factor: str, mfa_token: str, remember_device: bool, x_additional_headers: Optional[Dict[str, str]] = None, **kwargs): request = Verify2faCode.create( code=code, factor=factor, mfa_token=mfa_token, remember_device=remember_device, ) return await run_request_async(request, additional_headers=x_additional_headers, **kwargs)

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0.084591

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0.777135

0.775689

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0.152044

15,410

345

469

44.666667

0.836458

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1dddcabdf20b77b0faa9b957e37413e366d19b3f

441,138

py

Python

Tests/functions/test_signatures.py

tinyg/ironpython2

4e48642dee8785707dbdda330ac4dba19e1174a9

[ "Apache-2.0" ]

null

Tests/functions/test_signatures.py

tinyg/ironpython2

4e48642dee8785707dbdda330ac4dba19e1174a9

[ "Apache-2.0" ]

null

Tests/functions/test_signatures.py

tinyg/ironpython2

4e48642dee8785707dbdda330ac4dba19e1174a9

[ "Apache-2.0" ]

null

###################################################################################### # # Copyright (c) Microsoft Corporation. All rights reserved. # # This source code is subject to terms and conditions of the Apache License, Version 2.0. A # copy of the license can be found in the License.html file at the root of this distribution. If # you cannot locate the Apache License, Version 2.0, please send an email to # ironpy@microsoft.com. By using this source code in any fashion, you are agreeing to be bound # by the terms of the Apache License, Version 2.0. # # You must not remove this notice, or any other, from this software. # # ##################################################################################### import unittest from iptest import run_test def gen_major_cases(): ''' Helper function used to automatically and exhaustively generate test cases for all Python function signatures IronPython optimizes for. ''' NUM_PARAMS = 15 NUM_KWPARAMS = 15 NICE_SPACE = " " *3 for num_params in xrange(0, NUM_PARAMS): for num_kwparams in xrange(0, NUM_KWPARAMS): print NICE_SPACE, "#--%d params with %d keyword params--#" % (num_params, num_kwparams) print NICE_SPACE, "def f%dParams%dKwparams(self, " % (num_params, num_kwparams), for i in xrange(0, num_params): if i==num_params-1 and num_kwparams==0: print "p%d" % (i+1), else: print "p%d," % (i+1), for i in xrange(0, num_kwparams): if i!=num_kwparams-1: print "kw%d=%d," % (i+1, i+1), else: print "kw%d=%d" % (i+1, i+1), print "):" if num_params==0: print NICE_SPACE, " ret_valP = ()" else: print NICE_SPACE, " ret_valP = (", for i in xrange(0, num_params): print "p%d" % (i+1), print ",", if i==num_params-1: print ")" if num_kwparams==0: print NICE_SPACE, " ret_valKW = ()" else: print NICE_SPACE, " ret_valKW = (", for i in xrange(0, num_kwparams): print "kw%d" % (i+1), print ",", if i==num_kwparams-1: print ")" print NICE_SPACE, " return ret_valP, ret_valKW" print "" params = str(range(1, num_params+1))[1:-1] params_expected = params if num_params==1: params_expected += "," kwparams_expected = str(range(1, num_kwparams+1))[1:-1] if num_kwparams==1: kwparams_expected += "," print NICE_SPACE, "self.assertEqual(f%dParams%dKwparams(%s" % (num_params, num_kwparams, params), ")," print NICE_SPACE, " ((%s), " % params_expected, "(%s))" % kwparams_expected, ")" print NICE_SPACE, "" for i in xrange(0, num_kwparams): kwparams = str(["kw%d=None" % (x+1) for x in xrange(0, i+1)])[1:-1].replace("'", "") kwparams_expected = "None," * (i+1) kwparams_expected += str(range(i+2, num_kwparams+1))[1:-1] print NICE_SPACE, "self.assertEqual(f%dParams%dKwparams(%s" % (num_params, num_kwparams, params), if num_params!=0: print ",", print kwparams, ")," print NICE_SPACE, " ((%s), " % params_expected, "(%s))" % kwparams_expected, ")" print "" class SignaturesTest(unittest.TestCase): def test_args_plus_kwargs_optimizations(self): ''' Because IronPython utilizes DLR site caching for function signatures, we must exhaustively cover all Python signatures with up to 14 param/keyword parameters. ''' #--0 params with 0 keyword params--# def f0Params0Kwparams( ): ret_valP = () ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f0Params0Kwparams( ), ((), ()) ) #--0 params with 1 keyword params--# def f0Params1Kwparams( kw1=1 ): ret_valP = () ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f0Params1Kwparams( ), ((), (1,)) ) self.assertEqual(f0Params1Kwparams( kw1=None ), ((), (None,)) ) #--0 params with 2 keyword params--# def f0Params2Kwparams( kw1=1, kw2=2 ): ret_valP = () ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f0Params2Kwparams( ), ((), (1, 2)) ) self.assertEqual(f0Params2Kwparams( kw1=None ), ((), (None,2)) ) self.assertEqual(f0Params2Kwparams( kw1=None, kw2=None ), ((), (None,None,)) ) #--0 params with 3 keyword params--# def f0Params3Kwparams( kw1=1, kw2=2, kw3=3 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f0Params3Kwparams( ), ((), (1, 2, 3)) ) self.assertEqual(f0Params3Kwparams( kw1=None ), ((), (None,2, 3)) ) self.assertEqual(f0Params3Kwparams( kw1=None, kw2=None ), ((), (None,None,3)) ) self.assertEqual(f0Params3Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,)) ) #--0 params with 4 keyword params--# def f0Params4Kwparams( kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f0Params4Kwparams( ), ((), (1, 2, 3, 4)) ) self.assertEqual(f0Params4Kwparams( kw1=None ), ((), (None,2, 3, 4)) ) self.assertEqual(f0Params4Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4)) ) self.assertEqual(f0Params4Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4)) ) self.assertEqual(f0Params4Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,)) ) #--0 params with 5 keyword params--# def f0Params5Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f0Params5Kwparams( ), ((), (1, 2, 3, 4, 5)) ) self.assertEqual(f0Params5Kwparams( kw1=None ), ((), (None,2, 3, 4, 5)) ) self.assertEqual(f0Params5Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5)) ) self.assertEqual(f0Params5Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5)) ) self.assertEqual(f0Params5Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5)) ) self.assertEqual(f0Params5Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,)) ) #--0 params with 6 keyword params--# def f0Params6Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f0Params6Kwparams( ), ((), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f0Params6Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f0Params6Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6)) ) self.assertEqual(f0Params6Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6)) ) self.assertEqual(f0Params6Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6)) ) self.assertEqual(f0Params6Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6)) ) self.assertEqual(f0Params6Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,)) ) #--0 params with 7 keyword params--# def f0Params7Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f0Params7Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f0Params7Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f0Params7Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f0Params7Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f0Params7Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f0Params7Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7)) ) self.assertEqual(f0Params7Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7)) ) self.assertEqual(f0Params7Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,)) ) #--0 params with 8 keyword params--# def f0Params8Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f0Params8Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f0Params8Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,)) ) #--0 params with 9 keyword params--# def f0Params9Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f0Params9Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f0Params9Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((), (None,None,None,None,None,None,None,None,None,)) ) #--0 params with 10 keyword params--# def f0Params10Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f0Params10Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f0Params10Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((), (None,None,None,None,None,None,None,None,None,None,)) ) #--0 params with 11 keyword params--# def f0Params11Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f0Params11Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f0Params11Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--0 params with 12 keyword params--# def f0Params12Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f0Params12Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f0Params12Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--0 params with 13 keyword params--# def f0Params13Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f0Params13Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f0Params13Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--0 params with 14 keyword params--# def f0Params14Kwparams( kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = () ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f0Params14Kwparams( ), ((), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None ), ((), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None ), ((), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None ), ((), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None ), ((), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f0Params14Kwparams( kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--1 params with 0 keyword params--# def f1Params0Kwparams( p1 ): ret_valP = ( p1 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f1Params0Kwparams(1 ), ((1,), ()) ) #--1 params with 1 keyword params--# def f1Params1Kwparams( p1, kw1=1 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f1Params1Kwparams(1 ), ((1,), (1,)) ) self.assertEqual(f1Params1Kwparams(1 , kw1=None ), ((1,), (None,)) ) #--1 params with 2 keyword params--# def f1Params2Kwparams( p1, kw1=1, kw2=2 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f1Params2Kwparams(1 ), ((1,), (1, 2)) ) self.assertEqual(f1Params2Kwparams(1 , kw1=None ), ((1,), (None,2)) ) self.assertEqual(f1Params2Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,)) ) #--1 params with 3 keyword params--# def f1Params3Kwparams( p1, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f1Params3Kwparams(1 ), ((1,), (1, 2, 3)) ) self.assertEqual(f1Params3Kwparams(1 , kw1=None ), ((1,), (None,2, 3)) ) self.assertEqual(f1Params3Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3)) ) self.assertEqual(f1Params3Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,)) ) #--1 params with 4 keyword params--# def f1Params4Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f1Params4Kwparams(1 ), ((1,), (1, 2, 3, 4)) ) self.assertEqual(f1Params4Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4)) ) self.assertEqual(f1Params4Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4)) ) self.assertEqual(f1Params4Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4)) ) self.assertEqual(f1Params4Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,)) ) #--1 params with 5 keyword params--# def f1Params5Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f1Params5Kwparams(1 ), ((1,), (1, 2, 3, 4, 5)) ) self.assertEqual(f1Params5Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5)) ) self.assertEqual(f1Params5Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5)) ) self.assertEqual(f1Params5Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5)) ) self.assertEqual(f1Params5Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5)) ) self.assertEqual(f1Params5Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,)) ) #--1 params with 6 keyword params--# def f1Params6Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f1Params6Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f1Params6Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f1Params6Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6)) ) self.assertEqual(f1Params6Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6)) ) self.assertEqual(f1Params6Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6)) ) self.assertEqual(f1Params6Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6)) ) self.assertEqual(f1Params6Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,)) ) #--1 params with 7 keyword params--# def f1Params7Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f1Params7Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7)) ) self.assertEqual(f1Params7Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,)) ) #--1 params with 8 keyword params--# def f1Params8Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f1Params8Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f1Params8Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,)) ) #--1 params with 9 keyword params--# def f1Params9Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f1Params9Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f1Params9Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1,), (None,None,None,None,None,None,None,None,None,)) ) #--1 params with 10 keyword params--# def f1Params10Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f1Params10Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1,), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f1Params10Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,)) ) #--1 params with 11 keyword params--# def f1Params11Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f1Params11Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1,), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f1Params11Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--1 params with 12 keyword params--# def f1Params12Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f1Params12Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1,), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f1Params12Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--1 params with 13 keyword params--# def f1Params13Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f1Params13Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1,), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f1Params13Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--1 params with 14 keyword params--# def f1Params14Kwparams( p1, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f1Params14Kwparams(1 ), ((1,), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None ), ((1,), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None ), ((1,), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None ), ((1,), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1,), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1,), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1,), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1,), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1,), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1,), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f1Params14Kwparams(1 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1,), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--2 params with 0 keyword params--# def f2Params0Kwparams( p1, p2 ): ret_valP = ( p1 , p2 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f2Params0Kwparams(1, 2 ), ((1, 2), ()) ) #--2 params with 1 keyword params--# def f2Params1Kwparams( p1, p2, kw1=1 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f2Params1Kwparams(1, 2 ), ((1, 2), (1,)) ) self.assertEqual(f2Params1Kwparams(1, 2 , kw1=None ), ((1, 2), (None,)) ) #--2 params with 2 keyword params--# def f2Params2Kwparams( p1, p2, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f2Params2Kwparams(1, 2 ), ((1, 2), (1, 2)) ) self.assertEqual(f2Params2Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2)) ) self.assertEqual(f2Params2Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,)) ) #--2 params with 3 keyword params--# def f2Params3Kwparams( p1, p2, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f2Params3Kwparams(1, 2 ), ((1, 2), (1, 2, 3)) ) self.assertEqual(f2Params3Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3)) ) self.assertEqual(f2Params3Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3)) ) self.assertEqual(f2Params3Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,)) ) #--2 params with 4 keyword params--# def f2Params4Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f2Params4Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4)) ) self.assertEqual(f2Params4Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4)) ) self.assertEqual(f2Params4Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4)) ) self.assertEqual(f2Params4Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4)) ) self.assertEqual(f2Params4Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,)) ) #--2 params with 5 keyword params--# def f2Params5Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f2Params5Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5)) ) self.assertEqual(f2Params5Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5)) ) self.assertEqual(f2Params5Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5)) ) self.assertEqual(f2Params5Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5)) ) self.assertEqual(f2Params5Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5)) ) self.assertEqual(f2Params5Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,)) ) #--2 params with 6 keyword params--# def f2Params6Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f2Params6Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f2Params6Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f2Params6Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6)) ) self.assertEqual(f2Params6Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6)) ) self.assertEqual(f2Params6Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6)) ) self.assertEqual(f2Params6Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6)) ) self.assertEqual(f2Params6Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,)) ) #--2 params with 7 keyword params--# def f2Params7Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f2Params7Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7)) ) self.assertEqual(f2Params7Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,)) ) #--2 params with 8 keyword params--# def f2Params8Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f2Params8Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f2Params8Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,)) ) #--2 params with 9 keyword params--# def f2Params9Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f2Params9Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f2Params9Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,)) ) #--2 params with 10 keyword params--# def f2Params10Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f2Params10Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f2Params10Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,)) ) #--2 params with 11 keyword params--# def f2Params11Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f2Params11Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f2Params11Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--2 params with 12 keyword params--# def f2Params12Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f2Params12Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f2Params12Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--2 params with 13 keyword params--# def f2Params13Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f2Params13Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f2Params13Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--2 params with 14 keyword params--# def f2Params14Kwparams( p1, p2, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f2Params14Kwparams(1, 2 ), ((1, 2), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None ), ((1, 2), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None ), ((1, 2), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None ), ((1, 2), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f2Params14Kwparams(1, 2 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--3 params with 0 keyword params--# def f3Params0Kwparams( p1, p2, p3 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f3Params0Kwparams(1, 2, 3 ), ((1, 2, 3), ()) ) #--3 params with 1 keyword params--# def f3Params1Kwparams( p1, p2, p3, kw1=1 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f3Params1Kwparams(1, 2, 3 ), ((1, 2, 3), (1,)) ) self.assertEqual(f3Params1Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,)) ) #--3 params with 2 keyword params--# def f3Params2Kwparams( p1, p2, p3, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f3Params2Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2)) ) self.assertEqual(f3Params2Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2)) ) self.assertEqual(f3Params2Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,)) ) #--3 params with 3 keyword params--# def f3Params3Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f3Params3Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3)) ) self.assertEqual(f3Params3Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3)) ) self.assertEqual(f3Params3Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3)) ) self.assertEqual(f3Params3Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,)) ) #--3 params with 4 keyword params--# def f3Params4Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f3Params4Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4)) ) self.assertEqual(f3Params4Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4)) ) self.assertEqual(f3Params4Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4)) ) self.assertEqual(f3Params4Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4)) ) self.assertEqual(f3Params4Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,)) ) #--3 params with 5 keyword params--# def f3Params5Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f3Params5Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5)) ) self.assertEqual(f3Params5Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5)) ) self.assertEqual(f3Params5Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5)) ) self.assertEqual(f3Params5Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5)) ) self.assertEqual(f3Params5Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5)) ) self.assertEqual(f3Params5Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,)) ) #--3 params with 6 keyword params--# def f3Params6Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f3Params6Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f3Params6Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f3Params6Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6)) ) self.assertEqual(f3Params6Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6)) ) self.assertEqual(f3Params6Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6)) ) self.assertEqual(f3Params6Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6)) ) self.assertEqual(f3Params6Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,)) ) #--3 params with 7 keyword params--# def f3Params7Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f3Params7Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7)) ) self.assertEqual(f3Params7Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,)) ) #--3 params with 8 keyword params--# def f3Params8Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f3Params8Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f3Params8Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,)) ) #--3 params with 9 keyword params--# def f3Params9Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f3Params9Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f3Params9Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,)) ) #--3 params with 10 keyword params--# def f3Params10Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f3Params10Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f3Params10Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,)) ) #--3 params with 11 keyword params--# def f3Params11Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f3Params11Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f3Params11Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--3 params with 12 keyword params--# def f3Params12Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f3Params12Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f3Params12Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--3 params with 13 keyword params--# def f3Params13Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f3Params13Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f3Params13Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--3 params with 14 keyword params--# def f3Params14Kwparams( p1, p2, p3, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f3Params14Kwparams(1, 2, 3 ), ((1, 2, 3), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None ), ((1, 2, 3), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None ), ((1, 2, 3), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f3Params14Kwparams(1, 2, 3 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--4 params with 0 keyword params--# def f4Params0Kwparams( p1, p2, p3, p4 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f4Params0Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), ()) ) #--4 params with 1 keyword params--# def f4Params1Kwparams( p1, p2, p3, p4, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f4Params1Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1,)) ) self.assertEqual(f4Params1Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,)) ) #--4 params with 2 keyword params--# def f4Params2Kwparams( p1, p2, p3, p4, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f4Params2Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2)) ) self.assertEqual(f4Params2Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2)) ) self.assertEqual(f4Params2Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,)) ) #--4 params with 3 keyword params--# def f4Params3Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f4Params3Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3)) ) self.assertEqual(f4Params3Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3)) ) self.assertEqual(f4Params3Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3)) ) self.assertEqual(f4Params3Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,)) ) #--4 params with 4 keyword params--# def f4Params4Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f4Params4Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4)) ) self.assertEqual(f4Params4Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4)) ) self.assertEqual(f4Params4Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4)) ) self.assertEqual(f4Params4Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4)) ) self.assertEqual(f4Params4Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,)) ) #--4 params with 5 keyword params--# def f4Params5Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f4Params5Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5)) ) self.assertEqual(f4Params5Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5)) ) self.assertEqual(f4Params5Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5)) ) self.assertEqual(f4Params5Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5)) ) self.assertEqual(f4Params5Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5)) ) self.assertEqual(f4Params5Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,)) ) #--4 params with 6 keyword params--# def f4Params6Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6)) ) self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6)) ) self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6)) ) self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6)) ) self.assertEqual(f4Params6Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,)) ) #--4 params with 7 keyword params--# def f4Params7Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7)) ) self.assertEqual(f4Params7Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,)) ) #--4 params with 8 keyword params--# def f4Params8Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f4Params8Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,)) ) #--4 params with 9 keyword params--# def f4Params9Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f4Params9Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,)) ) #--4 params with 10 keyword params--# def f4Params10Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f4Params10Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,)) ) #--4 params with 11 keyword params--# def f4Params11Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f4Params11Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--4 params with 12 keyword params--# def f4Params12Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f4Params12Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--4 params with 13 keyword params--# def f4Params13Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f4Params13Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--4 params with 14 keyword params--# def f4Params14Kwparams( p1, p2, p3, p4, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 ), ((1, 2, 3, 4), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None ), ((1, 2, 3, 4), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None ), ((1, 2, 3, 4), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f4Params14Kwparams(1, 2, 3, 4 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--5 params with 0 keyword params--# def f5Params0Kwparams( p1, p2, p3, p4, p5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f5Params0Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), ()) ) #--5 params with 1 keyword params--# def f5Params1Kwparams( p1, p2, p3, p4, p5, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f5Params1Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1,)) ) self.assertEqual(f5Params1Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,)) ) #--5 params with 2 keyword params--# def f5Params2Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f5Params2Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2)) ) self.assertEqual(f5Params2Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2)) ) self.assertEqual(f5Params2Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,)) ) #--5 params with 3 keyword params--# def f5Params3Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f5Params3Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3)) ) self.assertEqual(f5Params3Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3)) ) self.assertEqual(f5Params3Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3)) ) self.assertEqual(f5Params3Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,)) ) #--5 params with 4 keyword params--# def f5Params4Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f5Params4Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4)) ) self.assertEqual(f5Params4Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4)) ) self.assertEqual(f5Params4Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4)) ) self.assertEqual(f5Params4Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4)) ) self.assertEqual(f5Params4Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,)) ) #--5 params with 5 keyword params--# def f5Params5Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f5Params5Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5)) ) self.assertEqual(f5Params5Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5)) ) self.assertEqual(f5Params5Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5)) ) self.assertEqual(f5Params5Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5)) ) self.assertEqual(f5Params5Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5)) ) self.assertEqual(f5Params5Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,)) ) #--5 params with 6 keyword params--# def f5Params6Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6)) ) self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6)) ) self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6)) ) self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6)) ) self.assertEqual(f5Params6Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,)) ) #--5 params with 7 keyword params--# def f5Params7Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7)) ) self.assertEqual(f5Params7Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,)) ) #--5 params with 8 keyword params--# def f5Params8Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f5Params8Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,)) ) #--5 params with 9 keyword params--# def f5Params9Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f5Params9Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,)) ) #--5 params with 10 keyword params--# def f5Params10Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f5Params10Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,)) ) #--5 params with 11 keyword params--# def f5Params11Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f5Params11Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--5 params with 12 keyword params--# def f5Params12Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f5Params12Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--5 params with 13 keyword params--# def f5Params13Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f5Params13Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--5 params with 14 keyword params--# def f5Params14Kwparams( p1, p2, p3, p4, p5, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 ), ((1, 2, 3, 4, 5), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None ), ((1, 2, 3, 4, 5), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f5Params14Kwparams(1, 2, 3, 4, 5 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--6 params with 0 keyword params--# def f6Params0Kwparams( p1, p2, p3, p4, p5, p6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f6Params0Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), ()) ) #--6 params with 1 keyword params--# def f6Params1Kwparams( p1, p2, p3, p4, p5, p6, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f6Params1Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1,)) ) self.assertEqual(f6Params1Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,)) ) #--6 params with 2 keyword params--# def f6Params2Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f6Params2Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2)) ) self.assertEqual(f6Params2Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2)) ) self.assertEqual(f6Params2Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,)) ) #--6 params with 3 keyword params--# def f6Params3Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f6Params3Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3)) ) self.assertEqual(f6Params3Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3)) ) self.assertEqual(f6Params3Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3)) ) self.assertEqual(f6Params3Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,)) ) #--6 params with 4 keyword params--# def f6Params4Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f6Params4Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4)) ) self.assertEqual(f6Params4Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4)) ) self.assertEqual(f6Params4Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4)) ) self.assertEqual(f6Params4Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4)) ) self.assertEqual(f6Params4Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,)) ) #--6 params with 5 keyword params--# def f6Params5Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f6Params5Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5)) ) self.assertEqual(f6Params5Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5)) ) self.assertEqual(f6Params5Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5)) ) self.assertEqual(f6Params5Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5)) ) self.assertEqual(f6Params5Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5)) ) self.assertEqual(f6Params5Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,)) ) #--6 params with 6 keyword params--# def f6Params6Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6)) ) self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6)) ) self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6)) ) self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6)) ) self.assertEqual(f6Params6Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,)) ) #--6 params with 7 keyword params--# def f6Params7Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7)) ) self.assertEqual(f6Params7Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,)) ) #--6 params with 8 keyword params--# def f6Params8Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f6Params8Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,)) ) #--6 params with 9 keyword params--# def f6Params9Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f6Params9Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,)) ) #--6 params with 10 keyword params--# def f6Params10Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f6Params10Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,)) ) #--6 params with 11 keyword params--# def f6Params11Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f6Params11Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--6 params with 12 keyword params--# def f6Params12Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f6Params12Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--6 params with 13 keyword params--# def f6Params13Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f6Params13Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--6 params with 14 keyword params--# def f6Params14Kwparams( p1, p2, p3, p4, p5, p6, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 ), ((1, 2, 3, 4, 5, 6), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None ), ((1, 2, 3, 4, 5, 6), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f6Params14Kwparams(1, 2, 3, 4, 5, 6 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--7 params with 0 keyword params--# def f7Params0Kwparams( p1, p2, p3, p4, p5, p6, p7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f7Params0Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), ()) ) #--7 params with 1 keyword params--# def f7Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f7Params1Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1,)) ) self.assertEqual(f7Params1Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,)) ) #--7 params with 2 keyword params--# def f7Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f7Params2Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2)) ) self.assertEqual(f7Params2Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2)) ) self.assertEqual(f7Params2Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,)) ) #--7 params with 3 keyword params--# def f7Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f7Params3Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3)) ) self.assertEqual(f7Params3Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3)) ) self.assertEqual(f7Params3Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3)) ) self.assertEqual(f7Params3Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,)) ) #--7 params with 4 keyword params--# def f7Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f7Params4Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4)) ) self.assertEqual(f7Params4Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4)) ) self.assertEqual(f7Params4Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4)) ) self.assertEqual(f7Params4Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4)) ) self.assertEqual(f7Params4Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,)) ) #--7 params with 5 keyword params--# def f7Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f7Params5Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5)) ) self.assertEqual(f7Params5Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5)) ) self.assertEqual(f7Params5Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5)) ) self.assertEqual(f7Params5Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5)) ) self.assertEqual(f7Params5Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5)) ) self.assertEqual(f7Params5Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,)) ) #--7 params with 6 keyword params--# def f7Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6)) ) self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6)) ) self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6)) ) self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6)) ) self.assertEqual(f7Params6Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,)) ) #--7 params with 7 keyword params--# def f7Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7)) ) self.assertEqual(f7Params7Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,)) ) #--7 params with 8 keyword params--# def f7Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f7Params8Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,)) ) #--7 params with 9 keyword params--# def f7Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f7Params9Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,)) ) #--7 params with 10 keyword params--# def f7Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f7Params10Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,)) ) #--7 params with 11 keyword params--# def f7Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f7Params11Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--7 params with 12 keyword params--# def f7Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f7Params12Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--7 params with 13 keyword params--# def f7Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f7Params13Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--7 params with 14 keyword params--# def f7Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 ), ((1, 2, 3, 4, 5, 6, 7), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f7Params14Kwparams(1, 2, 3, 4, 5, 6, 7 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--8 params with 0 keyword params--# def f8Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f8Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), ()) ) #--8 params with 1 keyword params--# def f8Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f8Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1,)) ) self.assertEqual(f8Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,)) ) #--8 params with 2 keyword params--# def f8Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f8Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2)) ) self.assertEqual(f8Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2)) ) self.assertEqual(f8Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,)) ) #--8 params with 3 keyword params--# def f8Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f8Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3)) ) self.assertEqual(f8Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3)) ) self.assertEqual(f8Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3)) ) self.assertEqual(f8Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,)) ) #--8 params with 4 keyword params--# def f8Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f8Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4)) ) self.assertEqual(f8Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4)) ) self.assertEqual(f8Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4)) ) self.assertEqual(f8Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4)) ) self.assertEqual(f8Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,)) ) #--8 params with 5 keyword params--# def f8Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f8Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5)) ) self.assertEqual(f8Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5)) ) self.assertEqual(f8Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5)) ) self.assertEqual(f8Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5)) ) self.assertEqual(f8Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5)) ) self.assertEqual(f8Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,)) ) #--8 params with 6 keyword params--# def f8Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6)) ) self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6)) ) self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6)) ) self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6)) ) self.assertEqual(f8Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,)) ) #--8 params with 7 keyword params--# def f8Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7)) ) self.assertEqual(f8Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,)) ) #--8 params with 8 keyword params--# def f8Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f8Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,)) ) #--8 params with 9 keyword params--# def f8Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f8Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,)) ) #--8 params with 10 keyword params--# def f8Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f8Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,)) ) #--8 params with 11 keyword params--# def f8Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f8Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--8 params with 12 keyword params--# def f8Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f8Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--8 params with 13 keyword params--# def f8Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f8Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--8 params with 14 keyword params--# def f8Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 ), ((1, 2, 3, 4, 5, 6, 7, 8), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f8Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--9 params with 0 keyword params--# def f9Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f9Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), ()) ) #--9 params with 1 keyword params--# def f9Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f9Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1,)) ) self.assertEqual(f9Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,)) ) #--9 params with 2 keyword params--# def f9Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f9Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2)) ) self.assertEqual(f9Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2)) ) self.assertEqual(f9Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,)) ) #--9 params with 3 keyword params--# def f9Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f9Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3)) ) self.assertEqual(f9Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3)) ) self.assertEqual(f9Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3)) ) self.assertEqual(f9Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,)) ) #--9 params with 4 keyword params--# def f9Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f9Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4)) ) self.assertEqual(f9Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4)) ) self.assertEqual(f9Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4)) ) self.assertEqual(f9Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4)) ) self.assertEqual(f9Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,)) ) #--9 params with 5 keyword params--# def f9Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f9Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5)) ) self.assertEqual(f9Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5)) ) self.assertEqual(f9Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5)) ) self.assertEqual(f9Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5)) ) self.assertEqual(f9Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5)) ) self.assertEqual(f9Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,)) ) #--9 params with 6 keyword params--# def f9Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6)) ) self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6)) ) self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6)) ) self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6)) ) self.assertEqual(f9Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,)) ) #--9 params with 7 keyword params--# def f9Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7)) ) self.assertEqual(f9Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,)) ) #--9 params with 8 keyword params--# def f9Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f9Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,)) ) #--9 params with 9 keyword params--# def f9Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f9Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,)) ) #--9 params with 10 keyword params--# def f9Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f9Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,)) ) #--9 params with 11 keyword params--# def f9Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f9Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--9 params with 12 keyword params--# def f9Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f9Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--9 params with 13 keyword params--# def f9Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f9Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--9 params with 14 keyword params--# def f9Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f9Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--10 params with 0 keyword params--# def f10Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f10Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), ()) ) #--10 params with 1 keyword params--# def f10Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f10Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1,)) ) self.assertEqual(f10Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,)) ) #--10 params with 2 keyword params--# def f10Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f10Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2)) ) self.assertEqual(f10Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2)) ) self.assertEqual(f10Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,)) ) #--10 params with 3 keyword params--# def f10Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f10Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3)) ) self.assertEqual(f10Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3)) ) self.assertEqual(f10Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3)) ) self.assertEqual(f10Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,)) ) #--10 params with 4 keyword params--# def f10Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f10Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4)) ) self.assertEqual(f10Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4)) ) self.assertEqual(f10Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4)) ) self.assertEqual(f10Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4)) ) self.assertEqual(f10Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,)) ) #--10 params with 5 keyword params--# def f10Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f10Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5)) ) self.assertEqual(f10Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5)) ) self.assertEqual(f10Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5)) ) self.assertEqual(f10Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5)) ) self.assertEqual(f10Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5)) ) self.assertEqual(f10Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,)) ) #--10 params with 6 keyword params--# def f10Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6)) ) self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6)) ) self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6)) ) self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6)) ) self.assertEqual(f10Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,)) ) #--10 params with 7 keyword params--# def f10Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7)) ) self.assertEqual(f10Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,)) ) #--10 params with 8 keyword params--# def f10Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f10Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,)) ) #--10 params with 9 keyword params--# def f10Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f10Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,)) ) #--10 params with 10 keyword params--# def f10Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f10Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,)) ) #--10 params with 11 keyword params--# def f10Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f10Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--10 params with 12 keyword params--# def f10Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f10Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--10 params with 13 keyword params--# def f10Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f10Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--10 params with 14 keyword params--# def f10Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f10Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--11 params with 0 keyword params--# def f11Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f11Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), ()) ) #--11 params with 1 keyword params--# def f11Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f11Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1,)) ) self.assertEqual(f11Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,)) ) #--11 params with 2 keyword params--# def f11Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f11Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2)) ) self.assertEqual(f11Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2)) ) self.assertEqual(f11Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,)) ) #--11 params with 3 keyword params--# def f11Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f11Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3)) ) self.assertEqual(f11Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3)) ) self.assertEqual(f11Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3)) ) self.assertEqual(f11Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,)) ) #--11 params with 4 keyword params--# def f11Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f11Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4)) ) self.assertEqual(f11Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4)) ) self.assertEqual(f11Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4)) ) self.assertEqual(f11Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4)) ) self.assertEqual(f11Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,)) ) #--11 params with 5 keyword params--# def f11Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f11Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5)) ) self.assertEqual(f11Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5)) ) self.assertEqual(f11Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5)) ) self.assertEqual(f11Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5)) ) self.assertEqual(f11Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5)) ) self.assertEqual(f11Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,)) ) #--11 params with 6 keyword params--# def f11Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6)) ) self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6)) ) self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6)) ) self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6)) ) self.assertEqual(f11Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,)) ) #--11 params with 7 keyword params--# def f11Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7)) ) self.assertEqual(f11Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,)) ) #--11 params with 8 keyword params--# def f11Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f11Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,)) ) #--11 params with 9 keyword params--# def f11Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f11Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,)) ) #--11 params with 10 keyword params--# def f11Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f11Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,)) ) #--11 params with 11 keyword params--# def f11Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f11Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--11 params with 12 keyword params--# def f11Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f11Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--11 params with 13 keyword params--# def f11Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f11Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--11 params with 14 keyword params--# def f11Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f11Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--12 params with 0 keyword params--# def f12Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f12Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), ()) ) #--12 params with 1 keyword params--# def f12Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f12Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1,)) ) self.assertEqual(f12Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,)) ) #--12 params with 2 keyword params--# def f12Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f12Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2)) ) self.assertEqual(f12Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2)) ) self.assertEqual(f12Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,)) ) #--12 params with 3 keyword params--# def f12Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f12Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3)) ) self.assertEqual(f12Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3)) ) self.assertEqual(f12Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3)) ) self.assertEqual(f12Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,)) ) #--12 params with 4 keyword params--# def f12Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f12Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4)) ) self.assertEqual(f12Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4)) ) self.assertEqual(f12Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4)) ) self.assertEqual(f12Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4)) ) self.assertEqual(f12Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,)) ) #--12 params with 5 keyword params--# def f12Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f12Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5)) ) self.assertEqual(f12Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5)) ) self.assertEqual(f12Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5)) ) self.assertEqual(f12Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5)) ) self.assertEqual(f12Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5)) ) self.assertEqual(f12Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,)) ) #--12 params with 6 keyword params--# def f12Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6)) ) self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6)) ) self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6)) ) self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6)) ) self.assertEqual(f12Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,)) ) #--12 params with 7 keyword params--# def f12Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7)) ) self.assertEqual(f12Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,)) ) #--12 params with 8 keyword params--# def f12Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f12Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,)) ) #--12 params with 9 keyword params--# def f12Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f12Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,)) ) #--12 params with 10 keyword params--# def f12Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f12Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,)) ) #--12 params with 11 keyword params--# def f12Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f12Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--12 params with 12 keyword params--# def f12Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f12Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--12 params with 13 keyword params--# def f12Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f12Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--12 params with 14 keyword params--# def f12Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f12Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--13 params with 0 keyword params--# def f13Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f13Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), ()) ) #--13 params with 1 keyword params--# def f13Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f13Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1,)) ) self.assertEqual(f13Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,)) ) #--13 params with 2 keyword params--# def f13Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f13Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2)) ) self.assertEqual(f13Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2)) ) self.assertEqual(f13Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,)) ) #--13 params with 3 keyword params--# def f13Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f13Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3)) ) self.assertEqual(f13Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3)) ) self.assertEqual(f13Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3)) ) self.assertEqual(f13Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,)) ) #--13 params with 4 keyword params--# def f13Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f13Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4)) ) self.assertEqual(f13Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4)) ) self.assertEqual(f13Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4)) ) self.assertEqual(f13Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4)) ) self.assertEqual(f13Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,)) ) #--13 params with 5 keyword params--# def f13Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f13Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5)) ) self.assertEqual(f13Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5)) ) self.assertEqual(f13Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5)) ) self.assertEqual(f13Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5)) ) self.assertEqual(f13Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5)) ) self.assertEqual(f13Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,)) ) #--13 params with 6 keyword params--# def f13Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6)) ) self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6)) ) self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6)) ) self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6)) ) self.assertEqual(f13Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,)) ) #--13 params with 7 keyword params--# def f13Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7)) ) self.assertEqual(f13Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,)) ) #--13 params with 8 keyword params--# def f13Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f13Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,)) ) #--13 params with 9 keyword params--# def f13Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f13Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,)) ) #--13 params with 10 keyword params--# def f13Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f13Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,)) ) #--13 params with 11 keyword params--# def f13Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f13Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--13 params with 12 keyword params--# def f13Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f13Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--13 params with 13 keyword params--# def f13Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f13Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--13 params with 14 keyword params--# def f13Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f13Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--14 params with 0 keyword params--# def f14Params0Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = () return ret_valP, ret_valKW self.assertEqual(f14Params0Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), ()) ) #--14 params with 1 keyword params--# def f14Params1Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , ) return ret_valP, ret_valKW self.assertEqual(f14Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1,)) ) self.assertEqual(f14Params1Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,)) ) #--14 params with 2 keyword params--# def f14Params2Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , ) return ret_valP, ret_valKW self.assertEqual(f14Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2)) ) self.assertEqual(f14Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2)) ) self.assertEqual(f14Params2Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,)) ) #--14 params with 3 keyword params--# def f14Params3Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , ) return ret_valP, ret_valKW self.assertEqual(f14Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3)) ) self.assertEqual(f14Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3)) ) self.assertEqual(f14Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3)) ) self.assertEqual(f14Params3Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,)) ) #--14 params with 4 keyword params--# def f14Params4Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , ) return ret_valP, ret_valKW self.assertEqual(f14Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4)) ) self.assertEqual(f14Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4)) ) self.assertEqual(f14Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4)) ) self.assertEqual(f14Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4)) ) self.assertEqual(f14Params4Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,)) ) #--14 params with 5 keyword params--# def f14Params5Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , ) return ret_valP, ret_valKW self.assertEqual(f14Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5)) ) self.assertEqual(f14Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5)) ) self.assertEqual(f14Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5)) ) self.assertEqual(f14Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5)) ) self.assertEqual(f14Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5)) ) self.assertEqual(f14Params5Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,)) ) #--14 params with 6 keyword params--# def f14Params6Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , ) return ret_valP, ret_valKW self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6)) ) self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6)) ) self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6)) ) self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6)) ) self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6)) ) self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6)) ) self.assertEqual(f14Params6Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,)) ) #--14 params with 7 keyword params--# def f14Params7Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , ) return ret_valP, ret_valKW self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7)) ) self.assertEqual(f14Params7Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,)) ) #--14 params with 8 keyword params--# def f14Params8Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , ) return ret_valP, ret_valKW self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8)) ) self.assertEqual(f14Params8Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,)) ) #--14 params with 9 keyword params--# def f14Params9Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , ) return ret_valP, ret_valKW self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8, 9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,9)) ) self.assertEqual(f14Params9Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,)) ) #--14 params with 10 keyword params--# def f14Params10Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , ) return ret_valP, ret_valKW self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8, 9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,9, 10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,10)) ) self.assertEqual(f14Params10Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,)) ) #--14 params with 11 keyword params--# def f14Params11Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , ) return ret_valP, ret_valKW self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8, 9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,9, 10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,10, 11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,11)) ) self.assertEqual(f14Params11Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,)) ) #--14 params with 12 keyword params--# def f14Params12Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , ) return ret_valP, ret_valKW self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8, 9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,9, 10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,10, 11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,11, 12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,12)) ) self.assertEqual(f14Params12Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--14 params with 13 keyword params--# def f14Params13Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , ) return ret_valP, ret_valKW self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,11, 12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,12, 13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,None,13)) ) self.assertEqual(f14Params13Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) #--14 params with 14 keyword params--# def f14Params14Kwparams( p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, kw1=1, kw2=2, kw3=3, kw4=4, kw5=5, kw6=6, kw7=7, kw8=8, kw9=9, kw10=10, kw11=11, kw12=12, kw13=13, kw14=14 ): ret_valP = ( p1 , p2 , p3 , p4 , p5 , p6 , p7 , p8 , p9 , p10 , p11 , p12 , p13 , p14 , ) ret_valKW = ( kw1 , kw2 , kw3 , kw4 , kw5 , kw6 , kw7 , kw8 , kw9 , kw10 , kw11 , kw12 , kw13 , kw14 , ) return ret_valP, ret_valKW self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,5, 6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,6, 7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,7, 8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,8, 9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,9, 10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,10, 11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,11, 12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,12, 13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,None,13, 14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,None,None,14)) ) self.assertEqual(f14Params14Kwparams(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 , kw1=None, kw2=None, kw3=None, kw4=None, kw5=None, kw6=None, kw7=None, kw8=None, kw9=None, kw10=None, kw11=None, kw12=None, kw13=None, kw14=None ), ((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14), (None,None,None,None,None,None,None,None,None,None,None,None,None,None,)) ) run_test(__name__)

80.177754

239

0.525446

76,377

441,138

3.022127

0.005159

0.240186

0.288587

0.30278

0.973919

0.971068

0.949155

0.945404

0.941478

0.934581

0

0.199738

0.272448

441,138

5,501

240

80.192329

0.519441

0.018876

0

0.152279

0

0.000801

0.000239

0

0.394829

0

null

0

0.000438

null

0.005916

0

null

1

0

null

0

1

0

10

383786a3c7f0dd92d724cae4be45a40615c6dc49

460

py

Python

vibrant_frequencies/colors/prototype_provider.py

garstka/vibrant-frequencies

e237bf97089c87ca3e9335ba0d2abd09756b98fc

[ "MIT" ]

2

2019-01-31T15:13:37.000Z

2020-11-19T03:24:12.000Z

vibrant_frequencies/colors/prototype_provider.py

garstka/vibrant-frequencies

e237bf97089c87ca3e9335ba0d2abd09756b98fc

[ "MIT" ]

null

vibrant_frequencies/colors/prototype_provider.py

garstka/vibrant-frequencies

e237bf97089c87ca3e9335ba0d2abd09756b98fc

[ "MIT" ]

null

import random class PrototypeColorProvider: def __init__(self): self.__set = [(229 / 255, 244 / 255, 227 / 255), (93 / 255, 169 / 255, 233 / 255), (0 / 255, 63 / 255, 145 / 255), (255 / 255, 255 / 255, 255 / 255), (109 / 255, 50 / 255, 109 / 255)] def random(self): return random.choice(self.__set) def all(self): return self.__set

27.058824

56

0.463043

53

460

3.830189

0.433962

0.17734

0.221675

0.236453

0.103448

0

0.313653

0.41087

460

16

57

28.75

0.435424

0

1

0.25

false

0

0.083333

0.166667

0.583333

0

null

0

1

0

1

0

1

0

null

0

1

0

1

0

7

69cc323696969cf9fcdd8535a0c0a63de57a7981

125

py

Python

app/engineer_django/main/gen.py

ministre/engineer

c23361527307f1a031a8d1ed658344b0ae0e8b89

[ "MIT" ]

null

app/engineer_django/main/gen.py

ministre/engineer

c23361527307f1a031a8d1ed658344b0ae0e8b89

[ "MIT" ]

null

app/engineer_django/main/gen.py

ministre/engineer

c23361527307f1a031a8d1ed658344b0ae0e8b89

[ "MIT" ]

null

# there should be a proprietary function to generate password def dynamic_pass(manufacturer: str, sn: str): return 'foo'

31.25

61

0.76

18

125

5.222222

0.944444

0

0.168

125

3

62

41.666667

0.903846

0.472

0

1

0

0.046875

0

1

0.5

false

0.5

0

0.5

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

c7f84f47f6d9b4cdd8d9ac8fb4986c899e942f83

152

py

Python

backend/server/models/__init__.py

ase-warriors/go-trucks

e92f6c646138a4398a0d9d0ac999f8c89041220d

[ "MIT" ]

1

2017-12-07T03:48:48.000Z

backend/server/models/__init__.py

ase-warriors/go-trucks

e92f6c646138a4398a0d9d0ac999f8c89041220d

[ "MIT" ]

1

2017-11-28T19:12:13.000Z

backend/server/models/__init__.py

ase-warriors/go-trucks

e92f6c646138a4398a0d9d0ac999f8c89041220d

[ "MIT" ]

1

2017-10-13T23:24:26.000Z

# server/models/__init__.py from server.models.vendor import Vendor from server.models.token import BlacklistToken from server.models.post import Post

25.333333

46

0.835526

22

152

5.590909

0.454545

0.390244

0

0.098684

152

5

47

30.4

0.89781

0.164474

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

2a63d31af6542bf23f735dec76f81278550b9440

3,369

py

Python

test/strings/format3.py

kylebarron/MagicPython

da6fa0793e2c85d3bf7709ff1d4f65ccf468db11

[ "MIT" ]

1,482

2015-10-16T21:59:32.000Z

2022-03-30T11:44:40.000Z

test/strings/format3.py

kylebarron/MagicPython

da6fa0793e2c85d3bf7709ff1d4f65ccf468db11

[ "MIT" ]

226

2015-10-15T15:53:44.000Z

2022-03-25T03:08:27.000Z

test/strings/format3.py

kylebarron/MagicPython

da6fa0793e2c85d3bf7709ff1d4f65ccf468db11

[ "MIT" ]

129

2015-10-20T02:41:49.000Z

2022-03-22T01:44:36.000Z

a = '%i' % 42 a = "%(language)s has %(number)03d quote types." a = b"%(language)s has %(number)03d quote types." a = R"%(language)s has %(number)03d quote types." a : source.python : source.python = : keyword.operator.assignment.python, source.python : source.python ' : punctuation.definition.string.begin.python, source.python, string.quoted.single.python %i : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.single.python ' : punctuation.definition.string.end.python, source.python, string.quoted.single.python : source.python % : keyword.operator.arithmetic.python, source.python : source.python 42 : constant.numeric.dec.python, source.python a : source.python : source.python = : keyword.operator.assignment.python, source.python : source.python " : punctuation.definition.string.begin.python, source.python, string.quoted.single.python %(language)s : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.single.python has : source.python, string.quoted.single.python %(number)03d : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.single.python quote types. : source.python, string.quoted.single.python " : punctuation.definition.string.end.python, source.python, string.quoted.single.python a : source.python : source.python = : keyword.operator.assignment.python, source.python : source.python b : source.python, storage.type.string.python, string.quoted.binary.single.python " : punctuation.definition.string.begin.python, source.python, string.quoted.binary.single.python %(language)s : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.binary.single.python has : source.python, string.quoted.binary.single.python %(number)03d : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.binary.single.python quote types. : source.python, string.quoted.binary.single.python " : punctuation.definition.string.end.python, source.python, string.quoted.binary.single.python a : source.python : source.python = : keyword.operator.assignment.python, source.python : source.python R : source.python, storage.type.string.python, string.quoted.raw.single.python " : punctuation.definition.string.begin.python, source.python, string.quoted.raw.single.python %(language)s : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.raw.single.python has : source.python, string.quoted.raw.single.python %(number)03d : constant.character.format.placeholder.other.python, meta.format.percent.python, source.python, string.quoted.raw.single.python quote types. : source.python, string.quoted.raw.single.python " : punctuation.definition.string.end.python, source.python, string.quoted.raw.single.python

66.058824

145

0.692194

392

3,369

5.94898

0.096939

0.221269

0.23928

0.216124

0.977702

0.953259

0.914666

0.818182

0.779588

0

0.005848

0.18789

3,369

50

146

67.38

0.846491

0

0.382979

0

0.234043

0.037993

0

null

0

null

0

null

1

0

1

0

null

0

1

0

11

aa612c91e2109221215ca4a177924d95802a4196

3,174

py

Python

scripts/controller.asm.py

devbored/pineapplecore

311f703347fc448ae5792bbb33e896d58931486a

[ "MIT" ]

null

scripts/controller.asm.py

devbored/pineapplecore

311f703347fc448ae5792bbb33e896d58931486a

[ "MIT" ]

null

scripts/controller.asm.py

devbored/pineapplecore

311f703347fc448ae5792bbb33e896d58931486a

[ "MIT" ]

null

#! /usr/bin/env python3 from common import * # Try each immediate-based RV32I instruction with random operands as the test vector test_assembly = f''' L1: lb x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) fence L6: addi x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} L19: auipc x{randReg(x0=False)}, {randImmU()} L15: sb x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) add x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} L18: lui x{randReg(x0=False)}, {randImmU()} beq x{randReg(x0=False)}, x{randReg(x0=False)}, L{random.randint(0,20)} L0: jalr x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} L20: jal x{randReg(x0=False)}, L{random.randint(0,20)} ecall L2: lh x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) L12: slli x{randReg(x0=False)}, x{randReg(x0=False)}, {randShamt()} L16: sh x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) sll x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} bne x{randReg(x0=False)}, x{randReg(x0=False)}, L{random.randint(0,20)} L3: lw x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) L7: slti x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} L17: sw x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) slt x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} L8: sltiu x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} sltu x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} L4: lbu x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) L9: xori x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} xor x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} blt x{randReg(x0=False)}, x{randReg(x0=False)}, L{random.randint(0,20)} L5: lhu x{randReg(x0=False)}, {randImmI()}(x{randReg(x0=False)}) L13: srli x{randReg(x0=False)}, x{randReg(x0=False)}, {randShamt()} srl x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} bge x{randReg(x0=False)}, x{randReg(x0=False)}, L{random.randint(0,20)} L10: ori x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} or x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} bltu x{randReg(x0=False)}, x{randReg(x0=False)}, L{random.randint(0,20)} L11: andi x{randReg(x0=False)}, x{randReg(x0=False)}, {randImmI()} and x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} bgeu x{randReg(x0=False)}, x{randReg(x0=False)}, L{random.randint(0,20)} ebreak sub x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} L14: srai x{randReg(x0=False)}, x{randReg(x0=False)}, {randShamt()} sra x{randReg(x0=False)}, x{randReg(x0=True)} , x{randReg(x0=True)} ''' if __name__ == "__main__": # Input test vector outfile = f"{basenameNoExt('build', __file__)}.s" with open(outfile, 'w') as fp: print(test_assembly, file=fp)

58.777778

84

0.596408

486

3,174

3.866255

0.222222

0.344864

0.43108

0.486961

0.786056

0.761575

0.378925

0

0.053474

0.192817

3,174

53

85

59.886792

0.679938

0.038752

0

0.744681

0.946194

0.514764

0

1

0

false

0

0.021277

0

0.021277

0

null

1

0

1

0

1

null

0

10

2ac5a6136e9e6e871fe942467153532bcac7dbdf

52,823

py

Python

napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

64

2016-10-20T15:47:18.000Z

2021-11-11T11:57:32.000Z

napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

126

2016-10-05T10:36:14.000Z

2019-05-15T08:43:23.000Z

napalm_yang/models/openconfig/network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

63

2016-11-07T15:23:08.000Z

2021-09-22T14:41:16.000Z

# -*- coding: utf-8 -*- from operator import attrgetter from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType from pyangbind.lib.yangtypes import RestrictedClassType from pyangbind.lib.yangtypes import TypedListType from pyangbind.lib.yangtypes import YANGBool from pyangbind.lib.yangtypes import YANGListType from pyangbind.lib.yangtypes import YANGDynClass from pyangbind.lib.yangtypes import ReferenceType from pyangbind.lib.base import PybindBase from collections import OrderedDict from decimal import Decimal from bitarray import bitarray import six # PY3 support of some PY2 keywords (needs improved) if six.PY3: import builtins as __builtin__ long = int elif six.PY2: import __builtin__ class state(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa-types/lsa-type/lsas/lsa/opaque-lsa/router-information/tlvs/tlv/informational-capabilities/state. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: State parameters of the informational capabilitis of the RI LSA """ __slots__ = ( "_path_helper", "_extmethods", "__graceful_restart_capable", "__graceful_restart_helper", "__stub_router", "__traffic_engineering", "__point_to_point_over_lan", "__experimental_te", ) _yang_name = "state" _pybind_generated_by = "container" def __init__(self, *args, **kwargs): self._path_helper = False self._extmethods = False self.__graceful_restart_capable = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__graceful_restart_helper = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__stub_router = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__traffic_engineering = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__point_to_point_over_lan = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__experimental_te = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path() + [self._yang_name] else: return [ "network-instances", "network-instance", "protocols", "protocol", "ospfv2", "areas", "area", "lsdb", "lsa-types", "lsa-type", "lsas", "lsa", "opaque-lsa", "router-information", "tlvs", "tlv", "informational-capabilities", "state", ] def _get_graceful_restart_capable(self): """ Getter method for graceful_restart_capable, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_capable (boolean) YANG Description: When this leaf is set to true, the advertising system is capable of OSPF graceful restart. """ return self.__graceful_restart_capable def _set_graceful_restart_capable(self, v, load=False): """ Setter method for graceful_restart_capable, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_capable (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_graceful_restart_capable is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_graceful_restart_capable() directly. YANG Description: When this leaf is set to true, the advertising system is capable of OSPF graceful restart. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """graceful_restart_capable must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__graceful_restart_capable = t if hasattr(self, "_set"): self._set() def _unset_graceful_restart_capable(self): self.__graceful_restart_capable = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_graceful_restart_helper(self): """ Getter method for graceful_restart_helper, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_helper (boolean) YANG Description: When this leaf is set to true, the advertising system is capable of being a helper for OSPF graceful restart """ return self.__graceful_restart_helper def _set_graceful_restart_helper(self, v, load=False): """ Setter method for graceful_restart_helper, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_helper (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_graceful_restart_helper is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_graceful_restart_helper() directly. YANG Description: When this leaf is set to true, the advertising system is capable of being a helper for OSPF graceful restart """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """graceful_restart_helper must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__graceful_restart_helper = t if hasattr(self, "_set"): self._set() def _unset_graceful_restart_helper(self): self.__graceful_restart_helper = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_stub_router(self): """ Getter method for stub_router, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/stub_router (boolean) YANG Description: When this leaf is set to true, the advertising system is able to advertise its status as a stub router """ return self.__stub_router def _set_stub_router(self, v, load=False): """ Setter method for stub_router, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/stub_router (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_stub_router is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_stub_router() directly. YANG Description: When this leaf is set to true, the advertising system is able to advertise its status as a stub router """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """stub_router must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__stub_router = t if hasattr(self, "_set"): self._set() def _unset_stub_router(self): self.__stub_router = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_traffic_engineering(self): """ Getter method for traffic_engineering, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/traffic_engineering (boolean) YANG Description: When this leaf is set to true, the advertising system supports OSPFv2 traffic engineering capabilities """ return self.__traffic_engineering def _set_traffic_engineering(self, v, load=False): """ Setter method for traffic_engineering, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/traffic_engineering (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_traffic_engineering is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_traffic_engineering() directly. YANG Description: When this leaf is set to true, the advertising system supports OSPFv2 traffic engineering capabilities """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """traffic_engineering must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__traffic_engineering = t if hasattr(self, "_set"): self._set() def _unset_traffic_engineering(self): self.__traffic_engineering = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_point_to_point_over_lan(self): """ Getter method for point_to_point_over_lan, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/point_to_point_over_lan (boolean) YANG Description: When this leaf is set to true, the advertising system supports treating LAN adjacencies as though they were point to point """ return self.__point_to_point_over_lan def _set_point_to_point_over_lan(self, v, load=False): """ Setter method for point_to_point_over_lan, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/point_to_point_over_lan (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_point_to_point_over_lan is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_point_to_point_over_lan() directly. YANG Description: When this leaf is set to true, the advertising system supports treating LAN adjacencies as though they were point to point """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """point_to_point_over_lan must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__point_to_point_over_lan = t if hasattr(self, "_set"): self._set() def _unset_point_to_point_over_lan(self): self.__point_to_point_over_lan = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_experimental_te(self): """ Getter method for experimental_te, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/experimental_te (boolean) YANG Description: When this leaf is set to ture, the advertising system supports the experimental extensions to OSPF for TE described in RFC4973 """ return self.__experimental_te def _set_experimental_te(self, v, load=False): """ Setter method for experimental_te, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/experimental_te (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_experimental_te is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_experimental_te() directly. YANG Description: When this leaf is set to ture, the advertising system supports the experimental extensions to OSPF for TE described in RFC4973 """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """experimental_te must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__experimental_te = t if hasattr(self, "_set"): self._set() def _unset_experimental_te(self): self.__experimental_te = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) graceful_restart_capable = __builtin__.property(_get_graceful_restart_capable) graceful_restart_helper = __builtin__.property(_get_graceful_restart_helper) stub_router = __builtin__.property(_get_stub_router) traffic_engineering = __builtin__.property(_get_traffic_engineering) point_to_point_over_lan = __builtin__.property(_get_point_to_point_over_lan) experimental_te = __builtin__.property(_get_experimental_te) _pyangbind_elements = OrderedDict( [ ("graceful_restart_capable", graceful_restart_capable), ("graceful_restart_helper", graceful_restart_helper), ("stub_router", stub_router), ("traffic_engineering", traffic_engineering), ("point_to_point_over_lan", point_to_point_over_lan), ("experimental_te", experimental_te), ] ) class state(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa-types/lsa-type/lsas/lsa/opaque-lsa/router-information/tlvs/tlv/informational-capabilities/state. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: State parameters of the informational capabilitis of the RI LSA """ __slots__ = ( "_path_helper", "_extmethods", "__graceful_restart_capable", "__graceful_restart_helper", "__stub_router", "__traffic_engineering", "__point_to_point_over_lan", "__experimental_te", ) _yang_name = "state" _pybind_generated_by = "container" def __init__(self, *args, **kwargs): self._path_helper = False self._extmethods = False self.__graceful_restart_capable = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__graceful_restart_helper = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__stub_router = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__traffic_engineering = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__point_to_point_over_lan = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) self.__experimental_te = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path() + [self._yang_name] else: return [ "network-instances", "network-instance", "protocols", "protocol", "ospfv2", "areas", "area", "lsdb", "lsa-types", "lsa-type", "lsas", "lsa", "opaque-lsa", "router-information", "tlvs", "tlv", "informational-capabilities", "state", ] def _get_graceful_restart_capable(self): """ Getter method for graceful_restart_capable, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_capable (boolean) YANG Description: When this leaf is set to true, the advertising system is capable of OSPF graceful restart. """ return self.__graceful_restart_capable def _set_graceful_restart_capable(self, v, load=False): """ Setter method for graceful_restart_capable, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_capable (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_graceful_restart_capable is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_graceful_restart_capable() directly. YANG Description: When this leaf is set to true, the advertising system is capable of OSPF graceful restart. """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """graceful_restart_capable must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__graceful_restart_capable = t if hasattr(self, "_set"): self._set() def _unset_graceful_restart_capable(self): self.__graceful_restart_capable = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-capable", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_graceful_restart_helper(self): """ Getter method for graceful_restart_helper, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_helper (boolean) YANG Description: When this leaf is set to true, the advertising system is capable of being a helper for OSPF graceful restart """ return self.__graceful_restart_helper def _set_graceful_restart_helper(self, v, load=False): """ Setter method for graceful_restart_helper, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/graceful_restart_helper (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_graceful_restart_helper is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_graceful_restart_helper() directly. YANG Description: When this leaf is set to true, the advertising system is capable of being a helper for OSPF graceful restart """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """graceful_restart_helper must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__graceful_restart_helper = t if hasattr(self, "_set"): self._set() def _unset_graceful_restart_helper(self): self.__graceful_restart_helper = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="graceful-restart-helper", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_stub_router(self): """ Getter method for stub_router, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/stub_router (boolean) YANG Description: When this leaf is set to true, the advertising system is able to advertise its status as a stub router """ return self.__stub_router def _set_stub_router(self, v, load=False): """ Setter method for stub_router, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/stub_router (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_stub_router is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_stub_router() directly. YANG Description: When this leaf is set to true, the advertising system is able to advertise its status as a stub router """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """stub_router must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__stub_router = t if hasattr(self, "_set"): self._set() def _unset_stub_router(self): self.__stub_router = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="stub-router", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_traffic_engineering(self): """ Getter method for traffic_engineering, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/traffic_engineering (boolean) YANG Description: When this leaf is set to true, the advertising system supports OSPFv2 traffic engineering capabilities """ return self.__traffic_engineering def _set_traffic_engineering(self, v, load=False): """ Setter method for traffic_engineering, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/traffic_engineering (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_traffic_engineering is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_traffic_engineering() directly. YANG Description: When this leaf is set to true, the advertising system supports OSPFv2 traffic engineering capabilities """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """traffic_engineering must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__traffic_engineering = t if hasattr(self, "_set"): self._set() def _unset_traffic_engineering(self): self.__traffic_engineering = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="traffic-engineering", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_point_to_point_over_lan(self): """ Getter method for point_to_point_over_lan, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/point_to_point_over_lan (boolean) YANG Description: When this leaf is set to true, the advertising system supports treating LAN adjacencies as though they were point to point """ return self.__point_to_point_over_lan def _set_point_to_point_over_lan(self, v, load=False): """ Setter method for point_to_point_over_lan, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/point_to_point_over_lan (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_point_to_point_over_lan is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_point_to_point_over_lan() directly. YANG Description: When this leaf is set to true, the advertising system supports treating LAN adjacencies as though they were point to point """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """point_to_point_over_lan must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__point_to_point_over_lan = t if hasattr(self, "_set"): self._set() def _unset_point_to_point_over_lan(self): self.__point_to_point_over_lan = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="point-to-point-over-lan", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) def _get_experimental_te(self): """ Getter method for experimental_te, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/experimental_te (boolean) YANG Description: When this leaf is set to ture, the advertising system supports the experimental extensions to OSPF for TE described in RFC4973 """ return self.__experimental_te def _set_experimental_te(self, v, load=False): """ Setter method for experimental_te, mapped from YANG variable /network_instances/network_instance/protocols/protocol/ospfv2/areas/area/lsdb/lsa_types/lsa_type/lsas/lsa/opaque_lsa/router_information/tlvs/tlv/informational_capabilities/state/experimental_te (boolean) If this variable is read-only (config: false) in the source YANG file, then _set_experimental_te is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_experimental_te() directly. YANG Description: When this leaf is set to ture, the advertising system supports the experimental extensions to OSPF for TE described in RFC4973 """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) except (TypeError, ValueError): raise ValueError( { "error-string": """experimental_te must be of a type compatible with boolean""", "defined-type": "boolean", "generated-type": """YANGDynClass(base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='boolean', is_config=False)""", } ) self.__experimental_te = t if hasattr(self, "_set"): self._set() def _unset_experimental_te(self): self.__experimental_te = YANGDynClass( base=YANGBool, default=YANGBool("false"), is_leaf=True, yang_name="experimental-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="boolean", is_config=False, ) graceful_restart_capable = __builtin__.property(_get_graceful_restart_capable) graceful_restart_helper = __builtin__.property(_get_graceful_restart_helper) stub_router = __builtin__.property(_get_stub_router) traffic_engineering = __builtin__.property(_get_traffic_engineering) point_to_point_over_lan = __builtin__.property(_get_point_to_point_over_lan) experimental_te = __builtin__.property(_get_experimental_te) _pyangbind_elements = OrderedDict( [ ("graceful_restart_capable", graceful_restart_capable), ("graceful_restart_helper", graceful_restart_helper), ("stub_router", stub_router), ("traffic_engineering", traffic_engineering), ("point_to_point_over_lan", point_to_point_over_lan), ("experimental_te", experimental_te), ] )

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2ad9a5624a6a3a732ce544d6a296513579ff70d3

14,762

py

Python

src/torch/nn/modules/padding.py

warcraft12321/Hyperfoods

b995cd7afe10fcbd338158c80f53ce637bfffc0c

[ "MIT" ]

51

2020-01-26T23:32:57.000Z

2022-03-20T14:49:57.000Z

src/torch/nn/modules/padding.py

warcraft12321/Hyperfoods

b995cd7afe10fcbd338158c80f53ce637bfffc0c

[ "MIT" ]

2

2020-12-19T20:00:28.000Z

2021-03-03T20:22:45.000Z

src/torch/nn/modules/padding.py

warcraft12321/Hyperfoods

b995cd7afe10fcbd338158c80f53ce637bfffc0c

[ "MIT" ]

33

2020-02-18T16:15:48.000Z

2022-03-24T15:12:05.000Z

from .module import Module from .utils import _pair, _quadruple, _ntuple from .. import functional as F # TODO: grad_output size asserts in THNN class _ConstantPadNd(Module): def __init__(self, value): super(_ConstantPadNd, self).__init__() self.value = value def forward(self, input): return F.pad(input, self.padding, 'constant', self.value) def extra_repr(self): return 'padding={}, value={}'.format(self.padding, self.value) class ConstantPad1d(_ConstantPadNd): r"""Pads the input tensor boundaries with a constant value. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in both boundaries. If a 2-`tuple`, uses (`paddingLeft`, `paddingRight`) Shape: - Input: :math:`(N, C, W_{in})` - Output: :math:`(N, C, W_{out})` where :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ConstantPad1d(2, 3.5) >>> input = torch.randn(1, 2, 4) >>> input (0 ,.,.) = 0.1875 0.5046 -1.0074 2.0005 -0.3540 -1.8645 1.1530 0.0632 [torch.FloatTensor of size (1,2,4)] >>> m(input) (0 ,.,.) = 3.5000 3.5000 0.1875 0.5046 -1.0074 2.0005 3.5000 3.5000 3.5000 3.5000 -0.3540 -1.8645 1.1530 0.0632 3.5000 3.5000 [torch.FloatTensor of size (1,2,8)] >>> # using different paddings >>> m = nn.ConstantPad1d((3, 1), 3.5) >>> m(input) (0 ,.,.) = 3.5000 3.5000 3.5000 0.1875 0.5046 -1.0074 2.0005 3.5000 3.5000 3.5000 3.5000 -0.3540 -1.8645 1.1530 0.0632 3.5000 [torch.FloatTensor of size (1,2,8)] """ def __init__(self, padding, value): super(ConstantPad1d, self).__init__(value) self.padding = _pair(padding) class ConstantPad2d(_ConstantPadNd): r"""Pads the input tensor boundaries with a constant value. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`) Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where :math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}` :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ConstantPad2d(2, 3.5) >>> input = torch.randn(1, 2, 2) >>> input (0 ,.,.) = -0.2295 -0.9774 -0.3335 -1.4178 [torch.FloatTensor of size (1,2,2)] >>> m(input) (0 ,.,.) = 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 -0.2295 -0.9774 3.5000 3.5000 3.5000 3.5000 -0.3335 -1.4178 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 [torch.FloatTensor of size (1,6,6)] >>> # using different paddings >>> m = nn.ConstantPad2d((3, 0, 2, 1), 3.5) >>> m(input) (0 ,.,.) = 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 3.5000 -0.2295 -0.9774 3.5000 3.5000 3.5000 -0.3335 -1.4178 3.5000 3.5000 3.5000 3.5000 3.5000 [torch.FloatTensor of size (1,5,5)] """ def __init__(self, padding, value): super(ConstantPad2d, self).__init__(value) self.padding = _quadruple(padding) class ConstantPad3d(_ConstantPadNd): r"""Pads the input tensor boundaries with a constant value. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`, `paddingFront`, `paddingBack`) Shape: - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` where :math:`D_{out} = D_{in} + \textit{paddingFront} + \textit{paddingBack}` :math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}` :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ConstantPad3d(3, 3.5) >>> input = torch.randn(16, 3, 10, 20, 30) >>> output = m(input) >>> # using different paddings >>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5) >>> output = m(input) """ def __init__(self, padding, value): super(ConstantPad3d, self).__init__(value) self.padding = _ntuple(6)(padding) class _ReflectionPadNd(Module): def forward(self, input): return F.pad(input, self.padding, 'reflect') def extra_repr(self): return '{}'.format(self.padding) class ReflectionPad1d(_ReflectionPadNd): r"""Pads the input tensor using the reflection of the input boundary. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 2-`tuple`, uses (`paddingLeft`, `paddingRight`) Shape: - Input: :math:`(N, C, W_{in})` - Output: :math:`(N, C, W_{out})` where :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ReflectionPad1d(2) >>> input = torch.arange(8).reshape(1, 2, 4) >>> input (0 ,.,.) = 0 1 2 3 4 5 6 7 [torch.FloatTensor of size (1,2,4)] >>> m(input) (0 ,.,.) = 2 1 0 1 2 3 2 1 6 5 4 5 6 7 6 5 [torch.FloatTensor of size (1,2,8)] >>> # using different paddings >>> m = nn.ReflectionPad1d((3, 1)) >>> m(input) (0 ,.,.) = 3 2 1 0 1 2 3 2 7 6 5 4 5 6 7 6 [torch.FloatTensor of size (1,2,8)] """ def __init__(self, padding): super(ReflectionPad1d, self).__init__() self.padding = _pair(padding) class ReflectionPad2d(_ReflectionPadNd): r"""Pads the input tensor using the reflection of the input boundary. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`) Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where :math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}` :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ReflectionPad2d(2) >>> input = torch.arange(9).reshape(1, 1, 3, 3) >>> input (0 ,0 ,.,.) = 0 1 2 3 4 5 6 7 8 [torch.FloatTensor of size (1,1,3,3)] >>> m(input) (0 ,0 ,.,.) = 8 7 6 7 8 7 6 5 4 3 4 5 4 3 2 1 0 1 2 1 0 5 4 3 4 5 4 3 8 7 6 7 8 7 6 5 4 3 4 5 4 3 2 1 0 1 2 1 0 [torch.FloatTensor of size (1,1,7,7)] >>> # using different paddings >>> m = nn.ReflectionPad2d((1, 1, 2, 0)) >>> m(input) (0 ,0 ,.,.) = 7 6 7 8 7 4 3 4 5 4 1 0 1 2 1 4 3 4 5 4 7 6 7 8 7 [torch.FloatTensor of size (1,1,5,5)] """ def __init__(self, padding): super(ReflectionPad2d, self).__init__() self.padding = _quadruple(padding) class _ReplicationPadNd(Module): def forward(self, input): return F.pad(input, self.padding, 'replicate') def extra_repr(self): return '{}'.format(self.padding) class ReplicationPad1d(_ReplicationPadNd): r"""Pads the input tensor using replication of the input boundary. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 2-`tuple`, uses (`paddingLeft`, `paddingRight`) Shape: - Input: :math:`(N, C, W_{in})` - Output: :math:`(N, C, W_{out})` where :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ReplicationPad1d(2) >>> input = torch.arange(8).reshape(1, 2, 4) >>> input (0 ,.,.) = 0 1 2 3 4 5 6 7 [torch.FloatTensor of size (1,2,4)] >>> m(input) (0 ,.,.) = 0 0 0 1 2 3 3 3 4 4 4 5 6 7 7 7 [torch.FloatTensor of size (1,2,8)] >>> # using different paddings >>> m = nn.ReplicationPad1d((3, 1)) >>> m(input) (0 ,.,.) = 0 0 0 0 1 2 3 3 4 4 4 4 5 6 7 7 [torch.FloatTensor of size (1,2,8)] """ def __init__(self, padding): super(ReplicationPad1d, self).__init__() self.padding = _pair(padding) class ReplicationPad2d(_ReplicationPadNd): r"""Pads the input tensor using replication of the input boundary. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`) Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where :math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}` :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ReplicationPad2d(2) >>> input = torch.arange(9).reshape(1, 1, 3, 3) >>> input (0 ,0 ,.,.) = 0 1 2 3 4 5 6 7 8 [torch.FloatTensor of size (1,1,3,3)] >>> m(input) (0 ,0 ,.,.) = 0 0 0 1 2 2 2 0 0 0 1 2 2 2 0 0 0 1 2 2 2 3 3 3 4 5 5 5 6 6 6 7 8 8 8 6 6 6 7 8 8 8 6 6 6 7 8 8 8 [torch.FloatTensor of size (1,1,7,7)] >>> # using different paddings >>> m = nn.ReplicationPad2d((1, 1, 2, 0)) >>> m(input) (0 ,0 ,.,.) = 0 0 1 2 2 0 0 1 2 2 0 0 1 2 2 3 3 4 5 5 6 6 7 8 8 [torch.FloatTensor of size (1,1,5,5)] """ def __init__(self, padding): super(ReplicationPad2d, self).__init__() self.padding = _quadruple(padding) class ReplicationPad3d(_ReplicationPadNd): r"""Pads the input tensor using replication of the input boundary. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 6-`tuple`, uses (`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`, `paddingFront`, `paddingBack`) Shape: - Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` - Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` where :math:`D_{out} = D_{in} + \textit{paddingFront} + \textit{paddingBack}` :math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}` :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ReplicationPad3d(3) >>> input = torch.randn(16, 3, 8, 320, 480) >>> output = m(input) >>> # using different paddings >>> m = nn.ReplicationPad3d((3, 3, 6, 6, 1, 1)) >>> output = m(input) """ def __init__(self, padding): super(ReplicationPad3d, self).__init__() self.padding = _ntuple(6)(padding) class ZeroPad2d(ConstantPad2d): r"""Pads the input tensor boundaries with zero. For `N`d-padding, use :func:`torch.nn.functional.pad()`. Args: padding (int, tuple): the size of the padding. If is `int`, uses the same padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`) Shape: - Input: :math:`(N, C, H_{in}, W_{in})` - Output: :math:`(N, C, H_{out}, W_{out})` where :math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}` :math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}` Examples:: >>> m = nn.ZeroPad2d(2) >>> input = torch.randn(1, 1, 3, 3) >>> input (0 ,0 ,.,.) = 1.4418 -1.9812 -0.3815 -0.3828 -0.6833 -0.2376 0.1433 0.0211 0.4311 [torch.FloatTensor of size (1,1,3,3)] >>> m(input) (0 ,0 ,.,.) = 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.4418 -1.9812 -0.3815 0.0000 0.0000 0.0000 0.0000 -0.3828 -0.6833 -0.2376 0.0000 0.0000 0.0000 0.0000 0.1433 0.0211 0.4311 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 [torch.FloatTensor of size (1,1,7,7)] >>> # using different paddings >>> m = nn.ZeroPad2d((1, 1, 2, 0)) >>> m(input) (0 ,0 ,.,.) = 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 1.4418 -1.9812 -0.3815 0.0000 0.0000 -0.3828 -0.6833 -0.2376 0.0000 0.0000 0.1433 0.0211 0.4311 0.0000 [torch.FloatTensor of size (1,1,5,5)] """ def __init__(self, padding): super(ZeroPad2d, self).__init__(padding, 0)

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Python

pymatflow/cp2k/base/motion_print.py

DeqiTang/pymatflow

bd8776feb40ecef0e6704ee898d9f42ded3b0186

[ "MIT" ]

6

2020-03-06T16:13:08.000Z

2022-03-09T07:53:34.000Z

pymatflow/cp2k/base/motion_print.py

DeqiTang/pymatflow

bd8776feb40ecef0e6704ee898d9f42ded3b0186

[ "MIT" ]

1

2021-10-02T02:23:08.000Z

2021-11-08T13:29:37.000Z

pymatflow/cp2k/base/motion_print.py

DeqiTang/pymatflow

bd8776feb40ecef0e6704ee898d9f42ded3b0186

[ "MIT" ]

1

2021-07-10T16:28:14.000Z

#!/usr/bin/env python # _*_ coding: utf-8 _*_ class cp2k_motion_print_cell_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_cell: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_cell_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&CELL\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&end CELL\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_core_forces_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_core_forces: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_core_forces_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&CORE_FORCES\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&end CORE_FORCES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_core_trajectory_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_core_trajectory: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_core_trajectory_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&CORE_TRAJECTORY\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END CORE_TRAJECTORY\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_core_velocities_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_core_velocities: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_core_velocities_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&CORE_VELOCITIES\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END CORE_VELOCITIES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_forces_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_forces: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_forces_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&FORCES\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END FORCES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_force_mixing_labels_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_force_mixing_labels: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_force_mixing_labels_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&FORCE_MIXING_LABELS\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END FORCE_MIXING_LABELS\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_mixed_energies_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_mixed_energies: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_mixed_energies_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&MIXED_ENERGIES\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END MIXED_ENERGIES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_restart_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&each\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end each\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] else: pass class cp2k_motion_print_restart: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_restart_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&restart\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&end restart\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_restart_history_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&end EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_restart_history: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_restart_history_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&restart_history\n") for item in self.params: if self.params[item] is not none: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&end restart_history\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) class cp2k_motion_print_shell_forces_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_shell_forces: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_shell_forces_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&SHELL_FORCES\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END SHELL_FORCES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_shell_trajectory_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_shell_trajectory: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_shell_trajectory_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&SHELL_TRAJECTORY\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END SHELL_TRAJECTORY\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_shell_velocities_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_shell_velocities: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_shell_velocities_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&SHELL_VELOCITIES\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END SHELL_VELOCITIES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_stress_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_stress: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_stress_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&STRESS\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END STRESS\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_structure_data_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_structure_data: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_structure_data_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&STRUCTURE_DATA\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END STRUCTURE_DATA\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_trajectory_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_trajectory: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_trajectory_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&TRAJECTORY\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END TRAJECTORY\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_translation_vector_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_translation_vector: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_translation_vector_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&TRANSLATION_VECTOR\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END TRANSLATION_VECTOR\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print_velocities_each: def __init__(self): self.params = {} self.status = False def to_input(self, fout): fout.write("\t\t\t&EACH\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) fout.write("\t\t\t&END EACH\n") def set_params(self, params): for item in params: if len(item.split("-")) == 4: self.params[item.split("-")[-1]] = params[item] class cp2k_motion_print_velocities: def __init__(self): self.params = {} self.status = False self.each = cp2k_motion_print_velocities_each() # basic setting self.each.status = True def to_input(self, fout): fout.write("\t\t&VELOCITIES\n") for item in self.params: if self.params[item] is not None: fout.write("\t\t\t%s %s\n" % (item, self.params[item])) if self.each.status == True: self.each.to_input(fout) fout.write("\t\t&END VELOCITIES\n") def set_params(self, params): for item in params: if len(item.split("-")) == 3: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[2] == "EACH": self.each.set_params({item: params[item]}) else: pass class cp2k_motion_print: def __init__(self): self.status = False self.cell = cp2k_motion_print_cell() self.core_forces = cp2k_motion_print_core_forces() self.core_trajectory = cp2k_motion_print_core_trajectory() self.core_velocities = cp2k_motion_print_core_velocities() self.forces = cp2k_motion_print_forces() self.force_mixing_labels = cp2k_motion_print_force_mixing_labels() self.mixed_energies = cp2k_motion_print_mixed_energies() self.restart = cp2k_motion_print_restart() self.restart_history = cp2k_motion_print_restart_history() self.shell_forces = cp2k_motion_print_shell_forces() self.shell_trajectory = cp2k_motion_print_shell_trajectory() self.shell_velocities = cp2k_motion_print_shell_velocities() self.stress = cp2k_motion_print_stress() self.structure_data = cp2k_motion_print_structure_data() self.trajectory = cp2k_motion_print_trajectory() self.translation_vector = cp2k_motion_print_translation_vector() self.velocities = cp2k_motion_print_velocities() # basic setting def to_input(self, fout): # fout: a file stream for writing fout.write("\t&PRINT\n") if self.cell.status == True: self.cell.to_input(fout) if self.core_forces.status == True: self.core_forces.to_input(fout) if self.core_trajectory.status == True: self.core_trajectory.to_input(fout) if self.core_velocities.status == True: self.core_velocities.to_input(fout) if self.forces.status == True: self.forces.to_input(fout) if self.force_mixing_labels.status == True: self.force_mixing_labels.to_input(fout) if self.mixed_energies.status == True: self.mixed_energies.to_input(fout) if self.restart.status == True: slef.restart.to_input(fout) if self.restart_history.status == True: self.restart_history.to_input(fout) if self.shell_forces.status == True: self.shell_forces.to_input(fout) if self.shell_trajectory.status == True: self.shell_trajectory.to_input(fout) if self.shell_velocities.status == True: self.shell_velocities.to_input(fout) if self.stress.status == True: self.stress.to_input(fout) if self.structure_data.status == True: self.structure_data.to_input(fout) if self.trajectory.status == True: self.trajectory.to_input(fout) if self.translation_vector.status == True: self.translation_vector.to_input(fout) if self.velocities.status == True: self.velocities.to_input(fout) fout.write("\t&END PRINT\n") def set_params(self, params): for item in params: if len(item.split("-")) == 2: self.params[item.split("-")[-1]] = params[item] elif item.split("-")[1] == "CELL": self.cell.set_params({item: params[item]}) elif item.split("-")[1] == "CORE_FORCES": self.core_forces.set_params({item: params[item]}) elif item.split("-")[1] == "CORE_TRAJECTORY": self.core_trajectory.set_params({item: params[item]}) elif item.split("-")[1] == "CORE_VELOCITIES": self.core_velocities.set_params({item: params[item]}) elif item.split("-")[1] == "FORCES": self.forces.set_params({item: params[item]}) elif item.split("-")[1] == "FORCE_MIXING_LABELS": self.force_mixing_labels.set_params({item: params[item]}) elif item.split("-")[1] == "MIXED_ENERGIES": self.mixed_energies.set_params({item: params[item]}) elif item.split("-")[1] == "RESTART": self.restart.set_params({item: params[item]}) elif item.split("-")[1] == "RESTART_HISTORY": slef.restart_history.set_params({item: params[item]}) elif item.split("-")[1] == "SHELL_FORCES": self.shell_forces.set_params({item: params[item]}) elif item.split("-")[1] == "SHELL_TRAJECTORY": self.shell_trajectory.set_params({item: params[item]}) elif item.split("-")[1] == "SHELL_VELOCITIES": slef.shell_velocities.set_params({item: params[item]}) elif item.split("-")[1] == "STRESS": self.stress.set_params({item: params[item]}) elif item.split("-")[1] == "STRUCTURE_DATA": self.structure_data.set_params({item: params[item]}) elif item.split("-")[1] == "TRAJECTORY": self.trajectory.set_params({item: params[item]}) elif item.split("-")[1] == "TRANSLATION_VECTOR": self.translation_vector.set_params({item: params[item]}) elif item.split("-")[1] == "VELOCITIES": self.velocities.set_params({item: params[item]}) else: pass

34.653199

75

0.529311

3,950

30,876

3.971899

0.017468

0.131302

0.066288

0.071515

0.902607

0.876856

0.843075

0.840015

0.838422

0.828542

0

0.008411

0.329965

30,876

890

76

34.692135

0.749952

0.010558

0

0.759336

0

0.07011

0.007625

0

1

0.145228

false

0.034578

0

0.193638

0.095436

0

null

0

1

0

1

0

null

0

7

9319dfb26094221cd76c129c500b0a753bbc25ae

259

py

Python

entity/cards/LETL_011H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

102

2021-10-20T09:06:39.000Z

2022-03-28T13:35:11.000Z

entity/cards/LETL_011H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

98

2021-10-19T16:13:27.000Z

2022-03-27T13:27:49.000Z

entity/cards/LETL_011H/__init__.py

x014/lushi_script

edab2b88e3f0de8139de2541ab2daa331f777c0e

[ "MIT" ]

55

2021-10-19T03:56:50.000Z

2022-03-25T08:25:26.000Z

# -*- coding: utf-8 -*- import entity.cards.LETL_011H.LETL_330 import entity.cards.LETL_011H.LETL_331 import entity.cards.LETL_011H.LETL_332_ import entity.cards.LETL_011H.LETL_333 import entity.cards.LETL_011H.LETL_334 import entity.cards.LETL_011H.LETL_335

32.375

39

0.830116

45

259

4.488889

0.311111

0.356436

0.504951

0.623762

0.861386

0

0.152893

0.065637

259

7

40

37

0.681818

0.081081

0

1

0

true

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

8

faa88ea73acc977bc409bc6ff5f45fd95bb704d0

2,047

py

Python

AdventOfCode2015/Day18/Day18.py

MattTitmas/AdventOfCode

36be4f6bf973f77ff93b08dc69c977bb11951f27

[ "MIT" ]

null

AdventOfCode2015/Day18/Day18.py

MattTitmas/AdventOfCode

36be4f6bf973f77ff93b08dc69c977bb11951f27

[ "MIT" ]

null

AdventOfCode2015/Day18/Day18.py

MattTitmas/AdventOfCode

36be4f6bf973f77ff93b08dc69c977bb11951f27

[ "MIT" ]

null

def part1(): values = [[True if j == "#" else False for j in i] for i in open("input.txt","r").read().split("\n")] for _ in range(100): copy = [row[:] for row in values] for i in range(0, len(values)): for j in range(0, len(values)): sum = 0 for x in range(max(0,i - 1), min(len(values), i + 2)): for y in range(max(0, j - 1), min(len(values), j + 2)): if x != i or y != j: sum += copy[x][y] if copy[i][j] and sum != 2 and sum != 3: values[i][j] = False if not copy[i][j] and sum == 3: values[i][j] = True total = 0 for i in range(0, len(values)): for j in range(0, len(values)): total += 1 if values[i][j] else 0 return total def part2(): values = [[True if j == "#" else False for j in i] for i in open("input.txt","r").read().split("\n")] for i, j in [(0,0),(0,99),(99,0),(99,99)]: values[i][j] = True for _ in range(100): copy = [row[:] for row in values] for i in range(0, len(values)): for j in range(0, len(values)): sum = 0 for x in range(max(0,i - 1), min(len(values), i + 2)): for y in range(max(0, j - 1), min(len(values), j + 2)): if x != i or y != j: sum += copy[x][y] if copy[i][j] and sum != 2 and sum != 3: values[i][j] = False if not copy[i][j] and sum == 3: values[i][j] = True if (i == 0 and j == 0) or (i == 99 and j == 99) or (i == 0 and j == 99) or (i == 99 and j == 0): values[i][j] = True total = 0 for i in range(0, len(values)): for j in range(0, len(values)): total += 1 if values[i][j] else 0 return total print(f"answer to part1: {part1()}") print(f"answer to part2: {part2()}")

40.94

112

0.426478

328

2,047

2.655488

0.131098

0.112514

0.073479

0.101033

0.881745

0.84845

0

0.057518

0.405471

2,047

49

113

41.77551

0.658176

0

0.869565

0

0.038123

0

1

0.043478

false

0

0.086957

0.043478

0

null

0

1

0

1

0

null

0

7

4f093fc1194a13fa2ad34e5ee560aff786a17094

12,135

py

Python

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/ocelot/phys/Phys_Studio_Connect.py

lmnotran/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

82

2016-06-29T17:24:43.000Z

2021-04-16T06:49:17.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/ocelot/phys/Phys_Studio_Connect.py

lmnotran/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

6

2022-01-12T18:22:08.000Z

2022-03-25T10:19:27.000Z

platform/radio/efr32_multiphy_configurator/pyradioconfig/parts/ocelot/phys/Phys_Studio_Connect.py

lmnotran/gecko_sdk

2e82050dc8823c9fe0e8908c1b2666fb83056230

[ "Zlib" ]

56

2016-08-02T10:50:50.000Z

2021-07-19T08:57:34.000Z

from pyradioconfig.calculator_model_framework.interfaces.iphy import IPhy from py_2_and_3_compatibility import * class PHYS_connect_Ocelot(IPhy): def Connect_base(self, phy, model): #Standard modulation, shaping, and framing settings for Connect PHYs #Modulation format and symbol mapping phy.profile_inputs.modulation_type.value = model.vars.modulation_type.var_enum.FSK2 phy.profile_inputs.fsk_symbol_map.value = model.vars.fsk_symbol_map.var_enum.MAP0 # Preamble and syncword definition phy.profile_inputs.preamble_pattern_len.value = 2 phy.profile_inputs.preamble_length.value = 32 phy.profile_inputs.preamble_pattern.value = 1 phy.profile_inputs.syncword_0.value = 0x2DD4 phy.profile_inputs.syncword_1.value = 0x0 phy.profile_inputs.syncword_length.value = 16 phy.profile_inputs.syncword_tx_skip.value = False #Shaping filter phy.profile_inputs.shaping_filter.value = model.vars.shaping_filter.var_enum.Gaussian phy.profile_inputs.shaping_filter_param.value = 0.5 #Xtal frequency and tolerance phy.profile_inputs.xtal_frequency_hz.value = 39000000 phy.profile_inputs.rx_xtal_error_ppm.value = 10 phy.profile_inputs.tx_xtal_error_ppm.value = 10 phy.profile_inputs.baudrate_tol_ppm.value = 0 #CRC settings phy.profile_inputs.crc_byte_endian.value = model.vars.crc_byte_endian.var_enum.MSB_FIRST phy.profile_inputs.crc_bit_endian.value = model.vars.crc_bit_endian.var_enum.MSB_FIRST #Encoding and whitening phy.profile_inputs.symbol_encoding.value = model.vars.symbol_encoding.var_enum.NRZ phy.profile_inputs.diff_encoding_mode.value = model.vars.diff_encoding_mode.var_enum.DISABLED phy.profile_inputs.dsss_chipping_code.value = 0 phy.profile_inputs.dsss_len.value = 0 phy.profile_inputs.dsss_spreading_factor.value = 0 phy.profile_inputs.white_poly.value = model.vars.white_poly.var_enum.PN9 # Owner: Casey Weltzin # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-140 def PHY_Studio_Connect_915MHz_2GFSK_500kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, 'US FCC 915, Brazil 915', readable_name="Connect 915MHz 2GFSK 500kbps", phy_name=phy_name) # Common base function for all connect PHYs self.Connect_base(phy, model) # Add data-rate specific parameters phy.profile_inputs.bitrate.value = 500000 phy.profile_inputs.deviation.value = 175000 # Add band-specific parameters phy.profile_inputs.base_frequency_hz.value = 915000000 phy.profile_inputs.channel_spacing_hz.value = 400000 # Preamble and syncword definition phy.profile_inputs.preamble_length.value = 40 return phy # Owner: Casey Weltzin # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-23 def PHY_Studio_Connect_169MHz_2GFSK_4p8kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='Europe 169', readable_name="Connect 169MHz 2GFSK 4.8kbps", phy_name=phy_name) # Common base function for all connect PHYs self.Connect_base(phy, model) # Add data-rate specific parameters phy.profile_inputs.bitrate.value = 4800 phy.profile_inputs.deviation.value = 1200 # Add band-specific parameters phy.profile_inputs.base_frequency_hz.value = 169000000 phy.profile_inputs.channel_spacing_hz.value = 12500 return phy # Owner: Casey Weltzin # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-139 def PHY_Studio_Connect_863MHz_2GFSK_100kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='Europe 868', readable_name="Connect 863MHz 2GFSK 100kbps", phy_name=phy_name) # Common base function for all connect PHYs self.Connect_base(phy, model) # Add data-rate specific parameters phy.profile_inputs.bitrate.value = 100000 phy.profile_inputs.deviation.value = 50000 # Add band-specific parameters phy.profile_inputs.base_frequency_hz.value = 863000000 phy.profile_inputs.channel_spacing_hz.value = 400000 return phy # Owner: Casey Weltzin # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-144 def PHY_Studio_Connect_920MHz_2GFSK_100kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='Japan 915', readable_name="Connect 920MHz 2GFSK 100kbps", phy_name=phy_name) # : Common base funtion for all connect PHYs self.Connect_base(phy, model) # Add data-rate specific parameters phy.profile_inputs.bitrate.value = 100000 phy.profile_inputs.deviation.value = 50000 # Add band-specific parameters phy.profile_inputs.base_frequency_hz.value = 920000000 phy.profile_inputs.channel_spacing_hz.value = 400000 return phy # Owner: Casey Weltzin # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-56 def PHY_Studio_Connect_434MHz_2GFSK_200kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='US FCC 434', readable_name="Connect 434MHz 2GFSK 200kbps", phy_name=phy_name) # : Common base funtion for all connect PHYs self.Connect_base(phy, model) # Add data-rate specific parameters phy.profile_inputs.bitrate.value = 200000 phy.profile_inputs.deviation.value = 100000 # Add band-specific parameters phy.profile_inputs.base_frequency_hz.value = 434000000 phy.profile_inputs.channel_spacing_hz.value = 500000 return phy # Owner: Young-Joon Choi # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-141 def PHY_Studio_Connect_915MHz_OQPSK_500kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='DSSS 500', readable_name="Connect 915MHz OQPSK 500Kbps", phy_name=phy_name) # Common base function for all connect PHYs self.Connect_base(phy, model) """ Channel and Bandwidth Settings """ phy.profile_inputs.base_frequency_hz.value = long(915000000) phy.profile_inputs.deviation.value = 250000 phy.profile_inputs.channel_spacing_hz.value = 400000 phy.profile_inputs.bitrate.value = 500000 phy.profile_inputs.baudrate_tol_ppm.value = 0 """ Modulation Type """ phy.profile_inputs.modulation_type.value = model.vars.modulation_type.var_enum.OQPSK """ Symbol Mapping and Encoding """ phy.profile_inputs.symbol_encoding.value = model.vars.symbol_encoding.var_enum.DSSS """ DSSS Parameters """ phy.profile_inputs.dsss_chipping_code.value = 31433 phy.profile_inputs.dsss_len.value = 16 phy.profile_inputs.dsss_spreading_factor.value = 4 """ Shaping Filter Parameters """ phy.profile_inputs.shaping_filter.value = model.vars.shaping_filter.var_enum.Custom_OQPSK phy.profile_inputs.shaping_filter_param.value = 0.5 """ Preamble Parameters """ phy.profile_inputs.preamble_pattern_len.value = 4 phy.profile_inputs.preamble_length.value = 40 phy.profile_inputs.preamble_pattern.value = 0 """ Syncword Parameters """ phy.profile_inputs.syncword_0.value = 0xA7 phy.profile_inputs.syncword_1.value = 0x0 phy.profile_inputs.syncword_length.value = 8 return phy # Owner: Young-Joon Choi # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-143 def PHY_Studio_Connect_915mhz_oqpsk_800kcps_100kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='DSSS 100', readable_name="Connect 915MHz OQPSK 800kcps 100kbps", phy_name=phy_name) # Common base function for all connect PHYs self.Connect_base(phy, model) """ Channel and Bandwidth Settings """ phy.profile_inputs.base_frequency_hz.value = 915000000 phy.profile_inputs.deviation.value = 200000 phy.profile_inputs.channel_spacing_hz.value = 2000000 phy.profile_inputs.bitrate.value = 100000 phy.profile_inputs.baudrate_tol_ppm.value = 4000 """ Modulation Type """ phy.profile_inputs.modulation_type.value = model.vars.modulation_type.var_enum.OQPSK """ Symbol Mapping and Encoding """ phy.profile_inputs.symbol_encoding.value = model.vars.symbol_encoding.var_enum.DSSS """ DSSS Parameters """ phy.profile_inputs.dsss_chipping_code.value = 1951056795 phy.profile_inputs.dsss_len.value = 32 phy.profile_inputs.dsss_spreading_factor.value = 8 """ Shaping Filter Parameters """ phy.profile_inputs.shaping_filter.value = model.vars.shaping_filter.var_enum.Custom_OQPSK phy.profile_inputs.shaping_filter_param.value = 0.5 """ Preamble Parameters """ phy.profile_inputs.preamble_pattern_len.value = 4 phy.profile_inputs.preamble_length.value = 32 phy.profile_inputs.preamble_pattern.value = 0 """ Syncword Parameters """ phy.profile_inputs.syncword_0.value = 0xA7 phy.profile_inputs.syncword_1.value = 0x0 phy.profile_inputs.syncword_length.value = 8 """ Frame Configurations """ # Packet Inputs phy.profile_inputs.crc_bit_endian.value = model.vars.crc_bit_endian.var_enum.LSB_FIRST phy.profile_inputs.crc_byte_endian.value = model.vars.crc_byte_endian.var_enum.LSB_FIRST return phy # Owner: Young-Joon Choi # Jira Link: https://jira.silabs.com/browse/PGOCELOTVALTEST-996 def PHY_Studio_Connect_915mhz_oqpsk_2Mcps_250kbps(self, model, phy_name=None): phy = self._makePhy(model, model.profiles.Connect, phy_description='DSSS 250', readable_name="Connect 915MHz OQPSK 2Mcps 250kbps", phy_name=phy_name) # Common base function for all connect PHYs self.Connect_base(phy, model) """ Channel and Bandwidth Settings """ phy.profile_inputs.base_frequency_hz.value = long(915000000) phy.profile_inputs.deviation.value = 500000 phy.profile_inputs.channel_spacing_hz.value = 2000000 phy.profile_inputs.bitrate.value = 250000 phy.profile_inputs.baudrate_tol_ppm.value = 4000 """ Modulation Type """ phy.profile_inputs.modulation_type.value = model.vars.modulation_type.var_enum.OQPSK """ Symbol Mapping and Encoding """ phy.profile_inputs.symbol_encoding.value = model.vars.symbol_encoding.var_enum.DSSS """ DSSS Parameters """ phy.profile_inputs.dsss_chipping_code.value = 1951056795 phy.profile_inputs.dsss_len.value = 32 phy.profile_inputs.dsss_spreading_factor.value = 8 """ Shaping Filter Parameters """ phy.profile_inputs.shaping_filter.value = model.vars.shaping_filter.var_enum.Custom_OQPSK phy.profile_inputs.shaping_filter_param.value = 0.5 """ Preamble Parameters """ phy.profile_inputs.preamble_pattern_len.value = 4 phy.profile_inputs.preamble_length.value = 32 phy.profile_inputs.preamble_pattern.value = 0 """ Syncword Parameters """ phy.profile_inputs.syncword_0.value = 0xA7 phy.profile_inputs.syncword_1.value = 0x0 phy.profile_inputs.syncword_length.value = 8 """ Frame Configurations """ # Packet Inputs phy.profile_inputs.crc_byte_endian.value = model.vars.crc_byte_endian.var_enum.LSB_FIRST phy.profile_inputs.crc_bit_endian.value = model.vars.crc_bit_endian.var_enum.LSB_FIRST return phy

43.808664

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0.070443

0.873768

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0.784236

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0.214586

12,135

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43.808664

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false

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null

0

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1

0

null

0

7

87dda1aeeff9efc2004a0e0af7c803217d8dc8a7

132

py

Python

projects/slots/custom_slot.py

only-romano/junkyard

b60a25b2643f429cdafee438d20f9966178d6f36

[ "MIT" ]

null

projects/slots/custom_slot.py

only-romano/junkyard

b60a25b2643f429cdafee438d20f9966178d6f36

[ "MIT" ]

null

projects/slots/custom_slot.py

only-romano/junkyard

b60a25b2643f429cdafee438d20f9966178d6f36

[ "MIT" ]

null

""" Custom slots interactive creation tool """ def create_custom_slots(slots): return slots __all__ = ['create_custom_slots']

14.666667

38

0.742424

16

132

5.625

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0.366667

0.377778

0

0.151515

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0.287879

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35b42e458282a515bb934f3c5cc032756c12404c

243,579

py

Python

src/AeroelasticSE/FAST_VT/FST_vartrees_out.py

kilojoules/AeroelasticSE

ffc412e33ac4bbf29470ada111e94408ef2098c5

[ "Apache-2.0" ]

1

2021-03-25T16:58:22.000Z

src/AeroelasticSE/FAST_VT/FST_vartrees_out.py

kilojoules/AeroelasticSE

ffc412e33ac4bbf29470ada111e94408ef2098c5

[ "Apache-2.0" ]

null

src/AeroelasticSE/FAST_VT/FST_vartrees_out.py

kilojoules/AeroelasticSE

ffc412e33ac4bbf29470ada111e94408ef2098c5

[ "Apache-2.0" ]

null

from openmdao.main.api import VariableTree, Container, Component from openmdao.lib.datatypes.api import Int, Str, Float, List, Array, Enum, Bool, VarTree, Dict # Output Channel Variable Trees class WindMotionsOut(VariableTree): # Wind Motions WindVxi = Bool(False, iotype='in', desc='Nominally downwind component of the hub-height wind velocity (Directed along the xi-axis ) , (.NOT. CompAero) units= m/s') WindVyi = Bool(False, iotype='in', desc='Cross-wind component of the hub-height wind velocity (Directed along the yi-axis ) , (.NOT. CompAero) units= m/s') WindVzi = Bool(False, iotype='in', desc='Vertical component of the hub-height wind velocity (Directed along the zi-axis ) , (.NOT. CompAero) units= m/s') TotWindV = Bool(False, iotype='in', desc='Total hub-height wind velocity magnitude (N/A ) , (.NOT. CompAero) units= m/s') HorWindV = Bool(False, iotype='in', desc='Horizontal hub-height wind velocity magnitude (In the xi- and yi-plane ) , (.NOT. CompAero) units= m/s') HorWndDir = Bool(False, iotype='in', desc='Horizontal hub-height wind direction. Please note that FAST uses the opposite sign convention that AeroDyn uses. Put a "-", "_", "m", or "M" character in front of this variable name if you want to use the AeroDyn convention. (About the zi-axis ) , (.NOT. CompAero) units= deg') VerWndDir = Bool(False, iotype='in', desc='Vertical hub-height wind direction (About an axis orthogonal to the zi-axis and the HorWindV-vector) , (.NOT. CompAero) units= deg') # Wind Motions Other Name(s) 1 uWind = Bool(False, iotype='in', desc='Nominally downwind component of the hub-height wind velocity (Directed along the xi-axis ) , (.NOT. CompAero) units= m/s') vWind = Bool(False, iotype='in', desc='Cross-wind component of the hub-height wind velocity (Directed along the yi-axis ) , (.NOT. CompAero) units= m/s') wWind = Bool(False, iotype='in', desc='Vertical component of the hub-height wind velocity (Directed along the zi-axis ) , (.NOT. CompAero) units= m/s') class BladeMotionsOut(VariableTree): # Blade 1 Tip Motions TipDxc1 = Bool(False, iotype='in', desc='Blade 1 out-of-plane tip deflection (relative to the undeflected position) (Directed along the xc1-axis) , () units= m') TipDyc1 = Bool(False, iotype='in', desc='Blade 1 in-plane tip deflection (relative to the undeflected position) (Directed along the yc1-axis) , () units= m') TipDzc1 = Bool(False, iotype='in', desc='Blade 1 axial tip deflection (relative to the undeflected position) (Directed along the zc1- and zb1-axes) , () units= m') TipDxb1 = Bool(False, iotype='in', desc='Blade 1 flapwise tip deflection (relative to the undeflected position) (Directed along the xb1-axis) , () units= m') TipDyb1 = Bool(False, iotype='in', desc='Blade 1 edgewise tip deflection (relative to the undeflected position) (Directed along the yb1-axis) , () units= m') TipALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise tip acceleration (absolute) (Directed along the local xb1-axis) , () units= m/s**2') TipALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise tip acceleration (absolute) (Directed along the local yb1-axis) , () units= m/s**2') TipALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial tip acceleration (absolute) (Directed along the local zb1-axis) , () units= m/s**2') TipRDxb1 = Bool(False, iotype='in', desc='Blade 1 roll (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the xb1-axis) , () units= deg') TipRDyb1 = Bool(False, iotype='in', desc='Blade 1 pitch (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the yb1-axis) , () units= deg') TipRDzc1 = Bool(False, iotype='in', desc='Blade 1 torsional tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc1- and zb1-axes) , () units= deg') TipClrnc1 = Bool(False, iotype='in', desc='Blade 1 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , () units= m') # Blade 2 Tip Motions TipDxc2 = Bool(False, iotype='in', desc='Blade 2 out-of-plane tip deflection (relative to the pitch axis) (Directed along the xc2-axis) , () units= m') TipDyc2 = Bool(False, iotype='in', desc='Blade 2 in-plane tip deflection (relative to the pitch axis) (Directed along the yc2-axis) , () units= m') TipDzc2 = Bool(False, iotype='in', desc='Blade 2 axial tip deflection (relative to the pitch axis) (Directed along the zc2- and zb2-axes) , () units= m') TipDxb2 = Bool(False, iotype='in', desc='Blade 2 flapwise tip deflection (relative to the pitch axis) (Directed along the xb2-axis) , () units= m') TipDyb2 = Bool(False, iotype='in', desc='Blade 2 edgewise tip deflection (relative to the pitch axis) (Directed along the yb2-axis) , () units= m') TipALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise tip acceleration (absolute) (Directed along the local xb2-axis) , () units= m/s**2') TipALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise tip acceleration (absolute) (Directed along the local yb2-axis) , () units= m/s**2') TipALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial tip acceleration (absolute) (Directed along the local zb2-axis) , () units= m/s**2') TipRDxb2 = Bool(False, iotype='in', desc='Blade 2 roll (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the xb2-axis) , () units= deg') TipRDyb2 = Bool(False, iotype='in', desc='Blade 2 pitch (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the yb2-axis) , () units= deg') TipRDzc2 = Bool(False, iotype='in', desc='Blade 2 torsional (angular/rotational) tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc2- and zb2-axes) , () units= deg') TipClrnc2 = Bool(False, iotype='in', desc='Blade 2 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , () units= m') # Blade 3 Tip Motions TipDxc3 = Bool(False, iotype='in', desc='Blade 3 out-of-plane tip deflection (relative to the pitch axis) (Directed along the xc3-axis) , (NumBl < 3) units= m') TipDyc3 = Bool(False, iotype='in', desc='Blade 3 in-plane tip deflection (relative to the pitch axis) (Directed along the yc3-axis) , (NumBl < 3) units= m') TipDzc3 = Bool(False, iotype='in', desc='Blade 3 axial tip deflection (relative to the pitch axis) (Directed along the zc3- and zb3-axes) , (NumBl < 3) units= m') TipDxb3 = Bool(False, iotype='in', desc='Blade 3 flapwise tip deflection (relative to the pitch axis) (Directed along the xb3-axis) , (NumBl < 3) units= m') TipDyb3 = Bool(False, iotype='in', desc='Blade 3 edgewise tip deflection (relative to the pitch axis) (Directed along the yb3-axis) , (NumBl < 3) units= m') TipALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise tip acceleration (absolute) (Directed along the local xb3-axis) , (NumBl < 3) units= m/s**2') TipALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise tip acceleration (absolute) (Directed along the local yb3-axis) , (NumBl < 3) units= m/s**2') TipALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial tip acceleration (absolute) (Directed along the local zb3-axis) , (NumBl < 3) units= m/s**2') TipRDxb3 = Bool(False, iotype='in', desc='Blade 3 roll (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the xb3-axis) , (NumBl < 3) units= deg') TipRDyb3 = Bool(False, iotype='in', desc='Blade 3 pitch (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the yb3-axis) , (NumBl < 3) units= deg') TipRDzc3 = Bool(False, iotype='in', desc='Blade 3 torsional tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc3- and zb3-axes) , (NumBl < 3) units= deg') TipClrnc3 = Bool(False, iotype='in', desc='Blade 3 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , (NumBl < 3) units= m') # Blade 1 Local Span Motions Spn1ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 1 (Directed along the local xb1-axis) , (NBlGages < 1) units= m/s**2') Spn1ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 1 (Directed along the local yb1-axis) , (NBlGages < 1) units= m/s**2') Spn1ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 1 (Directed along the local zb1-axis) , (NBlGages < 1) units= m/s**2') Spn2ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 2 (Directed along the local xb1-axis) , (NBlGages < 2) units= m/s**2') Spn2ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 2 (Directed along the local yb1-axis) , (NBlGages < 2) units= m/s**2') Spn2ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 2 (Directed along the local zb1-axis) , (NBlGages < 2) units= m/s**2') Spn3ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 3 (Directed along the local xb1-axis) , (NBlGages < 3) units= m/s**2') Spn3ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 3 (Directed along the local yb1-axis) , (NBlGages < 3) units= m/s**2') Spn3ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 3 (Directed along the local zb1-axis) , (NBlGages < 3) units= m/s**2') Spn4ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 4 (Directed along the local xb1-axis) , (NBlGages < 4) units= m/s**2') Spn4ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 4 (Directed along the local yb1-axis) , (NBlGages < 4) units= m/s**2') Spn4ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 4 (Directed along the local zb1-axis) , (NBlGages < 4) units= m/s**2') Spn5ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 5 (Directed along the local xb1-axis) , (NBlGages < 5) units= m/s**2') Spn5ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 5 (Directed along the local yb1-axis) , (NBlGages < 5) units= m/s**2') Spn5ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 5 (Directed along the local zb1-axis) , (NBlGages < 5) units= m/s**2') Spn6ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 6 (Directed along the local xb1-axis) , (NBlGages < 6) units= m/s**2') Spn6ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 6 (Directed along the local yb1-axis) , (NBlGages < 6) units= m/s**2') Spn6ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 6 (Directed along the local zb1-axis) , (NBlGages < 6) units= m/s**2') Spn7ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 7 (Directed along the local xb1-axis) , (NBlGages < 7) units= m/s**2') Spn7ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 7 (Directed along the local yb1-axis) , (NBlGages < 7) units= m/s**2') Spn7ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 7 (Directed along the local zb1-axis) , (NBlGages < 7) units= m/s**2') Spn8ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 8 (Directed along the local xb1-axis) , (NBlGages < 8) units= m/s**2') Spn8ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 8 (Directed along the local yb1-axis) , (NBlGages < 8) units= m/s**2') Spn8ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 8 (Directed along the local zb1-axis) , (NBlGages < 8) units= m/s**2') Spn9ALxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise acceleration (absolute) of span station 9 (Directed along the local xb1-axis) , (NBlGages < 9) units= m/s**2') Spn9ALyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise acceleration (absolute) of span station 9 (Directed along the local yb1-axis) , (NBlGages < 9) units= m/s**2') Spn9ALzb1 = Bool(False, iotype='in', desc='Blade 1 local axial acceleration (absolute) of span station 9 (Directed along the local zb1-axis) , (NBlGages < 9) units= m/s**2') Spn1TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the xb1-axis) , (NBlGages < 1) units= m') Spn1TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the yb1-axis) , (NBlGages < 1) units= m') Spn1TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the zb1-axis) , (NBlGages < 1) units= m') Spn2TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the xb1-axis) , (NBlGages < 2) units= m') Spn2TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the yb1-axis) , (NBlGages < 2) units= m') Spn2TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the zb1-axis) , (NBlGages < 2) units= m') Spn3TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the xb1-axis) , (NBlGages < 3) units= m') Spn3TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the yb1-axis) , (NBlGages < 3) units= m') Spn3TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the zb1-axis) , (NBlGages < 3) units= m') Spn4TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the xb1-axis) , (NBlGages < 4) units= m') Spn4TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the yb1-axis) , (NBlGages < 4) units= m') Spn4TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the zb1-axis) , (NBlGages < 4) units= m') Spn5TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the xb1-axis) , (NBlGages < 5) units= m') Spn5TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the yb1-axis) , (NBlGages < 5) units= m') Spn5TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the zb1-axis) , (NBlGages < 5) units= m') Spn6TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the xb1-axis) , (NBlGages < 6) units= m') Spn6TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the yb1-axis) , (NBlGages < 6) units= m') Spn6TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the zb1-axis) , (NBlGages < 6) units= m') Spn7TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the xb1-axis) , (NBlGages < 7) units= m') Spn7TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the yb1-axis) , (NBlGages < 7) units= m') Spn7TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the zb1-axis) , (NBlGages < 7) units= m') Spn8TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the xb1-axis) , (NBlGages < 8) units= m') Spn8TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the yb1-axis) , (NBlGages < 8) units= m') Spn8TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the zb1-axis) , (NBlGages < 8) units= m') Spn9TDxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the xb1-axis) , (NBlGages < 9) units= m') Spn9TDyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the yb1-axis) , (NBlGages < 9) units= m') Spn9TDzb1 = Bool(False, iotype='in', desc='Blade 1 local axial (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the zb1-axis) , (NBlGages < 9) units= m') Spn1RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 1. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 1) units= deg') Spn1RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 1. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 1) units= deg') Spn1RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 1. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 1) units= deg') Spn2RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 2. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 2) units= deg') Spn2RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 2. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 2) units= deg') Spn2RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 2. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 2) units= deg') Spn3RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 3. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 3) units= deg') Spn3RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 3. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 3) units= deg') Spn3RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 3. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 3) units= deg') Spn4RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 4. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 4) units= deg') Spn4RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 4. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 4) units= deg') Spn4RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 4. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 4) units= deg') Spn5RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 5. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 5) units= deg') Spn5RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 5. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 5) units= deg') Spn5RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 5. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 5) units= deg') Spn6RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 6. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 6) units= deg') Spn6RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 6. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 6) units= deg') Spn6RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 6. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 6) units= deg') Spn7RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 7. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 7) units= deg') Spn7RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 7. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 7) units= deg') Spn7RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 7. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 7) units= deg') Spn8RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 8. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 8) units= deg') Spn8RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 8. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 8) units= deg') Spn8RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 8. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 8) units= deg') Spn9RDxb1 = Bool(False, iotype='in', desc='Blade 1 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 9. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb1-axis) , (NBlGages < 9) units= deg') Spn9RDyb1 = Bool(False, iotype='in', desc='Blade 1 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 9. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb1-axis) , (NBlGages < 9) units= deg') Spn9RDzb1 = Bool(False, iotype='in', desc='Blade 1 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 9. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb1-axis) , (NBlGages < 9) units= deg') # Blade 2 Local Span Motions Spn1ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 1 (Directed along the local xb2-axis) , (NBlGages < 1) units= m/s**2') Spn1ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 1 (Directed along the local yb2-axis) , (NBlGages < 1) units= m/s**2') Spn1ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 1 (Directed along the local zb2-axis) , (NBlGages < 1) units= m/s**2') Spn2ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 2 (Directed along the local xb2-axis) , (NBlGages < 2) units= m/s**2') Spn2ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 2 (Directed along the local yb2-axis) , (NBlGages < 2) units= m/s**2') Spn2ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 2 (Directed along the local zb2-axis) , (NBlGages < 2) units= m/s**2') Spn3ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 3 (Directed along the local xb2-axis) , (NBlGages < 3) units= m/s**2') Spn3ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 3 (Directed along the local yb2-axis) , (NBlGages < 3) units= m/s**2') Spn3ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 3 (Directed along the local zb2-axis) , (NBlGages < 3) units= m/s**2') Spn4ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 4 (Directed along the local xb2-axis) , (NBlGages < 4) units= m/s**2') Spn4ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 4 (Directed along the local yb2-axis) , (NBlGages < 4) units= m/s**2') Spn4ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 4 (Directed along the local zb2-axis) , (NBlGages < 4) units= m/s**2') Spn5ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 5 (Directed along the local xb2-axis) , (NBlGages < 5) units= m/s**2') Spn5ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 5 (Directed along the local yb2-axis) , (NBlGages < 5) units= m/s**2') Spn5ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 5 (Directed along the local zb2-axis) , (NBlGages < 5) units= m/s**2') Spn6ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 6 (Directed along the local xb2-axis) , (NBlGages < 6) units= m/s**2') Spn6ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 6 (Directed along the local yb2-axis) , (NBlGages < 6) units= m/s**2') Spn6ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 6 (Directed along the local zb2-axis) , (NBlGages < 6) units= m/s**2') Spn7ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 7 (Directed along the local xb2-axis) , (NBlGages < 7) units= m/s**2') Spn7ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 7 (Directed along the local yb2-axis) , (NBlGages < 7) units= m/s**2') Spn7ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 7 (Directed along the local zb2-axis) , (NBlGages < 7) units= m/s**2') Spn8ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 8 (Directed along the local xb2-axis) , (NBlGages < 8) units= m/s**2') Spn8ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 8 (Directed along the local yb2-axis) , (NBlGages < 8) units= m/s**2') Spn8ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 8 (Directed along the local zb2-axis) , (NBlGages < 8) units= m/s**2') Spn9ALxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise acceleration (absolute) of span station 9 (Directed along the local xb2-axis) , (NBlGages < 9) units= m/s**2') Spn9ALyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise acceleration (absolute) of span station 9 (Directed along the local yb2-axis) , (NBlGages < 9) units= m/s**2') Spn9ALzb2 = Bool(False, iotype='in', desc='Blade 2 local axial acceleration (absolute) of span station 9 (Directed along the local zb2-axis) , (NBlGages < 9) units= m/s**2') Spn1TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the xb2-axis) , (NBlGages < 1) units= m') Spn1TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the yb2-axis) , (NBlGages < 1) units= m') Spn1TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the zb2-axis) , (NBlGages < 1) units= m') Spn2TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the xb2-axis) , (NBlGages < 2) units= m') Spn2TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the yb2-axis) , (NBlGages < 2) units= m') Spn2TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the zb2-axis) , (NBlGages < 2) units= m') Spn3TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the xb2-axis) , (NBlGages < 3) units= m') Spn3TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the yb2-axis) , (NBlGages < 3) units= m') Spn3TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the zb2-axis) , (NBlGages < 3) units= m') Spn4TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the xb2-axis) , (NBlGages < 4) units= m') Spn4TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the yb2-axis) , (NBlGages < 4) units= m') Spn4TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the zb2-axis) , (NBlGages < 4) units= m') Spn5TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the xb2-axis) , (NBlGages < 5) units= m') Spn5TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the yb2-axis) , (NBlGages < 5) units= m') Spn5TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the zb2-axis) , (NBlGages < 5) units= m') Spn6TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the xb2-axis) , (NBlGages < 6) units= m') Spn6TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the yb2-axis) , (NBlGages < 6) units= m') Spn6TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the zb2-axis) , (NBlGages < 6) units= m') Spn7TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the xb2-axis) , (NBlGages < 7) units= m') Spn7TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the yb2-axis) , (NBlGages < 7) units= m') Spn7TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the zb2-axis) , (NBlGages < 7) units= m') Spn8TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the xb2-axis) , (NBlGages < 8) units= m') Spn8TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the yb2-axis) , (NBlGages < 8) units= m') Spn8TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the zb2-axis) , (NBlGages < 8) units= m') Spn9TDxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the xb2-axis) , (NBlGages < 9) units= m') Spn9TDyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the yb2-axis) , (NBlGages < 9) units= m') Spn9TDzb2 = Bool(False, iotype='in', desc='Blade 2 local axial (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the zb2-axis) , (NBlGages < 9) units= m') Spn1RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 1. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 1) units= deg') Spn1RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 1. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 1) units= deg') Spn1RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 1. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 1) units= deg') Spn2RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 2. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 2) units= deg') Spn2RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 2. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 2) units= deg') Spn2RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 2. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 2) units= deg') Spn3RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 3. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 3) units= deg') Spn3RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 3. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 3) units= deg') Spn3RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 3. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 3) units= deg') Spn4RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 4. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 4) units= deg') Spn4RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 4. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 4) units= deg') Spn4RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 4. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 4) units= deg') Spn5RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 5. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 5) units= deg') Spn5RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 5. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 5) units= deg') Spn5RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 5. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 5) units= deg') Spn6RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 6. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 6) units= deg') Spn6RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 6. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 6) units= deg') Spn6RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 6. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 6) units= deg') Spn7RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 7. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 7) units= deg') Spn7RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 7. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 7) units= deg') Spn7RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 7. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 7) units= deg') Spn8RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 8. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 8) units= deg') Spn8RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 8. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 8) units= deg') Spn8RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 8. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 8) units= deg') Spn9RDxb2 = Bool(False, iotype='in', desc='Blade 2 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 9. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb2-axis) , (NBlGages < 9) units= deg') Spn9RDyb2 = Bool(False, iotype='in', desc='Blade 2 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 9. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb2-axis) , (NBlGages < 9) units= deg') Spn9RDzb2 = Bool(False, iotype='in', desc='Blade 2 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 9. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb2-axis) , (NBlGages < 9) units= deg') # Blade 3 Local Span Motions Spn1ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 1 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= m/s**2') Spn1ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 1 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= m/s**2') Spn1ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 1 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= m/s**2') Spn2ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 2 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= m/s**2') Spn2ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 2 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= m/s**2') Spn2ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 2 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= m/s**2') Spn3ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 3 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= m/s**2') Spn3ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 3 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= m/s**2') Spn3ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 3 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= m/s**2') Spn4ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 4 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= m/s**2') Spn4ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 4 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= m/s**2') Spn4ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 4 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= m/s**2') Spn5ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 5 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= m/s**2') Spn5ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 5 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= m/s**2') Spn5ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 5 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= m/s**2') Spn6ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 6 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= m/s**2') Spn6ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 6 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= m/s**2') Spn6ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 6 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= m/s**2') Spn7ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 7 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= m/s**2') Spn7ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 7 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= m/s**2') Spn7ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 7 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= m/s**2') Spn8ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 8 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= m/s**2') Spn8ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 8 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= m/s**2') Spn8ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 8 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= m/s**2') Spn9ALxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise acceleration (absolute) of span station 9 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= m/s**2') Spn9ALyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise acceleration (absolute) of span station 9 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= m/s**2') Spn9ALzb3 = Bool(False, iotype='in', desc='Blade 3 local axial acceleration (absolute) of span station 9 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= m/s**2') Spn1TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= m') Spn1TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= m') Spn1TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 1 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= m') Spn2TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= m') Spn2TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= m') Spn2TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 2 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= m') Spn3TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= m') Spn3TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= m') Spn3TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 3 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= m') Spn4TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= m') Spn4TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= m') Spn4TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 4 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= m') Spn5TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= m') Spn5TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= m') Spn5TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 5 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= m') Spn6TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= m') Spn6TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= m') Spn6TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 6 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= m') Spn7TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= m') Spn7TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= m') Spn7TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 7 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= m') Spn8TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= m') Spn8TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= m') Spn8TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 8 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= m') Spn9TDxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= m') Spn9TDyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= m') Spn9TDzb3 = Bool(False, iotype='in', desc='Blade 3 local axial (translational) deflection (relative to the undeflected position) of span station 9 (Directed along the zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= m') Spn1RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 1. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= deg') Spn1RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 1. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= deg') Spn1RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 1. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= deg') Spn2RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 2. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= deg') Spn2RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 2. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= deg') Spn2RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 2. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= deg') Spn3RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 3. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= deg') Spn3RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 3. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= deg') Spn3RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 3. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= deg') Spn4RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 4. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= deg') Spn4RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 4. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= deg') Spn4RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 4. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= deg') Spn5RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 5. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= deg') Spn5RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 5. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= deg') Spn5RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 5. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= deg') Spn6RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 6. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= deg') Spn6RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 6. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= deg') Spn6RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 6. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= deg') Spn7RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 7. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= deg') Spn7RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 7. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= deg') Spn7RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 7. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= deg') Spn8RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 8. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= deg') Spn8RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 8. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= deg') Spn8RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 8. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= deg') Spn9RDxb3 = Bool(False, iotype='in', desc='Blade 3 local roll (angular/rotational) deflection (relative to the undeflected position) of span station 9. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= deg') Spn9RDyb3 = Bool(False, iotype='in', desc='Blade 3 local pitch (angular/rotational) deflection (relative to the undeflected position) of span station 9. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= deg') Spn9RDzb3 = Bool(False, iotype='in', desc='Blade 3 local torsional (angular/rotational) deflection (relative to the undeflected position) of span station 9. This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= deg') # Other Names # Blade 1 Tip Motions OoPDefl1 = Bool(False, iotype='in', desc='Blade 1 out-of-plane tip deflection (relative to the undeflected position) (Directed along the xc1-axis) , () units= m') IPDefl1 = Bool(False, iotype='in', desc='Blade 1 in-plane tip deflection (relative to the undeflected position) (Directed along the yc1-axis) , () units= m') TipDzb1 = Bool(False, iotype='in', desc='Blade 1 axial tip deflection (relative to the undeflected position) (Directed along the zc1- and zb1-axes) , () units= m') RollDefl1 = Bool(False, iotype='in', desc='Blade 1 roll (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the xb1-axis) , () units= deg') PtchDefl1 = Bool(False, iotype='in', desc='Blade 1 pitch (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the yb1-axis) , () units= deg') TipRDzb1 = Bool(False, iotype='in', desc='Blade 1 torsional tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc1- and zb1-axes) , () units= deg') TwrClrnc1 = Bool(False, iotype='in', desc='Blade 1 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , () units= m') # Blade 1 Tip Motions TwstDefl1 = Bool(False, iotype='in', desc='Blade 1 torsional tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc1- and zb1-axes) , () units= deg') Tip2Twr1 = Bool(False, iotype='in', desc='Blade 1 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , () units= m') # Blade 2 Tip Motions TwrClrnc1 OoPDefl2 = Bool(False, iotype='in', desc='Blade 2 out-of-plane tip deflection (relative to the pitch axis) (Directed along the xc2-axis) , () units= m') IPDefl2 = Bool(False, iotype='in', desc='Blade 2 in-plane tip deflection (relative to the pitch axis) (Directed along the yc2-axis) , () units= m') TipDzb2 = Bool(False, iotype='in', desc='Blade 2 axial tip deflection (relative to the pitch axis) (Directed along the zc2- and zb2-axes) , () units= m') RollDefl2 = Bool(False, iotype='in', desc='Blade 2 roll (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the xb2-axis) , () units= deg') PtchDefl2 = Bool(False, iotype='in', desc='Blade 2 pitch (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the yb2-axis) , () units= deg') TipRDzb2 = Bool(False, iotype='in', desc='Blade 2 torsional (angular/rotational) tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc2- and zb2-axes) , () units= deg') TwrClrnc2 = Bool(False, iotype='in', desc='Blade 2 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , () units= m') # Blade 2 Tip Motions Tip2Twr1 TwstDefl2 = Bool(False, iotype='in', desc='Blade 2 torsional (angular/rotational) tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc2- and zb2-axes) , () units= deg') Tip2Twr2 = Bool(False, iotype='in', desc='Blade 2 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , () units= m') # Blade 3 Tip Motions TwrClrnc2 OoPDefl3 = Bool(False, iotype='in', desc='Blade 3 out-of-plane tip deflection (relative to the pitch axis) (Directed along the xc3-axis) , (NumBl < 3) units= m') IPDefl3 = Bool(False, iotype='in', desc='Blade 3 in-plane tip deflection (relative to the pitch axis) (Directed along the yc3-axis) , (NumBl < 3) units= m') TipDzb3 = Bool(False, iotype='in', desc='Blade 3 axial tip deflection (relative to the pitch axis) (Directed along the zc3- and zb3-axes) , (NumBl < 3) units= m') RollDefl3 = Bool(False, iotype='in', desc='Blade 3 roll (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the xb3-axis) , (NumBl < 3) units= deg') PtchDefl3 = Bool(False, iotype='in', desc='Blade 3 pitch (angular/rotational) tip deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small blade deflections, so that the rotation sequence does not matter. (About the yb3-axis) , (NumBl < 3) units= deg') TipRDzb3 = Bool(False, iotype='in', desc='Blade 3 torsional tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc3- and zb3-axes) , (NumBl < 3) units= deg') TwrClrnc3 = Bool(False, iotype='in', desc='Blade 3 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , (NumBl < 3) units= m') # Blade 3 Tip Motions Tip2Twr2 TwstDefl3 = Bool(False, iotype='in', desc='Blade 3 torsional tip deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining blade torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. Please note that this output uses the opposite of the sign convention used for blade pitch angles. (About the zc3- and zb3-axes) , (NumBl < 3) units= deg') Tip2Twr3 = Bool(False, iotype='in', desc='Blade 3 tip-to-tower clearance estimate. This is computed as the perpendicular distance from the yaw axis to the tip of blade 1 when the blade tip is below the yaw bearing. When the tip of blade 1 is above the yaw bearing, it is computed as the absolute distance from the yaw bearing to the blade tip. Please note that you should reduce this value by the tower radius to obtain the actual tower clearance. (N/A) , (NumBl < 3) units= m') class HubNacelleMotionsOut(VariableTree): # Blade Pitch Motions PtchPMzc1 = Bool(False, iotype='in', desc='Blade 1 pitch angle (position) (Positive towards feather about the minus zc1- and minus zb1-axes) , () units= deg') PtchPMzc2 = Bool(False, iotype='in', desc='Blade 2 pitch angle (position) (Positive towards feather about the minus zc2- and minus zb2-axes) , () units= deg') PtchPMzc3 = Bool(False, iotype='in', desc='Blade 3 pitch angle (position) (Positive towards feather about the minus zc3- and minus zb3-axes) , (NumBl < 3) units= deg') # Teeter Motions TeetPya = Bool(False, iotype='in', desc='Rotor teeter angle (position) (About the ya-axis) , (NumBl > 2) units= deg') TeetVya = Bool(False, iotype='in', desc='Rotor teeter angular velocity (About the ya-axis) , (NumBl > 2) units= deg/s') TeetAya = Bool(False, iotype='in', desc='Rotor teeter angular acceleration (About the ya-axis) , (NumBl > 2) units= deg/s**2') # Shaft Motions LSSTipPxa = Bool(False, iotype='in', desc='Rotor azimuth angle (position) (About the xa- and xs-axes) , () units= deg') LSSTipVxa = Bool(False, iotype='in', desc='Rotor azimuth angular speed (About the xa- and xs-axes) , () units= rpm') LSSTipAxa = Bool(False, iotype='in', desc='Rotor azimuth angular acceleration (About the xa- and xs-axes) , () units= deg/s**2') LSSGagPxa = Bool(False, iotype='in', desc='Low-speed shaft strain gage azimuth angle (position) (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= deg') LSSGagVxa = Bool(False, iotype='in', desc='Low-speed shaft strain gage angular speed (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= rpm') LSSGagAxa = Bool(False, iotype='in', desc='Low-speed shaft strain gage angular acceleration (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= deg/s**2') HSShftV = Bool(False, iotype='in', desc='Angular speed of the high-speed shaft and generator (Same sign as LSSGagVxa / LSSGagVxs / LSSGagV) , () units= rpm') HSShftA = Bool(False, iotype='in', desc='Angular acceleration of the high-speed shaft and generator (Same sign as LSSGagAxa / LSSGagAxs / LSSGagA) , () units= deg/s**2') TipSpdRat = Bool(False, iotype='in', desc='Rotor blade tip speed ratio (N/A) , (.NOT. CompAero) units= ') # Nacelle IMU Motions NcIMUTVxs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit translational velocity (absolute) (Directed along the xs-axis) , () units= m/s') NcIMUTVys = Bool(False, iotype='in', desc='Nacelle inertial measurement unit translational velocity (absolute) (Directed along the ys-axis) , () units= m/s') NcIMUTVzs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit translational velocity (absolute) (Directed along the zs-axis) , () units= m/s') NcIMUTAxs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit translational acceleration (absolute) (Directed along the xs-axis) , () units= m/s**2') NcIMUTAys = Bool(False, iotype='in', desc='Nacelle inertial measurement unit translational acceleration (absolute) (Directed along the ys-axis) , () units= m/s**2') NcIMUTAzs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit translational acceleration (absolute) (Directed along the zs-axis) , () units= m/s**2') NcIMURVxs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit angular (rotational) velocity (absolute) (About the xs-axis) , () units= deg/s') NcIMURVys = Bool(False, iotype='in', desc='Nacelle inertial measurement unit angular (rotational) velocity (absolute) (About the ys-axis) , () units= deg/s') NcIMURVzs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit angular (rotational) velocity (absolute) (About the zs-axis) , () units= deg/s') NcIMURAxs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit angular (rotational) acceleration (absolute) (About the xs-axis) , () units= deg/s**2') NcIMURAys = Bool(False, iotype='in', desc='Nacelle inertial measurement unit angular (rotational) acceleration (absolute) (About the ys-axis) , () units= deg/s**2') NcIMURAzs = Bool(False, iotype='in', desc='Nacelle inertial measurement unit angular (rotational) acceleration (absolute) (About the zs-axis) , () units= deg/s**2') # Rotor-Furl Motions RotFurlP = Bool(False, iotype='in', desc='Rotor-furl angle (position) (About the rotor-furl axis) , () units= deg') RotFurlV = Bool(False, iotype='in', desc='Rotor-furl angular velocity (About the rotor-furl axis) , () units= deg/s') RotFurlA = Bool(False, iotype='in', desc='Rotor-furl angular acceleration (About the rotor-furl axis) , () units= deg/s**2') # Tail-Furl Motions TailFurlP = Bool(False, iotype='in', desc='Tail-furl angle (position) (About the tail-furl axis) , () units= deg') TailFurlV = Bool(False, iotype='in', desc='Tail-furl angular velocity (About the tail-furl axis) , () units= deg/s') TailFurlA = Bool(False, iotype='in', desc='Tail-furl angular acceleration (About the tail-furl axis) , () units= deg/s**2') # Nacelle Yaw Motions YawPzn = Bool(False, iotype='in', desc='Nacelle yaw angle (position) (About the zn- and zp-axes) , () units= deg') YawVzn = Bool(False, iotype='in', desc='Nacelle yaw angular velocity (About the zn- and zp-axes) , () units= deg/s') YawAzn = Bool(False, iotype='in', desc='Nacelle yaw angular acceleration (About the zn- and zp-axes) , () units= deg/s**2') NacYawErr = Bool(False, iotype='in', desc='Nacelle yaw error estimate. This is computed as follows: NacYawErr = HorWndDir - YawPzn - YawBrRDzt - PtfmRDzi. This estimate is not accurate instantaneously in the presence of significant tower deflection or platform angular (rotational) displacement since the angles used in the computation are not all defined about the same axis of rotation. However, the estimate should be useful in a yaw controller if averaged over a time scale long enough to diminish the effects of tower and platform motions (i.e., much longer than the period of oscillation). (About the zi-axis) , (.NOT. CompAero) units= deg') # Other Names # Blade Pitch Motions PtchPMzb1 = Bool(False, iotype='in', desc='Blade 1 pitch angle (position) (Positive towards feather about the minus zc1- and minus zb1-axes) , () units= deg') PtchPMzb2 = Bool(False, iotype='in', desc='Blade 2 pitch angle (position) (Positive towards feather about the minus zc2- and minus zb2-axes) , () units= deg') PtchPMzb3 = Bool(False, iotype='in', desc='Blade 3 pitch angle (position) (Positive towards feather about the minus zc3- and minus zb3-axes) , (NumBl < 3) units= deg') # Blade Pitch Motions BldPitch1 = Bool(False, iotype='in', desc='Blade 1 pitch angle (position) (Positive towards feather about the minus zc1- and minus zb1-axes) , () units= deg') BldPitch2 = Bool(False, iotype='in', desc='Blade 2 pitch angle (position) (Positive towards feather about the minus zc2- and minus zb2-axes) , () units= deg') BldPitch3 = Bool(False, iotype='in', desc='Blade 3 pitch angle (position) (Positive towards feather about the minus zc3- and minus zb3-axes) , (NumBl < 3) units= deg') # Teeter Motions PtchPMzb3 RotTeetP = Bool(False, iotype='in', desc='Rotor teeter angle (position) (About the ya-axis) , (NumBl > 2) units= deg') RotTeetV = Bool(False, iotype='in', desc='Rotor teeter angular velocity (About the ya-axis) , (NumBl > 2) units= deg/s') RotTeetA = Bool(False, iotype='in', desc='Rotor teeter angular acceleration (About the ya-axis) , (NumBl > 2) units= deg/s**2') # Teeter Motions BldPitch3 TeetDefl = Bool(False, iotype='in', desc='Rotor teeter angle (position) (About the ya-axis) , (NumBl > 2) units= deg') RotTeetV = Bool(False, iotype='in', desc='Rotor teeter angular velocity (About the ya-axis) , (NumBl > 2) units= deg/s') RotTeetA = Bool(False, iotype='in', desc='Rotor teeter angular acceleration (About the ya-axis) , (NumBl > 2) units= deg/s**2') # Teeter Motions RotTeetV = Bool(False, iotype='in', desc='Rotor teeter angular velocity (About the ya-axis) , (NumBl > 2) units= deg/s') RotTeetA = Bool(False, iotype='in', desc='Rotor teeter angular acceleration (About the ya-axis) , (NumBl > 2) units= deg/s**2') # Shaft Motions RotTeetA LSSTipPxs = Bool(False, iotype='in', desc='Rotor azimuth angle (position) (About the xa- and xs-axes) , () units= deg') LSSTipVxs = Bool(False, iotype='in', desc='Rotor azimuth angular speed (About the xa- and xs-axes) , () units= rpm') LSSTipAxs = Bool(False, iotype='in', desc='Rotor azimuth angular acceleration (About the xa- and xs-axes) , () units= deg/s**2') LSSGagPxs = Bool(False, iotype='in', desc='Low-speed shaft strain gage azimuth angle (position) (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= deg') LSSGagVxs = Bool(False, iotype='in', desc='Low-speed shaft strain gage angular speed (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= rpm') LSSGagAxs = Bool(False, iotype='in', desc='Low-speed shaft strain gage angular acceleration (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= deg/s**2') GenSpeed = Bool(False, iotype='in', desc='Angular speed of the high-speed shaft and generator (Same sign as LSSGagVxa / LSSGagVxs / LSSGagV) , () units= rpm') # Shaft Motions RotTeetA LSSTipP = Bool(False, iotype='in', desc='Rotor azimuth angle (position) (About the xa- and xs-axes) , () units= deg') LSSTipV = Bool(False, iotype='in', desc='Rotor azimuth angular speed (About the xa- and xs-axes) , () units= rpm') LSSTipA = Bool(False, iotype='in', desc='Rotor azimuth angular acceleration (About the xa- and xs-axes) , () units= deg/s**2') LSSGagP = Bool(False, iotype='in', desc='Low-speed shaft strain gage azimuth angle (position) (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= deg') LSSGagV = Bool(False, iotype='in', desc='Low-speed shaft strain gage angular speed (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= rpm') LSSGagA = Bool(False, iotype='in', desc='Low-speed shaft strain gage angular acceleration (on the gearbox side of the low-speed shaft) (About the xa- and xs-axes) , () units= deg/s**2') # Shaft Motions RotTeetA Azimuth = Bool(False, iotype='in', desc='Rotor azimuth angle (position) (About the xa- and xs-axes) , () units= deg') RotSpeed = Bool(False, iotype='in', desc='Rotor azimuth angular speed (About the xa- and xs-axes) , () units= rpm') RotAccel = Bool(False, iotype='in', desc='Rotor azimuth angular acceleration (About the xa- and xs-axes) , () units= deg/s**2') # Rotor-Furl Motions RotFurl = Bool(False, iotype='in', desc='Rotor-furl angle (position) (About the rotor-furl axis) , () units= deg') # Tail-Furl Motions TailFurl = Bool(False, iotype='in', desc='Tail-furl angle (position) (About the tail-furl axis) , () units= deg') # Nacelle Yaw Motions YawPzp = Bool(False, iotype='in', desc='Nacelle yaw angle (position) (About the zn- and zp-axes) , () units= deg') YawVzp = Bool(False, iotype='in', desc='Nacelle yaw angular velocity (About the zn- and zp-axes) , () units= deg/s') YawAzp = Bool(False, iotype='in', desc='Nacelle yaw angular acceleration (About the zn- and zp-axes) , () units= deg/s**2') # Nacelle Yaw Motions NacYawP = Bool(False, iotype='in', desc='Nacelle yaw angle (position) (About the zn- and zp-axes) , () units= deg') NacYawV = Bool(False, iotype='in', desc='Nacelle yaw angular velocity (About the zn- and zp-axes) , () units= deg/s') NacYawA = Bool(False, iotype='in', desc='Nacelle yaw angular acceleration (About the zn- and zp-axes) , () units= deg/s**2') # Nacelle Yaw Motions NacYaw = Bool(False, iotype='in', desc='Nacelle yaw angle (position) (About the zn- and zp-axes) , () units= deg') YawRate = Bool(False, iotype='in', desc='Nacelle yaw angular velocity (About the zn- and zp-axes) , () units= deg/s') YawAccel = Bool(False, iotype='in', desc='Nacelle yaw angular acceleration (About the zn- and zp-axes) , () units= deg/s**2') # Nacelle Yaw Motions YawPos = Bool(False, iotype='in', desc='Nacelle yaw angle (position) (About the zn- and zp-axes) , () units= deg') class TowerSupportMotionsOut(VariableTree): # Tower-Top / Yaw Bearing Motions YawBrTDxp = Bool(False, iotype='in', desc='Tower-top / yaw bearing fore-aft (translational) deflection (relative to the undeflected position) (Directed along the xp-axis) , () units= m') YawBrTDyp = Bool(False, iotype='in', desc='Tower-top / yaw bearing side-to-side (translational) deflection (relative to the undeflected position) (Directed along the yp-axis) , () units= m') YawBrTDzp = Bool(False, iotype='in', desc='Tower-top / yaw bearing axial (translational) deflection (relative to the undeflected position) (Directed along the zp-axis) , () units= m') YawBrTDxt = Bool(False, iotype='in', desc='Tower-top / yaw bearing fore-aft (translational) deflection (relative to the undeflected position) (Directed along the xt-axis) , () units= m') YawBrTDyt = Bool(False, iotype='in', desc='Tower-top / yaw bearing side-to-side (translation) deflection (relative to the undeflected position) (Directed along the yt-axis) , () units= m') YawBrTDzt = Bool(False, iotype='in', desc='Tower-top / yaw bearing axial (translational) deflection (relative to the undeflected position) (Directed along the zt-axis) , () units= m') YawBrTAxp = Bool(False, iotype='in', desc='Tower-top / yaw bearing fore-aft (translational) acceleration (absolute) (Directed along the xp-axis) , () units= m/s**2') YawBrTAyp = Bool(False, iotype='in', desc='Tower-top / yaw bearing side-to-side (translational) acceleration (absolute) (Directed along the yp-axis) , () units= m/s**2') YawBrTAzp = Bool(False, iotype='in', desc='Tower-top / yaw bearing axial (translational) acceleration (absolute) (Directed along the zp-axis) , () units= m/s**2') YawBrRDxt = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) roll deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the xt-axis) , () units= deg') YawBrRDyt = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) pitch deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the yt-axis) , () units= deg') YawBrRDzt = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) torsion deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the zt-axis) , () units= deg') YawBrRVxp = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) roll velocity (absolute) (About the xp-axis) , () units= deg/s') YawBrRVyp = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) pitch velocity (absolute) (About the yp-axis) , () units= deg/s') YawBrRVzp = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) torsion velocity. This output will always be very close to zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. (absolute) (About the zp-axis) , () units= deg/s') YawBrRAxp = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) roll acceleration (absolute) (About the xp-axis) , () units= deg/s**2') YawBrRAyp = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) pitch acceleration (absolute) (About the yp-axis) , () units= deg/s**2') YawBrRAzp = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) torsion acceleration. This output will always be very close to zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. (absolute) (About the zp-axis) , () units= deg/s**2') # Local Tower Motions TwHt1ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 1 (Directed along the local xt-axis) , (NTwGages < 1) units= m/s**2') TwHt1ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 1 (Directed along the local yt-axis) , (NTwGages < 1) units= m/s**2') TwHt1ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 1 (Directed along the local zt-axis) , (NTwGages < 1) units= m/s**2') TwHt2ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 2 (Directed along the local xt-axis) , (NTwGages < 2) units= m/s**2') TwHt2ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 2 (Directed along the local yt-axis) , (NTwGages < 2) units= m/s**2') TwHt2ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 2 (Directed along the local zt-axis) , (NTwGages < 2) units= m/s**2') TwHt3ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 3 (Directed along the local xt-axis) , (NTwGages < 3) units= m/s**2') TwHt3ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 3 (Directed along the local yt-axis) , (NTwGages < 3) units= m/s**2') TwHt3ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 3 (Directed along the local zt-axis) , (NTwGages < 3) units= m/s**2') TwHt4ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 4 (Directed along the local xt-axis) , (NTwGages < 4) units= m/s**2') TwHt4ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 4 (Directed along the local yt-axis) , (NTwGages < 4) units= m/s**2') TwHt4ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 4 (Directed along the local zt-axis) , (NTwGages < 4) units= m/s**2') TwHt5ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 5 (Directed along the local xt-axis) , (NTwGages < 5) units= m/s**2') TwHt5ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 5 (Directed along the local yt-axis) , (NTwGages < 5) units= m/s**2') TwHt5ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 5 (Directed along the local zt-axis) , (NTwGages < 5) units= m/s**2') TwHt6ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 6 (Directed along the local xt-axis) , (NTwGages < 6) units= m/s**2') TwHt6ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 6 (Directed along the local yt-axis) , (NTwGages < 6) units= m/s**2') TwHt6ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 6 (Directed along the local zt-axis) , (NTwGages < 6) units= m/s**2') TwHt7ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 7 (Directed along the local xt-axis) , (NTwGages < 7) units= m/s**2') TwHt7ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 7 (Directed along the local yt-axis) , (NTwGages < 7) units= m/s**2') TwHt7ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 7 (Directed along the local zt-axis) , (NTwGages < 7) units= m/s**2') TwHt8ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 8 (Directed along the local xt-axis) , (NTwGages < 8) units= m/s**2') TwHt8ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 8 (Directed along the local yt-axis) , (NTwGages < 8) units= m/s**2') TwHt8ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 8 (Directed along the local zt-axis) , (NTwGages < 8) units= m/s**2') TwHt9ALxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) acceleration (absolute) of tower gage 9 (Directed along the local xt-axis) , (NTwGages < 9) units= m/s**2') TwHt9ALyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) acceleration (absolute) of tower gage 9 (Directed along the local yt-axis) , (NTwGages < 9) units= m/s**2') TwHt9ALzt = Bool(False, iotype='in', desc='Local tower axial (translational) acceleration (absolute) of tower gage 9 (Directed along the local zt-axis) , (NTwGages < 9) units= m/s**2') TwHt1TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 1 (Directed along the local xt-axis) , (NTwGages < 1) units= m') TwHt1TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 1 (Directed along the local yt-axis) , (NTwGages < 1) units= m') TwHt1TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 1 (Directed along the local zt-axis) , (NTwGages < 1) units= m') TwHt2TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 2 (Directed along the local xt-axis) , (NTwGages < 2) units= m') TwHt2TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 2 (Directed along the local yt-axis) , (NTwGages < 2) units= m') TwHt2TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 2 (Directed along the local zt-axis) , (NTwGages < 2) units= m') TwHt3TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 3 (Directed along the local xt-axis) , (NTwGages < 3) units= m') TwHt3TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 3 (Directed along the local yt-axis) , (NTwGages < 3) units= m') TwHt3TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 3 (Directed along the local zt-axis) , (NTwGages < 3) units= m') TwHt4TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 4 (Directed along the local xt-axis) , (NTwGages < 4) units= m') TwHt4TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 4 (Directed along the local yt-axis) , (NTwGages < 4) units= m') TwHt4TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 4 (Directed along the local zt-axis) , (NTwGages < 4) units= m') TwHt5TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 5 (Directed along the local xt-axis) , (NTwGages < 5) units= m') TwHt5TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 5 (Directed along the local yt-axis) , (NTwGages < 5) units= m') TwHt5TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 5 (Directed along the local zt-axis) , (NTwGages < 5) units= m') TwHt6TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 6 (Directed along the local xt-axis) , (NTwGages < 6) units= m') TwHt6TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 6 (Directed along the local yt-axis) , (NTwGages < 6) units= m') TwHt6TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 6 (Directed along the local zt-axis) , (NTwGages < 6) units= m') TwHt7TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 7 (Directed along the local xt-axis) , (NTwGages < 7) units= m') TwHt7TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 7 (Directed along the local yt-axis) , (NTwGages < 7) units= m') TwHt7TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 7 (Directed along the local zt-axis) , (NTwGages < 7) units= m') TwHt8TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 8 (Directed along the local xt-axis) , (NTwGages < 8) units= m') TwHt8TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 8 (Directed along the local yt-axis) , (NTwGages < 8) units= m') TwHt8TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 8 (Directed along the local zt-axis) , (NTwGages < 8) units= m') TwHt9TDxt = Bool(False, iotype='in', desc='Local tower fore-aft (translational) deflection (relative to the undeflected position) of tower gage 9 (Directed along the local xt-axis) , (NTwGages < 9) units= m') TwHt9TDyt = Bool(False, iotype='in', desc='Local tower side-to-side (translational) deflection (relative to the undeflected position) of tower gage 9 (Directed along the local yt-axis) , (NTwGages < 9) units= m') TwHt9TDzt = Bool(False, iotype='in', desc='Local tower axial (translational) deflection (relative to the undeflected position) of tower gage 9 (Directed along the local zt-axis) , (NTwGages < 9) units= m') TwHt1RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 1. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 1) units= deg') TwHt1RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 1. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 1) units= deg') TwHt1RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 1. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 1) units= deg') TwHt2RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 2. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 2) units= deg') TwHt2RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 2. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 2) units= deg') TwHt2RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 2. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 2) units= deg') TwHt3RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 3. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 3) units= deg') TwHt3RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 3. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 3) units= deg') TwHt3RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 3. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 3) units= deg') TwHt4RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 4. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 4) units= deg') TwHt4RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 4. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 4) units= deg') TwHt4RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 4. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 4) units= deg') TwHt5RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 5. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 5) units= deg') TwHt5RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 5. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 5) units= deg') TwHt5RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 5. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 5) units= deg') TwHt6RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 6. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 6) units= deg') TwHt6RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 6. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 6) units= deg') TwHt6RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 6. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 6) units= deg') TwHt7RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 7. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 7) units= deg') TwHt7RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 7. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 7) units= deg') TwHt7RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 7. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 7) units= deg') TwHt8RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 8. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 8) units= deg') TwHt8RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 8. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 8) units= deg') TwHt8RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 8. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 8) units= deg') TwHt9RDxt = Bool(False, iotype='in', desc='Local tower angular (rotational) roll deflection (relative to the undeflected position) of tower gage 9. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local xt-axis) , (NTwGages < 9) units= deg') TwHt9RDyt = Bool(False, iotype='in', desc='Local tower angular (rotational) pitch deflection (relative to the undeflected position) of tower gage 9. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the local yt-axis) , (NTwGages < 9) units= deg') TwHt9RDzt = Bool(False, iotype='in', desc='Local tower angular (rotational) torsion deflection (relative to the undeflected position) of tower gage 9. This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the local zt-axis) , (NTwGages < 9) units= deg') TwHt1TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 1 (Directed along the local xi-axis) , (NTwGages < 1) units= m') TwHt1TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 1 (Directed along the local yi-axis) , (NTwGages < 1) units= m') TwHt1TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 1 (Directed along the local zi-axis) , (NTwGages < 1) units= m') TwHt2TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 2 (Directed along the local xi-axis) , (NTwGages < 2) units= m') TwHt2TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 2 (Directed along the local yi-axis) , (NTwGages < 2) units= m') TwHt2TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 2 (Directed along the local zi-axis) , (NTwGages < 2) units= m') TwHt3TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 3 (Directed along the local xi-axis) , (NTwGages < 3) units= m') TwHt3TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 3 (Directed along the local yi-axis) , (NTwGages < 3) units= m') TwHt3TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 3 (Directed along the local zi-axis) , (NTwGages < 3) units= m') TwHt4TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 4 (Directed along the local xi-axis) , (NTwGages < 4) units= m') TwHt4TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 4 (Directed along the local yi-axis) , (NTwGages < 4) units= m') TwHt4TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 4 (Directed along the local zi-axis) , (NTwGages < 4) units= m') TwHt5TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 5 (Directed along the local xi-axis) , (NTwGages < 5) units= m') TwHt5TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 5 (Directed along the local yi-axis) , (NTwGages < 5) units= m') TwHt5TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 5 (Directed along the local zi-axis) , (NTwGages < 5) units= m') TwHt6TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 6 (Directed along the local xi-axis) , (NTwGages < 6) units= m') TwHt6TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 6 (Directed along the local yi-axis) , (NTwGages < 6) units= m') TwHt6TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 6 (Directed along the local zi-axis) , (NTwGages < 6) units= m') TwHt7TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 7 (Directed along the local xi-axis) , (NTwGages < 7) units= m') TwHt7TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 7 (Directed along the local yi-axis) , (NTwGages < 7) units= m') TwHt7TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 7 (Directed along the local zi-axis) , (NTwGages < 7) units= m') TwHt8TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 8 (Directed along the local xi-axis) , (NTwGages < 8) units= m') TwHt8TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 8 (Directed along the local yi-axis) , (NTwGages < 8) units= m') TwHt8TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 8 (Directed along the local zi-axis) , (NTwGages < 8) units= m') TwHt9TPxi = Bool(False, iotype='in', desc='xi-component of the translational position (relative to the inertia frame) of tower gage 9 (Directed along the local xi-axis) , (NTwGages < 9) units= m') TwHt9TPyi = Bool(False, iotype='in', desc='yi-component of the translational position (relative to the inertia frame) of tower gage 9 (Directed along the local yi-axis) , (NTwGages < 9) units= m') TwHt9TPzi = Bool(False, iotype='in', desc='zi-component of the translational position (relative to ground level [onshore] or MSL [offshore]) of tower gage 9 (Directed along the local zi-axis) , (NTwGages < 9) units= m') TwHt1RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 1. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 1) units= deg') TwHt1RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 1. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 1) units= deg') TwHt1RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 1. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 1) units= deg') TwHt2RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 2. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 2) units= deg') TwHt2RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 2. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 2) units= deg') TwHt2RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 2. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 2) units= deg') TwHt3RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 3. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 3) units= deg') TwHt3RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 3. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 3) units= deg') TwHt3RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 3. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 3) units= deg') TwHt4RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 4. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 4) units= deg') TwHt4RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 4. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 4) units= deg') TwHt4RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 4. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 4) units= deg') TwHt5RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 5. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 5) units= deg') TwHt5RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 5. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 5) units= deg') TwHt5RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 5. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 5) units= deg') TwHt6RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 6. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 6) units= deg') TwHt6RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 6. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 6) units= deg') TwHt6RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 6. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 6) units= deg') TwHt7RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 7. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 7) units= deg') TwHt7RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 7. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 7) units= deg') TwHt7RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 7. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 7) units= deg') TwHt8RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 8. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 8) units= deg') TwHt8RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 8. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 8) units= deg') TwHt8RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 8. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 8) units= deg') TwHt9RPxi = Bool(False, iotype='in', desc='xi-component of the rotational position (relative to the inertia frame) of tower gage 9. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local xi-axis) , (NTwGages < 9) units= deg') TwHt9RPyi = Bool(False, iotype='in', desc='yi-component of the rotational position (relative to the inertia frame) of tower gage 9. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local yi-axis) , (NTwGages < 9) units= deg') TwHt9RPzi = Bool(False, iotype='in', desc='zi-component of the rotational position (relative to the inertia frame) of tower gage 9. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower and platform rotational deflections, so that the rotation sequence does not matter. (About the local zi-axis) , (NTwGages < 9) units= deg') # Platform Motions PtfmTDxt = Bool(False, iotype='in', desc='Platform horizontal surge (translational) displacement (Directed along the xt-axis) , () units= m') PtfmTDyt = Bool(False, iotype='in', desc='Platform horizontal sway (translational) displacement (Directed along the yt-axis) , () units= m') PtfmTDzt = Bool(False, iotype='in', desc='Platform vertical heave (translational) displacement (Directed along the zt-axis) , () units= m') PtfmTDxi = Bool(False, iotype='in', desc='Platform horizontal surge (translational) displacement (Directed along the xi-axis) , () units= m') PtfmTDyi = Bool(False, iotype='in', desc='Platform horizontal sway (translational) displacement (Directed along the yi-axis) , () units= m') PtfmTDzi = Bool(False, iotype='in', desc='Platform vertical heave (translational) displacement (Directed along the zi-axis) , () units= m') PtfmTVxt = Bool(False, iotype='in', desc='Platform horizontal surge (translational) velocity (Directed along the xt-axis) , () units= m/s') PtfmTVyt = Bool(False, iotype='in', desc='Platform horizontal sway (translational) velocity (Directed along the yt-axis) , () units= m/s') PtfmTVzt = Bool(False, iotype='in', desc='Platform vertical heave (translational) velocity (Directed along the zt-axis) , () units= m/s') PtfmTVxi = Bool(False, iotype='in', desc='Platform horizontal surge (translational) velocity (Directed along the xi-axis) , () units= m/s') PtfmTVyi = Bool(False, iotype='in', desc='Platform horizontal sway (translational) velocity (Directed along the yi-axis) , () units= m/s') PtfmTVzi = Bool(False, iotype='in', desc='Platform vertical heave (translational) velocity (Directed along the zi-axis) , () units= m/s') PtfmTAxt = Bool(False, iotype='in', desc='Platform horizontal surge (translational) acceleration (Directed along the xt-axis) , () units= m/s**2') PtfmTAyt = Bool(False, iotype='in', desc='Platform horizontal sway (translational) acceleration (Directed along the yt-axis) , () units= m/s**2') PtfmTAzt = Bool(False, iotype='in', desc='Platform vertical heave (translational) acceleration (Directed along the zt-axis) , () units= m/s**2') PtfmTAxi = Bool(False, iotype='in', desc='Platform horizontal surge (translational) acceleration (Directed along the xi-axis) , () units= m/s**2') PtfmTAyi = Bool(False, iotype='in', desc='Platform horizontal sway (translational) acceleration (Directed along the yi-axis) , () units= m/s**2') PtfmTAzi = Bool(False, iotype='in', desc='Platform vertical heave (translational) acceleration (Directed along the zi-axis) , () units= m/s**2') PtfmRDxi = Bool(False, iotype='in', desc='Platform roll tilt angular (rotational) displacement. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small rotational platform displacements, so that the rotation sequence does not matter. (About the xi-axis) , () units= deg') PtfmRDyi = Bool(False, iotype='in', desc='Platform pitch tilt angular (rotational) displacement. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small rotational platform displacements, so that the rotation sequence does not matter. (About the yi-axis) , () units= deg') PtfmRDzi = Bool(False, iotype='in', desc='Platform yaw angular (rotational) displacement. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small rotational platform displacements, so that the rotation sequence does not matter. (About the zi-axis) , () units= deg') PtfmRVxt = Bool(False, iotype='in', desc='Platform roll tilt angular (rotational) velocity (About the xt-axis) , () units= deg/s') PtfmRVyt = Bool(False, iotype='in', desc='Platform pitch tilt angular (rotational) velocity (About the yt-axis) , () units= deg/s') PtfmRVzt = Bool(False, iotype='in', desc='Platform yaw angular (rotational) velocity (About the zt-axis) , () units= deg/s') PtfmRVxi = Bool(False, iotype='in', desc='Platform roll tilt angular (rotational) velocity (About the xi-axis) , () units= deg/s') PtfmRVyi = Bool(False, iotype='in', desc='Platform pitch tilt angular (rotational) velocity (About the yi-axis) , () units= deg/s') PtfmRVzi = Bool(False, iotype='in', desc='Platform yaw angular (rotational) velocity (About the zi-axis) , () units= deg/s') PtfmRAxt = Bool(False, iotype='in', desc='Platform roll tilt angular (rotational) acceleration (About the xt-axis) , () units= deg/s**2') PtfmRAyt = Bool(False, iotype='in', desc='Platform pitch tilt angular (rotational) acceleration (About the yt-axis) , () units= deg/s**2') PtfmRAzt = Bool(False, iotype='in', desc='Platform yaw angular (rotational) acceleration (About the zt-axis) , () units= deg/s**2') PtfmRAxi = Bool(False, iotype='in', desc='Platform roll tilt angular (rotational) acceleration (About the xi-axis) , () units= deg/s**2') PtfmRAyi = Bool(False, iotype='in', desc='Platform pitch tilt angular (rotational) acceleration (About the yi-axis) , () units= deg/s**2') PtfmRAzi = Bool(False, iotype='in', desc='Platform yaw angular (rotational) acceleration (About the zi-axis) , () units= deg/s**2') # Other Names # Tower-Top / Yaw Bearing Motions TTDspFA = Bool(False, iotype='in', desc='Tower-top / yaw bearing fore-aft (translational) deflection (relative to the undeflected position) (Directed along the xt-axis) , () units= m') TTDspSS = Bool(False, iotype='in', desc='Tower-top / yaw bearing side-to-side (translation) deflection (relative to the undeflected position) (Directed along the yt-axis) , () units= m') TTDspAx = Bool(False, iotype='in', desc='Tower-top / yaw bearing axial (translational) deflection (relative to the undeflected position) (Directed along the zt-axis) , () units= m') TTDspRoll = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) roll deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the xt-axis) , () units= deg') TTDspPtch = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) pitch deflection (relative to the undeflected position). In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small tower deflections, so that the rotation sequence does not matter. (About the yt-axis) , () units= deg') TTDspTwst = Bool(False, iotype='in', desc='Tower-top / yaw bearing angular (rotational) torsion deflection (relative to the undeflected position). This output will always be zero for FAST simulation results. Use it for examining tower torsional deflections of ADAMS simulations run using ADAMS datasets created using the FAST-to-ADAMS preprocessor. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. (About the zt-axis) , () units= deg') # Platform Motions PtfmSurge = Bool(False, iotype='in', desc='Platform horizontal surge (translational) displacement (Directed along the xi-axis) , () units= m') PtfmSway = Bool(False, iotype='in', desc='Platform horizontal sway (translational) displacement (Directed along the yi-axis) , () units= m') PtfmHeave = Bool(False, iotype='in', desc='Platform vertical heave (translational) displacement (Directed along the zi-axis) , () units= m') PtfmRoll = Bool(False, iotype='in', desc='Platform roll tilt angular (rotational) displacement. In ADAMS, it is output as an Euler angle computed as the 3rd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small rotational platform displacements, so that the rotation sequence does not matter. (About the xi-axis) , () units= deg') PtfmPitch = Bool(False, iotype='in', desc='Platform pitch tilt angular (rotational) displacement. In ADAMS, it is output as an Euler angle computed as the 2nd rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small rotational platform displacements, so that the rotation sequence does not matter. (About the yi-axis) , () units= deg') PtfmYaw = Bool(False, iotype='in', desc='Platform yaw angular (rotational) displacement. In ADAMS, it is output as an Euler angle computed as the 1st rotation in the yaw-pitch-roll rotation sequence. It is not output as an Euler angle in FAST, which assumes small rotational platform displacements, so that the rotation sequence does not matter. (About the zi-axis) , () units= deg') class BladeLoadsOut(VariableTree): # Blade 1 Root Loads RootFxc1 = Bool(False, iotype='in', desc='Blade 1 out-of-plane shear force at the blade root (Directed along the xc1-axis) , () units= kN') RootFyc1 = Bool(False, iotype='in', desc='Blade 1 in-plane shear force at the blade root (Directed along the yc1-axis) , () units= kN') RootFzc1 = Bool(False, iotype='in', desc='Blade 1 axial force at the blade root (Directed along the zc1- and zb1-axes) , () units= kN') RootFxb1 = Bool(False, iotype='in', desc='Blade 1 flapwise shear force at the blade root (Directed along the xb1-axis) , () units= kN') RootFyb1 = Bool(False, iotype='in', desc='Blade 1 edgewise shear force at the blade root (Directed along the yb1-axis) , () units= kN') RootMxc1 = Bool(False, iotype='in', desc='Blade 1 in-plane moment (i.e., the moment caused by in-plane forces) at the blade root (About the xc1-axis) , () units= kN*m') RootMyc1 = Bool(False, iotype='in', desc='Blade 1 out-of-plane moment (i.e., the moment caused by out-of-plane forces) at the blade root (About the yc1-axis) , () units= kN*m') RootMzc1 = Bool(False, iotype='in', desc='Blade 1 pitching moment at the blade root (About the zc1- and zb1-axes) , () units= kN*m') RootMxb1 = Bool(False, iotype='in', desc='Blade 1 edgewise moment (i.e., the moment caused by edgewise forces) at the blade root (About the xb1-axis) , () units= kN*m') RootMyb1 = Bool(False, iotype='in', desc='Blade 1 flapwise moment (i.e., the moment caused by flapwise forces) at the blade root (About the yb1-axis) , () units= kN*m') # Blade 2 Root Loads RootFxc2 = Bool(False, iotype='in', desc='Blade 2 out-of-plane shear force at the blade root (Directed along the xc2-axis) , () units= kN') RootFyc2 = Bool(False, iotype='in', desc='Blade 2 in-plane shear force at the blade root (Directed along the yc2-axis) , () units= kN') RootFzc2 = Bool(False, iotype='in', desc='Blade 2 axial force at the blade root (Directed along the zc2- and zb2-axes) , () units= kN') RootFxb2 = Bool(False, iotype='in', desc='Blade 2 flapwise shear force at the blade root (Directed along the xb2-axis) , () units= kN') RootFyb2 = Bool(False, iotype='in', desc='Blade 2 edgewise shear force at the blade root (Directed along the yb2-axis) , () units= kN') RootMxc2 = Bool(False, iotype='in', desc='Blade 2 in-plane moment (i.e., the moment caused by in-plane forces) at the blade root (About the xc2-axis) , () units= kN*m') RootMyc2 = Bool(False, iotype='in', desc='Blade 2 out-of-plane moment (i.e., the moment caused by out-of-plane forces) at the blade root (About the yc2-axis) , () units= kN*m') RootMzc2 = Bool(False, iotype='in', desc='Blade 2 pitching moment at the blade root (About the zc2- and zb2-axes) , () units= kN*m') RootMxb2 = Bool(False, iotype='in', desc='Blade 2 edgewise moment (i.e., the moment caused by edgewise forces) at the blade root (About the xb2-axis) , () units= kN*m') RootMyb2 = Bool(False, iotype='in', desc='Blade 2 flapwise moment (i.e., the moment caused by flapwise forces) at the blade root (About the yb2-axis) , () units= kN*m') # Blade 3 Root Loads RootFxc3 = Bool(False, iotype='in', desc='Blade 3 out-of-plane shear force at the blade root (Directed along the xc3-axis) , (NumBl < 3) units= kN') RootFyc3 = Bool(False, iotype='in', desc='Blade 3 in-plane shear force at the blade root (Directed along the yc3-axis) , (NumBl < 3) units= kN') RootFzc3 = Bool(False, iotype='in', desc='Blade 3 axial force at the blade root (Directed along the zc3- and zb3-axes) , (NumBl < 3) units= kN') RootFxb3 = Bool(False, iotype='in', desc='Blade 3 flapwise shear force at the blade root (Directed along the xb3-axis) , (NumBl < 3) units= kN') RootFyb3 = Bool(False, iotype='in', desc='Blade 3 edgewise shear force at the blade root (Directed along the yb3-axis) , (NumBl < 3) units= kN') RootMxc3 = Bool(False, iotype='in', desc='Blade 3 in-plane moment (i.e., the moment caused by in-plane forces) at the blade root (About the xc3-axis) , (NumBl < 3) units= kN*m') RootMyc3 = Bool(False, iotype='in', desc='Blade 3 out-of-plane moment (i.e., the moment caused by out-of-plane forces) at the blade root (About the yc3-axis) , (NumBl < 3) units= kN*m') RootMzc3 = Bool(False, iotype='in', desc='Blade 3 pitching moment at the blade root (About the zc3- and zb3-axes) , (NumBl < 3) units= kN*m') RootMxb3 = Bool(False, iotype='in', desc='Blade 3 edgewise moment (i.e., the moment caused by edgewise forces) at the blade root (About the xb3-axis) , (NumBl < 3) units= kN*m') RootMyb3 = Bool(False, iotype='in', desc='Blade 3 flapwise moment (i.e., the moment caused by flapwise forces) at the blade root (About the yb3-axis) , (NumBl < 3) units= kN*m') # Blade 1 Local Span Loads Spn1MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 1 (About the local xb1-axis) , (NBlGages < 1) units= kN*m') Spn1MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 1 (About the local yb1-axis) , (NBlGages < 1) units= kN*m') Spn1MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 1 (About the local zb1-axis) , (NBlGages < 1) units= kN*m') Spn2MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 2 (About the local xb1-axis) , (NBlGages < 2) units= kN*m') Spn2MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 2 (About the local yb1-axis) , (NBlGages < 2) units= kN*m') Spn2MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 2 (About the local zb1-axis) , (NBlGages < 2) units= kN*m') Spn3MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 3 (About the local xb1-axis) , (NBlGages < 3) units= kN*m') Spn3MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 3 (About the local yb1-axis) , (NBlGages < 3) units= kN*m') Spn3MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 3 (About the local zb1-axis) , (NBlGages < 3) units= kN*m') Spn4MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 4 (About the local xb1-axis) , (NBlGages < 4) units= kN*m') Spn4MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 4 (About the local yb1-axis) , (NBlGages < 4) units= kN*m') Spn4MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 4 (About the local zb1-axis) , (NBlGages < 4) units= kN*m') Spn5MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 5 (About the local xb1-axis) , (NBlGages < 5) units= kN*m') Spn5MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 5 (About the local yb1-axis) , (NBlGages < 5) units= kN*m') Spn5MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 5 (About the local zb1-axis) , (NBlGages < 5) units= kN*m') Spn6MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 6 (About the local xb1-axis) , (NBlGages < 6) units= kN*m') Spn6MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 6 (About the local yb1-axis) , (NBlGages < 6) units= kN*m') Spn6MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 6 (About the local zb1-axis) , (NBlGages < 6) units= kN*m') Spn7MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 7 (About the local xb1-axis) , (NBlGages < 7) units= kN*m') Spn7MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 7 (About the local yb1-axis) , (NBlGages < 7) units= kN*m') Spn7MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 7 (About the local zb1-axis) , (NBlGages < 7) units= kN*m') Spn8MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 8 (About the local xb1-axis) , (NBlGages < 8) units= kN*m') Spn8MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 8 (About the local yb1-axis) , (NBlGages < 8) units= kN*m') Spn8MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 8 (About the local zb1-axis) , (NBlGages < 8) units= kN*m') Spn9MLxb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise moment at span station 9 (About the local xb1-axis) , (NBlGages < 9) units= kN*m') Spn9MLyb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise moment at span station 9 (About the local yb1-axis) , (NBlGages < 9) units= kN*m') Spn9MLzb1 = Bool(False, iotype='in', desc='Blade 1 local pitching moment at span station 9 (About the local zb1-axis) , (NBlGages < 9) units= kN*m') Spn1FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 1 (Directed along the local xb1-axis) , (NBlGages < 1) units= kN') Spn1FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 1 (Directed along the local yb1-axis) , (NBlGages < 1) units= kN') Spn1FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 1 (Directed along the local zb1-axis) , (NBlGages < 1) units= kN') Spn2FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 2 (Directed along the local xb1-axis) , (NBlGages < 2) units= kN') Spn2FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 2 (Directed along the local yb1-axis) , (NBlGages < 2) units= kN') Spn2FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 2 (Directed along the local zb1-axis) , (NBlGages < 2) units= kN') Spn3FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 3 (Directed along the local xb1-axis) , (NBlGages < 3) units= kN') Spn3FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 3 (Directed along the local yb1-axis) , (NBlGages < 3) units= kN') Spn3FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 3 (Directed along the local zb1-axis) , (NBlGages < 3) units= kN') Spn4FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 4 (Directed along the local xb1-axis) , (NBlGages < 4) units= kN') Spn4FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 4 (Directed along the local yb1-axis) , (NBlGages < 4) units= kN') Spn4FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 4 (Directed along the local zb1-axis) , (NBlGages < 4) units= kN') Spn5FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 5 (Directed along the local xb1-axis) , (NBlGages < 5) units= kN') Spn5FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 5 (Directed along the local yb1-axis) , (NBlGages < 5) units= kN') Spn5FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 5 (Directed along the local zb1-axis) , (NBlGages < 5) units= kN') Spn6FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 6 (Directed along the local xb1-axis) , (NBlGages < 6) units= kN') Spn6FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 6 (Directed along the local yb1-axis) , (NBlGages < 6) units= kN') Spn6FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 6 (Directed along the local zb1-axis) , (NBlGages < 6) units= kN') Spn7FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 7 (Directed along the local xb1-axis) , (NBlGages < 7) units= kN') Spn7FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 7 (Directed along the local yb1-axis) , (NBlGages < 7) units= kN') Spn7FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 7 (Directed along the local zb1-axis) , (NBlGages < 7) units= kN') Spn8FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 8 (Directed along the local xb1-axis) , (NBlGages < 8) units= kN') Spn8FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 8 (Directed along the local yb1-axis) , (NBlGages < 8) units= kN') Spn8FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 8 (Directed along the local zb1-axis) , (NBlGages < 8) units= kN') Spn9FLxb1 = Bool(False, iotype='in', desc='Blade 1 local flapwise shear force at span station 9 (Directed along the local xb1-axis) , (NBlGages < 9) units= kN') Spn9FLyb1 = Bool(False, iotype='in', desc='Blade 1 local edgewise shear force at span station 9 (Directed along the local yb1-axis) , (NBlGages < 9) units= kN') Spn9FLzb1 = Bool(False, iotype='in', desc='Blade 1 local axial force at span station 9 (Directed along the local zb1-axis) , (NBlGages < 9) units= kN') # Blade 2 Local Span Loads Spn1MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 1 (About the local xb2-axis) , (NBlGages < 1) units= kN*m') Spn1MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 1 (About the local yb2-axis) , (NBlGages < 1) units= kN*m') Spn1MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 1 (About the local zb2-axis) , (NBlGages < 1) units= kN*m') Spn2MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 2 (About the local xb2-axis) , (NBlGages < 2) units= kN*m') Spn2MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 2 (About the local yb2-axis) , (NBlGages < 2) units= kN*m') Spn2MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 2 (About the local zb2-axis) , (NBlGages < 2) units= kN*m') Spn3MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 3 (About the local xb2-axis) , (NBlGages < 3) units= kN*m') Spn3MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 3 (About the local yb2-axis) , (NBlGages < 3) units= kN*m') Spn3MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 3 (About the local zb2-axis) , (NBlGages < 3) units= kN*m') Spn4MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 4 (About the local xb2-axis) , (NBlGages < 4) units= kN*m') Spn4MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 4 (About the local yb2-axis) , (NBlGages < 4) units= kN*m') Spn4MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 4 (About the local zb2-axis) , (NBlGages < 4) units= kN*m') Spn5MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 5 (About the local xb2-axis) , (NBlGages < 5) units= kN*m') Spn5MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 5 (About the local yb2-axis) , (NBlGages < 5) units= kN*m') Spn5MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 5 (About the local zb2-axis) , (NBlGages < 5) units= kN*m') Spn6MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 6 (About the local xb2-axis) , (NBlGages < 6) units= kN*m') Spn6MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 6 (About the local yb2-axis) , (NBlGages < 6) units= kN*m') Spn6MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 6 (About the local zb2-axis) , (NBlGages < 6) units= kN*m') Spn7MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 7 (About the local xb2-axis) , (NBlGages < 7) units= kN*m') Spn7MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 7 (About the local yb2-axis) , (NBlGages < 7) units= kN*m') Spn7MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 7 (About the local zb2-axis) , (NBlGages < 7) units= kN*m') Spn8MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 8 (About the local xb2-axis) , (NBlGages < 8) units= kN*m') Spn8MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 8 (About the local yb2-axis) , (NBlGages < 8) units= kN*m') Spn8MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 8 (About the local zb2-axis) , (NBlGages < 8) units= kN*m') Spn9MLxb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise moment at span station 9 (About the local xb2-axis) , (NBlGages < 9) units= kN*m') Spn9MLyb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise moment at span station 9 (About the local yb2-axis) , (NBlGages < 9) units= kN*m') Spn9MLzb2 = Bool(False, iotype='in', desc='Blade 2 local pitching moment at span station 9 (About the local zb2-axis) , (NBlGages < 9) units= kN*m') Spn1FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 1 (Directed along the local xb2-axis) , (NBlGages < 1) units= kN') Spn1FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 1 (Directed along the local yb2-axis) , (NBlGages < 1) units= kN') Spn1FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 1 (Directed along the local zb2-axis) , (NBlGages < 1) units= kN') Spn2FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 2 (Directed along the local xb2-axis) , (NBlGages < 2) units= kN') Spn2FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 2 (Directed along the local yb2-axis) , (NBlGages < 2) units= kN') Spn2FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 2 (Directed along the local zb2-axis) , (NBlGages < 2) units= kN') Spn3FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 3 (Directed along the local xb2-axis) , (NBlGages < 3) units= kN') Spn3FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 3 (Directed along the local yb2-axis) , (NBlGages < 3) units= kN') Spn3FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 3 (Directed along the local zb2-axis) , (NBlGages < 3) units= kN') Spn4FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 4 (Directed along the local xb2-axis) , (NBlGages < 4) units= kN') Spn4FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 4 (Directed along the local yb2-axis) , (NBlGages < 4) units= kN') Spn4FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 4 (Directed along the local zb2-axis) , (NBlGages < 4) units= kN') Spn5FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 5 (Directed along the local xb2-axis) , (NBlGages < 5) units= kN') Spn5FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 5 (Directed along the local yb2-axis) , (NBlGages < 5) units= kN') Spn5FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 5 (Directed along the local zb2-axis) , (NBlGages < 5) units= kN') Spn6FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 6 (Directed along the local xb2-axis) , (NBlGages < 6) units= kN') Spn6FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 6 (Directed along the local yb2-axis) , (NBlGages < 6) units= kN') Spn6FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 6 (Directed along the local zb2-axis) , (NBlGages < 6) units= kN') Spn7FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 7 (Directed along the local xb2-axis) , (NBlGages < 7) units= kN') Spn7FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 7 (Directed along the local yb2-axis) , (NBlGages < 7) units= kN') Spn7FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 7 (Directed along the local zb2-axis) , (NBlGages < 7) units= kN') Spn8FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 8 (Directed along the local xb2-axis) , (NBlGages < 8) units= kN') Spn8FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 8 (Directed along the local yb2-axis) , (NBlGages < 8) units= kN') Spn8FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 8 (Directed along the local zb2-axis) , (NBlGages < 8) units= kN') Spn9FLxb2 = Bool(False, iotype='in', desc='Blade 2 local flapwise shear force at span station 9 (Directed along the local xb2-axis) , (NBlGages < 9) units= kN') Spn9FLyb2 = Bool(False, iotype='in', desc='Blade 2 local edgewise shear force at span station 9 (Directed along the local yb2-axis) , (NBlGages < 9) units= kN') Spn9FLzb2 = Bool(False, iotype='in', desc='Blade 2 local axial force at span station 9 (Directed along the local zb2-axis) , (NBlGages < 9) units= kN') # Blade 3 Local Span Loads Spn1MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 1 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= kN*m') Spn1MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 1 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= kN*m') Spn1MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 1 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= kN*m') Spn2MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 2 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= kN*m') Spn2MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 2 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= kN*m') Spn2MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 2 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= kN*m') Spn3MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 3 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= kN*m') Spn3MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 3 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= kN*m') Spn3MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 3 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= kN*m') Spn4MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 4 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= kN*m') Spn4MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 4 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= kN*m') Spn4MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 4 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= kN*m') Spn5MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 5 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= kN*m') Spn5MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 5 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= kN*m') Spn5MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 5 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= kN*m') Spn6MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 6 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= kN*m') Spn6MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 6 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= kN*m') Spn6MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 6 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= kN*m') Spn7MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 7 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= kN*m') Spn7MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 7 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= kN*m') Spn7MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 7 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= kN*m') Spn8MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 8 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= kN*m') Spn8MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 8 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= kN*m') Spn8MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 8 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= kN*m') Spn9MLxb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise moment at span station 9 (About the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= kN*m') Spn9MLyb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise moment at span station 9 (About the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= kN*m') Spn9MLzb3 = Bool(False, iotype='in', desc='Blade 3 local pitching moment at span station 9 (About the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= kN*m') Spn1FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 1 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= kN') Spn1FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 1 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= kN') Spn1FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 1 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 1 )) units= kN') Spn2FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 2 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= kN') Spn2FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 2 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= kN') Spn2FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 2 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 2 )) units= kN') Spn3FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 3 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= kN') Spn3FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 3 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= kN') Spn3FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 3 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 3 )) units= kN') Spn4FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 4 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= kN') Spn4FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 4 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= kN') Spn4FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 4 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 4 )) units= kN') Spn5FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 5 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= kN') Spn5FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 5 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= kN') Spn5FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 5 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 5 )) units= kN') Spn6FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 6 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= kN') Spn6FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 6 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= kN') Spn6FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 6 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 6 )) units= kN') Spn7FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 7 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= kN') Spn7FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 7 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= kN') Spn7FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 7 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 7 )) units= kN') Spn8FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 8 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= kN') Spn8FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 8 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= kN') Spn8FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 8 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 8 )) units= kN') Spn9FLxb3 = Bool(False, iotype='in', desc='Blade 3 local flapwise shear force at span station 9 (Directed along the local xb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= kN') Spn9FLyb3 = Bool(False, iotype='in', desc='Blade 3 local edgewise shear force at span station 9 (Directed along the local yb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= kN') Spn9FLzb3 = Bool(False, iotype='in', desc='Blade 3 local axial force at span station 9 (Directed along the local zb3-axis) , (( NumBl < 3 ) .OR. ( NBlGages < 9 )) units= kN') # Other Names # Blade 1 Root Loads RootFzb1 = Bool(False, iotype='in', desc='Blade 1 axial force at the blade root (Directed along the zc1- and zb1-axes) , () units= kN') RootMIP1 = Bool(False, iotype='in', desc='Blade 1 in-plane moment (i.e., the moment caused by in-plane forces) at the blade root (About the xc1-axis) , () units= kN*m') RootMOoP1 = Bool(False, iotype='in', desc='Blade 1 out-of-plane moment (i.e., the moment caused by out-of-plane forces) at the blade root (About the yc1-axis) , () units= kN*m') RootMzb1 = Bool(False, iotype='in', desc='Blade 1 pitching moment at the blade root (About the zc1- and zb1-axes) , () units= kN*m') RootMEdg1 = Bool(False, iotype='in', desc='Blade 1 edgewise moment (i.e., the moment caused by edgewise forces) at the blade root (About the xb1-axis) , () units= kN*m') RootMFlp1 = Bool(False, iotype='in', desc='Blade 1 flapwise moment (i.e., the moment caused by flapwise forces) at the blade root (About the yb1-axis) , () units= kN*m') # Blade 2 Root Loads RootFzb2 = Bool(False, iotype='in', desc='Blade 2 axial force at the blade root (Directed along the zc2- and zb2-axes) , () units= kN') RootMIP2 = Bool(False, iotype='in', desc='Blade 2 in-plane moment (i.e., the moment caused by in-plane forces) at the blade root (About the xc2-axis) , () units= kN*m') RootMOoP2 = Bool(False, iotype='in', desc='Blade 2 out-of-plane moment (i.e., the moment caused by out-of-plane forces) at the blade root (About the yc2-axis) , () units= kN*m') RootMzb2 = Bool(False, iotype='in', desc='Blade 2 pitching moment at the blade root (About the zc2- and zb2-axes) , () units= kN*m') RootMEdg2 = Bool(False, iotype='in', desc='Blade 2 edgewise moment (i.e., the moment caused by edgewise forces) at the blade root (About the xb2-axis) , () units= kN*m') RootMFlp2 = Bool(False, iotype='in', desc='Blade 2 flapwise moment (i.e., the moment caused by flapwise forces) at the blade root (About the yb2-axis) , () units= kN*m') # Blade 3 Root Loads RootFzb3 = Bool(False, iotype='in', desc='Blade 3 axial force at the blade root (Directed along the zc3- and zb3-axes) , (NumBl < 3) units= kN') RootMIP3 = Bool(False, iotype='in', desc='Blade 3 in-plane moment (i.e., the moment caused by in-plane forces) at the blade root (About the xc3-axis) , (NumBl < 3) units= kN*m') RootMOoP3 = Bool(False, iotype='in', desc='Blade 3 out-of-plane moment (i.e., the moment caused by out-of-plane forces) at the blade root (About the yc3-axis) , (NumBl < 3) units= kN*m') RootMzb3 = Bool(False, iotype='in', desc='Blade 3 pitching moment at the blade root (About the zc3- and zb3-axes) , (NumBl < 3) units= kN*m') RootMEdg3 = Bool(False, iotype='in', desc='Blade 3 edgewise moment (i.e., the moment caused by edgewise forces) at the blade root (About the xb3-axis) , (NumBl < 3) units= kN*m') RootMFlp3 = Bool(False, iotype='in', desc='Blade 3 flapwise moment (i.e., the moment caused by flapwise forces) at the blade root (About the yb3-axis) , (NumBl < 3) units= kN*m') class HubNacelleLoadsOut(VariableTree): # Hub and Rotor Loads LSShftFxa = Bool(False, iotype='in', desc='Low-speed shaft thrust force (this is constant along the shaft and is equivalent to the rotor thrust force) (Directed along the xa- and xs-axes) , () units= kN') LSShftFya = Bool(False, iotype='in', desc='Rotating low-speed shaft shear force (this is constant along the shaft) (Directed along the ya-axis) , () units= kN') LSShftFza = Bool(False, iotype='in', desc='Rotating low-speed shaft shear force (this is constant along the shaft) (Directed along the za-axis) , () units= kN') LSShftFys = Bool(False, iotype='in', desc='Nonrotating low-speed shaft shear force (this is constant along the shaft) (Directed along the ys-axis) , () units= kN') LSShftFzs = Bool(False, iotype='in', desc='Nonrotating low-speed shaft shear force (this is constant along the shaft) (Directed along the zs-axis) , () units= kN') LSShftMxa = Bool(False, iotype='in', desc='Low-speed shaft torque (this is constant along the shaft and is equivalent to the rotor torque) (About the xa- and xs-axes) , () units= kN*m') LSSTipMya = Bool(False, iotype='in', desc='Rotating low-speed shaft bending moment at the shaft tip (teeter pin for 2-blader, apex of rotation for 3-blader) (About the ya-axis) , () units= kN*m') LSSTipMza = Bool(False, iotype='in', desc='Rotating low-speed shaft bending moment at the shaft tip (teeter pin for 2-blader, apex of rotation for 3-blader) (About the za-axis) , () units= kN*m') LSSTipMys = Bool(False, iotype='in', desc='Nonrotating low-speed shaft bending moment at the shaft tip (teeter pin for 2-blader, apex of rotation for 3-blader) (About the ys-axis) , () units= kN*m') LSSTipMzs = Bool(False, iotype='in', desc='Nonrotating low-speed shaft bending moment at the shaft tip (teeter pin for 2-blader, apex of rotation for 3-blader) (About the zs-axis) , () units= kN*m') CThrstAzm = Bool(False, iotype='in', desc='Azimuth location of the center of thrust. This is estimated using values of LSSTipMys, LSSTipMzs, and RotThrust. (About the xa- and xs-axes) , () units= deg') CThrstRad = Bool(False, iotype='in', desc='Dimensionless radial (arm) location of the center of thrust. This is estimated using values of LSSTipMys, LSSTipMzs, and RotThrust. (nondimensionalized using the undeflected tip radius normal to the shaft and limited to values between 0 and 1 (inclusive)) (Always positive (directed radially outboard at azimuth angle CThrstAzm)) , () units= ') RotPwr = Bool(False, iotype='in', desc='Rotor power (this is equivalent to the low-speed shaft power) (N/A) , () units= kW') RotCq = Bool(False, iotype='in', desc='Rotor torque coefficient (this is equivalent to the low-speed shaft torque coefficient) (N/A) , (.NOT. CompAero) units= ') RotCp = Bool(False, iotype='in', desc='Rotor power coefficient (this is equivalent to the low-speed shaft power coefficient) (N/A) , (.NOT. CompAero) units= ') RotCt = Bool(False, iotype='in', desc='Rotor thrust coefficient (this is equivalent to the low-speed shaft thrust coefficient) (N/A) , (.NOT. CompAero) units= ') # Shaft Strain Gage Loads LSSGagMya = Bool(False, iotype='in', desc='Rotating low-speed shaft bending moment at the shafts strain gage (shaft strain gage located by input ShftGagL) (About the ya-axis) , () units= kN*m') LSSGagMza = Bool(False, iotype='in', desc='Rotating low-speed shaft bending moment at the shafts strain gage (shaft strain gage located by input ShftGagL) (About the za-axis) , () units= kN*m') LSSGagMys = Bool(False, iotype='in', desc='Nonrotating low-speed shaft bending moment at the shafts strain gage (shaft strain gage located by input ShftGagL) (About the ys-axis) , () units= kN*m') LSSGagMzs = Bool(False, iotype='in', desc='Nonrotating low-speed shaft bending moment at the shafts strain gage (shaft strain gage located by input ShftGagL) (About the zs-axis) , () units= kN*m') # Generator and High-Speed Shaft Loads HSShftTq = Bool(False, iotype='in', desc='High-speed shaft torque (this is constant along the shaft) (Same sign as LSShftTq / RotTorq / LSShftMxa / LSShftMxs / LSSGagMxa / LSSGagMxs) , () units= kN*m') HSShftPwr = Bool(False, iotype='in', desc='High-speed shaft power (Same sign as HSShftTq) , () units= kW') HSShftCq = Bool(False, iotype='in', desc='High-speed shaft torque coefficient (N/A) , (.NOT. CompAero) units= ') HSShftCp = Bool(False, iotype='in', desc='High-speed shaft power coefficient (N/A) , (.NOT. CompAero) units= ') GenTq = Bool(False, iotype='in', desc='Electrical generator torque (Positive reflects power extracted and negative represents a motoring-up situation (power input)) , () units= kN*m') GenPwr = Bool(False, iotype='in', desc='Electrical generator power (Same sign as GenTq) , () units= kW') GenCq = Bool(False, iotype='in', desc='Electrical generator torque coefficient (N/A) , (.NOT. CompAero) units= ') GenCp = Bool(False, iotype='in', desc='Electrical generator power coefficient (N/A) , (.NOT. CompAero) units= ') HSSBrTq = Bool(False, iotype='in', desc='High-speed shaft brake torque (i.e., the moment applied to the high-speed shaft by the brake) (Always positive (indicating dissipation of power)) , () units= kN*m') # Rotor-Furl Bearing Loads RFrlBrM = Bool(False, iotype='in', desc='Rotor-furl bearing moment (About the rotor-furl axis) , () units= kN*m') # Tail-Furl Bearing Loads TFrlBrM = Bool(False, iotype='in', desc='Tail-furl bearing moment (About the tail-furl axis) , () units= kN*m') # Tail Fin Aerodynamic Loads TFinAlpha = Bool(False, iotype='in', desc='Tail fin angle of attack. This is the angle between the relative velocity of the wind-inflow at the tail fin center-of-pressure and the tail fin chordline. (About the tail fin z-axis, which is the axis in the tail fin plane normal to the chordline) , (.NOT. CompAero) units= deg') TFinCLift = Bool(False, iotype='in', desc='Tail fin dimensionless lift coefficient (N/A) , (.NOT. CompAero) units= ') TFinCDrag = Bool(False, iotype='in', desc='Tail fin dimensionless drag coefficient (N/A) , (.NOT. CompAero) units= ') TFinDnPrs = Bool(False, iotype='in', desc='Tail fin dynamic pressure, equal to 1/2*AirDens*Vrel^2 where Vrel is the relative velocity of the wind-inflow at the tail fin center-of-pressure (N/A) , (.NOT. CompAero) units= Pa') TFinCPFx = Bool(False, iotype='in', desc='Tangential aerodynamic force at the tail fin center-of-pressure (Directed along the tail fin x-axis, which is the axis along the chordline, positive towards the trailing edge) , (.NOT. CompAero) units= kN') TFinCPFy = Bool(False, iotype='in', desc='Normal aerodynamic force at the tail fin center-of-pressure (Directed along the tail fin y-axis, which is orthogonal to the tail fin plane) , (.NOT. CompAero) units= kN') # Other Names # Hub and Rotor Loads LSShftFxs = Bool(False, iotype='in', desc='Low-speed shaft thrust force (this is constant along the shaft and is equivalent to the rotor thrust force) (Directed along the xa- and xs-axes) , () units= kN') LSSGagFya = Bool(False, iotype='in', desc='Rotating low-speed shaft shear force (this is constant along the shaft) (Directed along the ya-axis) , () units= kN') LSSGagFza = Bool(False, iotype='in', desc='Rotating low-speed shaft shear force (this is constant along the shaft) (Directed along the za-axis) , () units= kN') LSSGagFys = Bool(False, iotype='in', desc='Nonrotating low-speed shaft shear force (this is constant along the shaft) (Directed along the ys-axis) , () units= kN') LSSGagFzs = Bool(False, iotype='in', desc='Nonrotating low-speed shaft shear force (this is constant along the shaft) (Directed along the zs-axis) , () units= kN') LSShftMxs = Bool(False, iotype='in', desc='Low-speed shaft torque (this is constant along the shaft and is equivalent to the rotor torque) (About the xa- and xs-axes) , () units= kN*m') CThrstArm = Bool(False, iotype='in', desc='Dimensionless radial (arm) location of the center of thrust. This is estimated using values of LSSTipMys, LSSTipMzs, and RotThrust. (nondimensionalized using the undeflected tip radius normal to the shaft and limited to values between 0 and 1 (inclusive)) (Always positive (directed radially outboard at azimuth angle CThrstAzm)) , () units= ') LSShftPwr = Bool(False, iotype='in', desc='Rotor power (this is equivalent to the low-speed shaft power) (N/A) , () units= kW') LSShftCq = Bool(False, iotype='in', desc='Rotor torque coefficient (this is equivalent to the low-speed shaft torque coefficient) (N/A) , (.NOT. CompAero) units= ') LSShftCp = Bool(False, iotype='in', desc='Rotor power coefficient (this is equivalent to the low-speed shaft power coefficient) (N/A) , (.NOT. CompAero) units= ') LSShftCt = Bool(False, iotype='in', desc='Rotor thrust coefficient (this is equivalent to the low-speed shaft thrust coefficient) (N/A) , (.NOT. CompAero) units= ') # Hub and Rotor Loads LSSGagFxa = Bool(False, iotype='in', desc='Low-speed shaft thrust force (this is constant along the shaft and is equivalent to the rotor thrust force) (Directed along the xa- and xs-axes) , () units= kN') LSSGagMxa = Bool(False, iotype='in', desc='Low-speed shaft torque (this is constant along the shaft and is equivalent to the rotor torque) (About the xa- and xs-axes) , () units= kN*m') # Hub and Rotor Loads LSSGagFxs = Bool(False, iotype='in', desc='Low-speed shaft thrust force (this is constant along the shaft and is equivalent to the rotor thrust force) (Directed along the xa- and xs-axes) , () units= kN') LSSGagMxs = Bool(False, iotype='in', desc='Low-speed shaft torque (this is constant along the shaft and is equivalent to the rotor torque) (About the xa- and xs-axes) , () units= kN*m') # Hub and Rotor Loads RotThrust = Bool(False, iotype='in', desc='Low-speed shaft thrust force (this is constant along the shaft and is equivalent to the rotor thrust force) (Directed along the xa- and xs-axes) , () units= kN') RotTorq = Bool(False, iotype='in', desc='Low-speed shaft torque (this is constant along the shaft and is equivalent to the rotor torque) (About the xa- and xs-axes) , () units= kN*m') # Hub and Rotor Loads LSShftTq = Bool(False, iotype='in', desc='Low-speed shaft torque (this is constant along the shaft and is equivalent to the rotor torque) (About the xa- and xs-axes) , () units= kN*m') class TowerSupportLoadsOut(VariableTree): # Tower-Top / Yaw Bearing Loads YawBrFxn = Bool(False, iotype='in', desc='Rotating (with nacelle) tower-top / yaw bearing shear force (Directed along the xn-axis) , () units= kN') YawBrFyn = Bool(False, iotype='in', desc='Rotating (with nacelle) tower-top / yaw bearing shear force (Directed along the yn-axis) , () units= kN') YawBrFzn = Bool(False, iotype='in', desc='Tower-top / yaw bearing axial force (Directed along the zn- and zp-axes) , () units= kN') YawBrFxp = Bool(False, iotype='in', desc='Tower-top / yaw bearing fore-aft (nonrotating) shear force (Directed along the xp-axis) , () units= kN') YawBrFyp = Bool(False, iotype='in', desc='Tower-top / yaw bearing side-to-side (nonrotating) shear force (Directed along the yp-axis) , () units= kN') YawBrMxn = Bool(False, iotype='in', desc='Rotating (with nacelle) tower-top / yaw bearing roll moment (About the xn-axis) , () units= kN*m') YawBrMyn = Bool(False, iotype='in', desc='Rotating (with nacelle) tower-top / yaw bearing pitch moment (About the yn-axis) , () units= kN*m') YawBrMzn = Bool(False, iotype='in', desc='Tower-top / yaw bearing yaw moment (About the zn- and zp-axes) , () units= kN*m') YawBrMxp = Bool(False, iotype='in', desc='Nonrotating tower-top / yaw bearing roll moment (About the xp-axis) , () units= kN*m') YawBrMyp = Bool(False, iotype='in', desc='Nonrotating tower-top / yaw bearing pitch moment (About the yp-axis) , () units= kN*m') # Tower Base Loads TwrBsFxt = Bool(False, iotype='in', desc='Tower base fore-aft shear force (Directed along the xt-axis) , () units= kN') TwrBsFyt = Bool(False, iotype='in', desc='Tower base side-to-side shear force (Directed along the yt-axis) , () units= kN') TwrBsFzt = Bool(False, iotype='in', desc='Tower base axial force (Directed along the zt-axis) , () units= kN') TwrBsMxt = Bool(False, iotype='in', desc='Tower base roll (or side-to-side) moment (i.e., the moment caused by side-to-side forces) (About the xt-axis) , () units= kN*m') TwrBsMyt = Bool(False, iotype='in', desc='Tower base pitching (or fore-aft) moment (i.e., the moment caused by fore-aft forces) (About the yt-axis) , () units= kN*m') TwrBsMzt = Bool(False, iotype='in', desc='Tower base yaw (or torsional) moment (About the zt-axis) , () units= kN*m') # Local Tower Loads TwHt1MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 1 (About the local xt-axis) , (NTwGages < 1) units= kN*m') TwHt1MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 1 (About the local yt-axis) , (NTwGages < 1) units= kN*m') TwHt1MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 1 (About the local zt-axis) , (NTwGages < 1) units= kN*m') TwHt2MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 2 (About the local xt-axis) , (NTwGages < 2) units= kN*m') TwHt2MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 2 (About the local yt-axis) , (NTwGages < 2) units= kN*m') TwHt2MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 2 (About the local zt-axis) , (NTwGages < 2) units= kN*m') TwHt3MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 3 (About the local xt-axis) , (NTwGages < 3) units= kN*m') TwHt3MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 3 (About the local yt-axis) , (NTwGages < 3) units= kN*m') TwHt3MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 3 (About the local zt-axis) , (NTwGages < 3) units= kN*m') TwHt4MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 4 (About the local xt-axis) , (NTwGages < 4) units= kN*m') TwHt4MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 4 (About the local yt-axis) , (NTwGages < 4) units= kN*m') TwHt4MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 4 (About the local zt-axis) , (NTwGages < 4) units= kN*m') TwHt5MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 5 (About the local xt-axis) , (NTwGages < 5) units= kN*m') TwHt5MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 5 (About the local yt-axis) , (NTwGages < 5) units= kN*m') TwHt5MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 5 (About the local zt-axis) , (NTwGages < 5) units= kN*m') TwHt6MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 6 (About the local xt-axis) , (NTwGages < 6) units= kN*m') TwHt6MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 6 (About the local yt-axis) , (NTwGages < 6) units= kN*m') TwHt6MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 6 (About the local zt-axis) , (NTwGages < 6) units= kN*m') TwHt7MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 7 (About the local xt-axis) , (NTwGages < 7) units= kN*m') TwHt7MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 7 (About the local yt-axis) , (NTwGages < 7) units= kN*m') TwHt7MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 7 (About the local zt-axis) , (NTwGages < 7) units= kN*m') TwHt8MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 8 (About the local xt-axis) , (NTwGages < 8) units= kN*m') TwHt8MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 8 (About the local yt-axis) , (NTwGages < 8) units= kN*m') TwHt8MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 8 (About the local zt-axis) , (NTwGages < 8) units= kN*m') TwHt9MLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) moment of tower gage 9 (About the local xt-axis) , (NTwGages < 9) units= kN*m') TwHt9MLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) moment of tower gage 9 (About the local yt-axis) , (NTwGages < 9) units= kN*m') TwHt9MLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) moment of tower gage 9 (About the local zt-axis) , (NTwGages < 9) units= kN*m') TwHt1FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 1 (About the local xt-axis) , (NTwGages < 1) units= kN') TwHt1FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 1 (About the local yt-axis) , (NTwGages < 1) units= kN') TwHt1FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 1 (About the local zt-axis) , (NTwGages < 1) units= kN') TwHt2FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 2 (About the local xt-axis) , (NTwGages < 2) units= kN') TwHt2FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 2 (About the local yt-axis) , (NTwGages < 2) units= kN') TwHt2FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 2 (About the local zt-axis) , (NTwGages < 2) units= kN') TwHt3FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 3 (About the local xt-axis) , (NTwGages < 3) units= kN') TwHt3FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 3 (About the local yt-axis) , (NTwGages < 3) units= kN') TwHt3FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 3 (About the local zt-axis) , (NTwGages < 3) units= kN') TwHt4FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 4 (About the local xt-axis) , (NTwGages < 4) units= kN') TwHt4FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 4 (About the local yt-axis) , (NTwGages < 4) units= kN') TwHt4FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 4 (About the local zt-axis) , (NTwGages < 4) units= kN') TwHt5FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 5 (About the local xt-axis) , (NTwGages < 5) units= kN') TwHt5FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 5 (About the local yt-axis) , (NTwGages < 5) units= kN') TwHt5FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 5 (About the local zt-axis) , (NTwGages < 5) units= kN') TwHt6FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 6 (About the local xt-axis) , (NTwGages < 6) units= kN') TwHt6FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 6 (About the local yt-axis) , (NTwGages < 6) units= kN') TwHt6FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 6 (About the local zt-axis) , (NTwGages < 6) units= kN') TwHt7FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 7 (About the local xt-axis) , (NTwGages < 7) units= kN') TwHt7FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 7 (About the local yt-axis) , (NTwGages < 7) units= kN') TwHt7FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 7 (About the local zt-axis) , (NTwGages < 7) units= kN') TwHt8FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 8 (About the local xt-axis) , (NTwGages < 8) units= kN') TwHt8FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 8 (About the local yt-axis) , (NTwGages < 8) units= kN') TwHt8FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 8 (About the local zt-axis) , (NTwGages < 8) units= kN') TwHt9FLxt = Bool(False, iotype='in', desc='Local tower roll (or side-to-side) force of tower gage 9 (About the local xt-axis) , (NTwGages < 9) units= kN') TwHt9FLyt = Bool(False, iotype='in', desc='Local tower pitching (or fore-aft) force of tower gage 9 (About the local yt-axis) , (NTwGages < 9) units= kN') TwHt9FLzt = Bool(False, iotype='in', desc='Local tower yaw (or torsional) force of tower gage 9 (About the local zt-axis) , (NTwGages < 9) units= kN') # Platform Loads PtfmFxt = Bool(False, iotype='in', desc='Platform horizontal surge shear force (Directed along the xt-axis) , () units= kN') PtfmFyt = Bool(False, iotype='in', desc='Platform horizontal sway shear force (Directed along the yt-axis) , () units= kN') PtfmFzt = Bool(False, iotype='in', desc='Platform vertical heave force (Directed along the zt-axis) , () units= kN') PtfmFxi = Bool(False, iotype='in', desc='Platform horizontal surge shear force (Directed along the xi-axis) , () units= kN') PtfmFyi = Bool(False, iotype='in', desc='Platform horizontal sway shear force (Directed along the yi-axis) , () units= kN') PtfmFzi = Bool(False, iotype='in', desc='Platform vertical heave force (Directed along the zi-axis) , () units= kN') PtfmMxt = Bool(False, iotype='in', desc='Platform roll tilt moment (About the xt-axis) , () units= kN*m') PtfmMyt = Bool(False, iotype='in', desc='Platform pitch tilt moment (About the yt-axis) , () units= kN*m') PtfmMzt = Bool(False, iotype='in', desc='Platform yaw moment (About the zt-axis) , () units= kN*m') PtfmMxi = Bool(False, iotype='in', desc='Platform roll tilt moment (About the xi-axis) , () units= kN*m') PtfmMyi = Bool(False, iotype='in', desc='Platform pitch tilt moment (About the yi-axis) , () units= kN*m') PtfmMzi = Bool(False, iotype='in', desc='Platform yaw moment (About the zi-axis) , () units= kN*m') # Mooring Line Loads Fair1Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 1 )) units= kN') Fair1Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 1 )) units= deg') Anch1Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 1 )) units= kN') Anch1Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 1 )) units= deg') Fair2Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 2 )) units= kN') Fair2Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 2 )) units= deg') Anch2Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 2 )) units= kN') Anch2Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 2 )) units= deg') Fair3Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 3 )) units= kN') Fair3Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 3 )) units= deg') Anch3Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 3 )) units= kN') Anch3Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 3 )) units= deg') Fair4Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 4 )) units= kN') Fair4Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 4 )) units= deg') Anch4Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 4 )) units= kN') Anch4Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 4 )) units= deg') Fair5Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 5 )) units= kN') Fair5Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 5 )) units= deg') Anch5Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 5 )) units= kN') Anch5Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 5 )) units= deg') Fair6Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 6 )) units= kN') Fair6Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 6 )) units= deg') Anch6Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 6 )) units= kN') Anch6Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 6 )) units= deg') Fair7Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 7 )) units= kN') Fair7Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 7 )) units= deg') Anch7Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 7 )) units= kN') Anch7Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 7 )) units= deg') Fair8Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 8 )) units= kN') Fair8Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 8 )) units= deg') Anch8Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 8 )) units= kN') Anch8Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 8 )) units= deg') Fair9Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 9 )) units= kN') Fair9Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 9 )) units= deg') Anch9Ten = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 9 )) units= kN') Anch9Ang = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NumLines < 9 )) units= deg') # Other Names # Tower-Top / Yaw Bearing Loads YawBrFzp = Bool(False, iotype='in', desc='Tower-top / yaw bearing axial force (Directed along the zn- and zp-axes) , () units= kN') YawBrMzp = Bool(False, iotype='in', desc='Tower-top / yaw bearing yaw moment (About the zn- and zp-axes) , () units= kN*m') # Tower-Top / Yaw Bearing Loads YawMom = Bool(False, iotype='in', desc='Tower-top / yaw bearing yaw moment (About the zn- and zp-axes) , () units= kN*m') class WaveMotionsOut(VariableTree): # Wave Motions WaveElev = Bool(False, iotype='in', desc=' () , (.NOT. CompHydro) units= m') Wave1Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 1 )) units= m/s') Wave1Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 1 )) units= m/s') Wave1Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 1 )) units= m/s') Wave1Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 1 )) units= m/s**2') Wave1Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 1 )) units= m/s**2') Wave1Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 1 )) units= m/s**2') Wave2Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 2 )) units= m/s') Wave2Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 2 )) units= m/s') Wave2Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 2 )) units= m/s') Wave2Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 2 )) units= m/s**2') Wave2Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 2 )) units= m/s**2') Wave2Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 2 )) units= m/s**2') Wave3Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 3 )) units= m/s') Wave3Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 3 )) units= m/s') Wave3Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 3 )) units= m/s') Wave3Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 3 )) units= m/s**2') Wave3Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 3 )) units= m/s**2') Wave3Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 3 )) units= m/s**2') Wave4Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 4 )) units= m/s') Wave4Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 4 )) units= m/s') Wave4Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 4 )) units= m/s') Wave4Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 4 )) units= m/s**2') Wave4Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 4 )) units= m/s**2') Wave4Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 4 )) units= m/s**2') Wave5Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 5 )) units= m/s') Wave5Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 5 )) units= m/s') Wave5Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 5 )) units= m/s') Wave5Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 5 )) units= m/s**2') Wave5Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 5 )) units= m/s**2') Wave5Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 5 )) units= m/s**2') Wave6Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 6 )) units= m/s') Wave6Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 6 )) units= m/s') Wave6Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 6 )) units= m/s') Wave6Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 6 )) units= m/s**2') Wave6Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 6 )) units= m/s**2') Wave6Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 6 )) units= m/s**2') Wave7Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 7 )) units= m/s') Wave7Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 7 )) units= m/s') Wave7Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 7 )) units= m/s') Wave7Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 7 )) units= m/s**2') Wave7Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 7 )) units= m/s**2') Wave7Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 7 )) units= m/s**2') Wave8Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 8 )) units= m/s') Wave8Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 8 )) units= m/s') Wave8Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 8 )) units= m/s') Wave8Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 8 )) units= m/s**2') Wave8Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 8 )) units= m/s**2') Wave8Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 8 )) units= m/s**2') Wave9Vxi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 9 )) units= m/s') Wave9Vyi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 9 )) units= m/s') Wave9Vzi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 9 )) units= m/s') Wave9Axi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 9 )) units= m/s**2') Wave9Ayi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 9 )) units= m/s**2') Wave9Azi = Bool(False, iotype='in', desc=' () , (( .NOT. CompHydro ) .OR. ( NWaveKin < 9 )) units= m/s**2') class DOFOut(VariableTree): # Internal Degrees of Freedom Q_B1E1 = Bool(False, iotype='in', desc='Displacement of 1st edgewise bending-mode DOF of blade 1 () , () units= m') Q_B2E1 = Bool(False, iotype='in', desc='Displacement of 1st edgewise bending-mode DOF of blade 2 () , () units= m') Q_B3E1 = Bool(False, iotype='in', desc='Displacement of 1st edgewise bending-mode DOF of blade 3 () , (NumBl < 3) units= m') Q_B1F1 = Bool(False, iotype='in', desc='Displacement of 1st flapwise bending-mode DOF of blade 1 () , () units= m') Q_B2F1 = Bool(False, iotype='in', desc='Displacement of 1st flapwise bending-mode DOF of blade 2 () , () units= m') Q_B3F1 = Bool(False, iotype='in', desc='Displacement of 1st flapwise bending-mode DOF of blade 3 () , (NumBl < 3) units= m') Q_B1F2 = Bool(False, iotype='in', desc='Displacement of 2nd flapwise bending-mode DOF of blade 1 () , () units= m') Q_B2F2 = Bool(False, iotype='in', desc='Displacement of 2nd flapwise bending-mode DOF of blade 2 () , () units= m') Q_B3F2 = Bool(False, iotype='in', desc='Displacement of 2nd flapwise bending-mode DOF of blade 3 () , (NumBl < 3) units= m') Q_Teet = Bool(False, iotype='in', desc='Displacement of hub teetering DOF () , (NumBl > 2) units= rad') Q_DrTr = Bool(False, iotype='in', desc='Displacement of drivetrain rotational-flexibility DOF () , () units= rad') Q_GeAz = Bool(False, iotype='in', desc='Displacement of variable speed generator DOF () , () units= rad') Q_RFrl = Bool(False, iotype='in', desc='Displacement of rotor-furl DOF () , () units= rad') Q_TFrl = Bool(False, iotype='in', desc='Displacement of tail-furl DOF () , () units= rad') Q_Yaw = Bool(False, iotype='in', desc='Displacement of nacelle yaw DOF () , () units= rad') Q_TFA1 = Bool(False, iotype='in', desc='Displacement of 1st tower fore-aft bending mode DOF () , () units= m') Q_TSS1 = Bool(False, iotype='in', desc='Displacement of 1st tower side-to-side bending mode DOF () , () units= m') Q_TFA2 = Bool(False, iotype='in', desc='Displacement of 2nd tower fore-aft bending mode DOF () , () units= m') Q_TSS2 = Bool(False, iotype='in', desc='Displacement of 2nd tower side-to-side bending mode DOF () , () units= m') Q_Sg = Bool(False, iotype='in', desc='Displacement of platform horizontal surge translation DOF () , () units= m') Q_Sw = Bool(False, iotype='in', desc='Displacement of platform horizontal sway translation DOF () , () units= m') Q_Hv = Bool(False, iotype='in', desc='Displacement of platform vertical heave translation DOF () , () units= m') Q_R = Bool(False, iotype='in', desc='Displacement of platform roll tilt rotation DOF () , () units= rad') Q_P = Bool(False, iotype='in', desc='Displacement of platform pitch tilt rotation DOF () , () units= rad') Q_Y = Bool(False, iotype='in', desc='Displacement of platform yaw rotation DOF () , () units= rad') QD_B1E1 = Bool(False, iotype='in', desc='Velocity of 1st edgewise bending-mode DOF of blade 1 () , () units= m/s') QD_B2E1 = Bool(False, iotype='in', desc='Velocity of 1st edgewise bending-mode DOF of blade 2 () , () units= m/s') QD_B3E1 = Bool(False, iotype='in', desc='Velocity of 1st edgewise bending-mode DOF of blade 3 () , (NumBl < 3) units= m/s') QD_B1F1 = Bool(False, iotype='in', desc='Velocity of 1st flapwise bending-mode DOF of blade 1 () , () units= m/s') QD_B2F1 = Bool(False, iotype='in', desc='Velocity of 1st flapwise bending-mode DOF of blade 2 () , () units= m/s') QD_B3F1 = Bool(False, iotype='in', desc='Velocity of 1st flapwise bending-mode DOF of blade 3 () , (NumBl < 3) units= m/s') QD_B1F2 = Bool(False, iotype='in', desc='Velocity of 2nd flapwise bending-mode DOF of blade 1 () , () units= m/s') QD_B2F2 = Bool(False, iotype='in', desc='Velocity of 2nd flapwise bending-mode DOF of blade 2 () , () units= m/s') QD_B3F2 = Bool(False, iotype='in', desc='Velocity of 2nd flapwise bending-mode DOF of blade 3 () , (NumBl < 3) units= m/s') QD_Teet = Bool(False, iotype='in', desc='Velocity of hub teetering DOF () , (NumBl > 2) units= rad/s') QD_DrTr = Bool(False, iotype='in', desc='Velocity of drivetrain rotational-flexibility DOF () , () units= rad/s') QD_GeAz = Bool(False, iotype='in', desc='Velocity of variable speed generator DOF () , () units= rad/s') QD_RFrl = Bool(False, iotype='in', desc='Velocity of rotor-furl DOF () , () units= rad/s') QD_TFrl = Bool(False, iotype='in', desc='Velocity of tail-furl DOF () , () units= rad/s') QD_Yaw = Bool(False, iotype='in', desc='Velocity of nacelle yaw DOF () , () units= rad/s') QD_TFA1 = Bool(False, iotype='in', desc='Velocity of 1st tower fore-aft bending mode DOF () , () units= m/s') QD_TSS1 = Bool(False, iotype='in', desc='Velocity of 1st tower side-to-side bending mode DOF () , () units= m/s') QD_TFA2 = Bool(False, iotype='in', desc='Velocity of 2nd tower fore-aft bending mode DOF () , () units= m/s') QD_TSS2 = Bool(False, iotype='in', desc='Velocity of 2nd tower side-to-side bending mode DOF () , () units= m/s') QD_Sg = Bool(False, iotype='in', desc='Velocity of platform horizontal surge translation DOF () , () units= m/s') QD_Sw = Bool(False, iotype='in', desc='Velocity of platform horizontal sway translation DOF () , () units= m/s') QD_Hv = Bool(False, iotype='in', desc='Velocity of platform vertical heave translation DOF () , () units= m/s') QD_R = Bool(False, iotype='in', desc='Velocity of platform roll tilt rotation DOF () , () units= rad/s') QD_P = Bool(False, iotype='in', desc='Velocity of platform pitch tilt rotation DOF () , () units= rad/s') QD_Y = Bool(False, iotype='in', desc='Velocity of platform yaw rotation DOF () , () units= rad/s') QD2_B1E1 = Bool(False, iotype='in', desc='Acceleration of 1st edgewise bending-mode DOF of blade 1 () , () units= m/s**2') QD2_B2E1 = Bool(False, iotype='in', desc='Acceleration of 1st edgewise bending-mode DOF of blade 2 () , () units= m/s**2') QD2_B3E1 = Bool(False, iotype='in', desc='Acceleration of 1st edgewise bending-mode DOF of blade 3 () , (NumBl < 3) units= m/s**2') QD2_B1F1 = Bool(False, iotype='in', desc='Acceleration of 1st flapwise bending-mode DOF of blade 1 () , () units= m/s**2') QD2_B2F1 = Bool(False, iotype='in', desc='Acceleration of 1st flapwise bending-mode DOF of blade 2 () , () units= m/s**2') QD2_B3F1 = Bool(False, iotype='in', desc='Acceleration of 1st flapwise bending-mode DOF of blade 3 () , (NumBl < 3) units= m/s**2') QD2_B1F2 = Bool(False, iotype='in', desc='Acceleration of 2nd flapwise bending-mode DOF of blade 1 () , () units= m/s**2') QD2_B2F2 = Bool(False, iotype='in', desc='Acceleration of 2nd flapwise bending-mode DOF of blade 2 () , () units= m/s**2') QD2_B3F2 = Bool(False, iotype='in', desc='Acceleration of 2nd flapwise bending-mode DOF of blade 3 () , (NumBl < 3) units= m/s**2') QD2_Teet = Bool(False, iotype='in', desc='Acceleration of hub teetering DOF () , (NumBl > 2) units= rad/s**2') QD2_DrTr = Bool(False, iotype='in', desc='Acceleration of drivetrain rotational-flexibility DOF () , () units= rad/s**2') QD2_GeAz = Bool(False, iotype='in', desc='Acceleration of variable speed generator DOF () , () units= rad/s**2') QD2_RFrl = Bool(False, iotype='in', desc='Acceleration of rotor-furl DOF () , () units= rad/s**2') QD2_TFrl = Bool(False, iotype='in', desc='Acceleration of tail-furl DOF () , () units= rad/s**2') QD2_Yaw = Bool(False, iotype='in', desc='Acceleration of nacelle yaw DOF () , () units= rad/s**2') QD2_TFA1 = Bool(False, iotype='in', desc='Acceleration of 1st tower fore-aft bending mode DOF () , () units= m/s**2') QD2_TSS1 = Bool(False, iotype='in', desc='Acceleration of 1st tower side-to-side bending mode DOF () , () units= m/s**2') QD2_TFA2 = Bool(False, iotype='in', desc='Acceleration of 2nd tower fore-aft bending mode DOF () , () units= m/s**2') QD2_TSS2 = Bool(False, iotype='in', desc='Acceleration of 2nd tower side-to-side bending mode DOF () , () units= m/s**2') QD2_Sg = Bool(False, iotype='in', desc='Acceleration of platform horizontal surge translation DOF () , () units= m/s**2') QD2_Sw = Bool(False, iotype='in', desc='Acceleration of platform horizontal sway translation DOF () , () units= m/s**2') QD2_Hv = Bool(False, iotype='in', desc='Acceleration of platform vertical heave translation DOF () , () units= m/s**2') QD2_R = Bool(False, iotype='in', desc='Acceleration of platform roll tilt rotation DOF () , () units= rad/s**2') QD2_P = Bool(False, iotype='in', desc='Acceleration of platform pitch tilt rotation DOF () , () units= rad/s**2') QD2_Y = Bool(False, iotype='in', desc='Acceleration of platform yaw rotation DOF () , () units= rad/s**2') # Output Variable Tree class FstOutput(VariableTree): # Motion output channels wind_mot_vt = VarTree(WindMotionsOut(), desc='wind motions output channels') blade_mot_vt = VarTree(BladeMotionsOut(), desc='blade motions output channels') hub_nacelle_mot_vt = VarTree(HubNacelleMotionsOut(), desc='hub and nacelle system motions output channels') tower_support_mot_vt = VarTree(TowerSupportMotionsOut(), desc='yaw bearing, tower and support motions output channels') wave_mot_vt = VarTree(WaveMotionsOut(), desc='wave motions output channels') # Loads output channels blade_loads_vt = VarTree(BladeLoadsOut(), desc='blade loads output channels') hub_nacelle_loads_vt = VarTree(HubNacelleLoadsOut(), desc='hub and nacelle system loads output channels') tower_support_loads_vt = VarTree(TowerSupportLoadsOut(), desc='tower and support loads output channels') # Other output channels dof_vt = VarTree(DOFOut(), desc='degree of freedom output channels')

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35d342fca510a2a929740b9e88696f41cfbeefb0

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py

Python

.local/share/Trash/files/instance/config.py

WayneMusungu/Akan-Names

f4241968636c8725d31bbbcf147134b450f30e79

[ "MIT" ]

null

.local/share/Trash/files/instance/config.py

WayneMusungu/Akan-Names

f4241968636c8725d31bbbcf147134b450f30e79

[ "MIT" ]

null

.local/share/Trash/files/instance/config.py

WayneMusungu/Akan-Names

f4241968636c8725d31bbbcf147134b450f30e79

[ "MIT" ]

null

NEWS_API_KEY = 'bdb929e9f536492187b6e3a21154bd50'

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Python

cfg_params/github_imagenet_cfg.py

zhjpqq/scaledensenet

5ae56786c7f628b8320b76d559ecaa6fa1d2ac0e

[ "MIT" ]

5

2019-08-27T20:15:05.000Z

2021-01-18T08:21:37.000Z

cfg_params/github_imagenet_cfg.py

zhjpqq/scaledensenet

5ae56786c7f628b8320b76d559ecaa6fa1d2ac0e

[ "MIT" ]

null

cfg_params/github_imagenet_cfg.py

zhjpqq/scaledensenet

5ae56786c7f628b8320b76d559ecaa6fa1d2ac0e

[ "MIT" ]

2

2020-03-12T04:41:49.000Z

2020-10-11T08:32:34.000Z

import math, os, time from math import ceil, floor import xtils # how-to-use: test a model.pth by run_main.py ##### # args.arch_name = 'scalenet' # args.arch_list = ['vo69'] # args.cfg_dict = 'cfgscale' # args.exp_version = 'exp.test' # args.gpu_ids = [1, 0] # args.arch_name = 'resnet' # args.arch_list = ['res50'] # args.cfg_dict = 'cfgtest' # args.exp_version = 'exp.test' # args.gpu_ids = [0, 1] # args.arch_name = 'fishnet' # args.arch_list = ['fish150'] # args.cfg_dict = 'cfgtest' # args.exp_version = 'exp.test' # args.gpu_ids = [1, 0] # args.arch_name = 'mobilev3' # args.arch_list = ['mbvxs'] # args.cfg_dict = 'cfgtest' # args.exp_version = 'exp.test' # args.gpu_ids = [0, 1] # args.arch_name = 'hrnet' # args.arch_list = ['hrw18'] # args.cfg_dict = 'cfgtest' # args.exp_version = 'exp.test' # args.arch_name = 'effnet' # args.arch_list = ['effb3'] # args.cfg_dict = 'cfgtest' # args.exp_version = 'exp.test' # args.gpu_ids = [1] # batch_nums = math.ceil(data_info['train_size']/bsize_train) train_size = 1281167 batch_size = 256 batch_size_val = 12 batch_nums = math.ceil(train_size / batch_size) BN = batch_nums # =>> Unit #5005 cfgtest = { # experiment config 'exp_version': 'exp.xxx', 'train_val_test': (False, True, False), # device config 'gpu_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8][0:4], # model config 'arch_name': 'xxx', 'arch_kwargs': {}, 'resume': '', # os.path.join(xtils.get_pretrained_models(), 'resnet50-19c8e357.pth'), 'resume_config': False, 'resume_optimizer': False, 'mgpus_to_sxpu': ['m2s', 's2m', 'none', 'auto'][3], # data config 'dataset': 'imagenet', 'data_info': {'train_size': train_size, 'val_size': 50000, 'test_size': 50000}, 'data_root': xtils.get_data_root(data='imagenet'), 'data_augment': {'train': 'rotate-rresize-1crop', 'val': '1resize-1crop', 'imsize': 256, 'insize': 224, 'color': True, 'degree': (0, 0), 'scale': (0.08, 1), 'ratio': (3. / 4, 4. / 3)}, 'data_kwargs': {}, 'data_workers': 4, # path config 'current_time': '', 'ckpt_suffix': '', # when save a ckpt, u can add a special mark to its filename. 'ckpt_base_dir': xtils.get_base_dir(k='ckpt'), 'ckpt_dir': 'auto-setting', 'log_base_dir': xtils.get_base_dir(k='log'), 'log_dir': 'auto-setting', # iter config 'start_iter': 0, 'max_iters': [100 * BN, 90 * BN, 60 * BN, 40 * BN, 120 * BN][1], 'start_epoch': 0, 'max_epochs': 0, 'bsize_train': batch_size, 'bsize_val': batch_size_val, 'batch_nums': batch_nums, 'Unit': {'epoch': batch_nums, 'iter': 1}[['epoch', 'iter'][0]], # 按epoch为单位调节还是按iteration为单位调节lr/bs？ 'BN': BN, # lr config 'optim_type': ['Adam', 'SGD'][1], 'lr_start': {'Adam': 0.01, 'SGD': 0.1}['SGD'], 'lr_end': 0.0, 'lr_decay_policy': ['regular', 'appoint', 'original', 'trace_prec'][1], 'lr_decay_start': 1 * BN, 'lr_decay_rate': 1 / 10, 'lr_decay_time': 15 * BN, 'lr_decay_appoint': ((30 * BN, 1 / 10), (60 * BN, 1 / 10), (90 * BN, 1 / 10)), 'momentum': 0.9, 'weight_decay': [0.0001, 0.0005, 0.00017, 0.00002][0], # frequency config # # Note: if val_freq: (0, plot_freq) # # the loss-curve/prec-curve of train and val can have same x-axis point. 'best_prec': {'train_prec1': 0, 'train_prec5': 0, 'val_prec1': 0, 'val_prec5': 0, 'best_start': 3, 'best_ok': False}, 'print_frequency': BN // 100, 'plot_frequency': BN // 100, # 5005/100=50 'val_frequency': (0 * BN, BN // 1), 'test_frequency': (999 * BN, BN // 1), 'save_frequency': (0 * BN, BN // 1), # forzen config 'mode_custom': False, 'train_which': [], 'eval_which': [], 'xfc_which': -1, # time config 'valid_total_time': 0, 'test_total_time': 0, 'exp_tic': time.time(), 'exclude_keys': ('exclude_keys', 'gpu_ids', 'device', 'resume'), } # batch_nums = math.ceil(data_info['train_size']/bsize_train) train_size = 1281167 batch_size = 256 batch_size_val = 128 batch_nums = math.ceil(train_size / batch_size) BN = batch_nums # =>> Unit #5005 cfgres1 = { # experiment config 'exp_version': 'exp.xxx', 'train_val_test': (True, True, True), # device config 'gpu_ids': [0, 1, 2, 3, 4, 5, 6, 7, 8][0:4], # model config 'arch_name': 'xxx', 'arch_kwargs': {}, 'resume': None or '/data1/zhangjp/classify/checkpoints/imagenet/resnet/resnet-exp.res2/' + 'imagenet-resnet54-ep81-it410409-acc75.76-best75.81-topv92.80-par25.56M-norm-exp.res2.ckpt', 'resume_config': True, 'resume_optimizer': True, 'mgpus_to_sxpu': ['m2s', 's2m', 'none', 'auto'][3], # data config 'dataset': 'imagenet', 'data_info': {'train_size': train_size, 'val_size': 50000, 'test_size': 50000}, 'data_root': xtils.get_data_root(data='imagenet'), 'data_augment': {'train': 'rotate-rresize-1crop', 'val': '1resize-1crop', 'imsize': 256, 'insize': 224, 'color': True, 'interp': 'bilinear', 'degree': (0, 0), 'scale': (0.08, 1), 'ratio': (3. / 4, 4. / 3)}, 'data_kwargs': {}, 'data_workers': 16, # path config 'current_time': '', 'ckpt_suffix': '', # when save a ckpt, u can add a special mark to its filename. 'ckpt_base_dir': xtils.get_base_dir(k='ckpt'), 'ckpt_dir': 'auto-setting', 'log_base_dir': xtils.get_base_dir(k='log'), 'log_dir': 'auto-setting', # iter config 'start_iter': 0, 'max_iters': [90 * BN, 100 * BN, 60 * BN, 40 * BN, 120 * BN][1], 'start_epoch': 0, 'max_epochs': 0, 'bsize_train': batch_size, 'bsize_val': batch_size_val, 'batch_nums': batch_nums, 'Unit': {'epoch': batch_nums, 'iter': 1}[['epoch', 'iter'][0]], # 按epoch为单位调节还是按iteration为单位调节lr/bs？ 'BN': BN, # lr config 'optim_type': ['Adam', 'SGD', 'RMSPROP'][1], 'lr_start': {'Adam': 0.01, 'SGD': 0.1, 'RMSPROP': 0.1}['SGD'], 'lr_decay_policy': ['regular', 'appoint', 'original', 'trace_prec'][1], 'lr_decay_appoint': ((30 * BN, 1 / 10), (60 * BN, 1 / 10), (90 * BN, 1 / 10)), 'momentum': 0.9, 'weight_decay': [0.0001, 0.0005, 0.00017, 0.00002][0], 'nesterov': False, 'rmsprop_alpha': '', 'rmsprop_centered': '', # frequency config # # Note: if val_freq: (0, plot_freq) # # the loss-curve/prec-curve of train and val can have same x-axis point. 'best_prec': {'train_prec1': 0, 'train_prec5': 0, 'val_prec1': 0, 'val_prec5': 0, 'best_start': 3, 'best_ok': False}, 'print_frequency': BN // 100, 'plot_frequency': BN // 100, # 5005/100=50 'val_frequency': (0 * BN, BN // 1), 'test_frequency': (999 * BN, BN // 1), 'save_frequency': (0 * BN, BN // 1), # forzen config 'mode_custom': False, 'train_which': [], 'eval_which': [], 'xfc_which': -1, # time config 'valid_total_time': 0, 'test_total_time': 0, 'exp_tic': time.time(), 'exclude_keys': ('exclude_keys', 'gpu_ids', 'device', 'resume'), } cfgres2 = cfgres1

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aa31d9c662ba6695ca092b0a30e3904eba7ba68f

11,491

py

Python

molsysmt/forms/api_mdtraj_PDBTrajectoryFile.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

molsysmt/forms/api_mdtraj_PDBTrajectoryFile.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

molsysmt/forms/api_mdtraj_PDBTrajectoryFile.py

dprada/molsysmt

83f150bfe3cfa7603566a0ed4aed79d9b0c97f5d

[ "MIT" ]

null

from molsysmt._private_tools.exceptions import * from molsysmt.forms.common_gets import * import numpy as np import importlib import sys from molsysmt import puw from molsysmt.native.molecular_system import molecular_system_components form_name='mdtraj.PDBTrajectoryFile' is_form={ 'mdtraj.PDBTrajectoryFile': form_name } info=["",""] has = molecular_system_components.copy() for ii in ['elements', 'bonds', 'coordinates', 'box']: has[ii]=True def to_mdtraj_Topology(item, molecular_system=None, atom_indices='all', frame_indices='all'): from molsysmt.forms.api_mdtraj_Topology import to_mdtraj_Topology as mdtraj_Topology_to_mdtraj_Topology tmp_molecular_system = None tmp_item = item.topology if molecular_system is not None: tmp_molecular_system = molecular_system.combine_with_items(tmp_item) tmp_item, tmp_molecular_system = mdtraj_Topology_to_mdtraj_Topology(tmp_item, tmp_molecular_system, atom_indices=atom_indices, frame_indices=frame_indices, copy_if_all=False) return tmp_item, tmp_molecular_system def to_mdtraj_PDBTrajectoryFile(item, molecular_system=None, atom_indices='all', frame_indices='all'): tmp_molecular_system = None if (atom_indices is 'all') and (frame_indices is 'all'): if copy_if_all: tmp_item = extract(item) if molecular_system is not None: tmp_molecular_system = molecular_system.combine_with_items(tmp_item) else: tmp_item = item if molecular_system is not None: tmp_molecular_system = molecular_system else: tmp_item = extract(item, atom_indices=atom_indices, frame_indices=frame_indices) if molecular_system is not None: tmp_molecular_system = molecular_system.combine_with_items(tmp_item, atom_indices=atom_indices, frame_indices=frame_indices) return tmp_item, tmp_molecular_system def extract(item, atom_indices='all', frame_indices='all'): if (atom_indices is 'all') and (frame_indices is 'all'): raise NotImplementedError() else: raise NotImplementedError() return tmp_item def merge(item_1, item_2): raise NotImplementedError() def add(to_item, item): raise NotImplementedError() def append_frames(item, step=None, time=None, coordinates=None, box=None): raise NotImplementedError() def concatenate_frames(item, step=None, time=None, coordinates=None, box=None): raise NotImplementedError #### Get def aux_get(item, indices='all', frame_indices='all'): from molsysmt.forms import forms method_name = sys._getframe(1).f_code.co_name if 'mdtraj.Topology' in forms: tmp_item, _ = to_mdtraj_Topology(item) module = importlib.import_module('molsysmt.forms.api_mdtraj_Topology') _get = getattr(module, method_name) output = _get(tmp_item, indices=indices, frame_indices=frame_indices) else: raise NotImplementedError return output ## atom def get_atom_index_from_atom(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_atom_id_from_atom(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_atom_name_from_atom(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_atom_type_from_atom(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_group_index_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_component_index_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_chain_index_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_molecule_index_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_entity_index_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_inner_bonded_atoms_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_inner_bonds_from_atom (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_coordinates_from_atom(item, indices='all', frame_indices='all'): xyz = item.positions if frame_indices is not 'all': xyz = xyz[frame_indices,:,:] if indices is not 'all': xyz = xyz[:,indices,:] xyz = xyz*puw.unit(item.distance_unit) xyz = puw.standardize(xyz) return xyz def get_frame_from_atom(item, indices='all', frame_indices='all'): tmp_step = get_step_from_system(item, frame_indices=frame_indices) tmp_time = get_time_from_system(item, frame_indices=frame_indices) tmp_box = get_box_from_system(item, frame_indices=frame_indices) tmp_coordinates = get_coordinates_from_atom(item, indices=indices, frame_indices=frame_indices) return tmp_step, tmp_time, tmp_coordinates, tmp_box ## group def get_group_id_from_group(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_group_name_from_group(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_group_type_from_group(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ## component def get_component_id_from_component (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_component_name_from_component (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_component_type_from_component (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ## molecule def get_molecule_id_from_molecule (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_molecule_name_from_molecule (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_molecule_type_from_molecule (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ## chain def get_chain_id_from_chain (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_chain_name_from_chain (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_chain_type_from_chain (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ## entity def get_entity_id_from_entity (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_entity_name_from_entity (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_entity_type_from_entity (item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ## system def get_n_atoms_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_groups_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_components_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_chains_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_molecules_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_entities_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_n_bonds_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_box_from_system(item, indices='all', frame_indices='all'): from molsysmt.pbc import box_vectors_from_box_lengths_and_angles cell_lengths = item.unitcell_lengths cell_angles = item.unitcell_angles if cell_lengths is not None: cell_lengths = cell_lengths*puw.unit(item.distance_unit) cell_angles = cell_angles*puw.unit('degrees') box = box_vectors_from_box_lengths_and_angles(cell_lengths, cell_angles) if frame_indices is not 'all': box = box[frame_indices,:,:] box = puw.standardize(box) else: box = None return box def get_box_shape_from_system (item, indices='all', frame_indices='all'): raise NotImplementedError def get_box_lengths_from_system(item, indices='all', frame_indices='all'): cell_lengths = item.unitcell_lengths if cell_lengths is not None: cell_lengths = cell_lengths*puw.unit(item.distance_unit) if frame_indices is not 'all': cell_lengths = cell_lengths[frame_indices] cell_lengths = puw.standardize(cell_lengths) else: cell_lengths = None return cell_lengths def get_box_angles_from_system(item, indices='all', frame_indices='all'): cell_angles = item.unitcell_angles if cell_lengths is not None: cell_angles = cell_angles*puw.unit('degrees') if frame_indices is not 'all': cell_angles = cell_angles[frame_indices] cell_angles = puw.standardize(cell_angles) else: cell_angles = None return cell_angles def get_box_volume_from_system(item, indices='all', frame_indices='all'): raise NotImplementedError def get_time_from_system(item, indices='all', frame_indices='all'): return None def get_step_from_system(item, indices='all', frame_indices='all'): return None def get_n_frames_from_system (item, indices='all', frame_indices='all'): return item.positions.shape[0] def get_bonded_atoms_from_system(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ## bond def get_bond_order_from_bond(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_bond_type_from_bond(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) def get_atom_index_from_bond(item, indices='all', frame_indices='all'): return aux_get(item, indices=indices, frame_indices=frame_indices) ###### Set def set_box_to_system(item, indices='all', frame_indices='all', value=None): raise NotImplementedError def set_coordinates_to_system(item, indices='all', frame_indices='all', value=None): raise NotImplementedError

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1

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null

0

1

0

1

0

8

a4bf283f2288d76fc1a221596fa81f628b23effe

326

py

Python

pydrot/drot/proximal/__init__.py

vienmai/drot

a5ffa08d6911c7245984d3b0e5835f90767801f4

[ "MIT" ]

null

pydrot/drot/proximal/__init__.py

vienmai/drot

a5ffa08d6911c7245984d3b0e5835f90767801f4

[ "MIT" ]

null

pydrot/drot/proximal/__init__.py

vienmai/drot

a5ffa08d6911c7245984d3b0e5835f90767801f4

[ "MIT" ]

null

from .proximal import generalized_doubly_stochastic_matrices_projection from .proximal import trace_nonnegative_prox from .proximal import primal_dual_linear_prox from .proximal import primal_dual_trace_nonnegative_prox from .proximal import trace_nonnegative_prox_nb from .proximal import apply_adjoint_operator_and_override

46.571429

71

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0.23913

0.565217

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326

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true

0

1

0

1

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1

0

null

0

1

0

1

0

1

0

7

352c2497bb5633fb0808aa78eab6aaee24a1f527

229

py

Python

tests/test_powerline_pulseaudio_volume.py

gmagno/powerline_pulseaudio_volume_segment

53f69eee0475bb44f64cb01427e7f7007d09692b

[ "MIT" ]

1

2021-09-15T15:13:38.000Z

tests/test_powerline_pulseaudio_volume.py

gmagno/powerline_pulseaudio_volume_segment

53f69eee0475bb44f64cb01427e7f7007d09692b

[ "MIT" ]

null

tests/test_powerline_pulseaudio_volume.py

gmagno/powerline_pulseaudio_volume_segment

53f69eee0475bb44f64cb01427e7f7007d09692b

[ "MIT" ]

null

#!/usr/bin/env python """Tests for `powerline_pulseaudio_volume` package.""" from powerline_pulseaudio_volume import powerline_pulseaudio_volume def test_current_volume(): powerline_pulseaudio_volume.current_volume(None)

22.9

67

0.820961

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0

1

0

1

0

1

0

8

52beda5b6ae1d9d238bc48b430370507536c27ae

24,384

py

Python

local_epifx/src/epifx/det.py

ruarai/epifx.covid

be7aecbf9e86c3402f6851ea65f6705cdb59f3cf

[ "BSD-3-Clause" ]

null

local_epifx/src/epifx/det.py

ruarai/epifx.covid

be7aecbf9e86c3402f6851ea65f6705cdb59f3cf

[ "BSD-3-Clause" ]

null

local_epifx/src/epifx/det.py

ruarai/epifx.covid

be7aecbf9e86c3402f6851ea65f6705cdb59f3cf

[ "BSD-3-Clause" ]

null

"""Deterministic models of infectious diseases.""" import logging from pathlib import Path import numpy as np import pypfilt from .model import Model class SEIR(Model): r""" An SEIR compartment model for a single circulating influenza strain, under the assumption that recovered individuals are completely protected against reinfection. .. math:: \frac{dS}{dt} &= - \beta S^\eta I \\[0.5em] \frac{dE}{dt} &= \beta S^\eta I - \sigma E \\[0.5em] \frac{dI}{dt} &= \sigma E - \gamma I \\[0.5em] \frac{dR}{dt} &= \gamma I \\[0.5em] \beta &= R_0 \cdot \gamma ============== ================================================ Parameter Meaning ============== ================================================ :math:`R_0` Basic reproduction number :math:`\sigma` Inverse of the incubation period (day :sup:`-1`) :math:`\gamma` Inverse of the infectious period (day :sup:`-1`) :math:`\eta` Inhomogeneous social mixing coefficient :math:`\alpha` Temporal forcing coefficient ============== ================================================ The force of infection can be subject to temporal forcing :math:`F(t)`, as mediated by :math:`\alpha`: .. math:: \beta(t) = \beta \cdot \left[1 + \alpha \cdot F(t)\right] Note that this requires the forcing time-series to be stored in the lookup table ``'R0_forcing'``. """ __info = [("S", False, 0, 1), ("E", False, 0, 1), ("I", False, 0, 1), ("R", False, 0, 1), ("R0", True, 1, 2), ("sigma", True, 1/3, 2), ("gamma", True, 1/3, 1), ("eta", True, 1, 2), ("alpha", True, -0.2, 0.2), ("t0", False, 0, 50), ] ix_S = 0 ix_E = 1 ix_I = 2 ix_R = 3 ix_R0 = 4 ix_sigma = 5 ix_gamma = 6 ix_eta = 7 ix_alpha = 8 ix_t0 = 9 def __init__(self): """Initialise the model instance.""" self.__Forcing_lookup = None def state_size(self): """Return the size of the state vector.""" return len(self.__info) def population_size(self): return self.popn_size def init(self, ctx, vec): """Initialise a state vector. :param ctx: The simulation context. :param vec: An uninitialised state vector of correct dimensions (see :py:func:`~state_size`). """ self.popn_size = ctx.params['model']['population_size'] self.__Forcing_lookup = None prior = ctx.params['model']['prior'] rnd = ctx.component['random']['model'] rnd_size = vec[..., 0].shape # Initialise the model state (fully susceptible population). initial_exposures = 1.0 / self.popn_size vec[..., :] = 0 vec[..., self.ix_S] = 1 - initial_exposures vec[..., self.ix_E] = initial_exposures vec[..., self.ix_R0] = prior['R0'](rnd, size=rnd_size) vec[..., self.ix_sigma] = prior['sigma'](rnd, size=rnd_size) vec[..., self.ix_gamma] = prior['gamma'](rnd, size=rnd_size) vec[..., self.ix_eta] = prior['eta'](rnd, size=rnd_size) vec[..., self.ix_alpha] = 0 vec[..., self.ix_t0] = prior['t0'](rnd, size=rnd_size) self.load_samples_file(ctx, vec) self.load_lookup_tables(ctx, vec, init_values=True) def sample_columns(self): """Identify parameters that can be saved and loaded.""" ix_tbl = { 'R0': self.ix_R0, 'sigma': self.ix_sigma, 'gamma': self.ix_gamma, 'eta': self.ix_eta, 'alpha': self.ix_alpha, 't0': self.ix_t0, } return ix_tbl def load_samples_file(self, ctx, vec): """Load initial parameter values from an external data file.""" if 'prior_samples' not in ctx.params['model']: return logger = logging.getLogger(__name__) samples = ctx.params['model']['prior_samples'] data_dir = Path(ctx.params['data_dir']) data_file = data_dir / samples['file'] columns = [(name, np.float) for name in samples['columns']] tbl = pypfilt.io.read_table(data_file, columns) if tbl.shape != vec[..., 0].shape: raise ValueError('Incompatible data shapes: {} and {}'.format( vec[..., 0].shape, tbl.shape)) ix_tbl = self.sample_columns() for name in samples['columns']: if name not in ix_tbl: raise ValueError('Unknown parameter {}'.format(name)) ix = ix_tbl[name] vec[..., ix] = tbl[name] # NOTE: warn if sampled values exceed the parameter bounds. min_val = np.min(tbl[name]) max_val = np.max(tbl[name]) if min_val < ctx.params['model']['param_min'][ix]: logger.warning('Sampled value for {} outside bounds' .format(name)) elif max_val > ctx.params['model']['param_max'][ix]: logger.warning('Sampled value for {} outside bounds' .format(name)) def load_lookup_tables(self, ctx, vec, init_values=False): logger = logging.getLogger(__name__) rnd = ctx.component['random']['model'] rnd_size = vec[..., 0].shape prior = ctx.params['model']['prior'] tables = ctx.component.get('lookup', {}) if 'R0_forcing' in tables and self.__Forcing_lookup is None: self.__Forcing_lookup = tables['R0_forcing'] logger.info('Using lookup table for R0 forcing with {} values' .format(self.__Forcing_lookup.value_count())) if init_values: vec[..., self.ix_alpha] = prior['alpha'](rnd, size=rnd_size) def update(self, ctx, step_date, dt, is_fs, prev, curr): """Perform a single time-step. :param ctx: The simulation context. :param step_date: The date and time of the current time-step. :param dt: The time-step size (days). :param is_fs: Indicates whether this is a forecasting simulation. :param prev: The state before the time-step. :param curr: The state after the time-step (destructively updated). """ # Update parameters and lookup tables that are defined in self.init() # and which will not exist if we are resuming from a cached state. self.popn_size = ctx.params['model']['population_size'] self.load_lookup_tables(ctx, prev, init_values=False) # Extract each parameter. R0 = prev[..., self.ix_R0].copy() sigma = prev[..., self.ix_sigma].copy() gamma = prev[..., self.ix_gamma].copy() eta = prev[..., self.ix_eta].copy() alpha = prev[..., self.ix_alpha].copy() t0 = prev[..., self.ix_t0].copy() beta = R0 * gamma if self.__Forcing_lookup is not None: # Modulate the force of infection with temporal forcing. force = alpha * self.__Forcing_lookup.lookup(step_date)[0] # Ensure the force of infection is non-negative (can be zero). beta *= np.maximum(1.0 + force, 0) epoch = ctx.component['time'].to_scalar(ctx.params['epoch']) curr_t = ctx.component['time'].to_scalar(step_date) zero_mask = t0 > (curr_t - epoch) R0[zero_mask] = 0 sigma[zero_mask] = 0 gamma[zero_mask] = 0 eta[zero_mask] = 0 alpha[zero_mask] = 0 t0[zero_mask] = 0 beta[zero_mask] = 0 # Extract each compartment. S = prev[..., self.ix_S] E = prev[..., self.ix_E] I = prev[..., self.ix_I] R = prev[..., self.ix_R] # Calculate flows between compartments. s_to_e = dt * beta * I * S ** eta e_to_i = dt * sigma * E i_to_r = dt * gamma * I # Update the compartment values. curr[..., self.ix_S] = S - s_to_e curr[..., self.ix_E] = E + s_to_e - e_to_i curr[..., self.ix_I] = I + e_to_i - i_to_r # Enforce invariants on the S, E, and I compartments. curr[..., :self.ix_R] = np.clip(curr[..., :self.ix_R], 0, 1) sum_SEI = np.sum(curr[..., :self.ix_R], axis=-1) mask_invalid = sum_SEI > 1 if np.any(mask_invalid): denom = sum_SEI[mask_invalid, None] curr[mask_invalid, :self.ix_R] = (curr[mask_invalid, :self.ix_R] / denom) # Calculate the size of the R compartment and clip appropriately. curr[..., self.ix_R] = np.clip( 1.0 - np.sum(curr[..., :self.ix_R], axis=-1), 0.0, 1.0) # Keep parameters fixed. curr[..., self.ix_R0:] = prev[..., self.ix_R0:] def pr_inf(self, prev, curr): """Calculate the likelihood of an individual becoming infected, for any number of state vectors. :param prev: The model states at the start of the observation period. :param curr: The model states at the end of the observation period. """ # Count the number of susceptible / exposed individuals at both ends # of the simulation period. prev_amt = np.sum(prev[..., 0:2], axis=-1) curr_amt = np.sum(curr[..., 0:2], axis=-1) # Avoid returning very small negative values (e.g., -1e-10). return np.maximum(prev_amt - curr_amt, 0) def is_seeded(self, hist): """Identify state vectors where infections have occurred. :param hist: A matrix of arbitrary dimensions, whose final dimension covers the model state space (i.e., has a length no smaller than that returned by :py:func:`state_size`). :type hist: numpy.ndarray :returns: A matrix of one fewer dimensions than ``hist`` that contains ``1`` for state vectors where infections have occurred and ``0`` for state vectors where they have not. :rtype: numpy.ndarray """ return np.ceil(1 - hist[..., 0]) def is_valid(self, hist): """Ignore state vectors where no infections have occurred, as their properties (such as parameter distributions) are uninformative.""" return self.is_seeded(hist) def describe(self): return self.__info def stat_info(self): """Return the details of each statistic that can be calculated by this model. Each such statistic is represented as a ``(name, stat_fn)`` pair, where ``name`` is a string that identifies the statistic and ``stat_fn`` is a function that calculates the statistic (see, e.g., :py:func:`stat_Reff`). """ return [("Reff", self.stat_Reff)] def stat_Reff(self, hist): """Calculate the effective reproduction number :math:`R_{eff}` for every particle. :param hist: The particle history matrix, or a subset thereof. """ return hist[..., self.ix_S] * hist[..., self.ix_R0] class SEEIIR(Model): r"""An SEEIIR compartment model for a single circulating influenza strain, under the assumption that recovered individuals are completely protected against reinfection. .. math:: \frac{dS}{dt} &= - \beta S^\eta (I_1 + I_2) \\[0.5em] \frac{dE_1}{dt} &= \beta S^\eta (I_1 + I_2) - 2 \sigma E_1 \\[0.5em] \frac{dE_2}{dt} &= 2 \sigma E_1 - 2 \sigma E_2 \\[0.5em] \frac{dI_1}{dt} &= 2 \sigma E_2 - 2 \gamma I_1 \\[0.5em] \frac{dI_2}{dt} &= 2 \gamma I_1 - 2 \gamma I_2 \\[0.5em] \frac{dR}{dt} &= 2 \gamma I_2 \\[0.5em] \beta &= R_0 \cdot \gamma ============== ================================================ Parameter Meaning ============== ================================================ :math:`R_0` Basic reproduction number :math:`\sigma` Inverse of the incubation period (day :sup:`-1`) :math:`\gamma` Inverse of the infectious period (day :sup:`-1`) :math:`\eta` Inhomogeneous social mixing coefficient :math:`\alpha` Temporal forcing coefficient ============== ================================================ The force of infection can be subject to temporal forcing :math:`F(t)`, as mediated by :math:`\alpha`: .. math:: \beta(t) = \beta \cdot \left[1 + \alpha \cdot F(t)\right] Note that this requires the forcing time-series to be stored in the lookup table ``'R0_forcing'``. """ __info = [ ("S", False, 0, 1), ("E1", False, 0, 1), ("E2", False, 0, 1), ("I1", False, 0, 1), ("I2", False, 0, 1), ("R", False, 0, 1), ("R0", True, 1, 2), ("sigma", True, 1/3, 2), ("gamma", True, 1/3, 1), ("eta", True, 1, 2), ("alpha", True, -0.2, 0.2), ("t0", False, 0, 100)] ix_S = 0 ix_E1 = 1 ix_E2 = 2 ix_I1 = 3 ix_I2 = 4 ix_R = 5 ix_R0 = 6 ix_sigma = 7 ix_gamma = 8 ix_eta = 9 ix_alpha = 10 ix_t0 = 11 def __init__(self): """Initialise the model instance.""" self.__R0_lookup = None self.__Overseas_lookup = None self.__Forcing_lookup = None def state_size(self): """Return the size of the state vector.""" return len(self.__info) def population_size(self): return self.popn_size def init(self, ctx, vec): """Initialise a state vector. :param ctx: The simulation context. :param vec: An uninitialised state vector of correct dimensions (see :py:func:`~state_size`). """ self.popn_size = ctx.params['model']['population_size'] self.__R0_lookup = None self.__Overseas_lookup = None self.__Forcing_lookup = None prior = ctx.params['model']['prior'] rnd = ctx.component['random']['model'] rnd_size = vec[..., 0].shape # Initialise the model state (fully susceptible population). initial_exposures = 1.0 / self.popn_size vec[..., :] = 0 vec[..., self.ix_S] = 1 - initial_exposures vec[..., self.ix_E1] = initial_exposures vec[..., self.ix_R0] = prior['R0'](rnd, size=rnd_size) vec[..., self.ix_sigma] = prior['sigma'](rnd, size=rnd_size) vec[..., self.ix_gamma] = prior['gamma'](rnd, size=rnd_size) vec[..., self.ix_eta] = prior['eta'](rnd, size=rnd_size) vec[..., self.ix_alpha] = 0 vec[..., self.ix_t0] = prior['t0'](rnd, size=rnd_size) self.load_samples_file(ctx, vec) self.load_lookup_tables(ctx, vec, init_values=True) def sample_columns(self): """Identify parameters that can be saved and loaded.""" ix_tbl = { 'R0': self.ix_R0, 'sigma': self.ix_sigma, 'gamma': self.ix_gamma, 'eta': self.ix_eta, 'alpha': self.ix_alpha, 't0': self.ix_t0, } return ix_tbl def load_samples_file(self, ctx, vec): """Load initial parameter values from an external data file.""" if 'prior_samples' not in ctx.params['model']: return logger = logging.getLogger(__name__) samples = ctx.params['model']['prior_samples'] data_dir = Path(ctx.params['data_dir']) data_file = data_dir / samples['file'] columns = [(name, np.float) for name in samples['columns']] tbl = pypfilt.io.read_table(data_file, columns) if tbl.shape != vec[..., 0].shape: raise ValueError('Incompatible data shapes: {} and {}'.format( vec[..., 0].shape, tbl.shape)) ix_tbl = self.sample_columns() for name in samples['columns']: if name not in ix_tbl: raise ValueError('Unknown parameter {}'.format(name)) ix = ix_tbl[name] vec[..., ix] = tbl[name] # NOTE: warn if sampled values exceed the parameter bounds. min_val = np.min(tbl[name]) max_val = np.max(tbl[name]) if min_val < ctx.params['model']['param_min'][ix]: logger.warning('Sampled value for {} outside bounds' .format(name)) elif max_val > ctx.params['model']['param_max'][ix]: logger.warning('Sampled value for {} outside bounds' .format(name)) def load_lookup_tables(self, ctx, vec, init_values=False): logger = logging.getLogger(__name__) rnd = ctx.component['random']['model'] rnd_size = vec[..., 0].shape prior = ctx.params['model']['prior'] tables = ctx.component.get('lookup', {}) if 'R0' in tables and self.__R0_lookup is None: # TODO: R0_ix and R0_val self.__R0_lookup = tables['R0'] num_values = self.__R0_lookup.value_count() logger.info('Using lookup table for R0 with {} values'.format( num_values)) if init_values and num_values > 1: vec[..., self.ix_R0_ix] = rnd.integers(num_values, size=rnd_size) elif init_values: vec[..., self.ix_R0_ix] = 0 if 'Overseas Cases' in tables and self.__Overseas_lookup is None: self.__Overseas_lookup = tables['Overseas Cases'] logger.info('Using lookup table for overseas cases with {} values' .format(self.__Overseas_lookup.value_count())) if 'R0_forcing' in tables and self.__Forcing_lookup is None: self.__Forcing_lookup = tables['R0_forcing'] logger.info('Using lookup table for R0 forcing with {} values' .format(self.__Forcing_lookup.value_count())) if init_values: vec[..., self.ix_alpha] = prior['alpha'](rnd, size=rnd_size) def update(self, ctx, step_date, dt, is_fs, prev, curr): """Perform a single time-step. :param ctx: The simulation context. :param step_date: The date and time of the current time-step. :param dt: The time-step size (days). :param is_fs: Indicates whether this is a forecasting simulation. :param prev: The state before the time-step. :param curr: The state after the time-step (destructively updated). """ # Update parameters and lookup tables that are defined in self.init() # and which will not exist if we are resuming from a cached state. self.popn_size = ctx.params['model']['population_size'] self.load_lookup_tables(ctx, prev, init_values=False) # Extract each parameter. R0 = prev[..., self.ix_R0].copy() sigma = prev[..., self.ix_sigma].copy() gamma = prev[..., self.ix_gamma].copy() eta = prev[..., self.ix_eta].copy() alpha = prev[..., self.ix_alpha].copy() t0 = prev[..., self.ix_t0].copy() # if self.__R0_lookup is not None: # start = ctx.component['time'].start # forecast_with_future_R0 = False # param_name = 'forecast_with_future_R0' # if 'model' in params and param_name in params['model']: # forecast_with_future_R0 = params['model'][param_name] # if is_fs and not forecast_with_future_R0: # # NOTE: Forecasting run, only using Reff(forecast_date). # when = start # else: # when = step_date # # Retrieve R0(t) values from the lookup table. # R0_values = self.__R0_lookup.lookup(when) # R0 = R0_values[R0_ix] beta = R0 * gamma if self.__Forcing_lookup is not None: # Modulate the force of infection with temporal forcing. force = alpha * self.__Forcing_lookup.lookup(step_date)[0] # Ensure the force of infection is non-negative (can be zero). beta *= np.maximum(1.0 + force, 0) import_rate = 0 if self.__Overseas_lookup is not None: imports = self.__Overseas_lookup.lookup(step_date)[0] import_rate = imports / self.popn_size epoch = ctx.component['time'].to_scalar(ctx.params['epoch']) curr_t = ctx.component['time'].to_scalar(step_date) zero_mask = t0 > (curr_t - epoch) R0[zero_mask] = 0 sigma[zero_mask] = 0 gamma[zero_mask] = 0 eta[zero_mask] = 0 alpha[zero_mask] = 0 t0[zero_mask] = 0 beta[zero_mask] = 0 # Extract each compartment. S = prev[..., self.ix_S] E1 = prev[..., self.ix_E1] E2 = prev[..., self.ix_E2] I1 = prev[..., self.ix_I1] I2 = prev[..., self.ix_I2] # Calculate flows between compartments. s_to_e1 = dt * (beta * (I1 + I2) * S ** eta + import_rate) e1_to_e2 = dt * 2 * sigma * E1 e2_to_i1 = dt * 2 * sigma * E2 i1_to_i2 = dt * 2 * gamma * I1 i2_to_r = dt * 2 * gamma * I2 # Update the compartment values. curr[..., self.ix_S] = S - s_to_e1 curr[..., self.ix_E1] = E1 + s_to_e1 - e1_to_e2 curr[..., self.ix_E2] = E2 + e1_to_e2 - e2_to_i1 curr[..., self.ix_I1] = I1 + e2_to_i1 - i1_to_i2 curr[..., self.ix_I2] = I2 + i1_to_i2 - i2_to_r # Enforce invariants on the S, E, and I compartments. curr[..., :self.ix_R] = np.clip(curr[..., :self.ix_R], 0, 1) sum_SEI = np.sum(curr[..., :self.ix_R], axis=-1) mask_invalid = sum_SEI > 1 if np.any(mask_invalid): denom = sum_SEI[mask_invalid, None] curr[mask_invalid, :self.ix_R] = (curr[mask_invalid, :self.ix_R] / denom) # Calculate the size of the R compartment and clip appropriately. curr[..., self.ix_R] = np.clip( 1.0 - np.sum(curr[..., :self.ix_R], axis=-1), 0.0, 1.0) # Keep parameters fixed. curr[..., self.ix_R0:] = prev[..., self.ix_R0:] def pr_inf(self, prev, curr): """Calculate the likelihood of an individual becoming infected, for any number of state vectors. :param prev: The model states at the start of the observation period. :param curr: The model states at the end of the observation period. """ # Count the number of susceptible / exposed individuals at both ends # of the simulation period. prev_amt = np.sum(prev[..., :self.ix_I2], axis=-1) curr_amt = np.sum(curr[..., :self.ix_I2], axis=-1) # Avoid returning very small negative values (e.g., -1e-10). return np.maximum(prev_amt - curr_amt, 0) def is_seeded(self, hist): """Identify state vectors where infections have occurred. :param hist: A matrix of arbitrary dimensions, whose final dimension covers the model state space (i.e., has a length no smaller than that returned by :py:func:`state_size`). :type hist: numpy.ndarray :returns: A matrix of one fewer dimensions than ``hist`` that contains ``1`` for state vectors where infections have occurred and ``0`` for state vectors where they have not. :rtype: numpy.ndarray """ return np.ceil(1 - hist[..., self.ix_S]) def is_valid(self, hist): """Ignore state vectors where no infections have occurred, as their properties (such as parameter distributions) are uninformative.""" return self.is_seeded(hist) def describe(self): return self.__info def stat_info(self): """Return the details of each statistic that can be calculated by this model. Each such statistic is represented as a ``(name, stat_fn)`` pair, where ``name`` is a string that identifies the statistic and ``stat_fn`` is a function that calculates the statistic (see, e.g., :py:func:`stat_Reff`). """ return [("Reff", self.stat_Reff)] def stat_Reff(self, hist): """Calculate the effective reproduction number :math:`R_{eff}` for every particle. :param hist: The particle history matrix, or a subset thereof. """ return hist[..., self.ix_S] * hist[..., self.ix_R0]

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Python

actstream/tests/__init__.py

slated/django-activity-stream

8d38fd45b4bef1f7137fb8185b04ee0d8cdb5e3b

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2015-01-02T02:46:30.000Z

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actstream/tests/__init__.py

slated/django-activity-stream

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[ "BSD-3-Clause" ]

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2015-01-02T19:54:09.000Z

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actstream/tests/__init__.py

slated/django-activity-stream

8d38fd45b4bef1f7137fb8185b04ee0d8cdb5e3b

[ "BSD-3-Clause" ]

345

2015-01-13T01:02:42.000Z

2022-03-21T09:39:26.000Z

from actstream.tests.test_gfk import GFKManagerTestCase from actstream.tests.test_zombies import ZombieTest from actstream.tests.test_activity import ActivityTestCase from actstream.tests.test_feeds import FeedsTestCase from actstream.tests.test_views import ViewsTest

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Python

inside/Config/SystemArgs.py

kangzai228/learning-power

31767fd61353cddc6a6d51286b7115f094a10f92

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2020-12-09T08:21:04.000Z

2021-07-06T08:01:12.000Z

inside/Config/SystemArgs.py

kangzai228/learning-power

31767fd61353cddc6a6d51286b7115f094a10f92

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2020-12-10T02:35:49.000Z

2021-06-21T04:34:23.000Z

inside/Config/SystemArgs.py

kangzai228/learning-power

31767fd61353cddc6a6d51286b7115f094a10f92

[ "MIT" ]

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2020-12-09T14:09:53.000Z

2021-06-26T10:08:37.000Z

#!/usr/bin/env python3 # -*- coding:utf-8 -*- # @Author : lisztomania # @Date : 2021/1/15 # @Software : Pycharm # @Version : Python 3.8.5 # @File : SystemArgs.py # @Function : 系统参数 from inside.Template.ABC_System_Args import SYSTEM_ARGS __all__ = ['WINDOWS', 'LINUX', 'MACOS'] class WINDOWS(SYSTEM_ARGS): """Windows操作系统类""" def __init__(self): """ WINDOWS() 初始化 """ import winreg self.__winreg = winreg self.__winreg_key = self.__winreg.OpenKey( key=self.__winreg.HKEY_CURRENT_USER, sub_key=r'Software\Google\Chrome\BLBeacon') @property def System(self) -> str: """ System -> str 操作系统 :return: str """ return 'Windowns' @property def Clear(self) -> str: """ Clear -> str 清空控制台命令 :return: str """ return 'cls' @property def Driver(self) -> str: """ Driver -> str 驱动文件名称 :return: str """ return 'chromedriver.exe' @property def Driver_Chmod(self) -> str: """ Driver_Chmod -> str 添加驱动文件执行权限命令 :return: str """ return '' @property def Chrome(self) -> bool: """ Chrome -> bool 谷歌浏览器是否安装 :return: bool """ try: return isinstance(self.Chrome_Version, str) except FileNotFoundError: return False @property def Chrome_Version(self) -> str: """ Chrome_Version -> str 谷歌浏览器版本号 :return: str """ return self.__winreg.QueryValueEx( self.__winreg_key, 'version' )[0] class LINUX(SYSTEM_ARGS): """Linux操作系统类""" def __init__(self): """ LINUX() 初始化 """ import os self.__os = os @property def System(self) -> str: """ System -> str 操作系统 :return: str """ return 'Linux' @property def Clear(self) -> str: """ Clear -> str 清空控制台命令 :return: str """ return 'clear' @property def Driver(self) -> str: """ Driver -> str 驱动文件名称 :return: str """ return 'chromedriver' @property def Driver_Chmod(self) -> str: """ Driver_Chmod -> str 添加驱动文件执行权限命令 :return: str """ return 'chmod +x ' @property def Chrome(self) -> bool: """ Chrome -> bool 谷歌浏览器是否安装 :return: bool """ try: return isinstance(self.Chrome_Version, str) except IndexError: return False @property def Chrome_Version(self) -> str: """ Chrome_Version -> str 谷歌浏览器版本号 :return: str """ version = self.__os.popen(cmd='google-chrome --version').readline() return version.strip().split()[-1] class MACOS(SYSTEM_ARGS): """MacOs操作系统类""" def __init__(self): """ MACOS() 初始化 """ import os self.__os = os @property def System(self) -> str: """ System -> str 操作系统 :return: str """ return 'macOs' @property def Clear(self) -> str: """ Clear -> str 清空控制台命令 :return: str """ return 'clear' @property def Driver(self) -> str: """ Driver -> str 驱动文件名称 :return: str """ return 'chromedriver' @property def Driver_Chmod(self) -> str: """ Driver_Chmod -> str 添加驱动文件执行权限命令 :return: str """ return 'chmod +x ' @property def Chrome(self) -> bool: """ Chrome -> bool 谷歌浏览器是否安装 :return: bool """ try: return isinstance(self.Chrome_Version, str) except IndexError: return False @property def Chrome_Version(self) -> str: """ Chrome_Version -> str 谷歌浏览器版本号 :return: str """ version = self.__os.popen(cmd=r'/Applications/Google\ ' r'Chrome.app/Contents/MacOS/Google\ ' r'Chrome --version').readline() return version.strip().split()[-1]

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Python

nfv/nfv-plugins/nfv_plugins/nfvi_plugins/openstack/sysinv.py

starlingx/nfv

849c386e84911af0ac8d0df815f911347b39f8d6

[ "Apache-2.0" ]

2

2020-02-07T19:01:36.000Z

2022-02-23T01:41:46.000Z

nfv/nfv-plugins/nfv_plugins/nfvi_plugins/openstack/sysinv.py

starlingx/nfv

849c386e84911af0ac8d0df815f911347b39f8d6

[ "Apache-2.0" ]

1

2021-01-14T12:02:25.000Z

nfv/nfv-plugins/nfv_plugins/nfvi_plugins/openstack/sysinv.py

starlingx/nfv

849c386e84911af0ac8d0df815f911347b39f8d6

[ "Apache-2.0" ]

2

2021-01-13T08:39:21.000Z

2022-02-09T00:21:55.000Z

# # Copyright (c) 2015-2021 Wind River Systems, Inc. # # SPDX-License-Identifier: Apache-2.0 # import copy import json from nfv_common import debug from nfv_plugins.nfvi_plugins.openstack.objects import PLATFORM_SERVICE from nfv_plugins.nfvi_plugins.openstack.rest_api import rest_api_request DLOG = debug.debug_get_logger('nfv_plugins.nfvi_plugins.openstack.sysinv') # WARNING: Any change to this timeout must be reflected in the config.ini # file for the nfvi plugins. REST_API_REQUEST_TIMEOUT = 45 KUBE_ROOTCA_UPDATE_ENDPOINT = "/kube_rootca_update" KUBE_ROOTCA_UPDATE_GENERATE_CERT_ENDPOINT = \ KUBE_ROOTCA_UPDATE_ENDPOINT + "/generate_cert" KUBE_ROOTCA_UPDATE_PODS_ENDPOINT = KUBE_ROOTCA_UPDATE_ENDPOINT + "/pods" KUBE_ROOTCA_UPDATE_HOSTS_ENDPOINT = KUBE_ROOTCA_UPDATE_ENDPOINT + "/hosts" KUBE_ROOTCA_UPDATE_UPLOAD_CERT_ENDPOINT = \ KUBE_ROOTCA_UPDATE_ENDPOINT + "/upload_cert" # todo(abailey): refactor _api_get, etc.. into rest_api.py def _api_cmd(token, endpoint): url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + endpoint return api_cmd def _api_cmd_headers(): api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" return api_cmd_headers def _api_delete(token, endpoint): """ Generic DELETE for a sysinv endpoint """ api_cmd = _api_cmd(token, endpoint) api_cmd_headers = _api_cmd_headers() response = rest_api_request(token, "DELETE", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def _api_get(token, endpoint): """ Perform a generic GET for a particular sysinv API endpoint """ api_cmd = _api_cmd(token, endpoint) response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def _api_patch_dict(token, endpoint, patch_dict): """ Generic PATCH for a sysinv endpoint that passes a json dict Some endpoints expect a dict, while others expect a list """ api_cmd = _api_cmd(token, endpoint) api_cmd_headers = _api_cmd_headers() return rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(patch_dict), timeout_in_secs=REST_API_REQUEST_TIMEOUT) def _api_post(token, endpoint, api_cmd_payload): """ Generic POST to a sysinv endpoint with a payload """ api_cmd = _api_cmd(token, endpoint) api_cmd_headers = _api_cmd_headers() response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_datanetworks(token, host_uuid): """ Get all data networks on a host. """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/" + host_uuid + "/interface_datanetworks" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" response = rest_api_request(token, "GET", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) result_data = response.result_data['interface_datanetworks'] return result_data def get_system_info(token): """ Asks System Inventory for information about the system, such as the name of the system """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/isystems" response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_hosts(token): """ Asks System Inventory for a list of hosts """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts" response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_host(token, host_uuid): """ Asks System Inventory for a host details """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_host_labels(token, host_uuid): """ Asks System Inventory for host label details """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/labels" % host_uuid response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_kube_host_upgrades(token): """ Asks System Inventory for information about the kube host upgrades """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_host_upgrades" response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_kube_rootca_update(token): """ Asks System Inventory for information about the kube rootca update """ return _api_get(token, KUBE_ROOTCA_UPDATE_ENDPOINT) def get_kube_rootca_host_update_list(token): """ Asks System Inventory for information about the kube rootca host updates """ return _api_get(token, KUBE_ROOTCA_UPDATE_HOSTS_ENDPOINT) def get_kube_upgrade(token): """ Asks System Inventory for information about the kube upgrade """ # todo(abailey): refactor using _api_get url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_upgrade" response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_kube_version(token, kube_version): """ Asks System Inventory for information a kube version """ # todo(abailey): refactor using _api_get url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_versions/" + kube_version response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_kube_versions(token): """ Asks System Inventory for information about the kube versions """ # todo(abailey): refactor using _api_get url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_versions" response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def kube_rootca_update_start(token, force=False, alarm_ignore_list=None): """ Ask System Inventory to start a kube rootca update """ api_cmd_payload = dict() api_cmd_payload['force'] = force if alarm_ignore_list is not None: api_cmd_payload['alarm_ignore_list'] = copy.copy(alarm_ignore_list) return _api_post(token, KUBE_ROOTCA_UPDATE_ENDPOINT, api_cmd_payload) def kube_rootca_update_generate_cert(token, expiry_date=None, subject=None): """ Ask System Inventory to kube rootca update generate a cert """ api_cmd_payload = dict() # even if these values are None, they need to be passed to sysinv API api_cmd_payload['expiry_date'] = expiry_date api_cmd_payload['subject'] = subject return _api_post(token, KUBE_ROOTCA_UPDATE_GENERATE_CERT_ENDPOINT, api_cmd_payload) def kube_rootca_update_upload_cert(token, cert_file): """ Ask System Inventory to kube rootca update upload a cert file This uses POST for a file, which urllib does not work well with. """ api_cmd = _api_cmd(token, KUBE_ROOTCA_UPDATE_UPLOAD_CERT_ENDPOINT) api_cmd_headers = _api_cmd_headers() api_cmd_payload = dict() # The API is expecting requests.post formatted data with open(cert_file, "rb") as cert_file_handle: # file handle automatically closed once this request is sent response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT, file_to_post=cert_file_handle) return response def kube_rootca_update_complete(token): """ Ask System Inventory to kube rootca update complete """ api_cmd_payload = list() state_data = dict() state_data['path'] = "/state" state_data['value'] = 'update-completed' state_data['op'] = "replace" api_cmd_payload.append(state_data) return _api_patch_dict(token, KUBE_ROOTCA_UPDATE_ENDPOINT + "?force=True", api_cmd_payload) def kube_rootca_update_host(token, host_uuid, phase): """ Utility method to post to kube rootca update pods endpoint phase Valid phase values are: [trust-both-cas, trust-new-ca, update-certs] """ api_cmd = "/ihosts/%s/kube_update_ca " % host_uuid api_cmd_payload = dict() api_cmd_payload['phase'] = phase return _api_post(token, api_cmd, api_cmd_payload) def kube_rootca_update_pods(token, phase): """ Utility method to post to kube rootca update pods endpoint phase Valid phase values are: [trust-both-cas, trust-new-ca] """ api_cmd_payload = dict() api_cmd_payload['phase'] = phase return _api_post(token, KUBE_ROOTCA_UPDATE_PODS_ENDPOINT, api_cmd_payload) def kube_upgrade_start(token, to_version, force=False, alarm_ignore_list=None): """ Ask System Inventory to start a kube upgrade """ # todo(abailey): refactor using _post_api_request url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_upgrade" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() api_cmd_payload['to_version'] = to_version api_cmd_payload['force'] = force if alarm_ignore_list is not None: api_cmd_payload['alarm_ignore_list'] = copy.copy(alarm_ignore_list) response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def _patch_kube_upgrade_state(token, new_value): url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_upgrade" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = list() host_data = dict() host_data['path'] = "/state" host_data['value'] = new_value host_data['op'] = "replace" api_cmd_payload.append(host_data) return rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) def kube_upgrade_cleanup(token): """ Ask System Inventory to delete the kube upgrade """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/kube_upgrade" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" response = rest_api_request(token, "DELETE", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def kube_upgrade_complete(token): """ Ask System Inventory to kube upgrade complete """ return _patch_kube_upgrade_state(token, "upgrade-complete") def kube_upgrade_download_images(token): """ Ask System Inventory to kube upgrade download images """ return _patch_kube_upgrade_state(token, "downloading-images") def kube_upgrade_networking(token): """ Ask System Inventory to kube upgrade networking """ return _patch_kube_upgrade_state(token, "upgrading-networking") def _kube_host_upgrade(token, host_uuid, target_operation, force): """ Invoke a POST for a host kube-upgrade operation target_operation one of: kube_upgrade_control_plane, kube_upgrade_kubelet force is a 'string' """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/%s" % (host_uuid, target_operation) api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() api_cmd_payload['force'] = force response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def kube_host_upgrade_control_plane(token, host_uuid, force="true"): """ Ask System Inventory to kube HOST upgrade control plane """ return _kube_host_upgrade(token, host_uuid, "kube_upgrade_control_plane", force) def kube_host_upgrade_kubelet(token, host_uuid, force="true"): """ Ask System Inventory to kube HOST upgrade kubelet """ return _kube_host_upgrade(token, host_uuid, "kube_upgrade_kubelet", force) def get_upgrade(token): """ Asks System Inventory for information about the upgrade """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/upgrade" response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def upgrade_start(token): """ Ask System Inventory to start an upgrade """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/upgrade" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() api_cmd_payload['force'] = "false" response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def upgrade_activate(token): """ Ask System Inventory to activate an upgrade """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/upgrade" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" host_data = dict() host_data['path'] = "/state" host_data['value'] = "activation-requested" host_data['op'] = "replace" api_cmd_payload = list() api_cmd_payload.append(host_data) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def upgrade_complete(token): """ Ask System Inventory to complete an upgrade """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/upgrade" api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" response = rest_api_request(token, "DELETE", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_host_lvgs(token, host_uuid): """ Asks System Inventory for a list logical volume groups for a host """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/ilvgs" % host_uuid response = rest_api_request(token, "GET", api_cmd, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def notify_host_services_enabled(token, host_uuid): """ Notify System Inventory that host services are enabled """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() api_cmd_payload['path'] = '/action' api_cmd_payload['value'] = 'services-enabled' api_cmd_payload['op'] = 'replace' api_cmd_list = list() api_cmd_list.append(api_cmd_payload) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_list), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def notify_host_services_disabled(token, host_uuid): """ Notify System Inventory that host services are disabled """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() api_cmd_payload['path'] = '/action' api_cmd_payload['value'] = 'services-disabled' api_cmd_payload['op'] = 'replace' api_cmd_list = list() api_cmd_list.append(api_cmd_payload) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_list), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def notify_host_services_disable_extend(token, host_uuid): """ Notify System Inventory that host services disable needs to be extended """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload_action = dict() api_cmd_payload_action['path'] = '/action' api_cmd_payload_action['value'] = 'services-disable-extend' api_cmd_payload_action['op'] = 'replace' api_cmd_list = list() api_cmd_list.append(api_cmd_payload_action) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_list), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def notify_host_services_disable_failed(token, host_uuid, reason): """ Notify System Inventory that host services disable failed """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload_action = dict() api_cmd_payload_action['path'] = '/action' api_cmd_payload_action['value'] = 'services-disable-failed' api_cmd_payload_action['op'] = 'replace' api_cmd_payload_reason = dict() api_cmd_payload_reason['path'] = '/vim_progress_status' api_cmd_payload_reason['value'] = str(reason) api_cmd_payload_reason['op'] = 'replace' api_cmd_list = list() api_cmd_list.append(api_cmd_payload_action) api_cmd_list.append(api_cmd_payload_reason) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_list), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def notify_host_services_deleted(token, host_uuid): """ Notify System Inventory that host services have been deleted """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" response = rest_api_request(token, "DELETE", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def notify_host_services_delete_failed(token, host_uuid, reason): """ Notify System Inventory that host services delete failed """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload_action = dict() api_cmd_payload_action['path'] = '/action' api_cmd_payload_action['value'] = 'services-delete-failed' api_cmd_payload_action['op'] = 'replace' api_cmd_payload_reason = dict() api_cmd_payload_reason['path'] = '/vim_progress_status' api_cmd_payload_reason['value'] = str(reason) api_cmd_payload_reason['op'] = 'replace' api_cmd_list = list() api_cmd_list.append(api_cmd_payload_action) api_cmd_list.append(api_cmd_payload_reason) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_list), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def lock_host(token, host_uuid): """ Ask System Inventory to lock a host """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" host_data = dict() host_data['path'] = "/action" host_data['value'] = "lock" host_data['op'] = "replace" api_cmd_payload = list() api_cmd_payload.append(host_data) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def unlock_host(token, host_uuid): """ Ask System Inventory to unlock a host """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" host_data = dict() host_data['path'] = "/action" host_data['value'] = "unlock" host_data['op'] = "replace" api_cmd_payload = list() api_cmd_payload.append(host_data) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def reboot_host(token, host_uuid): """ Ask System Inventory to reboot a host """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" host_data = dict() host_data['path'] = "/action" host_data['value'] = "reboot" host_data['op'] = "replace" api_cmd_payload = list() api_cmd_payload.append(host_data) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def upgrade_host(token, host_uuid): """ Ask System Inventory to upgrade a host """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/upgrade" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() api_cmd_payload['force'] = "false" response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def swact_from_host(token, host_uuid): """ Ask System Inventory to swact from a host """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" host_data = dict() host_data['path'] = "/action" host_data['value'] = "swact" host_data['op'] = "replace" api_cmd_payload = list() api_cmd_payload.append(host_data) response = rest_api_request(token, "PATCH", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_host_devices(token, host_uuid): """ Asks System Inventory for host device details """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/pci_devices" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" response = rest_api_request(token, "GET", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def get_host_device(token, device_uuid): """ Asks System Inventory for host details for specific device """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/pci_devices/%s" % device_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" response = rest_api_request(token, "GET", api_cmd, api_cmd_headers, timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def host_device_image_update(token, host_uuid): """ Asks System Inventory to start a host device image update """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/device_image_update" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response def host_device_image_update_abort(token, host_uuid): """ Asks System Inventory to abort a host device image update """ url = token.get_service_url(PLATFORM_SERVICE.SYSINV) if url is None: raise ValueError("OpenStack SysInv URL is invalid") api_cmd = url + "/ihosts/%s/device_image_update_abort" % host_uuid api_cmd_headers = dict() api_cmd_headers['Content-Type'] = "application/json" api_cmd_headers['User-Agent'] = "vim/1.0" api_cmd_payload = dict() response = rest_api_request(token, "POST", api_cmd, api_cmd_headers, json.dumps(api_cmd_payload), timeout_in_secs=REST_API_REQUEST_TIMEOUT) return response

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Python

beacon/__init__.py

aaronbieber/weather-beacon

45abb888fb519bacecd7fa21e69349d86cbc6185

[ "WTFPL" ]

1

2019-12-29T01:54:27.000Z

beacon/__init__.py

aaronbieber/weather-beacon

45abb888fb519bacecd7fa21e69349d86cbc6185

[ "WTFPL" ]

5

2021-06-01T21:47:26.000Z

2022-03-11T23:14:31.000Z

beacon/__init__.py

aaronbieber/weather-beacon

45abb888fb519bacecd7fa21e69349d86cbc6185

[ "WTFPL" ]

null

from . import lcd from . import weather

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py

Python

tests/data_gen.py

imi-bigpicture/wsidicom

4e9fb87f56766883397e31685a5e16c8f0941915

[ "Apache-2.0" ]

7

2021-11-30T08:33:19.000Z

2022-03-15T13:46:01.000Z

tests/data_gen.py

imi-bigpicture/wsidicom

4e9fb87f56766883397e31685a5e16c8f0941915

[ "Apache-2.0" ]

3

2021-12-15T14:39:15.000Z

2022-03-24T15:33:08.000Z

tests/data_gen.py

imi-bigpicture/wsidicom

4e9fb87f56766883397e31685a5e16c8f0941915

[ "Apache-2.0" ]

4

2022-02-04T09:57:49.000Z

2022-02-14T08:13:13.000Z

# Copyright 2021 SECTRA AB # # 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. # type: ignore from pydicom.dataset import Dataset, FileMetaDataset from pydicom.sequence import Sequence from pydicom.uid import generate_uid from pydicom.encaps import encapsulate from pathlib import Path def create_main_dataset() -> Dataset: # Main data elements ds = Dataset() ds.ImageType = ['DERIVED', 'PRIMARY', 'VOLUME', 'RESAMPLED'] ds.SOPClassUID = '1.2.840.10008.5.1.4.1.1.77.1.6' ds.SOPInstanceUID = generate_uid() ds.StudyDate = '' ds.ContentDate = '20210210' ds.AcquisitionDateTime = '' ds.StudyTime = '' ds.ContentTime = '142341.188035' ds.AccessionNumber = '?' ds.Modality = 'SM' ds.Manufacturer = '?' ds.ReferringPhysicianName = '' ds.TimezoneOffsetFromUTC = '+0100' ds.ManufacturerModelName = '?' ds.VolumetricProperties = 'VOLUME' ds.PatientName = '' ds.PatientID = '' ds.PatientBirthDate = '' ds.PatientSex = '' ds.DeviceSerialNumber = '?' ds.SoftwareVersions = ['?'] ds.AcquisitionDuration = 50.0 ds.StudyInstanceUID = generate_uid() ds.SeriesInstanceUID = generate_uid() ds.StudyID = '' ds.SeriesNumber = None ds.InstanceNumber = "1" ds.PatientOrientation = '' ds.FrameOfReferenceUID = generate_uid() ds.PositionReferenceIndicator = 'SLIDE_CORNER' # Dimension Organization Sequence dimension_organization_sequence = Sequence() ds.DimensionOrganizationSequence = dimension_organization_sequence dimensional_organization_uid = generate_uid() # Dimension Organization Sequence: Dimension Organization 1 dimension_organization1 = Dataset() dimension_organization1.DimensionOrganizationUID = dimensional_organization_uid dimension_organization_sequence.append(dimension_organization1) # Dimension Index Sequence dimension_index_sequence = Sequence() ds.DimensionIndexSequence = dimension_index_sequence # Dimension Index Sequence: Dimension Index 1 dimension_index1 = Dataset() dimension_index1.DimensionOrganizationUID = dimensional_organization_uid dimension_index1.DimensionIndexPointer = (0x48, 0x21a) dimension_index_sequence.append(dimension_index1) ds.SamplesPerPixel = 3 ds.PhotometricInterpretation = 'YBR_FULL_422' ds.PlanarConfiguration = 0 ds.NumberOfFrames = "1" ds.Rows = 1024 ds.Columns = 1024 ds.BitsAllocated = 8 ds.BitsStored = 8 ds.HighBit = 7 ds.PixelRepresentation = 0 ds.BurnedInAnnotation = 'NO' ds.LossyImageCompression = '01' ds.LossyImageCompressionRatio = "100.0" ds.LossyImageCompressionMethod = 'ISO_10918_1' ds.ContainerIdentifier = '?' # Issuer of the Container Identifier Sequence issuer_of_the_container_identifier_sequence = Sequence() ds.IssuerOfTheContainerIdentifierSequence = issuer_of_the_container_identifier_sequence # Container Type Code Sequence container_type_code_sequence = Sequence() ds.ContainerTypeCodeSequence = container_type_code_sequence # Acquisition Context Sequence acquisition_context_sequence = Sequence() ds.AcquisitionContextSequence = acquisition_context_sequence # Specimen Description Sequence specimen_description_sequence = Sequence() ds.SpecimenDescriptionSequence = specimen_description_sequence # Specimen Description Sequence: Specimen Description 1 specimen_description1 = Dataset() specimen_description1.SpecimenIdentifier = '?' specimen_description1.SpecimenUID = generate_uid() # Issuer of the Specimen Identifier Sequence issuer_of_the_specimen_identifier_sequence = Sequence() specimen_description1.IssuerOfTheSpecimenIdentifierSequence = issuer_of_the_specimen_identifier_sequence # Specimen Preparation Sequence specimen_preparation_sequence = Sequence() specimen_description1.SpecimenPreparationSequence = specimen_preparation_sequence specimen_description_sequence.append(specimen_description1) ds.ImagedVolumeWidth = 20.0 ds.ImagedVolumeHeight = 36.0 ds.ImagedVolumeDepth = 0.60 ds.TotalPixelMatrixColumns = 154 ds.TotalPixelMatrixRows = 290 # Total Pixel Matrix Origin Sequence total_pixel_matrix_origin_sequence = Sequence() ds.TotalPixelMatrixOriginSequence = total_pixel_matrix_origin_sequence # Total Pixel Matrix Origin Sequence: Total Pixel Matrix Origin 1 total_pixel_matrix_origin1 = Dataset() total_pixel_matrix_origin1.XOffsetInSlideCoordinateSystem = "60.0" total_pixel_matrix_origin1.YOffsetInSlideCoordinateSystem = "10.0" total_pixel_matrix_origin_sequence.append(total_pixel_matrix_origin1) ds.SpecimenLabelInImage = 'NO' ds.FocusMethod = 'AUTO' ds.ExtendedDepthOfField = 'NO' ds.ImageOrientationSlide = [-1, 0, 0, 0, -1, 0] # Optical Path Sequence optical_path_sequence = Sequence() ds.OpticalPathSequence = optical_path_sequence # Optical Path Sequence: Optical Path 1 optical_path1 = Dataset() # Illumination Type Code Sequence illumination_type_code_sequence = Sequence() optical_path1.IlluminationTypeCodeSequence = illumination_type_code_sequence # Illumination Type Code Sequence: Illumination Type Code 1 illumination_type_code1 = Dataset() illumination_type_code1.CodeValue = '111744' illumination_type_code1.CodingSchemeDesignator = 'DCM' illumination_type_code1.CodeMeaning = 'Brightfield illumination' illumination_type_code_sequence.append(illumination_type_code1) optical_path1.OpticalPathIdentifier = '0' # Illumination Color Code Sequence illumination_color_code_sequence = Sequence() optical_path1.IlluminationColorCodeSequence = illumination_color_code_sequence # Illumination Color Code Sequence: Illumination Color Code 1 illumination_color_code1 = Dataset() illumination_color_code1.CodeValue = 'R-102C0' illumination_color_code1.CodingSchemeDesignator = 'SRT' illumination_color_code1.CodeMeaning = 'Full Spectrum' illumination_color_code_sequence.append(illumination_color_code1) optical_path_sequence.append(optical_path1) ds.NumberOfOpticalPaths = 1 ds.TotalPixelMatrixFocalPlanes = 1 ds.LabelText = '' ds.BarcodeValue = '' # Shared Functional Groups Sequence shared_functional_groups_sequence = Sequence() ds.SharedFunctionalGroupsSequence = shared_functional_groups_sequence # Shared Functional Groups Sequence: Shared Functional Groups 1 shared_functional_groups1 = Dataset() # Pixel Measures Sequence pixel_measures_sequence = Sequence() shared_functional_groups1.PixelMeasuresSequence = pixel_measures_sequence # Pixel Measures Sequence: Pixel Measures 1 pixel_measures1 = Dataset() pixel_measures1.SliceThickness = "0.0006" pixel_measures1.SpacingBetweenSlices = "0.0006" pixel_measures1.PixelSpacing = [0.1242353, 0.1241778] pixel_measures_sequence.append(pixel_measures1) # Whole Slide Microscopy Image Frame Type Sequence whole_slide_microscopy_image_frame_type_sequence = Sequence() shared_functional_groups1.WholeSlideMicroscopyImageFrameTypeSequence = whole_slide_microscopy_image_frame_type_sequence # Whole Slide Microscopy Image Frame Type Sequence: Whole Slide Microscopy Image Frame Type 1 whole_slide_microscopy_image_frame_type1 = Dataset() whole_slide_microscopy_image_frame_type1.FrameType = ['DERIVED', 'PRIMARY', 'VOLUME', 'RESAMPLED'] whole_slide_microscopy_image_frame_type_sequence.append(whole_slide_microscopy_image_frame_type1) # Optical Path Identification Sequence optical_path_identification_sequence = Sequence() shared_functional_groups1.OpticalPathIdentificationSequence = optical_path_identification_sequence # Optical Path Identification Sequence: Optical Path Identification 1 optical_path_identification1 = Dataset() optical_path_identification1.OpticalPathIdentifier = '0' optical_path_identification_sequence.append(optical_path_identification1) shared_functional_groups_sequence.append(shared_functional_groups1) # Per-frame Functional Groups Sequence per_frame_functional_groups_sequence = Sequence() ds.PerFrameFunctionalGroupsSequence = per_frame_functional_groups_sequence # Per-frame Functional Groups Sequence: Per-frame Functional Groups 1 per_frame_functional_groups1 = Dataset() # Plane Position (Slide) Sequence plane_position_slide_sequence = Sequence() per_frame_functional_groups1.PlanePositionSlideSequence = plane_position_slide_sequence # Plane Position (Slide) Sequence: Plane Position (Slide) 1 plane_position_slide1 = Dataset() plane_position_slide1.XOffsetInSlideCoordinateSystem = "62.0" plane_position_slide1.YOffsetInSlideCoordinateSystem = "10.0" plane_position_slide1.ZOffsetInSlideCoordinateSystem = "0.0" plane_position_slide1.ColumnPositionInTotalImagePixelMatrix = 1 plane_position_slide1.RowPositionInTotalImagePixelMatrix = 1 plane_position_slide_sequence.append(plane_position_slide1) per_frame_functional_groups_sequence.append(per_frame_functional_groups1) frame = b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x00\x00\x01\x00\x01\x00\x00\xff\xdb\x00C\x00\x08\x06\x06\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f\x14\x1d\x1a\x1f\x1e\x1d\x1a\x1c\x1c $.\' 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ds.PixelData = encapsulate([frame]) return ds def create_meta_dataset() -> FileMetaDataset: # File meta info data elements file_meta = FileMetaDataset() file_meta.FileMetaInformationGroupLength = 204 file_meta.FileMetaInformationVersion = b'\x00\x01' file_meta.MediaStorageSOPClassUID = '1.2.840.10008.5.1.4.1.1.77.1.6' file_meta.MediaStorageSOPInstanceUID = generate_uid() file_meta.TransferSyntaxUID = '1.2.840.10008.1.2.4.50' file_meta.ImplementationClassUID = '1.2.826.0.1.3680043.8.498.1' file_meta.ImplementationVersionName = 'PYDICOM 2.2.2]' return file_meta def create_layer_file(filename: Path): file_meta = create_meta_dataset() ds = create_main_dataset() ds.file_meta = file_meta ds.is_implicit_VR = False ds.is_little_endian = True ds.save_as(filename, write_like_original=False)

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eabf194d7de98b3b9575c1c87992b536032e7d9a

15,104

py

Python

test/terra/reference/ref_conditionals.py

vm6502q/qiskit-qrack-provider

db082a0d196782b00cca484c61412775d70b9e0d

[ "Apache-2.0" ]

1

2020-05-26T15:10:50.000Z

test/terra/reference/ref_conditionals.py

vm6502q/qiskit-qrack-provider

db082a0d196782b00cca484c61412775d70b9e0d

[ "Apache-2.0" ]

3

2019-11-23T18:24:16.000Z

2020-01-13T00:30:58.000Z

test/terra/reference/ref_conditionals.py

vm6502q/qiskit-qrack-provider

db082a0d196782b00cca484c61412775d70b9e0d

[ "Apache-2.0" ]

null

# This code is part of Qiskit. # # (C) Copyright IBM 2018, 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """ Test circuits and reference outputs for conditional gates. """ import numpy as np from qiskit import QuantumRegister, ClassicalRegister, QuantumCircuit from qiskit.circuit import Instruction def add_conditional_x(circuit, qreg, creg, val, conditional_type): """Add a conditional instruction to a circuit. Args: circuit (QuantumCircuit): circuit to add instruction to. qreg (QuantumRegister): qubit to apply conditional X to creg (ClassicalRegister): classical reg to condition on val (int): Classical reg value to condition on. conditional_type (string): instruction type to add conditional X as. Conditional type can be 'gate', 'unitary', 'kraus', 'superop' and will apply a conditional X-gate in that representation """ # X-gate matrix x_mat = np.array([[0, 1], [1, 0]], dtype=complex) x_superop = Instruction('superop', 1, 0, [np.kron(x_mat, x_mat)]) x_kraus = Instruction('kraus', 1, 0, [x_mat]) if conditional_type == 'unitary': circuit.unitary(x_mat, [qreg]).c_if(creg, val) elif conditional_type == 'kraus': circuit.append(x_kraus, [qreg]).c_if(creg, val) elif conditional_type == 'superop': circuit.append(x_superop, [qreg]).c_if(creg, val) else: circuit.x(qreg).c_if(creg, val) # ========================================================================== # Conditionals on 1-bit register # ========================================================================== def conditional_circuits_1bit(final_measure=True, conditional_type='gate'): """Conditional gates on single bit classical register.""" circuits = [] qr = QuantumRegister(1) cond = ClassicalRegister(1, 'cond') if final_measure: cr = ClassicalRegister(1, 'meas') regs = (qr, cr, cond) else: regs = (qr, cond) # Conditional on 0 (cond = 0) circuit = QuantumCircuit(*regs) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 0, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 0 (cond = 1) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 0, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 1 (cond = 0) circuit = QuantumCircuit(*regs) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 1, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 1 (cond = 1) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 1, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) return circuits def conditional_counts_1bit(shots, hex_counts=True): """Conditional circuits reference counts.""" targets = [] if hex_counts: # Conditional on 0 (cond = 0), result "0 1" targets.append({'0x1': shots}) # Conditional on 0 (cond = 1), result "1 0" targets.append({'0x2': shots}) # Conditional on 1 (cond = 0), # result "0 0" targets.append({'0x0': shots}) # Conditional on 1 (cond = 1), # result "1 1" targets.append({'0x3': shots}) else: # Conditional on 0 (cond = 0), result "0 1" targets.append({'0 1': shots}) # Conditional on 0 (cond = 1), result "1 0" targets.append({'1 0': shots}) # Conditional on 1 (cond = 0), # result "0 0" targets.append({'0 0': shots}) # Conditional on 1 (cond = 1), # result "1 1" targets.append({'1 1': shots}) return targets def conditional_statevector_1bit(): """Conditional circuits reference statevector.""" targets = [] # Conditional on 0 (cond = 0) targets.append(np.array([0, 1])) # Conditional on 0 (cond = 1) targets.append(np.array([1, 0])) # Conditional on 1 (cond = 0) targets.append(np.array([1, 0])) # Conditional on 1 (cond = 1) targets.append(np.array([0, 1])) return targets # ========================================================================== # Conditionals on 2-bit register # ========================================================================== def conditional_circuits_2bit(final_measure=True, conditional_type='gate'): """Conditional test circuits on 2-bit classical register.""" circuits = [] qr = QuantumRegister(1) cond = ClassicalRegister(2, 'cond') if final_measure: cr = ClassicalRegister(1, 'meas') regs = (qr, cr, cond) else: regs = (qr, cond) # Conditional on 00 (cr = 00) circuit = QuantumCircuit(*regs) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 0, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 00 (cr = 01) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 0, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 00 (cr = 10) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 0, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 00 (cr = 11) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 0, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 01 (cr = 00) circuit = QuantumCircuit(*regs) add_conditional_x(circuit, qr[0], cond, 1, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 01 (cr = 01) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 1, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 01 (cr = 10) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 1, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 01 (cr = 11) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 1, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 10 (cr = 00) circuit = QuantumCircuit(*regs) circuit.x(qr).c_if(cond, 2) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 10 (cr = 01) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 2, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 10 (cr = 10) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 2, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 10 (cr = 11) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 2, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 11 (cr = 00) circuit = QuantumCircuit(*regs) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 3, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 11 (cr = 01) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 3, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 11 (cr = 10) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 3, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) # Conditional on 11 (cr = 11) circuit = QuantumCircuit(*regs) circuit.x(qr) circuit.measure(qr[0], cond[0]) circuit.measure(qr[0], cond[1]) circuit.x(qr) circuit.barrier(qr) add_conditional_x(circuit, qr[0], cond, 3, conditional_type) if final_measure: circuit.barrier(qr) circuit.measure(qr, cr) circuits.append(circuit) return circuits def conditional_counts_2bit(shots, hex_counts=True): """2-bit conditional circuits reference counts.""" targets = [] if hex_counts: # Conditional on 00 (cr = 00), result "00 1" targets.append({'0x1': shots}) # Conditional on 00 (cr = 01), result "01 0" targets.append({'0x2': shots}) # Conditional on 00 (cr = 10), result "10 0" targets.append({'0x4': shots}) # Conditional on 00 (cr = 11), result "11 0" targets.append({'0x6': shots}) # Conditional on 01 (cr = 00), result "00 0" targets.append({'0x0': shots}) # Conditional on 01 (cr = 01), result "01 1" targets.append({'0x3': shots}) # Conditional on 01 (cr = 10), result "10 0" targets.append({'0x4': shots}) # Conditional on 01 (cr = 11), result "11 0" targets.append({'0x6': shots}) # Conditional on 10 (cr = 00), result "00 0" targets.append({'0x0': shots}) # Conditional on 10 (cr = 01), result "01 0" targets.append({'0x2': shots}) # Conditional on 10 (cr = 10), result "10 1" targets.append({'0x5': shots}) # Conditional on 10 (cr = 11), result "11 0" targets.append({'0x6': shots}) # Conditional on 11 (cr = 00), result "00 0" targets.append({'0x0': shots}) # Conditional on 11 (cr = 01), result "01 0" targets.append({'0x2': shots}) # Conditional on 11 (cr = 10), result "10 0" targets.append({'0x4': shots}) # Conditional on 11 (cr = 11), result "11 1" targets.append({'0x7': shots}) else: # Conditional on 00 (cr = 00), result "00 1" targets.append({'00 1': shots}) # Conditional on 00 (cr = 01), result "01 0" targets.append({'01 0': shots}) # Conditional on 00 (cr = 10), result "10 0" targets.append({'10 0': shots}) # Conditional on 00 (cr = 11), result "11 0" targets.append({'11 0': shots}) # Conditional on 01 (cr = 00), result "00 0" targets.append({'00 0': shots}) # Conditional on 01 (cr = 01), result "01 1" targets.append({'01 1': shots}) # Conditional on 01 (cr = 10), result "10 0" targets.append({'10 0': shots}) # Conditional on 01 (cr = 11), result "11 0" targets.append({'11 0': shots}) # Conditional on 10 (cr = 00), result "00 0" targets.append({'00 0': shots}) # Conditional on 10 (cr = 01), result "01 0" targets.append({'01 0': shots}) # Conditional on 10 (cr = 10), result "10 1" targets.append({'10 0': shots}) # Conditional on 10 (cr = 11), result "11 0" targets.append({'11 0': shots}) # Conditional on 11 (cr = 00), result "00 0" targets.append({'00 0': shots}) # Conditional on 11 (cr = 01), result "01 0" targets.append({'01 0': shots}) # Conditional on 11 (cr = 10), result "10 0" targets.append({'10 0': shots}) # Conditional on 11 (cr = 11), result "11 1" targets.append({'11 1': shots}) return targets def conditional_statevector_2bit(): """2-bit conditional circuits reference statevector.""" state_0 = np.array([1, 0]) state_1 = np.array([0, 1]) targets = [] # Conditional on 00 (cr = 00) targets.append(state_1) # Conditional on 00 (cr = 01) targets.append(state_0) # Conditional on 00 (cr = 10) targets.append(state_0) # Conditional on 00 (cr = 11) targets.append(state_0) # Conditional on 01 (cr = 00) targets.append(state_0) # Conditional on 01 (cr = 01) targets.append(state_1) # Conditional on 01 (cr = 10) targets.append(state_0) # Conditional on 01 (cr = 11) targets.append(state_0) # Conditional on 10 (cr = 00) targets.append(state_0) # Conditional on 10 (cr = 01) targets.append(state_0) # Conditional on 10 (cr = 10) targets.append(state_1) # Conditional on 10 (cr = 11) targets.append(state_0) # Conditional on 11 (cr = 00) targets.append(state_0) # Conditional on 11 (cr = 01) targets.append(state_0) # Conditional on 11 (cr = 10) targets.append(state_0) # Conditional on 11 (cr = 11) targets.append(state_1) return targets

32.906318

77

0.606528

1,984

15,104

4.547883

0.073589

0.115261

0.067383

0.067827

0.841184

0.813588

0.792419

0.770808

0.712291

0.69744

0

0.055965

0.245233

15,104

458

78

32.978166

0.735526

0.299722

0

0.885522

0

0.01838

0

0.005774

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1

0.023569

false

0

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0

null

0

1

0

1

0

null

0

7

ead97d93a8163d2e6bca56594cbcda34ca6a35fc

3,468

py

Python

tests/test_01_stash_restore.py

RKrahl/git-attic

fcce4ed48ae216818aed52c7ce38f1605a34e612

[ "Apache-2.0" ]

null

tests/test_01_stash_restore.py

RKrahl/git-attic

fcce4ed48ae216818aed52c7ce38f1605a34e612

[ "Apache-2.0" ]

3

2021-03-30T20:36:50.000Z

2021-04-10T19:47:17.000Z

tests/test_01_stash_restore.py

RKrahl/git-attic

fcce4ed48ae216818aed52c7ce38f1605a34e612

[ "Apache-2.0" ]

null

"""Test list, stash and restore subcommands of git-attic. """ import pytest from conftest import * def test_stash_and_restore_simple(monkeypatch, gitrepo): """Stash and restore in the most simple case. """ monkeypatch.chdir(gitrepo) assert_refs(git_attic(("list", "-v")), ()) assert_refs(git_branches(), get_test_branches()) git_attic(("stash", "hawaii")) assert_refs(git_attic(("list", "-v")), get_test_branches(("hawaii",))) assert_refs(git_branches(), get_test_branches(("marinara", "master"))) git_attic(("stash", "marinara")) assert_refs(git_attic(("list", "-v")), get_test_branches(("hawaii", "marinara"))) assert_refs(git_branches(), get_test_branches(("master",))) assert set(git_attic(("list",)).stdout.split()) == {"hawaii", "marinara"} git_attic(("restore", "marinara")) assert_refs(git_attic(("list", "-v")), get_test_branches(("hawaii", "marinara"))) assert_refs(git_branches(), get_test_branches(("marinara", "master"))) def test_stash_and_restore_prefix(monkeypatch, gitrepo): """Use an alternative prefix. """ monkeypatch.chdir(gitrepo) assert_refs(git_attic(("list", "-v")), ()) assert_refs(git_branches(), get_test_branches()) git_attic(("--prefix", "archive", "stash", "hawaii")) assert_refs(git_attic(("--prefix", "archive", "list", "-v")), get_test_branches(("hawaii",))) assert_refs(git_attic(("list", "-v")), ()) assert_refs(git_branches(), get_test_branches(("marinara", "master"))) git_attic(("--prefix", "shed", "stash", "marinara")) assert_refs(git_attic(("--prefix", "shed", "list", "-v")), get_test_branches(("marinara",))) assert_refs(git_attic(("--prefix", "archive", "list", "-v")), get_test_branches(("hawaii",))) assert_refs(git_attic(("list", "-v")), ()) assert_refs(git_branches(), get_test_branches(("master",))) refs = git_attic(("--prefix", "archive", "list")).stdout.split() assert set(refs) == {"hawaii"} git_attic(("--prefix", "shed", "restore", "marinara")) assert_refs(git_attic(("--prefix", "shed", "list", "-v")), get_test_branches(("marinara",))) assert_refs(git_branches(), get_test_branches(("marinara", "master"))) def test_stash_and_restore_rename(monkeypatch, gitrepo): """Stash and restore refs using a different name. """ monkeypatch.chdir(gitrepo) assert_refs(git_attic(("list", "-v")), ()) assert_refs(git_branches(), get_test_branches()) git_attic(("stash", "hawaii", "a1")) assert_refs(git_attic(("list", "-v")), get_test_branches((("hawaii", "a1"),))) assert_refs(git_branches(), get_test_branches(("marinara", "master"))) git_attic(("stash", "marinara", "a2")) assert_refs(git_attic(("list", "-v")), get_test_branches((("hawaii", "a1"), ("marinara", "a2")))) assert_refs(git_branches(), get_test_branches(("master",))) assert set(git_attic(("list",)).stdout.split()) == {"a1", "a2"} git_attic(("restore", "a2", "b2")) assert_refs(git_attic(("list", "-v")), get_test_branches((("hawaii", "a1"), ("marinara", "a2")))) assert_refs(git_branches(), get_test_branches((("marinara", "b2"), "master")))

38.966292

77

0.5891

389

3,468

4.938303

0.120823

0.116606

0.182717

0.140552

0.844872

0.796981

0.766268

0.75898

0

0.004338

0.202422

3,468

88

78

39.409091

0.690166

0.053633

0

0.701493

0

0.172086

0

0.447761

1

0.044776

false

0

0.029851

0

0.074627

0

null

0

1

0

1

0

1

0

null

0

1

0

9

eaecad492a5febe29964e5ca6340ccefebe032c3

16,479

py

Python

src/ebay_rest/api/buy_browse/models/ship_to_region.py

matecsaj/ebay_rest

dd23236f39e05636eff222f99df1e3699ce47d4a

[ "MIT" ]

3

2021-12-12T04:28:03.000Z

2022-03-10T03:29:18.000Z

src/ebay_rest/api/buy_browse/models/ship_to_region.py

jdavv/ebay_rest

20fc88c6aefdae9ab90f9c1330e79abddcd750cd

[ "MIT" ]

33

2021-06-16T20:44:36.000Z

2022-03-30T14:55:06.000Z

src/ebay_rest/api/buy_browse/models/ship_to_region.py

jdavv/ebay_rest

20fc88c6aefdae9ab90f9c1330e79abddcd750cd

[ "MIT" ]

7

2021-06-03T09:30:23.000Z

2022-03-08T19:51:33.000Z

# coding: utf-8 """ Browse API The Browse API has the following resources: <ul> <li> item_summary: Lets shoppers search for specific items by keyword, GTIN, category, charity, product, or item aspects and refine the results by using filters, such as aspects, compatibility, and fields values.</li> <li> search_by_image: <a href=\"https://developer.ebay.com/api-docs/static/versioning.html#experimental\" target=\"_blank\"><img src=\"/cms/img/docs/experimental-icon.svg\" class=\"legend-icon experimental-icon\" alt=\"Experimental Release\" title=\"Experimental Release\" /> (Experimental)</a> Lets shoppers search for specific items by image. You can refine the results by using URI parameters and filters.</li> <li> item: <ul><li>Lets you retrieve the details of a specific item or all the items in an item group, which is an item with variations such as color and size and check if a product is compatible with the specified item, such as if a specific car is compatible with a specific part.</li> <li>Provides a bridge between the eBay legacy APIs, such as Finding, and the RESTful APIs, which use different formats for the item IDs.</li> </ul> </li> <li> shopping_cart: <a href=\"https://developer.ebay.com/api-docs/static/versioning.html#experimental\" target=\"_blank\"><img src=\"/cms/img/docs/experimental-icon.svg\" class=\"legend-icon experimental-icon\" alt=\"Experimental Release\" title=\"Experimental Release\" /> (Experimental)</a> <a href=\"https://developer.ebay.com/api-docs/static/versioning.html#limited\" target=\"_blank\"> <img src=\"/cms/img/docs/partners-api.svg\" class=\"legend-icon partners-icon\" title=\"Limited Release\" alt=\"Limited Release\" />(Limited Release)</a> Provides the ability for eBay members to see the contents of their eBay cart, and add, remove, and change the quantity of items in their eBay cart.   Note: This resource is not available in the eBay API Explorer.</li></ul> The item_summary, search_by_image, and item resource calls require an <a href=\"/api-docs/static/oauth-client-credentials-grant.html\">Application access token</a>. The shopping_cart resource calls require a <a href=\"/api-docs/static/oauth-authorization-code-grant.html\">User access token</a>. # noqa: E501 OpenAPI spec version: v1.11.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class ShipToRegion(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'region_id': 'str', 'region_name': 'str', 'region_type': 'str' } attribute_map = { 'region_id': 'regionId', 'region_name': 'regionName', 'region_type': 'regionType' } def __init__(self, region_id=None, region_name=None, region_type=None): # noqa: E501 """ShipToRegion - a model defined in Swagger""" # noqa: E501 self._region_id = None self._region_name = None self._region_type = None self.discriminator = None if region_id is not None: self.region_id = region_id if region_name is not None: self.region_name = region_name if region_type is not None: self.region_type = region_type @property def region_id(self): """Gets the region_id of this ShipToRegion. # noqa: E501 The unique identifier of the shipping region. The value returned here is dependent on the corresponding regionType value. The regionId value for a region does not vary based on the eBay marketplace. However, the corresponding regionName value for a region is a localized, text-based description of the shipping region. If the regionType value is <code>WORLDWIDE</code>, the regionId value will also be <code>WORLDWIDE</code>. If the regionType value is <code>WORLD_REGION</code>, the regionId value will be one of the following: <code>AFRICA</code>, <code>AMERICAS</code>, <code>ASIA</code>, <code>AUSTRALIA</code>, <code>CENTRAL_AMERICA_AND_CARIBBEAN</code>, <code>EUROPE</code>, <code>EUROPEAN_UNION</code>, <code>GREATER_CHINA</code>, <code>MIDDLE_EAST</code>, <code>NORTH_AMERICA</code>, <code>OCEANIA</code>, <code>SOUTH_AMERICA</code>, <code>SOUTHEAST_ASIA</code> or <code>CHANNEL_ISLANDS</code>. If the regionType value is <code>COUNTRY</code>, the regionId value will be the two-letter code for the country, as defined in the <a href=\"https://www.iso.org/iso-3166-country-codes.html\" target=\"_blank\">ISO 3166</a> standard. If the regionType value is <code>STATE_OR_PROVINCE</code>, the regionId value will either be the two-letter code for US states and DC (as defined on this <a href=\"https://www.ssa.gov/international/coc-docs/states.html\" target=\"_blank\">Social Security Administration</a> page), or the two-letter code for Canadian provinces (as defined by this <a href=\"https://www.canadapost.ca/tools/pg/manual/PGaddress-e.asp?ecid=murl10006450#1442131\" target=\"_blank\">Canada Post</a> page). If the regionType value is <code>COUNTRY_REGION</code>, the regionId value may be one of following: <code>_AH</code> (if a seller is not willing to ship to Alaska/Hawaii), <code>_PR</code> (if the seller is not willing to ship to US Protectorates), <code>_AP</code> (if seller is not willing to ship to a US Army or Fleet Post Office), and <code>PO_BOX</code> (if the seller is not willing to ship to a Post Office Box). # noqa: E501 :return: The region_id of this ShipToRegion. # noqa: E501 :rtype: str """ return self._region_id @region_id.setter def region_id(self, region_id): """Sets the region_id of this ShipToRegion. The unique identifier of the shipping region. The value returned here is dependent on the corresponding regionType value. The regionId value for a region does not vary based on the eBay marketplace. However, the corresponding regionName value for a region is a localized, text-based description of the shipping region. If the regionType value is <code>WORLDWIDE</code>, the regionId value will also be <code>WORLDWIDE</code>. If the regionType value is <code>WORLD_REGION</code>, the regionId value will be one of the following: <code>AFRICA</code>, <code>AMERICAS</code>, <code>ASIA</code>, <code>AUSTRALIA</code>, <code>CENTRAL_AMERICA_AND_CARIBBEAN</code>, <code>EUROPE</code>, <code>EUROPEAN_UNION</code>, <code>GREATER_CHINA</code>, <code>MIDDLE_EAST</code>, <code>NORTH_AMERICA</code>, <code>OCEANIA</code>, <code>SOUTH_AMERICA</code>, <code>SOUTHEAST_ASIA</code> or <code>CHANNEL_ISLANDS</code>. If the regionType value is <code>COUNTRY</code>, the regionId value will be the two-letter code for the country, as defined in the <a href=\"https://www.iso.org/iso-3166-country-codes.html\" target=\"_blank\">ISO 3166</a> standard. If the regionType value is <code>STATE_OR_PROVINCE</code>, the regionId value will either be the two-letter code for US states and DC (as defined on this <a href=\"https://www.ssa.gov/international/coc-docs/states.html\" target=\"_blank\">Social Security Administration</a> page), or the two-letter code for Canadian provinces (as defined by this <a href=\"https://www.canadapost.ca/tools/pg/manual/PGaddress-e.asp?ecid=murl10006450#1442131\" target=\"_blank\">Canada Post</a> page). If the regionType value is <code>COUNTRY_REGION</code>, the regionId value may be one of following: <code>_AH</code> (if a seller is not willing to ship to Alaska/Hawaii), <code>_PR</code> (if the seller is not willing to ship to US Protectorates), <code>_AP</code> (if seller is not willing to ship to a US Army or Fleet Post Office), and <code>PO_BOX</code> (if the seller is not willing to ship to a Post Office Box). # noqa: E501 :param region_id: The region_id of this ShipToRegion. # noqa: E501 :type: str """ self._region_id = region_id @property def region_name(self): """Gets the region_name of this ShipToRegion. # noqa: E501 A localized text string that indicates the name of the shipping region. The value returned here is dependent on the corresponding regionType value. If the regionType value is <code>WORLDWIDE</code>, the regionName value will show <code>Worldwide</code>. If the regionType value is <code>WORLD_REGION</code>, the regionName value will be a localized text string for one of the following large geographical regions: Africa, Americas, Asia, Australia, Central America and Caribbean, Europe, European Union, Greater China, Middle East, North America, Oceania, South America, Southeast Asia, or Channel Islands. If the regionType value is <code>COUNTRY</code>, the regionName value will be a localized text string for any country in the world. If the regionType value is <code>STATE_OR_PROVINCE</code>, the regionName value will be a localized text string for any US state or Canadian province. If the regionType value is <code>COUNTRY_REGION</code>, the regionName value may be a localized version of one of the following: Alaska/Hawaii, US Protectorates, APO/FPO (Army or Fleet Post Office), or PO BOX. # noqa: E501 :return: The region_name of this ShipToRegion. # noqa: E501 :rtype: str """ return self._region_name @region_name.setter def region_name(self, region_name): """Sets the region_name of this ShipToRegion. A localized text string that indicates the name of the shipping region. The value returned here is dependent on the corresponding regionType value. If the regionType value is <code>WORLDWIDE</code>, the regionName value will show <code>Worldwide</code>. If the regionType value is <code>WORLD_REGION</code>, the regionName value will be a localized text string for one of the following large geographical regions: Africa, Americas, Asia, Australia, Central America and Caribbean, Europe, European Union, Greater China, Middle East, North America, Oceania, South America, Southeast Asia, or Channel Islands. If the regionType value is <code>COUNTRY</code>, the regionName value will be a localized text string for any country in the world. If the regionType value is <code>STATE_OR_PROVINCE</code>, the regionName value will be a localized text string for any US state or Canadian province. If the regionType value is <code>COUNTRY_REGION</code>, the regionName value may be a localized version of one of the following: Alaska/Hawaii, US Protectorates, APO/FPO (Army or Fleet Post Office), or PO BOX. # noqa: E501 :param region_name: The region_name of this ShipToRegion. # noqa: E501 :type: str """ self._region_name = region_name @property def region_type(self): """Gets the region_type of this ShipToRegion. # noqa: E501 An enumeration value that indicates the level or type of shipping region. Valid Values: <ul><li> COUNTRY_REGION - Indicates the region is a domestic region or special location within a country.</li><li> STATE_OR_PROVINCE - Indicates the region is a state or province within a country, such as California or New York in the US, or Ontario or Alberta in Canada.</li><li> COUNTRY - Indicates the region is a single country.</li><li> WORLD_REGION - Indicates the region is a world region, such as Africa, the Middle East, or Southeast Asia.</li><li> WORLDWIDE - Indicates the region is the entire world. This value is only applicable for included shiping regions, and not excluded shipping regions.</li></ul> For more detail on the actual regionName/regionId values that will be returned based on the regionType value, see the <a href=\"/api-docs/buy/browse/resources/item/methods/getItem#response.shipToLocations.regionExcluded.regionId\">regionId</a> and/or <a href=\"/api-docs/buy/browse/resources/item/methods/getItem#response.shipToLocations.regionExcluded.regionName\">regionName</a> field descriptions. Code so that your app gracefully handles any future changes to this list. For implementation help, refer to <a href='https://developer.ebay.com/api-docs/buy/browse/types/ba:RegionTypeEnum'>eBay API documentation</a> # noqa: E501 :return: The region_type of this ShipToRegion. # noqa: E501 :rtype: str """ return self._region_type @region_type.setter def region_type(self, region_type): """Sets the region_type of this ShipToRegion. An enumeration value that indicates the level or type of shipping region. Valid Values: <ul><li> COUNTRY_REGION - Indicates the region is a domestic region or special location within a country.</li><li> STATE_OR_PROVINCE - Indicates the region is a state or province within a country, such as California or New York in the US, or Ontario or Alberta in Canada.</li><li> COUNTRY - Indicates the region is a single country.</li><li> WORLD_REGION - Indicates the region is a world region, such as Africa, the Middle East, or Southeast Asia.</li><li> WORLDWIDE - Indicates the region is the entire world. This value is only applicable for included shiping regions, and not excluded shipping regions.</li></ul> For more detail on the actual regionName/regionId values that will be returned based on the regionType value, see the <a href=\"/api-docs/buy/browse/resources/item/methods/getItem#response.shipToLocations.regionExcluded.regionId\">regionId</a> and/or <a href=\"/api-docs/buy/browse/resources/item/methods/getItem#response.shipToLocations.regionExcluded.regionName\">regionName</a> field descriptions. Code so that your app gracefully handles any future changes to this list. For implementation help, refer to <a href='https://developer.ebay.com/api-docs/buy/browse/types/ba:RegionTypeEnum'>eBay API documentation</a> # noqa: E501 :param region_type: The region_type of this ShipToRegion. # noqa: E501 :type: str """ self._region_type = region_type def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(ShipToRegion, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, ShipToRegion): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

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d80fa365450aa0fcca4ef9240a037f8b8c152dc6

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py

Python

ultracart/api/sso_api.py

UltraCart/rest_api_v2_sdk_python

d734ea13fabc7a57872ff68bac06861edb8fd882

[ "Apache-2.0" ]

1

2018-03-15T16:56:23.000Z

ultracart/api/sso_api.py

UltraCart/rest_api_v2_sdk_python

d734ea13fabc7a57872ff68bac06861edb8fd882

[ "Apache-2.0" ]

null

ultracart/api/sso_api.py

UltraCart/rest_api_v2_sdk_python

d734ea13fabc7a57872ff68bac06861edb8fd882

[ "Apache-2.0" ]

null

# coding: utf-8 """ UltraCart Rest API V2 UltraCart REST API Version 2 # noqa: E501 OpenAPI spec version: 2.0.0 Contact: support@ultracart.com 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 ultracart.api_client import ApiClient from ultracart.configuration import Configuration class SsoApi(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 @classmethod def fromApiKey(cls, apiKey, verify_ssl = True, debug = False): config = Configuration() config.api_key['x-ultracart-simple-key'] = apiKey config.debug = debug config.verify_ssl = verify_ssl api_client = ApiClient(configuration=config, header_name='X-UltraCart-Api-Version', header_value='2017-03-01') return SsoApi(api_client) def get_sso_session_user(self, **kwargs): # noqa: E501 """Get single sign on session user # noqa: E501 This is the equivalent of logging out of the single sign on session # 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_sso_session_user(async_req=True) >>> result = thread.get() :param async_req bool :return: User If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_sso_session_user_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_sso_session_user_with_http_info(**kwargs) # noqa: E501 return data def get_sso_session_user_with_http_info(self, **kwargs): # noqa: E501 """Get single sign on session user # noqa: E501 This is the equivalent of logging out of the single sign on session # 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_sso_session_user_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: User 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 get_sso_session_user" % 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 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json; charset=UTF-8']) # noqa: E501 # Authentication setting auth_settings = ['ultraCartOauth', 'ultraCartSimpleApiKey'] # noqa: E501 return self.api_client.call_api( '/sso/session/user', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='User', # 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 sso_authorize(self, authorization_request, **kwargs): # noqa: E501 """Authorize a single sign on session # noqa: E501 Starts the process of authorizing a single sign on session. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sso_authorize(authorization_request, async_req=True) >>> result = thread.get() :param async_req bool :param SingleSignOnAuthorizeRequest authorization_request: Authorization request (required) :return: SingleSignOnAuthorizeResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.sso_authorize_with_http_info(authorization_request, **kwargs) # noqa: E501 else: (data) = self.sso_authorize_with_http_info(authorization_request, **kwargs) # noqa: E501 return data def sso_authorize_with_http_info(self, authorization_request, **kwargs): # noqa: E501 """Authorize a single sign on session # noqa: E501 Starts the process of authorizing a single sign on session. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sso_authorize_with_http_info(authorization_request, async_req=True) >>> result = thread.get() :param async_req bool :param SingleSignOnAuthorizeRequest authorization_request: Authorization request (required) :return: SingleSignOnAuthorizeResponse If the method is called asynchronously, returns the request thread. """ all_params = ['authorization_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') 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 sso_authorize" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'authorization_request' is set if ('authorization_request' not in params or params['authorization_request'] is None): raise ValueError("Missing the required parameter `authorization_request` when calling `sso_authorize`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'authorization_request' in params: body_params = params['authorization_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; charset=UTF-8']) # noqa: E501 # Authentication setting auth_settings = ['ultraCartOauth', 'ultraCartSimpleApiKey'] # noqa: E501 return self.api_client.call_api( '/sso/authorize', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SingleSignOnAuthorizeResponse', # 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 sso_session_revoke(self, **kwargs): # noqa: E501 """Revoke single sign on session # noqa: E501 This is the equivalent of logging out of the single sign on session # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sso_session_revoke(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.sso_session_revoke_with_http_info(**kwargs) # noqa: E501 else: (data) = self.sso_session_revoke_with_http_info(**kwargs) # noqa: E501 return data def sso_session_revoke_with_http_info(self, **kwargs): # noqa: E501 """Revoke single sign on session # noqa: E501 This is the equivalent of logging out of the single sign on session # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sso_session_revoke_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 sso_session_revoke" % 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 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json; charset=UTF-8']) # noqa: E501 # Authentication setting auth_settings = ['ultraCartOauth', 'ultraCartSimpleApiKey'] # noqa: E501 return self.api_client.call_api( '/sso/session/revoke', 'DELETE', 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 sso_token(self, token_request, **kwargs): # noqa: E501 """Exchange a single sign on code for a simple key token # noqa: E501 Called by your application after receiving the code back on the redirect URI to obtain a simple key token to make API calls with # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sso_token(token_request, async_req=True) >>> result = thread.get() :param async_req bool :param SingleSignOnTokenRequest token_request: Token request (required) :return: SingleSignOnTokenResponse If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.sso_token_with_http_info(token_request, **kwargs) # noqa: E501 else: (data) = self.sso_token_with_http_info(token_request, **kwargs) # noqa: E501 return data def sso_token_with_http_info(self, token_request, **kwargs): # noqa: E501 """Exchange a single sign on code for a simple key token # noqa: E501 Called by your application after receiving the code back on the redirect URI to obtain a simple key token to make API calls with # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.sso_token_with_http_info(token_request, async_req=True) >>> result = thread.get() :param async_req bool :param SingleSignOnTokenRequest token_request: Token request (required) :return: SingleSignOnTokenResponse If the method is called asynchronously, returns the request thread. """ all_params = ['token_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') 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 sso_token" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'token_request' is set if ('token_request' not in params or params['token_request'] is None): raise ValueError("Missing the required parameter `token_request` when calling `sso_token`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'token_request' in params: body_params = params['token_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; charset=UTF-8']) # noqa: E501 # Authentication setting auth_settings = ['ultraCartOauth', 'ultraCartSimpleApiKey'] # noqa: E501 return self.api_client.call_api( '/sso/token', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SingleSignOnTokenResponse', # 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)

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1

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0

8

d835db30a879821dbbb8b1761f85a246bc598e02

1,953

py

Python

tests/test_compare_operators.py

Luavis/sherlock.py

6974244c5ddb2f5e41aea2fbc7f913917fd15532

[ "MIT" ]

55

2017-02-12T08:22:44.000Z

2022-01-08T12:34:53.000Z

tests/test_compare_operators.py

Ronlin1/sherlock.py

6974244c5ddb2f5e41aea2fbc7f913917fd15532

[ "MIT" ]

3

2017-02-24T16:23:40.000Z

2022-02-05T19:54:26.000Z

tests/test_compare_operators.py

Ronlin1/sherlock.py

6974244c5ddb2f5e41aea2fbc7f913917fd15532

[ "MIT" ]

7

2017-01-17T17:34:59.000Z

2022-02-15T15:23:17.000Z

from tests import analysis_code_list def test_number_equal_op(): code = [ 'a = 10', 'a == 10', ] assert analysis_code_list(code) == """export a=10 $a -eq 10 """ def test_number_gt_op(): code = [ 'a = 10', 'a > 10', ] assert analysis_code_list(code) == """export a=10 $a -gt 10 """ def test_number_ge_op(): code = [ 'a = 10', 'a >= 10', ] assert analysis_code_list(code) == """export a=10 $a -ge 10 """ def test_number_lt_op(): code = [ 'a = 10', 'a < 10', ] assert analysis_code_list(code) == """export a=10 $a -lt 10 """ def test_number_le_op(): code = [ 'a = 10', 'a <= 10', ] assert analysis_code_list(code) == """export a=10 $a -le 10 """ def test_number_le_op(): code = [ 'a = 10', 'a != 10', ] assert analysis_code_list(code) == """export a=10 $a -ne 10 """ def test_string_equal_op(): code = [ 'a = "10"', 'if a == "10":', ' pass' ] assert analysis_code_list(code) == """export a="10" if [ $a = "10" ]; then fi """ def test_string_not_equal_op(): code = [ 'a = "10"', 'if a != "10":', ' pass' ] assert analysis_code_list(code) == """export a="10" if [ $a ! "10" ]; then fi """ def test_string_is_op(): code = [ 'a = "10"', 'if a is "10":', ' pass' ] assert analysis_code_list(code) == """export a="10" if [ $a = "10" ]; then fi """ def test_string_not_equal_op(): code = [ 'a = "10"', 'if a != "10":', ' pass' ] assert analysis_code_list(code) == """export a="10" if [ $a ! "10" ]; then fi """ def test_string_not_equal_op(): code = [ 'a = "10"', 'if a is not "10":', ' pass' ] assert analysis_code_list(code) == """export a="10" if [ $a ! "10" ]; then fi """

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8

d838d43c3a4b54813b4d16d527988f14b77ce4c8

2,571

py

Python

torpido/wavelet/wavelets/sym8.py

AP-Atul/Torpido

a646b4d6de7f2e2c96de4c64ce3113f53e3931c2

[ "Unlicense" ]

21

2020-12-23T07:13:10.000Z

2022-01-12T10:32:22.000Z

wavelet/wavelets/sym8.py

AP-Atul/wavelets-ext

00ced22462c369584ebd32f9b5f357f092de0142

[ "MIT" ]

2

2020-12-30T10:45:42.000Z

2021-09-25T09:52:00.000Z

wavelet/wavelets/sym8.py

AP-Atul/wavelets-ext

00ced22462c369584ebd32f9b5f357f092de0142

[ "MIT" ]

1

2021-02-06T21:39:41.000Z

""" Symlet 8 wavelet """ class Symlet8: """ Properties ---------- near symmetric, orthogonal, biorthogonal All values are from http://wavelets.pybytes.com/wavelet/sym8/ """ __name__ = "Symlet Wavelet 8" __motherWaveletLength__ = 16 # length of the mother wavelet __transformWaveletLength__ = 2 # minimum wavelength of input signal # decomposition filter # low-pass decompositionLowFilter = [ -0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609, ] # high-pass decompositionHighFilter = [ -0.0018899503327594609, -0.0003029205147213668, 0.01495225833704823, 0.003808752013890615, -0.049137179673607506, -0.027219029917056003, 0.05194583810770904, 0.3644418948353314, -0.7771857517005235, 0.4813596512583722, 0.061273359067658524, -0.1432942383508097, -0.007607487324917605, 0.03169508781149298, 0.0005421323317911481, -0.0033824159510061256, ] # reconstruction filters # low pass reconstructionLowFilter = [ 0.0018899503327594609, -0.0003029205147213668, -0.01495225833704823, 0.003808752013890615, 0.049137179673607506, -0.027219029917056003, -0.05194583810770904, 0.3644418948353314, 0.7771857517005235, 0.4813596512583722, -0.061273359067658524, -0.1432942383508097, 0.007607487324917605, 0.03169508781149298, -0.0005421323317911481, -0.0033824159510061256, ] # high-pass reconstructionHighFilter = [ -0.0033824159510061256, 0.0005421323317911481, 0.03169508781149298, -0.007607487324917605, -0.1432942383508097, 0.061273359067658524, 0.4813596512583722, -0.7771857517005235, 0.3644418948353314, 0.05194583810770904, -0.027219029917056003, -0.049137179673607506, 0.003808752013890615, 0.01495225833704823, -0.0003029205147213668, -0.0018899503327594609, ]

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dc73d7d0399aa0dc9250fef06d513cded78a2b60

132

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Python

tkinter/minimal - function version/main-1.py

whitmans-max/python-examples

881a8f23f0eebc76816a0078e19951893f0daaaa

[ "MIT" ]

140

2017-02-21T22:49:04.000Z

2022-03-22T17:51:58.000Z

tkinter/minimal - function version/main-1.py

whitmans-max/python-examples

881a8f23f0eebc76816a0078e19951893f0daaaa

[ "MIT" ]

5

2017-12-02T19:55:00.000Z

2021-09-22T23:18:39.000Z

tkinter/minimal - function version/main-1.py

whitmans-max/python-examples

881a8f23f0eebc76816a0078e19951893f0daaaa

[ "MIT" ]

79

2017-01-25T10:53:33.000Z

2022-03-11T16:13:57.000Z

#!/usr/bin/env python try: import Tkinter as tk # Python 2 except: import tkinter as tk # Python 3 tk.Tk().mainloop()

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dca6e13ecfef528f5a2768898e9b06f128542515

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Python

form_app/migrations/0004_auto_20190428_0910.py

SaranGod/SportsManagement

2ad13b9d3ed5465cf0eb78a3b59b0773dfe6cdc9

[ "Apache-2.0" ]

1

2020-01-25T22:30:33.000Z

form_app/migrations/0004_auto_20190428_0910.py

SaranGod/SportsManagement

2ad13b9d3ed5465cf0eb78a3b59b0773dfe6cdc9

[ "Apache-2.0" ]

null

form_app/migrations/0004_auto_20190428_0910.py

SaranGod/SportsManagement

2ad13b9d3ed5465cf0eb78a3b59b0773dfe6cdc9

[ "Apache-2.0" ]

null

# Generated by Django 2.2 on 2019-04-28 09:10 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('form_app', '0003_owner_model_player'), ] operations = [ migrations.AlterField( model_name='owner_model', name='player1', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player1', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player10', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player10', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player11', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player11', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player12', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player12', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player13', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player13', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player2', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player2', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player3', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player3', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player4', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player4', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player5', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player5', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player6', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player6', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player7', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player7', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player8', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player8', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='player9', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='player9', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='runner', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='runner', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='runner2', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='runner2', to='form_app.player'), ), migrations.AlterField( model_name='owner_model', name='winner', field=models.ForeignKey(blank=True, on_delete=django.db.models.deletion.CASCADE, related_name='winner', to='form_app.player'), ), migrations.AlterField( model_name='player', name='id', field=models.DecimalField(decimal_places=0, max_digits=20, primary_key=True, serialize=False), ), ]

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dcbb07c74c314d0c5708a98cc0f55187713044fc

14,784

py

Python

angr/procedures/definitions/win32_wdsclientapi.py

r4b3rt/angr

c133cfd4f83ffea2a1d9e064241e9459eaabc55f

[ "BSD-2-Clause" ]

null

angr/procedures/definitions/win32_wdsclientapi.py

r4b3rt/angr

c133cfd4f83ffea2a1d9e064241e9459eaabc55f

[ "BSD-2-Clause" ]

null

angr/procedures/definitions/win32_wdsclientapi.py

r4b3rt/angr

c133cfd4f83ffea2a1d9e064241e9459eaabc55f

[ "BSD-2-Clause" ]

null

# pylint:disable=line-too-long import logging from ...sim_type import SimTypeFunction, SimTypeShort, SimTypeInt, SimTypeLong, SimTypeLongLong, SimTypeDouble, SimTypeFloat, SimTypePointer, SimTypeChar, SimStruct, SimTypeFixedSizeArray, SimTypeBottom, SimUnion, SimTypeBool from ...calling_conventions import SimCCStdcall, SimCCMicrosoftAMD64 from .. import SIM_PROCEDURES as P from . import SimLibrary _l = logging.getLogger(name=__name__) lib = SimLibrary() lib.set_default_cc('X86', SimCCStdcall) lib.set_default_cc('AMD64', SimCCMicrosoftAMD64) lib.set_library_names("wdsclientapi.dll") prototypes = \ { # 'WdsCliClose': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["Handle"]), # 'WdsCliRegisterTrace': SimTypeFunction([SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="SByte"), offset=0)], SimTypeBottom(label="Void"), arg_names=["pwszFormat", "Params"]), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pfn"]), # 'WdsCliFreeStringArray': SimTypeFunction([SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["ppwszArray", "ulCount"]), # 'WdsCliFindFirstImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "phFindHandle"]), # 'WdsCliFindNextImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["Handle"]), # 'WdsCliGetEnumerationFlags': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["Handle", "pdwFlags"]), # 'WdsCliGetImageHandleFromFindHandle': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["FindHandle", "phImageHandle"]), # 'WdsCliGetImageHandleFromTransferHandle': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hTransfer", "phImageHandle"]), # 'WdsCliCreateSession': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({"pwszUserName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszDomain": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszPassword": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="WDS_CLI_CRED", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszServer", "pCred", "phSession"]), # 'WdsCliAuthorizeSession': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimStruct({"pwszUserName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszDomain": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pwszPassword": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="WDS_CLI_CRED", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "pCred"]), # 'WdsCliInitializeLog': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="CPU_ARCHITECTURE"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "ulClientArchitecture", "pwszClientId", "pwszClientAddress"]), # 'WdsCliLog': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "ulLogLevel", "ulMessageCode"]), # 'WdsCliGetImageName': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageDescription': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageType': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="WDS_CLI_IMAGE_TYPE"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pImageType"]), # 'WdsCliGetImageFiles': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pppwszFiles", "pdwCount"]), # 'WdsCliGetImageLanguage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageLanguages': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="SByte"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pppszValues", "pdwNumValues"]), # 'WdsCliGetImageVersion': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImagePath': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageIndex': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pdwValue"]), # 'WdsCliGetImageArchitecture': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="CPU_ARCHITECTURE"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pdwValue"]), # 'WdsCliGetImageLastModifiedTime': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimStruct({"wYear": SimTypeShort(signed=False, label="UInt16"), "wMonth": SimTypeShort(signed=False, label="UInt16"), "wDayOfWeek": SimTypeShort(signed=False, label="UInt16"), "wDay": SimTypeShort(signed=False, label="UInt16"), "wHour": SimTypeShort(signed=False, label="UInt16"), "wMinute": SimTypeShort(signed=False, label="UInt16"), "wSecond": SimTypeShort(signed=False, label="UInt16"), "wMilliseconds": SimTypeShort(signed=False, label="UInt16")}, name="SYSTEMTIME", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppSysTimeValue"]), # 'WdsCliGetImageSize': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeLongLong(signed=False, label="UInt64"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pullValue"]), # 'WdsCliGetImageHalName': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageGroup': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageNamespace': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ppwszValue"]), # 'WdsCliGetImageParameter': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="WDS_CLI_IMAGE_PARAM_TYPE"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "ParamType", "pResponse", "uResponseLen"]), # 'WdsCliGetTransferSize': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeLongLong(signed=False, label="UInt64"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hIfh", "pullValue"]), # 'WdsCliSetTransferBufferSize': SimTypeFunction([SimTypeInt(signed=False, label="UInt32")], SimTypeBottom(label="Void"), arg_names=["ulSizeInBytes"]), # 'WdsCliTransferImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeFunction([SimTypeInt(signed=False, label="PFN_WDS_CLI_CALLBACK_MESSAGE_ID"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["dwMessageId", "wParam", "lParam", "pvUserData"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hImage", "pwszLocalPath", "dwFlags", "dwReserved", "pfnWdsCliCallback", "pvUserData", "phTransfer"]), # 'WdsCliTransferFile': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeFunction([SimTypeInt(signed=False, label="PFN_WDS_CLI_CALLBACK_MESSAGE_ID"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["dwMessageId", "wParam", "lParam", "pvUserData"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszServer", "pwszNamespace", "pwszRemoteFilePath", "pwszLocalFilePath", "dwFlags", "dwReserved", "pfnWdsCliCallback", "pvUserData", "phTransfer"]), # 'WdsCliCancelTransfer': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hTransfer"]), # 'WdsCliWaitForTransfer': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hTransfer"]), # 'WdsCliObtainDriverPackages': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hImage", "ppwszServerName", "pppwszDriverPackages", "pulCount"]), # 'WdsCliObtainDriverPackagesEx': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hSession", "pwszMachineInfo", "ppwszServerName", "pppwszDriverPackages", "pulCount"]), # 'WdsCliGetDriverQueryXml': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszWinDirPath", "ppwszDriverQuery"]), } lib.set_prototypes(prototypes)

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Python

core/ezybaasmain/views.py

bhavik1st/ezybaas

796db38e06c9ab6ac53987d86fd1a40e9a75fc63

[ "Apache-2.0" ]

17

2021-02-01T13:30:54.000Z

2022-02-15T02:30:27.000Z

core/ezybaasmain/views.py

bhavik1st/django-ezybaas

796db38e06c9ab6ac53987d86fd1a40e9a75fc63

[ "Apache-2.0" ]

4

2021-01-15T12:20:36.000Z

2021-06-07T04:36:21.000Z

core/ezybaasmain/views.py

bhavik1st/django-ezybaas

796db38e06c9ab6ac53987d86fd1a40e9a75fc63

[ "Apache-2.0" ]

1

2022-02-15T02:30:37.000Z

#from django.http import HttpResponse from django.http import HttpResponseRedirect from django.urls import reverse def index(request): return HttpResponseRedirect("/ezybaas") #return HttpResponse("Hello, world! Index Page")

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Python

tests/server/test_purge_on_delete.py

inmanta/inmanta-core

ae2153d57f124d00ad1b58e6d4bc6818364be4a8

[ "Apache-2.0" ]

6

2021-03-09T10:24:02.000Z

2022-01-16T03:52:11.000Z

tests/server/test_purge_on_delete.py

inmanta/inmanta-core

ae2153d57f124d00ad1b58e6d4bc6818364be4a8

[ "Apache-2.0" ]

1,319

2020-12-18T08:52:29.000Z

2022-03-31T18:17:32.000Z

tests/server/test_purge_on_delete.py

inmanta/inmanta-core

ae2153d57f124d00ad1b58e6d4bc6818364be4a8

[ "Apache-2.0" ]

4

2021-03-03T15:36:50.000Z

2022-03-11T11:41:51.000Z

""" Copyright 2019 Inmanta 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. Contact: code@inmanta.com """ import logging import uuid from datetime import datetime import pytest from inmanta import const, data from inmanta.agent.agent import Agent from inmanta.export import unknown_parameters from inmanta.main import Client from inmanta.server.protocol import Server from inmanta.util import get_compiler_version from utils import ClientHelper LOGGER = logging.getLogger(__name__) @pytest.fixture(scope="function") async def environment(environment, client): """ Override the environment fixture, defined in conftest.py, to make sure that the purge_on_delete environment setting is enabled for all tests defined in this file. """ result = await client.set_setting(tid=environment, id=data.PURGE_ON_DELETE, value=True) assert result.code == 200 yield environment @pytest.mark.asyncio async def test_purge_on_delete_requires(client: Client, server: Server, environment: str, clienthelper: ClientHelper): """ Test purge on delete of resources and inversion of requires """ agent = Agent("localhost", {"blah": "localhost"}, environment=environment, code_loader=False) await agent.start() aclient = agent._client version = await clienthelper.get_version() resources = [ { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file1],v=%d" % version, "owner": "root", "path": "/tmp/file1", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, }, { "group": "root", "hash": "b4350bef50c3ec3ee532d4a3f9d6daedec3d2aba", "id": "std::File[vm2,path=/tmp/file2],v=%d" % version, "owner": "root", "path": "/tmp/file2", "permissions": 644, "purged": False, "reload": False, "purge_on_delete": True, "requires": ["std::File[vm1,path=/tmp/file1],v=%d" % version], "version": version, }, ] await clienthelper.put_version_simple(resources, version) # Release the model and set all resources as deployed result = await client.release_version(environment, version, False) assert result.code == 200 now = datetime.now() result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file1],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await aclient.resource_action_update( environment, ["std::File[vm2,path=/tmp/file2],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["version"] == version assert result.result["model"]["total"] == len(resources) assert result.result["model"]["done"] == len(resources) assert result.result["model"]["released"] assert result.result["model"]["result"] == const.VersionState.success.name # validate requires and provides file1 = [x for x in result.result["resources"] if "file1" in x["id"]][0] file2 = [x for x in result.result["resources"] if "file2" in x["id"]][0] assert file2["id"] in file1["provides"] assert len(file1["attributes"]["requires"]) == 0 assert len(file2["provides"]) == 0 assert file1["id"] in file2["attributes"]["requires"] result = await client.decomission_environment(id=environment, metadata={"message": "test", "type": "test"}) assert result.code == 200 version = result.result["version"] result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["total"] == len(resources) # validate requires and provides file1 = [x for x in result.result["resources"] if "file1" in x["id"]][0] file2 = [x for x in result.result["resources"] if "file2" in x["id"]][0] assert file2["id"] in file1["attributes"]["requires"] assert type(file1["attributes"]["requires"]) == list assert len(file1["provides"]) == 0 assert len(file2["attributes"]["requires"]) == 0 assert file1["id"] in file2["provides"] await agent.stop() @pytest.mark.asyncio(timeout=20) async def test_purge_on_delete_compile_failed_with_compile( event_loop, client: Client, server: Server, environment: str, snippetcompiler ): snippetcompiler.setup_for_snippet( """ h = std::Host(name="test", os=std::linux) f = std::ConfigFile(host=h, path="/etc/motd", content="test", purge_on_delete=true) """ ) version, _ = await snippetcompiler.do_export_and_deploy(do_raise=False) result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["total"] == 1 snippetcompiler.setup_for_snippet( """ h = std::Host(name="test") """ ) # force deploy by having unknown unknown_parameters.append({"parameter": "a", "source": "b"}) version, _ = await snippetcompiler.do_export_and_deploy(do_raise=False) result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["total"] == 0 @pytest.mark.asyncio async def test_purge_on_delete_compile_failed(client: Client, server: Server, clienthelper: ClientHelper, environment: str): """ Test purge on delete of resources """ agent = Agent("localhost", {"blah": "localhost"}, environment=environment, code_loader=False) await agent.start() aclient = agent._client version = await clienthelper.get_version() resources = [ { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file1],v=%d" % version, "owner": "root", "path": "/tmp/file1", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, }, { "group": "root", "hash": "b4350bef50c3ec3ee532d4a3f9d6daedec3d2aba", "id": "std::File[vm1,path=/tmp/file2],v=%d" % version, "owner": "root", "path": "/tmp/file2", "permissions": 644, "purged": False, "reload": False, "purge_on_delete": True, "requires": ["std::File[vm1,path=/tmp/file1],v=%d" % version], "version": version, }, { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file3],v=%d" % version, "owner": "root", "path": "/tmp/file3", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, }, ] await clienthelper.put_version_simple(resources, version) # Release the model and set all resources as deployed result = await client.release_version(environment, version, False) assert result.code == 200 now = datetime.now() result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file1],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file2],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file3],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["version"] == version assert result.result["model"]["total"] == len(resources) assert result.result["model"]["done"] == len(resources) assert result.result["model"]["released"] assert result.result["model"]["result"] == const.VersionState.success.name # New version with only file3 version = await clienthelper.get_version() result = await client.put_version( tid=environment, version=version, resources=[], unknowns=[{"parameter": "a", "source": "b"}], version_info={const.EXPORT_META_DATA: {const.META_DATA_COMPILE_STATE: const.Compilestate.failed}}, compiler_version=get_compiler_version(), ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["total"] == 0 await agent.stop() assert len(result.result["unknowns"]) == 1 @pytest.mark.asyncio async def test_purge_on_delete(client: Client, clienthelper: ClientHelper, server: Server, environment: str): """ Test purge on delete of resources """ agent = Agent("localhost", {"blah": "localhost"}, environment=environment, code_loader=False) await agent.start() aclient = agent._client version = await clienthelper.get_version() resources = [ { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file1],v=%d" % version, "owner": "root", "path": "/tmp/file1", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, }, { "group": "root", "hash": "b4350bef50c3ec3ee532d4a3f9d6daedec3d2aba", "id": "std::File[vm1,path=/tmp/file2],v=%d" % version, "owner": "root", "path": "/tmp/file2", "permissions": 644, "purged": False, "reload": False, "purge_on_delete": True, "requires": ["std::File[vm1,path=/tmp/file1],v=%d" % version], "version": version, }, { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file3],v=%d" % version, "owner": "root", "path": "/tmp/file3", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, }, ] res = await client.put_version( tid=environment, version=version, resources=resources, unknowns=[], version_info={}, compiler_version=get_compiler_version(), ) assert res.code == 200 # Release the model and set all resources as deployed result = await client.release_version(environment, version, False) assert result.code == 200 now = datetime.now() result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file1],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file2],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file3],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["version"] == version assert result.result["model"]["total"] == len(resources) assert result.result["model"]["done"] == len(resources) assert result.result["model"]["released"] assert result.result["model"]["result"] == const.VersionState.success.name # New version with only file3 version = await clienthelper.get_version() res3 = { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file3],v=%d" % version, "owner": "root", "path": "/tmp/file3", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, } result = await client.put_version( tid=environment, version=version, resources=[res3], unknowns=[], version_info={}, compiler_version=get_compiler_version(), ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["total"] == 3 # validate requires and provides file1 = [x for x in result.result["resources"] if "file1" in x["id"]][0] file2 = [x for x in result.result["resources"] if "file2" in x["id"]][0] file3 = [x for x in result.result["resources"] if "file3" in x["id"]][0] assert file1["attributes"]["purged"] assert file2["attributes"]["purged"] assert not file3["attributes"]["purged"] await agent.stop() @pytest.mark.asyncio async def test_purge_on_delete_ignore(client: Client, clienthelper: ClientHelper, server: Server, environment: str): """ Test purge on delete behavior for resources that have not longer purged_on_delete set """ agent = Agent("localhost", {"blah": "localhost"}, environment=environment, code_loader=False) await agent.start() aclient = agent._client # Version 1 with purge_on_delete true version = await clienthelper.get_version() resources = [ { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file1],v=%d" % version, "owner": "root", "path": "/tmp/file1", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, } ] res = await client.put_version( tid=environment, version=version, resources=resources, unknowns=[], version_info={}, compiler_version=get_compiler_version(), ) assert res.code == 200 # Release the model and set all resources as deployed result = await client.release_version(environment, version, False) assert result.code == 200 now = datetime.now() result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file1],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["version"] == version assert result.result["model"]["total"] == len(resources) assert result.result["model"]["done"] == len(resources) assert result.result["model"]["released"] assert result.result["model"]["result"] == const.VersionState.success.name # Version 2 with purge_on_delete false version = await clienthelper.get_version() resources = [ { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file1],v=%d" % version, "owner": "root", "path": "/tmp/file1", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": False, "version": version, } ] res = await client.put_version( tid=environment, version=version, resources=resources, unknowns=[], version_info={}, compiler_version=get_compiler_version(), ) assert res.code == 200 # Release the model and set all resources as deployed result = await client.release_version(environment, version, False) assert result.code == 200 now = datetime.now() result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file1],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["version"] == version assert result.result["model"]["total"] == len(resources) assert result.result["model"]["done"] == len(resources) assert result.result["model"]["released"] assert result.result["model"]["result"] == const.VersionState.success.name # Version 3 with no resources version = await clienthelper.get_version() resources = [] res = await client.put_version( tid=environment, version=version, resources=resources, unknowns=[], version_info={}, compiler_version=get_compiler_version(), ) assert res.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["version"] == version assert result.result["model"]["total"] == len(resources) await agent.stop() @pytest.mark.asyncio async def test_disable_purge_on_delete(client: Client, clienthelper: ClientHelper, server: Server, environment: str): """ Test disable purge on delete of resources """ agent = Agent("localhost", {"blah": "localhost"}, environment=environment, code_loader=False) await agent.start() aclient = agent._client env = await data.Environment.get_by_id(environment) await env.set(data.PURGE_ON_DELETE, False) version = await clienthelper.get_version() resources = [ { "group": "root", "hash": "89bf880a0dc5ffc1156c8d958b4960971370ee6a", "id": "std::File[vm1,path=/tmp/file1],v=%d" % version, "owner": "root", "path": "/tmp/file1", "permissions": 644, "purged": False, "reload": False, "requires": [], "purge_on_delete": True, "version": version, } ] res = await client.put_version( tid=environment, version=version, resources=resources, unknowns=[], version_info={}, compiler_version=get_compiler_version(), ) assert res.code == 200 # Release the model and set all resources as deployed result = await client.release_version(environment, version, False) assert result.code == 200 now = datetime.now() result = await aclient.resource_action_update( environment, ["std::File[vm1,path=/tmp/file1],v=%d" % version], uuid.uuid4(), "deploy", now, now, "deployed", [], {} ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["result"] == const.VersionState.success.name # Empty version version = await clienthelper.get_version() result = await client.put_version( tid=environment, version=version, resources=[], unknowns=[], version_info={}, compiler_version=get_compiler_version() ) assert result.code == 200 result = await client.get_version(environment, version) assert result.code == 200 assert result.result["model"]["total"] == 0 await agent.stop()

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762f3bb77c7fa5a58a35312357c63ae0f2e2d48c

79

py

Python

qdeep/dqn/__init__.py

Talendar/qdeep

7228edc9cc7d7e6c6bc59e93a3eb726fda15704d

[ "MIT" ]

null

qdeep/dqn/__init__.py

Talendar/qdeep

7228edc9cc7d7e6c6bc59e93a3eb726fda15704d

[ "MIT" ]

null

qdeep/dqn/__init__.py

Talendar/qdeep

7228edc9cc7d7e6c6bc59e93a3eb726fda15704d

[ "MIT" ]

null

from qdeep.dqn.agent import DQNAgent from qdeep.dqn.learning import DQNLearner

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1

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1

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7

5212e9947c4bc922c6dd916641526b3424f184fc

197

py

Python

regseq/__init__.py

RPGroup-PBoC/RegSeq

d1df8727f27bd3a48f297974a4f9c5f170a34de4

[ "MIT" ]

3

2020-04-01T21:17:38.000Z

2022-03-08T00:54:42.000Z

regseq/__init__.py

RPGroup-PBoC/RegSeq

d1df8727f27bd3a48f297974a4f9c5f170a34de4

[ "MIT" ]

19

2020-03-30T21:02:28.000Z

2020-06-23T18:32:39.000Z

regseq/__init__.py

RPGroup-PBoC/RegSeq

d1df8727f27bd3a48f297974a4f9c5f170a34de4

[ "MIT" ]

2

2020-06-25T02:00:59.000Z

2020-10-05T06:58:06.000Z

from .utils import * from .prior_designs import * from .create_key import * from .seq_utils import * from .match_data import * from .inference import * from .information import * from .viz import *

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7

52618dad8c3c411dd46986fbb7ee90970e9b26ea

14,388

py

Python

sqlanalyzer/tests/test_query_analyzer.py

mathilda0902/sqlanalyzer

3512174068ae4125a78d578202a4bae845acb464

[ "MIT" ]

2

2020-07-22T13:52:37.000Z

2021-04-16T16:46:52.000Z

sqlanalyzer/tests/test_query_analyzer.py

vickyjkwan/sqlanalyzer

3512174068ae4125a78d578202a4bae845acb464

[ "MIT" ]

null

sqlanalyzer/tests/test_query_analyzer.py

vickyjkwan/sqlanalyzer

3512174068ae4125a78d578202a4bae845acb464

[ "MIT" ]

null

import pytest from sqlanalyzer import query_analyzer, unbundle @pytest.fixture def sample_query(): query = """SELECT * FROM (SELECT a.*, b.*, c.*, d.* FROM (SELECT DISTINCT anonymous_id, user_id FROM mapbox_customer_data.segment_identifies WHERE dt >= '2018-07-01' AND anonymous_id IS NOT NULL AND user_id IS NOT NULL ) a LEFT JOIN (SELECT id, email, created FROM mapbox_customer_data.accounts WHERE cast(dt AS DATE) = CURRENT_DATE - INTERVAL '1' DAY ) b ON a.user_id = b.id LEFT JOIN (SELECT anonymous_id AS anon_id_ad, context_campaign_name, min(TIMESTAMP) AS min_exposure FROM mapbox_customer_data.segment_pages WHERE dt >= '2018-07-01' AND context_campaign_name IS NOT NULL GROUP BY 1, 2) c ON a.anonymous_id = c.anon_id_ad LEFT JOIN (SELECT DISTINCT anonymous_id AS anon_id_event, original_timestamp, event, context_traits_email FROM mapbox_customer_data.segment_tracks WHERE dt >= '2018-07-01' AND event LIKE 'submitted_%form' AND context_traits_email IS NOT NULL ) d ON a.anonymous_id = d.anon_id_event LEFT JOIN (SELECT sfdc_accounts.platform, sfdc_accounts.mobile_os, sfdc_accounts.service_metadata, sfdc_cases.account, sfdc_cases.num_requests, sfdc_cases.owner, sfdc_accounts.user_id FROM sfdc.accounts sfdc_accounts LEFT JOIN (SELECT MAX(dt) FROM (SELECT dt FROM sfdc.oppty LEFT JOIN (SELECT MAX(dt) FROM (SELECT DISTINCT dt FROM sfdc.owner AS sfdc_owner) AS dt_owner ON sfdc_oppty.dt = sfdc_cases.dt) LEFT JOIN (SELECT dt FROM sfdc.cases) sfdc_cases ON sfdc_oppty.dt = sfdc_cases.dt) ) AS sfdc_cases_oppty ON sfdc_cases_oppty.dt = sfdc_accounts.dt LEFT JOIN sfdc.cases AS sfdc_cases ON sfdc_cases.id = sfdc_accounts.case_id WHERE sfdc_cases_oppty.dt > '2020-04-03' AND sfdc_cases_oppty.dt < '2020-05-04' ORDER BY 1 GROUP BY 3 LIMIT 20 ) e ON e.user_id = a.user_id ) WHERE context_campaign_name IS NOT NULL """ return query @pytest.fixture def analyzer(sample_query): analyzer = query_analyzer.Analyzer(sample_query) return analyzer @pytest.fixture def unbundled(sample_query): unbundled = unbundle.Unbundle(sample_query) return unbundled @pytest.fixture def cte_dict(): return {'appuserturnstile_30d': "SELECT CASE\n WHEN LOWER(useragent) RLIKE '(mapboxeventsnavigation|navigation-)' THEN 'navigation'\n WHEN LOWER(useragent) RLIKE 'mapboxvision' THEN 'vision'\n WHEN LOWER(useragent) RLIKE 'mapboxeventsunity' THEN 'unity'\n WHEN LOWER(useragent) RLIKE 'mapboxtelemetry' THEN 'telemetry'\n WHEN LOWER(useragent) RLIKE '(mapboxevents|mapbox-maps-)' THEN 'maps'\n ELSE 'other'\n END AS service,\n COALESCE(OWNER, 'unknown') AS account,\n userid\n FROM sdk_events.china_appuserturnstile\n WHERE dt BETWEEN DATE_SUB('{run_date}', 29) AND '{run_date}' ", 'appuserturnstile_7d': "SELECT CASE\n WHEN LOWER(useragent) RLIKE '(mapboxeventsnavigation|navigation-)' THEN 'navigation'\n WHEN LOWER(useragent) RLIKE 'mapboxvision' THEN 'vision'\n WHEN LOWER(useragent) RLIKE 'mapboxeventsunity' THEN 'unity'\n WHEN LOWER(useragent) RLIKE 'mapboxtelemetry' THEN 'telemetry'\n WHEN LOWER(useragent) RLIKE '(mapboxevents|mapbox-maps-)' THEN 'maps'\n ELSE 'other'\n END AS service,\n COALESCE(OWNER, 'unknown') AS account,\n userid\n FROM sdk_events.china_appuserturnstile\n WHERE dt BETWEEN DATE_SUB('{run_date}', 6) AND '{run_date}' ", 'appuserturnstile_1d': "SELECT CASE\n WHEN LOWER(useragent) RLIKE '(mapboxeventsnavigation|navigation-)' THEN 'navigation'\n WHEN LOWER(useragent) RLIKE 'mapboxvision' THEN 'vision'\n WHEN LOWER(useragent) RLIKE 'mapboxeventsunity' THEN 'unity'\n WHEN LOWER(useragent) RLIKE 'mapboxtelemetry' THEN 'telemetry'\n WHEN LOWER(useragent) RLIKE '(mapboxevents|mapbox-maps-)' THEN 'maps'\n ELSE 'other'\n END AS service,\n COALESCE(OWNER, 'unknown') AS account,\n userid\n FROM sdk_events.china_appuserturnstile\n WHERE dt = '{run_date}' ", 'mau_ts_cube': "SELECT '30d' AS aggregation,\n 'mobile' AS platform,\n COALESCE(service, '_all') AS service,\n COALESCE(account, '_all') AS account,\n num_users\n FROM\n (SELECT service,\n account,\n COUNT(DISTINCT userid) AS num_users\n FROM appuserturnstile_30d\n GROUP BY service,\n account WITH CUBE) m", 'wau_ts_cube': "SELECT '7d' AS aggregation,\n 'mobile' AS platform,\n COALESCE(service, '_all') AS service,\n COALESCE(account, '_all') AS account,\n num_users\n FROM\n (SELECT service,\n account,\n COUNT(DISTINCT userid) AS num_users\n FROM appuserturnstile_7d\n GROUP BY service,\n account WITH CUBE) w", 'dau_ts_cube': "SELECT '1d' AS aggregation,\n 'mobile' AS platform,\n COALESCE(service, '_all') AS service,\n COALESCE(account, '_all') AS account,\n num_users\n FROM\n (SELECT service,\n account,\n COUNT(DISTINCT userid) AS num_users\n FROM appuserturnstile_1d\n GROUP BY service,\n account WITH CUBE) d)\nSELECT aggregation,\n platform,\n service,\n account,\n num_users\nFROM mau_ts_cube\nUNION ALL\nSELECT aggregation,\n platform,\n service,\n account,\n num_users\nFROM wau_ts_cube\nUNION ALL", 'main_query': 'SELECT aggregation,\n platform,\n service,\n account,\n num_users\nFROM dau_ts_cube'} def test_flatten_pure_nested(analyzer, sample_query): assert analyzer.flatten_pure_nested(sample_query) == [{'level_1_main': 'SELECT * WHERE context_campaign_name IS NOT NULL FROM no alias '}, {'level_2_main': 'SELECT a.*, b.*, c.*, d.* FROM a LEFT JOIN b ON a.user_id = b.id LEFT JOIN c ON a.anonymous_id = c.anon_id_ad LEFT JOIN d ON a.anonymous_id = d.anon_id_event LEFT JOIN e ON e.user_id = a.user_id '}, {'a': "SELECT DISTINCT anonymous_id, user_id FROM mapbox_customer_data.segment_identifies WHERE dt >= '2018-07-01' AND anonymous_id IS NOT NULL AND user_id IS NOT NULL "}, {'c': "SELECT anonymous_id AS anon_id_ad, context_campaign_name, min(TIMESTAMP) AS min_exposure FROM mapbox_customer_data.segment_pages WHERE dt >= '2018-07-01' AND context_campaign_name IS NOT NULL GROUP BY 1, 2"}, {'b': "SELECT id, email, created FROM mapbox_customer_data.accounts WHERE cast(dt AS DATE) = CURRENT_DATE - INTERVAL '1' DAY "}, {'e': "SELECT sfdc_accounts.platform, sfdc_accounts.mobile_os, sfdc_accounts.service_metadata, sfdc_cases.account, sfdc_cases.num_requests, sfdc_cases.owner, sfdc_accounts.user_id WHERE sfdc_cases_oppty.dt > '2020-04-03' AND sfdc_cases_oppty.dt < '2020-05-04' FROM sfdc.accounts sfdc_accounts LEFT JOIN sfdc_cases_oppty ON sfdc_cases_oppty.dt = sfdc_accounts.dt LEFT JOIN sfdc.cases AS sfdc_cases ON sfdc_cases.id = sfdc_accounts.case_id "}, {'sfdc_cases_oppty': 'SELECT MAX(dt) FROM ( FROM sfdc.oppty LEFT JOIN dt_owner ON sfdc_oppty.dt = sfdc_cases.dt) LEFT JOIN sfdc_cases ON sfdc_oppty.dt = sfdc_cases.dt '}, {'sfdc_cases': 'SELECT dt FROM sfdc.cases'}, {'dt_owner': 'SELECT MAX(dt) FROM ( FROM sfdc.owner AS sfdc_owner '}] def test_flatten_cte_nested(analyzer, unbundled, cte_dict): assert analyzer.flatten_cte_nested(unbundled, cte_dict) == [{'appuserturnstile_30d': "SELECT CASE\n WHEN LOWER(useragent) RLIKE '(mapboxeventsnavigation|navigation-)' THEN 'navigation'\n WHEN LOWER(useragent) RLIKE 'mapboxvision' THEN 'vision'\n WHEN LOWER(useragent) RLIKE 'mapboxeventsunity' THEN 'unity'\n WHEN LOWER(useragent) RLIKE 'mapboxtelemetry' THEN 'telemetry'\n WHEN LOWER(useragent) RLIKE '(mapboxevents|mapbox-maps-)' THEN 'maps'\n ELSE 'other'\n END AS service,\n COALESCE(OWNER, 'unknown') AS account,\n userid\n FROM sdk_events.china_appuserturnstile\n WHERE dt BETWEEN DATE_SUB('{run_date}', 29) AND '{run_date}' "}, {'appuserturnstile_7d': "SELECT CASE\n WHEN LOWER(useragent) RLIKE '(mapboxeventsnavigation|navigation-)' THEN 'navigation'\n WHEN LOWER(useragent) RLIKE 'mapboxvision' THEN 'vision'\n WHEN LOWER(useragent) RLIKE 'mapboxeventsunity' THEN 'unity'\n WHEN LOWER(useragent) RLIKE 'mapboxtelemetry' THEN 'telemetry'\n WHEN LOWER(useragent) RLIKE '(mapboxevents|mapbox-maps-)' THEN 'maps'\n ELSE 'other'\n END AS service,\n COALESCE(OWNER, 'unknown') AS account,\n userid\n FROM sdk_events.china_appuserturnstile\n WHERE dt BETWEEN DATE_SUB('{run_date}', 6) AND '{run_date}' "}, {'appuserturnstile_1d': "SELECT CASE\n WHEN LOWER(useragent) RLIKE '(mapboxeventsnavigation|navigation-)' THEN 'navigation'\n WHEN LOWER(useragent) RLIKE 'mapboxvision' THEN 'vision'\n WHEN LOWER(useragent) RLIKE 'mapboxeventsunity' THEN 'unity'\n WHEN LOWER(useragent) RLIKE 'mapboxtelemetry' THEN 'telemetry'\n WHEN LOWER(useragent) RLIKE '(mapboxevents|mapbox-maps-)' THEN 'maps'\n ELSE 'other'\n END AS service,\n COALESCE(OWNER, 'unknown') AS account,\n userid\n FROM sdk_events.china_appuserturnstile\n WHERE dt = '{run_date}' "}, {'mau_ts_cube': [{'level_1_main': "SELECT '30d' AS aggregation, 'mobile' AS platform, COALESCE(service, '_all') AS service, COALESCE(account, '_all') AS account, num_users FROM m "}, {'m': 'SELECT service, account, COUNT(DISTINCT userid) AS num_users FROM appuserturnstile_30d GROUP BY service, account WITH CUBE'}]}, {'wau_ts_cube': [{'level_1_main': "SELECT '7d' AS aggregation, 'mobile' AS platform, COALESCE(service, '_all') AS service, COALESCE(account, '_all') AS account, num_users FROM w "}, {'w': 'SELECT service, account, COUNT(DISTINCT userid) AS num_users FROM appuserturnstile_7d GROUP BY service, account WITH CUBE'}]}, {'dau_ts_cube': [{'level_1_main': "SELECT '1d' AS aggregation, 'mobile' AS platform, COALESCE(service, '_all') AS service, COALESCE(account, '_all') AS account, num_users FROM no alias FROM mau_ts_cube FROM wau_ts_cube "}, {'no alias': 'SELECT service, account, COUNT(DISTINCT userid) AS num_users FROM appuserturnstile_1d GROUP BY service, account WITH CUBE) d'}]}, {'main_query': 'SELECT aggregation,\n platform,\n service,\n account,\n num_users\nFROM dau_ts_cube'}] def test_parse_query(analyzer, sample_query): assert analyzer.parse_query(sample_query) == [{'level_1_main': 'SELECT * WHERE context_campaign_name IS NOT NULL FROM no alias '}, {'level_2_main': 'SELECT a.*, b.*, c.*, d.* FROM a LEFT JOIN b ON a.user_id = b.id LEFT JOIN c ON a.anonymous_id = c.anon_id_ad LEFT JOIN d ON a.anonymous_id = d.anon_id_event LEFT JOIN e ON e.user_id = a.user_id '}, {'a': "SELECT DISTINCT anonymous_id, user_id FROM mapbox_customer_data.segment_identifies WHERE dt >= '2018-07-01' AND anonymous_id IS NOT NULL AND user_id IS NOT NULL "}, {'c': "SELECT anonymous_id AS anon_id_ad, context_campaign_name, min(TIMESTAMP) AS min_exposure FROM mapbox_customer_data.segment_pages WHERE dt >= '2018-07-01' AND context_campaign_name IS NOT NULL GROUP BY 1, 2"}, {'b': "SELECT id, email, created FROM mapbox_customer_data.accounts WHERE cast(dt AS DATE) = CURRENT_DATE - INTERVAL '1' DAY "}, {'e': "SELECT sfdc_accounts.platform, sfdc_accounts.mobile_os, sfdc_accounts.service_metadata, sfdc_cases.account, sfdc_cases.num_requests, sfdc_cases.owner, sfdc_accounts.user_id WHERE sfdc_cases_oppty.dt > '2020-04-03' AND sfdc_cases_oppty.dt < '2020-05-04' FROM sfdc.accounts sfdc_accounts LEFT JOIN sfdc_cases_oppty ON sfdc_cases_oppty.dt = sfdc_accounts.dt LEFT JOIN sfdc.cases AS sfdc_cases ON sfdc_cases.id = sfdc_accounts.case_id "}, {'sfdc_cases_oppty': 'SELECT MAX(dt) FROM ( FROM sfdc.oppty LEFT JOIN dt_owner ON sfdc_oppty.dt = sfdc_cases.dt) LEFT JOIN sfdc_cases ON sfdc_oppty.dt = sfdc_cases.dt '}, {'sfdc_cases': 'SELECT dt FROM sfdc.cases'}, {'dt_owner': 'SELECT MAX(dt) FROM ( FROM sfdc.owner AS sfdc_owner '}]

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null

0

9

8741e9fa7f88274b6bcc53c6780a739344f153b4

10,828

py

Python

test/test.py

amjabb/eegMindReader

58bb007790a03b41d2f431d208a3d83b8d1f875c

[ "Unlicense" ]

2

2019-05-14T13:58:11.000Z

2020-03-08T21:36:32.000Z

test/test.py

amjabb/eegMindReader

58bb007790a03b41d2f431d208a3d83b8d1f875c

[ "Unlicense" ]

12

2017-11-27T08:52:58.000Z

2017-12-14T01:33:07.000Z

test/test.py

amjabb/eegMindReader

58bb007790a03b41d2f431d208a3d83b8d1f875c

[ "Unlicense" ]

2

2017-11-27T09:11:42.000Z

2017-11-29T01:02:55.000Z

#!/usr/bin/env python2.7 import argparse # new in Python2.7 import os import time import string import atexit import threading import logging import sys logging.basicConfig(level=logging.ERROR) from yapsy.PluginManager import PluginManager manager = PluginManager() import open_bci_ganglion as bci plugins_paths = ["plugins"] manager.setPluginPlaces(plugins_paths) manager.collectPlugins() board = bci.OpenBCIBoard(port=None, daisy=False, filter_data=True, scaled_output=True, log=False, aux=False) plug_name = 'csv_collect' plug_args = 'record.csv' plug = manager.getPluginByName(plug_name) plug_list = [] callback_list = [] if plug == None: # eg: if an import fail inside a plugin, yapsy skip it print ("Error: [ " + plug_name + " ] not found or could not be loaded. Check name and requirements.") else: print ("\nActivating [ " + plug_name + " ] plugin...") if not plug.plugin_object.pre_activate(plug_args, sample_rate=board.getSampleRate(), eeg_channels=board.getNbEEGChannels(), aux_channels=board.getNbAUXChannels(), imp_channels=board.getNbImpChannels()): print ("Error while activating [ " + plug_name + " ], check output for more info.") else: print ("Plugin [ " + plug_name + "] added to the list") plug_list.append(plug.plugin_object) callback_list.append(plug.plugin_object) if len(plug_list) == 0: fun = None else: fun = callback_list print("\n-------------BEGIN---------------") # Init board state # s: stop board streaming; v: soft reset of the 32-bit board (no effect with 8bit board) s = 'sv' # Tell the board to enable or not daisy module if board.daisy: s = s + 'C' else: s = s + 'c' # d: Channels settings back to default s = s + 'd' while(s != "/exit"): # Send char and wait for registers to set if (not s): pass elif("help" in s): print ("View command map at: \ http://docs.openbci.com/software/01-OpenBCI_SDK.\n\ For user interface: read README or view \ https://github.com/OpenBCI/OpenBCI_Python") elif board.streaming and s != "/stop": print ("Error: the board is currently streaming data, please type '/stop' before issuing new commands.") else: # read silently incoming packet if set (used when stream is stopped) flush = False if('/' == s[0]): s = s[1:] rec = False # current command is recognized or fot if("T:" in s): lapse = int(s[string.find(s, "T:")+2:]) rec = True elif("t:" in s): lapse = int(s[string.find(s, "t:")+2:]) rec = True else: lapse = -1 if('startimp' in s): if board.getBoardType() == "cyton": print ("Impedance checking not supported on cyton.") else: board.setImpedance(True) if(fun != None): # start streaming in a separate thread so we could always send commands in here boardThread = threading.Thread(target=board.start_streaming, args=(fun, lapse)) boardThread.daemon = True # will stop on exit try: boardThread.start() except: raise else: print ("No function loaded") rec = True elif("start" in s): board.setImpedance(False) if(fun != None): # start streaming in a separate thread so we could always send commands in here boardThread = threading.Thread(target=board.start_streaming, args=(fun, lapse)) boardThread.daemon = True # will stop on exit try: boardThread.start() except: raise else: print ("No function loaded") rec = True elif('test' in s): test = int(s[s.find("test")+4:]) board.test_signal(test) rec = True elif('stop' in s): board.stop() rec = True flush = True if rec == False: print("Command not recognized...") elif s: for c in s: if sys.hexversion > 0x03000000: board.ser_write(bytes(c, 'utf-8')) else: board.ser_write(bytes(c)) time.sleep(0.100) line = '' time.sleep(0.1) #Wait to see if the board has anything to report # The Cyton nicely return incoming packets -- here supposedly messages -- whereas the Ganglion prints incoming ASCII message by itself if board.getBoardType() == "cyton": while board.ser_inWaiting(): c = board.ser_read().decode('utf-8', errors='replace') # we're supposed to get UTF8 text, but the board might behave otherwise line += c time.sleep(0.001) if (c == '\n') and not flush: print('%\t'+line[:-1]) line = '' elif board.getBoardType() == "ganglion": while board.ser_inWaiting(): board.waitForNotifications(0.001) if not flush: print(line) # Take user input #s = input('--> ') if sys.hexversion > 0x03000000: s = input('--> ') else: s = raw_input('--> ') print("\n-------------BEGIN---------------") # Init board state # s: stop board streaming; v: soft reset of the 32-bit board (no effect with 8bit board) s = 'sv' # Tell the board to enable or not daisy module if board.daisy: s = s + 'C' else: s = s + 'c' # d: Channels settings back to default s = s + 'd' while(s != "/exit"): # Send char and wait for registers to set if (not s): pass elif("help" in s): print ("View command map at: \ http://docs.openbci.com/software/01-OpenBCI_SDK.\n\ For user interface: read README or view \ https://github.com/OpenBCI/OpenBCI_Python") elif board.streaming and s != "/stop": print ("Error: the board is currently streaming data, please type '/stop' before issuing new commands.") else: # read silently incoming packet if set (used when stream is stopped) flush = False if('/' == s[0]): s = s[1:] rec = False # current command is recognized or fot if("T:" in s): lapse = int(s[string.find(s, "T:")+2:]) rec = True elif("t:" in s): lapse = int(s[string.find(s, "t:")+2:]) rec = True else: lapse = -1 if('startimp' in s): if board.getBoardType() == "cyton": print ("Impedance checking not supported on cyton.") else: board.setImpedance(True) if(fun != None): # start streaming in a separate thread so we could always send commands in here boardThread = threading.Thread(target=board.start_streaming, args=(fun, lapse)) boardThread.daemon = True # will stop on exit try: boardThread.start() except: raise else: print ("No function loaded") rec = True elif("start" in s): board.setImpedance(False) if(fun != None): # start streaming in a separate thread so we could always send commands in here boardThread = threading.Thread(target=board.start_streaming, args=(fun, lapse)) boardThread.daemon = True # will stop on exit try: boardThread.start() except: raise else: print ("No function loaded") rec = True elif('test' in s): test = int(s[s.find("test")+4:]) board.test_signal(test) rec = True elif('stop' in s): board.stop() rec = True flush = True if rec == False: print("Command not recognized...") elif s: for c in s: if sys.hexversion > 0x03000000: board.ser_write(bytes(c, 'utf-8')) else: board.ser_write(bytes(c)) time.sleep(0.100) line = '' time.sleep(0.1) #Wait to see if the board has anything to report # The Cyton nicely return incoming packets -- here supposedly messages -- whereas the Ganglion prints incoming ASCII message by itself if board.getBoardType() == "cyton": while board.ser_inWaiting(): c = board.ser_read().decode('utf-8', errors='replace') # we're supposed to get UTF8 text, but the board might behave otherwise line += c time.sleep(0.001) if (c == '\n') and not flush: print('%\t'+line[:-1]) line = '' elif board.getBoardType() == "ganglion": while board.ser_inWaiting(): board.waitForNotifications(0.001) if not flush: print(line) # Take user input #s = input('--> ') if sys.hexversion > 0x03000000: s = input('--> ') else: s = raw_input('--> ')

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0.097345

0

null

0

1

0

1

0

null

0

7

874fd43053f8a87a64f95c0c0eb6134e2c6bd77f

221

py

Python

lms/lms_app/Models/teste.py

Ney-Rocha/101018

25f50d99f875be61ba8b2d23ecb22a36c2475817

[ "Apache-2.0" ]

null

lms/lms_app/Models/teste.py

Ney-Rocha/101018

25f50d99f875be61ba8b2d23ecb22a36c2475817

[ "Apache-2.0" ]

null

lms/lms_app/Models/teste.py

Ney-Rocha/101018

25f50d99f875be61ba8b2d23ecb22a36c2475817

[ "Apache-2.0" ]

null

from django.db import models class teste(models.Model): def __str__(self): return self.nome + " - "+ self.email nome = models.TextField(max_length=255) email = models.TextField(max_length=255)

27.625

45

0.665158

29

221

4.862069

0.62069

0.212766

0.255319

0.340426

0.382979

0

0.034884

0.221719

221

8

46

27.625

0.784884

0

0.013953

0

1

0.166667

false

0

0.166667

1

0

1

0

null

1

0

1

0

null

0

1

0

7

0d883124a74357a99df4b50d8940b7483ee6f09c

28,859

py

Python

fhir/resources/tests/test_coverageeligibilityresponse.py

mmabey/fhir.resources

cc73718e9762c04726cd7de240c8f2dd5313cbe1

[ "BSD-3-Clause" ]

null

fhir/resources/tests/test_coverageeligibilityresponse.py

mmabey/fhir.resources

cc73718e9762c04726cd7de240c8f2dd5313cbe1

[ "BSD-3-Clause" ]

null

fhir/resources/tests/test_coverageeligibilityresponse.py

mmabey/fhir.resources

cc73718e9762c04726cd7de240c8f2dd5313cbe1

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Profile: http://hl7.org/fhir/StructureDefinition/CoverageEligibilityResponse Release: R4 Version: 4.0.1 Build ID: 9346c8cc45 Last updated: 2019-11-01T09:29:23.356+11:00 """ import io import json import os import unittest import pytest from .. import coverageeligibilityresponse from ..fhirdate import FHIRDate from .fixtures import force_bytes @pytest.mark.usefixtures("base_settings") class CoverageEligibilityResponseTests(unittest.TestCase): def instantiate_from(self, filename): datadir = os.environ.get("FHIR_UNITTEST_DATADIR") or "" with io.open(os.path.join(datadir, filename), "r", encoding="utf-8") as handle: js = json.load(handle) self.assertEqual("CoverageEligibilityResponse", js["resourceType"]) return coverageeligibilityresponse.CoverageEligibilityResponse(js) def testCoverageEligibilityResponse1(self): inst = self.instantiate_from("coverageeligibilityresponse-example.json") self.assertIsNotNone( inst, "Must have instantiated a CoverageEligibilityResponse instance" ) self.implCoverageEligibilityResponse1(inst) js = inst.as_json() self.assertEqual("CoverageEligibilityResponse", js["resourceType"]) inst2 = coverageeligibilityresponse.CoverageEligibilityResponse(js) self.implCoverageEligibilityResponse1(inst2) def implCoverageEligibilityResponse1(self, inst): self.assertEqual(inst.created.date, FHIRDate("2014-08-16").date) self.assertEqual(inst.created.as_json(), "2014-08-16") self.assertEqual( force_bytes(inst.disposition), force_bytes("Policy is currently in-force.") ) self.assertEqual(force_bytes(inst.id), force_bytes("E2500")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("http://www.BenefitsInc.com/fhir/coverageeligibilityresponse"), ) self.assertEqual(force_bytes(inst.identifier[0].value), force_bytes("881234")) self.assertTrue(inst.insurance[0].inforce) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(force_bytes(inst.outcome), force_bytes("complete")) self.assertEqual(force_bytes(inst.purpose[0]), force_bytes("validation")) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual( force_bytes(inst.text.div), force_bytes( '<div xmlns="http://www.w3.org/1999/xhtml">A human-readable rendering of the CoverageEligibilityResponse.</div>' ), ) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testCoverageEligibilityResponse2(self): inst = self.instantiate_from("coverageeligibilityresponse-example-error.json") self.assertIsNotNone( inst, "Must have instantiated a CoverageEligibilityResponse instance" ) self.implCoverageEligibilityResponse2(inst) js = inst.as_json() self.assertEqual("CoverageEligibilityResponse", js["resourceType"]) inst2 = coverageeligibilityresponse.CoverageEligibilityResponse(js) self.implCoverageEligibilityResponse2(inst2) def implCoverageEligibilityResponse2(self, inst): self.assertEqual(inst.created.date, FHIRDate("2014-09-16").date) self.assertEqual(inst.created.as_json(), "2014-09-16") self.assertEqual( force_bytes(inst.disposition), force_bytes( "Eligibiliy request could not be processed, please address errors before submitting." ), ) self.assertEqual( force_bytes(inst.error[0].code.coding[0].code), force_bytes("a001") ) self.assertEqual( force_bytes(inst.error[0].code.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/adjudication-error"), ) self.assertEqual( force_bytes(inst.form.coding[0].code), force_bytes("ELRSP/2017/01") ) self.assertEqual( force_bytes(inst.form.coding[0].system), force_bytes("http://national.org/form"), ) self.assertEqual(force_bytes(inst.id), force_bytes("E2503")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("http://www.BenefitsInc.com/fhir/coverageeligibilityresponse"), ) self.assertEqual(force_bytes(inst.identifier[0].value), force_bytes("8812343")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(force_bytes(inst.outcome), force_bytes("error")) self.assertEqual(force_bytes(inst.purpose[0]), force_bytes("validation")) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual( force_bytes(inst.text.div), force_bytes( '<div xmlns="http://www.w3.org/1999/xhtml">A human-readable rendering of the CoverageEligibilityResponse.</div>' ), ) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testCoverageEligibilityResponse3(self): inst = self.instantiate_from( "coverageeligibilityresponse-example-benefits-2.json" ) self.assertIsNotNone( inst, "Must have instantiated a CoverageEligibilityResponse instance" ) self.implCoverageEligibilityResponse3(inst) js = inst.as_json() self.assertEqual("CoverageEligibilityResponse", js["resourceType"]) inst2 = coverageeligibilityresponse.CoverageEligibilityResponse(js) self.implCoverageEligibilityResponse3(inst2) def implCoverageEligibilityResponse3(self, inst): self.assertEqual(force_bytes(inst.contained[0].id), force_bytes("coverage-1")) self.assertEqual(inst.created.date, FHIRDate("2014-09-16").date) self.assertEqual(inst.created.as_json(), "2014-09-16") self.assertEqual( force_bytes(inst.disposition), force_bytes("Policy is currently in-force.") ) self.assertEqual( force_bytes(inst.form.coding[0].code), force_bytes("ELRSP/2017/01") ) self.assertEqual( force_bytes(inst.form.coding[0].system), force_bytes("http://national.org/form"), ) self.assertEqual(force_bytes(inst.id), force_bytes("E2502")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("http://www.BenefitsInc.com/fhir/coverageeligibilityresponse"), ) self.assertEqual(force_bytes(inst.identifier[0].value), force_bytes("8812342")) self.assertTrue(inst.insurance[0].inforce) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[0].allowedMoney.currency), force_bytes("USD"), ) self.assertEqual( inst.insurance[0].item[0].benefit[0].allowedMoney.value, 500000 ) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[0].usedMoney.currency), force_bytes("USD"), ) self.assertEqual(inst.insurance[0].item[0].benefit[0].usedMoney.value, 3748.0) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[1].allowedMoney.currency), force_bytes("USD"), ) self.assertEqual(inst.insurance[0].item[0].benefit[1].allowedMoney.value, 100) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[1].type.coding[0].code), force_bytes("copay-maximum"), ) self.assertEqual(inst.insurance[0].item[0].benefit[2].allowedUnsignedInt, 20) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[2].type.coding[0].code), force_bytes("copay-percent"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].category.coding[0].code), force_bytes("30"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].category.coding[0].display), force_bytes("Health Benefit Plan Coverage"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].benefit[0].allowedMoney.currency), force_bytes("USD"), ) self.assertEqual(inst.insurance[0].item[1].benefit[0].allowedMoney.value, 15000) self.assertEqual( force_bytes(inst.insurance[0].item[1].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].category.coding[0].code), force_bytes("69"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].category.coding[0].display), force_bytes("Maternity"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].benefit[0].allowedMoney.currency), force_bytes("USD"), ) self.assertEqual(inst.insurance[0].item[2].benefit[0].allowedMoney.value, 2000) self.assertEqual( force_bytes(inst.insurance[0].item[2].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].category.coding[0].code), force_bytes("F3"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].category.coding[0].display), force_bytes("Dental Coverage"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].category.coding[0].code), force_bytes("F6"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].category.coding[0].display), force_bytes("Vision Coverage"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].description), force_bytes( "Vision products and services such as exams, glasses and contact lenses." ), ) self.assertTrue(inst.insurance[0].item[3].excluded) self.assertEqual( force_bytes(inst.insurance[0].item[3].name), force_bytes("Vision") ) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(force_bytes(inst.outcome), force_bytes("complete")) self.assertEqual(force_bytes(inst.purpose[0]), force_bytes("validation")) self.assertEqual(force_bytes(inst.purpose[1]), force_bytes("benefits")) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual( force_bytes(inst.text.div), force_bytes( '<div xmlns="http://www.w3.org/1999/xhtml">A human-readable rendering of the CoverageEligibilityResponse.</div>' ), ) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testCoverageEligibilityResponse4(self): inst = self.instantiate_from( "coverageeligibilityresponse-example-benefits.json" ) self.assertIsNotNone( inst, "Must have instantiated a CoverageEligibilityResponse instance" ) self.implCoverageEligibilityResponse4(inst) js = inst.as_json() self.assertEqual("CoverageEligibilityResponse", js["resourceType"]) inst2 = coverageeligibilityresponse.CoverageEligibilityResponse(js) self.implCoverageEligibilityResponse4(inst2) def implCoverageEligibilityResponse4(self, inst): self.assertEqual(inst.created.date, FHIRDate("2014-08-16").date) self.assertEqual(inst.created.as_json(), "2014-08-16") self.assertEqual( force_bytes(inst.disposition), force_bytes("Policy is currently in-force.") ) self.assertEqual(force_bytes(inst.id), force_bytes("E2501")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("http://www.BenefitsInc.com/fhir/coverageeligibilityresponse"), ) self.assertEqual(force_bytes(inst.identifier[0].value), force_bytes("881234")) self.assertTrue(inst.insurance[0].inforce) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[0].allowedMoney.currency), force_bytes("SAR"), ) self.assertEqual( inst.insurance[0].item[0].benefit[0].allowedMoney.value, 500000 ) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[1].allowedMoney.currency), force_bytes("SAR"), ) self.assertEqual(inst.insurance[0].item[0].benefit[1].allowedMoney.value, 100) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[1].type.coding[0].code), force_bytes("copay-maximum"), ) self.assertEqual(inst.insurance[0].item[0].benefit[2].allowedUnsignedInt, 20) self.assertEqual( force_bytes(inst.insurance[0].item[0].benefit[2].type.coding[0].code), force_bytes("copay-percent"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].category.coding[0].code), force_bytes("30"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].category.coding[0].display), force_bytes("Health Benefit Plan Coverage"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[0].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].benefit[0].allowedMoney.currency), force_bytes("SAR"), ) self.assertEqual(inst.insurance[0].item[1].benefit[0].allowedMoney.value, 15000) self.assertEqual( force_bytes(inst.insurance[0].item[1].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].category.coding[0].code), force_bytes("69"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].category.coding[0].display), force_bytes("Maternity"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[1].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].benefit[0].allowedMoney.currency), force_bytes("SAR"), ) self.assertEqual(inst.insurance[0].item[2].benefit[0].allowedMoney.value, 2000) self.assertEqual( force_bytes(inst.insurance[0].item[2].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].category.coding[0].code), force_bytes("F3"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].category.coding[0].display), force_bytes("Dental Coverage"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[2].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].benefit[0].allowedMoney.currency), force_bytes("SAR"), ) self.assertEqual(inst.insurance[0].item[3].benefit[0].allowedMoney.value, 400) self.assertEqual( force_bytes(inst.insurance[0].item[3].benefit[0].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].category.coding[0].code), force_bytes("F6"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].category.coding[0].display), force_bytes("Vision Coverage"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].term.coding[0].code), force_bytes("annual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[3].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].benefit[0].allowedString), force_bytes("shared"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].benefit[0].type.coding[0].code), force_bytes("room"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].benefit[1].allowedMoney.currency), force_bytes("SAR"), ) self.assertEqual(inst.insurance[0].item[4].benefit[1].allowedMoney.value, 600) self.assertEqual( force_bytes(inst.insurance[0].item[4].benefit[1].type.coding[0].code), force_bytes("benefit"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].category.coding[0].code), force_bytes("49"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].category.coding[0].display), force_bytes("Hospital Room and Board"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].category.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/ex-benefitcategory"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].network.coding[0].code), force_bytes("in"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].network.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-network"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].term.coding[0].code), force_bytes("day"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].term.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-term"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].unit.coding[0].code), force_bytes("individual"), ) self.assertEqual( force_bytes(inst.insurance[0].item[4].unit.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/benefit-unit"), ) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(force_bytes(inst.outcome), force_bytes("complete")) self.assertEqual(force_bytes(inst.purpose[0]), force_bytes("validation")) self.assertEqual(force_bytes(inst.purpose[1]), force_bytes("benefits")) self.assertEqual(inst.servicedDate.date, FHIRDate("2014-09-17").date) self.assertEqual(inst.servicedDate.as_json(), "2014-09-17") self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual( force_bytes(inst.text.div), force_bytes( '<div xmlns="http://www.w3.org/1999/xhtml">A human-readable rendering of the CoverageEligibilityResponse.</div>' ), ) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))

43.396992

128

0.615371

3,178

28,859

5.482379

0.069541

0.183091

0.182517

0.228147

0.909947

0.90237

0.899214

0.892211

0.879412

0.876428

0

0.03298

0.242455

28,859

664

129

43.462349

0.763974

0.006618

0

0.655712

0

0.00626

0.157083

0.020447

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0.014085

false

0

0.01252

0

0.029734

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null

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null

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df231ff484772e68ef6cd20c8e476180d044e593

69,681

py

Python

UFO_models/SMEFTsim_MFV_MwScheme_UFO/lorentz.py

matthewfeickert/SMEFTsim

db7d4a80bdcff424eee27dde71f1eb09ac894039

[ "MIT" ]

4

2020-12-29T03:42:43.000Z

2021-09-22T09:57:37.000Z

UFO_models/SMEFTsim_MFV_MwScheme_UFO/lorentz.py

matthewfeickert/SMEFTsim

db7d4a80bdcff424eee27dde71f1eb09ac894039

[ "MIT" ]

3

2021-05-19T11:06:59.000Z

2021-12-11T00:12:02.000Z

UFO_models/SMEFTsim_MFV_MwScheme_UFO/lorentz.py

matthewfeickert/SMEFTsim

db7d4a80bdcff424eee27dde71f1eb09ac894039

[ "MIT" ]

4

2021-09-22T09:57:39.000Z

2022-03-29T16:09:36.000Z

# This file was automatically created by FeynRules 2.3.35 # Mathematica version: 12.1.0 for Linux x86 (64-bit) (March 18, 2020) # Date: Thu 7 Jan 2021 11:40:43 from object_library import all_lorentz, Lorentz from function_library import complexconjugate, re, im, csc, sec, acsc, asec, cot try: import form_factors as ForFac except ImportError: pass SSS1 = Lorentz(name = 'SSS1', spins = [ 1, 1, 1 ], structure = '1') SSS2 = Lorentz(name = 'SSS2', spins = [ 1, 1, 1 ], structure = 'P(-1,1)*P(-1,2) + P(-1,1)*P(-1,3) + P(-1,2)*P(-1,3)') SSS3 = Lorentz(name = 'SSS3', spins = [ 1, 1, 1 ], structure = 'P(-1,1)**2 + (2*P(-1,1)*P(-1,2))/3. + P(-1,2)**2 + (2*P(-1,1)*P(-1,3))/3. + (2*P(-1,2)*P(-1,3))/3. + P(-1,3)**2') FFS1 = Lorentz(name = 'FFS1', spins = [ 2, 2, 1 ], structure = 'Identity(2,1)') FFS2 = Lorentz(name = 'FFS2', spins = [ 2, 2, 1 ], structure = 'ProjM(2,1)') FFS3 = Lorentz(name = 'FFS3', spins = [ 2, 2, 1 ], structure = 'ProjP(2,1)') FFV1 = Lorentz(name = 'FFV1', spins = [ 2, 2, 3 ], structure = 'Gamma(3,2,1)') FFV2 = Lorentz(name = 'FFV2', spins = [ 2, 2, 3 ], structure = 'Gamma(3,2,-1)*ProjM(-1,1)') FFV3 = Lorentz(name = 'FFV3', spins = [ 2, 2, 3 ], structure = '-(P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjM(-2,1)) + P(-1,3)*Gamma(-1,-3,-2)*Gamma(3,2,-3)*ProjM(-2,1)') FFV4 = Lorentz(name = 'FFV4', spins = [ 2, 2, 3 ], structure = 'Gamma(3,2,-1)*ProjP(-1,1)') FFV5 = Lorentz(name = 'FFV5', spins = [ 2, 2, 3 ], structure = 'Gamma(3,2,-1)*ProjM(-1,1) + (4*Gamma(3,2,-1)*ProjP(-1,1))/3.') FFV6 = Lorentz(name = 'FFV6', spins = [ 2, 2, 3 ], structure = 'Gamma(3,2,-1)*ProjM(-1,1) + (8*Gamma(3,2,-1)*ProjP(-1,1))/5.') FFV7 = Lorentz(name = 'FFV7', spins = [ 2, 2, 3 ], structure = 'Gamma(3,2,-1)*ProjM(-1,1) + 4*Gamma(3,2,-1)*ProjP(-1,1)') FFV8 = Lorentz(name = 'FFV8', spins = [ 2, 2, 3 ], structure = 'P(-1,3)*Gamma(-1,-2,1)*Gamma(3,2,-2) - P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjM(-2,1) - P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjP(-2,1)') FFV9 = Lorentz(name = 'FFV9', spins = [ 2, 2, 3 ], structure = '-(P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjP(-2,1)) + P(-1,3)*Gamma(-1,-3,-2)*Gamma(3,2,-3)*ProjP(-2,1)') FFV10 = Lorentz(name = 'FFV10', spins = [ 2, 2, 3 ], structure = '-(P(-1,3)*Gamma(-1,2,-2)*Gamma(3,-2,-3)*ProjP(-3,1)) + P(-1,3)*Gamma(-1,-3,-2)*Gamma(3,2,-3)*ProjP(-2,1)') VVS1 = Lorentz(name = 'VVS1', spins = [ 3, 3, 1 ], structure = 'Metric(1,2)') VVS2 = Lorentz(name = 'VVS2', spins = [ 3, 3, 1 ], structure = 'P(1,2)*P(2,1) - P(-1,1)*P(-1,2)*Metric(1,2)') VVS3 = Lorentz(name = 'VVS3', spins = [ 3, 3, 1 ], structure = 'P(-1,2)**2*P(1,1)*P(2,1) - P(-1,1)*P(-1,2)*P(1,1)*P(2,2) + P(-1,1)**2*P(1,2)*P(2,2) - P(-2,2)**2*P(-1,1)**2*Metric(1,2)') VVS4 = Lorentz(name = 'VVS4', spins = [ 3, 3, 1 ], structure = 'P(-1,1)**2*P(1,2)*P(2,1) + P(-1,2)**2*P(1,2)*P(2,1) - P(-2,1)*P(-2,2)*P(-1,1)**2*Metric(1,2) - P(-2,2)**2*P(-1,1)*P(-1,2)*Metric(1,2)') VVS5 = Lorentz(name = 'VVS5', spins = [ 3, 3, 1 ], structure = 'P(-1,1)*P(-1,2)*P(1,2)*P(2,1) - P(-2,1)*P(-2,2)*P(-1,1)*P(-1,2)*Metric(1,2)') VVV1 = Lorentz(name = 'VVV1', spins = [ 3, 3, 3 ], structure = 'P(3,1)*Metric(1,2) - P(3,2)*Metric(1,2) - P(2,1)*Metric(1,3) + P(1,2)*Metric(2,3)') VVV2 = Lorentz(name = 'VVV2', spins = [ 3, 3, 3 ], structure = 'P(3,1)*Metric(1,2) - P(3,2)*Metric(1,2) - P(2,1)*Metric(1,3) + P(2,3)*Metric(1,3) + P(1,2)*Metric(2,3) - P(1,3)*Metric(2,3)') VVV3 = Lorentz(name = 'VVV3', spins = [ 3, 3, 3 ], structure = 'P(3,2)*Metric(1,2) - P(2,3)*Metric(1,3) - P(1,2)*Metric(2,3) + P(1,3)*Metric(2,3)') VVV4 = Lorentz(name = 'VVV4', spins = [ 3, 3, 3 ], structure = '-(P(1,2)*P(2,3)*P(3,1)) + P(1,3)*P(2,1)*P(3,2) + P(-1,2)*P(-1,3)*P(3,1)*Metric(1,2) - P(-1,1)*P(-1,3)*P(3,2)*Metric(1,2) - P(-1,2)*P(-1,3)*P(2,1)*Metric(1,3) + P(-1,1)*P(-1,2)*P(2,3)*Metric(1,3) + P(-1,1)*P(-1,3)*P(1,2)*Metric(2,3) - P(-1,1)*P(-1,2)*P(1,3)*Metric(2,3)') SSSS1 = Lorentz(name = 'SSSS1', spins = [ 1, 1, 1, 1 ], structure = '1') SSSS2 = Lorentz(name = 'SSSS2', spins = [ 1, 1, 1, 1 ], structure = 'P(-1,1)*P(-1,2) + P(-1,1)*P(-1,3) + P(-1,2)*P(-1,3) + P(-1,1)*P(-1,4) + P(-1,2)*P(-1,4) + P(-1,3)*P(-1,4)') SSSS3 = Lorentz(name = 'SSSS3', spins = [ 1, 1, 1, 1 ], structure = 'P(-1,1)**2 + (2*P(-1,1)*P(-1,2))/3. + P(-1,2)**2 + (2*P(-1,1)*P(-1,3))/3. + (2*P(-1,2)*P(-1,3))/3. + P(-1,3)**2 + (2*P(-1,1)*P(-1,4))/3. + (2*P(-1,2)*P(-1,4))/3. + (2*P(-1,3)*P(-1,4))/3. + P(-1,4)**2') FFSS1 = Lorentz(name = 'FFSS1', spins = [ 2, 2, 1, 1 ], structure = 'Identity(2,1)') FFSS2 = Lorentz(name = 'FFSS2', spins = [ 2, 2, 1, 1 ], structure = 'ProjM(2,1)') FFSS3 = Lorentz(name = 'FFSS3', spins = [ 2, 2, 1, 1 ], structure = 'ProjP(2,1)') FFFF1 = Lorentz(name = 'FFFF1', spins = [ 2, 2, 2, 2 ], structure = 'ProjM(2,3)*ProjM(4,1)') FFFF2 = Lorentz(name = 'FFFF2', spins = [ 2, 2, 2, 2 ], structure = 'ProjM(2,1)*ProjM(4,3)') FFFF3 = Lorentz(name = 'FFFF3', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-3,1)*ProjM(-2,3)') FFFF4 = Lorentz(name = 'FFFF4', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-3,3)*ProjM(-2,1)') FFFF5 = Lorentz(name = 'FFFF5', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-2,-4,-3)*Gamma(-2,2,-6)*Gamma(-1,-6,-5)*Gamma(-1,4,-4)*ProjM(-5,1)*ProjM(-3,3)') FFFF6 = Lorentz(name = 'FFFF6', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-2,-4,-3)*Gamma(-2,4,-6)*Gamma(-1,-6,-5)*Gamma(-1,2,-4)*ProjM(-5,3)*ProjM(-3,1)') FFFF7 = Lorentz(name = 'FFFF7', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-2,-6,-5)*Gamma(-2,-4,-3)*Gamma(-1,2,-4)*Gamma(-1,4,-6)*ProjM(-5,3)*ProjM(-3,1)') FFFF8 = Lorentz(name = 'FFFF8', spins = [ 2, 2, 2, 2 ], structure = 'ProjM(4,3)*ProjP(2,1)') FFFF9 = Lorentz(name = 'FFFF9', spins = [ 2, 2, 2, 2 ], structure = 'ProjP(2,3)*ProjP(4,1)') FFFF10 = Lorentz(name = 'FFFF10', spins = [ 2, 2, 2, 2 ], structure = 'ProjM(2,1)*ProjP(4,3)') FFFF11 = Lorentz(name = 'FFFF11', spins = [ 2, 2, 2, 2 ], structure = 'ProjP(2,1)*ProjP(4,3)') FFFF12 = Lorentz(name = 'FFFF12', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-2,3)*ProjP(-3,1)') FFFF13 = Lorentz(name = 'FFFF13', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-2,3)*ProjP(-3,1)') FFFF14 = Lorentz(name = 'FFFF14', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjP(-3,1)*ProjP(-2,3)') FFFF15 = Lorentz(name = 'FFFF15', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-3)*Gamma(-1,4,-2)*ProjM(-2,1)*ProjP(-3,3)') FFFF16 = Lorentz(name = 'FFFF16', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjM(-2,1)*ProjP(-3,3)') FFFF17 = Lorentz(name = 'FFFF17', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-1,2,-2)*Gamma(-1,4,-3)*ProjP(-3,3)*ProjP(-2,1)') FFFF18 = Lorentz(name = 'FFFF18', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-2,-4,-3)*Gamma(-2,2,-6)*Gamma(-1,-6,-5)*Gamma(-1,4,-4)*ProjP(-5,1)*ProjP(-3,3)') FFFF19 = Lorentz(name = 'FFFF19', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-2,-4,-3)*Gamma(-2,4,-6)*Gamma(-1,-6,-5)*Gamma(-1,2,-4)*ProjP(-5,3)*ProjP(-3,1)') FFFF20 = Lorentz(name = 'FFFF20', spins = [ 2, 2, 2, 2 ], structure = 'Gamma(-2,-6,-5)*Gamma(-2,-4,-3)*Gamma(-1,2,-4)*Gamma(-1,4,-6)*ProjP(-5,3)*ProjP(-3,1)') FFVS1 = Lorentz(name = 'FFVS1', spins = [ 2, 2, 3, 1 ], structure = 'Gamma(3,2,-1)*ProjM(-1,1)') FFVS2 = Lorentz(name = 'FFVS2', spins = [ 2, 2, 3, 1 ], structure = '-(P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjM(-2,1)) + P(-1,3)*Gamma(-1,-3,-2)*Gamma(3,2,-3)*ProjM(-2,1)') FFVS3 = Lorentz(name = 'FFVS3', spins = [ 2, 2, 3, 1 ], structure = 'Gamma(3,2,-1)*ProjP(-1,1)') FFVS4 = Lorentz(name = 'FFVS4', spins = [ 2, 2, 3, 1 ], structure = 'P(-1,3)*Gamma(-1,-2,1)*Gamma(3,2,-2) - P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjM(-2,1) - P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjP(-2,1)') FFVS5 = Lorentz(name = 'FFVS5', spins = [ 2, 2, 3, 1 ], structure = '-(P(-1,3)*Gamma(-1,2,-3)*Gamma(3,-3,-2)*ProjP(-2,1)) + P(-1,3)*Gamma(-1,-3,-2)*Gamma(3,2,-3)*ProjP(-2,1)') FFVS6 = Lorentz(name = 'FFVS6', spins = [ 2, 2, 3, 1 ], structure = '-(P(-1,3)*Gamma(-1,2,-2)*Gamma(3,-2,-3)*ProjP(-3,1)) + P(-1,3)*Gamma(-1,-3,-2)*Gamma(3,2,-3)*ProjP(-2,1)') FFVV1 = Lorentz(name = 'FFVV1', spins = [ 2, 2, 3, 3 ], structure = 'Gamma(3,2,-2)*Gamma(4,-2,-1)*ProjM(-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjM(-1,1)') FFVV2 = Lorentz(name = 'FFVV2', spins = [ 2, 2, 3, 3 ], structure = 'Gamma(3,2,-1)*Gamma(4,-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjM(-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjP(-1,1)') FFVV3 = Lorentz(name = 'FFVV3', spins = [ 2, 2, 3, 3 ], structure = '-(Gamma(3,2,-2)*Gamma(4,-2,-1)*ProjP(-1,1)) + Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjP(-1,1)') FFVV4 = Lorentz(name = 'FFVV4', spins = [ 2, 2, 3, 3 ], structure = 'Gamma(3,2,-2)*Gamma(4,-2,-1)*ProjP(-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjP(-1,1)') FFVV5 = Lorentz(name = 'FFVV5', spins = [ 2, 2, 3, 3 ], structure = '-(Gamma(3,-1,-2)*Gamma(4,2,-1)*ProjP(-2,1)) + Gamma(3,2,-2)*Gamma(4,-2,-1)*ProjP(-1,1)') VVSS1 = Lorentz(name = 'VVSS1', spins = [ 3, 3, 1, 1 ], structure = 'Metric(1,2)') VVSS2 = Lorentz(name = 'VVSS2', spins = [ 3, 3, 1, 1 ], structure = 'P(1,2)*P(2,1) - P(-1,1)*P(-1,2)*Metric(1,2)') VVVS1 = Lorentz(name = 'VVVS1', spins = [ 3, 3, 3, 1 ], structure = 'P(3,1)*Metric(1,2) - P(2,1)*Metric(1,3)') VVVS2 = Lorentz(name = 'VVVS2', spins = [ 3, 3, 3, 1 ], structure = 'P(2,3)*Metric(1,3) - P(1,3)*Metric(2,3)') VVVS3 = Lorentz(name = 'VVVS3', spins = [ 3, 3, 3, 1 ], structure = 'P(3,1)*Metric(1,2) - P(3,2)*Metric(1,2) - P(2,1)*Metric(1,3) + P(2,3)*Metric(1,3) + P(1,2)*Metric(2,3) - P(1,3)*Metric(2,3)') VVVS4 = Lorentz(name = 'VVVS4', spins = [ 3, 3, 3, 1 ], structure = '-(P(1,2)*P(2,3)*P(3,1)) + P(1,3)*P(2,1)*P(3,2) + P(-1,2)*P(-1,3)*P(3,1)*Metric(1,2) - P(-1,1)*P(-1,3)*P(3,2)*Metric(1,2) - P(-1,2)*P(-1,3)*P(2,1)*Metric(1,3) + P(-1,1)*P(-1,2)*P(2,3)*Metric(1,3) + P(-1,1)*P(-1,3)*P(1,2)*Metric(2,3) - P(-1,1)*P(-1,2)*P(1,3)*Metric(2,3)') VVVS5 = Lorentz(name = 'VVVS5', spins = [ 3, 3, 3, 1 ], structure = 'P(1,2)*P(2,1)*P(3,1) - P(1,3)*P(2,1)*P(3,1) + P(1,3)*P(2,3)*P(3,1) - P(1,2)*P(2,1)*P(3,2) + P(1,2)*P(2,3)*P(3,2) - P(1,3)*P(2,3)*P(3,2) - 2*P(-1,1)*P(-1,2)*P(3,1)*Metric(1,2) - P(-1,1)*P(-1,3)*P(3,1)*Metric(1,2) + 2*P(-1,1)*P(-1,2)*P(3,2)*Metric(1,2) + P(-1,2)*P(-1,3)*P(3,2)*Metric(1,2) + P(-1,1)*P(-1,2)*P(2,1)*Metric(1,3) + 2*P(-1,1)*P(-1,3)*P(2,1)*Metric(1,3) - 2*P(-1,1)*P(-1,3)*P(2,3)*Metric(1,3) - P(-1,2)*P(-1,3)*P(2,3)*Metric(1,3) - P(-1,1)*P(-1,2)*P(1,2)*Metric(2,3) - 2*P(-1,2)*P(-1,3)*P(1,2)*Metric(2,3) + P(-1,1)*P(-1,3)*P(1,3)*Metric(2,3) + 2*P(-1,2)*P(-1,3)*P(1,3)*Metric(2,3)') VVVS6 = Lorentz(name = 'VVVS6', spins = [ 3, 3, 3, 1 ], structure = 'P(1,1)*P(2,2)*P(3,1) + 2*P(1,2)*P(2,2)*P(3,1) + 2*P(1,1)*P(2,3)*P(3,1) - 2*P(1,1)*P(2,1)*P(3,2) - P(1,1)*P(2,2)*P(3,2) - 2*P(1,3)*P(2,2)*P(3,2) - P(1,1)*P(2,1)*P(3,3) - 2*P(1,3)*P(2,1)*P(3,3) + P(1,2)*P(2,2)*P(3,3) - P(1,3)*P(2,2)*P(3,3) + P(1,1)*P(2,3)*P(3,3) + 2*P(1,2)*P(2,3)*P(3,3) - 2*P(-1,2)**2*P(3,1)*Metric(1,2) - P(-1,3)**2*P(3,1)*Metric(1,2) + 2*P(-1,1)**2*P(3,2)*Metric(1,2) + P(-1,3)**2*P(3,2)*Metric(1,2) + P(-1,1)**2*P(3,3)*Metric(1,2) - P(-1,2)**2*P(3,3)*Metric(1,2) + P(-1,1)*P(-1,3)*P(3,3)*Metric(1,2) - P(-1,2)*P(-1,3)*P(3,3)*Metric(1,2) + P(-1,2)**2*P(2,1)*Metric(1,3) + 2*P(-1,3)**2*P(2,1)*Metric(1,3) - P(-1,1)**2*P(2,2)*Metric(1,3) - P(-1,1)*P(-1,2)*P(2,2)*Metric(1,3) + P(-1,2)*P(-1,3)*P(2,2)*Metric(1,3) + P(-1,3)**2*P(2,2)*Metric(1,3) - 2*P(-1,1)**2*P(2,3)*Metric(1,3) - P(-1,2)**2*P(2,3)*Metric(1,3) + P(-1,1)*P(-1,2)*P(1,1)*Metric(2,3) + P(-1,2)**2*P(1,1)*Metric(2,3) - P(-1,1)*P(-1,3)*P(1,1)*Metric(2,3) - P(-1,3)**2*P(1,1)*Metric(2,3) - P(-1,1)**2*P(1,2)*Metric(2,3) - 2*P(-1,3)**2*P(1,2)*Metric(2,3) + P(-1,1)**2*P(1,3)*Metric(2,3) + 2*P(-1,2)**2*P(1,3)*Metric(2,3)') VVVS7 = Lorentz(name = 'VVVS7', spins = [ 3, 3, 3, 1 ], structure = 'P(1,2)*P(2,1)*P(3,1) - P(1,3)*P(2,1)*P(3,1) + 3*P(1,2)*P(2,3)*P(3,1) + P(1,3)*P(2,3)*P(3,1) - P(1,2)*P(2,1)*P(3,2) - 3*P(1,3)*P(2,1)*P(3,2) + P(1,2)*P(2,3)*P(3,2) - P(1,3)*P(2,3)*P(3,2) + (P(-1,1)**2*P(3,1)*Metric(1,2))/2. - P(-1,1)*P(-1,2)*P(3,1)*Metric(1,2) + (P(-1,2)**2*P(3,1)*Metric(1,2))/2. - 2*P(-1,2)*P(-1,3)*P(3,1)*Metric(1,2) + (P(-1,3)**2*P(3,1)*Metric(1,2))/2. - (P(-1,1)**2*P(3,2)*Metric(1,2))/2. + P(-1,1)*P(-1,2)*P(3,2)*Metric(1,2) - (P(-1,2)**2*P(3,2)*Metric(1,2))/2. + 2*P(-1,1)*P(-1,3)*P(3,2)*Metric(1,2) - (P(-1,3)**2*P(3,2)*Metric(1,2))/2. - (P(-1,1)**2*P(2,1)*Metric(1,3))/2. - (P(-1,2)**2*P(2,1)*Metric(1,3))/2. + P(-1,1)*P(-1,3)*P(2,1)*Metric(1,3) + 2*P(-1,2)*P(-1,3)*P(2,1)*Metric(1,3) - (P(-1,3)**2*P(2,1)*Metric(1,3))/2. + (P(-1,1)**2*P(2,3)*Metric(1,3))/2. - 2*P(-1,1)*P(-1,2)*P(2,3)*Metric(1,3) + (P(-1,2)**2*P(2,3)*Metric(1,3))/2. - P(-1,1)*P(-1,3)*P(2,3)*Metric(1,3) + (P(-1,3)**2*P(2,3)*Metric(1,3))/2. + (P(-1,1)**2*P(1,2)*Metric(2,3))/2. + (P(-1,2)**2*P(1,2)*Metric(2,3))/2. - 2*P(-1,1)*P(-1,3)*P(1,2)*Metric(2,3) - P(-1,2)*P(-1,3)*P(1,2)*Metric(2,3) + (P(-1,3)**2*P(1,2)*Metric(2,3))/2. - (P(-1,1)**2*P(1,3)*Metric(2,3))/2. + 2*P(-1,1)*P(-1,2)*P(1,3)*Metric(2,3) - (P(-1,2)**2*P(1,3)*Metric(2,3))/2. + P(-1,2)*P(-1,3)*P(1,3)*Metric(2,3) - (P(-1,3)**2*P(1,3)*Metric(2,3))/2.') VVVV1 = Lorentz(name = 'VVVV1', spins = [ 3, 3, 3, 3 ], structure = 'Metric(1,4)*Metric(2,3) - Metric(1,3)*Metric(2,4)') VVVV2 = Lorentz(name = 'VVVV2', spins = [ 3, 3, 3, 3 ], structure = 'P(3,2)*P(4,1)*Metric(1,2) - P(3,1)*P(4,2)*Metric(1,2) + P(2,1)*P(4,2)*Metric(1,3) + P(2,4)*P(4,3)*Metric(1,3) - P(2,1)*P(3,2)*Metric(1,4) - P(2,3)*P(3,4)*Metric(1,4) - P(1,2)*P(4,1)*Metric(2,3) - P(1,4)*P(4,3)*Metric(2,3) + P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3) + P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,2)*P(3,1)*Metric(2,4) + P(1,3)*P(3,4)*Metric(2,4) - P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4) - P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,4)*P(2,3)*Metric(3,4) - P(1,3)*P(2,4)*Metric(3,4)') VVVV3 = Lorentz(name = 'VVVV3', spins = [ 3, 3, 3, 3 ], structure = 'Metric(1,4)*Metric(2,3) + Metric(1,3)*Metric(2,4) - 2*Metric(1,2)*Metric(3,4)') VVVV4 = Lorentz(name = 'VVVV4', spins = [ 3, 3, 3, 3 ], structure = 'Metric(1,4)*Metric(2,3) - Metric(1,2)*Metric(3,4)') VVVV5 = Lorentz(name = 'VVVV5', spins = [ 3, 3, 3, 3 ], structure = 'Metric(1,3)*Metric(2,4) - Metric(1,2)*Metric(3,4)') VVVV6 = Lorentz(name = 'VVVV6', spins = [ 3, 3, 3, 3 ], structure = 'Metric(1,4)*Metric(2,3) - (Metric(1,3)*Metric(2,4))/2. - (Metric(1,2)*Metric(3,4))/2.') VVVV7 = Lorentz(name = 'VVVV7', spins = [ 3, 3, 3, 3 ], structure = 'P(3,4)*P(4,1)*Metric(1,2) + P(3,2)*P(4,3)*Metric(1,2) - P(2,4)*P(4,1)*Metric(1,3) - P(2,3)*P(4,2)*Metric(1,3) + P(2,4)*P(3,1)*Metric(1,4) - P(2,1)*P(3,4)*Metric(1,4) + P(1,3)*P(4,2)*Metric(2,3) - P(1,2)*P(4,3)*Metric(2,3) - P(1,4)*P(3,1)*Metric(2,4) - P(1,3)*P(3,2)*Metric(2,4) + P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4) + P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,4)*P(2,1)*Metric(3,4) + P(1,2)*P(2,3)*Metric(3,4) - P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVV8 = Lorentz(name = 'VVVV8', spins = [ 3, 3, 3, 3 ], structure = 'P(3,4)*P(4,2)*Metric(1,2) + P(3,1)*P(4,3)*Metric(1,2) + P(2,3)*P(4,1)*Metric(1,3) - P(2,1)*P(4,3)*Metric(1,3) - P(2,3)*P(3,1)*Metric(1,4) - P(2,4)*P(3,2)*Metric(1,4) - P(1,3)*P(4,1)*Metric(2,3) - P(1,4)*P(4,2)*Metric(2,3) + P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,4)*P(3,2)*Metric(2,4) - P(1,2)*P(3,4)*Metric(2,4) + P(1,3)*P(2,1)*Metric(3,4) + P(1,2)*P(2,4)*Metric(3,4) - P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVV9 = Lorentz(name = 'VVVV9', spins = [ 3, 3, 3, 3 ], structure = 'P(3,2)*P(4,1)*Metric(1,2) - P(3,1)*P(4,2)*Metric(1,2) + P(3,4)*P(4,2)*Metric(1,2) + P(3,1)*P(4,3)*Metric(1,2) + P(2,3)*P(4,1)*Metric(1,3) + P(2,1)*P(4,2)*Metric(1,3) - P(2,1)*P(4,3)*Metric(1,3) + P(2,4)*P(4,3)*Metric(1,3) - P(2,3)*P(3,1)*Metric(1,4) - P(2,1)*P(3,2)*Metric(1,4) - P(2,4)*P(3,2)*Metric(1,4) - P(2,3)*P(3,4)*Metric(1,4) - P(1,2)*P(4,1)*Metric(2,3) - P(1,3)*P(4,1)*Metric(2,3) - P(1,4)*P(4,2)*Metric(2,3) - P(1,4)*P(4,3)*Metric(2,3) + P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3) + P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,2)*P(3,1)*Metric(2,4) + P(1,4)*P(3,2)*Metric(2,4) - P(1,2)*P(3,4)*Metric(2,4) + P(1,3)*P(3,4)*Metric(2,4) - P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4) - P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,3)*P(2,1)*Metric(3,4) + P(1,4)*P(2,3)*Metric(3,4) + P(1,2)*P(2,4)*Metric(3,4) - P(1,3)*P(2,4)*Metric(3,4) - P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVV10 = Lorentz(name = 'VVVV10', spins = [ 3, 3, 3, 3 ], structure = 'P(3,4)*P(4,1)*Metric(1,2) + P(3,4)*P(4,2)*Metric(1,2) + P(3,1)*P(4,3)*Metric(1,2) + P(3,2)*P(4,3)*Metric(1,2) + P(2,3)*P(4,1)*Metric(1,3) - P(2,4)*P(4,1)*Metric(1,3) - P(2,3)*P(4,2)*Metric(1,3) - P(2,1)*P(4,3)*Metric(1,3) - P(2,3)*P(3,1)*Metric(1,4) + P(2,4)*P(3,1)*Metric(1,4) - P(2,4)*P(3,2)*Metric(1,4) - P(2,1)*P(3,4)*Metric(1,4) - P(1,3)*P(4,1)*Metric(2,3) + P(1,3)*P(4,2)*Metric(2,3) - P(1,4)*P(4,2)*Metric(2,3) - P(1,2)*P(4,3)*Metric(2,3) + P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) - P(1,4)*P(3,1)*Metric(2,4) - P(1,3)*P(3,2)*Metric(2,4) + P(1,4)*P(3,2)*Metric(2,4) - P(1,2)*P(3,4)*Metric(2,4) + P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4) + P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,3)*P(2,1)*Metric(3,4) + P(1,4)*P(2,1)*Metric(3,4) + P(1,2)*P(2,3)*Metric(3,4) + P(1,2)*P(2,4)*Metric(3,4) - P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4)') SSSSS1 = Lorentz(name = 'SSSSS1', spins = [ 1, 1, 1, 1, 1 ], structure = '1') FFSSS1 = Lorentz(name = 'FFSSS1', spins = [ 2, 2, 1, 1, 1 ], structure = 'Identity(2,1)') FFSSS2 = Lorentz(name = 'FFSSS2', spins = [ 2, 2, 1, 1, 1 ], structure = 'ProjM(2,1)') FFSSS3 = Lorentz(name = 'FFSSS3', spins = [ 2, 2, 1, 1, 1 ], structure = 'ProjP(2,1)') FFVSS1 = Lorentz(name = 'FFVSS1', spins = [ 2, 2, 3, 1, 1 ], structure = 'Gamma(3,2,-1)*ProjM(-1,1)') FFVSS2 = Lorentz(name = 'FFVSS2', spins = [ 2, 2, 3, 1, 1 ], structure = 'Gamma(3,2,-1)*ProjP(-1,1)') FFVVS1 = Lorentz(name = 'FFVVS1', spins = [ 2, 2, 3, 3, 1 ], structure = 'Gamma(3,2,-2)*Gamma(4,-2,-1)*ProjM(-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjM(-1,1)') FFVVS2 = Lorentz(name = 'FFVVS2', spins = [ 2, 2, 3, 3, 1 ], structure = 'Gamma(3,2,-1)*Gamma(4,-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjM(-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjP(-1,1)') FFVVS3 = Lorentz(name = 'FFVVS3', spins = [ 2, 2, 3, 3, 1 ], structure = 'Gamma(3,2,-2)*Gamma(4,-2,-1)*ProjP(-1,1) - Gamma(3,-2,-1)*Gamma(4,2,-2)*ProjP(-1,1)') VVSSS1 = Lorentz(name = 'VVSSS1', spins = [ 3, 3, 1, 1, 1 ], structure = 'Metric(1,2)') VVVSS1 = Lorentz(name = 'VVVSS1', spins = [ 3, 3, 3, 1, 1 ], structure = 'P(3,1)*Metric(1,2) - P(2,1)*Metric(1,3)') VVVSS2 = Lorentz(name = 'VVVSS2', spins = [ 3, 3, 3, 1, 1 ], structure = 'P(2,3)*Metric(1,3) - P(1,3)*Metric(2,3)') VVVSS3 = Lorentz(name = 'VVVSS3', spins = [ 3, 3, 3, 1, 1 ], structure = 'P(3,1)*Metric(1,2) - P(3,2)*Metric(1,2) - P(2,1)*Metric(1,3) + P(2,3)*Metric(1,3) + P(1,2)*Metric(2,3) - P(1,3)*Metric(2,3)') VVVVS1 = Lorentz(name = 'VVVVS1', spins = [ 3, 3, 3, 3, 1 ], structure = 'Metric(1,4)*Metric(2,3) - Metric(1,3)*Metric(2,4)') VVVVS2 = Lorentz(name = 'VVVVS2', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(2,1)*P(4,1)*Metric(1,3) - 2*P(2,3)*P(4,1)*Metric(1,3) - P(2,4)*P(4,1)*Metric(1,3) - P(2,3)*P(4,2)*Metric(1,3) + P(2,4)*P(4,2)*Metric(1,3) + P(2,3)*P(4,3)*Metric(1,3) - P(2,1)*P(3,1)*Metric(1,4) + P(2,3)*P(3,1)*Metric(1,4) + 2*P(2,4)*P(3,1)*Metric(1,4) - P(2,3)*P(3,2)*Metric(1,4) + P(2,4)*P(3,2)*Metric(1,4) - P(2,4)*P(3,4)*Metric(1,4) + P(1,3)*P(4,1)*Metric(2,3) - P(1,4)*P(4,1)*Metric(2,3) - P(1,2)*P(4,2)*Metric(2,3) + 2*P(1,3)*P(4,2)*Metric(2,3) + P(1,4)*P(4,2)*Metric(2,3) - P(1,3)*P(4,3)*Metric(2,3) + P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + 2*P(-1,2)*P(-1,3)*Metric(1,4)*Metric(2,3) + 2*P(-1,1)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,3)*P(3,1)*Metric(2,4) - P(1,4)*P(3,1)*Metric(2,4) + P(1,2)*P(3,2)*Metric(2,4) - P(1,3)*P(3,2)*Metric(2,4) - 2*P(1,4)*P(3,2)*Metric(2,4) + P(1,4)*P(3,4)*Metric(2,4) - 2*P(-1,1)*P(-1,3)*Metric(1,3)*Metric(2,4) - P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4) - P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4) - 2*P(-1,2)*P(-1,4)*Metric(1,3)*Metric(2,4)') VVVVS3 = Lorentz(name = 'VVVVS3', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,2)*P(4,1)*Metric(1,2) - P(3,1)*P(4,2)*Metric(1,2) + P(2,1)*P(4,2)*Metric(1,3) + P(2,4)*P(4,3)*Metric(1,3) - P(2,1)*P(3,2)*Metric(1,4) - P(2,3)*P(3,4)*Metric(1,4) - P(1,2)*P(4,1)*Metric(2,3) - P(1,4)*P(4,3)*Metric(2,3) + P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3) + P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,2)*P(3,1)*Metric(2,4) + P(1,3)*P(3,4)*Metric(2,4) - P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4) - P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,4)*P(2,3)*Metric(3,4) - P(1,3)*P(2,4)*Metric(3,4)') VVVVS4 = Lorentz(name = 'VVVVS4', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,2)*P(4,1)*Metric(1,2) - P(3,1)*P(4,2)*Metric(1,2) + (P(2,1)*P(4,1)*Metric(1,3))/3. - (2*P(2,3)*P(4,1)*Metric(1,3))/3. - (P(2,4)*P(4,1)*Metric(1,3))/3. + P(2,1)*P(4,2)*Metric(1,3) - (P(2,3)*P(4,2)*Metric(1,3))/3. + (P(2,4)*P(4,2)*Metric(1,3))/3. + (P(2,3)*P(4,3)*Metric(1,3))/3. + P(2,4)*P(4,3)*Metric(1,3) - (P(2,1)*P(3,1)*Metric(1,4))/3. + (P(2,3)*P(3,1)*Metric(1,4))/3. + (2*P(2,4)*P(3,1)*Metric(1,4))/3. - P(2,1)*P(3,2)*Metric(1,4) - (P(2,3)*P(3,2)*Metric(1,4))/3. + (P(2,4)*P(3,2)*Metric(1,4))/3. - P(2,3)*P(3,4)*Metric(1,4) - (P(2,4)*P(3,4)*Metric(1,4))/3. - P(1,2)*P(4,1)*Metric(2,3) + (P(1,3)*P(4,1)*Metric(2,3))/3. - (P(1,4)*P(4,1)*Metric(2,3))/3. - (P(1,2)*P(4,2)*Metric(2,3))/3. + (2*P(1,3)*P(4,2)*Metric(2,3))/3. + (P(1,4)*P(4,2)*Metric(2,3))/3. - (P(1,3)*P(4,3)*Metric(2,3))/3. - P(1,4)*P(4,3)*Metric(2,3) - (P(-1,1)**2*Metric(1,4)*Metric(2,3))/6. + (2*P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3))/3. - (P(-1,2)**2*Metric(1,4)*Metric(2,3))/6. + (P(-1,2)*P(-1,3)*Metric(1,4)*Metric(2,3))/3. - (P(-1,3)**2*Metric(1,4)*Metric(2,3))/6. + (P(-1,1)*P(-1,4)*Metric(1,4)*Metric(2,3))/3. + (2*P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3))/3. - (P(-1,4)**2*Metric(1,4)*Metric(2,3))/6. + P(1,2)*P(3,1)*Metric(2,4) + (P(1,3)*P(3,1)*Metric(2,4))/3. - (P(1,4)*P(3,1)*Metric(2,4))/3. + (P(1,2)*P(3,2)*Metric(2,4))/3. - (P(1,3)*P(3,2)*Metric(2,4))/3. - (2*P(1,4)*P(3,2)*Metric(2,4))/3. + P(1,3)*P(3,4)*Metric(2,4) + (P(1,4)*P(3,4)*Metric(2,4))/3. + (P(-1,1)**2*Metric(1,3)*Metric(2,4))/6. - (2*P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4))/3. + (P(-1,2)**2*Metric(1,3)*Metric(2,4))/6. - (P(-1,1)*P(-1,3)*Metric(1,3)*Metric(2,4))/3. + (P(-1,3)**2*Metric(1,3)*Metric(2,4))/6. - (P(-1,2)*P(-1,4)*Metric(1,3)*Metric(2,4))/3. - (2*P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4))/3. + (P(-1,4)**2*Metric(1,3)*Metric(2,4))/6. + P(1,4)*P(2,3)*Metric(3,4) - P(1,3)*P(2,4)*Metric(3,4)') VVVVS5 = Lorentz(name = 'VVVVS5', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,2)*P(4,1)*Metric(1,2) + (P(3,3)*P(4,1)*Metric(1,2))/6. - P(3,1)*P(4,2)*Metric(1,2) - (P(3,3)*P(4,2)*Metric(1,2))/6. - (P(3,1)*P(4,4)*Metric(1,2))/6. + (P(3,2)*P(4,4)*Metric(1,2))/6. + (P(2,1)*P(4,1)*Metric(1,3))/3. - (P(2,2)*P(4,1)*Metric(1,3))/6. - (2*P(2,3)*P(4,1)*Metric(1,3))/3. - (P(2,4)*P(4,1)*Metric(1,3))/3. + P(2,1)*P(4,2)*Metric(1,3) - (P(2,3)*P(4,2)*Metric(1,3))/3. + (P(2,4)*P(4,2)*Metric(1,3))/3. + (P(2,2)*P(4,3)*Metric(1,3))/6. + (P(2,3)*P(4,3)*Metric(1,3))/3. + P(2,4)*P(4,3)*Metric(1,3) + (P(2,1)*P(4,4)*Metric(1,3))/6. - (P(2,3)*P(4,4)*Metric(1,3))/6. - (P(2,1)*P(3,1)*Metric(1,4))/3. + (P(2,2)*P(3,1)*Metric(1,4))/6. + (P(2,3)*P(3,1)*Metric(1,4))/3. + (2*P(2,4)*P(3,1)*Metric(1,4))/3. - P(2,1)*P(3,2)*Metric(1,4) - (P(2,3)*P(3,2)*Metric(1,4))/3. + (P(2,4)*P(3,2)*Metric(1,4))/3. - (P(2,1)*P(3,3)*Metric(1,4))/6. + (P(2,4)*P(3,3)*Metric(1,4))/6. - (P(2,2)*P(3,4)*Metric(1,4))/6. - P(2,3)*P(3,4)*Metric(1,4) - (P(2,4)*P(3,4)*Metric(1,4))/3. - P(1,2)*P(4,1)*Metric(2,3) + (P(1,3)*P(4,1)*Metric(2,3))/3. - (P(1,4)*P(4,1)*Metric(2,3))/3. + (P(1,1)*P(4,2)*Metric(2,3))/6. - (P(1,2)*P(4,2)*Metric(2,3))/3. + (2*P(1,3)*P(4,2)*Metric(2,3))/3. + (P(1,4)*P(4,2)*Metric(2,3))/3. - (P(1,1)*P(4,3)*Metric(2,3))/6. - (P(1,3)*P(4,3)*Metric(2,3))/3. - P(1,4)*P(4,3)*Metric(2,3) - (P(1,2)*P(4,4)*Metric(2,3))/6. + (P(1,3)*P(4,4)*Metric(2,3))/6. - (P(-1,1)**2*Metric(1,4)*Metric(2,3))/6. + (2*P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3))/3. - (P(-1,2)**2*Metric(1,4)*Metric(2,3))/6. + (P(-1,2)*P(-1,3)*Metric(1,4)*Metric(2,3))/3. - (P(-1,3)**2*Metric(1,4)*Metric(2,3))/6. + (P(-1,1)*P(-1,4)*Metric(1,4)*Metric(2,3))/3. + (2*P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3))/3. - (P(-1,4)**2*Metric(1,4)*Metric(2,3))/6. + P(1,2)*P(3,1)*Metric(2,4) + (P(1,3)*P(3,1)*Metric(2,4))/3. - (P(1,4)*P(3,1)*Metric(2,4))/3. - (P(1,1)*P(3,2)*Metric(2,4))/6. + (P(1,2)*P(3,2)*Metric(2,4))/3. - (P(1,3)*P(3,2)*Metric(2,4))/3. - (2*P(1,4)*P(3,2)*Metric(2,4))/3. + (P(1,2)*P(3,3)*Metric(2,4))/6. - (P(1,4)*P(3,3)*Metric(2,4))/6. + (P(1,1)*P(3,4)*Metric(2,4))/6. + P(1,3)*P(3,4)*Metric(2,4) + (P(1,4)*P(3,4)*Metric(2,4))/3. + (P(-1,1)**2*Metric(1,3)*Metric(2,4))/6. - (2*P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4))/3. + (P(-1,2)**2*Metric(1,3)*Metric(2,4))/6. - (P(-1,1)*P(-1,3)*Metric(1,3)*Metric(2,4))/3. + (P(-1,3)**2*Metric(1,3)*Metric(2,4))/6. - (P(-1,2)*P(-1,4)*Metric(1,3)*Metric(2,4))/3. - (2*P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4))/3. + (P(-1,4)**2*Metric(1,3)*Metric(2,4))/6. - (P(1,3)*P(2,2)*Metric(3,4))/6. + (P(1,4)*P(2,2)*Metric(3,4))/6. + (P(1,1)*P(2,3)*Metric(3,4))/6. + P(1,4)*P(2,3)*Metric(3,4) - (P(1,1)*P(2,4)*Metric(3,4))/6. - P(1,3)*P(2,4)*Metric(3,4)') VVVVS6 = Lorentz(name = 'VVVVS6', spins = [ 3, 3, 3, 3, 1 ], structure = 'Metric(1,4)*Metric(2,3) + Metric(1,3)*Metric(2,4) - 2*Metric(1,2)*Metric(3,4)') VVVVS7 = Lorentz(name = 'VVVVS7', spins = [ 3, 3, 3, 3, 1 ], structure = 'Metric(1,4)*Metric(2,3) - Metric(1,2)*Metric(3,4)') VVVVS8 = Lorentz(name = 'VVVVS8', spins = [ 3, 3, 3, 3, 1 ], structure = 'Metric(1,3)*Metric(2,4) - Metric(1,2)*Metric(3,4)') VVVVS9 = Lorentz(name = 'VVVVS9', spins = [ 3, 3, 3, 3, 1 ], structure = 'Metric(1,4)*Metric(2,3) - (Metric(1,3)*Metric(2,4))/2. - (Metric(1,2)*Metric(3,4))/2.') VVVVS10 = Lorentz(name = 'VVVVS10', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,4)*P(4,1)*Metric(1,2) + P(3,2)*P(4,3)*Metric(1,2) - P(2,4)*P(4,1)*Metric(1,3) - P(2,3)*P(4,2)*Metric(1,3) + P(2,4)*P(3,1)*Metric(1,4) - P(2,1)*P(3,4)*Metric(1,4) + P(1,3)*P(4,2)*Metric(2,3) - P(1,2)*P(4,3)*Metric(2,3) - P(1,4)*P(3,1)*Metric(2,4) - P(1,3)*P(3,2)*Metric(2,4) + P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4) + P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,4)*P(2,1)*Metric(3,4) + P(1,2)*P(2,3)*Metric(3,4) - P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVVS11 = Lorentz(name = 'VVVVS11', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,4)*P(4,2)*Metric(1,2) + P(3,1)*P(4,3)*Metric(1,2) + P(2,3)*P(4,1)*Metric(1,3) - P(2,1)*P(4,3)*Metric(1,3) - P(2,3)*P(3,1)*Metric(1,4) - P(2,4)*P(3,2)*Metric(1,4) - P(1,3)*P(4,1)*Metric(2,3) - P(1,4)*P(4,2)*Metric(2,3) + P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,4)*P(3,2)*Metric(2,4) - P(1,2)*P(3,4)*Metric(2,4) + P(1,3)*P(2,1)*Metric(3,4) + P(1,2)*P(2,4)*Metric(3,4) - P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVVS12 = Lorentz(name = 'VVVVS12', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,3)*P(4,1)*Metric(1,2) + 2*P(3,4)*P(4,1)*Metric(1,2) - P(3,3)*P(4,2)*Metric(1,2) - 2*P(3,4)*P(4,2)*Metric(1,2) - 2*P(3,1)*P(4,3)*Metric(1,2) + 2*P(3,2)*P(4,3)*Metric(1,2) - P(3,1)*P(4,4)*Metric(1,2) + P(3,2)*P(4,4)*Metric(1,2) + P(2,2)*P(4,2)*Metric(1,3) + 4*P(2,1)*P(4,3)*Metric(1,3) + 2*P(2,2)*P(4,3)*Metric(1,3) + 2*P(2,1)*P(4,4)*Metric(1,3) + P(2,2)*P(4,4)*Metric(1,3) + P(2,4)*P(4,4)*Metric(1,3) - P(2,2)*P(3,2)*Metric(1,4) - 2*P(2,1)*P(3,3)*Metric(1,4) - P(2,2)*P(3,3)*Metric(1,4) - P(2,3)*P(3,3)*Metric(1,4) - 4*P(2,1)*P(3,4)*Metric(1,4) - 2*P(2,2)*P(3,4)*Metric(1,4) - P(1,1)*P(4,1)*Metric(2,3) - 2*P(1,1)*P(4,3)*Metric(2,3) - 4*P(1,2)*P(4,3)*Metric(2,3) - P(1,1)*P(4,4)*Metric(2,3) - 2*P(1,2)*P(4,4)*Metric(2,3) - P(1,4)*P(4,4)*Metric(2,3) + P(-1,1)**2*Metric(1,4)*Metric(2,3) + P(-1,2)**2*Metric(1,4)*Metric(2,3) + P(-1,3)**2*Metric(1,4)*Metric(2,3) + P(-1,4)**2*Metric(1,4)*Metric(2,3) + P(1,1)*P(3,1)*Metric(2,4) + P(1,1)*P(3,3)*Metric(2,4) + 2*P(1,2)*P(3,3)*Metric(2,4) + P(1,3)*P(3,3)*Metric(2,4) + 2*P(1,1)*P(3,4)*Metric(2,4) + 4*P(1,2)*P(3,4)*Metric(2,4) - P(-1,1)**2*Metric(1,3)*Metric(2,4) - P(-1,2)**2*Metric(1,3)*Metric(2,4) - P(-1,3)**2*Metric(1,3)*Metric(2,4) - P(-1,4)**2*Metric(1,3)*Metric(2,4) - 2*P(1,3)*P(2,1)*Metric(3,4) + 2*P(1,4)*P(2,1)*Metric(3,4) - P(1,3)*P(2,2)*Metric(3,4) + P(1,4)*P(2,2)*Metric(3,4) + P(1,1)*P(2,3)*Metric(3,4) + 2*P(1,2)*P(2,3)*Metric(3,4) - P(1,1)*P(2,4)*Metric(3,4) - 2*P(1,2)*P(2,4)*Metric(3,4) + P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4) + P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVVS13 = Lorentz(name = 'VVVVS13', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,1)*Metric(1,2) - 2*P(3,2)*P(4,1)*Metric(1,2) - P(3,4)*P(4,1)*Metric(1,2) + P(3,2)*P(4,2)*Metric(1,2) - P(3,2)*P(4,3)*Metric(1,2) + P(3,4)*P(4,3)*Metric(1,2) - P(2,1)*P(3,1)*Metric(1,4) + P(2,1)*P(3,2)*Metric(1,4) - P(2,3)*P(3,2)*Metric(1,4) + 2*P(2,1)*P(3,4)*Metric(1,4) + P(2,3)*P(3,4)*Metric(1,4) - P(2,4)*P(3,4)*Metric(1,4) + P(1,2)*P(4,1)*Metric(2,3) - P(1,4)*P(4,1)*Metric(2,3) - P(1,2)*P(4,2)*Metric(2,3) + 2*P(1,2)*P(4,3)*Metric(2,3) - P(1,3)*P(4,3)*Metric(2,3) + P(1,4)*P(4,3)*Metric(2,3) + P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3) + 2*P(-1,2)*P(-1,3)*Metric(1,4)*Metric(2,3) + 2*P(-1,1)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3) + P(1,2)*P(2,1)*Metric(3,4) - P(1,4)*P(2,1)*Metric(3,4) - P(1,2)*P(2,3)*Metric(3,4) + P(1,3)*P(2,3)*Metric(3,4) - 2*P(1,4)*P(2,3)*Metric(3,4) + P(1,4)*P(2,4)*Metric(3,4) - 2*P(-1,1)*P(-1,2)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4) - 2*P(-1,3)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVVS14 = Lorentz(name = 'VVVVS14', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,1)*Metric(1,2) - 2*P(3,1)*P(4,2)*Metric(1,2) + P(3,2)*P(4,2)*Metric(1,2) - P(3,4)*P(4,2)*Metric(1,2) - P(3,1)*P(4,3)*Metric(1,2) + P(3,4)*P(4,3)*Metric(1,2) - P(2,1)*P(4,1)*Metric(1,3) + P(2,1)*P(4,2)*Metric(1,3) - P(2,4)*P(4,2)*Metric(1,3) + 2*P(2,1)*P(4,3)*Metric(1,3) - P(2,3)*P(4,3)*Metric(1,3) + P(2,4)*P(4,3)*Metric(1,3) + P(1,2)*P(3,1)*Metric(2,4) - P(1,3)*P(3,1)*Metric(2,4) - P(1,2)*P(3,2)*Metric(2,4) + 2*P(1,2)*P(3,4)*Metric(2,4) + P(1,3)*P(3,4)*Metric(2,4) - P(1,4)*P(3,4)*Metric(2,4) + P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4) + 2*P(-1,1)*P(-1,3)*Metric(1,3)*Metric(2,4) + 2*P(-1,2)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(1,2)*P(2,1)*Metric(3,4) - P(1,3)*P(2,1)*Metric(3,4) + P(1,3)*P(2,3)*Metric(3,4) - P(1,2)*P(2,4)*Metric(3,4) - 2*P(1,3)*P(2,4)*Metric(3,4) + P(1,4)*P(2,4)*Metric(3,4) - 2*P(-1,1)*P(-1,2)*Metric(1,2)*Metric(3,4) - P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) - P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4) - 2*P(-1,3)*P(-1,4)*Metric(1,2)*Metric(3,4)') VVVVS15 = Lorentz(name = 'VVVVS15', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,2)*P(4,1)*Metric(1,2) + (P(3,3)*P(4,1)*Metric(1,2))/2. + (P(3,3)*P(4,3)*Metric(1,2))/4. + (P(3,2)*P(4,4)*Metric(1,2))/2. + (P(3,3)*P(4,4)*Metric(1,2))/4. + (P(3,4)*P(4,4)*Metric(1,2))/4. - (P(2,2)*P(4,1)*Metric(1,3))/2. - P(2,3)*P(4,1)*Metric(1,3) - (P(2,2)*P(4,2)*Metric(1,3))/4. - (P(2,2)*P(4,4)*Metric(1,3))/4. - (P(2,3)*P(4,4)*Metric(1,3))/2. - (P(2,4)*P(4,4)*Metric(1,3))/4. + (P(2,2)*P(3,1)*Metric(1,4))/4. + (P(2,3)*P(3,1)*Metric(1,4))/2. - (P(2,1)*P(3,2)*Metric(1,4))/2. + (P(2,4)*P(3,2)*Metric(1,4))/2. - (P(2,1)*P(3,3)*Metric(1,4))/4. + (P(2,4)*P(3,3)*Metric(1,4))/4. - (P(2,2)*P(3,4)*Metric(1,4))/4. - (P(2,3)*P(3,4)*Metric(1,4))/2. - (P(1,2)*P(4,1)*Metric(2,3))/2. + (P(1,3)*P(4,1)*Metric(2,3))/2. + (P(1,1)*P(4,2)*Metric(2,3))/4. + (P(1,4)*P(4,2)*Metric(2,3))/2. - (P(1,1)*P(4,3)*Metric(2,3))/4. - (P(1,4)*P(4,3)*Metric(2,3))/2. - (P(1,2)*P(4,4)*Metric(2,3))/4. + (P(1,3)*P(4,4)*Metric(2,3))/4. + (P(-1,1)*P(-1,2)*Metric(1,4)*Metric(2,3))/4. - (P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3))/4. - (P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3))/4. + (P(-1,3)*P(-1,4)*Metric(1,4)*Metric(2,3))/4. - (P(1,1)*P(3,1)*Metric(2,4))/4. - (P(1,1)*P(3,2)*Metric(2,4))/2. - P(1,4)*P(3,2)*Metric(2,4) - (P(1,1)*P(3,3)*Metric(2,4))/4. - (P(1,3)*P(3,3)*Metric(2,4))/4. - (P(1,4)*P(3,3)*Metric(2,4))/2. + (P(-1,1)**2*Metric(1,3)*Metric(2,4))/4. + (P(-1,2)**2*Metric(1,3)*Metric(2,4))/4. + (P(-1,3)**2*Metric(1,3)*Metric(2,4))/4. + (P(-1,4)**2*Metric(1,3)*Metric(2,4))/4. + (P(1,1)*P(2,1)*Metric(3,4))/4. + (P(1,1)*P(2,2)*Metric(3,4))/4. + (P(1,2)*P(2,2)*Metric(3,4))/4. + (P(1,4)*P(2,2)*Metric(3,4))/2. + (P(1,1)*P(2,3)*Metric(3,4))/2. + P(1,4)*P(2,3)*Metric(3,4) - (P(-1,1)**2*Metric(1,2)*Metric(3,4))/4. - (P(-1,2)**2*Metric(1,2)*Metric(3,4))/4. - (P(-1,3)**2*Metric(1,2)*Metric(3,4))/4. - (P(-1,4)**2*Metric(1,2)*Metric(3,4))/4.') VVVVS16 = Lorentz(name = 'VVVVS16', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,2)*Metric(1,2) + (P(3,3)*P(4,2)*Metric(1,2))/2. + (P(3,3)*P(4,3)*Metric(1,2))/4. + (P(3,1)*P(4,4)*Metric(1,2))/2. + (P(3,3)*P(4,4)*Metric(1,2))/4. + (P(3,4)*P(4,4)*Metric(1,2))/4. + (P(2,2)*P(4,1)*Metric(1,3))/4. + (P(2,4)*P(4,1)*Metric(1,3))/2. - (P(2,1)*P(4,2)*Metric(1,3))/2. + (P(2,3)*P(4,2)*Metric(1,3))/2. - (P(2,2)*P(4,3)*Metric(1,3))/4. - (P(2,4)*P(4,3)*Metric(1,3))/2. - (P(2,1)*P(4,4)*Metric(1,3))/4. + (P(2,3)*P(4,4)*Metric(1,3))/4. - (P(2,2)*P(3,1)*Metric(1,4))/2. - P(2,4)*P(3,1)*Metric(1,4) - (P(2,2)*P(3,2)*Metric(1,4))/4. - (P(2,2)*P(3,3)*Metric(1,4))/4. - (P(2,3)*P(3,3)*Metric(1,4))/4. - (P(2,4)*P(3,3)*Metric(1,4))/2. - (P(1,1)*P(4,1)*Metric(2,3))/4. - (P(1,1)*P(4,2)*Metric(2,3))/2. - P(1,3)*P(4,2)*Metric(2,3) - (P(1,1)*P(4,4)*Metric(2,3))/4. - (P(1,3)*P(4,4)*Metric(2,3))/2. - (P(1,4)*P(4,4)*Metric(2,3))/4. + (P(-1,1)**2*Metric(1,4)*Metric(2,3))/4. + (P(-1,2)**2*Metric(1,4)*Metric(2,3))/4. + (P(-1,3)**2*Metric(1,4)*Metric(2,3))/4. + (P(-1,4)**2*Metric(1,4)*Metric(2,3))/4. - (P(1,2)*P(3,1)*Metric(2,4))/2. + (P(1,4)*P(3,1)*Metric(2,4))/2. + (P(1,1)*P(3,2)*Metric(2,4))/4. + (P(1,3)*P(3,2)*Metric(2,4))/2. - (P(1,2)*P(3,3)*Metric(2,4))/4. + (P(1,4)*P(3,3)*Metric(2,4))/4. - (P(1,1)*P(3,4)*Metric(2,4))/4. - (P(1,3)*P(3,4)*Metric(2,4))/2. + (P(-1,1)*P(-1,2)*Metric(1,3)*Metric(2,4))/4. - (P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4))/4. - (P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4))/4. + (P(-1,3)*P(-1,4)*Metric(1,3)*Metric(2,4))/4. + (P(1,1)*P(2,1)*Metric(3,4))/4. + (P(1,1)*P(2,2)*Metric(3,4))/4. + (P(1,2)*P(2,2)*Metric(3,4))/4. + (P(1,3)*P(2,2)*Metric(3,4))/2. + (P(1,1)*P(2,4)*Metric(3,4))/2. + P(1,3)*P(2,4)*Metric(3,4) - (P(-1,1)**2*Metric(1,2)*Metric(3,4))/4. - (P(-1,2)**2*Metric(1,2)*Metric(3,4))/4. - (P(-1,3)**2*Metric(1,2)*Metric(3,4))/4. - (P(-1,4)**2*Metric(1,2)*Metric(3,4))/4.') VVVVS17 = Lorentz(name = 'VVVVS17', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,1)*Metric(1,2) + 3*P(3,4)*P(4,1)*Metric(1,2) - 2*P(3,1)*P(4,2)*Metric(1,2) + P(3,2)*P(4,2)*Metric(1,2) - P(3,4)*P(4,2)*Metric(1,2) - P(3,1)*P(4,3)*Metric(1,2) + 3*P(3,2)*P(4,3)*Metric(1,2) + P(3,4)*P(4,3)*Metric(1,2) - P(2,1)*P(4,1)*Metric(1,3) - 3*P(2,4)*P(4,1)*Metric(1,3) + P(2,1)*P(4,2)*Metric(1,3) - 3*P(2,3)*P(4,2)*Metric(1,3) - P(2,4)*P(4,2)*Metric(1,3) + 2*P(2,1)*P(4,3)*Metric(1,3) - P(2,3)*P(4,3)*Metric(1,3) + P(2,4)*P(4,3)*Metric(1,3) + 3*P(2,4)*P(3,1)*Metric(1,4) - 3*P(2,1)*P(3,4)*Metric(1,4) + 3*P(1,3)*P(4,2)*Metric(2,3) - 3*P(1,2)*P(4,3)*Metric(2,3) + P(1,2)*P(3,1)*Metric(2,4) - P(1,3)*P(3,1)*Metric(2,4) - 3*P(1,4)*P(3,1)*Metric(2,4) - P(1,2)*P(3,2)*Metric(2,4) - 3*P(1,3)*P(3,2)*Metric(2,4) + 2*P(1,2)*P(3,4)*Metric(2,4) + P(1,3)*P(3,4)*Metric(2,4) - P(1,4)*P(3,4)*Metric(2,4) - (P(-1,1)**2*Metric(1,3)*Metric(2,4))/2. - (P(-1,2)**2*Metric(1,3)*Metric(2,4))/2. + P(-1,1)*P(-1,3)*Metric(1,3)*Metric(2,4) + 2*P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4) - (P(-1,3)**2*Metric(1,3)*Metric(2,4))/2. + 2*P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(-1,2)*P(-1,4)*Metric(1,3)*Metric(2,4) - (P(-1,4)**2*Metric(1,3)*Metric(2,4))/2. + P(1,2)*P(2,1)*Metric(3,4) - P(1,3)*P(2,1)*Metric(3,4) + 3*P(1,4)*P(2,1)*Metric(3,4) + 3*P(1,2)*P(2,3)*Metric(3,4) + P(1,3)*P(2,3)*Metric(3,4) - P(1,2)*P(2,4)*Metric(3,4) - 2*P(1,3)*P(2,4)*Metric(3,4) + P(1,4)*P(2,4)*Metric(3,4) + (P(-1,1)**2*Metric(1,2)*Metric(3,4))/2. - P(-1,1)*P(-1,2)*Metric(1,2)*Metric(3,4) + (P(-1,2)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) + (P(-1,3)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4) - P(-1,3)*P(-1,4)*Metric(1,2)*Metric(3,4) + (P(-1,4)**2*Metric(1,2)*Metric(3,4))/2.') VVVVS18 = Lorentz(name = 'VVVVS18', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,1)*Metric(1,2) + (P(3,3)*P(4,1)*Metric(1,2))/2. + 3*P(3,4)*P(4,1)*Metric(1,2) - 2*P(3,1)*P(4,2)*Metric(1,2) + P(3,2)*P(4,2)*Metric(1,2) - (P(3,3)*P(4,2)*Metric(1,2))/2. - P(3,4)*P(4,2)*Metric(1,2) - P(3,1)*P(4,3)*Metric(1,2) + 3*P(3,2)*P(4,3)*Metric(1,2) + P(3,4)*P(4,3)*Metric(1,2) - (P(3,1)*P(4,4)*Metric(1,2))/2. + (P(3,2)*P(4,4)*Metric(1,2))/2. - P(2,1)*P(4,1)*Metric(1,3) - (P(2,2)*P(4,1)*Metric(1,3))/2. - 3*P(2,4)*P(4,1)*Metric(1,3) + P(2,1)*P(4,2)*Metric(1,3) - 3*P(2,3)*P(4,2)*Metric(1,3) - P(2,4)*P(4,2)*Metric(1,3) + 2*P(2,1)*P(4,3)*Metric(1,3) + (P(2,2)*P(4,3)*Metric(1,3))/2. - P(2,3)*P(4,3)*Metric(1,3) + P(2,4)*P(4,3)*Metric(1,3) + (P(2,1)*P(4,4)*Metric(1,3))/2. - (P(2,3)*P(4,4)*Metric(1,3))/2. + (P(2,2)*P(3,1)*Metric(1,4))/2. + 3*P(2,4)*P(3,1)*Metric(1,4) - (P(2,1)*P(3,3)*Metric(1,4))/2. + (P(2,4)*P(3,3)*Metric(1,4))/2. - 3*P(2,1)*P(3,4)*Metric(1,4) - (P(2,2)*P(3,4)*Metric(1,4))/2. + (P(1,1)*P(4,2)*Metric(2,3))/2. + 3*P(1,3)*P(4,2)*Metric(2,3) - (P(1,1)*P(4,3)*Metric(2,3))/2. - 3*P(1,2)*P(4,3)*Metric(2,3) - (P(1,2)*P(4,4)*Metric(2,3))/2. + (P(1,3)*P(4,4)*Metric(2,3))/2. + P(1,2)*P(3,1)*Metric(2,4) - P(1,3)*P(3,1)*Metric(2,4) - 3*P(1,4)*P(3,1)*Metric(2,4) - (P(1,1)*P(3,2)*Metric(2,4))/2. - P(1,2)*P(3,2)*Metric(2,4) - 3*P(1,3)*P(3,2)*Metric(2,4) + (P(1,2)*P(3,3)*Metric(2,4))/2. - (P(1,4)*P(3,3)*Metric(2,4))/2. + (P(1,1)*P(3,4)*Metric(2,4))/2. + 2*P(1,2)*P(3,4)*Metric(2,4) + P(1,3)*P(3,4)*Metric(2,4) - P(1,4)*P(3,4)*Metric(2,4) - (P(-1,1)**2*Metric(1,3)*Metric(2,4))/2. - (P(-1,2)**2*Metric(1,3)*Metric(2,4))/2. + P(-1,1)*P(-1,3)*Metric(1,3)*Metric(2,4) + 2*P(-1,2)*P(-1,3)*Metric(1,3)*Metric(2,4) - (P(-1,3)**2*Metric(1,3)*Metric(2,4))/2. + 2*P(-1,1)*P(-1,4)*Metric(1,3)*Metric(2,4) + P(-1,2)*P(-1,4)*Metric(1,3)*Metric(2,4) - (P(-1,4)**2*Metric(1,3)*Metric(2,4))/2. + P(1,2)*P(2,1)*Metric(3,4) - P(1,3)*P(2,1)*Metric(3,4) + 3*P(1,4)*P(2,1)*Metric(3,4) - (P(1,3)*P(2,2)*Metric(3,4))/2. + (P(1,4)*P(2,2)*Metric(3,4))/2. + (P(1,1)*P(2,3)*Metric(3,4))/2. + 3*P(1,2)*P(2,3)*Metric(3,4) + P(1,3)*P(2,3)*Metric(3,4) - (P(1,1)*P(2,4)*Metric(3,4))/2. - P(1,2)*P(2,4)*Metric(3,4) - 2*P(1,3)*P(2,4)*Metric(3,4) + P(1,4)*P(2,4)*Metric(3,4) + (P(-1,1)**2*Metric(1,2)*Metric(3,4))/2. - P(-1,1)*P(-1,2)*Metric(1,2)*Metric(3,4) + (P(-1,2)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,2)*P(-1,3)*Metric(1,2)*Metric(3,4) + (P(-1,3)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,1)*P(-1,4)*Metric(1,2)*Metric(3,4) - P(-1,3)*P(-1,4)*Metric(1,2)*Metric(3,4) + (P(-1,4)**2*Metric(1,2)*Metric(3,4))/2.') VVVVS19 = Lorentz(name = 'VVVVS19', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,1)*Metric(1,2) - 2*P(3,2)*P(4,1)*Metric(1,2) - P(3,4)*P(4,1)*Metric(1,2) + P(3,2)*P(4,2)*Metric(1,2) + 3*P(3,4)*P(4,2)*Metric(1,2) + 3*P(3,1)*P(4,3)*Metric(1,2) - P(3,2)*P(4,3)*Metric(1,2) + P(3,4)*P(4,3)*Metric(1,2) + 3*P(2,3)*P(4,1)*Metric(1,3) - 3*P(2,1)*P(4,3)*Metric(1,3) - P(2,1)*P(3,1)*Metric(1,4) - 3*P(2,3)*P(3,1)*Metric(1,4) + P(2,1)*P(3,2)*Metric(1,4) - P(2,3)*P(3,2)*Metric(1,4) - 3*P(2,4)*P(3,2)*Metric(1,4) + 2*P(2,1)*P(3,4)*Metric(1,4) + P(2,3)*P(3,4)*Metric(1,4) - P(2,4)*P(3,4)*Metric(1,4) + P(1,2)*P(4,1)*Metric(2,3) - 3*P(1,3)*P(4,1)*Metric(2,3) - P(1,4)*P(4,1)*Metric(2,3) - P(1,2)*P(4,2)*Metric(2,3) - 3*P(1,4)*P(4,2)*Metric(2,3) + 2*P(1,2)*P(4,3)*Metric(2,3) - P(1,3)*P(4,3)*Metric(2,3) + P(1,4)*P(4,3)*Metric(2,3) - (P(-1,1)**2*Metric(1,4)*Metric(2,3))/2. - (P(-1,2)**2*Metric(1,4)*Metric(2,3))/2. + 2*P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,3)*Metric(1,4)*Metric(2,3) - (P(-1,3)**2*Metric(1,4)*Metric(2,3))/2. + P(-1,1)*P(-1,4)*Metric(1,4)*Metric(2,3) + 2*P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) - (P(-1,4)**2*Metric(1,4)*Metric(2,3))/2. + 3*P(1,4)*P(3,2)*Metric(2,4) - 3*P(1,2)*P(3,4)*Metric(2,4) + P(1,2)*P(2,1)*Metric(3,4) + 3*P(1,3)*P(2,1)*Metric(3,4) - P(1,4)*P(2,1)*Metric(3,4) - P(1,2)*P(2,3)*Metric(3,4) + P(1,3)*P(2,3)*Metric(3,4) - 2*P(1,4)*P(2,3)*Metric(3,4) + 3*P(1,2)*P(2,4)*Metric(3,4) + P(1,4)*P(2,4)*Metric(3,4) + (P(-1,1)**2*Metric(1,2)*Metric(3,4))/2. - P(-1,1)*P(-1,2)*Metric(1,2)*Metric(3,4) + (P(-1,2)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) + (P(-1,3)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4) - P(-1,3)*P(-1,4)*Metric(1,2)*Metric(3,4) + (P(-1,4)**2*Metric(1,2)*Metric(3,4))/2.') VVVVS20 = Lorentz(name = 'VVVVS20', spins = [ 3, 3, 3, 3, 1 ], structure = 'P(3,1)*P(4,1)*Metric(1,2) - 2*P(3,2)*P(4,1)*Metric(1,2) - (P(3,3)*P(4,1)*Metric(1,2))/2. - P(3,4)*P(4,1)*Metric(1,2) + P(3,2)*P(4,2)*Metric(1,2) + (P(3,3)*P(4,2)*Metric(1,2))/2. + 3*P(3,4)*P(4,2)*Metric(1,2) + 3*P(3,1)*P(4,3)*Metric(1,2) - P(3,2)*P(4,3)*Metric(1,2) + P(3,4)*P(4,3)*Metric(1,2) + (P(3,1)*P(4,4)*Metric(1,2))/2. - (P(3,2)*P(4,4)*Metric(1,2))/2. + (P(2,2)*P(4,1)*Metric(1,3))/2. + 3*P(2,3)*P(4,1)*Metric(1,3) - 3*P(2,1)*P(4,3)*Metric(1,3) - (P(2,2)*P(4,3)*Metric(1,3))/2. - (P(2,1)*P(4,4)*Metric(1,3))/2. + (P(2,3)*P(4,4)*Metric(1,3))/2. - P(2,1)*P(3,1)*Metric(1,4) - (P(2,2)*P(3,1)*Metric(1,4))/2. - 3*P(2,3)*P(3,1)*Metric(1,4) + P(2,1)*P(3,2)*Metric(1,4) - P(2,3)*P(3,2)*Metric(1,4) - 3*P(2,4)*P(3,2)*Metric(1,4) + (P(2,1)*P(3,3)*Metric(1,4))/2. - (P(2,4)*P(3,3)*Metric(1,4))/2. + 2*P(2,1)*P(3,4)*Metric(1,4) + (P(2,2)*P(3,4)*Metric(1,4))/2. + P(2,3)*P(3,4)*Metric(1,4) - P(2,4)*P(3,4)*Metric(1,4) + P(1,2)*P(4,1)*Metric(2,3) - 3*P(1,3)*P(4,1)*Metric(2,3) - P(1,4)*P(4,1)*Metric(2,3) - (P(1,1)*P(4,2)*Metric(2,3))/2. - P(1,2)*P(4,2)*Metric(2,3) - 3*P(1,4)*P(4,2)*Metric(2,3) + (P(1,1)*P(4,3)*Metric(2,3))/2. + 2*P(1,2)*P(4,3)*Metric(2,3) - P(1,3)*P(4,3)*Metric(2,3) + P(1,4)*P(4,3)*Metric(2,3) + (P(1,2)*P(4,4)*Metric(2,3))/2. - (P(1,3)*P(4,4)*Metric(2,3))/2. - (P(-1,1)**2*Metric(1,4)*Metric(2,3))/2. - (P(-1,2)**2*Metric(1,4)*Metric(2,3))/2. + 2*P(-1,1)*P(-1,3)*Metric(1,4)*Metric(2,3) + P(-1,2)*P(-1,3)*Metric(1,4)*Metric(2,3) - (P(-1,3)**2*Metric(1,4)*Metric(2,3))/2. + P(-1,1)*P(-1,4)*Metric(1,4)*Metric(2,3) + 2*P(-1,2)*P(-1,4)*Metric(1,4)*Metric(2,3) - (P(-1,4)**2*Metric(1,4)*Metric(2,3))/2. + (P(1,1)*P(3,2)*Metric(2,4))/2. + 3*P(1,4)*P(3,2)*Metric(2,4) - (P(1,2)*P(3,3)*Metric(2,4))/2. + (P(1,4)*P(3,3)*Metric(2,4))/2. - (P(1,1)*P(3,4)*Metric(2,4))/2. - 3*P(1,2)*P(3,4)*Metric(2,4) + P(1,2)*P(2,1)*Metric(3,4) + 3*P(1,3)*P(2,1)*Metric(3,4) - P(1,4)*P(2,1)*Metric(3,4) + (P(1,3)*P(2,2)*Metric(3,4))/2. - (P(1,4)*P(2,2)*Metric(3,4))/2. - (P(1,1)*P(2,3)*Metric(3,4))/2. - P(1,2)*P(2,3)*Metric(3,4) + P(1,3)*P(2,3)*Metric(3,4) - 2*P(1,4)*P(2,3)*Metric(3,4) + (P(1,1)*P(2,4)*Metric(3,4))/2. + 3*P(1,2)*P(2,4)*Metric(3,4) + P(1,4)*P(2,4)*Metric(3,4) + (P(-1,1)**2*Metric(1,2)*Metric(3,4))/2. - P(-1,1)*P(-1,2)*Metric(1,2)*Metric(3,4) + (P(-1,2)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,1)*P(-1,3)*Metric(1,2)*Metric(3,4) + (P(-1,3)**2*Metric(1,2)*Metric(3,4))/2. - 2*P(-1,2)*P(-1,4)*Metric(1,2)*Metric(3,4) - P(-1,3)*P(-1,4)*Metric(1,2)*Metric(3,4) + (P(-1,4)**2*Metric(1,2)*Metric(3,4))/2.') VVVVV1 = Lorentz(name = 'VVVVV1', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,3)*Metric(2,4) - P(3,1)*Metric(1,5)*Metric(2,4) - P(4,1)*Metric(1,3)*Metric(2,5) + P(3,1)*Metric(1,4)*Metric(2,5) - P(5,1)*Metric(1,2)*Metric(3,4) + P(2,1)*Metric(1,5)*Metric(3,4) + P(4,1)*Metric(1,2)*Metric(3,5) - P(2,1)*Metric(1,4)*Metric(3,5)') VVVVV2 = Lorentz(name = 'VVVVV2', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,2)*Metric(1,4)*Metric(2,3) - P(4,2)*Metric(1,5)*Metric(2,3) + P(3,2)*Metric(1,5)*Metric(2,4) - P(3,2)*Metric(1,4)*Metric(2,5) - P(5,2)*Metric(1,2)*Metric(3,4) + P(1,2)*Metric(2,5)*Metric(3,4) + P(4,2)*Metric(1,2)*Metric(3,5) - P(1,2)*Metric(2,4)*Metric(3,5)') VVVVV3 = Lorentz(name = 'VVVVV3', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,4)*Metric(2,3) - (P(5,2)*Metric(1,4)*Metric(2,3))/2. - (P(5,3)*Metric(1,4)*Metric(2,3))/2. - P(4,1)*Metric(1,5)*Metric(2,3) + (P(4,2)*Metric(1,5)*Metric(2,3))/2. + (P(4,3)*Metric(1,5)*Metric(2,3))/2. - (P(5,1)*Metric(1,3)*Metric(2,4))/2. + P(5,2)*Metric(1,3)*Metric(2,4) - (P(5,3)*Metric(1,3)*Metric(2,4))/2. + (P(3,1)*Metric(1,5)*Metric(2,4))/2. - (P(3,2)*Metric(1,5)*Metric(2,4))/2. + (P(4,1)*Metric(1,3)*Metric(2,5))/2. - P(4,2)*Metric(1,3)*Metric(2,5) + (P(4,3)*Metric(1,3)*Metric(2,5))/2. - (P(3,1)*Metric(1,4)*Metric(2,5))/2. + (P(3,2)*Metric(1,4)*Metric(2,5))/2. - (P(5,1)*Metric(1,2)*Metric(3,4))/2. - (P(5,2)*Metric(1,2)*Metric(3,4))/2. + P(5,3)*Metric(1,2)*Metric(3,4) + (P(2,1)*Metric(1,5)*Metric(3,4))/2. - (P(2,3)*Metric(1,5)*Metric(3,4))/2. + (P(1,2)*Metric(2,5)*Metric(3,4))/2. - (P(1,3)*Metric(2,5)*Metric(3,4))/2. + (P(4,1)*Metric(1,2)*Metric(3,5))/2. + (P(4,2)*Metric(1,2)*Metric(3,5))/2. - P(4,3)*Metric(1,2)*Metric(3,5) - (P(2,1)*Metric(1,4)*Metric(3,5))/2. + (P(2,3)*Metric(1,4)*Metric(3,5))/2. - (P(1,2)*Metric(2,4)*Metric(3,5))/2. + (P(1,3)*Metric(2,4)*Metric(3,5))/2.') VVVVV4 = Lorentz(name = 'VVVVV4', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,3)*Metric(1,4)*Metric(2,3) - P(4,3)*Metric(1,5)*Metric(2,3) - P(5,3)*Metric(1,3)*Metric(2,4) + P(4,3)*Metric(1,3)*Metric(2,5) + P(2,3)*Metric(1,5)*Metric(3,4) - P(1,3)*Metric(2,5)*Metric(3,4) - P(2,3)*Metric(1,4)*Metric(3,5) + P(1,3)*Metric(2,4)*Metric(3,5)') VVVVV5 = Lorentz(name = 'VVVVV5', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,4)*Metric(2,3) - P(4,1)*Metric(1,5)*Metric(2,3) + P(4,1)*Metric(1,3)*Metric(2,5) - P(3,1)*Metric(1,4)*Metric(2,5) - P(5,1)*Metric(1,2)*Metric(3,4) + P(2,1)*Metric(1,5)*Metric(3,4) + P(3,1)*Metric(1,2)*Metric(4,5) - P(2,1)*Metric(1,3)*Metric(4,5)') VVVVV6 = Lorentz(name = 'VVVVV6', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,4)*Metric(2,3) - P(4,1)*Metric(1,5)*Metric(2,3) - P(5,1)*Metric(1,3)*Metric(2,4) + P(3,1)*Metric(1,5)*Metric(2,4) + P(4,1)*Metric(1,2)*Metric(3,5) - P(2,1)*Metric(1,4)*Metric(3,5) - P(3,1)*Metric(1,2)*Metric(4,5) + P(2,1)*Metric(1,3)*Metric(4,5)') VVVVV7 = Lorentz(name = 'VVVVV7', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,4)*Metric(1,3)*Metric(2,4) - P(3,4)*Metric(1,4)*Metric(2,5) - P(5,4)*Metric(1,2)*Metric(3,4) + P(1,4)*Metric(2,5)*Metric(3,4) + P(2,4)*Metric(1,4)*Metric(3,5) - P(1,4)*Metric(2,4)*Metric(3,5) + P(3,4)*Metric(1,2)*Metric(4,5) - P(2,4)*Metric(1,3)*Metric(4,5)') VVVVV8 = Lorentz(name = 'VVVVV8', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(3,5)*Metric(1,5)*Metric(2,4) - P(4,5)*Metric(1,3)*Metric(2,5) - P(2,5)*Metric(1,5)*Metric(3,4) + P(1,5)*Metric(2,5)*Metric(3,4) + P(4,5)*Metric(1,2)*Metric(3,5) - P(1,5)*Metric(2,4)*Metric(3,5) - P(3,5)*Metric(1,2)*Metric(4,5) + P(2,5)*Metric(1,3)*Metric(4,5)') VVVVV9 = Lorentz(name = 'VVVVV9', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,3)*Metric(2,4) - P(5,4)*Metric(1,3)*Metric(2,4) - P(3,1)*Metric(1,5)*Metric(2,4) + 2*P(3,4)*Metric(1,5)*Metric(2,4) - P(3,5)*Metric(1,5)*Metric(2,4) + P(4,1)*Metric(1,3)*Metric(2,5) - P(4,5)*Metric(1,3)*Metric(2,5) - P(3,1)*Metric(1,4)*Metric(2,5) - P(3,4)*Metric(1,4)*Metric(2,5) + 2*P(3,5)*Metric(1,4)*Metric(2,5) - P(5,1)*Metric(1,2)*Metric(3,4) + P(5,4)*Metric(1,2)*Metric(3,4) + P(2,1)*Metric(1,5)*Metric(3,4) - 2*P(2,4)*Metric(1,5)*Metric(3,4) + P(2,5)*Metric(1,5)*Metric(3,4) + P(1,4)*Metric(2,5)*Metric(3,4) - P(1,5)*Metric(2,5)*Metric(3,4) - P(4,1)*Metric(1,2)*Metric(3,5) + P(4,5)*Metric(1,2)*Metric(3,5) + P(2,1)*Metric(1,4)*Metric(3,5) + P(2,4)*Metric(1,4)*Metric(3,5) - 2*P(2,5)*Metric(1,4)*Metric(3,5) - P(1,4)*Metric(2,4)*Metric(3,5) + P(1,5)*Metric(2,4)*Metric(3,5) + 2*P(3,1)*Metric(1,2)*Metric(4,5) - P(3,4)*Metric(1,2)*Metric(4,5) - P(3,5)*Metric(1,2)*Metric(4,5) - 2*P(2,1)*Metric(1,3)*Metric(4,5) + P(2,4)*Metric(1,3)*Metric(4,5) + P(2,5)*Metric(1,3)*Metric(4,5)') VVVVV10 = Lorentz(name = 'VVVVV10', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(4,2)*Metric(1,5)*Metric(2,3) + P(5,2)*Metric(1,3)*Metric(2,4) - P(3,2)*Metric(1,5)*Metric(2,4) - P(4,2)*Metric(1,3)*Metric(2,5) - P(5,2)*Metric(1,2)*Metric(3,4) + P(1,2)*Metric(2,5)*Metric(3,4) + P(3,2)*Metric(1,2)*Metric(4,5) - P(1,2)*Metric(2,3)*Metric(4,5)') VVVVV11 = Lorentz(name = 'VVVVV11', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,2)*Metric(1,4)*Metric(2,3) - P(5,2)*Metric(1,3)*Metric(2,4) + P(4,2)*Metric(1,3)*Metric(2,5) - P(3,2)*Metric(1,4)*Metric(2,5) - P(4,2)*Metric(1,2)*Metric(3,5) + P(1,2)*Metric(2,4)*Metric(3,5) + P(3,2)*Metric(1,2)*Metric(4,5) - P(1,2)*Metric(2,3)*Metric(4,5)') VVVVV12 = Lorentz(name = 'VVVVV12', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,3)*Metric(1,4)*Metric(2,3) - P(4,3)*Metric(1,3)*Metric(2,5) - P(5,3)*Metric(1,2)*Metric(3,4) + P(1,3)*Metric(2,5)*Metric(3,4) + P(4,3)*Metric(1,2)*Metric(3,5) - P(2,3)*Metric(1,4)*Metric(3,5) + P(2,3)*Metric(1,3)*Metric(4,5) - P(1,3)*Metric(2,3)*Metric(4,5)') VVVVV13 = Lorentz(name = 'VVVVV13', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(4,3)*Metric(1,5)*Metric(2,3) - P(5,3)*Metric(1,3)*Metric(2,4) + P(5,3)*Metric(1,2)*Metric(3,4) - P(2,3)*Metric(1,5)*Metric(3,4) - P(4,3)*Metric(1,2)*Metric(3,5) + P(1,3)*Metric(2,4)*Metric(3,5) + P(2,3)*Metric(1,3)*Metric(4,5) - P(1,3)*Metric(2,3)*Metric(4,5)') VVVVV14 = Lorentz(name = 'VVVVV14', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,4)*Metric(1,4)*Metric(2,3) - P(3,4)*Metric(1,5)*Metric(2,4) - P(5,4)*Metric(1,2)*Metric(3,4) + P(2,4)*Metric(1,5)*Metric(3,4) - P(2,4)*Metric(1,4)*Metric(3,5) + P(1,4)*Metric(2,4)*Metric(3,5) + P(3,4)*Metric(1,2)*Metric(4,5) - P(1,4)*Metric(2,3)*Metric(4,5)') VVVVV15 = Lorentz(name = 'VVVVV15', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,4)*Metric(1,4)*Metric(2,3) - P(5,4)*Metric(1,3)*Metric(2,4) + P(3,4)*Metric(1,5)*Metric(2,4) - P(3,4)*Metric(1,4)*Metric(2,5) - P(2,4)*Metric(1,5)*Metric(3,4) + P(1,4)*Metric(2,5)*Metric(3,4) + P(2,4)*Metric(1,3)*Metric(4,5) - P(1,4)*Metric(2,3)*Metric(4,5)') VVVVV16 = Lorentz(name = 'VVVVV16', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,4)*Metric(2,3) + P(5,2)*Metric(1,4)*Metric(2,3) - P(5,3)*Metric(1,4)*Metric(2,3) - P(5,4)*Metric(1,4)*Metric(2,3) - P(4,1)*Metric(1,5)*Metric(2,3) + P(4,3)*Metric(1,5)*Metric(2,3) + P(5,1)*Metric(1,3)*Metric(2,4) + P(5,2)*Metric(1,3)*Metric(2,4) - P(5,3)*Metric(1,3)*Metric(2,4) - P(5,4)*Metric(1,3)*Metric(2,4) - P(3,1)*Metric(1,5)*Metric(2,4) + P(3,4)*Metric(1,5)*Metric(2,4) - P(4,2)*Metric(1,3)*Metric(2,5) + P(4,3)*Metric(1,3)*Metric(2,5) - P(3,2)*Metric(1,4)*Metric(2,5) + P(3,4)*Metric(1,4)*Metric(2,5) - 2*P(5,1)*Metric(1,2)*Metric(3,4) - 2*P(5,2)*Metric(1,2)*Metric(3,4) + 2*P(5,3)*Metric(1,2)*Metric(3,4) + 2*P(5,4)*Metric(1,2)*Metric(3,4) + 2*P(2,1)*Metric(1,5)*Metric(3,4) - P(2,3)*Metric(1,5)*Metric(3,4) - P(2,4)*Metric(1,5)*Metric(3,4) + 2*P(1,2)*Metric(2,5)*Metric(3,4) - P(1,3)*Metric(2,5)*Metric(3,4) - P(1,4)*Metric(2,5)*Metric(3,4) + P(4,1)*Metric(1,2)*Metric(3,5) + P(4,2)*Metric(1,2)*Metric(3,5) - 2*P(4,3)*Metric(1,2)*Metric(3,5) - P(2,1)*Metric(1,4)*Metric(3,5) + P(2,3)*Metric(1,4)*Metric(3,5) - P(1,2)*Metric(2,4)*Metric(3,5) + P(1,3)*Metric(2,4)*Metric(3,5) + P(3,1)*Metric(1,2)*Metric(4,5) + P(3,2)*Metric(1,2)*Metric(4,5) - 2*P(3,4)*Metric(1,2)*Metric(4,5) - P(2,1)*Metric(1,3)*Metric(4,5) + P(2,4)*Metric(1,3)*Metric(4,5) - P(1,2)*Metric(2,3)*Metric(4,5) + P(1,4)*Metric(2,3)*Metric(4,5)') VVVVV17 = Lorentz(name = 'VVVVV17', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(4,5)*Metric(1,5)*Metric(2,3) - P(3,5)*Metric(1,4)*Metric(2,5) - P(2,5)*Metric(1,5)*Metric(3,4) + P(1,5)*Metric(2,5)*Metric(3,4) - P(4,5)*Metric(1,2)*Metric(3,5) + P(2,5)*Metric(1,4)*Metric(3,5) + P(3,5)*Metric(1,2)*Metric(4,5) - P(1,5)*Metric(2,3)*Metric(4,5)') VVVVV18 = Lorentz(name = 'VVVVV18', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(4,5)*Metric(1,5)*Metric(2,3) - P(3,5)*Metric(1,5)*Metric(2,4) - P(4,5)*Metric(1,3)*Metric(2,5) + P(3,5)*Metric(1,4)*Metric(2,5) - P(2,5)*Metric(1,4)*Metric(3,5) + P(1,5)*Metric(2,4)*Metric(3,5) + P(2,5)*Metric(1,3)*Metric(4,5) - P(1,5)*Metric(2,3)*Metric(4,5)') VVVVV19 = Lorentz(name = 'VVVVV19', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,1)*Metric(1,4)*Metric(2,3) - P(5,2)*Metric(1,4)*Metric(2,3) - P(4,1)*Metric(1,5)*Metric(2,3) + 2*P(4,2)*Metric(1,5)*Metric(2,3) - P(4,5)*Metric(1,5)*Metric(2,3) - P(5,1)*Metric(1,3)*Metric(2,4) + P(5,2)*Metric(1,3)*Metric(2,4) + P(3,1)*Metric(1,5)*Metric(2,4) - 2*P(3,2)*Metric(1,5)*Metric(2,4) + P(3,5)*Metric(1,5)*Metric(2,4) + 2*P(4,1)*Metric(1,3)*Metric(2,5) - P(4,2)*Metric(1,3)*Metric(2,5) - P(4,5)*Metric(1,3)*Metric(2,5) - 2*P(3,1)*Metric(1,4)*Metric(2,5) + P(3,2)*Metric(1,4)*Metric(2,5) + P(3,5)*Metric(1,4)*Metric(2,5) - P(4,1)*Metric(1,2)*Metric(3,5) - P(4,2)*Metric(1,2)*Metric(3,5) + 2*P(4,5)*Metric(1,2)*Metric(3,5) + P(2,1)*Metric(1,4)*Metric(3,5) - P(2,5)*Metric(1,4)*Metric(3,5) + P(1,2)*Metric(2,4)*Metric(3,5) - P(1,5)*Metric(2,4)*Metric(3,5) + P(3,1)*Metric(1,2)*Metric(4,5) + P(3,2)*Metric(1,2)*Metric(4,5) - 2*P(3,5)*Metric(1,2)*Metric(4,5) - P(2,1)*Metric(1,3)*Metric(4,5) + P(2,5)*Metric(1,3)*Metric(4,5) - P(1,2)*Metric(2,3)*Metric(4,5) + P(1,5)*Metric(2,3)*Metric(4,5)') VVVVV20 = Lorentz(name = 'VVVVV20', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,3)*Metric(1,4)*Metric(2,3) - P(5,4)*Metric(1,4)*Metric(2,3) + P(4,3)*Metric(1,5)*Metric(2,3) - P(4,5)*Metric(1,5)*Metric(2,3) - P(5,3)*Metric(1,3)*Metric(2,4) + P(5,4)*Metric(1,3)*Metric(2,4) + P(3,4)*Metric(1,5)*Metric(2,4) - P(3,5)*Metric(1,5)*Metric(2,4) - P(4,3)*Metric(1,3)*Metric(2,5) + P(4,5)*Metric(1,3)*Metric(2,5) - P(3,4)*Metric(1,4)*Metric(2,5) + P(3,5)*Metric(1,4)*Metric(2,5) - P(2,3)*Metric(1,5)*Metric(3,4) - P(2,4)*Metric(1,5)*Metric(3,4) + 2*P(2,5)*Metric(1,5)*Metric(3,4) + P(1,3)*Metric(2,5)*Metric(3,4) + P(1,4)*Metric(2,5)*Metric(3,4) - 2*P(1,5)*Metric(2,5)*Metric(3,4) - P(2,3)*Metric(1,4)*Metric(3,5) + 2*P(2,4)*Metric(1,4)*Metric(3,5) - P(2,5)*Metric(1,4)*Metric(3,5) + P(1,3)*Metric(2,4)*Metric(3,5) - 2*P(1,4)*Metric(2,4)*Metric(3,5) + P(1,5)*Metric(2,4)*Metric(3,5) + 2*P(2,3)*Metric(1,3)*Metric(4,5) - P(2,4)*Metric(1,3)*Metric(4,5) - P(2,5)*Metric(1,3)*Metric(4,5) - 2*P(1,3)*Metric(2,3)*Metric(4,5) + P(1,4)*Metric(2,3)*Metric(4,5) + P(1,5)*Metric(2,3)*Metric(4,5)') VVVVV21 = Lorentz(name = 'VVVVV21', spins = [ 3, 3, 3, 3, 3 ], structure = 'P(5,2)*Metric(1,4)*Metric(2,3) + P(5,3)*Metric(1,4)*Metric(2,3) - 2*P(5,4)*Metric(1,4)*Metric(2,3) + P(4,2)*Metric(1,5)*Metric(2,3) + P(4,3)*Metric(1,5)*Metric(2,3) - 2*P(4,5)*Metric(1,5)*Metric(2,3) - P(5,3)*Metric(1,3)*Metric(2,4) + P(5,4)*Metric(1,3)*Metric(2,4) - P(3,2)*Metric(1,5)*Metric(2,4) + P(3,5)*Metric(1,5)*Metric(2,4) - P(4,3)*Metric(1,3)*Metric(2,5) + P(4,5)*Metric(1,3)*Metric(2,5) - P(3,2)*Metric(1,4)*Metric(2,5) + P(3,4)*Metric(1,4)*Metric(2,5) - P(5,2)*Metric(1,2)*Metric(3,4) + P(5,4)*Metric(1,2)*Metric(3,4) - P(2,3)*Metric(1,5)*Metric(3,4) + P(2,5)*Metric(1,5)*Metric(3,4) + P(1,2)*Metric(2,5)*Metric(3,4) + P(1,3)*Metric(2,5)*Metric(3,4) - P(1,4)*Metric(2,5)*Metric(3,4) - P(1,5)*Metric(2,5)*Metric(3,4) - P(4,2)*Metric(1,2)*Metric(3,5) + P(4,5)*Metric(1,2)*Metric(3,5) - P(2,3)*Metric(1,4)*Metric(3,5) + P(2,4)*Metric(1,4)*Metric(3,5) + P(1,2)*Metric(2,4)*Metric(3,5) + P(1,3)*Metric(2,4)*Metric(3,5) - P(1,4)*Metric(2,4)*Metric(3,5) - P(1,5)*Metric(2,4)*Metric(3,5) + 2*P(3,2)*Metric(1,2)*Metric(4,5) - P(3,4)*Metric(1,2)*Metric(4,5) - P(3,5)*Metric(1,2)*Metric(4,5) + 2*P(2,3)*Metric(1,3)*Metric(4,5) - P(2,4)*Metric(1,3)*Metric(4,5) - P(2,5)*Metric(1,3)*Metric(4,5) - 2*P(1,2)*Metric(2,3)*Metric(4,5) - 2*P(1,3)*Metric(2,3)*Metric(4,5) + 2*P(1,4)*Metric(2,3)*Metric(4,5) + 2*P(1,5)*Metric(2,3)*Metric(4,5)') SSSSSS1 = Lorentz(name = 'SSSSSS1', spins = [ 1, 1, 1, 1, 1, 1 ], structure = '1') VVSSSS1 = Lorentz(name = 'VVSSSS1', spins = [ 3, 3, 1, 1, 1, 1 ], structure = 'Metric(1,2)') VVVVSS1 = Lorentz(name = 'VVVVSS1', spins = [ 3, 3, 3, 3, 1, 1 ], structure = 'Metric(1,4)*Metric(2,3) - Metric(1,3)*Metric(2,4)') VVVVSS2 = Lorentz(name = 'VVVVSS2', spins = [ 3, 3, 3, 3, 1, 1 ], structure = 'Metric(1,4)*Metric(2,3) + Metric(1,3)*Metric(2,4) - 2*Metric(1,2)*Metric(3,4)') VVVVSS3 = Lorentz(name = 'VVVVSS3', spins = [ 3, 3, 3, 3, 1, 1 ], structure = 'Metric(1,4)*Metric(2,3) - Metric(1,2)*Metric(3,4)') VVVVSS4 = Lorentz(name = 'VVVVSS4', spins = [ 3, 3, 3, 3, 1, 1 ], structure = 'Metric(1,3)*Metric(2,4) - Metric(1,2)*Metric(3,4)') VVVVSS5 = Lorentz(name = 'VVVVSS5', spins = [ 3, 3, 3, 3, 1, 1 ], structure = 'Metric(1,4)*Metric(2,3) - (Metric(1,3)*Metric(2,4))/2. - (Metric(1,2)*Metric(3,4))/2.') VVVVVV1 = Lorentz(name = 'VVVVVV1', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,4)*Metric(3,5) - Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,5)*Metric(2,4)*Metric(3,6) + Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,3)*Metric(2,6)*Metric(4,5) + Metric(1,5)*Metric(2,3)*Metric(4,6) - Metric(1,3)*Metric(2,5)*Metric(4,6)') VVVVVV2 = Lorentz(name = 'VVVVVV2', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,5)*Metric(2,6)*Metric(3,4) + Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,5)*Metric(2,3)*Metric(4,6) - Metric(1,2)*Metric(3,5)*Metric(4,6)') VVVVVV3 = Lorentz(name = 'VVVVVV3', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,5)*Metric(2,6)*Metric(3,4) - Metric(1,6)*Metric(2,4)*Metric(3,5) + Metric(1,5)*Metric(2,4)*Metric(3,6) + Metric(1,3)*Metric(2,6)*Metric(4,5) - Metric(1,2)*Metric(3,6)*Metric(4,5) - Metric(1,3)*Metric(2,5)*Metric(4,6) + Metric(1,2)*Metric(3,5)*Metric(4,6)') VVVVVV4 = Lorentz(name = 'VVVVVV4', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,5)*Metric(2,6)*Metric(3,4) + Metric(1,5)*Metric(2,4)*Metric(3,6) - Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,3)*Metric(2,6)*Metric(4,5) + Metric(1,4)*Metric(2,3)*Metric(5,6) - Metric(1,3)*Metric(2,4)*Metric(5,6)') VVVVVV5 = Lorentz(name = 'VVVVVV5', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,4)*Metric(3,5) - Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,3)*Metric(2,5)*Metric(4,6) - Metric(1,2)*Metric(3,5)*Metric(4,6) + Metric(1,4)*Metric(2,3)*Metric(5,6) - Metric(1,3)*Metric(2,4)*Metric(5,6)') VVVVVV6 = Lorentz(name = 'VVVVVV6', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,5)*Metric(2,6)*Metric(3,4) - Metric(1,6)*Metric(2,4)*Metric(3,5) + Metric(1,4)*Metric(2,6)*Metric(3,5) + Metric(1,5)*Metric(2,3)*Metric(4,6) - Metric(1,3)*Metric(2,5)*Metric(4,6) - Metric(1,4)*Metric(2,3)*Metric(5,6) + Metric(1,3)*Metric(2,4)*Metric(5,6)') VVVVVV7 = Lorentz(name = 'VVVVVV7', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,5)*Metric(2,4)*Metric(3,6) - Metric(1,3)*Metric(2,6)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,5)*Metric(2,3)*Metric(4,6) - Metric(1,2)*Metric(3,5)*Metric(4,6) - Metric(1,4)*Metric(2,3)*Metric(5,6) + Metric(1,3)*Metric(2,4)*Metric(5,6)') VVVVVV8 = Lorentz(name = 'VVVVVV8', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) + Metric(1,5)*Metric(2,6)*Metric(3,4) - (Metric(1,6)*Metric(2,4)*Metric(3,5))/2. - (Metric(1,4)*Metric(2,6)*Metric(3,5))/2. - (Metric(1,5)*Metric(2,4)*Metric(3,6))/2. - (Metric(1,4)*Metric(2,5)*Metric(3,6))/2. - (Metric(1,6)*Metric(2,3)*Metric(4,5))/2. - (Metric(1,3)*Metric(2,6)*Metric(4,5))/2. + Metric(1,2)*Metric(3,6)*Metric(4,5) - (Metric(1,5)*Metric(2,3)*Metric(4,6))/2. - (Metric(1,3)*Metric(2,5)*Metric(4,6))/2. + Metric(1,2)*Metric(3,5)*Metric(4,6) + Metric(1,4)*Metric(2,3)*Metric(5,6) + Metric(1,3)*Metric(2,4)*Metric(5,6) - 2*Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV9 = Lorentz(name = 'VVVVVV9', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,5)*Metric(2,6)*Metric(3,4) + Metric(1,6)*Metric(2,4)*Metric(3,5) - Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,5)*Metric(2,4)*Metric(3,6) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,4)*Metric(2,3)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV10 = Lorentz(name = 'VVVVVV10', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,5)*Metric(2,6)*Metric(3,4) - Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,3)*Metric(2,6)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) - Metric(1,5)*Metric(2,3)*Metric(4,6) + Metric(1,3)*Metric(2,5)*Metric(4,6) + Metric(1,4)*Metric(2,3)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV11 = Lorentz(name = 'VVVVVV11', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,6)*Metric(2,4)*Metric(3,5) + Metric(1,5)*Metric(2,4)*Metric(3,6) - Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,5)*Metric(2,3)*Metric(4,6) + Metric(1,2)*Metric(3,5)*Metric(4,6) + Metric(1,4)*Metric(2,3)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV12 = Lorentz(name = 'VVVVVV12', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,3)*Metric(2,6)*Metric(4,5) - Metric(1,3)*Metric(2,5)*Metric(4,6) + Metric(1,2)*Metric(3,5)*Metric(4,6) + Metric(1,4)*Metric(2,3)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV13 = Lorentz(name = 'VVVVVV13', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,6)*Metric(2,4)*Metric(3,5) + Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,3)*Metric(2,6)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,3)*Metric(2,4)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV14 = Lorentz(name = 'VVVVVV14', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - Metric(1,5)*Metric(2,4)*Metric(3,6) - Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,5)*Metric(2,3)*Metric(4,6) - Metric(1,3)*Metric(2,5)*Metric(4,6) + Metric(1,3)*Metric(2,4)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV15 = Lorentz(name = 'VVVVVV15', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,5)*Metric(2,6)*Metric(3,4) - Metric(1,6)*Metric(2,4)*Metric(3,5) + Metric(1,6)*Metric(2,3)*Metric(4,5) - Metric(1,3)*Metric(2,6)*Metric(4,5) - Metric(1,5)*Metric(2,3)*Metric(4,6) + Metric(1,2)*Metric(3,5)*Metric(4,6) + Metric(1,3)*Metric(2,4)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV16 = Lorentz(name = 'VVVVVV16', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,5)*Metric(2,6)*Metric(3,4) - Metric(1,4)*Metric(2,6)*Metric(3,5) - Metric(1,5)*Metric(2,4)*Metric(3,6) + Metric(1,4)*Metric(2,5)*Metric(3,6) - Metric(1,3)*Metric(2,5)*Metric(4,6) + Metric(1,2)*Metric(3,5)*Metric(4,6) + Metric(1,3)*Metric(2,4)*Metric(5,6) - Metric(1,2)*Metric(3,4)*Metric(5,6)') VVVVVV17 = Lorentz(name = 'VVVVVV17', spins = [ 3, 3, 3, 3, 3, 3 ], structure = 'Metric(1,6)*Metric(2,5)*Metric(3,4) - (Metric(1,5)*Metric(2,6)*Metric(3,4))/2. + Metric(1,6)*Metric(2,4)*Metric(3,5) - (Metric(1,4)*Metric(2,6)*Metric(3,5))/2. - (Metric(1,5)*Metric(2,4)*Metric(3,6))/2. - (Metric(1,4)*Metric(2,5)*Metric(3,6))/2. - 2*Metric(1,6)*Metric(2,3)*Metric(4,5) + Metric(1,3)*Metric(2,6)*Metric(4,5) + Metric(1,2)*Metric(3,6)*Metric(4,5) + Metric(1,5)*Metric(2,3)*Metric(4,6) - (Metric(1,3)*Metric(2,5)*Metric(4,6))/2. - (Metric(1,2)*Metric(3,5)*Metric(4,6))/2. + Metric(1,4)*Metric(2,3)*Metric(5,6) - (Metric(1,3)*Metric(2,4)*Metric(5,6))/2. - (Metric(1,2)*Metric(3,4)*Metric(5,6))/2.')

107.036866

2,677

0.490048

16,821

69,681

2.029784

0.013436

0.209736

0.073807

0.021556

0.884457

0.881088

0.872331

0.865302

0.855168

0.826494

0

0.195713

0.157762

69,681

650

2,678

107.201538

0.386058

0.002196

0

0.463768

0

0.267081

0.759835

0.66565

0

1

0

false

0.00207

0.008282

0

0.008282

0

null

1

0

1

0

1

0

1

null

0

13

df8752e311ac91c8893b7ab5e7e238d8f07eac15

3,776

py

Python

Final_code/strawberry.py

tjddus0403/osscap2020

642691e8e30bbf14ec6acd177da1ad58456c3a4c

[ "Apache-2.0" ]

1

2020-10-07T12:49:33.000Z

Game/strawberry.py

tjddus0403/osscap2020

642691e8e30bbf14ec6acd177da1ad58456c3a4c

[ "Apache-2.0" ]

9

2020-10-09T08:58:09.000Z

2020-11-30T12:21:14.000Z

memory_gallery/strawberry.py

tjddus0403/osscap2020

642691e8e30bbf14ec6acd177da1ad58456c3a4c

[ "Apache-2.0" ]

3

2020-10-07T12:49:35.000Z

2020-10-11T12:53:39.000Z

#딸기 위 QarrayScreen9=[ [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] ] #딸기 아래 QarrayScreen10=[ [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,7,7,7,7,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] ] #딸기 QarrayScreen=[ [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,4,7,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,4,7,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,4,4,4,4,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] ]

65.103448

77

0.419492

1,544

3,776

1.025907

0.006477

0.782828

1.083333

1.323232

0.972222

0

0.477209

0.145922

3,776

57

78

66.245614

0.013953

0.002913

0

0.941176

0

1

0

false

0

1

null

1

0

1

0

1

0

1

0

null

0

13

10d7d589ada1d89745f7dcaf36b417c103b02427

13,273

py

Python

app/seeds/survey_questions.py

nappernick/envelope

af4f574c04c51293b90ee2e09d0f95d12ca36d2c

[ "MIT" ]

2

2021-01-13T22:52:16.000Z

2021-01-29T18:37:51.000Z

app/seeds/survey_questions.py

nappernick/envelope

af4f574c04c51293b90ee2e09d0f95d12ca36d2c

[ "MIT" ]

32

2021-01-08T19:05:33.000Z

2021-04-07T22:01:54.000Z

app/seeds/survey_questions.py

nappernick/envelope

af4f574c04c51293b90ee2e09d0f95d12ca36d2c

[ "MIT" ]

null

# Don't know fields: [ "q7", "q7_1", "q8", "q9", "q6", "q12", "q14", "a1", "a2", "a4", "a4_gpslati~e", "a4_gpslong~e", "a4_gpsalti~e", "a4_gpsaccu~y", "a4_yes_wrong", "a4_no_wrong1", "a4_no_wrong2", "a5", "a6", "a6_no", "a7", "a8_day", "a8_month", "a8_year", "a9", "a10", "a10_2", "a12_day", "a12_month", "a12_year", "a13", "a14_1", "a14_2", "a14_3", "a14_4", "a14_5", "a14_6", "a14_7", "a14_8", "a14_9", "a14_10", "a14_11", "a14_12", "a14__97", "a15_day", "a15_month", "a15_year", "a16", "a17_day", "a17_month", "a17_year", "a18", "a19", "a20", "a22", "a23_1", "a23_2", "a23_3", "a23_4", "a23_5", "a23_6", "a23_7", "a23_8", "a23_9", "a23_10", "a23_11", "a23_12", "a23__97", "a24", "a24a", "a24b", "a25", "a27", "a27_fix", "a28", "a28_fix", "intr~atitude", "intro~gitude", "intro_gpsa~e", "intro_gpsa~y", "q16", "_120_1_1_s~e", "_120_1_2_s~e", "_120_1_c~11a", "_120_1_c~11b", "_120_1_c~11c", "_120_2_1_s~e", "_120_2_2_s~e", "_120_2_c~11a", "_120_2_c~11b", "_120_2_c~11c", "_120_3_1_s~e", "_120_3_2_s~e", "_120_3_c~11a", "_120_3_c~11b", "_120_3_c~11c", "_720_1_1_s~e", "_720_1_2_s~e", "_720_1_c~11a", "_720_1_c~11b", "_720_1_c~11c", "_720_2_1_s~e", "_720_2_2_s~e", "_720_2_c~11a", "_720_2_c~11b", "_720_2_c~11c", "_720_3_1_s~e", "_720_3_2_s~e", "_720_3_c~11a", "_720_3_c~11b", "_720_3_c~11c", "q103", "q104", "q105", "q106", "q108", "q109", "num_hh_mem~s", "q111_1", "q113_1", "q111_2", "q113_2", "q111_3", "q113_3", "q111_4", "q113_4", "q111_5", "q113_5", "q111_6", "q113_6", "q111_7", "q113_7", "q111_8", "q113_8", "q111_9", "q113_9", "q111_10", "q113_10", "q111_11", "q113_11", "q111_12", "q113_12", "q111_13", "q113_13", "q111_14", "q113_14", "q111_15", "q113_15", "q111_16", "q113_16", "q111_17", "q113_17", "q111_18", "q113_18", "q111_19", "q113_19", "q111_20", "q113_20", "under5chil~p", "q115b_1", "q115b_2", "q115b_3", "q115b_4", "q115b_5", "q115b_6", "q115b_7", "q115b_8", "q115b_9", "q115b_10", "q115b_11", "q115b_12", "q115b_13", "q115b_14", "q115b_15", "q115b_16", "q115b_17", "q115b_18", "q115b_19", "q115b_20", "q116a_1", "q116b_1", "q116c_1", "q117_1", "q116a_2", "q116b_2", "q116c_2", "q117_2", "q116a_3", "q116b_3", "q116c_3", "q117_3", "q116a_4", "q116b_4", "q116c_4", "q117_4", "q116a_5", "q116b_5", "q116c_5", "q117_5", "q201_1", "q202_1", "q205_1", "q206_1", "q206_1_1", "q206_2_1", "q206_3_1", "q207_1", "q208_1", "q207_1_1_1", "q207_2_1_1", "q207_3_1_1", "q207_1_2_1", "q207_2_2_1", "q207_3_2_1", "q207_1_3_1", "q207_2_3_1", "q207_3_3_1", "q207_1_4_1", "q207_2_4_1", "q207_3_4_1", "q209_1", "q210_1", "q209_1_1_1", "q209_2_1_1", "q209_3_1_1", "q209_1_2_1", "q209_2_2_1", "q209_3_2_1", "q209_1_3_1", "q209_2_3_1", "q209_3_3_1", "q211_1", "q212_1", "q211_1_1_1", "q211_2_1_1", "q211_3_1_1", "q211_1_2_1", "q211_2_2_1", "q211_3_2_1", "q211_1_3_1", "q211_2_3_1", "q211_3_3_1", "q213_1", "q213_1_1", "q213_2_1", "q213_3_1", "q215_1", "q216_1", "q217_1", "q218_1", "q219_1", "q221_1", "q222_1", "q224_1", "q225_1", "q226_1", "q227_1", "q228_1", "q230_1", "q231_1", "q201_2", "q202_2", "q205_2", "q206_2", "q206_1_2", "q206_2_2", "q206_3_2", "q207_2", "q208_2", "q207_1_1_2", "q207_2_1_2", "q207_3_1_2", "q207_1_2_2", "q207_2_2_2", "q207_3_2_2", "q207_1_3_2", "q207_2_3_2", "q207_3_3_2", "q207_1_4_2", "q207_2_4_2", "q207_3_4_2", "q209_2", "q210_2", "q209_1_1_2", "q209_2_1_2", "q209_3_1_2", "q209_1_2_2", "q209_2_2_2", "q209_3_2_2", "q209_1_3_2", "q209_2_3_2", "q209_3_3_2", "q211_2", "q212_2", "q211_1_1_2", "q211_2_1_2", "q211_3_1_2", "q211_1_2_2", "q211_2_2_2", "q211_3_2_2", "q211_1_3_2", "q211_2_3_2", "q211_3_3_2", "q213_2", "q213_1_2", "q213_2_2", "q213_3_2", "q215_2", "q216_2", "q217_2", "q218_2", "q219_2", "q221_2", "q222_2", "q224_2", "q225_2", "q226_2", "q227_2", "q228_2", "q230_2", "q231_2", "q201_3", "q202_3", "q205_3", "q206_3", "q206_1_3", "q206_2_3", "q206_3_3", "q207_3", "q208_3", "q207_1_1_3", "q207_2_1_3", "q207_3_1_3", "q207_1_2_3", "q207_2_2_3", "q207_3_2_3", "q207_1_3_3", "q207_2_3_3", "q207_3_3_3", "q207_1_4_3", "q207_2_4_3", "q207_3_4_3", "q209_3", "q210_3", "q209_1_1_3", "q209_2_1_3", "q209_3_1_3", "q209_1_2_3", "q209_2_2_3", "q209_3_2_3", "q209_1_3_3", "q209_2_3_3", "q209_3_3_3", "q211_3", "q212_3", "q211_1_1_3", "q211_2_1_3", "q211_3_1_3", "q211_1_2_3", "q211_2_2_3", "q211_3_2_3", "q211_1_3_3", "q211_2_3_3", "q211_3_3_3", "q213_3", "q213_1_3", "q213_2_3", "q213_3_3", "q215_3", "q216_3", "q217_3", "q218_3", "q219_3", "q221_3", "q222_3", "q224_3", "q225_3", "q226_3", "q227_3", "q228_3", "q230_3", "q231_3", "q201_4", "q202_4", "q205_4", "q206_4", "q206_1_4", "q206_2_4", "q206_3_4", "q207_4", "q208_4", "q207_1_1_4", "q207_2_1_4", "q207_3_1_4", "q207_1_2_4", "q207_2_2_4", "q207_3_2_4", "q207_1_3_4", "q207_2_3_4", "q207_3_3_4", "q207_1_4_4", "q207_2_4_4", "q207_3_4_4", "q209_4", "q210_4", "q209_1_1_4", "q209_2_1_4", "q209_3_1_4", "q209_1_2_4", "q209_2_2_4", "q209_3_2_4", "q209_1_3_4", "q209_2_3_4", "q209_3_3_4", "q211_4", "q212_4", "q211_1_1_4", "q211_2_1_4", "q211_3_1_4", "q211_1_2_4", "q211_2_2_4", "q211_3_2_4", "q211_1_3_4", "q211_2_3_4", "q211_3_3_4", "q213_4", "q213_1_4", "q213_2_4", "q213_3_4", "q215_4", "q216_4", "q217_4", "q218_4", "q219_4", "q221_4", "q222_4", "q224_4", "q225_4", "q226_4", "q227_4", "q228_4", "q230_4", "q231_4", "q201_5", "q202_5", "q205_5", "q206_5", "q206_1_5", "q206_2_5", "q206_3_5", "q207_5", "q208_5", "q207_1_1_5", "q207_2_1_5", "q207_3_1_5", "q207_1_2_5", "q207_2_2_5", "q207_3_2_5", "q207_1_3_5", "q207_2_3_5", "q207_3_3_5", "q207_1_4_5", "q207_2_4_5", "q207_3_4_5", "q209_5", "q210_5", "q209_1_1_5", "q209_2_1_5", "q209_3_1_5", "q209_1_2_5", "q209_2_2_5", "q209_3_2_5", "q209_1_3_5", "q209_2_3_5", "q209_3_3_5", "q211_5", "q212_5", "q211_1_1_5", "q211_2_1_5", "q211_3_1_5", "q211_1_2_5", "q211_2_2_5", "q211_3_2_5", "q211_1_3_5", "q211_2_3_5", "q211_3_3_5", "q213_5", "q213_1_5", "q213_2_5", "q213_3_5", "q215_5", "q216_5", "q217_5", "q218_5", "q219_5", "q221_5", "q222_5", "q224_5", "q225_5", "q226_5", "q227_5", "q228_5", "q230_5", "q231_5", "q201_6", "q202_6", "q205_6", "q206_6", "q206_1_6", "q206_2_6", "q206_3_6", "q207_6", "q208_6", "q207_1_1_6", "q207_2_1_6", "q207_3_1_6", "q207_1_2_6", "q207_2_2_6", "q207_3_2_6", "q207_1_3_6", "q207_2_3_6", "q207_3_3_6", "q207_1_4_6", "q207_2_4_6", "q207_3_4_6", "q209_6", "q210_6", "q209_1_1_6", "q209_2_1_6", "q209_3_1_6", "q209_1_2_6", "q209_2_2_6", "q209_3_2_6", "q209_1_3_6", "q209_2_3_6", "q209_3_3_6", "q211_6", "q212_6", "q211_1_1_6", "q211_2_1_6", "q211_3_1_6", "q211_1_2_6", "q211_2_2_6", "q211_3_2_6", "q211_1_3_6", "q211_2_3_6", "q211_3_3_6", "q213_6", "q213_1_6", "q213_2_6", "q213_3_6", "q215_6", "q216_6", "q217_6", "q218_6", "q219_6", "q221_6", "q222_6", "q224_6", "q225_6", "q226_6", "q227_6", "q228_6", "q230_6", "q231_6", "q201_7", "q202_7", "q205_7", "q206_7", "q206_1_7", "q206_2_7", "q206_3_7", "q207_7", "q208_7", "q207_1_1_7", "q207_2_1_7", "q207_3_1_7", "q207_1_2_7", "q207_2_2_7", "q207_3_2_7", "q207_1_3_7", "q207_2_3_7", "q207_3_3_7", "q207_1_4_7", "q207_2_4_7", "q207_3_4_7", "q209_7", "q210_7", "q209_1_1_7", "q209_2_1_7", "q209_3_1_7", "q209_1_2_7", "q209_2_2_7", "q209_3_2_7", "q209_1_3_7", "q209_2_3_7", "q209_3_3_7", "q211_7", "q212_7", "q211_1_1_7", "q211_2_1_7", "q211_3_1_7", "q211_1_2_7", "q211_2_2_7", "q211_3_2_7", "q211_1_3_7", "q211_2_3_7", "q211_3_3_7", "q213_7", "q213_1_7", "q213_2_7", "q213_3_7", "q215_7", "q216_7", "q217_7", "q218_7", "q219_7", "q221_7", "q222_7", "q224_7", "q225_7", "q226_7", "q227_7", "q228_7", "q230_7", "q231_7", "q201_8", "q202_8", "q205_8", "q206_8", "q206_1_8", "q206_2_8", "q206_3_8", "q207_8", "q208_8", "q207_1_1_8", "q207_2_1_8", "q207_3_1_8", "q207_1_2_8", "q207_2_2_8", "q207_3_2_8", "q207_1_3_8", "q207_2_3_8", "q207_3_3_8", "q207_1_4_8", "q207_2_4_8", "q207_3_4_8", "q209_8", "q210_8", "q209_1_1_8", "q209_2_1_8", "q209_3_1_8", "q209_1_2_8", "q209_2_2_8", "q209_3_2_8", "q209_1_3_8", "q209_2_3_8", "q209_3_3_8", "q211_8", "q212_8", "q211_1_1_8", "q211_2_1_8", "q211_3_1_8", "q211_1_2_8", "q211_2_2_8", "q211_3_2_8", "q211_1_3_8", "q211_2_3_8", "q211_3_3_8", "q213_8", "q213_1_8", "q213_2_8", "q213_3_8", "q215_8", "q216_8", "q217_8", "q218_8", "q219_8", "q221_8", "q222_8", "q224_8", "q225_8", "q226_8", "q227_8", "q228_8", "q230_8", "q231_8", "q201_9", "q202_9", "q205_9", "q206_9", "q206_1_9", "q206_2_9", "q206_3_9", "q207_9", "q208_9", "q207_1_1_9", "q207_2_1_9", "q207_3_1_9", "q207_1_2_9", "q207_2_2_9", "q207_3_2_9", "q207_1_3_9", "q207_2_3_9", "q207_3_3_9", "q207_1_4_9", "q207_2_4_9", "q207_3_4_9", "q209_9", "q210_9", "q209_1_1_9", "q209_2_1_9", "q209_3_1_9", "q209_1_2_9", "q209_2_2_9", "q209_3_2_9", "q209_1_3_9", "q209_2_3_9", "q209_3_3_9", "q211_9", "q212_9", "q211_1_1_9", "q211_2_1_9", "q211_3_1_9", "q211_1_2_9", "q211_2_2_9", "q211_3_2_9", "q211_1_3_9", "q211_2_3_9", "q211_3_3_9", "q213_9", "q213_1_9", "q213_2_9", "q213_3_9", "q215_9", "q216_9", "q217_9", "q218_9", "q219_9", "q221_9", "q222_9", "q224_9", "q225_9", "q226_9", "q227_9", "q228_9", "q230_9", "q231_9", "q301", "q302", "preg_count~1", "preg_count~2", "preg_s~1_num", "preg_s~2_num", "q304", "q304_week", "q304_month", "q306", "q307_1", "q307_2", "q307_3", "q307_4", "q307_5", "q307__97", "q307__98", "q308_1", "q308_2", "q308_3", "q308_4", "q308__97", "q308__98", "q309_1", "q309_2", "q309_3", "q309_4", "q309_5", "q309_6", "q309_7", "q309__97", "q309__98", "q310", "q310_week", "q310_month", "q311", "q312", "q318", "q319", "q320", "q320a", "q320b", "q322a_1", "q322b_1", "q322c_1", "q322a_2", "q322b_2", "q322c_2", "q322a_3", "q322b_3", "q322c_3", "q322a_4", "q322b_4", "q322c_4", "birth_coun~1", "birth_coun~2", "birth_~1_num", "birth_~2_num", "q324", "q325_1", "q325_2", "q325_3", "q325_4", "q325_6", "q325__97", "q325__98", "q326_1", "q326_2", "q326_3", "q326_4", "q326__97", "q326__98", "q327_1", "q327_2", "q327_3", "q327_4", "q327_5", "q327_6", "q327__97", "q327__98", "q328", "q328_week", "q328_month", "q329", "v2419", "q330", "q336", "q337", "q338", "q340", "q341", "q342", "q343", "v2431", "q346", "q347", "q352", "q355", "q356_weeks", "q356_months", "q400", "q401", "q403_1", "q404_day_1", "q404_month_1", "q404_year_1", "q403_2", "q404_day_2", "q404_month_2", "q404_year_2", "q403_3", "q404_day_3", "q404_month_3", "q404_year_3", "q405", "q406", "q408_1", "q409_1", "q409a_num_1", "q409b_num_1", "q410_day_1", "q410_month_1", "q410_year_1", "q408_2", "q409_2", "q409a_num_2", "q409b_num_2", "q410_day_2", "q410_month_2", "q410_year_2", "q408_3", "q409_3", "q409a_num_3", "q409b_num_3", "q410_day_3", "q410_month_3", "q410_year_3", "q413", "q414", "q416_1", "q417_day_1", "q417_month_1", "q417_year_1", "q418_1", "q419_1", "q420_1", "q421_1", "q416_2", "q417_day_2", "q417_month_2", "q417_year_2", "q418_2", "q419_2", "q420_2", "q421_2", "q501", "q502", "q502a", "q502b_1", "q502b_2", "q502b_3", "q502b_4", "q502b_5", "q502b_6", "q502b_7", "q502b_8", "q502b_9", "q502b_10", "q502b_11", "q502b_12", "q502b_13", "q502b_14", "q502b_15", "q502b_16", "q502b_17", "q502b_18", "q502b_19", "q502b_20", "q502c", "q504", "q504_num", "q507", "q508_1", "q508_2", "q508_3", "q508_4", "q508_5", "q508_6", "q508_7", "q508_8", "q508_9", "q508_10", "q508_11", "q508_12", "q508_97", "q508__98", "q509", "q510", "q601", "q511_0", "q511_day", "q511_month", "q511_year", "q512", "q513", "q514_1", "q514_2", "q514_3", "q514_4", "q514_5", "q514__97", "q514__98", "q515_minutes", "q515_hours", "q516_1", "q516_2", "q516_3", "q516_4", "q516_5", "q516__98", "q517", "q518", "q519", "q520", "q523", "q524", "q525_1", "q525_2", "q525_3", "q525_4", "q525_5", "q525_6", "q525_7", "q525_8", "q525__97", "q525_9", "q525__98", "q526", "q527", "q528", "q530", "q531", "q532", "q533", "q535", "q536", "q537_1", "q537_2", "q537_3", "q537_4", "q537_5", "q537_6", "q537__97", "q537__98", "q538", "q543", "q544", "q545", "q546", "q547", "q548", "q549", "q552", "q553", "q554", "q555","q701","q703","q704","q705","q706","q707","q708","q702_1","q702_2","q702_3","q702_4","q702_5","q702_6","q702_7","q702_8","q702_9","q702_10","q702_11","q702__97","q702__98","q708a","q709","q710","q711","q712_month","q712_year","q713_1","q713_2","q713_3","q713_4","q713_5","q713_6","q713__98","q714","q714b_month","q714b_year","q714j","q714k_1","q714k_2","q714k_3","q714k_4","q714k_5","q714k__98","q801","q802","q803","q805","q806","q807","q808","v2732","q809","q810","q811","q812","q813","q816","q817","q818","q819","q820","q821","q822","q901","q904","q905","q906","q907","q908","q909","q910","q911","q912","q913","q914","q915_1","q915_2","q915_3","q915_4","q915_5","q915_6","q915__97","q915__98","q916_1","q916_2","q916_3","q916_4","q916_5","q916_6","q916_7","q916_8","q916_9","q916_10","q916_11","q916_12","q916__97","q916__98","q917","q919_a","q919_a_num","q919_b","q919_b_num","q919_c","q919_c_num","q919_d","q919_d_num","q919_e","q919_e_num","q919_f","q919_f_num","q919_g","q919_g_num","q919_i","q919_i_num","q919_k","q919_k_num","q919_l","q919_l_num","q919_m","q919_m_num","q919_n","q919_n_num","q919_o","q919_o_num","q919_o_more","q919_p","q919_p_num","move1","migration1","migration2","migration3","phone_perm~n","phone_number","s1_15_2","urbanrural","q1001","q1002","q1004","q1005","q1006","q1007","q1008","q1009","q1009","q1009","q1009"] # Integer fields, some of which include wildcards! ["q222_" *, "q311", "q414", "q510", "q515_minutes", "q515_hours", "q526", "q528", "q919_" * "_num"]

2,654.6

13,119

0.652754

2,909

13,273

2.378824

0.114129

0.011561

0.007803

0.002601

0

0.409663

0.07843

13,273

4

13,120

3,318.25

0.156066

0.999473

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

1

0

null

0

1

0

7