hexsha string | size int64 | ext string | lang string | max_stars_repo_path string | max_stars_repo_name string | max_stars_repo_head_hexsha string | max_stars_repo_licenses list | max_stars_count int64 | max_stars_repo_stars_event_min_datetime string | max_stars_repo_stars_event_max_datetime string | max_issues_repo_path string | max_issues_repo_name string | max_issues_repo_head_hexsha string | max_issues_repo_licenses list | max_issues_count int64 | max_issues_repo_issues_event_min_datetime string | max_issues_repo_issues_event_max_datetime string | max_forks_repo_path string | max_forks_repo_name string | max_forks_repo_head_hexsha string | max_forks_repo_licenses list | max_forks_count int64 | max_forks_repo_forks_event_min_datetime string | max_forks_repo_forks_event_max_datetime string | content string | avg_line_length float64 | max_line_length int64 | alphanum_fraction float64 | qsc_code_num_words_quality_signal int64 | qsc_code_num_chars_quality_signal float64 | qsc_code_mean_word_length_quality_signal float64 | qsc_code_frac_words_unique_quality_signal float64 | qsc_code_frac_chars_top_2grams_quality_signal float64 | qsc_code_frac_chars_top_3grams_quality_signal float64 | qsc_code_frac_chars_top_4grams_quality_signal float64 | qsc_code_frac_chars_dupe_5grams_quality_signal float64 | qsc_code_frac_chars_dupe_6grams_quality_signal float64 | qsc_code_frac_chars_dupe_7grams_quality_signal float64 | qsc_code_frac_chars_dupe_8grams_quality_signal float64 | qsc_code_frac_chars_dupe_9grams_quality_signal float64 | qsc_code_frac_chars_dupe_10grams_quality_signal float64 | qsc_code_frac_chars_replacement_symbols_quality_signal float64 | qsc_code_frac_chars_digital_quality_signal float64 | qsc_code_frac_chars_whitespace_quality_signal float64 | qsc_code_size_file_byte_quality_signal float64 | qsc_code_num_lines_quality_signal float64 | qsc_code_num_chars_line_max_quality_signal float64 | qsc_code_num_chars_line_mean_quality_signal float64 | qsc_code_frac_chars_alphabet_quality_signal float64 | qsc_code_frac_chars_comments_quality_signal float64 | qsc_code_cate_xml_start_quality_signal float64 | qsc_code_frac_lines_dupe_lines_quality_signal float64 | qsc_code_cate_autogen_quality_signal float64 | qsc_code_frac_lines_long_string_quality_signal float64 | qsc_code_frac_chars_string_length_quality_signal float64 | qsc_code_frac_chars_long_word_length_quality_signal float64 | qsc_code_frac_lines_string_concat_quality_signal float64 | qsc_code_cate_encoded_data_quality_signal float64 | qsc_code_frac_chars_hex_words_quality_signal float64 | qsc_code_frac_lines_prompt_comments_quality_signal float64 | qsc_code_frac_lines_assert_quality_signal float64 | qsc_codepython_cate_ast_quality_signal float64 | qsc_codepython_frac_lines_func_ratio_quality_signal float64 | qsc_codepython_cate_var_zero_quality_signal bool | qsc_codepython_frac_lines_pass_quality_signal float64 | qsc_codepython_frac_lines_import_quality_signal float64 | qsc_codepython_frac_lines_simplefunc_quality_signal float64 | qsc_codepython_score_lines_no_logic_quality_signal float64 | qsc_codepython_frac_lines_print_quality_signal float64 | qsc_code_num_words int64 | qsc_code_num_chars int64 | qsc_code_mean_word_length int64 | qsc_code_frac_words_unique null | qsc_code_frac_chars_top_2grams int64 | qsc_code_frac_chars_top_3grams int64 | qsc_code_frac_chars_top_4grams int64 | qsc_code_frac_chars_dupe_5grams int64 | qsc_code_frac_chars_dupe_6grams int64 | qsc_code_frac_chars_dupe_7grams int64 | qsc_code_frac_chars_dupe_8grams int64 | qsc_code_frac_chars_dupe_9grams int64 | qsc_code_frac_chars_dupe_10grams int64 | qsc_code_frac_chars_replacement_symbols int64 | qsc_code_frac_chars_digital int64 | qsc_code_frac_chars_whitespace int64 | qsc_code_size_file_byte int64 | qsc_code_num_lines int64 | qsc_code_num_chars_line_max int64 | qsc_code_num_chars_line_mean int64 | qsc_code_frac_chars_alphabet int64 | qsc_code_frac_chars_comments int64 | qsc_code_cate_xml_start int64 | qsc_code_frac_lines_dupe_lines int64 | qsc_code_cate_autogen int64 | qsc_code_frac_lines_long_string int64 | qsc_code_frac_chars_string_length int64 | qsc_code_frac_chars_long_word_length int64 | qsc_code_frac_lines_string_concat null | qsc_code_cate_encoded_data int64 | qsc_code_frac_chars_hex_words int64 | qsc_code_frac_lines_prompt_comments int64 | qsc_code_frac_lines_assert int64 | qsc_codepython_cate_ast int64 | qsc_codepython_frac_lines_func_ratio int64 | qsc_codepython_cate_var_zero int64 | qsc_codepython_frac_lines_pass int64 | qsc_codepython_frac_lines_import int64 | qsc_codepython_frac_lines_simplefunc int64 | qsc_codepython_score_lines_no_logic int64 | qsc_codepython_frac_lines_print int64 | effective string | hits int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5b52f9869c381ea3bb983fb9f1dc8ab10435d302 | 795 | py | Python | indexapp/models.py | mesic-m/competitivenessindex | d67dda3525c5f942ee33e0ce1f490afd7582856b | [
"MIT"
] | null | null | null | indexapp/models.py | mesic-m/competitivenessindex | d67dda3525c5f942ee33e0ce1f490afd7582856b | [
"MIT"
] | null | null | null | indexapp/models.py | mesic-m/competitivenessindex | d67dda3525c5f942ee33e0ce1f490afd7582856b | [
"MIT"
] | null | null | null | from django.db import models
class rca(models.Model):
file = models.FileField(upload_to="obradeni_excel/", max_length=250)
def __str__(self):
return self.title
class gliit(models.Model):
file = models.FileField(upload_to="obradeni_excel/", max_length=250)
def __str__(self):
return self.title
class tfcc(models.Model):
file = models.FileField(upload_to="obradeni_excel/", max_length=250)
def __str__(self):
return self.title
class ci(models.Model):
file = models.FileField(upload_to="obradeni_excel/", max_length=250)
def __str__(self):
return self.title
class svi(models.Model):
file = models.FileField(upload_to="obradeni_excel/", max_length=250)
def __str__(self):
return self.title
| 22.714286 | 72 | 0.680503 | 105 | 795 | 4.819048 | 0.247619 | 0.108696 | 0.148221 | 0.20751 | 0.909091 | 0.909091 | 0.909091 | 0.909091 | 0.909091 | 0.909091 | 0 | 0.023734 | 0.205031 | 795 | 34 | 73 | 23.382353 | 0.776899 | 0 | 0 | 0.714286 | 0 | 0 | 0.094578 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.238095 | false | 0 | 0.047619 | 0.238095 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 11 |
750c9ac0cb90c9eeed6574813ad54d32a9fa171b | 2,481 | py | Python | QRFactorization/TestQR.py | jsbrenner-smu/MathTest | e67565fafbad2912ea2c61ede37bf6729e509238 | [
"CC0-1.0"
] | 2 | 2021-01-26T19:13:57.000Z | 2021-03-05T05:43:59.000Z | QRFactorization/TestQR.py | jsbrenner-smu/MathTest | e67565fafbad2912ea2c61ede37bf6729e509238 | [
"CC0-1.0"
] | null | null | null | QRFactorization/TestQR.py | jsbrenner-smu/MathTest | e67565fafbad2912ea2c61ede37bf6729e509238 | [
"CC0-1.0"
] | 1 | 2021-02-26T22:12:24.000Z | 2021-02-26T22:12:24.000Z | #!/usr/bin/env python3
#
# Script to test QRFactors on a variety of matrices.
#
# Daniel R. Reynolds
# SMU Mathematics
# Math 4315
# imports
import numpy
from QRFactors import QRFactors
# set matrix sizes for tests
nvals = [50, 100, 200, 400]
# full-rank square matrix tests
for n in nvals:
print("Testing with full-rank square matrix of dimension ", n)
# create the matrix
I = numpy.eye(n)
A = numpy.random.rand(n,n) + I
# call QRFactors
Q, R = QRFactors(A)
# output results
print(" ||I-Q^TQ|| = ", numpy.linalg.norm(I-Q.T@Q,2))
print(" ||I-QQ^T|| = ", numpy.linalg.norm(I-Q@Q.T,2))
print(" ||A-QR|| = ", numpy.linalg.norm(A-Q@R,2))
print(" ||tril(R,-1)|| = ", numpy.linalg.norm(numpy.tril(R,-1),2))
# full-rank rectangular matrix tests
for n in nvals:
print("Testing with full-rank rectangular matrix of dimension ", 2*n, "x", n)
# create the matrix
I = numpy.eye(2*n)
A = numpy.random.rand(2*n,n) + I[:,:n]
# call QRFactors
Q, R = QRFactors(A)
# output results
print(" ||I-Q^TQ|| = ", numpy.linalg.norm(I-Q.T@Q,2))
print(" ||I-QQ^T|| = ", numpy.linalg.norm(I-Q@Q.T,2))
print(" ||A-QR|| = ", numpy.linalg.norm(A-Q@R,2))
print(" ||tril(R,-1)|| = ", numpy.linalg.norm(numpy.tril(R,-1),2))
# rank-deficient square matrix tests
for n in nvals:
print("Testing with rank-deficient square matrix of dimension ", n)
# create the matrix
I = numpy.eye(n)
A = numpy.random.rand(n,n) + I
A[:,2] = 2*A[:,1]
# call QRFactors
Q, R = QRFactors(A)
# output results
print(" ||I-Q^TQ|| = ", numpy.linalg.norm(I-Q.T@Q,2))
print(" ||I-QQ^T|| = ", numpy.linalg.norm(I-Q@Q.T,2))
print(" ||A-QR|| = ", numpy.linalg.norm(A-Q@R,2))
print(" ||tril(R,-1)|| = ", numpy.linalg.norm(numpy.tril(R,-1),2))
# rank-deficient rectangular matrix tests
for n in nvals:
print("Testing with rank-deficient rectangular matrix of dimension ", 2*n, "x", n)
# create the matrix
I = numpy.eye(2*n)
A = numpy.random.rand(2*n,n) + I[:,:n]
# call QRFactors
Q, R = QRFactors(A)
# output results
print(" ||I-Q^TQ|| = ", numpy.linalg.norm(I-Q.T@Q,2))
print(" ||I-QQ^T|| = ", numpy.linalg.norm(I-Q@Q.T,2))
print(" ||A-QR|| = ", numpy.linalg.norm(A-Q@R,2))
print(" ||tril(R,-1)|| = ", numpy.linalg.norm(numpy.tril(R,-1),2))
# end of script
| 27.263736 | 86 | 0.565095 | 405 | 2,481 | 3.461728 | 0.162963 | 0.125535 | 0.171184 | 0.091298 | 0.850214 | 0.850214 | 0.850214 | 0.850214 | 0.850214 | 0.850214 | 0 | 0.025871 | 0.236598 | 2,481 | 90 | 87 | 27.566667 | 0.714361 | 0.200726 | 0 | 0.8 | 0 | 0 | 0.276955 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.05 | 0 | 0.05 | 0.5 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 8 |
75342d65f284960b1d5c01caf8e6da2f6ef9f1d8 | 3,529 | py | Python | spyder-remote-client/spyder_remote_client/spyder/discover.py | goanpeca/spyder-remote | 5c4d702c2f1ad0c924e328ca6c32d15e5a9b38d6 | [
"MIT"
] | 3 | 2020-09-08T19:47:29.000Z | 2022-03-31T19:18:45.000Z | spyder-remote-client/spyder_remote_client/spyder/discover.py | goanpeca/spyder-remote | 5c4d702c2f1ad0c924e328ca6c32d15e5a9b38d6 | [
"MIT"
] | 21 | 2020-08-02T13:20:08.000Z | 2021-08-19T12:52:42.000Z | spyder-remote-client/spyder_remote_client/spyder/discover.py | goanpeca/spyder-remote | 5c4d702c2f1ad0c924e328ca6c32d15e5a9b38d6 | [
"MIT"
] | 3 | 2020-07-31T11:36:21.000Z | 2022-03-31T19:19:00.000Z | # Copyright (c) Semi-ATE
# Distributed under the terms of the MIT License
"""
Spyder Remote zeroconf Qt listener.
"""
from qtpy.QtCore import QObject, Signal
from spyder_remote_client.discover import SpyderRemoteListener
class QSpyderRemoteListener(QObject, SpyderRemoteListener):
"""
A Zeroconf server listener for spyder-kernels services.
Connects the SpyderRemoteListener to Qt Signals.
"""
sig_service_added = Signal(object, str, str)
"""
This signal is emitted when a zeroconf service is added.
Parameters
----------
zeroconf_instance: Zeroconf
The zerconf instance.
service_type: str
The service type. Example: '_sdk._tcp.local.'
name: str
The name of the host exposing the service.
"""
sig_service_removed = Signal(object, str, str)
"""
This signal is emitted when a zeroconf service is removed.
Parameters
----------
zeroconf_instance: Zeroconf
The zerconf instance.
service_type: str
The service type. Example: '_sdk._tcp.local.'
name: str
The name of the host exposing the service.
"""
sig_service_updated = Signal(object, str, str)
"""
This signal is emitted when a zeroconf service is updated.
Parameters
----------
zeroconf_instance: Zeroconf
The zerconf instance.
service_type: str
The service type. Example: '_sdk._tcp.local.'
name: str
The name of the host exposing the service.
"""
def __init__(self):
super().__init__()
def add_service(self, zeroconf_instance, service_type, name):
"""
Handle a zeroconf service addition.
Parameters
----------
zeroconf_instance: Zeroconf
The zerconf instance.
service_type: str
The service type. Example: '_sdk._tcp.local.'
name: str
The name of the host exposing the service.
Notes
-----
This method emits a Qt signal.
"""
super().add_service(zeroconf_instance, service_type, name)
self.sig_service_added.emit(zeroconf_instance, service_type, name)
def remove_service(self, zeroconf_instance, service_type, name):
"""
Handle a zeroconf service addition.
Parameters
----------
zeroconf_instance: Zeroconf
The zerconf instance.
service_type: str
The service type. Example: '_sdk._tcp.local.'
name: str
The name of the host exposing the service.
Notes
-----
This method emits a Qt signal.
"""
super().remove_service(zeroconf_instance, service_type, name)
self.sig_service_removed.emit(zeroconf_instance, service_type, name)
def update_service(self, zeroconf_instance, service_type, name):
"""
Handle a zeroconf service addition.
Parameters
----------
zeroconf_instance: Zeroconf
The zerconf instance.
service_type: str
The service type. Example: '_sdk._tcp.local.'
name: str
The name of the host exposing the service.
Notes
-----
This method emits a Qt signal.
"""
super().update_service(zeroconf_instance, service_type, name)
self.sig_service_updated.emit(zeroconf_instance, service_type, name)
| 28.691057 | 77 | 0.601587 | 383 | 3,529 | 5.360313 | 0.182768 | 0.112518 | 0.138821 | 0.118363 | 0.787141 | 0.787141 | 0.770093 | 0.733074 | 0.733074 | 0.657087 | 0 | 0 | 0.313403 | 3,529 | 122 | 78 | 28.92623 | 0.847297 | 0.299802 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.235294 | false | 0 | 0.117647 | 0 | 0.588235 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
7539e30d4b271a70277aacb7200cbbab119ee407 | 221 | py | Python | tests/__init__.py | gabicavalcante/pymoo | 1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846 | [
"Apache-2.0"
] | 11 | 2018-05-22T17:38:02.000Z | 2022-02-28T03:34:33.000Z | tests/__init__.py | gabicavalcante/pymoo | 1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846 | [
"Apache-2.0"
] | 15 | 2022-01-03T19:36:36.000Z | 2022-03-30T03:57:58.000Z | tests/__init__.py | gabicavalcante/pymoo | 1711ce3a96e5ef622d0116d6c7ea4d26cbe2c846 | [
"Apache-2.0"
] | 3 | 2021-11-22T08:01:47.000Z | 2022-03-11T08:53:58.000Z | import os
from pymoo.configuration import get_pymoo
def get_pymoo_test():
return os.path.join(get_pymoo(), "tests")
def path_to_test_resources(*args):
return os.path.join(get_pymoo_test(), "resources", *args) | 20.090909 | 61 | 0.742081 | 34 | 221 | 4.558824 | 0.441176 | 0.206452 | 0.154839 | 0.206452 | 0.309677 | 0.309677 | 0 | 0 | 0 | 0 | 0 | 0 | 0.131222 | 221 | 11 | 61 | 20.090909 | 0.807292 | 0 | 0 | 0 | 0 | 0 | 0.063063 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0.333333 | 1 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 7 |
755276c6ed874684e8aefb4692d771759280d73e | 95,747 | py | Python | tb_rest_client/api/api_pe/dashboard_controller_api.py | samson0v/python_tb_rest_client | 08ff7898740f7cec2170e85d5c3c89e222e967f7 | [
"Apache-2.0"
] | 30 | 2020-06-19T06:42:50.000Z | 2021-08-23T21:16:36.000Z | tb_rest_client/api/api_pe/dashboard_controller_api.py | samson0v/python_tb_rest_client | 08ff7898740f7cec2170e85d5c3c89e222e967f7 | [
"Apache-2.0"
] | 25 | 2021-08-30T01:17:27.000Z | 2022-03-16T14:10:14.000Z | tb_rest_client/api/api_pe/dashboard_controller_api.py | samson0v/python_tb_rest_client | 08ff7898740f7cec2170e85d5c3c89e222e967f7 | [
"Apache-2.0"
] | 23 | 2020-07-06T13:41:54.000Z | 2021-08-23T21:04:50.000Z | # coding: utf-8
"""
ThingsBoard REST API
ThingsBoard Professional Edition IoT platform REST API documentation. # noqa: E501
OpenAPI spec version: 3.3.3PAAS-RC1
Contact: info@thingsboard.io
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 tb_rest_client.api_client import ApiClient
class DashboardControllerApi(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 delete_dashboard_using_delete(self, dashboard_id, **kwargs): # noqa: E501
"""Delete the Dashboard (deleteDashboard) # noqa: E501
Delete the Dashboard. Only users with 'TENANT_ADMIN') authority may delete the dashboards. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_dashboard_using_delete(dashboard_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_id: A string value representing the device id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_dashboard_using_delete_with_http_info(dashboard_id, **kwargs) # noqa: E501
else:
(data) = self.delete_dashboard_using_delete_with_http_info(dashboard_id, **kwargs) # noqa: E501
return data
def delete_dashboard_using_delete_with_http_info(self, dashboard_id, **kwargs): # noqa: E501
"""Delete the Dashboard (deleteDashboard) # noqa: E501
Delete the Dashboard. Only users with 'TENANT_ADMIN') authority may delete the dashboards. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_dashboard_using_delete_with_http_info(dashboard_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_id: A string value representing the device id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['dashboard_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_dashboard_using_delete" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'dashboard_id' is set
if ('dashboard_id' not in params or
params['dashboard_id'] is None):
raise ValueError("Missing the required parameter `dashboard_id` when calling `delete_dashboard_using_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'dashboard_id' in params:
path_params['dashboardId'] = params['dashboard_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/{dashboardId}', '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 export_group_dashboards_using_get(self, entity_group_id, limit, **kwargs): # noqa: E501
"""Export Dashboards (exportGroupDashboards) # noqa: E501
Export the dashboards that belong to specified group id.The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.export_group_dashboards_using_get(entity_group_id, limit, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int limit: Limit of the entities to export (required)
:return: list[Dashboard]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.export_group_dashboards_using_get_with_http_info(entity_group_id, limit, **kwargs) # noqa: E501
else:
(data) = self.export_group_dashboards_using_get_with_http_info(entity_group_id, limit, **kwargs) # noqa: E501
return data
def export_group_dashboards_using_get_with_http_info(self, entity_group_id, limit, **kwargs): # noqa: E501
"""Export Dashboards (exportGroupDashboards) # noqa: E501
Export the dashboards that belong to specified group id.The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.export_group_dashboards_using_get_with_http_info(entity_group_id, limit, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int limit: Limit of the entities to export (required)
:return: list[Dashboard]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_group_id', 'limit'] # 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 export_group_dashboards_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_group_id' is set
if ('entity_group_id' not in params or
params['entity_group_id'] is None):
raise ValueError("Missing the required parameter `entity_group_id` when calling `export_group_dashboards_using_get`") # noqa: E501
# verify the required parameter 'limit' is set
if ('limit' not in params or
params['limit'] is None):
raise ValueError("Missing the required parameter `limit` when calling `export_group_dashboards_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'entity_group_id' in params:
path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501
query_params = []
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
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityGroup/{entityGroupId}/dashboards/export{?limit}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[Dashboard]', # 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 get_customer_home_dashboard_info_using_get(self, **kwargs): # noqa: E501
"""Get Customer Home Dashboard Info (getCustomerHomeDashboardInfo) # noqa: E501
Returns the home dashboard info object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the corresponding customer. Available for users with 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the white labeling resource. # 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_customer_home_dashboard_info_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_customer_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_customer_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_customer_home_dashboard_info_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get Customer Home Dashboard Info (getCustomerHomeDashboardInfo) # noqa: E501
Returns the home dashboard info object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the corresponding customer. Available for users with 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the white labeling resource. # 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_customer_home_dashboard_info_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboardInfo
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_customer_home_dashboard_info_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/customer/dashboard/home/info', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HomeDashboardInfo', # 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 get_dashboard_by_id_using_get(self, dashboard_id, **kwargs): # noqa: E501
"""Get Dashboard (getDashboardById) # noqa: E501
Get the dashboard based on 'dashboardId' parameter. The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_dashboard_by_id_using_get(dashboard_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_id: A string value representing the device id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: Dashboard
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_dashboard_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501
else:
(data) = self.get_dashboard_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501
return data
def get_dashboard_by_id_using_get_with_http_info(self, dashboard_id, **kwargs): # noqa: E501
"""Get Dashboard (getDashboardById) # noqa: E501
Get the dashboard based on 'dashboardId' parameter. The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_dashboard_by_id_using_get_with_http_info(dashboard_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_id: A string value representing the device id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: Dashboard
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['dashboard_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_dashboard_by_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'dashboard_id' is set
if ('dashboard_id' not in params or
params['dashboard_id'] is None):
raise ValueError("Missing the required parameter `dashboard_id` when calling `get_dashboard_by_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'dashboard_id' in params:
path_params['dashboardId'] = params['dashboard_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/{dashboardId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Dashboard', # 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 get_dashboard_info_by_id_using_get(self, dashboard_id, **kwargs): # noqa: E501
"""Get Dashboard Info (getDashboardInfoById) # noqa: E501
Get the information about the dashboard based on 'dashboardId' parameter. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. # 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_dashboard_info_by_id_using_get(dashboard_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_id: A string value representing the device id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: DashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_dashboard_info_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501
else:
(data) = self.get_dashboard_info_by_id_using_get_with_http_info(dashboard_id, **kwargs) # noqa: E501
return data
def get_dashboard_info_by_id_using_get_with_http_info(self, dashboard_id, **kwargs): # noqa: E501
"""Get Dashboard Info (getDashboardInfoById) # noqa: E501
Get the information about the dashboard based on 'dashboardId' parameter. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. # 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_dashboard_info_by_id_using_get_with_http_info(dashboard_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_id: A string value representing the device id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:return: DashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['dashboard_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_dashboard_info_by_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'dashboard_id' is set
if ('dashboard_id' not in params or
params['dashboard_id'] is None):
raise ValueError("Missing the required parameter `dashboard_id` when calling `get_dashboard_info_by_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'dashboard_id' in params:
path_params['dashboardId'] = params['dashboard_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/info/{dashboardId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='DashboardInfo', # 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 get_dashboards_by_entity_group_id_using_get(self, entity_group_id, page_size, page, **kwargs): # noqa: E501
"""Get dashboards by Entity Group Id (getDashboardsByEntityGroupId) # noqa: E501
Returns a page of Dashboard objects that belongs to specified Entity Group Id. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_dashboards_by_entity_group_id_using_get(entity_group_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_dashboards_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_dashboards_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, **kwargs) # noqa: E501
return data
def get_dashboards_by_entity_group_id_using_get_with_http_info(self, entity_group_id, page_size, page, **kwargs): # noqa: E501
"""Get dashboards by Entity Group Id (getDashboardsByEntityGroupId) # noqa: E501
Returns a page of Dashboard objects that belongs to specified Entity Group Id. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for specified group. # 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_dashboards_by_entity_group_id_using_get_with_http_info(entity_group_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_group_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # 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_dashboards_by_entity_group_id_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_group_id' is set
if ('entity_group_id' not in params or
params['entity_group_id'] is None):
raise ValueError("Missing the required parameter `entity_group_id` when calling `get_dashboards_by_entity_group_id_using_get`") # noqa: E501
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_dashboards_by_entity_group_id_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_dashboards_by_entity_group_id_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'entity_group_id' in params:
path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityGroup/{entityGroupId}/dashboards{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataDashboardInfo', # 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 get_dashboards_by_ids_using_get(self, dashboard_ids, **kwargs): # noqa: E501
"""Get dashboards by Dashboard Ids (getDashboardsByIds) # noqa: E501
Returns a list of DashboardInfo objects based on the provided ids. Filters the list based on the user permissions. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_dashboards_by_ids_using_get(dashboard_ids, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_ids: A list of dashboard ids, separated by comma ',' (required)
:return: list[DashboardInfo]
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_dashboards_by_ids_using_get_with_http_info(dashboard_ids, **kwargs) # noqa: E501
else:
(data) = self.get_dashboards_by_ids_using_get_with_http_info(dashboard_ids, **kwargs) # noqa: E501
return data
def get_dashboards_by_ids_using_get_with_http_info(self, dashboard_ids, **kwargs): # noqa: E501
"""Get dashboards by Dashboard Ids (getDashboardsByIds) # noqa: E501
Returns a list of DashboardInfo objects based on the provided ids. Filters the list based on the user permissions. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_dashboards_by_ids_using_get_with_http_info(dashboard_ids, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str dashboard_ids: A list of dashboard ids, separated by comma ',' (required)
:return: list[DashboardInfo]
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['dashboard_ids'] # 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_dashboards_by_ids_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'dashboard_ids' is set
if ('dashboard_ids' not in params or
params['dashboard_ids'] is None):
raise ValueError("Missing the required parameter `dashboard_ids` when calling `get_dashboards_by_ids_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'dashboard_ids' in params:
query_params.append(('dashboardIds', params['dashboard_ids'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboards{?dashboardIds}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='list[DashboardInfo]', # 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 get_home_dashboard_info_using_get(self, **kwargs): # noqa: E501
"""Get Home Dashboard Info (getHomeDashboardInfo) # noqa: E501
Returns the home dashboard info object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the User. If 'homeDashboardId' parameter is not set on the User level and the User has authority 'CUSTOMER_USER', check the same parameter for the corresponding Customer. If 'homeDashboardId' parameter is not set on the User and Customer levels then checks the same parameter for the Tenant that owns the user. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_home_dashboard_info_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_home_dashboard_info_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get Home Dashboard Info (getHomeDashboardInfo) # noqa: E501
Returns the home dashboard info object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the User. If 'homeDashboardId' parameter is not set on the User level and the User has authority 'CUSTOMER_USER', check the same parameter for the corresponding Customer. If 'homeDashboardId' parameter is not set on the User and Customer levels then checks the same parameter for the Tenant that owns the user. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_home_dashboard_info_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboardInfo
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_home_dashboard_info_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/home/info', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HomeDashboardInfo', # 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 get_home_dashboard_using_get(self, **kwargs): # noqa: E501
"""Get Home Dashboard (getHomeDashboard) # noqa: E501
Returns the home dashboard object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the User. If 'homeDashboardId' parameter is not set on the User level and the User has authority 'CUSTOMER_USER', check the same parameter for the corresponding Customer. If 'homeDashboardId' parameter is not set on the User and Customer levels then checks the same parameter for the Tenant that owns the user. The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_home_dashboard_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboard
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_home_dashboard_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_home_dashboard_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_home_dashboard_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get Home Dashboard (getHomeDashboard) # noqa: E501
Returns the home dashboard object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the User. If 'homeDashboardId' parameter is not set on the User level and the User has authority 'CUSTOMER_USER', check the same parameter for the corresponding Customer. If 'homeDashboardId' parameter is not set on the User and Customer levels then checks the same parameter for the Tenant that owns the user. The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # 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_home_dashboard_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboard
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_home_dashboard_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/home', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HomeDashboard', # 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 get_max_datapoints_limit_using_get(self, **kwargs): # noqa: E501
"""Get max data points limit (getMaxDatapointsLimit) # noqa: E501
Get the maximum number of data points that dashboard may request from the server per in a single subscription command. This value impacts the time window behavior. It impacts 'Max values' parameter in case user selects 'None' as 'Data aggregation function'. It also impacts the 'Grouping interval' in case of any other 'Data aggregation function' is selected. The actual value of the limit is configurable in the system configuration file. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_max_datapoints_limit_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: int
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_max_datapoints_limit_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_max_datapoints_limit_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_max_datapoints_limit_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get max data points limit (getMaxDatapointsLimit) # noqa: E501
Get the maximum number of data points that dashboard may request from the server per in a single subscription command. This value impacts the time window behavior. It impacts 'Max values' parameter in case user selects 'None' as 'Data aggregation function'. It also impacts the 'Grouping interval' in case of any other 'Data aggregation function' is selected. The actual value of the limit is configurable in the system configuration file. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_max_datapoints_limit_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: int
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_max_datapoints_limit_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/maxDatapointsLimit', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='int', # 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 get_server_time_using_get(self, **kwargs): # noqa: E501
"""Get server time (getServerTime) # noqa: E501
Get the server time (milliseconds since January 1, 1970 UTC). Used to adjust view of the dashboards according to the difference between browser and server time. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_server_time_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: int
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_server_time_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_server_time_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_server_time_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get server time (getServerTime) # noqa: E501
Get the server time (milliseconds since January 1, 1970 UTC). Used to adjust view of the dashboards according to the difference between browser and server time. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_server_time_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: int
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_server_time_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard/serverTime', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='int', # 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 get_tenant_dashboards_using_get(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Dashboards (getTenantDashboards) # noqa: E501
Returns a page of dashboard info objects owned by the tenant of a current user. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' authority. # 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_tenant_dashboards_using_get(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param bool mobile: Exclude dashboards that are hidden for mobile
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_tenant_dashboards_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_dashboards_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
return data
def get_tenant_dashboards_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Dashboards (getTenantDashboards) # noqa: E501
Returns a page of dashboard info objects owned by the tenant of a current user. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'TENANT_ADMIN' authority. # 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_tenant_dashboards_using_get_with_http_info(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param bool mobile: Exclude dashboards that are hidden for mobile
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page_size', 'page', 'mobile', 'text_search', 'sort_property', 'sort_order'] # 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_tenant_dashboards_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_tenant_dashboards_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_tenant_dashboards_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'mobile' in params:
query_params.append(('mobile', params['mobile'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/tenant/dashboards{?mobile,page,pageSize,sortOrder,sortProperty,textSearch}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataDashboardInfo', # 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 get_tenant_dashboards_using_get1(self, tenant_id, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Dashboards by System Administrator (getTenantDashboards) # noqa: E501
Returns a page of dashboard info objects owned by tenant. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'SYS_ADMIN' authority. # 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_tenant_dashboards_using_get1(tenant_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_id: A string value representing the tenant id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_tenant_dashboards_using_get1_with_http_info(tenant_id, page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_tenant_dashboards_using_get1_with_http_info(tenant_id, page_size, page, **kwargs) # noqa: E501
return data
def get_tenant_dashboards_using_get1_with_http_info(self, tenant_id, page_size, page, **kwargs): # noqa: E501
"""Get Tenant Dashboards by System Administrator (getTenantDashboards) # noqa: E501
Returns a page of dashboard info objects owned by tenant. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. Available for users with 'SYS_ADMIN' authority. # 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_tenant_dashboards_using_get1_with_http_info(tenant_id, page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str tenant_id: A string value representing the tenant id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['tenant_id', 'page_size', 'page', 'text_search', 'sort_property', 'sort_order'] # 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_tenant_dashboards_using_get1" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'tenant_id' is set
if ('tenant_id' not in params or
params['tenant_id'] is None):
raise ValueError("Missing the required parameter `tenant_id` when calling `get_tenant_dashboards_using_get1`") # noqa: E501
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_tenant_dashboards_using_get1`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_tenant_dashboards_using_get1`") # noqa: E501
collection_formats = {}
path_params = {}
if 'tenant_id' in params:
path_params['tenantId'] = params['tenant_id'] # noqa: E501
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/tenant/{tenantId}/dashboards{?page,pageSize,sortOrder,sortProperty,textSearch}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataDashboardInfo', # 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 get_tenant_home_dashboard_info_using_get(self, **kwargs): # noqa: E501
"""Get Tenant Home Dashboard Info (getTenantHomeDashboardInfo) # noqa: E501
Returns the home dashboard info object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the corresponding tenant. Available for users with 'TENANT_ADMIN' authority. Security check is performed to verify that the user has 'READ' permission for the white labeling resource. # 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_tenant_home_dashboard_info_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_tenant_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_tenant_home_dashboard_info_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_tenant_home_dashboard_info_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Get Tenant Home Dashboard Info (getTenantHomeDashboardInfo) # noqa: E501
Returns the home dashboard info object that is configured as 'homeDashboardId' parameter in the 'additionalInfo' of the corresponding tenant. Available for users with 'TENANT_ADMIN' authority. Security check is performed to verify that the user has 'READ' permission for the white labeling resource. # 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_tenant_home_dashboard_info_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:return: HomeDashboardInfo
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_tenant_home_dashboard_info_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/tenant/dashboard/home/info', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='HomeDashboardInfo', # 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 get_user_dashboards_using_get(self, page_size, page, **kwargs): # noqa: E501
"""Get Dashboards (getUserDashboards) # noqa: E501
Returns a page of Dashboard Info objects available for specified or current user. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_user_dashboards_using_get(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param bool mobile: Exclude dashboards that are hidden for mobile
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:param str operation: Filter by allowed operations for the current user
:param str user_id: A string value representing the user id. For example, '784f394c-42b6-435a-983c-b7beff2784f9'
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_user_dashboards_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
else:
(data) = self.get_user_dashboards_using_get_with_http_info(page_size, page, **kwargs) # noqa: E501
return data
def get_user_dashboards_using_get_with_http_info(self, page_size, page, **kwargs): # noqa: E501
"""Get Dashboards (getUserDashboards) # noqa: E501
Returns a page of Dashboard Info objects available for specified or current user. You can specify parameters to filter the results. The result is wrapped with PageData object that allows you to iterate over result set using pagination. See the 'Model' tab of the Response Class for more details. The Dashboard Info object contains lightweight information about the dashboard (e.g. title, image, assigned customers) but does not contain the heavyweight configuration JSON. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'READ' permission for the entity (entities). # 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_user_dashboards_using_get_with_http_info(page_size, page, async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page_size: Maximum amount of entities in a one page (required)
:param int page: Sequence number of page starting from 0 (required)
:param bool mobile: Exclude dashboards that are hidden for mobile
:param str text_search: The case insensitive 'startsWith' filter based on the dashboard title.
:param str sort_property: Property of entity to sort by
:param str sort_order: Sort order. ASC (ASCENDING) or DESC (DESCENDING)
:param str operation: Filter by allowed operations for the current user
:param str user_id: A string value representing the user id. For example, '784f394c-42b6-435a-983c-b7beff2784f9'
:return: PageDataDashboardInfo
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page_size', 'page', 'mobile', 'text_search', 'sort_property', 'sort_order', 'operation', 'user_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_user_dashboards_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'page_size' is set
if ('page_size' not in params or
params['page_size'] is None):
raise ValueError("Missing the required parameter `page_size` when calling `get_user_dashboards_using_get`") # noqa: E501
# verify the required parameter 'page' is set
if ('page' not in params or
params['page'] is None):
raise ValueError("Missing the required parameter `page` when calling `get_user_dashboards_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
if 'page_size' in params:
query_params.append(('pageSize', params['page_size'])) # noqa: E501
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'mobile' in params:
query_params.append(('mobile', params['mobile'])) # noqa: E501
if 'text_search' in params:
query_params.append(('textSearch', params['text_search'])) # noqa: E501
if 'sort_property' in params:
query_params.append(('sortProperty', params['sort_property'])) # noqa: E501
if 'sort_order' in params:
query_params.append(('sortOrder', params['sort_order'])) # noqa: E501
if 'operation' in params:
query_params.append(('operation', params['operation'])) # noqa: E501
if 'user_id' in params:
query_params.append(('userId', params['user_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/user/dashboards{?mobile,operation,page,pageSize,sortOrder,sortProperty,textSearch,userId}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageDataDashboardInfo', # 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 import_group_dashboards_using_post(self, entity_group_id, **kwargs): # noqa: E501
"""Import Dashboards (importGroupDashboards) # noqa: E501
Import the dashboards to specified group.The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.import_group_dashboards_using_post(entity_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param list[Dashboard] body:
:param bool overwrite: Overwrite dashboards with the same name
: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.import_group_dashboards_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501
else:
(data) = self.import_group_dashboards_using_post_with_http_info(entity_group_id, **kwargs) # noqa: E501
return data
def import_group_dashboards_using_post_with_http_info(self, entity_group_id, **kwargs): # noqa: E501
"""Import Dashboards (importGroupDashboards) # noqa: E501
Import the dashboards to specified group.The Dashboard object is a heavyweight object that contains information about the dashboard (e.g. title, image, assigned customers) and also configuration JSON (e.g. layouts, widgets, entity aliases). Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for specified group. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.import_group_dashboards_using_post_with_http_info(entity_group_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str entity_group_id: A string value representing the Entity Group Id. For example, '784f394c-42b6-435a-983c-b7beff2784f9' (required)
:param list[Dashboard] body:
:param bool overwrite: Overwrite dashboards with the same name
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['entity_group_id', 'body', 'overwrite'] # 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 import_group_dashboards_using_post" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'entity_group_id' is set
if ('entity_group_id' not in params or
params['entity_group_id'] is None):
raise ValueError("Missing the required parameter `entity_group_id` when calling `import_group_dashboards_using_post`") # noqa: E501
collection_formats = {}
path_params = {}
if 'entity_group_id' in params:
path_params['entityGroupId'] = params['entity_group_id'] # noqa: E501
query_params = []
if 'overwrite' in params:
query_params.append(('overwrite', params['overwrite'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# 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 = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/entityGroup/{entityGroupId}/dashboards/import{?overwrite}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def save_dashboard_using_post(self, **kwargs): # noqa: E501
"""Create Or Update Dashboard (saveDashboard) # noqa: E501
Create or update the Dashboard. When creating dashboard, platform generates Dashboard Id as [time-based UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier#Version_1_(date-time_and_MAC_address)). The newly created Dashboard id will be present in the response. Specify existing Dashboard id to update the dashboard. Referencing non-existing dashboard Id will cause 'Not Found' error. Only users with 'TENANT_ADMIN') authority may create the dashboards. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.save_dashboard_using_post(async_req=True)
>>> result = thread.get()
:param async_req bool
:param Dashboard body:
:param str entity_group_id: entityGroupId
:return: Dashboard
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.save_dashboard_using_post_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.save_dashboard_using_post_with_http_info(**kwargs) # noqa: E501
return data
def save_dashboard_using_post_with_http_info(self, **kwargs): # noqa: E501
"""Create Or Update Dashboard (saveDashboard) # noqa: E501
Create or update the Dashboard. When creating dashboard, platform generates Dashboard Id as [time-based UUID](https://en.wikipedia.org/wiki/Universally_unique_identifier#Version_1_(date-time_and_MAC_address)). The newly created Dashboard id will be present in the response. Specify existing Dashboard id to update the dashboard. Referencing non-existing dashboard Id will cause 'Not Found' error. Only users with 'TENANT_ADMIN') authority may create the dashboards. Available for users with 'TENANT_ADMIN' or 'CUSTOMER_USER' authority. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.save_dashboard_using_post_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param Dashboard body:
:param str entity_group_id: entityGroupId
:return: Dashboard
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body', 'entity_group_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method save_dashboard_using_post" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'entity_group_id' in params:
query_params.append(('entityGroupId', params['entity_group_id'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# 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 = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/dashboard{?entityGroupId}', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='Dashboard', # 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 set_customer_home_dashboard_info_using_post(self, **kwargs): # noqa: E501
"""Update Customer Home Dashboard Info (setCustomerHomeDashboardInfo) # noqa: E501
Update the home dashboard assignment for the current customer. Available for users with 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for the white labeling resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.set_customer_home_dashboard_info_using_post(async_req=True)
>>> result = thread.get()
:param async_req bool
:param HomeDashboardInfo body:
: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.set_customer_home_dashboard_info_using_post_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.set_customer_home_dashboard_info_using_post_with_http_info(**kwargs) # noqa: E501
return data
def set_customer_home_dashboard_info_using_post_with_http_info(self, **kwargs): # noqa: E501
"""Update Customer Home Dashboard Info (setCustomerHomeDashboardInfo) # noqa: E501
Update the home dashboard assignment for the current customer. Available for users with 'CUSTOMER_USER' authority. Security check is performed to verify that the user has 'WRITE' permission for the white labeling resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.set_customer_home_dashboard_info_using_post_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param HomeDashboardInfo body:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body'] # 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 set_customer_home_dashboard_info_using_post" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# 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 = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/customer/dashboard/home/info', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def set_tenant_home_dashboard_info_using_post(self, **kwargs): # noqa: E501
"""Update Tenant Home Dashboard Info (getTenantHomeDashboardInfo) # noqa: E501
Update the home dashboard assignment for the current tenant. Available for users with 'TENANT_ADMIN' authority. Security check is performed to verify that the user has 'WRITE' permission for the white labeling resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.set_tenant_home_dashboard_info_using_post(async_req=True)
>>> result = thread.get()
:param async_req bool
:param HomeDashboardInfo body:
: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.set_tenant_home_dashboard_info_using_post_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.set_tenant_home_dashboard_info_using_post_with_http_info(**kwargs) # noqa: E501
return data
def set_tenant_home_dashboard_info_using_post_with_http_info(self, **kwargs): # noqa: E501
"""Update Tenant Home Dashboard Info (getTenantHomeDashboardInfo) # noqa: E501
Update the home dashboard assignment for the current tenant. Available for users with 'TENANT_ADMIN' authority. Security check is performed to verify that the user has 'WRITE' permission for the white labeling resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.set_tenant_home_dashboard_info_using_post_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param HomeDashboardInfo body:
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['body'] # 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 set_tenant_home_dashboard_info_using_post" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'body' in params:
body_params = params['body']
# 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 = ['X-Authorization'] # noqa: E501
return self.api_client.call_api(
'/api/tenant/dashboard/home/info', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type=None, # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
| 49.687078 | 714 | 0.655718 | 11,662 | 95,747 | 5.14663 | 0.033185 | 0.03852 | 0.017727 | 0.022792 | 0.983322 | 0.977074 | 0.973092 | 0.968211 | 0.965245 | 0.958747 | 0 | 0.017629 | 0.263611 | 95,747 | 1,926 | 715 | 49.712876 | 0.833634 | 0.441674 | 0 | 0.817132 | 0 | 0.000962 | 0.203728 | 0.069121 | 0 | 0 | 0 | 0 | 0 | 1 | 0.037536 | false | 0 | 0.010587 | 0 | 0.103946 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
7560225df7dd6338e8888c2c91a15648de3f4dfc | 49 | py | Python | apps/websocket/consumers/__init__.py | lovebirdegg/nnms-server | 9fd4563ccca9f29add375d346cdd1c2dd636c512 | [
"MIT"
] | null | null | null | apps/websocket/consumers/__init__.py | lovebirdegg/nnms-server | 9fd4563ccca9f29add375d346cdd1c2dd636c512 | [
"MIT"
] | null | null | null | apps/websocket/consumers/__init__.py | lovebirdegg/nnms-server | 9fd4563ccca9f29add375d346cdd1c2dd636c512 | [
"MIT"
] | null | null | null | # @Time : 2019/3/19 18:39
# @Author : xufqing | 24.5 | 28 | 0.571429 | 8 | 49 | 3.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.297297 | 0.244898 | 49 | 2 | 29 | 24.5 | 0.459459 | 0.918367 | 0 | null | 0 | null | 0 | 0 | null | 0 | 0 | 0 | null | 1 | null | true | 0 | 0 | null | null | null | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
f3b695dedb71c577db6db1eed1720bb255e79528 | 22,274 | py | Python | RecoEgamma/ElectronIdentification/python/Identification/cutBasedElectronID_CSA14_50ns_V1_cff.py | pasmuss/cmssw | 566f40c323beef46134485a45ea53349f59ae534 | [
"Apache-2.0"
] | null | null | null | RecoEgamma/ElectronIdentification/python/Identification/cutBasedElectronID_CSA14_50ns_V1_cff.py | pasmuss/cmssw | 566f40c323beef46134485a45ea53349f59ae534 | [
"Apache-2.0"
] | null | null | null | RecoEgamma/ElectronIdentification/python/Identification/cutBasedElectronID_CSA14_50ns_V1_cff.py | pasmuss/cmssw | 566f40c323beef46134485a45ea53349f59ae534 | [
"Apache-2.0"
] | null | null | null | import FWCore.ParameterSet.Config as cms
from PhysicsTools.SelectorUtils.centralIDRegistry import central_id_registry
ebCutOff = 1.479
#
# The ID cuts below are optimized IDs for CSA14 Scenario 1 (50ns, with misalignment)
# The cut values are taken from the twiki:
# https://twiki.cern.ch/twiki/bin/view/CMS/CutBasedElectronIdentificationRun2
# (where they may not stay, if a newer version of cuts becomes available for these
# conditions)
# See also the presentation explaining these working points (this will not change):
# https://indico.cern.ch/event/298244/contribution/1/material/slides/1.pdf
cutBasedElectronID_CSA14_50ns_V1_standalone_veto = cms.PSet(
idName = cms.string("cutBasedElectronID-CSA14-50ns-V1-standalone-veto"),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MinPtCut"),
minPt = cms.double(5.0),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("GsfEleSCEtaMultiRangeCut"),
useAbsEta = cms.bool(True),
allowedEtaRanges = cms.VPSet(
cms.PSet( minEta = cms.double(0.0),
maxEta = cms.double(ebCutOff) ),
cms.PSet( minEta = cms.double(ebCutOff),
maxEta = cms.double(2.5) )
),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDEtaInCut'),
dEtaInCutValueEB = cms.double(0.021156),
dEtaInCutValueEE = cms.double(0.028286),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDPhiInCut'),
dPhiInCutValueEB = cms.double(0.247197),
dPhiInCutValueEE = cms.double(0.228815),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleFull5x5SigmaIEtaIEtaCut'),
full5x5SigmaIEtaIEtaCutValueEB = cms.double(0.012236),
full5x5SigmaIEtaIEtaCutValueEE = cms.double(0.035176),
full5x5SigmaIEtaIEtaMap = cms.InputTag('electronIDValueMapProducer:eleFull5x5SigmaIEtaIEta'),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleHadronicOverEMCut'),
hadronicOverEMCutValueEB = cms.double(0.241641),
hadronicOverEMCutValueEE = cms.double(0.185910),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDxyCut'),
dxyCutValueEB = cms.double(0.031812),
dxyCutValueEE = cms.double(0.216331),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDzCut'),
dzCutValueEB = cms.double(0.499344),
dzCutValueEE = cms.double(0.911467),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleEInverseMinusPInverseCut'),
eInverseMinusPInverseCutValueEB = cms.double(0.323747),
eInverseMinusPInverseCutValueEE = cms.double(0.133209),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDeltaBetaIsoCutStandalone'),
isoCutEBLowPt = cms.double(0.239832),
isoCutEBHighPt = cms.double(0.239832),
isoCutEELowPt = cms.double(0.237643),
isoCutEEHighPt = cms.double(0.237643),
isRelativeIso = cms.bool(True),
deltaBetaConstant = cms.double(0.5),
ptCutOff = cms.double(20.0),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleConversionVetoCut'),
conversionSrc = cms.InputTag('allConversions'),
conversionSrcMiniAOD = cms.InputTag('reducedEgamma:reducedConversions'),
beamspotSrc = cms.InputTag('offlineBeamSpot'),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleMissingHitsCut'),
maxMissingHitsEB = cms.uint32(2),
maxMissingHitsEE = cms.uint32(3),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False))
)
)
cutBasedElectronID_CSA14_50ns_V1_standalone_loose = cms.PSet(
idName = cms.string("cutBasedElectronID-CSA14-50ns-V1-standalone-loose"),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MinPtCut"),
minPt = cms.double(5.0),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("GsfEleSCEtaMultiRangeCut"),
useAbsEta = cms.bool(True),
allowedEtaRanges = cms.VPSet(
cms.PSet( minEta = cms.double(0.0),
maxEta = cms.double(ebCutOff) ),
cms.PSet( minEta = cms.double(ebCutOff),
maxEta = cms.double(2.5) )
),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDEtaInCut'),
dEtaInCutValueEB = cms.double(0.015792),
dEtaInCutValueEE = cms.double(0.024830),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDPhiInCut'),
dPhiInCutValueEB = cms.double(0.079938),
dPhiInCutValueEE = cms.double(0.096950),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleFull5x5SigmaIEtaIEtaCut'),
full5x5SigmaIEtaIEtaCutValueEB = cms.double(0.011836),
full5x5SigmaIEtaIEtaCutValueEE = cms.double(0.032479),
full5x5SigmaIEtaIEtaMap = cms.InputTag('electronIDValueMapProducer:eleFull5x5SigmaIEtaIEta'),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleHadronicOverEMCut'),
hadronicOverEMCutValueEB = cms.double(0.145705),
hadronicOverEMCutValueEE = cms.double(0.115660),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDxyCut'),
dxyCutValueEB = cms.double(0.019094),
dxyCutValueEE = cms.double(0.098601),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDzCut'),
dzCutValueEB = cms.double(0.035802),
dzCutValueEE = cms.double(0.879581),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleEInverseMinusPInverseCut'),
eInverseMinusPInverseCutValueEB = cms.double(0.105459),
eInverseMinusPInverseCutValueEE = cms.double(0.110190),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDeltaBetaIsoCutStandalone'),
isoCutEBLowPt = cms.double(0.183981),
isoCutEBHighPt = cms.double(0.183981),
isoCutEELowPt = cms.double(0.211836),
isoCutEEHighPt = cms.double(0.211836),
isRelativeIso = cms.bool(True),
deltaBetaConstant = cms.double(0.5),
ptCutOff = cms.double(20.0),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleConversionVetoCut'),
conversionSrc = cms.InputTag('allConversions'),
conversionSrcMiniAOD = cms.InputTag('reducedEgamma:reducedConversions'),
beamspotSrc = cms.InputTag('offlineBeamSpot'),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleMissingHitsCut'),
maxMissingHitsEB = cms.uint32(1),
maxMissingHitsEE = cms.uint32(1),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False))
)
)
cutBasedElectronID_CSA14_50ns_V1_standalone_medium = cms.PSet(
idName = cms.string("cutBasedElectronID-CSA14-50ns-V1-standalone-medium"),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MinPtCut"),
minPt = cms.double(5.0),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("GsfEleSCEtaMultiRangeCut"),
useAbsEta = cms.bool(True),
allowedEtaRanges = cms.VPSet(
cms.PSet( minEta = cms.double(0.0),
maxEta = cms.double(ebCutOff) ),
cms.PSet( minEta = cms.double(ebCutOff),
maxEta = cms.double(2.5) )
),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDEtaInCut'),
dEtaInCutValueEB = cms.double(0.015382),
dEtaInCutValueEE = cms.double(0.022962),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDPhiInCut'),
dPhiInCutValueEB = cms.double(0.050766),
dPhiInCutValueEE = cms.double(0.056415),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleFull5x5SigmaIEtaIEtaCut'),
full5x5SigmaIEtaIEtaCutValueEB = cms.double(0.010326),
full5x5SigmaIEtaIEtaCutValueEE = cms.double(0.030011),
full5x5SigmaIEtaIEtaMap = cms.InputTag('electronIDValueMapProducer:eleFull5x5SigmaIEtaIEta'),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleHadronicOverEMCut'),
hadronicOverEMCutValueEB = cms.double(0.100644),
hadronicOverEMCutValueEE = cms.double(0.099253),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDxyCut'),
dxyCutValueEB = cms.double(0.012012),
dxyCutValueEE = cms.double(0.067921),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDzCut'),
dzCutValueEB = cms.double(0.030398),
dzCutValueEE = cms.double(0.781003),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleEInverseMinusPInverseCut'),
eInverseMinusPInverseCutValueEB = cms.double(0.052553),
eInverseMinusPInverseCutValueEE = cms.double(0.109146),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDeltaBetaIsoCutStandalone'),
isoCutEBLowPt = cms.double(0.135355),
isoCutEBHighPt = cms.double(0.135355),
isoCutEELowPt = cms.double(0.146530),
isoCutEEHighPt = cms.double(0.146530),
isRelativeIso = cms.bool(True),
deltaBetaConstant = cms.double(0.5),
ptCutOff = cms.double(20.0),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleConversionVetoCut'),
conversionSrc = cms.InputTag('allConversions'),
conversionSrcMiniAOD = cms.InputTag('reducedEgamma:reducedConversions'),
beamspotSrc = cms.InputTag('offlineBeamSpot'),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleMissingHitsCut'),
maxMissingHitsEB = cms.uint32(1),
maxMissingHitsEE = cms.uint32(1),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False))
)
)
cutBasedElectronID_CSA14_50ns_V1_standalone_tight = cms.PSet(
idName = cms.string("cutBasedElectronID-CSA14-50ns-V1-standalone-tight"),
cutFlow = cms.VPSet(
cms.PSet( cutName = cms.string("MinPtCut"),
minPt = cms.double(5.0),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False) ),
cms.PSet( cutName = cms.string("GsfEleSCEtaMultiRangeCut"),
useAbsEta = cms.bool(True),
allowedEtaRanges = cms.VPSet(
cms.PSet( minEta = cms.double(0.0),
maxEta = cms.double(ebCutOff) ),
cms.PSet( minEta = cms.double(ebCutOff),
maxEta = cms.double(2.5) )
),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDEtaInCut'),
dEtaInCutValueEB = cms.double(0.012316),
dEtaInCutValueEE = cms.double(0.019221),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDPhiInCut'),
dPhiInCutValueEB = cms.double(0.024063),
dPhiInCutValueEE = cms.double(0.042714),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleFull5x5SigmaIEtaIEtaCut'),
full5x5SigmaIEtaIEtaCutValueEB = cms.double(0.010244),
full5x5SigmaIEtaIEtaCutValueEE = cms.double(0.028612),
full5x5SigmaIEtaIEtaMap = cms.InputTag('electronIDValueMapProducer:eleFull5x5SigmaIEtaIEta'),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleHadronicOverEMCut'),
hadronicOverEMCutValueEB = cms.double(0.073594),
hadronicOverEMCutValueEE = cms.double(0.080394),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDxyCut'),
dxyCutValueEB = cms.double(0.009088),
dxyCutValueEE = cms.double(0.037208),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDzCut'),
dzCutValueEB = cms.double(0.016881),
dzCutValueEE = cms.double(0.064947),
vertexSrc = cms.InputTag("offlinePrimaryVertices"),
vertexSrcMiniAOD = cms.InputTag("offlineSlimmedPrimaryVertices"),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleEInverseMinusPInverseCut'),
eInverseMinusPInverseCutValueEB = cms.double(0.025730),
eInverseMinusPInverseCutValueEE = cms.double(0.076298),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleDeltaBetaIsoCutStandalone'),
isoCutEBLowPt = cms.double(0.100254),
isoCutEBHighPt = cms.double(0.100254),
isoCutEELowPt = cms.double(0.137026),
isoCutEEHighPt = cms.double(0.137026),
isRelativeIso = cms.bool(True),
deltaBetaConstant = cms.double(0.5),
ptCutOff = cms.double(20.0),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleConversionVetoCut'),
conversionSrc = cms.InputTag('allConversions'),
conversionSrcMiniAOD = cms.InputTag('reducedEgamma:reducedConversions'),
beamspotSrc = cms.InputTag('offlineBeamSpot'),
needsAdditionalProducts = cms.bool(True),
isIgnored = cms.bool(False)),
cms.PSet( cutName = cms.string('GsfEleMissingHitsCut'),
maxMissingHitsEB = cms.uint32(1),
maxMissingHitsEE = cms.uint32(1),
barrelCutOff = cms.double(ebCutOff),
needsAdditionalProducts = cms.bool(False),
isIgnored = cms.bool(False))
)
)
central_id_registry.register(cutBasedElectronID_CSA14_50ns_V1_standalone_veto.idName,
'0443b6d07f602b4ee4ccabd851e4d364')
central_id_registry.register(cutBasedElectronID_CSA14_50ns_V1_standalone_loose.idName,
'078717cbe5967273b808379cf4319e6c')
central_id_registry.register(cutBasedElectronID_CSA14_50ns_V1_standalone_medium.idName,
'6ae65b48e6f5efcc468fcad883a599fb')
central_id_registry.register(cutBasedElectronID_CSA14_50ns_V1_standalone_tight.idName,
'767014939fe5066f79e4d9b9dc92bb09')
| 56.676845 | 111 | 0.562315 | 1,752 | 22,274 | 7.120434 | 0.124429 | 0.098116 | 0.080802 | 0.065411 | 0.821884 | 0.821884 | 0.813948 | 0.813948 | 0.813948 | 0.793427 | 0 | 0.052236 | 0.337344 | 22,274 | 392 | 112 | 56.821429 | 0.792954 | 0.021236 | 0 | 0.724796 | 0 | 0 | 0.098242 | 0.078043 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.00545 | 0 | 0.00545 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
3442e28098c0e85f5e8f723815758d225be999e7 | 104 | py | Python | model/__init__.py | hyeyeon08/Multimodal-FFM-TLD | afb2b525c3c2abb6d396d5e4429a072ad3e18894 | [
"MIT"
] | 3 | 2022-03-07T10:41:38.000Z | 2022-03-07T10:42:20.000Z | model/__init__.py | TatheerHussain/Multimodal-FFM-TLD | afb2b525c3c2abb6d396d5e4429a072ad3e18894 | [
"MIT"
] | null | null | null | model/__init__.py | TatheerHussain/Multimodal-FFM-TLD | afb2b525c3c2abb6d396d5e4429a072ad3e18894 | [
"MIT"
] | 2 | 2022-03-08T09:13:50.000Z | 2022-03-26T09:34:01.000Z | from .Unet_original_4c import Unet as Unet_original_4c
from .Unet_proposed import Unet as Unet_proposed
| 34.666667 | 54 | 0.865385 | 18 | 104 | 4.666667 | 0.388889 | 0.190476 | 0.333333 | 0.380952 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.021739 | 0.115385 | 104 | 2 | 55 | 52 | 0.891304 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
344f77e240f92bbcd581bf418e5b6c841bbd032f | 7,340 | py | Python | tests/test_plain_text_render.py | rgonalo/sdklib | e70d473c0160be8d91c2edf0d43cb5ebf7bc558f | [
"BSD-2-Clause"
] | 3 | 2016-12-15T15:54:37.000Z | 2021-08-10T03:16:18.000Z | tests/test_plain_text_render.py | rgonalo/sdklib | e70d473c0160be8d91c2edf0d43cb5ebf7bc558f | [
"BSD-2-Clause"
] | 44 | 2016-04-13T08:19:45.000Z | 2022-01-14T12:58:44.000Z | tests/test_plain_text_render.py | rgonalo/sdklib | e70d473c0160be8d91c2edf0d43cb5ebf7bc558f | [
"BSD-2-Clause"
] | 5 | 2016-11-22T11:23:28.000Z | 2020-01-28T12:26:10.000Z | # -*- coding: utf-8 -*-
import unittest
from sdklib.http.renderers import PlainTextRenderer
class TestPlainTextRender(unittest.TestCase):
def test_encode_plain_data_files(self):
files = {"file_upload": "tests/resources/file.pdf", "file_upload2": "tests/resources/file.png"}
data = {"param1": "value1", "param2": "value2"}
r = PlainTextRenderer()
body, content_type = r.encode_params(data, files=files)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value1", body)
self.assertIn(b"param2=value2", body)
self.assertNotIn(b"file_upload", body)
def test_encode_plain_data_as_2tuple_parameter(self):
data = [("param1", "value 1"), ("param2", "value2"), ("param2", "value3")]
r = PlainTextRenderer()
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertEqual(b"param1=value 1\nparam2=value2\nparam2=value3", body)
def test_encode_plain_no_data(self):
r = PlainTextRenderer()
body, content_type = r.encode_params()
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertEqual(b"", body)
def test_encode_plain_data_charset_default(self):
data = {"param1": "value 1", "param2": ["value2", "value3"]}
r = PlainTextRenderer()
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=value2", body)
self.assertIn(b"param2=value3", body)
def test_encode_plain_data_charset_ascii(self):
data = {"param1": "value 1", "param2": "value2"}
r = PlainTextRenderer(charset='ascii')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=ascii")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=value2", body)
def test_encode_plain_data_no_charset(self):
data = {"param1": "value 1", "param2": "value2"}
r = PlainTextRenderer(charset=None)
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=value2", body)
def test_encode_plain_data_unicode(self):
data = u"Hello! I'm Iván Martín!"
r = PlainTextRenderer(charset=None)
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertEqual(b"Hello! I'm Iv\xc3\xa1n Mart\xc3\xadn!", body)
def test_encode_plain_data_unicode_utf8(self):
data = u"Hello! I'm Iván Martín!"
r = PlainTextRenderer()
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertEqual(b"Hello! I'm Iv\xc3\xa1n Mart\xc3\xadn!", body)
def test_encode_plain_data_array_csv(self):
data = {"param1": "value 1", "param2": ["value2", "value3"]}
r = PlainTextRenderer(collection_format='csv')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2[]=value2,value3", body)
def test_encode_plain_data_array_ssv(self):
data = {"param1": "value 1", "param2": ["value2", "value3"]}
r = PlainTextRenderer(collection_format='ssv')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2[]=value2 value3", body)
def test_encode_plain_data_array_tsv(self):
data = {"param1": "value 1", "param2": ["value2", "value3"]}
r = PlainTextRenderer(collection_format='tsv')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2[]=value2\tvalue3", body)
def test_encode_plain_data_array_pipes(self):
data = {"param1": "value 1", "param2": ["value2", "value3"]}
r = PlainTextRenderer(collection_format='pipes')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2[]=value2|value3", body)
def test_encode_plain_data_array_plain(self):
data = {"param1": "value 1", "param2": ["value2", "value3"]}
r = PlainTextRenderer(collection_format='plain')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=['value2', 'value3']", body)
def test_encode_plain_data_boolean(self):
data = {"param1": "value 1", "param2": False}
r = PlainTextRenderer(collection_format='plain')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=false", body)
def test_encode_plain_data_none(self):
data = {"param1": "value 1", "param2": None}
r = PlainTextRenderer(collection_format='plain')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=null", body)
def test_encode_plain_data_none_csharp(self):
data = {"param1": "value 1", "param2": None}
r = PlainTextRenderer(collection_format='plain', output_str='csharp')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=Null", body)
def test_encode_plain_data_true_csharp(self):
data = {"param1": "value 1", "param2": True}
r = PlainTextRenderer(collection_format='plain', output_str='csharp')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=True", body)
def test_encode_plain_data_none_python(self):
data = {"param1": "value 1", "param2": None}
r = PlainTextRenderer(collection_format='plain', output_str='python')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=None", body)
def test_encode_plain_data_true_python(self):
data = {"param1": "value 1", "param2": True}
r = PlainTextRenderer(collection_format='plain', output_str='python')
body, content_type = r.encode_params(data)
self.assertEqual(content_type, "text/plain; charset=utf-8")
self.assertIn(b"param1=value 1", body)
self.assertIn(b"param2=True", body)
| 41.468927 | 103 | 0.655313 | 938 | 7,340 | 4.950959 | 0.08742 | 0.090009 | 0.086779 | 0.073643 | 0.910853 | 0.902885 | 0.885659 | 0.827089 | 0.808355 | 0.797588 | 0 | 0.027526 | 0.203134 | 7,340 | 176 | 104 | 41.704545 | 0.766456 | 0.002861 | 0 | 0.656716 | 0 | 0 | 0.227826 | 0.019954 | 0 | 0 | 0 | 0 | 0.410448 | 1 | 0.141791 | false | 0 | 0.014925 | 0 | 0.164179 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
caf6394632d8c891588f279d704ef324a87c0cd7 | 9,756 | py | Python | lib/spot-2.8.1/tests/python/satmin.py | AlessandroCaste/SynkrisisJupyter | a9c2b21ec1ae7ac0c05ef5deebc63a369274650f | [
"Unlicense"
] | 1 | 2018-03-02T14:29:57.000Z | 2018-03-02T14:29:57.000Z | lib/spot-2.8.1/tests/python/satmin.py | AlessandroCaste/SynkrisisJupyter | a9c2b21ec1ae7ac0c05ef5deebc63a369274650f | [
"Unlicense"
] | null | null | null | lib/spot-2.8.1/tests/python/satmin.py | AlessandroCaste/SynkrisisJupyter | a9c2b21ec1ae7ac0c05ef5deebc63a369274650f | [
"Unlicense"
] | 1 | 2015-06-05T12:42:07.000Z | 2015-06-05T12:42:07.000Z | # -*- mode: python; coding: utf-8 -*-
# Copyright (C) 2015 Laboratoire de Recherche et Développement
# de l'Epita
#
# This file is part of Spot, a model checking library.
#
# Spot is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# Spot is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
# License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import spot
aut = spot.translate('GFa & GFb', 'BA')
assert aut.num_sets() == 1
assert aut.num_states() == 3
assert aut.is_deterministic()
min1 = spot.sat_minimize(aut, acc='Rabin 1')
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_langmap=True)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=1)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=1, sat_incr_steps=0)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=1, sat_incr_steps=1)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=1, sat_incr_steps=2)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=1, sat_incr_steps=50)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=2)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=2, sat_incr_steps=-1)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=2, sat_incr_steps=0)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=2, sat_incr_steps=1)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=2, sat_incr_steps=2)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_incr=2, sat_incr_steps=50)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min1 = spot.sat_minimize(aut, acc='Rabin 1', sat_naive=True)
assert min1.num_sets() == 2
assert min1.num_states() == 2
min2 = spot.sat_minimize(aut, acc='Streett 2')
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_langmap=True)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=1)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=1, sat_incr_steps=0)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=1, sat_incr_steps=1)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=1, sat_incr_steps=2)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=1, sat_incr_steps=50)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=2)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=2, sat_incr_steps=-1)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=2, sat_incr_steps=0)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=2, sat_incr_steps=1)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=2, sat_incr_steps=2)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_incr=2, sat_incr_steps=50)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min2 = spot.sat_minimize(aut, acc='Streett 2', sat_naive=True)
assert min2.num_sets() == 4
assert min2.num_states() == 1
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_langmap=True)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=1)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=1,
sat_incr_steps=0)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=1,
sat_incr_steps=1)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=1,
sat_incr_steps=2)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=1,
sat_incr_steps=50)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=2)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=2,
sat_incr_steps=-1)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=2,
sat_incr_steps=0)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=2,
sat_incr_steps=1)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=2,
sat_incr_steps=2)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_incr=2,
sat_incr_steps=50)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min3 = spot.sat_minimize(aut, acc='Rabin 2',
state_based=True, max_states=5, sat_naive=True)
assert min3.num_sets() == 4
assert min3.num_states() == 3
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_langmap=True)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=1)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=1, sat_incr_steps=0)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=1, sat_incr_steps=1)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=1, sat_incr_steps=2)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=1, sat_incr_steps=50)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=2)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=2, sat_incr_steps=-1)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=2, sat_incr_steps=0)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=2, sat_incr_steps=1)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=2, sat_incr_steps=2)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_incr=2, sat_incr_steps=50)
assert min4.num_sets() == 3
assert min4.num_states() == 2
min4 = spot.sat_minimize(aut, acc='parity max odd 3',
colored=True, sat_naive=True)
assert min4.num_sets() == 3
assert min4.num_states() == 2
| 40.65 | 77 | 0.658159 | 1,598 | 9,756 | 3.81602 | 0.076971 | 0.091833 | 0.13775 | 0.1653 | 0.89308 | 0.888816 | 0.879633 | 0.879633 | 0.869465 | 0.869465 | 0 | 0.057385 | 0.203362 | 9,756 | 239 | 78 | 40.820084 | 0.727226 | 0.077388 | 0 | 0.747619 | 0 | 0 | 0.062013 | 0 | 0 | 0 | 0 | 0 | 0.547619 | 1 | 0 | false | 0 | 0.004762 | 0 | 0.004762 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
caf88b96e5b5ecc7652b933ca10bc742597f9592 | 4,374 | py | Python | decl_spec_lang/DeclSpecLanguageVisitor.py | jacopoMauro/abs_deployer | f52e0fe51d640669b62039fac682632fa549c55f | [
"0BSD"
] | 2 | 2015-03-04T08:55:51.000Z | 2019-11-25T20:08:29.000Z | decl_spec_lang/DeclSpecLanguageVisitor.py | jacopoMauro/abs_deployer | f52e0fe51d640669b62039fac682632fa549c55f | [
"0BSD"
] | null | null | null | decl_spec_lang/DeclSpecLanguageVisitor.py | jacopoMauro/abs_deployer | f52e0fe51d640669b62039fac682632fa549c55f | [
"0BSD"
] | null | null | null | # Generated from DeclSpecLanguage.g4 by ANTLR 4.7
from antlr4 import *
# This class defines a complete generic visitor for a parse tree produced by DeclSpecLanguageParser.
class DeclSpecLanguageVisitor(ParseTreeVisitor):
# Visit a parse tree produced by DeclSpecLanguageParser#statement.
def visitStatement(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#b_expr.
def visitB_expr(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#b_term.
def visitB_term(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#b_factor.
def visitB_factor(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#relation.
def visitRelation(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#expr.
def visitExpr(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AtermQuantifier.
def visitAtermQuantifier(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AtermInt.
def visitAtermInt(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AtermId.
def visitAtermId(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AtermDCObj.
def visitAtermDCObj(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AtermSum.
def visitAtermSum(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AexprUnaryArithmetic.
def visitAexprUnaryArithmetic(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AtermBrackets.
def visitAtermBrackets(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#typeV.
def visitTypeV(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AdcIDID.
def visitAdcIDID(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AdcIDVar.
def visitAdcIDVar(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AdcIDNum.
def visitAdcIDNum(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AobjIDID.
def visitAobjIDID(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AobjIDVar.
def visitAobjIDVar(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AobjIDScenario.
def visitAobjIDScenario(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#AobjIDRE.
def visitAobjIDRE(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#Avariable.
def visitAvariable(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#bool_binary_op.
def visitBool_binary_op(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#arith_binary_op.
def visitArith_binary_op(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#arith_unary_op.
def visitArith_unary_op(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#comparison_op.
def visitComparison_op(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#unaryOp.
def visitUnaryOp(self, ctx):
return self.visitChildren(ctx)
# Visit a parse tree produced by DeclSpecLanguageParser#boolFact.
def visitBoolFact(self, ctx):
return self.visitChildren(ctx)
| 29.554054 | 100 | 0.728395 | 496 | 4,374 | 6.383065 | 0.171371 | 0.054959 | 0.091598 | 0.164877 | 0.731207 | 0.731207 | 0.707517 | 0.692672 | 0.692672 | 0.692672 | 0 | 0.001151 | 0.205533 | 4,374 | 147 | 101 | 29.755102 | 0.909928 | 0.446959 | 0 | 0.482759 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.482759 | false | 0 | 0.017241 | 0.482759 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 8 |
1ba5cf4b0edd8f0938238e148de3577907304a58 | 3,767 | py | Python | python_test/FlyMeToTheMoon_eq.py | LovelyA72/ScoreDraft | dd344a49a5eec2670110cc43d672936cd1c27844 | [
"MIT"
] | 1 | 2020-03-26T15:48:49.000Z | 2020-03-26T15:48:49.000Z | python_test/FlyMeToTheMoon_eq.py | LovelyA72/ScoreDraft | dd344a49a5eec2670110cc43d672936cd1c27844 | [
"MIT"
] | null | null | null | python_test/FlyMeToTheMoon_eq.py | LovelyA72/ScoreDraft | dd344a49a5eec2670110cc43d672936cd1c27844 | [
"MIT"
] | null | null | null | #!/usr/bin/python3
import ScoreDraft
from ScoreDraft.Notes import *
def soS(octave=5, duration=48):
return note(octave,Freqs[8],duration)
doc=ScoreDraft.Document()
doc.setReferenceFrequency(264.0 *1.25)
doc.setTempo(120)
seq1 = [do(6,72), ti(5,24), la(5,24), so(5,72)]
seq2 = [la(3,192), BK(144), mi(4,48), so(4,48), do(5,48)]
seq1 = seq1 + [fa(5,96), BL(24), so(5,24), la(5,24), do(6,24)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48),fa(4,48)]
seq1 = seq1 + [ti(5,72), la(5,24), so(5,24), fa(5,72)]
seq2 = seq2 + [so(3,192), BK(144), re(4,48), fa(4,48), ti(4,48)]
seq1 = seq1 + [mi(5,144), BL(48)]
seq2 = seq2 + [do(3,192), BK(144), so(3,48), do(4,48), mi(4,48)]
seq1 = seq1 + [la(5,72), so(5,24), fa(5,24), mi(5,72)]
seq2 = seq2 + [fa(3,192), BK(144), do(4,48), mi(4,48), la(4,48)]
seq1 = seq1 + [re(5,72), mi(5,24), fa(5,24), la(5,72)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48), fa(4,48)]
seq1 = seq1 + [soS(5,72), fa(5,24), mi(5,24), re(5,72)]
seq2 = seq2 + [mi(3,192), BK(144), ti(3,48), re(4,48), soS(4,48)]
seq1 = seq1 + [do(5,144), BL(48)]
seq2 = seq2 + [la(3,192), BK(144), mi(4,48), so(4,48), do(5,48)]
seq1 = seq1 + [re(5,24), la(5,72), la(5,96)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48), fa(4,48)]
seq1 = seq1 + [BL(96), do(6,24), ti(5,72)]
seq2 = seq2 + [so(3,192), BK(144), re(4,48), fa(4,48), ti(4,48)]
seq1 = seq1 + [so(5,144), BL(48)]
seq2 = seq2 + [mi(3,192), BK(144), ti(3,48), re(4,48), so(4,48)]
seq1 = seq1 + [la(4,24), fa(5,72), fa(5,96)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48), fa(4,48)]
seq1 = seq1 + [BL(96), la(5,72), so(5,24)]
seq2 = seq2 + [so(3,192), BK(144), re(4,48), fa(4,48), ti(4,48)]
seq1 = seq1 + [fa(5,24), mi(5,120), BL(48)]
seq2 = seq2 + [do(3,192), BK(144), so(3,48), do(4,48), mi(4,48)]
seq1 = seq1 + [do(6,72), ti(5,24), la(5,24), so(5,72)]
seq2 = seq2 + [la(3,192), BK(144), mi(4,48), so(4,48), do(5,48)]
seq1 = seq1 + [fa(5,96), BL(24), so(5,24), la(5,24), do(6,24)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48),fa(4,48)]
seq1 = seq1 + [ti(5,72), la(5,24), so(5,24), fa(5,72)]
seq2 = seq2 + [so(3,192), BK(144), re(4,48), fa(4,48), ti(4,48)]
seq1 = seq1 + [mi(5,144), BL(48)]
seq2 = seq2 + [do(3,192), BK(144), so(3,48), do(4,48), mi(4,48)]
seq1 = seq1 + [la(5,72), so(5,24), fa(5,24), mi(5,72)]
seq2 = seq2 + [fa(3,192), BK(144), do(4,48), mi(4,48), la(4,48)]
seq1 = seq1 + [re(5,72), mi(5,24), fa(5,24), la(5,72)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48), fa(4,48)]
seq1 = seq1 + [soS(5,72), la(5,24), ti(5,24), ti(5,72)]
seq2 = seq2 + [mi(3,192), BK(144), ti(3,48), re(4,48), soS(4,48)]
seq1 = seq1 + [do(6,24), ti(5,24), la(5,96), BL(48)]
seq2 = seq2 + [la(3,192), BK(144), mi(4,48), so(4,48), do(5,48)]
seq1 = seq1 + [la(5,24), so(5,72), la(5,24), so(5,24), fa(5,48)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48), fa(4,48)]
seq1 = seq1 + [BL(96), do(6,24), ti(5,72)]
seq2 = seq2 + [so(3,192), BK(144), re(4,48), fa(4,48), ti(4,48)]
seq1 = seq1 + [mi(6,144), BL(48)]
seq2 = seq2 + [fa(3,192), BK(144), do(4,48), mi(4,48), la(4,48)]
seq1 = seq1 + [mi(6,24), do(6,72), do(6,96)]
seq2 = seq2 + [re(3,192), BK(144), la(3,48), do(4,48), fa(4,48)]
seq1 = seq1 + [BL(96), ti(5,24), re(6,72)]
seq2 = seq2 + [so(3,192), BK(144), re(4,48), fa(4,48), ti(4,48)]
seq1 = seq1 + [do(6,192)]
seq2 = seq2 + [do(3,192), BK(180), so(3,180), BK(168), do(4,168), BK(156), mi(4,156), BK(144), so(4,144), BK(132), do(5,132) ]
# instrument=ScoreDraft.Piano()
instrument = ScoreDraft.SF2Instrument('florestan-subset.sf2', 0)
doc.playNoteSeq(seq1, instrument)
doc.playNoteSeq(seq2, instrument)
doc.mixDown('FlyMeToTheMoon_eq.wav')
ScoreDraft.WriteNoteSequencesToMidi([seq1, seq2], 120, 264.0 *1.25, "FlyMeToTheMoon.mid")
| 3,767 | 3,767 | 0.551102 | 852 | 3,767 | 2.435446 | 0.07277 | 0.091084 | 0.080964 | 0.117108 | 0.756145 | 0.725783 | 0.698313 | 0.698313 | 0.698313 | 0.698313 | 0 | 0.275062 | 0.142023 | 3,767 | 1 | 3,767 | 3,767 | 0.366955 | 0.999204 | 0 | 0.544118 | 0 | 0 | 0.015869 | 0.005648 | 0 | 0 | 0 | 0 | 0 | 1 | 0.014706 | false | 0 | 0.029412 | 0.014706 | 0.058824 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
1badb7f0b7dabc22f3f079cc3c2b10b196989b9a | 86 | py | Python | example_package/some_other_sub_module/some_other_sub_module_1.py | jerabaul29/example_python_package | 4c9a47709e0317eaa00e5d78815da9568cbe51d0 | [
"MIT"
] | null | null | null | example_package/some_other_sub_module/some_other_sub_module_1.py | jerabaul29/example_python_package | 4c9a47709e0317eaa00e5d78815da9568cbe51d0 | [
"MIT"
] | 2 | 2021-05-05T20:51:44.000Z | 2021-05-09T20:11:07.000Z | example_package/some_other_sub_module/some_other_sub_module_1.py | jerabaul29/example_python_package | 4c9a47709e0317eaa00e5d78815da9568cbe51d0 | [
"MIT"
] | 1 | 2021-02-01T08:37:28.000Z | 2021-02-01T08:37:28.000Z | def some_other_sub_module_2_hello():
print("hello from some_other_sub_module_2")
| 21.5 | 47 | 0.802326 | 15 | 86 | 4 | 0.6 | 0.3 | 0.4 | 0.6 | 0.633333 | 0 | 0 | 0 | 0 | 0 | 0 | 0.026316 | 0.116279 | 86 | 3 | 48 | 28.666667 | 0.763158 | 0 | 0 | 0 | 0 | 0 | 0.4 | 0.270588 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 0.5 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 8 |
1bd208c3aaac64795cc80707d8cd16b2949c8340 | 2,535 | py | Python | tests/test_DCD002.py | joelschutz/flake8-declarative-dict | 32e63397ebedb1a54f05696fdc15ddeb7570e5b1 | [
"MIT"
] | null | null | null | tests/test_DCD002.py | joelschutz/flake8-declarative-dict | 32e63397ebedb1a54f05696fdc15ddeb7570e5b1 | [
"MIT"
] | null | null | null | tests/test_DCD002.py | joelschutz/flake8-declarative-dict | 32e63397ebedb1a54f05696fdc15ddeb7570e5b1 | [
"MIT"
] | null | null | null | """Tests for DCD002 case in `flake8_declarative_dict` package."""
# Core Library
from flake8_plugin_utils import assert_error, assert_not_error
# First party
from flake8_declarative_dict.visitor import Visitor
from flake8_declarative_dict.error import DCD002
def test_valid_call(default_config):
case = 'f(a)'
assert_not_error(
Visitor,
case,
default_config
)
def test_invalid_call(default_config):
case = 'f(a, b)'
assert_error(
Visitor,
case,
DCD002,
default_config,
size=2,
max_size=default_config.args_size_limit
)
def test_valid_nested_call(default_config):
case = 'f(a=ff(b))'
assert_not_error(
Visitor,
case,
default_config
)
def test_invalid_nested_call(default_config):
case = 'f(a=ff(b, c))'
assert_error(
Visitor,
case,
DCD002,
default_config,
size=2,
max_size=default_config.args_size_limit
)
def test_valid_call_unpack_1(default_config):
case = '''
f(**a)
'''
assert_not_error(
Visitor,
case,
default_config
)
def test_valid_call_unpack_2(default_config):
case = '''
f(*b, **a)
'''
assert_not_error(
Visitor,
case,
default_config
)
def test_invalid_call_unpack_1(default_config):
case = '''
f(a, **b)
'''
assert_error(
Visitor,
case,
DCD002,
default_config,
size=2,
max_size=default_config.args_size_limit
)
def test_invalid_call_unpack_2(default_config):
case = '''
f(a, *b)
'''
assert_error(
Visitor,
case,
DCD002,
default_config,
size=2,
max_size=default_config.args_size_limit
)
def test_invalid_call_unpack_3(default_config):
case = '''
f(a, *b, **c)
'''
assert_error(
Visitor,
case,
DCD002,
default_config,
size=3,
max_size=default_config.args_size_limit
)
def test_invalid_call_unpack_4(default_config):
case = '''
f(*a, *b)
'''
assert_error(
Visitor,
case,
DCD002,
default_config,
size=2,
max_size=default_config.args_size_limit
)
def test_invalid_call_unpack_5(default_config):
case = '''
f(**a, **b)
'''
assert_error(
Visitor,
case,
DCD002,
default_config,
size=2,
max_size=default_config.args_size_limit
)
| 19.204545 | 65 | 0.592505 | 305 | 2,535 | 4.55082 | 0.144262 | 0.271614 | 0.134726 | 0.142651 | 0.837896 | 0.834294 | 0.817723 | 0.79611 | 0.783141 | 0.737752 | 0 | 0.025714 | 0.309665 | 2,535 | 131 | 66 | 19.351145 | 0.767429 | 0.033531 | 0 | 0.678261 | 0 | 0 | 0.058125 | 0 | 0 | 0 | 0 | 0 | 0.104348 | 1 | 0.095652 | false | 0 | 0.026087 | 0 | 0.121739 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
94432813a88fc1837d090b95054757b28dfef14c | 90 | py | Python | cfpq_data/graphs/readwrite/__init__.py | viabzalov/CFPQ_Data | 67239c876897d04ba2f4ef88a75fd4a38a494efa | [
"Apache-2.0"
] | 8 | 2020-03-30T17:47:31.000Z | 2022-01-27T13:36:39.000Z | cfpq_data/graphs/readwrite/__init__.py | viabzalov/CFPQ_Data | 67239c876897d04ba2f4ef88a75fd4a38a494efa | [
"Apache-2.0"
] | 27 | 2019-10-21T09:31:08.000Z | 2021-11-07T03:19:15.000Z | cfpq_data/graphs/readwrite/__init__.py | viabzalov/CFPQ_Data | 67239c876897d04ba2f4ef88a75fd4a38a494efa | [
"Apache-2.0"
] | 14 | 2019-10-18T12:49:47.000Z | 2021-08-03T14:20:17.000Z | from cfpq_data.graphs.readwrite.rdf import *
from cfpq_data.graphs.readwrite.txt import *
| 30 | 44 | 0.822222 | 14 | 90 | 5.142857 | 0.571429 | 0.222222 | 0.333333 | 0.5 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.088889 | 90 | 2 | 45 | 45 | 0.878049 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
ca2578a001dcf9055d1dda091e76dd0992978ed8 | 7,152 | py | Python | borsachart/charts/tests/mock_data.py | azaleas/borsachart | 08b70ce8851b639bc695314c39eba1841e300d8c | [
"MIT"
] | 4 | 2017-11-27T15:27:32.000Z | 2018-11-09T22:20:30.000Z | borsachart/charts/tests/mock_data.py | azaleas/borsachart | 08b70ce8851b639bc695314c39eba1841e300d8c | [
"MIT"
] | 2 | 2020-02-12T00:51:35.000Z | 2020-06-05T18:27:25.000Z | borsachart/charts/tests/mock_data.py | azaleas/borsachart | 08b70ce8851b639bc695314c39eba1841e300d8c | [
"MIT"
] | null | null | null | import json
quandl_results = r'''{
"datatable": {
"data": [
[
"V",
"2017-06-01",
95.4,
95.45,
94.61,
95.4,
8887847,
0,
1,
95.4,
95.45,
94.61,
95.4,
8887847
],
[
"V",
"2017-06-02",
95.41,
96.19,
95.41,
96.15,
8528039,
0,
1,
95.41,
96.19,
95.41,
96.15,
8528039
],
[
"V",
"2017-06-05",
96.32,
96.59,
96.11,
96.55,
14490708,
0,
1,
96.32,
96.59,
96.11,
96.55,
14490708
]
],
"columns": [
{
"name": "ticker",
"type": "String"
},
{
"name": "date",
"type": "Date"
},
{
"name": "open",
"type": "BigDecimal(34,12)"
},
{
"name": "high",
"type": "BigDecimal(34,12)"
},
{
"name": "low",
"type": "BigDecimal(34,12)"
},
{
"name": "close",
"type": "BigDecimal(34,12)"
},
{
"name": "volume",
"type": "BigDecimal(37,15)"
},
{
"name": "ex-dividend",
"type": "BigDecimal(42,20)"
},
{
"name": "split_ratio",
"type": "double"
},
{
"name": "adj_open",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_high",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_low",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_close",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_volume",
"type": "double"
}
]
},
"meta": {
"next_cursor_id": null
}
}
'''
quandl_results_json = json.loads(quandl_results)
quandl_results_not_found = r'''{
"datatable": {
"data": [],
"columns": [
{
"name": "ticker",
"type": "String"
},
{
"name": "date",
"type": "Date"
},
{
"name": "open",
"type": "BigDecimal(34,12)"
},
{
"name": "high",
"type": "BigDecimal(34,12)"
},
{
"name": "low",
"type": "BigDecimal(34,12)"
},
{
"name": "close",
"type": "BigDecimal(34,12)"
},
{
"name": "volume",
"type": "BigDecimal(37,15)"
},
{
"name": "ex-dividend",
"type": "BigDecimal(42,20)"
},
{
"name": "split_ratio",
"type": "double"
},
{
"name": "adj_open",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_high",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_low",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_close",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_volume",
"type": "double"
}
]
},
"meta": {
"next_cursor_id": null
}
}
'''
quandl_results_not_found_json = json.loads(quandl_results_not_found)
db_data = r'''{
"datatable": {
"data": [
[
"G",
"2017-06-01",
95.4,
95.45,
94.61,
95.4,
8887847,
0,
1,
95.4,
95.45,
94.61,
95.4,
8887847
],
[
"G",
"2017-06-02",
95.41,
96.19,
95.41,
96.15,
8528039,
0,
1,
95.41,
96.19,
95.41,
96.15,
8528039
],
[
"G",
"2017-06-05",
96.32,
96.59,
96.11,
96.55,
14490708,
0,
1,
96.32,
96.59,
96.11,
96.55,
14490708
]
],
"columns": [
{
"name": "ticker",
"type": "String"
},
{
"name": "date",
"type": "Date"
},
{
"name": "open",
"type": "BigDecimal(34,12)"
},
{
"name": "high",
"type": "BigDecimal(34,12)"
},
{
"name": "low",
"type": "BigDecimal(34,12)"
},
{
"name": "close",
"type": "BigDecimal(34,12)"
},
{
"name": "volume",
"type": "BigDecimal(37,15)"
},
{
"name": "ex-dividend",
"type": "BigDecimal(42,20)"
},
{
"name": "split_ratio",
"type": "double"
},
{
"name": "adj_open",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_high",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_low",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_close",
"type": "BigDecimal(50,28)"
},
{
"name": "adj_volume",
"type": "double"
}
]
},
"meta": {
"next_cursor_id": null
}
}
'''
db_data_json = json.loads(db_data) | 23.070968 | 68 | 0.246225 | 455 | 7,152 | 3.778022 | 0.145055 | 0.244328 | 0.111693 | 0.125654 | 0.919139 | 0.888889 | 0.888889 | 0.888889 | 0.888889 | 0.888889 | 0 | 0.165507 | 0.618149 | 7,152 | 310 | 69 | 23.070968 | 0.463933 | 0 | 0 | 0.630363 | 0 | 0 | 0.96505 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.0033 | 0 | 0.0033 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
ca6b9384da5e4f817a0e06eed94f33654a25dc6e | 126 | py | Python | typeform_feedback/api/views/__init__.py | exolever/django-typeform-feedback | 5784523b880e4890172b9f61d848187f5c24237e | [
"MIT"
] | null | null | null | typeform_feedback/api/views/__init__.py | exolever/django-typeform-feedback | 5784523b880e4890172b9f61d848187f5c24237e | [
"MIT"
] | 15 | 2019-03-22T09:04:53.000Z | 2019-12-13T08:15:10.000Z | typeform_feedback/api/views/__init__.py | exolever/django-typeform-feedback | 5784523b880e4890172b9f61d848187f5c24237e | [
"MIT"
] | null | null | null | from .user_generic_typeform import UserTypeformViewSet # noqa
from .generic_typeform import GenericTypeformApiView # noqa
| 42 | 62 | 0.84127 | 13 | 126 | 7.923077 | 0.615385 | 0.291262 | 0.407767 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.126984 | 126 | 2 | 63 | 63 | 0.936364 | 0.071429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
04a6bdcd9cdf173593bb11cdc5594e91a13b4db5 | 1,285 | py | Python | app_user/migrations/0014_auto_20211101_1424.py | lurdray/crypto_invest | 8e10bda0771ac48c22827021935be3a28c9c3107 | [
"Apache-2.0"
] | 1 | 2021-11-20T14:09:58.000Z | 2021-11-20T14:09:58.000Z | app_user/migrations/0014_auto_20211101_1424.py | lurdray/crypto_invest | 8e10bda0771ac48c22827021935be3a28c9c3107 | [
"Apache-2.0"
] | null | null | null | app_user/migrations/0014_auto_20211101_1424.py | lurdray/crypto_invest | 8e10bda0771ac48c22827021935be3a28c9c3107 | [
"Apache-2.0"
] | null | null | null | # Generated by Django 3.1.7 on 2021-11-01 14:24
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('app_user', '0013_auto_20211021_1427'),
]
operations = [
migrations.AlterField(
model_name='appuser',
name='id',
field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='claimer',
name='id',
field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='investment',
name='id',
field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='nft',
name='id',
field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
migrations.AlterField(
model_name='nftclaimerconnector',
name='id',
field=models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID'),
),
]
| 32.948718 | 108 | 0.603113 | 135 | 1,285 | 5.562963 | 0.333333 | 0.079893 | 0.166445 | 0.193076 | 0.700399 | 0.700399 | 0.700399 | 0.700399 | 0.700399 | 0.700399 | 0 | 0.033298 | 0.275486 | 1,285 | 38 | 109 | 33.815789 | 0.773362 | 0.035019 | 0 | 0.625 | 1 | 0 | 0.078352 | 0.018578 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.03125 | 0 | 0.125 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
04b0316b3f891382f2fd788eb88d1202d9a5e26c | 1,010 | bzl | Python | tests/test_content.bzl | flarebuild/starlark_yaml | 20ed599003104eda6573fed6f561dfa070bfc3d5 | [
"MIT"
] | 3 | 2020-10-02T12:48:24.000Z | 2021-09-03T00:19:02.000Z | tests/test_content.bzl | flarebuild/bazel_utility | b0497c4b634e2d1e0fd2ce924f6d0abfec23a537 | [
"MIT"
] | null | null | null | tests/test_content.bzl | flarebuild/bazel_utility | b0497c4b634e2d1e0fd2ce924f6d0abfec23a537 | [
"MIT"
] | null | null | null | TEST_CONTENT = """
root_1:
inner_1:
# some comment
inner_inner_1:
# some comment
some_key: some_value
some_int_key: 123
some_another_key: "some_quoted_value"
inner_inner_2:
some_key: some_value
some_nested_array:
- whoop1
- whoop2
some_other_key: some_other_value
inner_2:
# some comment
inner_inner_1:
# some comment
some_key: some_value
some_int_key: 123
some_another_key: "some_quoted_value"
inner_inner_2:
some_key: some_value
some_nested_array:
- whoop1
- whoop2
some_other_key: some_other_value
"root_2": root2_value
root_3:
inner_1:
- one_value_in_array
inner_2:
some_key: some_value:with_colon
inner_3:
- some_key: some_value
some_int_key: 123
some_another_key: "some_quoted_value"
- some_key: some_value
some_int_key: 123
some_another_key: "some_quoted_value"
inner_4:
some_key: some_value
""" | 22.954545 | 43 | 0.654455 | 142 | 1,010 | 4.126761 | 0.183099 | 0.167235 | 0.150171 | 0.21843 | 0.827645 | 0.827645 | 0.790102 | 0.790102 | 0.790102 | 0.790102 | 0 | 0.041494 | 0.284158 | 1,010 | 44 | 44 | 22.954545 | 0.769018 | 0 | 0 | 0.795455 | 0 | 0 | 0.978239 | 0.020772 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
8e4b24fefaac690921d3ac3392a3b72b7f9f0f3c | 78 | py | Python | baseline/tf/tagger/__init__.py | shar999/mead-baseline | bd9cd02c0a1d9c0df91aca171774a6967e6ce190 | [
"Apache-2.0"
] | 241 | 2016-04-25T20:02:31.000Z | 2019-09-03T05:44:09.000Z | baseline/tf/tagger/__init__.py | shar999/mead-baseline | bd9cd02c0a1d9c0df91aca171774a6967e6ce190 | [
"Apache-2.0"
] | 42 | 2017-08-21T16:04:36.000Z | 2019-09-30T20:45:17.000Z | baseline/tf/tagger/__init__.py | shar999/mead-baseline | bd9cd02c0a1d9c0df91aca171774a6967e6ce190 | [
"Apache-2.0"
] | 75 | 2016-06-28T01:18:58.000Z | 2019-08-29T06:47:22.000Z | from baseline.tf.tagger.model import *
from baseline.tf.tagger.train import *
| 26 | 38 | 0.794872 | 12 | 78 | 5.166667 | 0.583333 | 0.387097 | 0.451613 | 0.645161 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.102564 | 78 | 2 | 39 | 39 | 0.885714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
6d61630e480f4bf2251eeeb01e23571ba084c106 | 59 | py | Python | src/mul.py | hygull/playing-git | 777757853aff71bfa1876bbfc77ae66baa1ba421 | [
"MIT"
] | null | null | null | src/mul.py | hygull/playing-git | 777757853aff71bfa1876bbfc77ae66baa1ba421 | [
"MIT"
] | null | null | null | src/mul.py | hygull/playing-git | 777757853aff71bfa1876bbfc77ae66baa1ba421 | [
"MIT"
] | null | null | null | def mul(a, b):
print("MUL: ", a, b);
print(a * b);
| 14.75 | 25 | 0.423729 | 11 | 59 | 2.272727 | 0.454545 | 0.24 | 0.4 | 0.8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.305085 | 59 | 3 | 26 | 19.666667 | 0.609756 | 0 | 0 | 0 | 0 | 0 | 0.084746 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0 | 0.333333 | 0.666667 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
6d630d1bacee624cee7c350c8586abb74a4d1c42 | 16,009 | py | Python | tests/artifactstorage/artifactory/test_artifactory.py | jajomi/flow | c984be6f7de1a34192601c129dbc19f2ce45f135 | [
"Apache-2.0"
] | null | null | null | tests/artifactstorage/artifactory/test_artifactory.py | jajomi/flow | c984be6f7de1a34192601c129dbc19f2ce45f135 | [
"Apache-2.0"
] | 6 | 2021-03-05T16:39:42.000Z | 2021-06-11T01:04:57.000Z | tests/artifactstorage/artifactory/test_artifactory.py | jajomi/flow | c984be6f7de1a34192601c129dbc19f2ce45f135 | [
"Apache-2.0"
] | null | null | null | import os
from unittest.mock import MagicMock
from unittest.mock import patch
import pytest
import responses
from flow.buildconfig import BuildConfig
from requests.exceptions import HTTPError
from flow.artifactstorage.artifactory.artifactory import Artifactory, ArtifactException
mock_build_config_dict = {
"projectInfo": {
"name": "testproject"
},
"artifact": {
"artifactoryDomain": "https://testdomain/artifactory",
"artifactoryRepoKey": "release-repo",
"artifactoryRepoKeySnapshot": "snapshot-repo",
"artifactoryGroup": "group",
"artifactType": "type",
"artifactDirectory": "directory"
},
"environments": {
"unittest": {
"artifactCategory": "release"
}
},
"slack": {
"botName": "Flow",
"emoji": ":robot_face:",
"channel": "#spigot-ci"
}
}
mock_build_config_artifactoryConfig_include_POM = {
"projectInfo": {
"name": "testproject"
},
"artifactoryConfig": {
"artifactoryDomain": "https://testdomain/artifactory",
"artifactoryRepoKey": "release-repo",
"artifactoryRepoKeySnapshot": "snapshot-repo",
"artifactoryGroup": "group",
"artifactType": "type",
"artifactDirectory": "directory",
"includePom": "true"
},
"environments": {
"unittest": {
"artifactCategory": "release"
}
},
"slack": {
"botName": "Flow",
"emoji": ":robot_face:",
"channel": "#spigot-ci"
}
}
mock_build_config_missing_artifact_dict = {
"projectInfo": {
"name": "testproject"
},
"environments": {
"unittest": {
"artifactCategory": "release"
}
},
"slack": {
"botName": "Flow",
"emoji": ":robot_face:",
"channel": "#spigot-ci"
}
}
response_body_artifactory = """
{
"repo" : "release-repo",
"path" : "/group/testproject/v1.0.0",
"created" : "2016-09-09T13:02:49.851-04:00",
"createdBy" : "svc_cicd",
"lastModified" : "2016-09-09T13:02:49.851-04:00",
"modifiedBy" : "svc_cicd",
"lastUpdated" : "2016-09-09T13:02:49.851-04:00",
"children" : [ {
"uri" : "/unittest",
"folder" : true
}, {
"uri" : "/testproject.bob",
"folder" : false
},
{
"uri" : "/testproject.vcl",
"folder" : false
}
],
"uri" : "https://maven.artifactory.fake.com/artifactory/api/storage/libs-release-local/com/fake/thd-store-info-service/v1.2.0"
}
"""
response_body_artifactory_no_matching_children = """
{
"repo" : "release-repo",
"path" : "/group/testproject/v1.0.0",
"created" : "2016-09-09T13:02:49.851-04:00",
"createdBy" : "svc_cicd",
"lastModified" : "2016-09-09T13:02:49.851-04:00",
"modifiedBy" : "svc_cicd",
"lastUpdated" : "2016-09-09T13:02:49.851-04:00",
"children" : [ {
"uri" : "/unittest",
"folder" : true
}, {
"uri" : "/testproject.nonexistenttype",
"folder" : false
} ],
"uri" : "https://maven.artifactory.fake.com/artifactory/api/storage/libs-release-local/com/fake/thd-store-info-service/v1.2.0"
}
"""
response_body_artifactory_no_children = """
{
"repo" : "release-repo",
"path" : "/group/testproject/v1.0.0",
"created" : "2016-09-09T13:02:49.851-04:00",
"createdBy" : "svc_cicd",
"lastModified" : "2016-09-09T13:02:49.851-04:00",
"modifiedBy" : "svc_cicd",
"lastUpdated" : "2016-09-09T13:02:49.851-04:00",
"children" : [ ],
"uri" : "https://maven.artifactory.fake.com/artifactory/api/storage/libs-release-local/com/fake/thd-store-info-service/v1.2.0"
}
"""
response_body_artifactory_not_found = """
{
"errors" : [ {
"status" : 404,
"message" : "Unable to find item"
} ]
}
"""
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_urls_of_artifacts():
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
_b.artifact_extension = None
_b.artifact_extensions = ["bob", "vcl"]
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
responses.add(responses.GET,
test_url,
body=response_body_artifactory,
status=200,
content_type="application/json")
urls = art.get_urls_of_artifacts()
assert urls == ["https://testdomain/artifactory/release-repo/group/testproject/v1.0.0/testproject.bob", "https://testdomain/artifactory/release-repo/group/testproject/v1.0.0/testproject.vcl"]
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_artifact_url():
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
_b.artifact_extension = 'bob'
_b.artifact_extensions = None
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
responses.add(responses.GET,
test_url,
body=response_body_artifactory,
status=200,
content_type="application/json")
url = art.get_artifact_url()
assert url == "https://testdomain/artifactory/release-repo/group/testproject/v1.0.0/testproject.bob"
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_artifact_with_include_pom():
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_artifactoryConfig_include_POM['environments']['unittest']
_b.json_config = mock_build_config_artifactoryConfig_include_POM
_b.project_name = mock_build_config_artifactoryConfig_include_POM['projectInfo']['name']
_b.include_pom = mock_build_config_artifactoryConfig_include_POM['artifactoryConfig']['includePom']
_b.version_number = 'v1.0.0'
_b.artifact_extension = 'bob'
_b.artifact_extensions = None
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
responses.add(responses.GET,
test_url,
body=response_body_artifactory,
status=200,
content_type="application/json")
url = art.get_artifact_url()
assert url == "https://testdomain/artifactory/release-repo/group/testproject/v1.0.0/testproject.bob"
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_artifact_url_failure():
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
exception = HTTPError('Something went wrong')
responses.add(responses.GET,
test_url,
body=exception)
with pytest.raises(ArtifactException):
art.get_artifact_url()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('Artifactory', 'get_artifact_url', 'Unable to locate artifactory path https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0', 'ERROR')
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_artifact_url_not_found():
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
responses.add(responses.GET,
test_url,
body=response_body_artifactory_not_found,
status=404,
content_type="application/json")
with pytest.raises(ArtifactException):
art.get_artifact_url()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('Artifactory', 'get_artifact_url', 'Unable to locate artifactory path https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0\r\n Response: \n{\n "errors" : [ {\n "status" : 404,\n "message" : "Unable to find item"\n } ]\n}\n', 'ERROR')
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_artifact_url_specified_type_does_not_exist():
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
_b.artifact_extension = 'bob'
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
responses.add(responses.GET,
test_url,
body=response_body_artifactory_no_matching_children,
status=200,
content_type="application/json")
with pytest.raises(ArtifactException):
art.get_artifact_url()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('Artifactory', 'get_artifact_url', 'Could not locate artifact bob',
'ERROR')
# noinspection PyUnresolvedReferences
@responses.activate
def test_get_artifact_url_specified_path_has_no_children():
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
_b.artifact_extension = 'bob'
_b.artifact_extensions = None
art = Artifactory(config_override=_b)
test_url = "https://testdomain/artifactory/api/storage/release-repo/group/testproject/v1.0.0"
responses.add(responses.GET,
test_url,
body=response_body_artifactory_no_children,
status=200,
content_type="application/json")
with pytest.raises(ArtifactException):
art.get_artifact_url()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('Artifactory', 'get_artifact_url', 'Could not locate artifact bob',
'ERROR')
def test__get_artifactory_file_name_directory_not_defined(monkeypatch):
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
art = Artifactory(config_override=_b)
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
if os.getenv('ARTIFACT_BUILD_DIRECTORY'):
monkeypatch.delenv('ARTIFACT_BUILD_DIRECTORY')
with pytest.raises(SystemExit):
art._get_artifactory_files_name_from_build_dir()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('Artifactory', '_get_artifactory_files_name_from_build_dir', 'Missing artifact build path. Did you forget to define the environment variable \'ARTIFACT_BUILD_DIRECTORY\'? ', 'ERROR')
# def foo(self, test1, test2):
# print(test1)
# print(test2)
# pass
# @patch('utils.commons.CommonUtils.get_files_of_type_from_directory', new=foo)
def test__get_artifactory_files_name_no_artifact_found(monkeypatch):
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = 'v1.0.0'
_b.artifact_extension = 'bob'
_b.artifact_extensions = None
art = Artifactory(config_override=_b)
def _get_files_of_type_from_directory(filetype, directory):
print(filetype)
print(directory)
with patch('flow.utils.commons.get_files_of_type_from_directory', new=_get_files_of_type_from_directory):
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
monkeypatch.setenv('ARTIFACT_BUILD_DIRECTORY', 'mydir')
with pytest.raises(SystemExit):
art._get_artifactory_files_name_from_build_dir()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('Artifactory', '_get_artifactory_files_name_from_build_dir',
'Failed to find artifact of type bob in mydir', 'ERROR')
def test_get_artifact_home_url_no_defined_version():
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
with pytest.raises(SystemExit):
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = None
art = Artifactory(config_override=_b)
art.get_artifact_home_url()
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('commons', 'verify_version', 'Version not defined. Is your '
'repo tagged with a version '
'number?', 'ERROR')
def test_download_and_extract_artifacts_locally_no_defined_version():
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
with pytest.raises(SystemExit):
_b = MagicMock(BuildConfig)
_b.build_env_info = mock_build_config_dict['environments']['unittest']
_b.json_config = mock_build_config_dict
_b.project_name = mock_build_config_dict['projectInfo']['name']
_b.version_number = None
art = Artifactory(config_override=_b)
art.download_and_extract_artifacts_locally('download_dir')
print(str(mock_printmsg_fn.mock_calls))
mock_printmsg_fn.assert_called_with('commons', 'verify_version', 'Version not defined. Is your '
'repo tagged with a version '
'number?', 'ERROR')
def test_init_missing_artifactory():
_b = MagicMock(BuildConfig)
_b.json_config = mock_build_config_missing_artifact_dict
with patch('flow.utils.commons.print_msg') as mock_printmsg_fn:
with pytest.raises(SystemExit):
Artifactory(config_override=_b)
mock_printmsg_fn.assert_called_with('Artifactory', '__init__', "The build config associated with artifactory is missing key 'artifact'", 'ERROR')
| 36.633867 | 313 | 0.657068 | 1,824 | 16,009 | 5.435307 | 0.108553 | 0.043272 | 0.057494 | 0.059411 | 0.866451 | 0.853339 | 0.82207 | 0.802199 | 0.787573 | 0.780008 | 0 | 0.024214 | 0.223499 | 16,009 | 436 | 314 | 36.71789 | 0.773309 | 0.025048 | 0 | 0.701183 | 0 | 0.073965 | 0.343231 | 0.058616 | 0 | 0 | 0 | 0 | 0.035503 | 1 | 0.038462 | false | 0 | 0.023669 | 0 | 0.06213 | 0.08284 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
eddfe5871d2bc571ea101a87612ca8ea339385f1 | 92 | py | Python | parameters_8001.py | AnujBrandy/AdsIdeaInQBO | 561b096a0e5db3acddbf9f1fc57d29ac8fe1791d | [
"BSD-3-Clause"
] | 2 | 2015-07-05T12:25:08.000Z | 2015-07-05T15:39:32.000Z | parameters_8001.py | AnujBrandy/AdsIdeaInQBO | 561b096a0e5db3acddbf9f1fc57d29ac8fe1791d | [
"BSD-3-Clause"
] | null | null | null | parameters_8001.py | AnujBrandy/AdsIdeaInQBO | 561b096a0e5db3acddbf9f1fc57d29ac8fe1791d | [
"BSD-3-Clause"
] | null | null | null | password="pbkdf2(1000,20,sha512)$b7be3eabb9e0f671$42805bad515a5f87e5b75c18f3abe6182f5c2545"
| 46 | 91 | 0.891304 | 7 | 92 | 11.714286 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.472527 | 0.01087 | 92 | 1 | 92 | 92 | 0.428571 | 0 | 0 | 0 | 0 | 0 | 0.869565 | 0.869565 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
6121a154b017a0353317c55befafbb9deb343a21 | 8,910 | py | Python | test/test_complete_html_tags.py | scop/pymarkdown | 562ba8f7857d99ba09e86e42de5a37ec6d9b2c30 | [
"MIT"
] | null | null | null | test/test_complete_html_tags.py | scop/pymarkdown | 562ba8f7857d99ba09e86e42de5a37ec6d9b2c30 | [
"MIT"
] | null | null | null | test/test_complete_html_tags.py | scop/pymarkdown | 562ba8f7857d99ba09e86e42de5a37ec6d9b2c30 | [
"MIT"
] | null | null | null | """
Tests for the functions that deal with parsing of complete html tags.
"""
from pymarkdown.html_helper import HtmlHelper
def test_simple_complete_html_end_tag():
"""
Make sure to test a simple complete html tag.
"""
# Arrange
input_tag_name = "a"
string_to_parse = ">"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_end_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_simple_complete_html_end_tag_with_invalid_tag_name():
"""
Make sure to test a simple complete html tag.
"""
# Arrange
input_tag_name = "a*b"
string_to_parse = ">"
parse_index = 0
expected_is_valid = False
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_end_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_simple_complete_html_end_tag_with_whitespace():
"""
Make sure to test a simple complete html tag with extra whitespace.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " >"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_end_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 2
def test_complete_html_end_tag_with_bad_attribute():
"""
Make sure to test a complete html tag with a attribute specified (bad).
"""
# Arrange
input_tag_name = "a"
string_to_parse = " foo>"
parse_index = 0
expected_is_valid = False
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_end_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 2
def test_complete_html_end_tag_with_no_more_string():
"""
Make sure to test a complete html tag that isn't terminated.
"""
# Arrange
input_tag_name = "a"
string_to_parse = ""
parse_index = 0
expected_is_valid = False
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_end_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_simple_complete_html_start_tag_with_no_attributes():
"""
Make sure to test a simple complete html start tag with no attributes.
"""
# Arrange
input_tag_name = "a"
string_to_parse = ">"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_simple_complete_html_start_tag_with_bad_tag_name():
"""
Make sure to test a simple complete html start tag with a bad tag name.
"""
# Arrange
input_tag_name = "a*b"
string_to_parse = ">"
parse_index = 0
expected_is_valid = False
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_simple_complete_html_start_tag_with_no_attributes_and_whitespace():
"""
Make sure to test a simple complete html start tag with no attributes and whitespace.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " >"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 2
def test_complete_html_start_tag_with_single_no_value_attributes():
"""
Make sure to test a simple complete html start tag with a single attribute with no value.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show>"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 6
def test_complete_html_start_tag_with_invalidly_named_no_value_attributes():
"""
Make sure to test a simple complete html start tag with a single attribute that has an invalid name.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " sh*ow>"
parse_index = 0
expected_is_valid = False
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_complete_html_start_tag_with_single_no_value_attributes_and_whitespace():
"""
Make sure to test a simple complete html start tag with a single attribute with no value and whitespace.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show >"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 7
def test_complete_html_start_tag_with_single_attribute():
"""
Make sure to test a simple complete html start tag with a single attribute.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show=1>"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 8
def test_complete_html_start_tag_with_single_attribute_with_bad_value():
"""
Make sure to test a simple complete html start tag with a single attribute with bad value.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show=>"
parse_index = 0
expected_is_valid = False
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 1
def test_complete_html_start_tag_with_single_attribute_with_whitespace():
"""
Make sure to test a simple complete html start tag with a single attribute with whitespace.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show = '1' >"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 13
def test_complete_html_start_tag_with_multiple_attributes():
"""
Make sure to test a simple complete html start tag with multiple attributes.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show=1 maximize=1 opacity='70'>"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 32
def test_complete_html_start_tag_with_self_closing_tag():
"""
Make sure to test a simple complete html start tag with multiple attributes.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show/>"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 7
def test_complete_html_start_tag_with_normal_opening_tag():
"""
Make sure to test a simple complete html start tag with multiple attributes.
"""
# Arrange
input_tag_name = "a"
string_to_parse = " show>"
parse_index = 0
expected_is_valid = True
# Act
actual_is_valid, parse_index = HtmlHelper.is_complete_html_start_tag(
input_tag_name, string_to_parse, parse_index
)
# Assert
assert expected_is_valid == actual_is_valid
assert parse_index == 6
| 24.61326 | 108 | 0.699439 | 1,252 | 8,910 | 4.53754 | 0.064696 | 0.119697 | 0.107728 | 0.126738 | 0.953353 | 0.951417 | 0.948425 | 0.925717 | 0.903538 | 0.899842 | 0 | 0.006161 | 0.234905 | 8,910 | 361 | 109 | 24.68144 | 0.827197 | 0.190909 | 0 | 0.730994 | 0 | 0 | 0.018416 | 0 | 0 | 0 | 0 | 0 | 0.19883 | 1 | 0.099415 | false | 0 | 0.005848 | 0 | 0.105263 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
612a101b9127ed0550671115e217471e726bd7dd | 159 | py | Python | ipygis/__init__.py | GispoCoding/ipypostgis | 51535ade348ea037d47a78106f7679b78a4e30b0 | [
"MIT"
] | 4 | 2021-06-16T06:28:01.000Z | 2022-01-19T16:09:46.000Z | ipygis/__init__.py | GispoCoding/ipypostgis | 51535ade348ea037d47a78106f7679b78a4e30b0 | [
"MIT"
] | 9 | 2021-06-11T15:12:11.000Z | 2021-10-04T12:22:59.000Z | ipygis/__init__.py | GispoCoding/ipygis | 51535ade348ea037d47a78106f7679b78a4e30b0 | [
"MIT"
] | null | null | null | from .clusters import generate_clusters
from .db_utils import get_connection_url
from .gis_utils import get_map, get_h3_map, generate_map, QueryResult, to_gdf
| 39.75 | 77 | 0.855346 | 26 | 159 | 4.846154 | 0.576923 | 0.174603 | 0.222222 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006993 | 0.100629 | 159 | 3 | 78 | 53 | 0.874126 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
b6591675f8102f0e8a18bb7ec5846ac6bc4d9d7a | 15,312 | py | Python | examples/rule_based.py | Borororo/interpretable_ropes | c083cc388998a1cfb6720a92ecb943b0edcee204 | [
"Apache-2.0"
] | 3 | 2020-10-03T04:07:19.000Z | 2020-11-29T13:48:18.000Z | examples/rule_based.py | Borororo/Interpretable-Modular-KR-for-MRC | cc921b0e04947663fcc60f0552ca8d8907ca6a1b | [
"Apache-2.0"
] | null | null | null | examples/rule_based.py | Borororo/Interpretable-Modular-KR-for-MRC | cc921b0e04947663fcc60f0552ca8d8907ca6a1b | [
"Apache-2.0"
] | null | null | null | # -*- coding：utf-8 -*-
import json
from transformers.data.metrics.squad_metrics import (
compute_f1,compute_exact
)
import os
import random
def make_choice_pos(predicts):
if int(predicts['TP_relevance']) ==0:
return predicts["object1"]
else:
return predicts["object2"]
def make_choice_neg(predicts):
if int(predicts['TP_relevance']) ==1:
return predicts["object1"]
else:
return predicts["object2"]
def remove_punc(text):
ends =['.',',',"'s"]
if text.endswith('.') or text.endswith(','):
text = text[:-1]
elif text.endswith("'s"):
text = text[:-2]
return text
def make_find_answer_by_rule(no_label_synthetic,output):
Object_question_words ={"which",'in which','whose','who','what','for which','on which'," which",'when','at which','where','during which','when'}
positive_words = ['more', 'higher', 'increase', 'high', 'harder', 'increasing', 'up', 'larger', 'better', 'faster', 'stronger', 'closer', 'louder', 'correctly']
negative_words = ['less', 'lower', 'decrease', 'low', 'easier', 'decreasing', 'down', 'smaller', 'worse', 'slower', 'weaker', 'farther', 'quieter', 'incorrectly','fewer','not','avoid']
comparative_words = [
'more', 'less', 'higher', 'lower', 'increase', 'decrease', 'harder', 'easier', 'increasing',
'decreasing', 'larger', 'smaller', 'better', 'worse', 'faster', 'slower', 'weaker', 'stronger',
'closer', 'farther', 'louder', 'quieter', 'correctly', 'incorrectly', 'not', 'yes', 'no', 'not'
]
pairs = {
'more': 'less',
'higher': 'lower',
'increase': 'decrease',
'harder': 'easier',
'increasing': 'decreasing',
'larger': 'smaller',
'better': 'worse',
'faster': 'slower',
'stronger': 'weaker',
'closer': 'farther',
'louder': 'quieter',
'correctly': 'incorrectly',
'increased':"reduced",
"warmer":"colder",
'high':'low',
'turn on':"turn off",
'rise':'fall',
'up':'down',
'longer':'shorter',
'deeper':"shallower",
'positively':'negatively',
}
final_answer = {}
f1 =[]
unsolved= []
exact = []
with open(no_label_synthetic, "r", encoding="utf-8")as reader:
input_data = json.load(reader)
for entry in input_data["data"]:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["background"]
situation_text = paragraph['situation']
for qa in paragraph['qas']:
qas_id = qa['id']
question_text = qa['question']
try:
answer_text = qa['answers'][0]['text']
except:
answer_text="null"
predicts = qa['predicts']
# Object Type:
if any(question_text.lower().startswith(i) for i in Object_question_words):
predicts_answer = make_choice_pos(predicts)
for word in positive_words:
if word in question_text.lower():
predicts_answer = make_choice_pos(predicts)
break
for word in negative_words:
if word in question_text.lower():
if word not in predicts['TP in back'].lower():
predicts_answer = make_choice_neg(predicts)
break
f1.append(compute_f1(remove_punc(answer_text), remove_punc(predicts_answer)))
else:
candidate = []
for key, val in pairs.items():
if key in question_text.lower() and val in question_text.lower():
candidate = [key, val]
break
# comparative
if candidate:
o1_ind = question_text.lower().find(remove_punc(predicts["object1"].lower()))
o2_ind = question_text.lower().find(remove_punc(predicts["object2"].lower()))
than_ind = question_text.lower().find("than")
indx = [o1_ind, o2_ind, than_ind]
# if (o1_ind == -1 and o2_ind == -1) or o1_ind == o2_ind:
# # print(qas_id,question_text)
# continue
if o1_ind != -1 and o2_ind == -1:
if than_ind != -1:
if o1_ind < than_ind:
o2_ind = 1000
elif o1_ind == -1 and o2_ind != -1:
if than_ind != -1:
if o2_ind < than_ind:
o1_ind = 1000
if o1_ind < o2_ind:
if int(predicts['TP_relevance']) == 0:
predicts_answer = candidate[0]
f1.append(compute_f1(answer_text, candidate[0]))
else:
predicts_answer = candidate[1]
f1.append(compute_f1(answer_text, candidate[1]))
else:
if int(predicts['TP_relevance']) == 0:
predicts_answer = candidate[1]
f1.append(compute_f1(answer_text, candidate[1]))
else:
predicts_answer = candidate[0]
f1.append(compute_f1(answer_text, candidate[0]))
else:
unsolved.append([qas_id,question_text,answer_text])
predicts_answer = predicts["object1"] +predicts["object2"]
# +predicts["SP_object1"]+predicts["SP_object2"]
than_ind = question_text.lower().find("or")
o1_f1 = compute_f1(question_text,predicts["object1"])
o2_f1 = compute_f1(question_text, predicts["object2"])
sp_o1_f1 = compute_f1(question_text, predicts["SP_object1"])
sp_o2_f1 = compute_f1(question_text, predicts["SP_object2"])
posssible_answer = [predicts["object1"] ,predicts["object2"],predicts["SP_object1"],predicts["SP_object2"]]
predicts_answer = posssible_answer[[o1_f1,o2_f1,sp_o1_f1,sp_o2_f1].index(max([o1_f1,o2_f1,sp_o1_f1,sp_o2_f1]))]
f1.append(compute_f1(remove_punc(answer_text), predicts_answer))
predicts_answer = remove_punc(predicts_answer)
final_answer[qas_id] = predicts_answer
exact.append(compute_exact(remove_punc(answer_text),remove_punc(predicts_answer)))
with open(output, "w+") as writer:
writer.write(json.dumps(final_answer, indent=4) + "\n")
return final_answer,f1,exact
def make_find_answer_by_rule_uncovered(no_label_synthetic,output):
Object_question_words ={"which",'in which','whose','who','what','for which','on which'," which",'when','at which','where','during which','when'}
positive_words = ['more', 'higher', 'increase', 'high', 'harder', 'increasing', 'up', 'larger', 'better', 'faster', 'stronger', 'closer', 'louder', 'correctly']
negative_words = ['less', 'lower', 'decrease', 'low', 'easier', 'decreasing', 'down', 'smaller', 'worse', 'slower', 'weaker', 'farther', 'quieter', 'incorrectly','fewer','not','avoid']
comparative_words = [
'more', 'less', 'higher', 'lower', 'increase', 'decrease', 'harder', 'easier', 'increasing',
'decreasing', 'larger', 'smaller', 'better', 'worse', 'faster', 'slower', 'weaker', 'stronger',
'closer', 'farther', 'louder', 'quieter', 'correctly', 'incorrectly', 'not', 'yes', 'no', 'not'
]
pairs = {
'more': 'less',
'higher': 'lower',
'increase': 'decrease',
'harder': 'easier',
'increasing': 'decreasing',
'larger': 'smaller',
'better': 'worse',
'faster': 'slower',
'stronger': 'weaker',
'closer': 'farther',
'louder': 'quieter',
'correctly': 'incorrectly',
'increased':"reduced",
"warmer":"colder",
'high':'low',
'turn on':"turn off",
'rise':'fall',
'up':'down',
'longer':'shorter',
'deeper':"shallower",
'positively':'negatively',
}
filtered = [1912355095, 580926078, 2893035919, 3005625773, 4037359159, 1090395805, 1111432861, 1772822810,
1779114266, 3003444033, 1687481522, 373839753, 3209385804, 3128907286, 1746228971, 4088330372,
4227418477, 4047850835,
302402461, 603409309, 2849858743, 2050057087, 336287454, 1787516699, 184586157, 2999947384, 1202686909,
1223723965, 2335934912, 2677049981, 1182285725, 430326667, 4183507762, 1091390005, 1076054581,
3847534556,
4074158044, 955038082, 1423883267,
1626887498, 184177891, 1586058524, 156980418, 1607423284, 1608996147, 3071497657,
3865282641, 1588430868, 3876948090, 3344467660, 3346695902, 3600844723, 3227879116, 3484256179,
2934519278, 3876075109, 2041918832, 1115960569, 1108882681, 2013214064, 3895408229, 2975020526,
3885579536, 356328641, 985605428, 809437101, 494407430, 3999682891, 1562329124, 1505378340, 2022868633,
2022147737, 1729594789, 1730315685, 1082622173, 1205043665, 1081901277, 1204322769]
filtered_id = [str(i) for i in filtered]
final_answer = {}
f1 =[]
unsolved= []
exact = []
# writer_ref = open(os.path.join(output, "refs_filtered.txt"), 'w+', encoding='utf-8')
# writer_hyps = open(os.path.join(out,"hyps_a_1_base.txt"),'w+',encoding='utf-8')
writer_hyps = open(os.path.join(output,"hyps_rule_based"),'w+',encoding='utf-8')
with open(no_label_synthetic, "r", encoding="utf-8")as reader:
input_data = json.load(reader)
for entry in input_data["data"]:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["background"]
situation_text = paragraph['situation']
for qa in paragraph['qas']:
qas_id = qa['id']
question_text = qa['question']
try:
answer_text = qa['answers'][0]['text']
except:
answer_text="null"
predicts = qa['predicts']
# if qas_id in filtered_id:
# continue
# Object Type:
if any(question_text.lower().startswith(i) for i in Object_question_words):
predicts_answer = make_choice_pos(predicts)
for word in positive_words:
if word in question_text.lower():
predicts_answer = make_choice_pos(predicts)
break
for word in negative_words:
if word in question_text.lower():
if word not in predicts['TP in back'].lower():
predicts_answer = make_choice_neg(predicts)
break
f1.append(compute_f1(remove_punc(answer_text), remove_punc(predicts_answer)))
else:
candidate = []
for key, val in pairs.items():
if key in question_text.lower() and val in question_text.lower():
candidate = [key, val]
break
# comparative
if candidate:
o1_ind = question_text.lower().find(remove_punc(predicts["object1"].lower()))
o2_ind = question_text.lower().find(remove_punc(predicts["object2"].lower()))
than_ind = question_text.lower().find("than")
indx = [o1_ind, o2_ind, than_ind]
# if (o1_ind == -1 and o2_ind == -1) or o1_ind == o2_ind:
# # print(qas_id,question_text)
# continue
if o1_ind != -1 and o2_ind == -1:
if than_ind != -1:
if o1_ind < than_ind:
o2_ind = 1000
elif o1_ind == -1 and o2_ind != -1:
if than_ind != -1:
if o2_ind < than_ind:
o1_ind = 1000
if o1_ind < o2_ind:
if int(predicts['TP_relevance']) == 0:
predicts_answer = candidate[0]
f1.append(compute_f1(answer_text, candidate[0]))
else:
predicts_answer = candidate[1]
f1.append(compute_f1(answer_text, candidate[1]))
else:
if int(predicts['TP_relevance']) == 0:
predicts_answer = candidate[1]
f1.append(compute_f1(answer_text, candidate[1]))
else:
predicts_answer = candidate[0]
f1.append(compute_f1(answer_text, candidate[0]))
else:
unsolved.append([qas_id,question_text,answer_text])
predicts_answer = predicts["object1"] +predicts["object2"]
# +predicts["SP_object1"]+predicts["SP_object2"]
than_ind = question_text.lower().find("or")
o1_f1 = compute_f1(question_text,predicts["object1"])
o2_f1 = compute_f1(question_text, predicts["object2"])
sp_o1_f1 = compute_f1(question_text, predicts["SP_object1"])
sp_o2_f1 = compute_f1(question_text, predicts["SP_object2"])
posssible_answer = [predicts["object1"] ,predicts["object2"],predicts["SP_object1"],predicts["SP_object2"]]
predicts_answer = posssible_answer[[o1_f1,o2_f1,sp_o1_f1,sp_o2_f1].index(max([o1_f1,o2_f1,sp_o1_f1,sp_o2_f1]))]
f1.append(compute_f1(remove_punc(answer_text), predicts_answer))
predicts_answer = remove_punc(predicts_answer)
ref2 = answer_text
# writer_ref.write(ref2)
# writer_ref.write('\n')
writer_hyps.write(predicts_answer)
writer_hyps.write('\n')
final_answer[qas_id] = predicts_answer
exact.append(compute_exact(remove_punc(answer_text),remove_punc(predicts_answer)))
# writer_ref.close()
writer_hyps.close()
return final_answer,f1,exact | 51.210702 | 188 | 0.512539 | 1,494 | 15,312 | 5.034806 | 0.17336 | 0.05105 | 0.040681 | 0.02712 | 0.825977 | 0.809226 | 0.79447 | 0.79447 | 0.781441 | 0.781441 | 0 | 0.101148 | 0.362722 | 15,312 | 299 | 189 | 51.210702 | 0.669707 | 0.041601 | 0 | 0.840304 | 0 | 0 | 0.132942 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.019011 | false | 0 | 0.015209 | 0 | 0.060837 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
fcb109045992d0d3f9289b280d05a59236b8f84b | 2,009 | py | Python | src/genie/libs/parser/iosxe/tests/ShowIpOspfFastRerouteTiLfa/cli/equal/golden_output_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/iosxe/tests/ShowIpOspfFastRerouteTiLfa/cli/equal/golden_output_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/iosxe/tests/ShowIpOspfFastRerouteTiLfa/cli/equal/golden_output_expected.py | balmasea/genieparser | d1e71a96dfb081e0a8591707b9d4872decd5d9d3 | [
"Apache-2.0"
] | 309 | 2019-01-16T20:21:07.000Z | 2022-03-30T12:56:41.000Z | expected_output = {
"process_id": {
65109: {
"router_id": "10.4.1.1",
"ospf_object": {
"Process ID (65109)": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"Area 8": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"Loopback0": {
"ipfrr_enabled": "no",
"sr_enabled": "no",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"GigabitEthernet0/1/2": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"GigabitEthernet0/1/1": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"GigabitEthernet0/1/0": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"TenGigabitEthernet0/0/": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
"AS external": {
"ipfrr_enabled": "no",
"sr_enabled": "yes",
"ti_lfa_configured": "no",
"ti_lfa_enabled": "no",
},
},
}
}
}
| 34.637931 | 46 | 0.333499 | 144 | 2,009 | 4.291667 | 0.194444 | 0.247573 | 0.101942 | 0.20712 | 0.81877 | 0.781553 | 0.781553 | 0.781553 | 0.781553 | 0.781553 | 0 | 0.029598 | 0.529119 | 2,009 | 57 | 47 | 35.245614 | 0.623679 | 0 | 0 | 0.54386 | 0 | 0 | 0.332006 | 0.010951 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
fcbac97bf7fb8fa51f971e979f604696341d47a2 | 20,441 | py | Python | molecule/python/molecule_api/api/webhook_api.py | sumit4-ttn/SDK | b3ae385e5415e47ac70abd0b3fdeeaeee9aa7cff | [
"Apache-2.0"
] | null | null | null | molecule/python/molecule_api/api/webhook_api.py | sumit4-ttn/SDK | b3ae385e5415e47ac70abd0b3fdeeaeee9aa7cff | [
"Apache-2.0"
] | null | null | null | molecule/python/molecule_api/api/webhook_api.py | sumit4-ttn/SDK | b3ae385e5415e47ac70abd0b3fdeeaeee9aa7cff | [
"Apache-2.0"
] | null | null | null | # coding: utf-8
"""
Molecule API Documentation
The Hydrogen Molecule API # noqa: E501
OpenAPI spec version: 1.3.0
Contact: info@hydrogenplatform.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 molecule_api.api_client import ApiClient
class WebhookApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
Ref: https://github.com/swagger-api/swagger-codegen
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def create_webhook_using_post(self, webhook_params, **kwargs): # noqa: E501
"""Creates a new Webhook record # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_webhook_using_post(webhook_params, async_req=True)
>>> result = thread.get()
:param async_req bool
:param WebhookParams webhook_params: It enables a user to create a Webhook record (required)
:return: WebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.create_webhook_using_post_with_http_info(webhook_params, **kwargs) # noqa: E501
else:
(data) = self.create_webhook_using_post_with_http_info(webhook_params, **kwargs) # noqa: E501
return data
def create_webhook_using_post_with_http_info(self, webhook_params, **kwargs): # noqa: E501
"""Creates a new Webhook record # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_webhook_using_post_with_http_info(webhook_params, async_req=True)
>>> result = thread.get()
:param async_req bool
:param WebhookParams webhook_params: It enables a user to create a Webhook record (required)
:return: WebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['webhook_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 create_webhook_using_post" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'webhook_params' is set
if ('webhook_params' not in params or
params['webhook_params'] is None):
raise ValueError("Missing the required parameter `webhook_params` when calling `create_webhook_using_post`") # noqa: E501
collection_formats = {}
path_params = {}
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'webhook_params' in params:
body_params = params['webhook_params']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/webhook', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='WebhookResponse', # noqa: E501
auth_settings=auth_settings,
async_req=params.get('async_req'),
_return_http_data_only=params.get('_return_http_data_only'),
_preload_content=params.get('_preload_content', True),
_request_timeout=params.get('_request_timeout'),
collection_formats=collection_formats)
def delete_webhook_using_delete(self, webhook_id, **kwargs): # noqa: E501
"""Delete Webhook # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_webhook_using_delete(webhook_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str webhook_id: Webhook ID (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.delete_webhook_using_delete_with_http_info(webhook_id, **kwargs) # noqa: E501
else:
(data) = self.delete_webhook_using_delete_with_http_info(webhook_id, **kwargs) # noqa: E501
return data
def delete_webhook_using_delete_with_http_info(self, webhook_id, **kwargs): # noqa: E501
"""Delete Webhook # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_webhook_using_delete_with_http_info(webhook_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str webhook_id: Webhook ID (required)
:return: None
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['webhook_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method delete_webhook_using_delete" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'webhook_id' is set
if ('webhook_id' not in params or
params['webhook_id'] is None):
raise ValueError("Missing the required parameter `webhook_id` when calling `delete_webhook_using_delete`") # noqa: E501
collection_formats = {}
path_params = {}
if 'webhook_id' in params:
path_params['webhook_id'] = params['webhook_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/webhook/{webhook_id}', '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 get_webhook_all_using_get(self, **kwargs): # noqa: E501
"""Fetch Webhook list # 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_webhook_all_using_get(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page: To filter response webhook list by page number
:param int size: Number of records per page
:param str order_by: Field to sort record list
:param bool ascending: Sorting order
:param bool get_latest: To fetch latest (one) record
:return: PageWebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_webhook_all_using_get_with_http_info(**kwargs) # noqa: E501
else:
(data) = self.get_webhook_all_using_get_with_http_info(**kwargs) # noqa: E501
return data
def get_webhook_all_using_get_with_http_info(self, **kwargs): # noqa: E501
"""Fetch Webhook list # 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_webhook_all_using_get_with_http_info(async_req=True)
>>> result = thread.get()
:param async_req bool
:param int page: To filter response webhook list by page number
:param int size: Number of records per page
:param str order_by: Field to sort record list
:param bool ascending: Sorting order
:param bool get_latest: To fetch latest (one) record
:return: PageWebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['page', 'size', 'order_by', 'ascending', 'get_latest'] # 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_webhook_all_using_get" % key
)
params[key] = val
del params['kwargs']
collection_formats = {}
path_params = {}
query_params = []
if 'page' in params:
query_params.append(('page', params['page'])) # noqa: E501
if 'size' in params:
query_params.append(('size', params['size'])) # noqa: E501
if 'order_by' in params:
query_params.append(('order_by', params['order_by'])) # noqa: E501
if 'ascending' in params:
query_params.append(('ascending', params['ascending'])) # noqa: E501
if 'get_latest' in params:
query_params.append(('get_latest', params['get_latest'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/webhook', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='PageWebhookResponse', # 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 get_webhook_using_get(self, webhook_id, **kwargs): # noqa: E501
"""Fetch Webhook details # 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_webhook_using_get(webhook_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str webhook_id: Webhook ID (required)
:return: WebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.get_webhook_using_get_with_http_info(webhook_id, **kwargs) # noqa: E501
else:
(data) = self.get_webhook_using_get_with_http_info(webhook_id, **kwargs) # noqa: E501
return data
def get_webhook_using_get_with_http_info(self, webhook_id, **kwargs): # noqa: E501
"""Fetch Webhook details # 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_webhook_using_get_with_http_info(webhook_id, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str webhook_id: Webhook ID (required)
:return: WebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['webhook_id'] # noqa: E501
all_params.append('async_req')
all_params.append('_return_http_data_only')
all_params.append('_preload_content')
all_params.append('_request_timeout')
params = locals()
for key, val in six.iteritems(params['kwargs']):
if key not in all_params:
raise TypeError(
"Got an unexpected keyword argument '%s'"
" to method get_webhook_using_get" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'webhook_id' is set
if ('webhook_id' not in params or
params['webhook_id'] is None):
raise ValueError("Missing the required parameter `webhook_id` when calling `get_webhook_using_get`") # noqa: E501
collection_formats = {}
path_params = {}
if 'webhook_id' in params:
path_params['webhook_id'] = params['webhook_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/webhook/{webhook_id}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='WebhookResponse', # 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 update_webhook_using_put(self, webhook_id, webhook_update_params, **kwargs): # noqa: E501
"""Update Webhook details # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_webhook_using_put(webhook_id, webhook_update_params, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str webhook_id: Webhook ID (required)
:param WebhookParams webhook_update_params: Webhook details to be updated (required)
:return: WebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
kwargs['_return_http_data_only'] = True
if kwargs.get('async_req'):
return self.update_webhook_using_put_with_http_info(webhook_id, webhook_update_params, **kwargs) # noqa: E501
else:
(data) = self.update_webhook_using_put_with_http_info(webhook_id, webhook_update_params, **kwargs) # noqa: E501
return data
def update_webhook_using_put_with_http_info(self, webhook_id, webhook_update_params, **kwargs): # noqa: E501
"""Update Webhook details # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_webhook_using_put_with_http_info(webhook_id, webhook_update_params, async_req=True)
>>> result = thread.get()
:param async_req bool
:param str webhook_id: Webhook ID (required)
:param WebhookParams webhook_update_params: Webhook details to be updated (required)
:return: WebhookResponse
If the method is called asynchronously,
returns the request thread.
"""
all_params = ['webhook_id', 'webhook_update_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 update_webhook_using_put" % key
)
params[key] = val
del params['kwargs']
# verify the required parameter 'webhook_id' is set
if ('webhook_id' not in params or
params['webhook_id'] is None):
raise ValueError("Missing the required parameter `webhook_id` when calling `update_webhook_using_put`") # noqa: E501
# verify the required parameter 'webhook_update_params' is set
if ('webhook_update_params' not in params or
params['webhook_update_params'] is None):
raise ValueError("Missing the required parameter `webhook_update_params` when calling `update_webhook_using_put`") # noqa: E501
collection_formats = {}
path_params = {}
if 'webhook_id' in params:
path_params['webhook_id'] = params['webhook_id'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
if 'webhook_update_params' in params:
body_params = params['webhook_update_params']
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['oauth2'] # noqa: E501
return self.api_client.call_api(
'/webhook/{webhook_id}', 'PUT',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='WebhookResponse', # 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)
| 39.309615 | 140 | 0.620811 | 2,379 | 20,441 | 5.047919 | 0.075662 | 0.042635 | 0.023316 | 0.029978 | 0.928054 | 0.905987 | 0.894746 | 0.865184 | 0.853776 | 0.846948 | 0 | 0.014244 | 0.2925 | 20,441 | 519 | 141 | 39.385356 | 0.816139 | 0.322391 | 0 | 0.741935 | 1 | 0 | 0.19109 | 0.060492 | 0 | 0 | 0 | 0 | 0 | 1 | 0.039427 | false | 0 | 0.014337 | 0 | 0.111111 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
1e0c52e072034391ee7ca15a507b36a69b50841a | 217 | py | Python | src/card_game_analysis/__init__.py | nwnordahl/daifugo-analysis | d0e8c302e5de01d11c5915cfd8ff91f7a23b8155 | [
"MIT"
] | 1 | 2021-12-24T20:48:16.000Z | 2021-12-24T20:48:16.000Z | src/card_game_analysis/__init__.py | nwnordahl/daifugo-analysis | d0e8c302e5de01d11c5915cfd8ff91f7a23b8155 | [
"MIT"
] | null | null | null | src/card_game_analysis/__init__.py | nwnordahl/daifugo-analysis | d0e8c302e5de01d11c5915cfd8ff91f7a23b8155 | [
"MIT"
] | null | null | null | from card_game_analysis.card import Card
from card_game_analysis.deck import Deck
from card_game_analysis.stack import Stack
from card_game_analysis.player import Player
from card_game_analysis.players import Players
| 36.166667 | 46 | 0.884793 | 35 | 217 | 5.2 | 0.257143 | 0.21978 | 0.32967 | 0.549451 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.092166 | 217 | 5 | 47 | 43.4 | 0.923858 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
1e18d0f0675e8dbbe0d0e73d47eed02f175fc490 | 348 | py | Python | logfury/v1/__init__.py | ppolewicz/logfury | 78687dd422cb77d365a36e5d047d0c71a11065d0 | [
"BSD-3-Clause"
] | 2 | 2016-10-31T17:00:53.000Z | 2017-07-21T13:45:27.000Z | logfury/v1/__init__.py | reef-technologies/logfury | 78687dd422cb77d365a36e5d047d0c71a11065d0 | [
"BSD-3-Clause"
] | 1 | 2021-05-30T21:33:52.000Z | 2021-05-30T21:55:41.000Z | logfury/v1/__init__.py | reef-technologies/logfury | 78687dd422cb77d365a36e5d047d0c71a11065d0 | [
"BSD-3-Clause"
] | 3 | 2020-04-23T22:24:16.000Z | 2021-05-30T17:08:42.000Z | from .._logfury.meta import AbstractTracePublicCallsMeta
from .._logfury.meta import DefaultTraceAbstractMeta
from .._logfury.meta import DefaultTraceMeta
from .._logfury.meta import TraceAllPublicCallsMeta
from .._logfury.trace_call import trace_call
from .._logfury.tuning import disable_trace
from .._logfury.tuning import limit_trace_arguments
| 43.5 | 56 | 0.859195 | 40 | 348 | 7.175 | 0.35 | 0.268293 | 0.209059 | 0.292683 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08046 | 348 | 7 | 57 | 49.714286 | 0.896875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
9497b8d1b03efb52e94c1626a200749b60872bd4 | 2,171 | py | Python | catkin_generated/pkg.installspace.context.pc.py | kimfeel/small_lidar_rpi | e7f26805da2d0efd3c017f6556572fb11842ba87 | [
"BSD-2-Clause"
] | null | null | null | catkin_generated/pkg.installspace.context.pc.py | kimfeel/small_lidar_rpi | e7f26805da2d0efd3c017f6556572fb11842ba87 | [
"BSD-2-Clause"
] | null | null | null | catkin_generated/pkg.installspace.context.pc.py | kimfeel/small_lidar_rpi | e7f26805da2d0efd3c017f6556572fb11842ba87 | [
"BSD-2-Clause"
] | null | null | null | # generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "/opt/ros/kinetic/include;/usr/include/pcl-1.7;/usr/include/eigen3;/usr/include/ni;/usr/include/vtk-6.2;/usr/include/freetype2;/usr/include/arm-linux-gnueabihf/freetype2;/usr/include/arm-linux-gnueabihf;/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent;/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent/include;/usr/lib/openmpi/include;/usr/lib/openmpi/include/openmpi;/usr/include/python2.7;/usr/include/jsoncpp;/usr/include/hdf5/openmpi;/usr/include/libxml2;/usr/include/tcl".split(';') if "/opt/ros/kinetic/include;/usr/include/pcl-1.7;/usr/include/eigen3;/usr/include/ni;/usr/include/vtk-6.2;/usr/include/freetype2;/usr/include/arm-linux-gnueabihf/freetype2;/usr/include/arm-linux-gnueabihf;/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent;/usr/lib/openmpi/include/openmpi/opal/mca/event/libevent2021/libevent/include;/usr/lib/openmpi/include;/usr/lib/openmpi/include/openmpi;/usr/include/python2.7;/usr/include/jsoncpp;/usr/include/hdf5/openmpi;/usr/include/libxml2;/usr/include/tcl" != "" else []
PROJECT_CATKIN_DEPENDS = "".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-lpcl_ros_filters;-lpcl_ros_io;-lpcl_ros_tf;-lsqlite3;-ldynamic_reconfigure_config_init_mutex;-lnodeletlib;-lbondcpp;-lclass_loader;-ldl;-lroslib;-lrospack;-lrosbag;-lrosbag_storage;-lroslz4;-ltopic_tools;-ltf;-ltf2_ros;-lactionlib;-lmessage_filters;-lroscpp;-lpthread;-lxmlrpcpp;-ltf2;-lroscpp_serialization;-lrosconsole;-lrosconsole_log4cxx;-lrosconsole_backend_interface;-lrostime;-lcpp_common".split(';') if "-lpcl_ros_filters;-lpcl_ros_io;-lpcl_ros_tf;-lsqlite3;-ldynamic_reconfigure_config_init_mutex;-lnodeletlib;-lbondcpp;-lclass_loader;-ldl;-lroslib;-lrospack;-lrosbag;-lrosbag_storage;-lroslz4;-ltopic_tools;-ltf;-ltf2_ros;-lactionlib;-lmessage_filters;-lroscpp;-lpthread;-lxmlrpcpp;-ltf2;-lroscpp_serialization;-lrosconsole;-lrosconsole_log4cxx;-lrosconsole_backend_interface;-lrostime;-lcpp_common" != "" else []
PROJECT_NAME = "rplidar_ros"
PROJECT_SPACE_DIR = "/usr"
PROJECT_VERSION = "1.5.7"
| 241.222222 | 1,097 | 0.808844 | 305 | 2,171 | 5.570492 | 0.301639 | 0.14126 | 0.061212 | 0.094173 | 0.867569 | 0.867569 | 0.867569 | 0.867569 | 0.867569 | 0.867569 | 0 | 0.023921 | 0.017964 | 2,171 | 8 | 1,098 | 271.375 | 0.772983 | 0.024873 | 0 | 0 | 1 | 0.571429 | 0.872813 | 0.861466 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 13 |
94d09b8282a7e833699a41d2f04f55f34df5d121 | 138 | py | Python | africanus/model/wsclean/__init__.py | JoshVStaden/codex-africanus | 4a38994431d51510b1749fa0e4b8b6190b8b530f | [
"BSD-3-Clause"
] | 13 | 2018-04-06T09:36:13.000Z | 2021-04-13T13:11:00.000Z | africanus/model/wsclean/__init__.py | JoshVStaden/codex-africanus | 4a38994431d51510b1749fa0e4b8b6190b8b530f | [
"BSD-3-Clause"
] | 153 | 2018-03-28T14:13:48.000Z | 2022-02-03T07:49:17.000Z | africanus/model/wsclean/__init__.py | JoshVStaden/codex-africanus | 4a38994431d51510b1749fa0e4b8b6190b8b530f | [
"BSD-3-Clause"
] | 14 | 2018-03-29T13:30:52.000Z | 2021-06-12T02:56:55.000Z | __all__ = ["load", "spectra"]
from africanus.model.wsclean.file_model import load
from africanus.model.wsclean.spec_model import spectra
| 27.6 | 54 | 0.804348 | 19 | 138 | 5.526316 | 0.526316 | 0.247619 | 0.342857 | 0.47619 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.094203 | 138 | 4 | 55 | 34.5 | 0.84 | 0 | 0 | 0 | 0 | 0 | 0.07971 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.666667 | 0 | 0.666667 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
94d0f42f2eb0aa0ea7daa64050c4ba1ade03e6fc | 17 | py | Python | test01.py | zzpython/python001 | 4eb5447e648cc250104d3ee95a4a42d152095b6a | [
"MIT"
] | null | null | null | test01.py | zzpython/python001 | 4eb5447e648cc250104d3ee95a4a42d152095b6a | [
"MIT"
] | null | null | null | test01.py | zzpython/python001 | 4eb5447e648cc250104d3ee95a4a42d152095b6a | [
"MIT"
] | null | null | null |
b = 100
a = 1
| 2.833333 | 7 | 0.352941 | 4 | 17 | 1.5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5 | 0.529412 | 17 | 5 | 8 | 3.4 | 0.25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
a204995b256eec9633e8fdbc5bb1a144c0bf6a7e | 19,373 | py | Python | csdl/tests/test_sum.py | LSDOlab/csdl | 04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3 | [
"MIT"
] | null | null | null | csdl/tests/test_sum.py | LSDOlab/csdl | 04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3 | [
"MIT"
] | null | null | null | csdl/tests/test_sum.py | LSDOlab/csdl | 04c2c5764f6ca9b865ec87ecfeaf6f22ecacc5a3 | [
"MIT"
] | 1 | 2021-10-04T19:40:32.000Z | 2021-10-04T19:40:32.000Z | import numpy as np
from numpy.testing._private.utils import assert_
import pytest
def test_sum_single_vector(backend):
from csdl.examples.valid.ex_sum_single_vector import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
v1 = np.arange(n)
desired_vector_sum = np.sum(v1)
np.testing.assert_almost_equal(sim['single_vector_sum'],
desired_vector_sum)
assert sim['v1'].shape == (n, )
assert sim['single_vector_sum'].shape == (1, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_single_vector_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_matrix(backend):
from csdl.examples.valid.ex_sum_single_matrix import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
desired_matrix_sum = np.sum(M1)
np.testing.assert_almost_equal(sim['single_matrix_sum'],
desired_matrix_sum)
assert sim['M1'].shape == (n, m)
assert sim['single_matrix_sum'].shape == (1, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_single_matrix_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_tensor(backend):
from csdl.examples.valid.ex_sum_single_tensor import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 4
p = 5
q = 6
T1 = np.arange(n * m * p * q).reshape((n, m, p, q))
desired_tensor_sum = np.sum(T1)
np.testing.assert_almost_equal(sim['single_tensor_sum'],
desired_tensor_sum)
assert sim['single_tensor_sum'].shape == (1, )
partials_error_tensor_sum = sim.check_partials(
includes=['comp_single_tensor_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_tensor_sum,
atol=1.e-5,
rtol=1.e-5)
def test_sum_multiple_vector(backend):
from csdl.examples.valid.ex_sum_multiple_vector import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
v1 = np.arange(n)
v2 = np.arange(n, 2 * n)
desired_vector_sum = v1 + v2
np.testing.assert_almost_equal(sim['multiple_vector_sum'],
desired_vector_sum)
assert sim['multiple_vector_sum'].shape == (n, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_multiple_vector_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix(backend):
from csdl.examples.valid.ex_sum_multiple_matrix import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
M2 = np.arange(n * m, 2 * n * m).reshape((n, m))
desired_matrix_sum = M1 + M2
np.testing.assert_almost_equal(sim['multiple_matrix_sum'],
desired_matrix_sum)
assert sim['multiple_matrix_sum'].shape == (n, m)
partials_error_matrix_sum = sim.check_partials(
includes=['comp_multiple_matrix_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_matrix_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_tensor(backend):
from csdl.examples.valid.ex_sum_multiple_tensor import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
p = 7
q = 10
T1 = np.arange(n * m * p * q).reshape((n, m, p, q))
T2 = np.arange(n * m * p * q, 2 * n * m * p * q).reshape(
(n, m, p, q))
desired_tensor_sum = T1 + T2
np.testing.assert_almost_equal(sim['multiple_tensor_sum'],
desired_tensor_sum)
assert sim['multiple_tensor_sum'].shape == (n, m, p, q)
partials_error_tensor_sum = sim.check_partials(
includes=['comp_multiple_tensor_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_tensor_sum,
atol=1.e-5,
rtol=1.e-5)
def test_sum_single_matrix_along0(backend):
from csdl.examples.valid.ex_sum_single_matrix_along0 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
desired_single_matrix_sum_axis_0 = np.sum(M1, axis=0)
np.testing.assert_almost_equal(sim['single_matrix_sum_along_0'],
desired_single_matrix_sum_axis_0)
assert sim['single_matrix_sum_along_0'].shape == (m, )
partials_error_single_matrix_axis_0 = sim.check_partials(
includes=['comp_single_matrix_sum_along_0'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_single_matrix_axis_0,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_matrix_along1(backend):
from csdl.examples.valid.ex_sum_single_matrix_along1 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
desired_single_matrix_sum_axis_1 = np.sum(M1, axis=1)
np.testing.assert_almost_equal(sim['single_matrix_sum_along_1'],
desired_single_matrix_sum_axis_1)
assert sim['single_matrix_sum_along_1'].shape == (n, )
partials_error_single_matrix_axis_1 = sim.check_partials(
includes=['comp_single_matrix_sum_along_1'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_single_matrix_axis_1,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_along0(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_along0 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
M2 = np.arange(n * m, 2 * n * m).reshape((n, m))
desired_multiple_matrix_sum_axis_0 = np.sum(M1 + M2, axis=0)
np.testing.assert_almost_equal(sim['multiple_matrix_sum_along_0'],
desired_multiple_matrix_sum_axis_0)
partials_error_multiple_matrix_axis_0 = sim.check_partials(
includes=['comp_multiple_matrix_sum_along_0'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_0,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_along1(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_along1 import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
M1 = np.arange(n * m).reshape((n, m))
M2 = np.arange(n * m, 2 * n * m).reshape((n, m))
desired_multiple_matrix_sum_axis_1 = np.sum(M1 + M2, axis=1)
np.testing.assert_almost_equal(sim['multiple_matrix_sum_along_1'],
desired_multiple_matrix_sum_axis_1)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
includes=['comp_multiple_matrix_sum_along_1'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-6,
rtol=1.e-6)
def test_sum_concatenate_sums(backend):
from csdl.examples.valid.ex_sum_concatenate import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
x = np.array([np.sum(np.arange(5)), np.sum(np.arange(4)), 0])
np.testing.assert_almost_equal(sim['sum_vector'], x)
np.testing.assert_almost_equal(
sim['single_vector_sum_1a'],
sim['single_vector_sum_1a'],
)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
out_stream=None, compact_print=True, method='fd')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-8,
rtol=1.e-8)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
out_stream=None, compact_print=True, method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-8,
rtol=1.e-8)
def test_sum_single_vector_random(backend):
from csdl.examples.valid.ex_sum_single_vector_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
np.random.seed(0)
v1 = np.random.rand(n)
desired_vector_sum = np.sum(v1)
np.testing.assert_almost_equal(sim['single_vector_sum'],
desired_vector_sum)
assert sim['v1'].shape == (n, )
assert sim['single_vector_sum'].shape == (1, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_single_vector_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_matrix_random(backend):
from csdl.examples.valid.ex_sum_single_matrix_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
np.random.seed(0)
M1 = np.random.rand(n * m).reshape((n, m))
desired_matrix_sum = np.sum(M1)
np.testing.assert_almost_equal(sim['single_matrix_sum'],
desired_matrix_sum)
assert sim['M1'].shape == (n, m)
assert sim['single_matrix_sum'].shape == (1, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_single_matrix_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_tensor_random(backend):
from csdl.examples.valid.ex_sum_single_tensor_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 4
p = 5
q = 6
np.random.seed(0)
T1 = np.random.rand(n * m * p * q).reshape((n, m, p, q))
desired_tensor_sum = np.sum(T1)
np.testing.assert_almost_equal(sim['single_tensor_sum'],
desired_tensor_sum)
assert sim['single_tensor_sum'].shape == (1, )
partials_error_tensor_sum = sim.check_partials(
includes=['comp_single_tensor_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_tensor_sum,
atol=1.e-5,
rtol=1.e-5)
def test_sum_multiple_vector_random(backend):
from csdl.examples.valid.ex_sum_multiple_vector_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
np.random.seed(0)
v1 = np.random.rand(n)
v2 = np.random.rand(n)
desired_vector_sum = v1 + v2
np.testing.assert_almost_equal(sim['multiple_vector_sum'],
desired_vector_sum)
assert sim['multiple_vector_sum'].shape == (n, )
partials_error_vector_sum = sim.check_partials(
includes=['comp_multiple_vector_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_vector_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_random(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
np.random.seed(0)
M1 = np.random.rand(n * m).reshape((n, m))
M2 = np.random.rand(n * m).reshape((n, m))
desired_matrix_sum = M1 + M2
np.testing.assert_almost_equal(sim['multiple_matrix_sum'],
desired_matrix_sum)
assert sim['multiple_matrix_sum'].shape == (n, m)
partials_error_matrix_sum = sim.check_partials(
includes=['comp_multiple_matrix_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_matrix_sum,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_tensor_random(backend):
from csdl.examples.valid.ex_sum_multiple_tensor_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
p = 7
q = 10
np.random.seed(0)
T1 = np.random.rand(n * m * p * q).reshape((n, m, p, q))
T2 = np.random.rand(n * m * p * q).reshape((n, m, p, q))
desired_tensor_sum = T1 + T2
np.testing.assert_almost_equal(sim['multiple_tensor_sum'],
desired_tensor_sum)
assert sim['multiple_tensor_sum'].shape == (n, m, p, q)
partials_error_tensor_sum = sim.check_partials(
includes=['comp_multiple_tensor_sum'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_tensor_sum,
atol=1.e-5,
rtol=1.e-5)
def test_sum_single_matrix_along0_random(backend):
from csdl.examples.valid.ex_sum_single_matrix_along0_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
np.random.seed(0)
M1 = np.random.rand(n * m).reshape((n, m))
desired_single_matrix_sum_axis_0 = np.sum(M1, axis=0)
np.testing.assert_almost_equal(sim['single_matrix_sum_along_0'],
desired_single_matrix_sum_axis_0)
assert sim['single_matrix_sum_along_0'].shape == (m, )
partials_error_single_matrix_axis_0 = sim.check_partials(
includes=['comp_single_matrix_sum_along_0'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_single_matrix_axis_0,
atol=1.e-6,
rtol=1.e-6)
def test_sum_single_matrix_along1_random(backend):
from csdl.examples.valid.ex_sum_single_matrix_along1_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
np.random.seed(0)
M1 = np.random.rand(n * m).reshape((n, m))
desired_single_matrix_sum_axis_1 = np.sum(M1, axis=1)
np.testing.assert_almost_equal(sim['single_matrix_sum_along_1'],
desired_single_matrix_sum_axis_1)
assert sim['single_matrix_sum_along_1'].shape == (n, )
partials_error_single_matrix_axis_1 = sim.check_partials(
includes=['comp_single_matrix_sum_along_1'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_single_matrix_axis_1,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_along0_random(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_along0_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
np.random.seed(0)
M1 = np.random.rand(n * m).reshape((n, m))
M2 = np.random.rand(n * m).reshape((n, m))
desired_multiple_matrix_sum_axis_0 = np.sum(M1 + M2, axis=0)
np.testing.assert_almost_equal(sim['multiple_matrix_sum_along_0'],
desired_multiple_matrix_sum_axis_0)
partials_error_multiple_matrix_axis_0 = sim.check_partials(
includes=['comp_multiple_matrix_sum_along_0'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_0,
atol=1.e-6,
rtol=1.e-6)
def test_sum_multiple_matrix_along1_random(backend):
from csdl.examples.valid.ex_sum_multiple_matrix_along1_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
n = 3
m = 6
np.random.seed(0)
M1 = np.random.rand(n * m).reshape((n, m))
M2 = np.random.rand(n * m).reshape((n, m))
desired_multiple_matrix_sum_axis_1 = np.sum(M1 + M2, axis=1)
np.testing.assert_almost_equal(sim['multiple_matrix_sum_along_1'],
desired_multiple_matrix_sum_axis_1)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
includes=['comp_multiple_matrix_sum_along_1'],
out_stream=None,
compact_print=True,
method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-6,
rtol=1.e-6)
def test_sum_concatenate_sums_random(backend):
from csdl.examples.valid.ex_sum_concatenate_random import example
exec('from {} import Simulator'.format(backend))
sim = example(eval('Simulator'))
np.random.seed(0)
x = np.array(
[np.sum(np.random.rand(5)),
np.sum(np.random.rand(4)), 0])
np.testing.assert_almost_equal(sim['sum_vector'], x)
np.testing.assert_almost_equal(
sim['single_vector_sum_1a'],
sim['single_vector_sum_1a'],
)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
out_stream=None, compact_print=True, method='fd')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-8,
rtol=1.e-8)
partials_error_multiple_matrix_axis_1 = sim.check_partials(
out_stream=None, compact_print=True, method='cs')
sim.assert_check_partials(partials_error_multiple_matrix_axis_1,
atol=1.e-8,
rtol=1.e-8)
| 32.181063 | 80 | 0.619625 | 2,598 | 19,373 | 4.295612 | 0.032333 | 0.048387 | 0.008602 | 0.045161 | 0.989606 | 0.984319 | 0.980735 | 0.97966 | 0.970699 | 0.932168 | 0 | 0.022788 | 0.270634 | 19,373 | 601 | 81 | 32.234609 | 0.767021 | 0 | 0 | 0.871397 | 0 | 0 | 0.110773 | 0.042946 | 0 | 0 | 0 | 0 | 0.152993 | 1 | 0.04878 | false | 0 | 0.104213 | 0 | 0.152993 | 0.053215 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
bf3fdcafc963b2ca5971b7949406e4d75bd95b54 | 3,918 | py | Python | state_test.py | echo8/editor | 8f8a0dafe22792cd9885ebd0406205de020891bc | [
"MIT"
] | null | null | null | state_test.py | echo8/editor | 8f8a0dafe22792cd9885ebd0406205de020891bc | [
"MIT"
] | null | null | null | state_test.py | echo8/editor | 8f8a0dafe22792cd9885ebd0406205de020891bc | [
"MIT"
] | null | null | null | # coding=utf-8
import unittest
import sdl2.ext
from state import *
class TextBufferMock:
def __init__(self):
self.changed = False
class TextAreaMock:
def __init__(self):
self.text_buffer = TextBufferMock()
def draw(self):
pass
def handle_input(self, events):
pass
class StatusBarMock:
def __init__(self):
pass
def draw(self):
pass
def init_input(self, label, text=None):
pass
def draw_label(self):
pass
def draw_input(self):
pass
def handle_input(self, events):
pass
def display_msg(self, msg):
pass
class EventMock:
def __init__(self):
pass
def get_key_down_event(sym):
e = EventMock()
e.type = sdl2.SDL_KEYDOWN
e.key = EventMock()
e.key.keysym = EventMock()
e.key.keysym.sym = sym
return e
class StateTestCase(unittest.TestCase):
def setUp(self):
self.state = EditState(Editor(None, None, TextAreaMock(), StatusBarMock()))
self.state.update_only = True
class EditTestCases(StateTestCase):
def test_quit(self):
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_q)])
self.assertTrue(isinstance(res, QuitState))
res = self.state.update([get_key_down_event(sdl2.SDLK_RCTRL), get_key_down_event(sdl2.SDLK_q)])
self.assertTrue(isinstance(res, QuitState))
def test_new(self):
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_n)])
self.assertTrue(isinstance(res, NewState))
res = self.state.update([get_key_down_event(sdl2.SDLK_RCTRL), get_key_down_event(sdl2.SDLK_n)])
self.assertTrue(isinstance(res, NewState))
def test_open(self):
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_o)])
self.assertTrue(isinstance(res, OpenState))
res = self.state.update([get_key_down_event(sdl2.SDLK_RCTRL), get_key_down_event(sdl2.SDLK_o)])
self.assertTrue(isinstance(res, OpenState))
def test_save(self):
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_s)])
self.assertTrue(isinstance(res, SaveState))
res = self.state.update([get_key_down_event(sdl2.SDLK_RCTRL), get_key_down_event(sdl2.SDLK_s)])
self.assertTrue(isinstance(res, SaveState))
def test_save_as(self):
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_LSHIFT),
get_key_down_event(sdl2.SDLK_s)])
self.assertTrue(isinstance(res, SaveState))
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_RSHIFT),
get_key_down_event(sdl2.SDLK_s)])
self.assertTrue(isinstance(res, SaveState))
res = self.state.update([get_key_down_event(sdl2.SDLK_RCTRL), get_key_down_event(sdl2.SDLK_LSHIFT),
get_key_down_event(sdl2.SDLK_s)])
self.assertTrue(isinstance(res, SaveState))
res = self.state.update([get_key_down_event(sdl2.SDLK_RCTRL), get_key_down_event(sdl2.SDLK_RSHIFT),
get_key_down_event(sdl2.SDLK_s)])
self.assertTrue(isinstance(res, SaveState))
def test_quit_with_changes(self):
self.state.editor.text_area.text_buffer.changed = True
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_q)])
self.assertTrue(isinstance(res, SaveChangesState))
def test_open_with_changes(self):
self.state.editor.text_area.text_buffer.changed = True
res = self.state.update([get_key_down_event(sdl2.SDLK_LCTRL), get_key_down_event(sdl2.SDLK_o)])
self.assertTrue(isinstance(res, SaveChangesState))
| 33.775862 | 107 | 0.679173 | 535 | 3,918 | 4.652336 | 0.142056 | 0.07955 | 0.132583 | 0.198875 | 0.760948 | 0.724789 | 0.724789 | 0.724789 | 0.695862 | 0.695862 | 0 | 0.011309 | 0.210056 | 3,918 | 115 | 108 | 34.069565 | 0.792892 | 0.003063 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.166667 | 1 | 0.25 | false | 0.119048 | 0.035714 | 0 | 0.369048 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
bf4bd88a4e79f7896be0d44f71a1f161648c5472 | 4,365 | py | Python | pydory_getPD.py | nihcompmed/Dory | eda581666d13f1536d7e21c78277fb7f29aaee42 | [
"MIT"
] | 1 | 2021-04-27T09:13:52.000Z | 2021-04-27T09:13:52.000Z | pydory_getPD.py | nihcompmed/Dory | eda581666d13f1536d7e21c78277fb7f29aaee42 | [
"MIT"
] | null | null | null | pydory_getPD.py | nihcompmed/Dory | eda581666d13f1536d7e21c78277fb7f29aaee42 | [
"MIT"
] | 1 | 2021-12-15T19:18:11.000Z | 2021-12-15T19:18:11.000Z | import pydory as dory
import sys
import time
main_dirr = 'Datasets/'
compute_cycles = 0
reduce_cyc_lengths = 0
suppress_output = 1
threads = 4
lower_thresh = 0
# Filetype details
# Accepts comma-separated files
#
# 0: Distance square matrix
# 1: Point-cloud (locations)
# 2: List of edges in the format --- v1, v2, edge length
# dim = 1 for up to and including H1
# dim = 2 for up to and including H2
#################################
## dragon (point-cloud, H1)
#################################
dataset = 'Dragon'
dirr = main_dirr+dataset+'/'
source = dirr+'dragon2000_locs.csv'
target = dirr + 'Dory'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 1000
filetype = 1
dim = 1
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ':', time.time() - start)
#################################
#################################
## fract (distance matrix, H2)
#################################
dataset = 'fract'
dirr = main_dirr+dataset+'/'
source = dirr+'fractal_r_distmat.csv'
target = dirr + 'Dory'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 1000
filetype = 0
dim = 2
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ':', time.time() - start)
#################################
## o3 (point-cloud, H2)
#################################
dataset = 'o3'
dirr = main_dirr+dataset+'/'
source = dirr+'o3_8192.csv'
target = dirr + 'Dory'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 1
filetype = 1
dim = 2
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ':', time.time() - start)
#################################
## torus4 (point-cloud, H1)
#################################
dataset = 'torus4'
dirr = main_dirr+dataset+'/'
source = dirr+'torus4_locs.csv'
target = dirr + 'Dory'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 0.15
filetype = 1
dim = 1
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ' (H1):', time.time() - start)
#################################
## torus4 (point-cloud, H2)
#################################
dataset = 'torus4'
dirr = main_dirr+dataset+'/'
source = dirr+'torus4_locs.csv'
target = dirr + 'Dory'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 0.15
filetype = 1
dim = 2
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ' (H2):', time.time() - start)
#################################
## HiC control (list of edges, H2)
#################################
dataset = 'HiC'
dirr = main_dirr+dataset+'/'
source = dirr+'control_500.csv'
target = dirr + 'Dory_control_'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 400
filetype = 2
dim = 2
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ':', time.time() - start)
#################################
## HiC auxin (list of edges, H2)
#################################
dataset = 'HiC'
dirr = main_dirr+dataset+'/'
source = dirr+'auxin_500.csv'
target = dirr + 'Dory_auxin_'
print('#########################')
print('Processing', dataset)
print('#########################')
thresh = 400
filetype = 2
dim = 2
start = time.time()
dory.compute_PH(source, lower_thresh, thresh, filetype, threads, target, dim, compute_cycles, reduce_cyc_lengths, thresh, suppress_output)
print('Time taken for', dataset, ':', time.time() - start)
| 24.385475 | 138 | 0.565865 | 491 | 4,365 | 4.894094 | 0.160896 | 0.046608 | 0.053267 | 0.055347 | 0.819809 | 0.787349 | 0.732418 | 0.732418 | 0.732418 | 0.732418 | 0 | 0.021061 | 0.1189 | 4,365 | 178 | 139 | 24.522472 | 0.603744 | 0.093929 | 0 | 0.77 | 0 | 0 | 0.213912 | 0.107974 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.03 | 0 | 0.03 | 0.28 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
bf9d99234396eff7867f303adb6b58306eb85bd1 | 126 | py | Python | ipuz/structures/cluenum.py | maiamcc/ipuz | fbe6f663b28ad42754622bf2d3bbe59a26be2615 | [
"MIT"
] | null | null | null | ipuz/structures/cluenum.py | maiamcc/ipuz | fbe6f663b28ad42754622bf2d3bbe59a26be2615 | [
"MIT"
] | null | null | null | ipuz/structures/cluenum.py | maiamcc/ipuz | fbe6f663b28ad42754622bf2d3bbe59a26be2615 | [
"MIT"
] | null | null | null | import six
def validate_cluenum(field_data):
return type(field_data) is int or isinstance(field_data, six.string_types)
| 21 | 78 | 0.793651 | 20 | 126 | 4.75 | 0.75 | 0.284211 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.134921 | 126 | 5 | 79 | 25.2 | 0.87156 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 7 |
44d24ea1690ad2fd6e306dd6d105fb82a748b6c6 | 68,638 | py | Python | benchmarks/SimResults/Paper2_pinned_spec_base/cmp_tontoh264reflbmomnetpp/power.py | TugberkArkose/MLScheduler | e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061 | [
"Unlicense"
] | null | null | null | benchmarks/SimResults/Paper2_pinned_spec_base/cmp_tontoh264reflbmomnetpp/power.py | TugberkArkose/MLScheduler | e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061 | [
"Unlicense"
] | null | null | null | benchmarks/SimResults/Paper2_pinned_spec_base/cmp_tontoh264reflbmomnetpp/power.py | TugberkArkose/MLScheduler | e493b6cbf7b9d29a2c9300d7dd6f0c2f102e4061 | [
"Unlicense"
] | null | null | null | power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate Leakage': 0.00662954,
'Peak Dynamic': 0.0,
'Runtime Dynamic': 0.0,
'Subthreshold Leakage': 0.0691322,
'Subthreshold Leakage with power gating': 0.0259246},
'Core': [{'Area': 32.6082,
'Execution Unit/Area': 8.2042,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 6.51836e-05,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.20274,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.000427576,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.122718,
'Execution Unit/Instruction Scheduler/Area': 2.17927,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.577531,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 1.00008,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.573571,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 2.15118,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.570801,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 6.01424,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 8.07783e-05,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.020936,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.151415,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.154834,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.151496,
'Execution Unit/Register Files/Runtime Dynamic': 0.17577,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.365898,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 1.1494,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155,
'Execution Unit/Runtime Dynamic': 4.08446,
'Execution Unit/Subthreshold Leakage': 1.83518,
'Execution Unit/Subthreshold Leakage with power gating': 0.709678,
'Gate Leakage': 0.372997,
'Instruction Fetch Unit/Area': 5.86007,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00227979,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00227979,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00197937,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000762789,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00222421,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00876316,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0220843,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0590479,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.148846,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.43323,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.407138,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.505548,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 8.96874,
'Instruction Fetch Unit/Runtime Dynamic': 1.09238,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932587,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0268003,
'L2/Runtime Dynamic': 0.00888509,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80969,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 6.7184,
'Load Store Unit/Data Cache/Runtime Dynamic': 2.64438,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0351387,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.177332,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.177332,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 7.55921,
'Load Store Unit/Runtime Dynamic': 3.69626,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.437271,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.874543,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591622,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283406,
'Memory Management Unit/Area': 0.434579,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.155189,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.155556,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00813591,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.399995,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0668479,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.826786,
'Memory Management Unit/Runtime Dynamic': 0.222404,
'Memory Management Unit/Subthreshold Leakage': 0.0769113,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462,
'Peak Dynamic': 27.9575,
'Renaming Unit/Area': 0.369768,
'Renaming Unit/FP Front End RAT/Area': 0.168486,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.000281584,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925,
'Renaming Unit/Free List/Area': 0.0414755,
'Renaming Unit/Free List/Gate Leakage': 4.15911e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0401324,
'Renaming Unit/Free List/Runtime Dynamic': 0.0295351,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987,
'Renaming Unit/Gate Leakage': 0.00863632,
'Renaming Unit/Int Front End RAT/Area': 0.114751,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.312385,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781,
'Renaming Unit/Peak Dynamic': 4.56169,
'Renaming Unit/Runtime Dynamic': 0.342202,
'Renaming Unit/Subthreshold Leakage': 0.070483,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779,
'Runtime Dynamic': 9.44659,
'Subthreshold Leakage': 6.21877,
'Subthreshold Leakage with power gating': 2.58311},
{'Area': 32.0201,
'Execution Unit/Area': 7.68434,
'Execution Unit/Complex ALUs/Area': 0.235435,
'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646,
'Execution Unit/Complex ALUs/Peak Dynamic': 0.0602325,
'Execution Unit/Complex ALUs/Runtime Dynamic': 0.249998,
'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111,
'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163,
'Execution Unit/Floating Point Units/Area': 4.6585,
'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156,
'Execution Unit/Floating Point Units/Peak Dynamic': 0.275432,
'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033,
'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829,
'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061,
'Execution Unit/Gate Leakage': 0.120359,
'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.214636,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.346199,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.17475,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.735585,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.203254,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 4.73451,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.0520351,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00900278,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0896783,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0665811,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.141713,
'Execution Unit/Register Files/Runtime Dynamic': 0.0755839,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.203988,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.53552,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 2.00206,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00139655,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00139655,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
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'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000956443,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00501337,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0116953,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0640061,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.07134,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.181097,
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'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.217394,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
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'Instruction Fetch Unit/Peak Dynamic': 6.48745,
'Instruction Fetch Unit/Runtime Dynamic': 0.479205,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
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'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0155427,
'L2/Runtime Dynamic': 0.00374887,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
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'Load Store Unit/Data Cache/Area': 6.84535,
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'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
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'Load Store Unit/LoadQ/Peak Dynamic': 0.0771939,
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'Load Store Unit/Peak Dynamic': 3.98768,
'Load Store Unit/Runtime Dynamic': 1.61132,
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'Load Store Unit/Subthreshold Leakage': 0.591321,
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'Memory Management Unit/Gate Leakage': 0.00808595,
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'Memory Management Unit/Peak Dynamic': 0.525297,
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'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Renaming Unit/Peak Dynamic': 3.58947,
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'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
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'Execution Unit/Area': 7.68434,
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Gate Leakage': 0.120359,
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'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
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'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
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'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
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'Execution Unit/Peak Dynamic': 4.8251,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.104067,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00509376,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
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'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.060677,
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'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.164744,
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'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
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'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
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'Execution Unit/Runtime Dynamic': 1.46595,
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'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
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'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 2.50205e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
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'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000541153,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00075729,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.000739173,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0362145,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.30355,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0726214,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.123001,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 4.63386,
'Instruction Fetch Unit/Runtime Dynamic': 0.233333,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.089648,
'L2/Runtime Dynamic': 0.0548592,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 2.04714,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.521589,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0262061,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0262061,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 2.17089,
'Load Store Unit/Runtime Dynamic': 0.677035,
'Load Store Unit/StoreQ/Area': 0.322079,
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'Load Store Unit/StoreQ/Peak Dynamic': 0.0646198,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.129239,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
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'Memory Management Unit/Gate Leakage': 0.00808595,
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'Renaming Unit/FP Front End RAT/Area': 0.131045,
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'Renaming Unit/Free List/Area': 0.0340654,
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'Renaming Unit/Int Front End RAT/Area': 0.0941223,
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'Runtime Dynamic': 2.80515,
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'Subthreshold Leakage with power gating': 2.55328},
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'Execution Unit/Floating Point Units/Area': 4.6585,
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'Execution Unit/Instruction Scheduler/Area': 1.66526,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.0580421,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453,
'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519,
'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913,
'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223,
'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562,
'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763,
'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.0936197,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755,
'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964,
'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262,
'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388,
'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608,
'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451,
'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0472561,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853,
'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446,
'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.198918,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892,
'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346,
'Execution Unit/Integer ALUs/Area': 0.47087,
'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291,
'Execution Unit/Integer ALUs/Peak Dynamic': 0.0662712,
'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344,
'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222,
'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833,
'Execution Unit/Peak Dynamic': 3.95341,
'Execution Unit/Register Files/Area': 0.570804,
'Execution Unit/Register Files/Floating Point RF/Area': 0.208131,
'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788,
'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.00013718,
'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00243454,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698,
'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968,
'Execution Unit/Register Files/Gate Leakage': 0.000622708,
'Execution Unit/Register Files/Integer RF/Area': 0.362673,
'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992,
'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.017606,
'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0180049,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175,
'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675,
'Execution Unit/Register Files/Peak Dynamic': 0.0177432,
'Execution Unit/Register Files/Runtime Dynamic': 0.0204395,
'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387,
'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643,
'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912,
'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402,
'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0371038,
'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.0973803,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478,
'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543,
'Execution Unit/Runtime Dynamic': 0.924844,
'Execution Unit/Subthreshold Leakage': 1.79543,
'Execution Unit/Subthreshold Leakage with power gating': 0.688821,
'Gate Leakage': 0.368936,
'Instruction Fetch Unit/Area': 5.85939,
'Instruction Fetch Unit/Branch Predictor/Area': 0.138516,
'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000845929,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000845929,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719,
'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.0007604,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344,
'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000307269,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347,
'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045,
'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838,
'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732,
'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05,
'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602,
'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000258642,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505,
'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733,
'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.0027109,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703,
'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282,
'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954,
'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758,
'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867,
'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0072676,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682,
'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357,
'Instruction Fetch Unit/Gate Leakage': 0.0589979,
'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323,
'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05,
'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827,
'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0173086,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885,
'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682,
'Instruction Fetch Unit/Instruction Cache/Area': 3.14635,
'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931,
'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.10098,
'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0680732,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022,
'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386,
'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799,
'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493,
'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404,
'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.0587878,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943,
'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104,
'Instruction Fetch Unit/Peak Dynamic': 3.37292,
'Instruction Fetch Unit/Runtime Dynamic': 0.154148,
'Instruction Fetch Unit/Subthreshold Leakage': 0.932286,
'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843,
'L2/Area': 4.53318,
'L2/Gate Leakage': 0.015464,
'L2/Peak Dynamic': 0.0280726,
'L2/Runtime Dynamic': 0.00871061,
'L2/Subthreshold Leakage': 0.834142,
'L2/Subthreshold Leakage with power gating': 0.401066,
'Load Store Unit/Area': 8.80901,
'Load Store Unit/Data Cache/Area': 6.84535,
'Load Store Unit/Data Cache/Gate Leakage': 0.0279261,
'Load Store Unit/Data Cache/Peak Dynamic': 1.85339,
'Load Store Unit/Data Cache/Runtime Dynamic': 0.30892,
'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675,
'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085,
'Load Store Unit/Gate Leakage': 0.0350888,
'Load Store Unit/LoadQ/Area': 0.0836782,
'Load Store Unit/LoadQ/Gate Leakage': 0.00059896,
'Load Store Unit/LoadQ/Peak Dynamic': 0.0199378,
'Load Store Unit/LoadQ/Runtime Dynamic': 0.0199379,
'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961,
'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918,
'Load Store Unit/Peak Dynamic': 1.94754,
'Load Store Unit/Runtime Dynamic': 0.427185,
'Load Store Unit/StoreQ/Area': 0.322079,
'Load Store Unit/StoreQ/Gate Leakage': 0.00329971,
'Load Store Unit/StoreQ/Peak Dynamic': 0.0491633,
'Load Store Unit/StoreQ/Runtime Dynamic': 0.0983271,
'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621,
'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004,
'Load Store Unit/Subthreshold Leakage': 0.591321,
'Load Store Unit/Subthreshold Leakage with power gating': 0.283293,
'Memory Management Unit/Area': 0.4339,
'Memory Management Unit/Dtlb/Area': 0.0879726,
'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729,
'Memory Management Unit/Dtlb/Peak Dynamic': 0.0174482,
'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0178557,
'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699,
'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485,
'Memory Management Unit/Gate Leakage': 0.00808595,
'Memory Management Unit/Itlb/Area': 0.301552,
'Memory Management Unit/Itlb/Gate Leakage': 0.00393464,
'Memory Management Unit/Itlb/Peak Dynamic': 0.0684547,
'Memory Management Unit/Itlb/Runtime Dynamic': 0.0112016,
'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758,
'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842,
'Memory Management Unit/Peak Dynamic': 0.254536,
'Memory Management Unit/Runtime Dynamic': 0.0290573,
'Memory Management Unit/Subthreshold Leakage': 0.0766103,
'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333,
'Peak Dynamic': 13.146,
'Renaming Unit/Area': 0.303608,
'Renaming Unit/FP Front End RAT/Area': 0.131045,
'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123,
'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468,
'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.000360828,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571,
'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885,
'Renaming Unit/Free List/Area': 0.0340654,
'Renaming Unit/Free List/Gate Leakage': 2.5481e-05,
'Renaming Unit/Free List/Peak Dynamic': 0.0306032,
'Renaming Unit/Free List/Runtime Dynamic': 0.00262309,
'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144,
'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064,
'Renaming Unit/Gate Leakage': 0.00708398,
'Renaming Unit/Int Front End RAT/Area': 0.0941223,
'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242,
'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965,
'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0294031,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488,
'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228,
'Renaming Unit/Peak Dynamic': 3.58947,
'Renaming Unit/Runtime Dynamic': 0.0323871,
'Renaming Unit/Subthreshold Leakage': 0.0552466,
'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461,
'Runtime Dynamic': 1.57633,
'Subthreshold Leakage': 6.16288,
'Subthreshold Leakage with power gating': 2.55328}],
'DRAM': {'Area': 0,
'Gate Leakage': 0,
'Peak Dynamic': 6.111274730646621,
'Runtime Dynamic': 6.111274730646621,
'Subthreshold Leakage': 4.252,
'Subthreshold Leakage with power gating': 4.252},
'L3': [{'Area': 61.9075,
'Gate Leakage': 0.0484137,
'Peak Dynamic': 0.211788,
'Runtime Dynamic': 0.1455,
'Subthreshold Leakage': 6.80085,
'Subthreshold Leakage with power gating': 3.32364}],
'Processor': {'Area': 191.908,
'Gate Leakage': 1.53485,
'Peak Dynamic': 76.3029,
'Peak Power': 109.415,
'Runtime Dynamic': 18.4235,
'Subthreshold Leakage': 31.5774,
'Subthreshold Leakage with power gating': 13.9484,
'Total Cores/Area': 128.669,
'Total Cores/Gate Leakage': 1.4798,
'Total Cores/Peak Dynamic': 76.0911,
'Total Cores/Runtime Dynamic': 18.278,
'Total Cores/Subthreshold Leakage': 24.7074,
'Total Cores/Subthreshold Leakage with power gating': 10.2429,
'Total L3s/Area': 61.9075,
'Total L3s/Gate Leakage': 0.0484137,
'Total L3s/Peak Dynamic': 0.211788,
'Total L3s/Runtime Dynamic': 0.1455,
'Total L3s/Subthreshold Leakage': 6.80085,
'Total L3s/Subthreshold Leakage with power gating': 3.32364,
'Total Leakage': 33.1122,
'Total NoCs/Area': 1.33155,
'Total NoCs/Gate Leakage': 0.00662954,
'Total NoCs/Peak Dynamic': 0.0,
'Total NoCs/Runtime Dynamic': 0.0,
'Total NoCs/Subthreshold Leakage': 0.0691322,
'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}} | 75.09628 | 124 | 0.682115 | 8,089 | 68,638 | 5.78205 | 0.067623 | 0.123495 | 0.11289 | 0.093391 | 0.938659 | 0.929486 | 0.917406 | 0.886254 | 0.860704 | 0.841804 | 0 | 0.132158 | 0.224234 | 68,638 | 914 | 125 | 75.09628 | 0.74622 | 0 | 0 | 0.642232 | 0 | 0 | 0.657134 | 0.048078 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
44fc017a24deb7cbb48547ef8f6c27a0edef3be8 | 17,508 | py | Python | app.py | shen92/USC-Films-Flask | 323912efb5f316f4d51e51cbed9ce1ccc28afef7 | [
"MIT"
] | null | null | null | app.py | shen92/USC-Films-Flask | 323912efb5f316f4d51e51cbed9ce1ccc28afef7 | [
"MIT"
] | 1 | 2021-07-04T23:24:41.000Z | 2021-07-04T23:24:41.000Z | app.py | shen92/USC-Films-Flask | 323912efb5f316f4d51e51cbed9ce1ccc28afef7 | [
"MIT"
] | null | null | null | from flask import Flask, request
from flask import jsonify
from flask_cors import CORS
import requests
from string import Template
import json
app = Flask(__name__, static_url_path='', static_folder='static')
CORS(app)
BASE_URL = "https://api.themoviedb.org/3"
API_KEY = 'ffacc501334b9c13b0136b785a4a2d81'
NUM_RESULTS = 10
NUM_CASTS = 8
NUM_COMMENTS = 5
@app.route('/', methods=['GET'])
@app.route('/index.html', methods=['GET'])
def get_landing_page():
return app.send_static_file("index.html")
#2.1.1 TMDB Trending Endpoint
@app.route('/home/movie', methods=['GET'])
def get_home_movies():
url = Template('$base_url/trending/movie/week?api_key=$api_key').substitute(base_url=BASE_URL, api_key=API_KEY)
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
results = results[0:5]
response = []
for result in results:
movie = {
"title": result["title"] if "title" in result else None,
"backdrop_path": result["backdrop_path"] if "backdrop_path" in result else None,
"release_date": result["release_date"] if "release_date" in result else None,
"media_type": "movie" #Additional field
}
response.append(movie)
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.1.2 TMDB TV Airing Today Endpoint
@app.route('/home/tv', methods=['GET'])
def get_home_tv_shows():
url = Template('$base_url/tv/airing_today?api_key=$api_key').substitute(base_url=BASE_URL, api_key=API_KEY)
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
results = results[0:5]
response = []
for result in results:
tvshow = {
"name": result["name"] if "name" in result else None,
"backdrop_path": result["backdrop_path"] if "backdrop_path" in result else None,
"first_air_date": result["first_air_date"] if "first_air_date" in result else None,
"media_type": "tv" #Additional field
}
response.append(tvshow)
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.3.1 Search Movie Endpoint
@app.route('/search/movie', methods=['GET'])
def get_search_movies():
params = request.args
url = Template('$base_url/search/movie?api_key=$api_key&query=$keyword&language=en-US&page=1&include_adult=false').substitute(base_url=BASE_URL, api_key=API_KEY,keyword=params["keyword"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
results = results[0:NUM_RESULTS]
response = []
for result in results:
movie = {
"id": result["id"] if "id" in result else None,
"title": result["title"] if "title" in result else None,
"overview": result["overview"] if "overview" in result else None,
"poster_path": result["poster_path"] if "poster_path" in result else None,
"release_date": result["release_date"] if "release_date" in result else None,
"vote_average": result["vote_average"] if "vote_average" in result else None,
"vote_count": result["vote_count"] if "vote_count" in result else None,
"genre_ids": result["genre_ids"] if "genre_ids" in result else None,
"media_type": "movie" #Additional field
}
response.append(movie)
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.3.2 Search TV Endpoint
@app.route('/search/tv', methods=['GET'])
def get_search_tv_shows():
params = request.args
url = Template('$base_url/search/tv?api_key=$api_key&query=$keyword&language=en-US&page=1&include_adult=false').substitute(base_url=BASE_URL, api_key=API_KEY,keyword=params["keyword"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
results = results[0:NUM_RESULTS]
response = []
for result in results:
tvshow = {
"id": result["id"] if "id" in result else None,
"name": result["name"] if "name" in result else None,
"overview": result["overview"] if "overview" in result else None,
"poster_path": result["poster_path"] if "poster_path" in result else None,
"first_air_date": result["first_air_date"] if "first_air_date" in result else None,
"vote_average": result["vote_average"] if "vote_average" in result else None,
"vote_count": result["vote_count"] if "vote_count" in result else None,
"genre_ids": result["genre_ids"] if "genre_ids" in result else None,
"media_type": "tv" #Additional field
}
response.append(tvshow)
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.3.3 Multi-Search Endpoint
@app.route('/search/multi', methods=['GET'])
def get_search_multi():
params = request.args
url = Template('$base_url/search/multi?api_key=$api_key&query=$keyword&language=en-US&page=1&include_adult=false').substitute(base_url=BASE_URL, api_key=API_KEY,keyword=params["keyword"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
response = []
for result in results:
if("media_type" in result and result["media_type"] == "movie"):
data = {
"id": result["id"] if "id" in result else None,
"title": result["title"] if "title" in result else None,
"overview": result["overview"] if "overview" in result else None,
"poster_path": result["poster_path"] if "poster_path" in result else None,
"release_date": result["release_date"] if "release_date" in result else None,
"vote_average": result["vote_average"] if "vote_average" in result else None,
"vote_count": result["vote_count"] if "vote_count" in result else None,
"genre_ids": result["genre_ids"] if "genre_ids" in result else None,
"media_type": "movie" #Additional field
}
response.append(data)
elif("media_type" in result and result["media_type"] == "tv"):
data = {
"id": result["id"] if "id" in result else None,
"name": result["name"] if "name" in result else None,
"overview": result["overview"] if "overview" in result else None,
"poster_path": result["poster_path"] if "poster_path" in result else None,
"first_air_date": result["first_air_date"] if "first_air_date" in result else None,
"vote_average": result["vote_average"] if "vote_average" in result else None,
"vote_count": result["vote_count"] if "vote_count" in result else None,
"genre_ids": result["genre_ids"] if "genre_ids" in result else None,
"media_type": "tv" #Additional field
}
response.append(data)
response = response[0:NUM_RESULTS]
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.5.1 TMDB Movie Genres Endpoint
@app.route('/genres/movie', methods=['GET'])
def get_movie_genres():
url = Template('$base_url/genre/movie/list?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY)
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
response = results["genres"] if "genres" in results else []
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.5.2 TMDB TV Genres Endpoint
@app.route('/genres/tv', methods=['GET'])
def get_tv_show_genres():
url = Template('$base_url/genre/tv/list?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY)
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
response = results["genres"] if "genres" in results else []
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.4.1 Get Movie Details Endpoint
@app.route('/details/movie', methods=['GET'])
def get_movie_details():
params = request.args
url = Template('$base_url/movie/$id?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY,id=params["id"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
result = api_response.json()
data = {
"id": result["id"] if "id" in result else None,
"title": result["title"] if "title" in result else None,
"runtime": result["runtime"] if "runtime" in result else None,
"release_date": result["release_date"] if "release_date" in result else None,
"spoken_languages": result["spoken_languages"] if "spoken_languages" in result else None,
"vote_average": result["vote_average"] if "vote_average" in result else None,
"vote_count": result["vote_count"] if "vote_count" in result else None,
"poster_path": result["poster_path"] if "poster_path" in result else None,
"backdrop_path": result["backdrop_path"] if "backdrop_path" in result else None,
"genres": result["genres"] if "genres" in result else None,
"overview": result["overview"] if "overview" in result else None, #Additional field
"media_type": "movie" #Additional field
}
return jsonify({"data": data})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.4.4 Get TV Show Details Endpoint
@app.route('/details/tv', methods=['GET'])
def get_tv_details():
params = request.args
url = Template('$base_url/tv/$id?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY,id=params["id"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
result = api_response.json()
data = {
"backdrop_path": result["backdrop_path"] if "backdrop_path" in result else None,
"episode_run_time": result["episode_run_time"] if "episode_run_time" in result else None,
"first_air_date": result["first_air_date"] if "first_air_date" in result else None,
"genres": result["genres"] if "genres" in result else None,
"id": result["id"] if "id" in result else None,
"name": result["name"] if "name" in result else None,
"number_of_seasons": result["number_of_seasons"] if "number_of_seasons" in result else None,
"overview": result["overview"] if "overview" in result else None,
"poster_path": result["poster_path"] if "poster_path" in result else None,
"spoken languages": result["spoken languages"] if "spoken languages" in result else None,
"vote_average": result["vote_average"] if "vote_average" in result else None,
"vote_count": result["vote_count"] if "vote_count" in result else None, #Additional field
"overview": result["overview"] if "overview" in result else None, #Additional field
"media_type": "tv"
}
return jsonify({"data": data})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.4.2 Get Movie Credits Endpoint
@app.route('/cast/movie', methods=['GET'])
def get_movie_cast():
params = request.args
url = Template('$base_url/movie/$id/credits?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY,id=params["id"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["cast"] if "cast" in results else []
results = results[0:NUM_CASTS]
response = []
for result in results:
data = {
"name": result["name"] if "name" in result else None,
"profile_path": result["profile_path"] if "profile_path" in result else None,
"character": result["character"] if "character" in result else None,
}
response.append(data)
return jsonify({"data": results})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.4.5 Get TV Show Credits Endpoint
@app.route('/cast/tv', methods=['GET'])
def get_tv_cast():
params = request.args
url = Template('$base_url/tv/$id/credits?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY,id=params["id"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["cast"] if "cast" in results else []
results = results[0:NUM_CASTS]
response = []
for result in results:
data = {
"name": result["name"] if "name" in result else None,
"profile_path": result["profile_path"] if "profile_path" in result else None,
"character": result["character"] if "character" in result else None,
}
response.append(data)
return jsonify({"data": results})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.4.3 Get Movie Reviews Endpoint
@app.route('/reviews/movie', methods=['GET'])
def get_movie_reviews():
params = request.args
url = Template('$base_url/movie/$id/reviews?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY,id=params["id"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
results = results[0:NUM_COMMENTS]
response = []
for result in results:
author_details = result["author_details"] if "author_details" in result else None
data = {
"username": author_details["username"] if author_details is not None and "username" in author_details else None,
"content": result["content"] if "content" in result else None,
"rating": author_details["rating"] if author_details is not None and "rating" in author_details else None,
"created_at": result["created_at"] if "created_at" in result else None,
}
response.append(data)
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
#2.4.6 Get TV Show Reviews Endpoint
@app.route('/reviews/tv', methods=['GET'])
def get_tv_reviews():
params = request.args
url = Template('$base_url/tv/$id/reviews?api_key=$api_key&language=en-US').substitute(base_url=BASE_URL, api_key=API_KEY,id=params["id"])
api_response = requests.get(url)
if api_response.status_code == 200:
#Extract response
results = api_response.json()
results = results["results"] if "results" in results else []
results = results[0:NUM_COMMENTS]
response = []
for result in results:
author_details = result["author_details"] if "author_details" in result else None
data = {
"username": author_details["username"] if author_details is not None and "username" in author_details else None,
"content": result["content"] if "content" in result else None,
"rating": author_details["rating"] if author_details is not None and "rating" in author_details else None,
"created_at": result["created_at"] if "created_at" in result else None,
}
response.append(data)
return jsonify({"data": response})
else:
response = {"message": "Unknown error occurred."}
return jsonify(response)
if __name__ == '__main__':
app.run() | 48.364641 | 191 | 0.622801 | 2,221 | 17,508 | 4.738856 | 0.061234 | 0.059287 | 0.084371 | 0.112494 | 0.913919 | 0.873634 | 0.847126 | 0.847126 | 0.825368 | 0.799335 | 0 | 0.008931 | 0.251714 | 17,508 | 362 | 192 | 48.364641 | 0.794443 | 0.043866 | 0 | 0.754717 | 0 | 0.009434 | 0.23927 | 0.050703 | 0 | 0 | 0 | 0 | 0 | 1 | 0.044025 | false | 0 | 0.018868 | 0.003145 | 0.147799 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
78394e64ecab39941dcf970b247a9ac7ee2850be | 125 | py | Python | src/hub/dataload/sources/dbsnp/__init__.py | erikyao/myvariant.info | a4eaaca7ab6c069199f8942d5afae2dece908147 | [
"Apache-2.0"
] | 39 | 2017-07-01T22:34:39.000Z | 2022-03-15T22:25:59.000Z | src/hub/dataload/sources/dbsnp/__init__.py | erikyao/myvariant.info | a4eaaca7ab6c069199f8942d5afae2dece908147 | [
"Apache-2.0"
] | 105 | 2017-06-28T17:26:06.000Z | 2022-03-17T17:49:53.000Z | src/hub/dataload/sources/dbsnp/__init__.py | erikyao/myvariant.info | a4eaaca7ab6c069199f8942d5afae2dece908147 | [
"Apache-2.0"
] | 15 | 2015-10-15T20:46:50.000Z | 2021-07-12T19:17:49.000Z | from .dbsnp_dump import DBSNPDumper
from .dbsnp_upload import DBSNPHg19Uploader
from .dbsnp_upload import DBSNPHg38Uploader
| 25 | 43 | 0.872 | 15 | 125 | 7.066667 | 0.533333 | 0.254717 | 0.283019 | 0.396226 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.035714 | 0.104 | 125 | 4 | 44 | 31.25 | 0.910714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
7857545c95584fdb66e6de7218d08c5db2b9be23 | 2,298 | py | Python | npcl/solvers/deconv.py | ncianeo/numpycl | d66c26e91ef4d9c0a452e189ea8ad6d4713ec211 | [
"MIT"
] | 2 | 2020-07-24T09:27:53.000Z | 2021-02-04T07:34:32.000Z | npcl/solvers/deconv.py | ncianeo/numpycl | d66c26e91ef4d9c0a452e189ea8ad6d4713ec211 | [
"MIT"
] | null | null | null | npcl/solvers/deconv.py | ncianeo/numpycl | d66c26e91ef4d9c0a452e189ea8ad6d4713ec211 | [
"MIT"
] | 1 | 2021-02-04T07:34:35.000Z | 2021-02-04T07:34:35.000Z | import numpy as np
from npcl.ops.convolve import convolve2d, transpose2d
from npcl.ops.convolve import convolve2d_sv, transpose2d_sv
from npcl.regularizers.local import denoise_tv
from .fbs import solve_fbs
from .fista import solve_fista
def deconv_fbs(
img_blurry, psf, kernel=None, denoiser=denoise_tv,
mu=np.float32(1e-3), tol=np.float32(1e-4), delta=np.float32(1.5),
max_iter=50, verbose=False,
):
"""
Deconvolution with Forward-Backward Splitting Method.
"""
x_0 = img_blurry.copy()
if kernel is None:
kernel = transpose2d(psf)
mu = np.float32(mu)
delta = np.float32(delta)
tol = np.float32(tol)
ATb = convolve2d(x_0, psf)
def ATA(x):
return convolve2d(convolve2d(x, kernel), psf)
ProxR = denoiser
x, k = solve_fbs(
ATA, ATb, x_0, ProxR,
delta=delta, mu=mu, tol=tol, verbose=verbose, max_iter=max_iter,
)
return x, k
def deconv_fista(
img_blurry, psf, kernel=None, denoiser=denoise_tv,
mu=np.float32(1e-3), tol=np.float32(1e-4), delta=np.float32(1.5),
max_iter=50, verbose=False,
):
"""
Deconvolution with FISTA method.
"""
x_0 = img_blurry.copy()
if kernel is None:
kernel = transpose2d(psf)
mu = np.float32(mu)
delta = np.float32(delta)
tol = np.float32(tol)
ATb = convolve2d(x_0, psf)
def ATA(x):
return convolve2d(convolve2d(x, kernel), psf)
ProxR = denoiser
x, k = solve_fista(
ATA, ATb, x_0, ProxR,
delta=delta, mu=mu, tol=tol, verbose=verbose, max_iter=max_iter,
)
return x, k
def deconv_sv_fbs(
img_blurry, psf, kernel=None, denoiser=denoise_tv,
mu=np.float32(1e-3), tol=np.float32(1e-4), delta=np.float32(1.5),
max_iter=50, verbose=False,
):
x_0 = img_blurry.copy()
if kernel is None:
kernel = transpose2d_sv(psf)
mu = np.float32(mu)
delta = np.float32(delta)
tol = np.float32(tol)
ATb = convolve2d_sv(x_0, psf)
def ATA(x):
return convolve2d_sv(convolve2d_sv(x, kernel), psf)
ProxR = denoiser
x, k = solve_fbs(
ATA, ATb, x_0, ProxR,
delta=delta, mu=mu, tol=tol, verbose=verbose, max_iter=max_iter,
)
return x, k
| 27.035294 | 73 | 0.620975 | 337 | 2,298 | 4.106825 | 0.166172 | 0.117052 | 0.047688 | 0.039017 | 0.859104 | 0.859104 | 0.808526 | 0.808526 | 0.788295 | 0.788295 | 0 | 0.049883 | 0.258486 | 2,298 | 84 | 74 | 27.357143 | 0.762324 | 0.037424 | 0 | 0.757576 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.090909 | false | 0 | 0.090909 | 0.045455 | 0.272727 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
78674a022661cac654bd134406cff9a38b82963c | 149 | py | Python | backend/Chats/admin.py | Fowerus/drf-blog | 61f792ee73022d26129ca24a11113b28323c0de5 | [
"MIT"
] | 5 | 2021-03-21T06:51:44.000Z | 2021-04-19T12:29:52.000Z | backend/Chats/admin.py | Fowerus/drf-soc-net | 61f792ee73022d26129ca24a11113b28323c0de5 | [
"MIT"
] | null | null | null | backend/Chats/admin.py | Fowerus/drf-soc-net | 61f792ee73022d26129ca24a11113b28323c0de5 | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import *
admin.site.register(Chats)
admin.site.register(Messages)
admin.site.register(Chats_admins)
| 16.555556 | 33 | 0.805369 | 21 | 149 | 5.666667 | 0.52381 | 0.226891 | 0.428571 | 0.369748 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09396 | 149 | 8 | 34 | 18.625 | 0.881481 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.4 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
15a240493bf8537ac5cdea74f8999f95ff40320f | 1,599 | py | Python | problems/test_ic_14_inflight_entertainment.py | gregdferrell/algo | 974ae25b028d49bcb7ded6655a7e11dcf6aa221d | [
"MIT"
] | null | null | null | problems/test_ic_14_inflight_entertainment.py | gregdferrell/algo | 974ae25b028d49bcb7ded6655a7e11dcf6aa221d | [
"MIT"
] | null | null | null | problems/test_ic_14_inflight_entertainment.py | gregdferrell/algo | 974ae25b028d49bcb7ded6655a7e11dcf6aa221d | [
"MIT"
] | null | null | null | import pytest
from ic_14_inflight_entertainment import flight_time_movies_1_brute_force, \
flight_time_movies_2_binary_search, flight_time_movies_3_utilize_set
def test_flight_time_movies_algorithms_not_enough_movies():
movie_lengths = [90]
flight_length = 180
with pytest.raises(ValueError) as e:
flight_time_movies_1_brute_force(movie_lengths, flight_length)
with pytest.raises(ValueError) as e:
flight_time_movies_2_binary_search(movie_lengths, flight_length)
with pytest.raises(ValueError) as e:
flight_time_movies_3_utilize_set(movie_lengths, flight_length)
def test_flight_time_movies_algorithms_none():
movie_lengths = [80, 60, 110, 150, 75, 115]
flight_length = 180
assert not flight_time_movies_1_brute_force(movie_lengths, flight_length)
assert not flight_time_movies_2_binary_search(movie_lengths, flight_length)
assert not flight_time_movies_3_utilize_set(movie_lengths, flight_length)
def test_flight_time_movies_algorithms_found():
movie_lengths = [80, 60, 110, 150, 75, 105]
flight_length = 180
assert flight_time_movies_1_brute_force(movie_lengths, flight_length)
assert flight_time_movies_2_binary_search(movie_lengths, flight_length)
assert flight_time_movies_3_utilize_set(movie_lengths, flight_length)
def test_flight_time_movies_algorithms_cant_use_same_movie_twice():
movie_lengths = [90, 80, 70]
flight_length = 180
assert not flight_time_movies_1_brute_force(movie_lengths, flight_length)
assert not flight_time_movies_2_binary_search(movie_lengths, flight_length)
assert not flight_time_movies_3_utilize_set(movie_lengths, flight_length)
| 39.975 | 76 | 0.855535 | 249 | 1,599 | 4.947791 | 0.196787 | 0.154221 | 0.246753 | 0.233766 | 0.86526 | 0.86526 | 0.771104 | 0.732143 | 0.732143 | 0.708604 | 0 | 0.045922 | 0.087555 | 1,599 | 39 | 77 | 41 | 0.798492 | 0 | 0 | 0.433333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.3 | 1 | 0.133333 | false | 0 | 0.066667 | 0 | 0.2 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
ec8d1940ad8736c170e5361bed1ebb77896be288 | 51 | py | Python | pychastic/__init__.py | RadostW/stochastic | 1d437900e0314f18678353fd4794ecefb197761d | [
"MIT"
] | 2 | 2022-03-01T11:48:21.000Z | 2022-03-01T11:48:22.000Z | pychastic/__init__.py | RadostW/stochastic | 1d437900e0314f18678353fd4794ecefb197761d | [
"MIT"
] | null | null | null | pychastic/__init__.py | RadostW/stochastic | 1d437900e0314f18678353fd4794ecefb197761d | [
"MIT"
] | 2 | 2021-11-16T15:44:39.000Z | 2021-12-15T22:59:49.000Z | from . import sde_solver
from . import sde_problem
| 17 | 25 | 0.803922 | 8 | 51 | 4.875 | 0.625 | 0.512821 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.156863 | 51 | 2 | 26 | 25.5 | 0.906977 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
ec9027c3fe4b4c0d9c79240968fea0839b6b0598 | 3,644 | py | Python | model.py | orla84/YOLO_keras | 87e43268d20f98196ebb72e9a1896c3bd8181519 | [
"MIT"
] | null | null | null | model.py | orla84/YOLO_keras | 87e43268d20f98196ebb72e9a1896c3bd8181519 | [
"MIT"
] | null | null | null | model.py | orla84/YOLO_keras | 87e43268d20f98196ebb72e9a1896c3bd8181519 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 3 15:52:43 2019
@author: Orlando Ciricosta
"""
from keras.layers import Dense, Conv2D, LeakyReLU, MaxPool2D
from keras.layers import InputLayer, Reshape, Flatten
from keras.models import Sequential
yolo = Sequential()
yolo.add(InputLayer(input_shape = (448,448,3)))
yolo.add(Conv2D(64,7, strides=2, padding='same', name='conv-0_block-0'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-0_block-0'))
yolo.add(MaxPool2D(strides=2, name='pool_block-0'))
yolo.add(Conv2D(192,3, padding='same', name='conv-0_block-1'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-0_block-1'))
yolo.add(MaxPool2D(strides=2, name='pool_block-1'))
yolo.add(Conv2D(128,1, padding='same', name='conv-0_block-2'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-0_block-2'))
yolo.add(Conv2D(256,3, padding='same', name='conv-1_block-2'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-1_block-2'))
yolo.add(Conv2D(256,1, padding='same', name='conv-2_block-2'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-2_block-2'))
yolo.add(Conv2D(512,3, padding='same', name='conv-3_block-2'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-3_block-2'))
yolo.add(MaxPool2D(strides=2, name='pool_block-2'))
yolo.add(Conv2D(256,1, padding='same', name='conv-0_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-0_block-3'))
yolo.add(Conv2D(512,3, padding='same', name='conv-1_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-1_block-3'))
yolo.add(Conv2D(256,1, padding='same', name='conv-2_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-2_block-3'))
yolo.add(Conv2D(512,3, padding='same', name='conv-3_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-3_block-3'))
yolo.add(Conv2D(256,1, padding='same', name='conv-4_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-4_block-3'))
yolo.add(Conv2D(512,3, padding='same', name='conv-5_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-5_block-3'))
yolo.add(Conv2D(256,1, padding='same', name='conv-6_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-6_block-3'))
yolo.add(Conv2D(512,3, padding='same', name='conv-7_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-7_block-3'))
yolo.add(Conv2D(512,1, padding='same', name='conv-8_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-8_block-3'))
yolo.add(Conv2D(1024,3, padding='same', name='conv-9_block-3'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-9_block-3'))
yolo.add(MaxPool2D(strides=2, name='pool_block-3'))
yolo.add(Conv2D(512,1, padding='same', name='conv-0_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-0_block-4'))
yolo.add(Conv2D(1024,3, padding='same', name='conv-1_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-1_block-4'))
yolo.add(Conv2D(512,1, padding='same', name='conv-2_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-2_block-4'))
yolo.add(Conv2D(1024,3, padding='same', name='conv-3_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-3_block-4'))
yolo.add(Conv2D(1024,3, padding='same', name='conv-4_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-4_block-4'))
yolo.add(Conv2D(1024,3, strides=2, padding='same', name='conv-5_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-5_block-4'))
yolo.add(Conv2D(1024,3, padding='same', name='conv-6_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-6_block-4'))
yolo.add(Conv2D(1024,3, padding='same', name='conv-7_block-4'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-7_block-4'))
yolo.add(Flatten(name='flatten'))
yolo.add(Dense(4096, name='dense_0'))
yolo.add(LeakyReLU(alpha=0.1, name='leaky-0_block-5'))
yolo.add(Dense(1470, name='dense_1'))
yolo.add(Reshape((7,7,30), name='reshape_out'))
yolo.summary() | 45.55 | 74 | 0.715148 | 682 | 3,644 | 3.737537 | 0.095308 | 0.159278 | 0.156924 | 0.205963 | 0.83876 | 0.826599 | 0.784229 | 0.75716 | 0.703021 | 0.674382 | 0 | 0.093913 | 0.053238 | 3,644 | 80 | 75 | 45.55 | 0.644928 | 0.029638 | 0 | 0 | 0 | 0 | 0.251417 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.047619 | 0 | 0.047619 | 0 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ecb0d44cf00de50df25aaa4e3c0dbea54445562f | 11,800 | py | Python | lang/python/github/com/metaprov/modelaapi/services/apitoken/v1/apitoken_pb2_grpc.py | metaprov/modeldapi | ee05693832051dcd990ee4f061715d7ae0787340 | [
"Apache-2.0"
] | 5 | 2022-02-18T03:40:10.000Z | 2022-03-01T16:11:24.000Z | lang/python/github/com/metaprov/modelaapi/services/apitoken/v1/apitoken_pb2_grpc.py | metaprov/modeldapi | ee05693832051dcd990ee4f061715d7ae0787340 | [
"Apache-2.0"
] | 1 | 2022-01-07T19:59:25.000Z | 2022-02-04T01:21:14.000Z | lang/python/github/com/metaprov/modelaapi/services/apitoken/v1/apitoken_pb2_grpc.py | metaprov/modeldapi | ee05693832051dcd990ee4f061715d7ae0787340 | [
"Apache-2.0"
] | 1 | 2022-03-25T10:21:43.000Z | 2022-03-25T10:21:43.000Z | # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc
from github.com.metaprov.modelaapi.services.apitoken.v1 import apitoken_pb2 as github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2
class ApiTokenServiceStub(object):
"""Missing associated documentation comment in .proto file."""
def __init__(self, channel):
"""Constructor.
Args:
channel: A grpc.Channel.
"""
self.ListApiTokens = channel.unary_unary(
'/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/ListApiTokens',
request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.ListApiTokensRequest.SerializeToString,
response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.ListApiTokensResponse.FromString,
)
self.CreateApiToken = channel.unary_unary(
'/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/CreateApiToken',
request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.CreateApiTokenRequest.SerializeToString,
response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.CreateApiTokenResponse.FromString,
)
self.GetApiToken = channel.unary_unary(
'/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/GetApiToken',
request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.GetApiTokenRequest.SerializeToString,
response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.GetApiTokenResponse.FromString,
)
self.UpdateApiToken = channel.unary_unary(
'/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/UpdateApiToken',
request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.UpdateApiTokenRequest.SerializeToString,
response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.UpdateApiTokenResponse.FromString,
)
self.DeleteApiToken = channel.unary_unary(
'/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/DeleteApiToken',
request_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.DeleteApiTokenRequest.SerializeToString,
response_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.DeleteApiTokenResponse.FromString,
)
class ApiTokenServiceServicer(object):
"""Missing associated documentation comment in .proto file."""
def ListApiTokens(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def CreateApiToken(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def GetApiToken(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def UpdateApiToken(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def DeleteApiToken(self, request, context):
"""Missing associated documentation comment in .proto file."""
context.set_code(grpc.StatusCode.UNIMPLEMENTED)
context.set_details('Method not implemented!')
raise NotImplementedError('Method not implemented!')
def add_ApiTokenServiceServicer_to_server(servicer, server):
rpc_method_handlers = {
'ListApiTokens': grpc.unary_unary_rpc_method_handler(
servicer.ListApiTokens,
request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.ListApiTokensRequest.FromString,
response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.ListApiTokensResponse.SerializeToString,
),
'CreateApiToken': grpc.unary_unary_rpc_method_handler(
servicer.CreateApiToken,
request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.CreateApiTokenRequest.FromString,
response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.CreateApiTokenResponse.SerializeToString,
),
'GetApiToken': grpc.unary_unary_rpc_method_handler(
servicer.GetApiToken,
request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.GetApiTokenRequest.FromString,
response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.GetApiTokenResponse.SerializeToString,
),
'UpdateApiToken': grpc.unary_unary_rpc_method_handler(
servicer.UpdateApiToken,
request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.UpdateApiTokenRequest.FromString,
response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.UpdateApiTokenResponse.SerializeToString,
),
'DeleteApiToken': grpc.unary_unary_rpc_method_handler(
servicer.DeleteApiToken,
request_deserializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.DeleteApiTokenRequest.FromString,
response_serializer=github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.DeleteApiTokenResponse.SerializeToString,
),
}
generic_handler = grpc.method_handlers_generic_handler(
'github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService', rpc_method_handlers)
server.add_generic_rpc_handlers((generic_handler,))
# This class is part of an EXPERIMENTAL API.
class ApiTokenService(object):
"""Missing associated documentation comment in .proto file."""
@staticmethod
def ListApiTokens(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/ListApiTokens',
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.ListApiTokensRequest.SerializeToString,
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.ListApiTokensResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def CreateApiToken(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/CreateApiToken',
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.CreateApiTokenRequest.SerializeToString,
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.CreateApiTokenResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def GetApiToken(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/GetApiToken',
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.GetApiTokenRequest.SerializeToString,
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.GetApiTokenResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def UpdateApiToken(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/UpdateApiToken',
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.UpdateApiTokenRequest.SerializeToString,
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.UpdateApiTokenResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
@staticmethod
def DeleteApiToken(request,
target,
options=(),
channel_credentials=None,
call_credentials=None,
insecure=False,
compression=None,
wait_for_ready=None,
timeout=None,
metadata=None):
return grpc.experimental.unary_unary(request, target, '/github.com.metaprov.modelaapi.services.apitoken.v1.ApiTokenService/DeleteApiToken',
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.DeleteApiTokenRequest.SerializeToString,
github_dot_com_dot_metaprov_dot_modelaapi_dot_services_dot_apitoken_dot_v1_dot_apitoken__pb2.DeleteApiTokenResponse.FromString,
options, channel_credentials,
insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
| 59.296482 | 174 | 0.744322 | 1,233 | 11,800 | 6.623682 | 0.089213 | 0.083507 | 0.045549 | 0.056936 | 0.887107 | 0.887107 | 0.887107 | 0.858455 | 0.844619 | 0.814497 | 0 | 0.007893 | 0.194746 | 11,800 | 198 | 175 | 59.59596 | 0.85161 | 0.058136 | 0 | 0.493827 | 1 | 0 | 0.106369 | 0.079551 | 0 | 0 | 0 | 0 | 0 | 1 | 0.074074 | false | 0 | 0.012346 | 0.030864 | 0.135802 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
ecba953f71bd984ec7db536ee82b665620eac3de | 11,921 | py | Python | flask_web/flask_app/bearing_master/forms.py | Yakings/system_demo | 6ec9596db1e60e221054282a06d9129246e88f54 | [
"Apache-2.0"
] | null | null | null | flask_web/flask_app/bearing_master/forms.py | Yakings/system_demo | 6ec9596db1e60e221054282a06d9129246e88f54 | [
"Apache-2.0"
] | null | null | null | flask_web/flask_app/bearing_master/forms.py | Yakings/system_demo | 6ec9596db1e60e221054282a06d9129246e88f54 | [
"Apache-2.0"
] | 1 | 2020-08-18T10:55:10.000Z | 2020-08-18T10:55:10.000Z | #!/usr/bin/python
#-*- coding: UTF-8 -*-
from __future__ import unicode_literals
from flask_wtf import FlaskForm
from wtforms import RadioField, SubmitField, StringField, PasswordField, SelectField, BooleanField, FileField
from wtforms.validators import DataRequired, Length
class TodoListForm(FlaskForm):
title = StringField('标题', validators=[DataRequired(), Length(1, 64)])
status = RadioField('是否完成', validators=[DataRequired()], choices=[("0", '机器学习模型'),("1",'深度神经网络')])
submit = SubmitField('提交')
class DataListForm(FlaskForm):
title = StringField('标题', validators=[DataRequired(), Length(1, 64)])
submit = SubmitField('提交')
class LoginForm(FlaskForm):
username = StringField('用户名', validators=[DataRequired(), Length(1, 24)])
password = PasswordField('密码', validators=[DataRequired(), Length(1, 24)])
submit = SubmitField('登录')
class RegistorForm(FlaskForm):
username = StringField('用户名', validators=[DataRequired(), Length(1, 24)])
password = PasswordField('密码', validators=[DataRequired(), Length(1, 24)])
submit = SubmitField('注册')
class CNNForm(FlaskForm):
# submit = SubmitField("Submit")
# username = StringField('网络类型名', validators=['cnn', Length(0, 0)])
submit = SubmitField('深度学习')
class MLForm(FlaskForm):
# submit = SubmitField("Submit")
# username = StringField('网络类型名', validators=['cnn', Length(0, 0)])
submit = SubmitField('机器学习')
class CNNSetting(FlaskForm):
"""电影表单"""
tag_id00 = SelectField(
label="网络类型",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id01 = SelectField(
label="激活函数",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id02 = SelectField(
label="优化器",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id03 = SelectField(
label="batch size",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id013 = StringField('输入维度', validators=[DataRequired(), Length(1, 24)])
tag_id014 = StringField('输出维度', validators=[DataRequired(), Length(1, 24)])
tag_id015 = StringField('网络层数', validators=[DataRequired(), Length(1, 24)])
tag_id016 = StringField('学习率', validators=[DataRequired(), Length(1, 24)])
tag_id017 = StringField('权值衰减率', validators=[DataRequired(), Length(1, 24)])
# tag_id18 = BooleanField('是否选择', validators=[DataRequired()])
# tag_id19 = BooleanField('是否选择2', validators=[DataRequired()])
# submit = SubmitField('确定')
# star = SelectField(
# label="星级",
# validators=[DataRequired("请选择星际")],
# description="星级",
# coerce=int,
# choices=[(1, "1星"), (2, "2星"), (3, "3星"), (4, "4星"), (5, "5星")],
# render_kw={"class": "form-control"})
# def __init__(self, *args, **kwargs):
# super(CNNSetting, self).__init__(*args, **kwargs)
# self.tag_id.choices = [(v.id, v.name) for v in Tag.query.all()]
def __init__(self, args):
super(CNNSetting, self).__init__(args)
# super(CNNSetting, self).__init__()
print(args)
self.tag_id00.choices = [(i, args[0][i]) for i in range(len(args[0]))]
self.tag_id01.choices = [(i, args[1][i]) for i in range(len(args[1]))]
self.tag_id02.choices = [(i, args[2][i]) for i in range(len(args[2]))]
self.tag_id03.choices = [(i, args[3][i]) for i in range(len(args[3]))]
class Data_Select_Form():
# tag_id18 = BooleanField('是否选择1', validators=[DataRequired()])
# tag_id19 = BooleanField('是否选择1', validators=[DataRequired()])
# tag_id = []
# for i in range(100):
# tag_id.append(BooleanField('是否选择', validators=[DataRequired()]))
def __init__(self, args):
print(args)
class DynamicForm(FlaskForm):
#############
#############
def mygetForm(self,str_name):
return self.__getattribute__(str_name)
pass
for i in range(args[0]):
# setattr(DynamicForm, 'tag_id' + str(i), StringField(i))
# setattr(DynamicForm, 'tag_id' + str(i), BooleanField(str(i)))
setattr(DynamicForm, 'tag_id' + str(i), BooleanField(str(i), validators=[DataRequired()]))
setattr(DynamicForm, 'submit', SubmitField('确定'))
self.form = DynamicForm()
def __call__(self):
return self.form
# for i in range(args[0]):
# self.tag_id.append(BooleanField('是否选择', validators=[DataRequired()]))
class All_Set_Form():
# tag_id18 = BooleanField('是否选择1', validators=[DataRequired()])
# tag_id19 = BooleanField('是否选择1', validators=[DataRequired()])
# tag_id = []
# for i in range(100):
# tag_id.append(BooleanField('是否选择', validators=[DataRequired()]))
def __init__(self, args):
print(args)
class DynamicForm(FlaskForm):
tag_id00 = SelectField(
label="网络类型",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id01 = SelectField(
label="激活函数",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id02 = SelectField(
label="优化器",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id03 = SelectField(
label="batch size",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id04 = SelectField(
label="是否做数据增强",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id05 = SelectField(
label="损失函数",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id013 = StringField('输入维度', validators=[DataRequired(), Length(1, 24)])
tag_id014 = StringField('输出维度', validators=[DataRequired(), Length(1, 24)])
tag_id015 = StringField('网络层数', validators=[DataRequired(), Length(1, 24)])
tag_id016 = StringField('学习率', validators=[DataRequired(), Length(1, 24)])
tag_id017 = StringField('权值衰减率', validators=[DataRequired(), Length(1, 24)])
# whether_data_augment =, deep_model_class =, ml_model_class = -1, input_dim =, output_dim =, weight_decay =,
# learning_rate =, activation_class =, layers_num =
def __init__(self, args):
# super(DynamicForm, self).__init__(args)
super(DynamicForm, self).__init__()
print(args)
# self.tag_id00.choices = [(1, v[1]) for v in args]
# self.tag_id01.choices = [(1, v[1]) for v in args]
# self.tag_id02.choices = [(1, v[1]) for v in args]
# self.tag_id03.choices = [(1, v[1]) for v in args]
self.tag_id00.choices = [(i, args[0][i]) for i in range(len(args[0]))]
self.tag_id01.choices = [(i, args[1][i]) for i in range(len(args[1]))]
self.tag_id02.choices = [(i, args[2][i]) for i in range(len(args[2]))]
self.tag_id03.choices = [(i, args[3][i]) for i in range(len(args[3]))]
self.tag_id04.choices = [(i, args[4][i]) for i in range(len(args[4]))]
self.tag_id05.choices = [(i, args[5][i]) for i in range(len(args[5]))]
#############
#############
def mygetForm(self,str_name):
return self.__getattribute__(str_name)
pass
for i in range(args[0]):
# setattr(DynamicForm, 'tag_id' + str(i), StringField(i))
# setattr(DynamicForm, 'tag_id_dym' + str(i), BooleanField(str(i), validators=[DataRequired()]))
setattr(DynamicForm, 'tag_id_dym' + str(i), BooleanField(str(i), validators=[]))
setattr(DynamicForm, 'submit', SubmitField('确定'))
self.form = DynamicForm(args[1])
def __call__(self):
return self.form
class ML_Set_Form():
def __init__(self, args):
print('arg out:',args)
class DynamicForm(FlaskForm):
tag_id00 = SelectField(
label="网络类型",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id01 = SelectField(
label="激活函数",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id013 = StringField('输入维度', validators=[DataRequired(), Length(1, 24)])
tag_id014 = StringField('输出维度', validators=[DataRequired(), Length(1, 24)])
tag_id016 = StringField('学习率', validators=[DataRequired(), Length(1, 24)])
tag_id017 = StringField('权值衰减率', validators=[DataRequired(), Length(1, 24)])
def __init__(self, args):
super(DynamicForm, self).__init__(args)
# super(DynamicForm, self).__init__()
print('arg',args)
self.tag_id00.choices = [(i, args[0][i]) for i in range(len(args[0]))]
self.tag_id01.choices = [(i, args[1][i]) for i in range(len(args[1]))]
#############
def mygetForm(self,str_name):
return self.__getattribute__(str_name)
pass
for i in range(args[0]):
# setattr(DynamicForm, 'tag_id' + str(i), StringField(i))
# setattr(DynamicForm, 'tag_id_dym' + str(i), BooleanField(str(i), validators=[DataRequired()]))
setattr(DynamicForm, 'tag_id_dym' + str(i), BooleanField(str(i), validators=[]))
setattr(DynamicForm, 'submit', SubmitField('确定'))
self.form = DynamicForm(args[1])
def __call__(self):
return self.form
class DataSetting(FlaskForm):
"""电影表单"""
tag_id = SelectField(
label="输入维度",
validators=[DataRequired("请选择标签")],
description="",
coerce=int,
render_kw={"class": "form-control"}
)
tag_id13 = StringField('均值', validators=[DataRequired(), Length(1, 24)])
# submit = SubmitField('确定')
# star = SelectField(
# label="星级",
# validators=[DataRequired("请选择星际")],
# description="星级",
# coerce=int,
# choices=[(1, "1星"), (2, "2星"), (3, "3星"), (4, "4星"), (5, "5星")],
# render_kw={"class": "form-control"})
# def __init__(self, *args, **kwargs):
# super(CNNSetting, self).__init__(*args, **kwargs)
# self.tag_id.choices = [(v.id, v.name) for v in Tag.query.all()]
def __init__(self, args):
# super(DataSetting, self).__init__(args)
super(DataSetting, self).__init__()
print(args)
self.tag_id.choices = [(v[0], v[1]) for v in args]
class UploadForm(FlaskForm):
image = FileField('选择数据')
submit = SubmitField('提交')
| 36.68 | 121 | 0.553225 | 1,245 | 11,921 | 5.123695 | 0.13012 | 0.168992 | 0.092177 | 0.09547 | 0.860009 | 0.836965 | 0.824424 | 0.805926 | 0.795579 | 0.771124 | 0 | 0.029718 | 0.28303 | 11,921 | 324 | 122 | 36.79321 | 0.716626 | 0.227498 | 0 | 0.732323 | 0 | 0 | 0.057741 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.065657 | false | 0.030303 | 0.020202 | 0.030303 | 0.323232 | 0.035354 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
019dc6f6de70fac77d22bd0568c608094caa204c | 12,919 | py | Python | pyaz/iot/hub/__init__.py | py-az-cli/py-az-cli | 9a7dc44e360c096a5a2f15595353e9dad88a9792 | [
"MIT"
] | null | null | null | pyaz/iot/hub/__init__.py | py-az-cli/py-az-cli | 9a7dc44e360c096a5a2f15595353e9dad88a9792 | [
"MIT"
] | null | null | null | pyaz/iot/hub/__init__.py | py-az-cli/py-az-cli | 9a7dc44e360c096a5a2f15595353e9dad88a9792 | [
"MIT"
] | 1 | 2022-02-03T09:12:01.000Z | 2022-02-03T09:12:01.000Z | '''
Manage Azure IoT hubs.
'''
from ... pyaz_utils import _call_az
from . import certificate, consumer_group, devicestream, identity, message_enrichment, policy, route, routing_endpoint
def create(name, resource_group, c2d_max_delivery_count=None, c2d_ttl=None, disable_device_sas=None, disable_local_auth=None, disable_module_sas=None, feedback_lock_duration=None, feedback_max_delivery_count=None, feedback_ttl=None, fileupload_notification_lock_duration=None, fileupload_notification_max_delivery_count=None, fileupload_notification_ttl=None, fileupload_notifications=None, fileupload_sas_ttl=None, fileupload_storage_auth_type=None, fileupload_storage_connectionstring=None, fileupload_storage_container_name=None, fileupload_storage_container_uri=None, fileupload_storage_identity=None, location=None, mi_system_assigned=None, mi_user_assigned=None, min_tls_version=None, partition_count=None, retention_day=None, role=None, scopes=None, sku=None, tags=None, unit=None):
'''
Create an Azure IoT hub.
Required Parameters:
- name -- IoT Hub name.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
Optional Parameters:
- c2d_max_delivery_count -- The number of times the IoT hub will attempt to deliver a cloud-to-device message to a device, between 1 and 100.
- c2d_ttl -- The amount of time a message is available for the device to consume before it is expired by IoT Hub, between 1 and 48 hours.
- disable_device_sas -- A boolean indicating whether or not to disable all device (including Edge devices but excluding modules) scoped SAS keys for authentication
- disable_local_auth -- A boolean indicating whether or not to disable IoT hub scoped SAS keys for authentication.
- disable_module_sas -- A boolean indicating whether or not to disable module-scoped SAS keys for authentication.
- feedback_lock_duration -- The lock duration for the feedback queue, between 5 and 300 seconds.
- feedback_max_delivery_count -- The number of times the IoT hub attempts to deliver a message on the feedback queue, between 1 and 100.
- feedback_ttl -- The period of time for which the IoT hub will maintain the feedback for expiration or delivery of cloud-to-device messages, between 1 and 48 hours.
- fileupload_notification_lock_duration -- The lock duration for the file upload notifications queue, between 5 and 300 seconds.
- fileupload_notification_max_delivery_count -- The number of times the IoT hub will attempt to deliver a file notification message, between 1 and 100.
- fileupload_notification_ttl -- The amount of time a file upload notification is available for the service to consume before it is expired by IoT Hub, between 1 and 48 hours.
- fileupload_notifications -- A boolean indicating whether to log information about uploaded files to the messages/servicebound/filenotifications IoT Hub endpoint.
- fileupload_sas_ttl -- The amount of time a SAS URI generated by IoT Hub is valid before it expires, between 1 and 24 hours.
- fileupload_storage_auth_type -- The authentication type for the Azure Storage account to which files are uploaded.
- fileupload_storage_connectionstring -- The connection string for the Azure Storage account to which files are uploaded.
- fileupload_storage_container_name -- The name of the root container where you upload files. The container need not exist but should be creatable using the connectionString specified.
- fileupload_storage_container_uri -- The container URI for the Azure Storage account to which files are uploaded.
- fileupload_storage_identity -- The managed identity to use for file upload authentication. Use '[system]' to refer to the system-assigned managed identity or a resource ID to refer to a user-assigned managed identity.
- location -- Location of your IoT Hub. Default is the location of target resource group.
- mi_system_assigned -- Enable system-assigned managed identity for this hub
- mi_user_assigned -- Enable user-assigned managed identities for this hub. Accept space-separated list of identity resource IDs.
- min_tls_version -- Specify the minimum TLS version to support for this hub. Can be set to "1.2" to have clients that use a TLS version below 1.2 to be rejected.
- partition_count -- The number of partitions of the backing Event Hub for device-to-cloud messages.
- retention_day -- Specifies how long this IoT hub will maintain device-to-cloud events, between 1 and 7 days.
- role -- Role to assign to the hub's system-assigned managed identity.
- scopes -- Space separated list of scopes to assign the role (--role) for the system-assigned managed identity.
- sku -- Pricing tier for Azure IoT Hub. Note that only one free IoT hub instance (F1) is allowed in each subscription. Exception will be thrown if free instances exceed one.
- tags -- space-separated tags: key[=value] [key[=value] ...]. Use '' to clear existing tags.
- unit -- Units in your IoT Hub.
'''
return _call_az("az iot hub create", locals())
def list(resource_group=None):
'''
List IoT hubs.
Optional Parameters:
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub list", locals())
def show_connection_string(all=None, hub_name=None, key=None, policy_name=None, resource_group=None):
'''
Show the connection strings for an IoT hub.
Optional Parameters:
- all -- Allow to show all shared access policies.
- hub_name -- IoT Hub name.
- key -- The key to use.
- policy_name -- Shared access policy to use.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub show-connection-string", locals())
def show(name, resource_group=None):
'''
Get the details of an IoT hub.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub show", locals())
def update(name, add=None, c2d_max_delivery_count=None, c2d_ttl=None, disable_device_sas=None, disable_local_auth=None, disable_module_sas=None, feedback_lock_duration=None, feedback_max_delivery_count=None, feedback_ttl=None, fileupload_notification_lock_duration=None, fileupload_notification_max_delivery_count=None, fileupload_notification_ttl=None, fileupload_notifications=None, fileupload_sas_ttl=None, fileupload_storage_auth_type=None, fileupload_storage_connectionstring=None, fileupload_storage_container_name=None, fileupload_storage_container_uri=None, fileupload_storage_identity=None, force_string=None, remove=None, resource_group=None, retention_day=None, set=None, sku=None, tags=None, unit=None):
'''
Update metadata for an IoT hub.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- add -- Add an object to a list of objects by specifying a path and key value pairs. Example: --add property.listProperty <key=value, string or JSON string>
- c2d_max_delivery_count -- The number of times the IoT hub will attempt to deliver a cloud-to-device message to a device, between 1 and 100.
- c2d_ttl -- The amount of time a message is available for the device to consume before it is expired by IoT Hub, between 1 and 48 hours.
- disable_device_sas -- A boolean indicating whether or not to disable all device (including Edge devices but excluding modules) scoped SAS keys for authentication
- disable_local_auth -- A boolean indicating whether or not to disable IoT hub scoped SAS keys for authentication.
- disable_module_sas -- A boolean indicating whether or not to disable module-scoped SAS keys for authentication.
- feedback_lock_duration -- The lock duration for the feedback queue, between 5 and 300 seconds.
- feedback_max_delivery_count -- The number of times the IoT hub attempts to deliver a message on the feedback queue, between 1 and 100.
- feedback_ttl -- The period of time for which the IoT hub will maintain the feedback for expiration or delivery of cloud-to-device messages, between 1 and 48 hours.
- fileupload_notification_lock_duration -- The lock duration for the file upload notifications queue, between 5 and 300 seconds.
- fileupload_notification_max_delivery_count -- The number of times the IoT hub will attempt to deliver a file notification message, between 1 and 100.
- fileupload_notification_ttl -- The amount of time a file upload notification is available for the service to consume before it is expired by IoT Hub, between 1 and 48 hours.
- fileupload_notifications -- A boolean indicating whether to log information about uploaded files to the messages/servicebound/filenotifications IoT Hub endpoint.
- fileupload_sas_ttl -- The amount of time a SAS URI generated by IoT Hub is valid before it expires, between 1 and 24 hours.
- fileupload_storage_auth_type -- The authentication type for the Azure Storage account to which files are uploaded.
- fileupload_storage_connectionstring -- The connection string for the Azure Storage account to which files are uploaded.
- fileupload_storage_container_name -- The name of the root container where you upload files. The container need not exist but should be creatable using the connectionString specified.
- fileupload_storage_container_uri -- The container URI for the Azure Storage account to which files are uploaded.
- fileupload_storage_identity -- The managed identity to use for file upload authentication. Use '[system]' to refer to the system-assigned managed identity or a resource ID to refer to a user-assigned managed identity.
- force_string -- When using 'set' or 'add', preserve string literals instead of attempting to convert to JSON.
- remove -- Remove a property or an element from a list. Example: --remove property.list <indexToRemove> OR --remove propertyToRemove
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
- retention_day -- Specifies how long this IoT hub will maintain device-to-cloud events, between 1 and 7 days.
- set -- Update an object by specifying a property path and value to set. Example: --set property1.property2=<value>
- sku -- Pricing tier for Azure IoT Hub. Note that only one free IoT hub instance (F1) is allowed in each subscription. Exception will be thrown if free instances exceed one.
- tags -- space-separated tags: key[=value] [key[=value] ...]. Use '' to clear existing tags.
- unit -- Units in your IoT Hub.
'''
return _call_az("az iot hub update", locals())
def delete(name, resource_group=None):
'''
Delete an IoT hub.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub delete", locals())
def list_skus(name, resource_group=None):
'''
List available pricing tiers.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub list-skus", locals())
def show_quota_metrics(name, resource_group=None):
'''
Get the quota metrics for an IoT hub.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub show-quota-metrics", locals())
def show_stats(name, resource_group=None):
'''
Get the statistics for an IoT hub.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub show-stats", locals())
def manual_failover(name, no_wait=None, resource_group=None):
'''
Initiate a manual failover for the IoT Hub to the geo-paired disaster recovery region.
Required Parameters:
- name -- IoT Hub name.
Optional Parameters:
- no_wait -- Do not wait for the long-running operation to finish.
- resource_group -- Name of resource group. You can configure the default group using `az configure --defaults group=<name>`
'''
return _call_az("az iot hub manual-failover", locals())
| 67.638743 | 789 | 0.755554 | 1,895 | 12,919 | 5.022691 | 0.140369 | 0.034041 | 0.018491 | 0.019962 | 0.782517 | 0.779155 | 0.770645 | 0.764972 | 0.755726 | 0.755726 | 0 | 0.007897 | 0.176639 | 12,919 | 190 | 790 | 67.994737 | 0.886904 | 0.749826 | 0 | 0 | 0 | 0 | 0.078096 | 0.008181 | 0 | 0 | 0 | 0 | 0 | 1 | 0.454545 | false | 0 | 0.090909 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 7 |
01b610e69371f6c5198eb652d10981858d6aa52a | 32 | py | Python | function_19373249.py | yannnyiii/ASMART-18 | 1cf175c6bc7ba13f152df02ab560bd0596b087a2 | [
"MIT"
] | 4 | 2021-04-21T15:10:22.000Z | 2021-05-17T07:29:56.000Z | function_19373249.py | yannnyiii/ASMART-18 | 1cf175c6bc7ba13f152df02ab560bd0596b087a2 | [
"MIT"
] | null | null | null | function_19373249.py | yannnyiii/ASMART-18 | 1cf175c6bc7ba13f152df02ab560bd0596b087a2 | [
"MIT"
] | 2 | 2021-04-21T15:10:28.000Z | 2021-05-15T16:00:35.000Z | print('My student_id: 19373249') | 32 | 32 | 0.78125 | 5 | 32 | 4.8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.266667 | 0.0625 | 32 | 1 | 32 | 32 | 0.533333 | 0 | 0 | 0 | 0 | 0 | 0.69697 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
01bf6febe6c5c645ee5bac0ee70387daef8bb512 | 163 | py | Python | openprocurement/auctions/core/plugins/contracting/v3/tests/blanks/fixtures/prolongation.py | EBRD-ProzorroSale/openprocurement.auctions.core | 52bd59f193f25e4997612fca0f87291decf06966 | [
"Apache-2.0"
] | 2 | 2016-09-15T20:17:43.000Z | 2017-01-08T03:32:43.000Z | openprocurement/auctions/core/plugins/contracting/v3/tests/blanks/fixtures/prolongation.py | EBRD-ProzorroSale/openprocurement.auctions.core | 52bd59f193f25e4997612fca0f87291decf06966 | [
"Apache-2.0"
] | 183 | 2017-12-21T11:04:37.000Z | 2019-03-27T08:14:34.000Z | openprocurement/auctions/core/plugins/contracting/v3/tests/blanks/fixtures/prolongation.py | EBRD-ProzorroSale/openprocurement.auctions.core | 52bd59f193f25e4997612fca0f87291decf06966 | [
"Apache-2.0"
] | 12 | 2016-09-05T12:07:48.000Z | 2019-02-26T09:24:17.000Z | from zope import deprecation
deprecation.moved('openprocurement.auctions.core.tests.plugins.contracting.v3.tests.blanks.fixtures.prolongation', 'version update')
| 40.75 | 132 | 0.840491 | 19 | 163 | 7.210526 | 0.894737 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.006452 | 0.04908 | 163 | 3 | 133 | 54.333333 | 0.877419 | 0 | 0 | 0 | 0 | 0.5 | 0.656442 | 0.570552 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
01ca05e2028d54f76cf9ec48120d4f6848c30b37 | 6,800 | py | Python | ifx_db/tests/test_158_FetchAssocNestedSelects_02.py | ifxdb/PythonIfxDB | a9c64e8ade1329b7102f0bf356c0e4b6d230ca95 | [
"Apache-2.0"
] | 3 | 2017-05-01T10:22:27.000Z | 2021-12-29T11:02:34.000Z | ifx_db/tests/test_158_FetchAssocNestedSelects_02.py | ifxdb/PythonIfxDB | a9c64e8ade1329b7102f0bf356c0e4b6d230ca95 | [
"Apache-2.0"
] | 1 | 2020-01-07T12:56:26.000Z | 2020-01-07T12:56:26.000Z | ifx_db/tests/test_158_FetchAssocNestedSelects_02.py | ifxdb/PythonIfxDB | a9c64e8ade1329b7102f0bf356c0e4b6d230ca95 | [
"Apache-2.0"
] | 3 | 2017-05-10T16:03:25.000Z | 2018-03-19T14:59:41.000Z | #
# Licensed Materials - Property of IBM
#
# (c) Copyright IBM Corp. 2007-2008
#
import unittest, sys
import ifx_db
import config
from testfunctions import IfxDbTestFunctions
class IfxDbTestCase(unittest.TestCase):
def test_158_FetchAssocNestedSelects_02(self):
obj = IfxDbTestFunctions()
obj.assert_expect(self.run_test_158)
def run_test_158(self):
conn = ifx_db.connect(config.ConnStr, config.user, config.password)
server = ifx_db.server_info( conn )
if (server.DBMS_NAME[0:3] == 'Inf'):
op = {ifx_db.ATTR_CASE: ifx_db.CASE_UPPER}
ifx_db.set_option(conn, op, 1)
result = ifx_db.exec_immediate(conn, "SELECT * FROM staff WHERE id < 50")
output = ''
row = ifx_db.fetch_assoc(result)
while ( row ):
output += str(row['ID']) + ', ' + row['NAME'] + ', ' + str(row['DEPT']) + ', ' + row['JOB'] + ', ' + str(row['YEARS']) + ', ' + str(row['SALARY']) + ', ' + str(row['COMM'])
row = ifx_db.fetch_assoc(result)
result2 = ifx_db.exec_immediate(conn,"SELECT * FROM department WHERE substr(deptno,1,1) in ('A','B','C','D','E')")
row2 = ifx_db.fetch_assoc(result2)
while ( row2 ):
if (row2['MGRNO'] == None):
row2['MGRNO'] = ''
if (row2['LOCATION'] == None):
row2['LOCATION'] = ''
output += str(row2['DEPTNO']) + ', ' + row2['DEPTNAME'] + ', ' + str(row2['MGRNO']) + ', ' + row2['ADMRDEPT'] + ', ' + row2['LOCATION']
row2 = ifx_db.fetch_assoc(result2)
result3 = ifx_db.exec_immediate(conn,"SELECT * FROM employee WHERE lastname IN ('HAAS','THOMPSON', 'KWAN', 'GEYER', 'STERN', 'PULASKI', 'HENDERSON', 'SPENSER', 'LUCCHESSI', 'OCONNELL', 'QUINTANA', 'NICHOLLS', 'ADAMSON', 'PIANKA', 'YOSHIMURA', 'SCOUTTEN', 'WALKER', 'BROWN', 'JONES', 'LUTZ', 'JEFFERSON', 'MARINO', 'SMITH', 'JOHNSON', 'PEREZ', 'SCHNEIDER', 'PARKER', 'SMITH', 'SETRIGHT', 'MEHTA', 'LEE', 'GOUNOT')")
row3 = ifx_db.fetch_tuple(result3)
while ( row3 ):
output += row3[0] + ', ' + row3[3] + ', ' + row3[5]
row3=ifx_db.fetch_tuple(result3)
print output
#__END__
#__LUW_EXPECTED__
#10, Sanders, 20, Mgr , 7, 18357.50, None20, Pernal, 20, Sales, 8, 18171.25, 612.4530, Marenghi, 38, Mgr , 5, 17506.75, None40, OBrien, 38, Sales, 6, 18006.00, 846.55A00, SPIFFY COMPUTER SERVICE DIV., 000010, A00, B01, PLANNING, 000020, A00, C01, INFORMATION CENTER, 000030, A00, D01, DEVELOPMENT CENTER, , A00, D11, MANUFACTURING SYSTEMS, 000060, D01, D21, ADMINISTRATION SYSTEMS, 000070, D01, E01, SUPPORT SERVICES, 000050, A00, E11, OPERATIONS, 000090, E01, E21, SOFTWARE SUPPORT, 000100, E01, 000010, HAAS, 3978000020, THOMPSON, 3476000030, KWAN, 4738000050, GEYER, 6789000060, STERN, 6423000070, PULASKI, 7831000090, HENDERSON, 5498000100, SPENSER, 0972000110, LUCCHESSI, 3490000120, OCONNELL, 2167000130, QUINTANA, 4578000140, NICHOLLS, 1793000150, ADAMSON, 4510000160, PIANKA, 3782000170, YOSHIMURA, 2890000180, SCOUTTEN, 1682000190, WALKER, 2986000200, BROWN, 4501000210, JONES, 0942000220, LUTZ, 0672000230, JEFFERSON, 2094000240, MARINO, 3780000250, SMITH, 0961000260, JOHNSON, 8953000270, PEREZ, 9001000280, SCHNEIDER, 8997000290, PARKER, 4502000300, SMITH, 2095000310, SETRIGHT, 3332000320, MEHTA, 9990000330, LEE, 2103000340, GOUNOT, 5698
#__ZOS_EXPECTED__
#10, Sanders, 20, Mgr , 7, 18357.50, None20, Pernal, 20, Sales, 8, 18171.25, 612.4530, Marenghi, 38, Mgr , 5, 17506.75, None40, OBrien, 38, Sales, 6, 18006.00, 846.55A00, SPIFFY COMPUTER SERVICE DIV., 000010, A00, B01, PLANNING, 000020, A00, C01, INFORMATION CENTER, 000030, A00, D01, DEVELOPMENT CENTER, , A00, D11, MANUFACTURING SYSTEMS, 000060, D01, D21, ADMINISTRATION SYSTEMS, 000070, D01, E01, SUPPORT SERVICES, 000050, A00, E11, OPERATIONS, 000090, E01, E21, SOFTWARE SUPPORT, 000100, E01, 000010, HAAS, 3978000020, THOMPSON, 3476000030, KWAN, 4738000050, GEYER, 6789000060, STERN, 6423000070, PULASKI, 7831000090, HENDERSON, 5498000100, SPENSER, 0972000110, LUCCHESSI, 3490000120, OCONNELL, 2167000130, QUINTANA, 4578000140, NICHOLLS, 1793000150, ADAMSON, 4510000160, PIANKA, 3782000170, YOSHIMURA, 2890000180, SCOUTTEN, 1682000190, WALKER, 2986000200, BROWN, 4501000210, JONES, 0942000220, LUTZ, 0672000230, JEFFERSON, 2094000240, MARINO, 3780000250, SMITH, 0961000260, JOHNSON, 8953000270, PEREZ, 9001000280, SCHNEIDER, 8997000290, PARKER, 4502000300, SMITH, 2095000310, SETRIGHT, 3332000320, MEHTA, 9990000330, LEE, 2103000340, GOUNOT, 5698
#__SYSTEMI_EXPECTED__
#10, Sanders, 20, Mgr , 7, 18357.50, None20, Pernal, 20, Sales, 8, 18171.25, 612.4530, Marenghi, 38, Mgr , 5, 17506.75, None40, OBrien, 38, Sales, 6, 18006.00, 846.55A00, SPIFFY COMPUTER SERVICE DIV., 000010, A00, B01, PLANNING, 000020, A00, C01, INFORMATION CENTER, 000030, A00, D01, DEVELOPMENT CENTER, , A00, D11, MANUFACTURING SYSTEMS, 000060, D01, D21, ADMINISTRATION SYSTEMS, 000070, D01, E01, SUPPORT SERVICES, 000050, A00, E11, OPERATIONS, 000090, E01, E21, SOFTWARE SUPPORT, 000100, E01, 000010, HAAS, 3978000020, THOMPSON, 3476000030, KWAN, 4738000050, GEYER, 6789000060, STERN, 6423000070, PULASKI, 7831000090, HENDERSON, 5498000100, SPENSER, 0972000110, LUCCHESSI, 3490000120, OCONNELL, 2167000130, QUINTANA, 4578000140, NICHOLLS, 1793000150, ADAMSON, 4510000160, PIANKA, 3782000170, YOSHIMURA, 2890000180, SCOUTTEN, 1682000190, WALKER, 2986000200, BROWN, 4501000210, JONES, 0942000220, LUTZ, 0672000230, JEFFERSON, 2094000240, MARINO, 3780000250, SMITH, 0961000260, JOHNSON, 8953000270, PEREZ, 9001000280, SCHNEIDER, 8997000290, PARKER, 4502000300, SMITH, 2095000310, SETRIGHT, 3332000320, MEHTA, 9990000330, LEE, 2103000340, GOUNOT, 5698
#__IDS_EXPECTED__
#10, Sanders, 20, Mgr , 7, 18357.50, None20, Pernal, 20, Sales, 8, 18171.25, 612.4530, Marenghi, 38, Mgr , 5, 17506.75, None40, OBrien, 38, Sales, 6, 18006.00, 846.55A00, SPIFFY COMPUTER SERVICE DIV., 000010, A00, B01, PLANNING, 000020, A00, C01, INFORMATION CENTER, 000030, A00, D01, DEVELOPMENT CENTER, , A00, D11, MANUFACTURING SYSTEMS, 000060, D01, D21, ADMINISTRATION SYSTEMS, 000070, D01, E01, SUPPORT SERVICES, 000050, A00, E11, OPERATIONS, 000090, E01, E21, SOFTWARE SUPPORT, 000100, E01, 000010, HAAS, 3978000020, THOMPSON, 3476000030, KWAN, 4738000050, GEYER, 6789000060, STERN, 6423000070, PULASKI, 7831000090, HENDERSON, 5498000100, SPENSER, 0972000110, LUCCHESSI, 3490000120, OCONNELL, 2167000130, QUINTANA, 4578000140, NICHOLLS, 1793000150, ADAMSON, 4510000160, PIANKA, 3782000170, YOSHIMURA, 2890000180, SCOUTTEN, 1682000190, WALKER, 2986000200, BROWN, 4501000210, JONES, 0942000220, LUTZ, 0672000230, JEFFERSON, 2094000240, MARINO, 3780000250, SMITH, 0961000260, JOHNSON, 8953000270, PEREZ, 9001000280, SCHNEIDER, 8997000290, PARKER, 4502000300, SMITH, 2095000310, SETRIGHT, 3332000320, MEHTA, 9990000330, LEE, 2103000340, GOUNOT, 5698
| 113.333333 | 1,153 | 0.716912 | 824 | 6,800 | 5.842233 | 0.265777 | 0.01558 | 0.012464 | 0.012464 | 0.792688 | 0.792688 | 0.761113 | 0.741172 | 0.741172 | 0.741172 | 0 | 0.326913 | 0.144853 | 6,800 | 59 | 1,154 | 115.254237 | 0.500946 | 0.699118 | 0 | 0.171429 | 0 | 0.057143 | 0.294234 | 0.01035 | 0 | 0 | 0 | 0 | 0.028571 | 0 | null | null | 0.028571 | 0.114286 | null | null | 0.028571 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
bf3858620f0326fe84311a064fb8aa2c87260aec | 382,982 | pyt | Python | eran/NNet/nnet/ACASXU_run2a_3_9_batch_2000_16bit.pyt | pauls658/ReluDiff-ICSE2020-Artifact | 212854fe04f482183c239e5dfec70106a9a83df8 | [
"Apache-2.0"
] | 7 | 2020-01-27T21:25:49.000Z | 2022-01-07T04:37:37.000Z | eran/NNet/nnet/ACASXU_run2a_3_9_batch_2000_16bit.pyt | yqtianust/ReluDiff-ICSE2020-Artifact | 149f6efe4799602db749faa576980c36921a07c7 | [
"Apache-2.0"
] | 1 | 2022-01-25T17:41:54.000Z | 2022-01-26T02:27:51.000Z | eran/NNet/nnet/ACASXU_run2a_3_9_batch_2000_16bit.pyt | yqtianust/ReluDiff-ICSE2020-Artifact | 149f6efe4799602db749faa576980c36921a07c7 | [
"Apache-2.0"
] | 3 | 2020-03-14T17:12:17.000Z | 2022-03-16T09:50:46.000Z | ReLU
[[0.16055, -1.93743, 2.16966, 0.14185, 0.45792], [0.102468, 1.47801, -1.77639, 0.136121, -0.652599], [0.021776, 0.812071, 0.10097, 0.601396, -0.650466], [-0.00950781, 0.514973, 1.63132, -0.147942, 0.189796], [0.00905549, -0.98364, 1.10534, -0.0939109, 0.184957], [-0.335828, -2.45704, 2.51648, -0.0140635, -0.79112], [-0.0524343, 1.75508, -2.13176, -0.0700412, 0.559414], [0.157758, -1.60981, 1.31247, 0.109598, -0.236515], [0.00993427, -0.0107234, -0.0137423, 0.0118399, -0.0008458], [0.00481964, 0.0230415, -0.00382064, 8.15532e-05, 0.00961506], [-0.0596997, 0.249071, 0.431907, -0.44248, -0.0931362], [0.0454656, -0.072114, 0.215416, -0.0817889, -1.86461], [0.734947, -0.00848052, -0.0105886, 0.142879, 0.0804103], [2.22717, -0.03765, 0.0421522, 0.0236535, 0.0394441], [0.0246505, 0.588099, 0.849283, 0.42998, -0.0453454], [-0.0503312, 1.16585, -1.40683, -0.0822027, 0.259667], [-0.022408, -0.29715, -0.942976, 0.0817271, 0.0724324], [-0.0299618, 0.399278, -0.270132, -0.0161721, -1.10913], [0.00249462, -0.245844, 1.05074, -0.055542, -0.664153], [0.00563379, -1.44155, 1.95187, -0.173351, 0.641072], [-0.0612014, -0.555451, -1.33673, 0.509084, -0.330187], [0.0362268, 1.4365, -1.79658, 0.276646, -0.610691], [-0.154587, 0.803696, 0.972237, 0.735818, -0.789723], [0.0584895, -0.299756, 0.476021, 1.07472, -1.117], [0.372501, 0.874059, -0.953925, 0.00840965, 0.170885], [0.0592025, 0.496511, -0.010033, 0.185213, 0.379766], [-0.0473153, -1.09769, -0.435223, 0.529828, -0.227492], [0.0721142, 2.0345, 0.293713, 0.12686, -0.144498], [0.00470876, -1.23624, 1.59784, -0.139188, 0.39208], [0.111645, -0.453259, 1.14149, 0.460199, -0.530753], [-0.0830952, 1.85342, -0.0225459, 0.0209698, -0.686452], [-0.0170882, 0.00445536, -0.0320548, 0.325966, 0.0988783], [-0.126202, -0.6329, -0.0163662, -0.497119, 0.616975], [0.0951966, -1.25394, -1.43282, 0.169484, 0.281459], [-0.0254539, -0.921215, -0.571918, 0.395195, -0.305011], [-0.0561084, -0.595043, -0.730692, -0.357, 0.0534088], [0.00459689, 0.794308, 0.26104, -0.119769, 0.0587317], [0.180918, -0.205884, 0.518442, -0.642233, 0.72614], [-0.0300941, -0.663809, -0.11896, -0.271273, 0.0133629], [0.0201892, -0.564035, 0.591202, -0.43197, 0.466915], [-1.10763, -0.104696, -0.0627237, 0.0604764, -0.0440033], [-0.064018, -0.0407361, 0.00386754, -0.704302, 0.703241], [-2.48828, -0.213725, 0.147825, 0.0157724, -0.0164491], [0.0199359, 0.000862705, 0.00144215, -0.0170411, 0.00306012], [0.0831467, -1.35103, -0.0746848, 0.0972335, -0.0355858], [-0.704046, 0.160448, 0.859789, 0.263744, 0.153123], [-0.170612, 2.31342, -2.86201, -0.152773, 0.902843], [8.87804e-05, -1.98099, 2.09834, 0.972495, -0.860263], [0.122332, -1.09439, -0.0202138, 0.733083, -0.852861], [0.148678, -1.45188, 0.451811, 0.320701, -0.441691], [0.1605, -1.9375, 2.17, 0.1418, 0.458], [0.1025, 1.478, -1.776, 0.1361, -0.653], [0.02177, 0.812, 0.10095, 0.6016, -0.6504], [-0.00951, 0.515, 1.631, -0.148, 0.1898], [0.009056, -0.9834, 1.105, -0.09393, 0.1849], [-0.336, -2.457, 2.516, -0.01406, -0.791], [-0.05243, 1.755, -2.13, -0.07007, 0.5596], [0.1577, -1.609, 1.3125, 0.1096, -0.2366], [0.00993, -0.01073, -0.01374, 0.01184, -0.000846], [0.004818, 0.02304, -0.00382, 8.154e-05, 0.00961], [-0.0597, 0.249, 0.432, -0.4424, -0.09314], [0.04547, -0.07214, 0.2155, -0.0818, -1.864], [0.735, -0.008484, -0.01059, 0.1428, 0.0804], [2.227, -0.03766, 0.04214, 0.02365, 0.03946], [0.02464, 0.588, 0.849, 0.43, -0.04535], [-0.05032, 1.166, -1.407, -0.0822, 0.2598], [-0.02242, -0.297, -0.943, 0.0817, 0.07245], [-0.02997, 0.3992, -0.27, -0.01617, -1.109], [0.002495, -0.2458, 1.051, -0.05554, -0.664], [0.005634, -1.441, 1.952, -0.1733, 0.641], [-0.0612, -0.5557, -1.337, 0.5093, -0.33], [0.03622, 1.437, -1.797, 0.2766, -0.611], [-0.1545, 0.8037, 0.972, 0.736, -0.7896], [0.0585, -0.2998, 0.476, 1.075, -1.117], [0.3726, 0.874, -0.954, 0.00841, 0.1709], [0.0592, 0.4966, -0.01003, 0.1852, 0.38], [-0.0473, -1.098, -0.4353, 0.53, -0.2275], [0.07214, 2.035, 0.2937, 0.1268, -0.1445], [0.004707, -1.236, 1.598, -0.1392, 0.392], [0.11163, -0.4534, 1.142, 0.4602, -0.531], [-0.08307, 1.854, -0.02255, 0.02097, -0.6865], [-0.01709, 0.004456, -0.03204, 0.326, 0.0989], [-0.1262, -0.633, -0.01637, -0.497, 0.617], [0.0952, -1.254, -1.433, 0.1694, 0.2815], [-0.02545, -0.9214, -0.572, 0.3953, -0.305], [-0.05612, -0.595, -0.7305, -0.357, 0.0534], [0.004597, 0.7944, 0.261, -0.11975, 0.05875], [0.1809, -0.2059, 0.5186, -0.642, 0.726], [-0.03009, -0.6636, -0.11896, -0.2712, 0.01337], [0.02019, -0.564, 0.5913, -0.432, 0.4668], [-1.107, -0.1047, -0.06274, 0.0605, -0.044], [-0.064, -0.04074, 0.003868, -0.704, 0.703], [-2.488, -0.2137, 0.1478, 0.01578, -0.01645], [0.01994, 0.0008626, 0.001442, -0.01704, 0.00306], [0.0831, -1.351, -0.0747, 0.0972, -0.03558], [-0.704, 0.1604, 0.86, 0.2637, 0.1531], [-0.1707, 2.312, -2.861, -0.1528, 0.903], [8.875e-05, -1.981, 2.098, 0.9727, -0.8604], [0.1223, -1.095, -0.02022, 0.733, -0.853], [0.1487, -1.452, 0.452, 0.3208, -0.4417]]
[0.0306229, 0.244152, 0.0133718, 0.0288753, -0.535883, -0.102602, -0.644674, -0.4497, -0.0275754, -0.0219069, -0.0110854, -0.754237, 0.231571, 0.652223, -0.00140033, -0.54398, -0.0412755, -0.49519, -0.293582, -0.526703, -0.0222066, -0.37771, 0.0617968, 0.230671, -0.395854, 0.0292626, 0.177872, -0.0998932, -0.59666, 0.22983, -0.203298, 0.110547, 0.106396, -0.152288, -0.0270308, 0.120276, 0.0397293, 0.100904, 0.0299061, 0.128694, -0.0925063, 0.00146498, -0.565617, -0.0254389, -0.0199489, -0.105925, -0.390361, -0.257498, -0.126947, -0.232132, 0.03062, 0.2441, 0.013374, 0.02887, -0.5356, -0.1026, -0.6445, -0.4497, -0.02757, -0.02191, -0.011086, -0.7544, 0.2316, 0.6523, -0.0014, -0.544, -0.0413, -0.495, -0.2937, -0.527, -0.0222, -0.3777, 0.0618, 0.2307, -0.3958, 0.02927, 0.1779, -0.0999, -0.5967, 0.2299, -0.2032, 0.11053, 0.1064, -0.1523, -0.02702, 0.1203, 0.03973, 0.1009, 0.0299, 0.1287, -0.0925, 0.001465, -0.5654, -0.02544, -0.01994, -0.1059, -0.3904, -0.2576, -0.127, -0.2322]
ReLU
[[-1.00992, -0.972259, -0.0896375, 1.42594, 1.13969, -0.997028, -0.529877, 2.07285, -0.0433804, -0.0140184, 0.260731, -1.20925, 0.0100026, 0.603408, 1.43794, 0.408145, 0.867734, 1.08515, 0.851034, -0.466417, -0.0182557, 0.0119225, 0.0264918, -0.809281, 0.3673, -0.410726, 0.666128, 0.491479, -0.19179, -0.225307, -0.105765, 0.71746, 0.303928, 0.512574, 0.271449, 1.36935, 0.779905, 0.304166, 0.744547, 0.154391, -0.0304542, 0.718146, 2.03514, 0.0397703, 2.49627, 0.189292, -1.0865, 0.089238, 0.252777, 0.513409, 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, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0233752, 0.0306728, 0.00344369, -0.0249603, 0.0409448, 0.0278939, -0.0481839, -0.00537117, -0.0100825, 0.0294559, -0.0191859, 0.0033202, 0.00960662, -0.0278179, 0.0197037, 0.00334849, -0.000161443, -0.0166474, -0.024136, -0.0302382, 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ReLU
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[-0.940152, -0.576253, 0.135058, -0.959974, -0.405695, 0.282358, 0.550596, -0.0190258, -0.0220899, -0.940892, 0.0350431, -0.186477, -0.360106, -0.568107, 1.30177, -0.706881, 0.325825, -0.805309, -0.417132, -0.327362, -1.42743, 0.0520682, -1.20555, 1.82806, -0.0465577, -0.0395666, -0.727403, -1.22361, -1.49032, 0.0927208, 0.5289, -0.947843, -0.620888, 0.672177, -0.604776, -1.1046, -0.368531, -0.867185, -0.248366, -0.0255894, 1.93771, 0.993855, -0.341596, -0.00603874, -0.200336, 0.101579, -0.0100161, -0.578079, -0.911568, -1.04393, -0.94, -0.576, 0.135, -0.96, -0.4058, 0.2825, 0.551, -0.01903, -0.0221, -0.941, 0.03503, -0.1865, -0.36, -0.568, 1.302, -0.707, 0.326, -0.805, -0.4172, -0.3274, -1.428, 0.05206, -1.205, 1.828, -0.04657, -0.03958, -0.7275, -1.224, -1.49, 0.0927, 0.529, -0.9478, -0.621, 0.6724, -0.605, -1.1045, -0.3687, -0.867, -0.2484, -0.02559, 1.9375, 0.9937, -0.3416, -0.00604, -0.2003, 0.10156, -0.01002, -0.578, -0.9116, -1.044]
ReLU
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ReLU
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ReLU
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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, 0.0, 0.0001906, 0.000927, 0.0003197, 0.007996, -0.000152, 0.0004332, 0.001197, -4.93e-05, 5.144e-05, -0.0001944, -0.007664, 0.001267, 0.0001095, 0.000617, 5.08e-05, 9.704e-05, 9.14e-05, 0.000547, 0.00997, -7.79e-05, 0.003637, 0.000315, 0.0002396, -5.4e-07, 0.000373, 0.0006323, -0.000389, -0.0003445, -0.0005636, 0.001297, 0.001015, -0.001735, -0.000251, -0.002121, -0.000516, 0.000122, -0.00011945, 2.76e-05, 0.0009985, -0.0002084, 0.001116, 0.000605, -0.00010914, 0.001431, 0.000631, 0.0004067, 4.345e-05, -0.0001786, 4.63e-05, -0.000827], [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, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0001915, 0.000902, 8.076e-05, 0.00188, -0.000123, 0.0001476, 0.001336, -2.676e-05, 0.000225, 0.0004082, -0.00712, 0.001282, 0.0001744, 0.001443, 0.0003963, 1.884e-05, 0.0005875, 0.001083, 0.01375, 5.16e-05, 0.003313, 0.000975, 6.38e-05, -6.044e-05, 0.0007944, 0.0006585, -0.0005007, 0.0004404, -0.0005007, 0.0006285, 0.0010195, 0.0021, 0.0003383, -0.0005894, 3.46e-06, 6.956e-05, 5.21e-05, 0.001375, -9.06e-05, 0.00012016, 0.0004325, -2.956e-05, 0.001387, 0.000429, 0.0002227, -0.0002229, 0.0002713, -0.0002768, -0.0001642, -0.0001048]]
[-0.00293663, 0.0208524, -0.0188575, 0.0218464, -0.0148489, -0.002937, 0.02086, -0.01886, 0.02185, -0.01485]
| 17,408.272727 | 74,865 | 0.552504 | 104,325 | 382,982 | 2.028267 | 0.180379 | 0.480664 | 0.713808 | 0.942216 | 0.244193 | 0.243966 | 0.243966 | 0.243966 | 0.243966 | 0.243966 | 0 | 0.639146 | 0.136082 | 382,982 | 21 | 74,866 | 18,237.238095 | 0.000387 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
bd6c5743b6c999afecf355f4279bfd24a518b1a8 | 38,291 | py | Python | OmniDB/OmniDB_app/views/monitoring_units/postgresql.py | lejmr/OmniDB | 52c1c5a726a322f537a8e65f71d77ce322344d35 | [
"MIT"
] | 2,982 | 2016-04-12T13:33:50.000Z | 2022-03-31T14:16:43.000Z | OmniDB/OmniDB_app/views/monitoring_units/postgresql.py | lejmr/OmniDB | 52c1c5a726a322f537a8e65f71d77ce322344d35 | [
"MIT"
] | 704 | 2016-04-30T14:44:11.000Z | 2022-03-18T09:39:41.000Z | OmniDB/OmniDB_app/views/monitoring_units/postgresql.py | lejmr/OmniDB | 52c1c5a726a322f537a8e65f71d77ce322344d35 | [
"MIT"
] | 452 | 2016-04-25T23:50:25.000Z | 2022-03-28T15:03:52.000Z | monitoring_units = [{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 0,
'title': 'Transaction Rate',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "TPS"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from random import randint
if previous_data != None:
query = "select round((sum(xact_commit+xact_rollback) - " + previous_data["current_count"] + ")/(extract(epoch from now()::time - '" + previous_data["current_time"] + "'::time))::numeric,2) as tps, sum(xact_commit+xact_rollback) as current_count, now()::time as current_time FROM pg_stat_database"
else:
query = 'select 0 as tps, sum(xact_commit+xact_rollback) as current_count, now()::time as current_time FROM pg_stat_database'
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Rate',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]['tps']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_count": query_data.Rows[0]['current_count'],
'current_time': query_data.Rows[0]['current_time']
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 1,
'title': 'Backends',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
max_connections = connection.ExecuteScalar('SHOW max_connections')
result = {
"type": "line",
"data": None,
"options": {
"responsive": True,
"title":{
"display":True,
"text":"Backends (max_connections: " + str(max_connections) + ")"
},
"legend": {
"display": False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Value"
},
"ticks": {
"beginAtZero": True,
"max": int(max_connections)
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from random import randint
backends = connection.Query('''
SELECT count(*) as count
FROM pg_stat_activity
''')
datasets = []
datasets.append({
"label": 'Backends',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [backends.Rows[0]["count"]]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 2,
'title': 'Autovacuum Workers Usage',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "%"
},
"ticks": {
"beginAtZero": True,
"max": 100.0
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from random import randint
query_data = connection.Query('''
SELECT current_setting('autovacuum_max_workers')::bigint - (SELECT count(*) FROM pg_stat_activity WHERE query LIKE 'autovacuum: %') free,
(SELECT count(*) FROM pg_stat_activity WHERE query LIKE 'autovacuum: %') used,
current_setting('autovacuum_max_workers')::bigint total
''')
perc = round((float(query_data.Rows[0]['used']))/(float(query_data.Rows[0]['total']))*100,1)
datasets = []
datasets.append({
"label": 'Workers busy (%)',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [perc]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 3,
'title': 'WAL Production Rate',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "MB/s"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from random import randint
version = int(connection.Query('show server_version_num').Rows[0][0])
if version < 100000:
if previous_data == None:
r = connection.Query(\"\"\"
SELECT 0 as rate,
current_lsn,
current_time::text
FROM (
SELECT CASE WHEN pg_is_in_recovery() THEN null
ELSE pg_current_xlog_location()
END as current_lsn,
now()::text as current_time) t
\"\"\")
else:
r = connection.Query(\"\"\"
SELECT round((pg_xlog_location_diff(current_lsn,'\"\"\" + previous_data["current_lsn"] + \"\"\"')/1048576.0)/(extract(epoch from now()::time - '\"\"\" + previous_data["current_time"] + \"\"\"'::time))::numeric,2) as rate,
current_lsn,
current_time::text
FROM (
SELECT CASE WHEN pg_is_in_recovery() THEN null
ELSE pg_current_xlog_location()
END as current_lsn,
now() as current_time) t
\"\"\")
else:
if previous_data == None:
r = connection.Query(\"\"\"
SELECT 0 as rate,
current_lsn,
current_time::text
FROM (
SELECT CASE WHEN pg_is_in_recovery() THEN null
ELSE pg_current_wal_lsn()
END as current_lsn,
now() as current_time) t
\"\"\")
else:
r = connection.Query(\"\"\"
SELECT round((pg_wal_lsn_diff(current_lsn,'\"\"\" + previous_data["current_lsn"] + \"\"\"')/1048576.0)/(extract(epoch from now()::time - '\"\"\" + previous_data["current_time"] + \"\"\"'::time))::numeric,2) as rate,
current_lsn,
current_time::text
FROM (
SELECT CASE WHEN pg_is_in_recovery() THEN null
ELSE pg_current_wal_lsn()
END as current_lsn,
now() as current_time) t
\"\"\")
datasets = []
datasets.append({
"label": 'Rate (MB/s)',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [r.Rows[0]['rate']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_lsn": r.Rows[0]['current_lsn'],
'current_time': r.Rows[0]['current_time']
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 4,
'title': 'Temp Files Creation Rate',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "MB/s"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from random import randint
if previous_data == None:
r = connection.Query(\"\"\"
SELECT 0 as rate,
sum(temp_bytes) current_temp_bytes,
now()::text as current_time
FROM pg_stat_database
\"\"\")
else:
r = connection.Query(\"\"\"
SELECT round(((sum(temp_bytes) - \"\"\" + previous_data["current_temp_bytes"] + \"\"\")/1048576.0)/(extract(epoch from now()::time - '\"\"\" + previous_data["current_time"] + \"\"\"'::time))::numeric,2) as rate,
sum(temp_bytes) current_temp_bytes,
now()::text as current_time
FROM pg_stat_database
\"\"\")
datasets = []
datasets.append({
"label": 'Rate (MB/s)',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [r.Rows[0]['rate']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_temp_bytes": r.Rows[0]['current_temp_bytes'],
'current_time': r.Rows[0]['current_time']
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 5,
'title': 'Autovacuum Freeze',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
max_age = connection.ExecuteScalar('SHOW autovacuum_freeze_max_age')
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":True,
"text":"Autovacuum Freeze (autovacuum_freeze_max_age: " + str(max_age) + ")"
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "%"
},
"ticks": {
"beginAtZero": True,
"max": 100.0
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from random import randint
r = connection.Query('''
SELECT round(max(t.perc::numeric),2) as perc
FROM (
SELECT (greatest(age(c.relfrozenxid), age(t.relfrozenxid))::INT8 / current_setting('autovacuum_freeze_max_age')::FLOAT)*100 as perc
FROM (pg_class c
JOIN pg_namespace n ON (c.relnamespace=n.oid))
LEFT JOIN pg_class t ON c.reltoastrelid = t.oid
WHERE c.relkind = 'r') t
''')
datasets = []
datasets.append({
"label": 'Freeze (%)',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [r.Rows[0]['perc']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 6,
'title': 'Blocked Locks',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"responsive": True,
"title":{
"display":True,
"text":"Locks Blocked"
},
"legend": {
"display": False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Value"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
query_data = connection.Query('''
SELECT count(*)
FROM pg_catalog.pg_locks blocked_locks
WHERE NOT blocked_locks.GRANTED;
''')
datasets = []
datasets.append({
"label": 'Locks Blocked',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]["count"]]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 7,
'title': 'Database Size',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"responsive": True,
"title":{
"display":True,
"text":"Database Size"
},
"legend": {
"display": False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Size (MB)"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
from decimal import Decimal
query_data = connection.Query('''
SELECT sum(pg_database_size(datname)) AS sum
FROM pg_stat_database
WHERE datname IS NOT NULL
''')
datasets = []
datasets.append({
"label": 'Database Size',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [round(query_data.Rows[0]["sum"] / Decimal(1048576.0),1)]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
}, {
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 8,
'title': 'Database Growth Rate',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":True,
"text": "Database Growth Rate"
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "MB/s"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if previous_data != None:
query = '''
SELECT round(
((sum(pg_database_size(datname)) - {0})/1048576.0) / (extract(epoch from now()::time - '{1}'::time))::numeric,
2
) AS database_growth,
sum(pg_database_size(datname)) AS current_sum,
now()::text AS current_time
FROM pg_stat_database
WHERE datname IS NOT NULL
'''.format(
previous_data['current_sum'],
previous_data['current_time']
)
else:
query = '''
SELECT 0 AS database_growth,
sum(pg_database_size(datname)) AS current_sum,
now()::text AS current_time
FROM pg_stat_database
WHERE datname IS NOT NULL
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Rate',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]['database_growth']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_sum": query_data.Rows[0]['current_sum'],
'current_time': query_data.Rows[0]['current_time']
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 9,
'title': 'Heap Cache Miss Ratio',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
database_name = connection.ExecuteScalar('SELECT current_database()')
result = {
"type": "line",
"data": None,
"options": {
"responsive": True,
"title":{
"display":True,
"text":"Heap Cache Miss Ratio (Database: {0})".format(database_name)
},
"legend": {
"display": False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "%"
},
"ticks": {
"beginAtZero": True,
"max": 100.0
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if previous_data != None:
query = '''
SELECT sum(heap_blks_read) AS current_reads,
sum(heap_blks_hit) AS current_hits,
now()::time AS current_time,
CASE (sum(heap_blks_read) + sum(heap_blks_hit) - {1})
WHEN 0 THEN 0.0
ELSE
round(
((sum(heap_blks_read) - {0})*100::float / (sum(heap_blks_read) + sum(heap_blks_hit) - {1}))::numeric,
2
) END AS miss_ratio
FROM pg_statio_all_tables
'''.format(
previous_data['current_reads'],
int(previous_data['current_hits']) + int(previous_data['current_reads'])
)
else:
query = '''
SELECT sum(heap_blks_read) AS current_reads,
sum(heap_blks_hit) AS current_hits,
now()::time AS current_time,
0.0 AS miss_ratio
FROM pg_statio_all_tables
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Miss Ratio',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]["miss_ratio"]]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_reads": query_data.Rows[0]['current_reads'],
"current_hits": query_data.Rows[0]['current_hits'],
'current_time': query_data.Rows[0]['current_time']
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 10,
'title': 'Index Cache Miss Ratio',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
database_name = connection.ExecuteScalar('SELECT current_database()')
result = {
"type": "line",
"data": None,
"options": {
"responsive": True,
"title":{
"display":True,
"text":"Index Cache Miss Ratio (Database: {0})".format(database_name)
},
"legend": {
"display": False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "%"
},
"ticks": {
"beginAtZero": True,
"max": 100.0
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if previous_data != None:
query = '''
SELECT sum(idx_blks_read) AS current_reads,
sum(idx_blks_hit) AS current_hits,
now()::time AS current_time,
CASE (sum(idx_blks_read) + sum(idx_blks_hit) - {1})
WHEN 0 THEN 0.0
ELSE
round(
((sum(idx_blks_read) - {0})*100::float / (sum(idx_blks_read) + sum(idx_blks_hit) - {1}))::numeric,
2
) END AS miss_ratio
FROM pg_statio_all_tables
'''.format(
previous_data['current_reads'],
int(previous_data['current_hits']) + int(previous_data['current_reads'])
)
else:
query = '''
SELECT sum(idx_blks_read) AS current_reads,
sum(idx_blks_hit) AS current_hits,
now()::time AS current_time,
0.0 AS miss_ratio
FROM pg_statio_all_tables
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Miss Ratio',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]["miss_ratio"]]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_reads": query_data.Rows[0]['current_reads'],
"current_hits": query_data.Rows[0]['current_hits'],
'current_time': query_data.Rows[0]['current_time']
}
"""
},
{
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 11,
'title': 'Seq Scan Ratio',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
database_name = connection.ExecuteScalar('SELECT current_database()')
result = {
"type": "line",
"data": None,
"options": {
"responsive": True,
"title":{
"display":True,
"text":"Seq Scan Ratio (Database: {0})".format(database_name)
},
"legend": {
"display": False
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "%"
},
"ticks": {
"beginAtZero": True,
"max": 100.0
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if previous_data != None:
query = '''
SELECT sum(seq_scan) as current_seq,
sum(idx_scan) as current_idx,
now()::time AS current_time,
CASE (sum(seq_scan) + sum(idx_scan) - {1})
WHEN 0 THEN 0.0
ELSE
round(
((sum(seq_scan) - {0})*100::float / (sum(seq_scan) + sum(idx_scan) - {1}))::numeric,
2
) END AS ratio
FROM pg_stat_all_tables
'''.format(
previous_data['current_seq'],
int(previous_data['current_seq']) + int(previous_data['current_idx'])
)
else:
query = '''
SELECT sum(seq_scan) as current_seq,
sum(idx_scan) as current_idx,
now()::time AS current_time,
0.0 AS ratio
FROM pg_stat_all_tables
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Seq Scan Ratio',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]["ratio"]]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_seq": query_data.Rows[0]['current_seq'],
"current_idx": query_data.Rows[0]['current_idx'],
'current_time': query_data.Rows[0]['current_time']
}
"""
}, {
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 12,
'title': 'Long Transaction',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":True,
"text": "Long Transaction"
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Seconds"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if int(connection.ExecuteScalar('show server_version_num')) < 100000:
query = '''
SELECT seconds
FROM (
SELECT ROUND(EXTRACT(EPOCH FROM (clock_timestamp()-xact_start))::numeric,2) as seconds
FROM pg_stat_activity
WHERE xact_start is not null
AND datid is not null
AND query NOT LIKE 'autovacuum: %'
) x
ORDER BY seconds DESC
LIMIT 1
'''
else:
query = '''
SELECT seconds
FROM (
SELECT ROUND(EXTRACT(EPOCH FROM (clock_timestamp()-xact_start))::numeric,2) as seconds
FROM pg_stat_activity
WHERE xact_start is not null
AND datid is not null
AND backend_type NOT IN ('walreceiver','walsender','walwriter','autovacuum worker')
) x
ORDER BY seconds DESC
LIMIT 1
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Seconds',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]['seconds']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
}, {
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 13,
'title': 'Long Query',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":True,
"text": "Long Query"
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Seconds"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if int(connection.ExecuteScalar('show server_version_num')) < 100000:
query = '''
SELECT seconds
FROM (
SELECT ROUND(EXTRACT(EPOCH FROM (clock_timestamp()-query_start))::numeric,2) as seconds
FROM pg_stat_activity
WHERE state='active'
AND query_start is not null
AND datid is not null
AND query NOT LIKE 'autovacuum: %'
UNION ALL
SELECT 0.0
) x
ORDER BY seconds DESC
LIMIT 1
'''
else:
query = '''
SELECT seconds
FROM (
SELECT ROUND(EXTRACT(EPOCH FROM (clock_timestamp()-query_start))::numeric,2) as seconds
FROM pg_stat_activity
WHERE state='active'
AND query_start is not null
AND datid is not null
AND backend_type NOT IN ('walreceiver','walsender','walwriter','autovacuum worker')
UNION ALL
SELECT 0.0
) x
ORDER BY seconds DESC
LIMIT 1
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Seconds',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]['seconds']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
}, {
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 14,
'title': 'Long Autovacuum',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":True,
"text": "Long Autovacuum"
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Seconds"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if int(connection.ExecuteScalar('show server_version_num')) < 100000:
query = '''
SELECT seconds
FROM (
SELECT ROUND(EXTRACT(EPOCH FROM (clock_timestamp()-query_start))::numeric,2) as seconds
FROM pg_stat_activity
WHERE state='active'
AND query_start is not null
AND datid is not null
AND query LIKE 'autovacuum: %'
UNION ALL
SELECT 0.0
) x
ORDER BY seconds DESC
LIMIT 1
'''
else:
query = '''
SELECT seconds
FROM (
SELECT ROUND(EXTRACT(EPOCH FROM (clock_timestamp()-query_start))::numeric,2) as seconds
FROM pg_stat_activity
WHERE state='active'
AND query_start is not null
AND datid is not null
AND backend_type = 'autovacuum worker'
UNION ALL
SELECT 0.0
) x
ORDER BY seconds DESC
LIMIT 1
'''
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Seconds',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]['seconds']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets
}
"""
}, {
'dbms': 'postgresql',
'plugin_name': 'postgresql',
'id': 15,
'title': 'Checkpoints',
'type': 'timeseries',
'interval': 10,
'default': True,
'script_chart': """
result = {
"type": "line",
"data": None,
"options": {
"legend": {
"display": False
},
"responsive": True,
"title":{
"display":True,
"text": "Checkpoints"
},
"tooltips": {
"mode": "index",
"intersect": False
},
"hover": {
"mode": "nearest",
"intersect": True
},
"scales": {
"xAxes": [{
"display": True,
"scaleLabel": {
"display": False,
"labelString": "Time"
}
}],
"yAxes": [{
"display": True,
"scaleLabel": {
"display": True,
"labelString": "Checkpoints"
},
"ticks": {
"beginAtZero": True
}
}]
}
}
}
""",
'script_data': """
from datetime import datetime
if previous_data != None:
query = "select (checkpoints_timed+checkpoints_req) - " + str(previous_data["current_checkpoints"]) + " as checkpoints_diff, (checkpoints_timed+checkpoints_req) as current_checkpoints FROM pg_stat_bgwriter"
else:
query = 'select 0 as checkpoints_diff, (checkpoints_timed+checkpoints_req) as current_checkpoints FROM pg_stat_bgwriter'
query_data = connection.Query(query)
datasets = []
datasets.append({
"label": 'Checkpoints',
"backgroundColor": 'rgba(129,223,129,0.4)',
"borderColor": 'rgba(129,223,129,1)',
"lineTension": 0,
"pointRadius": 0,
"borderWidth": 1,
"data": [query_data.Rows[0]['checkpoints_diff']]
})
result = {
"labels": [datetime.now().strftime('%H:%M:%S')],
"datasets": datasets,
"current_checkpoints": query_data.Rows[0]['current_checkpoints']
}
"""
}]
| 25.595588 | 301 | 0.466611 | 3,418 | 38,291 | 5.092744 | 0.062902 | 0.038548 | 0.038605 | 0.051474 | 0.904464 | 0.880738 | 0.868501 | 0.860861 | 0.854656 | 0.835641 | 0 | 0.026679 | 0.372542 | 38,291 | 1,495 | 302 | 25.612709 | 0.697827 | 0 | 0 | 0.772632 | 0 | 0.009825 | 0.963804 | 0.139693 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.01614 | 0 | 0.01614 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
bdc8079eff8d89b3f2369de8ae89ec0df268736b | 3,056 | py | Python | test/bug-attr-mem.py | Cam2337/snap-python | 0bf722b461f8b5aae3ecb2757313521e9e9e76f1 | [
"BSD-3-Clause"
] | 242 | 2015-01-01T08:40:28.000Z | 2022-03-18T05:22:09.000Z | test/bug-attr-mem.py | Cam2337/snap-python | 0bf722b461f8b5aae3ecb2757313521e9e9e76f1 | [
"BSD-3-Clause"
] | 99 | 2015-01-24T07:55:27.000Z | 2021-10-30T18:20:13.000Z | test/bug-attr-mem.py | Cam2337/snap-python | 0bf722b461f8b5aae3ecb2757313521e9e9e76f1 | [
"BSD-3-Clause"
] | 105 | 2015-03-03T06:45:17.000Z | 2022-02-24T15:52:40.000Z | #!/usr/bin/python
#
# the memory keeps growing while traversing the graph
# find out why this is happening
#
import os, sys
sys.path.append('/home/user/snap-python/swig')
import snap as sn
import numpy as np
import memory_profiler
import gc
def writeLoop(graph):
nID = graph.BegNI()
while nID < graph.EndNI():
#write attribute
graph.AddFltAttrDatN(nID,np.random.random(1),'attr')
nID.Next()
return graph
def readLoop(graph):
NI = graph.BegNAFltI('attr')
while NI < graph.EndNAFltI('attr'):
NI.GetDat()
NI.Next()
return graph
@profile
def repeatChangeAttr(graph):
for i in range(1,10):
writeLoop(graph)
#graph = writeLoop(graph)
for i in range(1,10):
readLoop(graph)
#graph = readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
readLoop(graph)
graph = sn.TNEANet()
#graph.AddFltAttrN("attr", 1.)
for i in range(1000):
graph.AddNode(i)
repeatChangeAttr(graph)
| 20.373333 | 60 | 0.64267 | 336 | 3,056 | 5.842262 | 0.178571 | 0.715232 | 0.971982 | 1.390728 | 0.723892 | 0.723892 | 0.723892 | 0.704534 | 0.704534 | 0.704534 | 0 | 0.005245 | 0.251309 | 3,056 | 149 | 61 | 20.510067 | 0.85271 | 0.067408 | 0 | 0.827068 | 0 | 0 | 0.013732 | 0.009507 | 0 | 0 | 0 | 0 | 0 | 1 | 0.022556 | false | 0 | 0.037594 | 0 | 0.075188 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
bdcf5bbd86e13ececbb2a3e260c3108cbc2d27a3 | 197 | py | Python | pysnoonotes/errors.py | mmshivesh/PySnooNotes | 8783e12237d8f83468c4e57714d8c04d5ff020ba | [
"MIT"
] | 1 | 2020-07-18T22:18:04.000Z | 2020-07-18T22:18:04.000Z | pysnoonotes/errors.py | mmshivesh/PySnooNotes | 8783e12237d8f83468c4e57714d8c04d5ff020ba | [
"MIT"
] | 2 | 2020-07-19T16:53:33.000Z | 2020-07-20T19:40:43.000Z | pysnoonotes/errors.py | mmshivesh/PySnooNotes | 8783e12237d8f83468c4e57714d8c04d5ff020ba | [
"MIT"
] | null | null | null | class LoginFailedError(Exception):
"""Raised when login to Snoonotes API fails."""
pass
class RequestFailedError(Exception):
"""Raised when a query to Snoonotes API fails."""
pass
| 24.625 | 53 | 0.705584 | 23 | 197 | 6.043478 | 0.608696 | 0.215827 | 0.273381 | 0.273381 | 0.330935 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.192893 | 197 | 7 | 54 | 28.142857 | 0.874214 | 0.431472 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 0 | 0 | 0.5 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
bdd6f2f4adfe7d0c936e1b6c6c4ae88e4ef61dc0 | 580 | py | Python | tests/integration/flags/test_interthread_flag.py | thoughteer/edera | c4ddb5d8a25906c3bd773c91afb3260fc0b704f2 | [
"MIT"
] | 3 | 2018-11-27T15:45:19.000Z | 2018-12-21T20:32:10.000Z | tests/integration/flags/test_interthread_flag.py | thoughteer/edera | c4ddb5d8a25906c3bd773c91afb3260fc0b704f2 | [
"MIT"
] | 18 | 2018-12-02T18:38:59.000Z | 2020-02-05T22:09:37.000Z | tests/integration/flags/test_interthread_flag.py | thoughteer/edera | c4ddb5d8a25906c3bd773c91afb3260fc0b704f2 | [
"MIT"
] | null | null | null | import threading
def test_flag_can_be_raised_from_another_thread(interthread_flag):
def raise_it():
interthread_flag.up()
raiser = threading.Thread(target=raise_it)
raiser.daemon = True
raiser.start()
raiser.join()
assert interthread_flag.raised
def test_flag_can_be_lowered_from_another_thread(interthread_flag):
def raise_it():
interthread_flag.down()
interthread_flag.up()
raiser = threading.Thread(target=raise_it)
raiser.daemon = True
raiser.start()
raiser.join()
assert not interthread_flag.raised
| 21.481481 | 67 | 0.725862 | 74 | 580 | 5.351351 | 0.337838 | 0.265152 | 0.055556 | 0.070707 | 0.805556 | 0.724747 | 0.724747 | 0.724747 | 0.724747 | 0.724747 | 0 | 0 | 0.189655 | 580 | 26 | 68 | 22.307692 | 0.842553 | 0 | 0 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 1 | 0.222222 | false | 0 | 0.055556 | 0 | 0.277778 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
da16e4b28c72293de8c05d38c07c5469d0c01c4d | 124 | py | Python | alphapose/__init__.py | hjpotter92/AlphaPose | d8c2156fa088394264b72199b56408d52ac56462 | [
"Apache-2.0"
] | null | null | null | alphapose/__init__.py | hjpotter92/AlphaPose | d8c2156fa088394264b72199b56408d52ac56462 | [
"Apache-2.0"
] | null | null | null | alphapose/__init__.py | hjpotter92/AlphaPose | d8c2156fa088394264b72199b56408d52ac56462 | [
"Apache-2.0"
] | null | null | null | from .datasets import * # noqa: F401,F403
from .models import * # noqa: F401,F403
from .utils import * # noqa: F401,F403
| 31 | 42 | 0.685484 | 18 | 124 | 4.722222 | 0.444444 | 0.352941 | 0.494118 | 0.635294 | 0.517647 | 0 | 0 | 0 | 0 | 0 | 0 | 0.18 | 0.193548 | 124 | 3 | 43 | 41.333333 | 0.67 | 0.379032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
e5496f70b2e4d96204338c1e9a2aff60e04d82ce | 15,982 | py | Python | AsyncLine/lib/Gen/liff/f_LiffService.py | NeolithEra/AsyncLine | 4f84785c7f987475a11a1334b74b06892a2b1e0b | [
"MIT"
] | 42 | 2019-07-26T09:47:25.000Z | 2020-10-15T15:50:35.000Z | AsyncLine/lib/Gen/liff/f_LiffService.py | NeolithEra/AsyncLine | 4f84785c7f987475a11a1334b74b06892a2b1e0b | [
"MIT"
] | 11 | 2019-07-24T18:07:41.000Z | 2021-06-01T13:00:58.000Z | AsyncLine/lib/Gen/liff/f_LiffService.py | NeolithEra/AsyncLine | 4f84785c7f987475a11a1334b74b06892a2b1e0b | [
"MIT"
] | 28 | 2019-07-26T00:42:15.000Z | 2021-03-29T20:34:10.000Z | #
# Autogenerated by Frugal Compiler (3.4.3)
#
# DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
#
import asyncio
from datetime import timedelta
import inspect
from frugal.aio.processor import FBaseProcessor
from frugal.aio.processor import FProcessorFunction
from frugal.exceptions import TApplicationExceptionType
from frugal.exceptions import TTransportExceptionType
from frugal.middleware import Method
from frugal.transport import TMemoryOutputBuffer
from frugal.util.deprecate import deprecated
from thrift.Thrift import TApplicationException
from thrift.Thrift import TMessageType
from thrift.transport.TTransport import TTransportException
from .ttypes import *
class Iface(object):
async def issueLiffView(self, ctx, request):
"""
Args:
ctx: FContext
request: LiffViewRequest
"""
pass
async def revokeToken(self, ctx, request):
"""
Args:
ctx: FContext
request: RevokeTokenRequest
"""
pass
class Client(Iface):
def __init__(self, provider, middleware=None):
"""
Create a new Client with an FServiceProvider containing a transport
and protocol factory.
Args:
provider: FServiceProvider
middleware: ServiceMiddleware or list of ServiceMiddleware
"""
middleware = middleware or []
if middleware and not isinstance(middleware, list):
middleware = [middleware]
self._transport = provider.get_transport()
self._protocol_factory = provider.get_protocol_factory()
middleware += provider.get_middleware()
self._methods = {
'issueLiffView': Method(self._issueLiffView, middleware),
'revokeToken': Method(self._revokeToken, middleware),
}
async def issueLiffView(self, ctx, request):
"""
Args:
ctx: FContext
request: LiffViewRequest
"""
return await self._methods['issueLiffView']([ctx, request])
async def _issueLiffView(self, ctx, request):
memory_buffer = TMemoryOutputBuffer(self._transport.get_request_size_limit())
oprot = self._protocol_factory.get_protocol(memory_buffer)
oprot.write_request_headers(ctx)
oprot.writeMessageBegin('issueLiffView', TMessageType.CALL, 0)
args = issueLiffView_args()
args.request = request
args.write(oprot)
oprot.writeMessageEnd()
response_transport = await self._transport.request(ctx, memory_buffer.getvalue())
iprot = self._protocol_factory.get_protocol(response_transport)
iprot.read_response_headers(ctx)
_, mtype, _ = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
if x.type == TApplicationExceptionType.RESPONSE_TOO_LARGE:
raise TTransportException(type=TTransportExceptionType.RESPONSE_TOO_LARGE, message=x.message)
raise x
result = issueLiffView_result()
result.read(iprot)
iprot.readMessageEnd()
if result.e is not None:
raise result.e
if result.success is not None:
return result.success
raise TApplicationException(TApplicationExceptionType.MISSING_RESULT, "issueLiffView failed: unknown result")
async def revokeToken(self, ctx, request):
"""
Args:
ctx: FContext
request: RevokeTokenRequest
"""
return await self._methods['revokeToken']([ctx, request])
async def _revokeToken(self, ctx, request):
memory_buffer = TMemoryOutputBuffer(self._transport.get_request_size_limit())
oprot = self._protocol_factory.get_protocol(memory_buffer)
oprot.write_request_headers(ctx)
oprot.writeMessageBegin('revokeToken', TMessageType.CALL, 0)
args = revokeToken_args()
args.request = request
args.write(oprot)
oprot.writeMessageEnd()
response_transport = await self._transport.request(ctx, memory_buffer.getvalue())
iprot = self._protocol_factory.get_protocol(response_transport)
iprot.read_response_headers(ctx)
_, mtype, _ = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
if x.type == TApplicationExceptionType.RESPONSE_TOO_LARGE:
raise TTransportException(type=TTransportExceptionType.RESPONSE_TOO_LARGE, message=x.message)
raise x
result = revokeToken_result()
result.read(iprot)
iprot.readMessageEnd()
if result.e is not None:
raise result.e
class Processor(FBaseProcessor):
def __init__(self, handler, middleware=None):
"""
Create a new Processor.
Args:
handler: Iface
"""
if middleware and not isinstance(middleware, list):
middleware = [middleware]
super(Processor, self).__init__()
self.add_to_processor_map('issueLiffView', _issueLiffView(Method(handler.issueLiffView, middleware), self.get_write_lock()))
self.add_to_processor_map('revokeToken', _revokeToken(Method(handler.revokeToken, middleware), self.get_write_lock()))
class _issueLiffView(FProcessorFunction):
def __init__(self, handler, lock):
super(_issueLiffView, self).__init__(handler, lock)
async def process(self, ctx, iprot, oprot):
args = issueLiffView_args()
args.read(iprot)
iprot.readMessageEnd()
result = issueLiffView_result()
try:
ret = self._handler([ctx, args.request])
if inspect.iscoroutine(ret):
ret = await ret
result.success = ret
except TApplicationException as ex:
async with self._lock:
_write_application_exception(ctx, oprot, "issueLiffView", exception=ex)
return
except LiffException as e:
result.e = e
except Exception as e:
async with self._lock:
_write_application_exception(ctx, oprot, "issueLiffView", ex_code=TApplicationExceptionType.INTERNAL_ERROR, message=str(e))
raise
async with self._lock:
try:
oprot.write_response_headers(ctx)
oprot.writeMessageBegin('issueLiffView', TMessageType.REPLY, 0)
result.write(oprot)
oprot.writeMessageEnd()
oprot.get_transport().flush()
except TTransportException as e:
# catch a request too large error because the TMemoryOutputBuffer always throws that if too much data is written
if e.type == TTransportExceptionType.REQUEST_TOO_LARGE:
raise _write_application_exception(ctx, oprot, "issueLiffView", ex_code=TApplicationExceptionType.RESPONSE_TOO_LARGE, message=e.message)
else:
raise e
class _revokeToken(FProcessorFunction):
def __init__(self, handler, lock):
super(_revokeToken, self).__init__(handler, lock)
async def process(self, ctx, iprot, oprot):
args = revokeToken_args()
args.read(iprot)
iprot.readMessageEnd()
result = revokeToken_result()
try:
ret = self._handler([ctx, args.request])
if inspect.iscoroutine(ret):
ret = await ret
except TApplicationException as ex:
async with self._lock:
_write_application_exception(ctx, oprot, "revokeToken", exception=ex)
return
except LiffException as e:
result.e = e
except Exception as e:
async with self._lock:
_write_application_exception(ctx, oprot, "revokeToken", ex_code=TApplicationExceptionType.INTERNAL_ERROR, message=str(e))
raise
async with self._lock:
try:
oprot.write_response_headers(ctx)
oprot.writeMessageBegin('revokeToken', TMessageType.REPLY, 0)
result.write(oprot)
oprot.writeMessageEnd()
oprot.get_transport().flush()
except TTransportException as e:
# catch a request too large error because the TMemoryOutputBuffer always throws that if too much data is written
if e.type == TTransportExceptionType.REQUEST_TOO_LARGE:
raise _write_application_exception(ctx, oprot, "revokeToken", ex_code=TApplicationExceptionType.RESPONSE_TOO_LARGE, message=e.message)
else:
raise e
def _write_application_exception(ctx, oprot, method, ex_code=None, message=None, exception=None):
if exception is not None:
x = exception
else:
x = TApplicationException(type=ex_code, message=message)
oprot.write_response_headers(ctx)
oprot.writeMessageBegin(method, TMessageType.EXCEPTION, 0)
x.write(oprot)
oprot.writeMessageEnd()
oprot.get_transport().flush()
return x
class issueLiffView_args(object):
"""
Attributes:
- request
"""
def __init__(self, request=None):
self.request = request
def read(self, iprot):
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.request = LiffViewRequest()
self.request.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
self.validate()
def write(self, oprot):
self.validate()
oprot.writeStructBegin('issueLiffView_args')
if self.request is not None:
oprot.writeFieldBegin('request', TType.STRUCT, 1)
self.request.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(make_hashable(self.request))
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class issueLiffView_result(object):
"""
Attributes:
- success
- e
"""
def __init__(self, success=None, e=None):
self.success = success
self.e = e
def read(self, iprot):
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.STRUCT:
self.success = LiffViewResponse()
self.success.read(iprot)
else:
iprot.skip(ftype)
elif fid == 1:
if ftype == TType.STRUCT:
self.e = LiffException()
self.e.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
self.validate()
def write(self, oprot):
self.validate()
oprot.writeStructBegin('issueLiffView_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.STRUCT, 0)
self.success.write(oprot)
oprot.writeFieldEnd()
if self.e is not None:
oprot.writeFieldBegin('e', TType.STRUCT, 1)
self.e.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(make_hashable(self.success))
value = (value * 31) ^ hash(make_hashable(self.e))
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class revokeToken_args(object):
"""
Attributes:
- request
"""
def __init__(self, request=None):
self.request = request
def read(self, iprot):
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.request = RevokeTokenRequest()
self.request.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
self.validate()
def write(self, oprot):
self.validate()
oprot.writeStructBegin('revokeToken_args')
if self.request is not None:
oprot.writeFieldBegin('request', TType.STRUCT, 1)
self.request.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(make_hashable(self.request))
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class revokeToken_result(object):
"""
Attributes:
- e
"""
def __init__(self, e=None):
self.e = e
def read(self, iprot):
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.e = LiffException()
self.e.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
self.validate()
def write(self, oprot):
self.validate()
oprot.writeStructBegin('revokeToken_result')
if self.e is not None:
oprot.writeFieldBegin('e', TType.STRUCT, 1)
self.e.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __hash__(self):
value = 17
value = (value * 31) ^ hash(make_hashable(self.e))
return value
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
| 33.226611 | 156 | 0.596296 | 1,593 | 15,982 | 5.755179 | 0.118016 | 0.016798 | 0.016361 | 0.01767 | 0.778141 | 0.747382 | 0.732221 | 0.707788 | 0.702116 | 0.685973 | 0 | 0.003264 | 0.309911 | 15,982 | 480 | 157 | 33.295833 | 0.827999 | 0.041296 | 0 | 0.777159 | 1 | 0 | 0.025396 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.103064 | false | 0.005571 | 0.038997 | 0.033426 | 0.239554 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
e55b798a15c381f89b4ec836dbc216cf463b66e3 | 3,093 | py | Python | test/pyaz/monitor/action_group/__init__.py | bigdatamoore/py-az-cli | 54383a4ee7cc77556f6183e74e992eec95b28e01 | [
"MIT"
] | null | null | null | test/pyaz/monitor/action_group/__init__.py | bigdatamoore/py-az-cli | 54383a4ee7cc77556f6183e74e992eec95b28e01 | [
"MIT"
] | 9 | 2021-09-24T16:37:24.000Z | 2021-12-24T00:39:19.000Z | test/pyaz/monitor/action_group/__init__.py | bigdatamoore/py-az-cli | 54383a4ee7cc77556f6183e74e992eec95b28e01 | [
"MIT"
] | null | null | null | import json, subprocess
from ... pyaz_utils import get_cli_name, get_params
def show(resource_group, name):
params = get_params(locals())
command = "az monitor action-group show " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def create(action=None, short_name=None, tags=None, resource_group, name, __ACTION_GROUP=None):
params = get_params(locals())
command = "az monitor action-group create " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def delete(resource_group, name):
params = get_params(locals())
command = "az monitor action-group delete " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def enable_receiver(resource_group, action_group, name):
params = get_params(locals())
command = "az monitor action-group enable-receiver " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def list(resource_group=None):
params = get_params(locals())
command = "az monitor action-group list " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
def update(add_action=None, remove_action=None, resource_group, name, tags=None, short_name=None, set=None, add=None, remove=None, force_string=None):
params = get_params(locals())
command = "az monitor action-group update " + params
print(command)
output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
stdout = output.stdout.decode("utf-8")
stderr = output.stderr.decode("utf-8")
if stdout:
return json.loads(stdout)
print(stdout)
else:
raise Exception(stderr)
print(stderr)
| 35.147727 | 150 | 0.666667 | 382 | 3,093 | 5.329843 | 0.13089 | 0.082515 | 0.058939 | 0.061886 | 0.840864 | 0.840864 | 0.840864 | 0.840864 | 0.840864 | 0.840864 | 0 | 0.004953 | 0.216618 | 3,093 | 87 | 151 | 35.551724 | 0.835328 | 0 | 0 | 0.825 | 0 | 0 | 0.081151 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.025 | null | null | 0.225 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
e56b5d0dbae7d4e9dcda1f589417ddacd21e1cd9 | 4,389 | py | Python | datasetutsigsplit.py | TanweerulHaque/Signature-Verification | ee30b99d8ca8bc4a51494734791cc7b3c43002e5 | [
"MIT"
] | null | null | null | datasetutsigsplit.py | TanweerulHaque/Signature-Verification | ee30b99d8ca8bc4a51494734791cc7b3c43002e5 | [
"MIT"
] | null | null | null | datasetutsigsplit.py | TanweerulHaque/Signature-Verification | ee30b99d8ca8bc4a51494734791cc7b3c43002e5 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
import os
import shutil
def main():
for root, dirs, files in os.walk("C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig\\Forgery\\Simple"):
for dir in dirs:
for i, fp in enumerate(sorted(os.listdir(os.path.join(root, dir)))):
filepath = os.path.join(root, dir, fp)
fp = "s" + fp
filepath2 = os.path.join(
root, dir, fp).replace("Simple", "Skilled")
shutil.move(filepath, filepath2)
for root, dirs, files in os.walk("C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig\\Forgery\\Opposite Hand"):
for dir in dirs:
for i, fp in enumerate(sorted(os.listdir(os.path.join(root, dir)))):
filepath = os.path.join(root, dir, fp)
fp = "oh" + fp
filepath2 = os.path.join(root, dir, fp).replace(
"Opposite Hand", "Skilled")
shutil.move(filepath, filepath2)
count = 0
for root, dirs, files in os.walk("C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig\\Forgery\\Skilled"):
for dir in dirs:
fol = sorted(os.listdir(os.path.join(root, dir)))
for i, fp in enumerate(fol):
count += 1
filepath = os.path.join(root, dir, fp)
fp = str(count) + "ab" + fp
if i < 3:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\train\\FORGED", fp))
continue
if i < 5:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\test\\FORGED", fp))
continue
if i < 6:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\val\\FORGED", fp))
continue
if i < 7:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\train\\FORGED", fp))
continue
if i < 8:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\test\\FORGED", fp))
continue
if i < 9:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\val\\FORGED", fp))
continue
if i < 35:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\train\\FORGED", fp))
continue
if i < 44:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\test\\FORGED", fp))
continue
if i < 45:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\val\\FORGED", fp))
continue
print("Done")
countx = 0
for root, dirs, files in os.walk("C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig\\Genuine"):
for dir in dirs:
fol = sorted(os.listdir(os.path.join(root, dir)))
for i, fp in enumerate(fol):
countx += 1
filepath = os.path.join(root, dir, fp)
fp1 = str(countx) + "pq" + fp
if i < 16:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\train\\GENUINE", fp1))
continue
if i < 24:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\test\\GENUINE", fp1))
continue
if i < 27:
shutil.move(filepath, os.path.join(
"C:\\Users\\Tanweer\\Downloads\\UTSigSplit\\UTSig_updated\\val\\GENUINE", fp1))
continue
print("Done")
if __name__ == "__main__":
main()
| 45.71875 | 113 | 0.486899 | 470 | 4,389 | 4.504255 | 0.148936 | 0.062352 | 0.103921 | 0.166273 | 0.898914 | 0.846953 | 0.846953 | 0.846953 | 0.819556 | 0.784601 | 0 | 0.011289 | 0.374345 | 4,389 | 95 | 114 | 46.2 | 0.75965 | 0.004785 | 0 | 0.586207 | 0 | 0 | 0.26546 | 0.251489 | 0 | 0 | 0 | 0 | 0 | 1 | 0.011494 | false | 0 | 0.022989 | 0 | 0.034483 | 0.022989 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
e5e262e328a4937aa88a36d8a552c91e03975d19 | 138 | py | Python | ansiotropy/metrics/__init__.py | ethankim00/soft_prompt_ansiotropy | 4b0c337135264c7bfb8dd60f78544aa55f55f59d | [
"FTL"
] | null | null | null | ansiotropy/metrics/__init__.py | ethankim00/soft_prompt_ansiotropy | 4b0c337135264c7bfb8dd60f78544aa55f55f59d | [
"FTL"
] | null | null | null | ansiotropy/metrics/__init__.py | ethankim00/soft_prompt_ansiotropy | 4b0c337135264c7bfb8dd60f78544aa55f55f59d | [
"FTL"
] | null | null | null | from .ansiotropy_metrics import get_average_mev, intra_sentence_cosine_similarity, inter_context_cosine_similarity, word_cosine_similarity | 138 | 138 | 0.927536 | 18 | 138 | 6.5 | 0.777778 | 0.410256 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.043478 | 138 | 1 | 138 | 138 | 0.886364 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
e5e4ba1483b8057d2705c8404ab0b7fa4eef7449 | 14,592 | py | Python | p4v1_1/simple_router/send5.py | vibhaa/iw15 | c2a316499dbd3e7459aed2cacf0612df0b7dcec2 | [
"Apache-2.0"
] | 14 | 2019-02-25T22:42:15.000Z | 2021-12-22T06:29:20.000Z | p4v1_1/simple_router/send5.py | vibhaa/iw15 | c2a316499dbd3e7459aed2cacf0612df0b7dcec2 | [
"Apache-2.0"
] | null | null | null | p4v1_1/simple_router/send5.py | vibhaa/iw15 | c2a316499dbd3e7459aed2cacf0612df0b7dcec2 | [
"Apache-2.0"
] | 8 | 2018-11-25T11:42:24.000Z | 2021-03-11T07:23:21.000Z | #!/usr/bin/python
# Copyright 2013-present Barefoot Networks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from scapy.all import sniff, sendp
from scapy.all import Packet
from scapy.all import ShortField, IntField, LongField, BitField
from scapy.all import Ether, IP, TCP
import networkx as nx
import sys
def main():
if len(sys.argv) != 1:
print "Usage: send5.py"
sys.exit(1)
srcmac = '00:aa:bb:00:00:00'
dstmac = '00:aa:bb:00:00:01'
port = 80
msg = 'hi'
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '6.172.207.28', dst = '208.89.117.253') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '205.38.229.230', dst = '66.216.25.163') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '6.172.207.28', dst = '208.89.117.253') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '112.119.182.32', dst = '1.102.100.56') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '108.8.178.13', dst = '1.96.167.17') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '197.78.57.178', dst = '66.216.25.163') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '109.225.203.112', dst = '3.248.156.73') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.84.69.158', dst = '1.34.151.28') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '104.209.92.60', dst = '111.37.201.173') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '119.228.217.83', dst = '1.96.222.150') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '181.183.122.237', dst = '74.238.204.108') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '104.209.92.60', dst = '111.37.201.173') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '43.139.101.154', dst = '1.38.59.94') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '119.228.217.83', dst = '1.96.222.150') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '131.57.76.156', dst = '153.193.46.95') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '108.8.178.13', dst = '1.96.167.17') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '112.119.190.33', dst = '1.96.223.203') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '181.183.122.237', dst = '74.238.204.108') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '43.139.101.135', dst = '1.103.139.4') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '108.8.178.13', dst = '1.96.167.17') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '13.1.149.6', dst = '1.96.164.152') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.184.175.251', dst = '1.96.166.240') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.8.16.84', dst = '1.96.167.92') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '108.8.178.13', dst = '1.96.167.17') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '28.67.127.118', dst = '1.96.223.248') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '131.204.248.226', dst = '35.240.203.196') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '131.57.76.156', dst = '153.193.46.95') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '144.187.28.60', dst = '1.66.27.105') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '117.91.221.69', dst = '1.96.228.67') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '108.8.78.68', dst = '1.102.89.68') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '67.216.134.53', dst = '213.203.225.60') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '43.139.101.99', dst = '1.0.67.66') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '108.8.178.13', dst = '1.96.167.17') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.61.56.89', dst = '1.96.223.155') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '127.54.208.195', dst = '221.46.221.124') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.136.123.163', dst = '1.96.223.185') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.25.175.14', dst = '1.96.167.92') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '130.77.108.7', dst = '145.103.141.135') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '130.77.108.7', dst = '145.103.141.135') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.108.124.177', dst = '1.144.12.13') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '109.87.11.100', dst = '1.65.181.172') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '39.198.15.118', dst = '3.254.16.73') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '181.183.122.237', dst = '74.238.204.108') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '196.135.73.233', dst = '66.216.25.163') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '43.139.101.126', dst = '1.158.17.123') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '205.99.120.234', dst = '145.103.120.99') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.138.181.127', dst = '1.96.223.228') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.108.124.177', dst = '1.144.12.13') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.61.56.89', dst = '1.96.223.155') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.45.119.245', dst = '1.96.167.9') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '121.129.221.24', dst = '43.239.203.91') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '6.172.207.28', dst = '208.89.117.253') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '128.45.108.137', dst = '1.96.222.237') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.136.45.240', dst = '1.96.166.250') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.131.188.142', dst = '1.96.222.150') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.64.177', dst = '73.240.167.133') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '119.250.216.198', dst = '1.96.166.223') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '43.139.101.115', dst = '1.101.127.52') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '199.130.180.67', dst = '12.63.53.16') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.131.184.181', dst = '1.96.166.230') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.131.188.142', dst = '1.96.222.150') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '119.250.216.198', dst = '1.96.166.223') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '207.131.188.142', dst = '1.96.222.150') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '109.229.135.120', dst = '137.182.8.31') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '39.242.255.94', dst = '5.252.42.107') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '119.250.216.198', dst = '1.96.166.223') / msg
sendp(p, iface = "veth0", verbose = 0)
p = Ether(src=srcmac, dst=dstmac, type=0x0800) / IP(src = '1.96.91.57', dst = '111.205.228.195') / msg
sendp(p, iface = "veth0", verbose = 0)
if __name__ == '__main__':
main()
| 66.630137 | 110 | 0.596491 | 2,503 | 14,592 | 3.474231 | 0.111067 | 0.062098 | 0.093146 | 0.155244 | 0.846251 | 0.843951 | 0.843951 | 0.843951 | 0.843951 | 0.843951 | 0 | 0.204893 | 0.193257 | 14,592 | 218 | 111 | 66.93578 | 0.533809 | 0.040228 | 0 | 0.734694 | 0 | 0 | 0.200272 | 0 | 0 | 0 | 0.038596 | 0 | 0 | 0 | null | null | 0 | 0.030612 | null | null | 0.005102 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
e5f461b50826f969ab3810d16e214352398cbe11 | 164,028 | py | Python | vectorai/api/api.py | Tiamat-Tech/vectorai | 79e088a70ff79fc6bf18c6a6c0a4f367c1113648 | [
"Apache-2.0"
] | 255 | 2020-09-30T12:32:20.000Z | 2022-03-19T16:12:35.000Z | vectorai/api/api.py | Tiamat-Tech/vectorai | 79e088a70ff79fc6bf18c6a6c0a4f367c1113648 | [
"Apache-2.0"
] | 20 | 2020-10-01T06:14:35.000Z | 2021-04-12T07:22:57.000Z | vectorai/api/api.py | Tiamat-Tech/vectorai | 79e088a70ff79fc6bf18c6a6c0a4f367c1113648 | [
"Apache-2.0"
] | 33 | 2020-10-01T20:52:39.000Z | 2022-03-18T07:17:25.000Z | # This python file is auto-generated. Please do not edit.
import requests
import requests
from vectorai.api.utils import retry, return_curl_or_response
class _ViAPIClient:
def __init__(self, username, api_key, url, ):
self.username = username
self.api_key = api_key
self.url = url
@retry()
@return_curl_or_response('json')
def request_api_key(self, email, description, referral_code="api_referred", **kwargs):
"""Request an api key
Make sure to save the api key somewhere safe. If you have a valid referral code, you can recieve the api key more quickly.
Args
========
username: Username you'd like to create, lowercase only
email: Email you are using to sign up
description: Description of your intended use case
referral_code: The referral code you've been given to allow you to register for an api key before others
"""
return requests.post(
url=self.url+'/project/request_api_key',
json=dict(
username=self.username,
email=email,
description=description,
referral_code=referral_code,
))
@retry()
@return_curl_or_response('json')
def list_jobs(self,show_active_only=True, **kwargs):
return requests.get(
url=self.url+'/project/list_jobs',
params=dict(
show_active_only=show_active_only,
username=self.username,
api_key=self.api_key,
))
@retry()
@return_curl_or_response('json')
def request_read_api_key(self, read_username, **kwargs):
"""Request a read api key for your collections
Creates a read only key for your collections. Make sure to save the api key somewhere safe. When doing a search the admin username should still be used.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
read_username: Username for read only key
"""
return requests.post(
url=self.url+'/project/request_read_api_key',
json=dict(
username=self.username,
api_key=self.api_key,
read_username=read_username,
))
@retry()
@return_curl_or_response('json')
def _create_collection(self, collection_name, collection_schema={}, **kwargs):
"""Creates a collection
A collection can store documents to be **searched, retrieved, filtered and aggregated** _(similar to Collections in MongoDB, Tables in SQL, Indexes in ElasticSearch)_.
If you are inserting your own vector use the suffix (ends with) **"\_vector\_"** for the field name. and specify the length of the vector in colletion_schema like below example:
{
"collection_schema": {
"celebrity_image_vector_": 1024,
"celebrity_audio_vector" : 512,
"product_description_vector" : 128
}
}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
collection_schema: Schema for specifying the field that are vectors and its length
"""
return requests.post(
url=self.url+'/project/create_collection',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
collection_schema=collection_schema,
))
@retry()
@return_curl_or_response('json')
def _create_collection_from_document(self, collection_name, document={}, **kwargs):
"""Creates a collection by infering the schema from a document
If you are inserting your own vector use the suffix (ends with) **"\_vector\_"** for the field name. e.g. "product\_description\_vector\_"
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
document: A Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
"""
return requests.post(
url=self.url+'/project/create_collection_from_document',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
document=document,
))
@retry()
@return_curl_or_response('json')
def _delete_collection(self,collection_name, **kwargs):
return requests.get(
url=self.url+'/project/delete_collection',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def _list_collections(self,sort_by_created_at_date=False, asc=False, **kwargs):
return requests.get(
url=self.url+'/project/list_collections',
params=dict(
username=self.username,
api_key=self.api_key,
sort_by_created_at_date=sort_by_created_at_date,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def search_collections(self, collection_search_query, sort_by_created_at_date=False, asc=False, **kwargs):
"""Search collections
Search collections by their names
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_search_query: The collection search query
sort_by_created_at_date: Sort by created at date. By default shows the newest collections. Set reverse=False to get oldest collection.
asc: Sort by created at date. By default shows the newest collections. Set reverse=False to get oldest collection.
"""
return requests.post(
url=self.url+'/project/search_collections',
json=dict(
username=self.username,
api_key=self.api_key,
collection_search_query=collection_search_query,
sort_by_created_at_date=sort_by_created_at_date,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def list_collections_info(self,schema=True, stats=True, metadata=True, schema_stats=False, vector_health=False, active_jobs=False, collection_names=[], sort_by_created_at_date=False, asc=False, page_size=20, page=1, **kwargs):
return requests.get(
url=self.url+'/project/list_collections_info',
params=dict(
username=self.username,
api_key=self.api_key,
schema=schema,
stats=stats,
metadata=metadata,
schema_stats=schema_stats,
vector_health=vector_health,
active_jobs=active_jobs,
collection_names=collection_names,
sort_by_created_at_date=sort_by_created_at_date,
asc=asc,
page_size=page_size,
page=page,
))
@retry()
@return_curl_or_response('json')
def collection_stats(self,collection_name, seperate_chunks=False, **kwargs):
return requests.get(
url=self.url+'/project/collection_stats',
params=dict(
seperate_chunks=seperate_chunks,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def collection_schema(self,collection_name, **kwargs):
return requests.get(
url=self.url+'/project/collection_schema',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def collection_schema_stats(self,collection_name, include_zero_vectors=True, **kwargs):
return requests.get(
url=self.url+'/project/collection_schema_stats',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
include_zero_vectors=include_zero_vectors,
))
@retry()
@return_curl_or_response('json')
def collection_vector_health(self,collection_name, **kwargs):
return requests.get(
url=self.url+'/project/collection_vector_health',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def add_collection_metadata(self, collection_name, metadata, **kwargs):
"""Add metadata about a collection
Add metadata about a collection. notably description, data source, etc
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
metadata: Metadata for a collection, e.g. {'description' : 'collection for searching products'}
"""
return requests.post(
url=self.url+'/project/add_collection_metadata',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
metadata=metadata,
))
@retry()
@return_curl_or_response('json')
def collection_metadata(self,collection_name, **kwargs):
return requests.get(
url=self.url+'/project/collection_metadata',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def copy_collection(self, collection_name, original_collection_name, collection_schema={}, rename_fields={}, remove_fields=[], **kwargs):
"""Copy a collection into a new collection
Copy a collection into a new collection. You can use this to rename fields and change data schema. This is considered a project job.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
original_collection_name: Name of collection to copy from
collection_schema: Schema to change, if unspecified then schema is unchanged. Defaults to no schema change
rename_fields: Fields to rename {'old_field': 'new_field'}. Defaults to no renames
remove_fields: Fields to remove ['random_field', 'another_random_field']. Defaults to no removes
"""
return requests.post(
url=self.url+'/project/copy_collection',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
original_collection_name=original_collection_name,
collection_schema=collection_schema,
rename_fields=rename_fields,
remove_fields=remove_fields,
))
@retry()
@return_curl_or_response('json')
def job_status(self,job_id, **kwargs):
return requests.get(
url=self.url+'/project/job/job_status',
params=dict(
job_id=job_id,
username=self.username,
api_key=self.api_key,
))
@retry()
@return_curl_or_response('json')
def insert(self, collection_name, document={}, insert_date=True, overwrite=True, update_schema=True, quick=False, pipeline=[], **kwargs):
"""Insert a document into a Collection
When inserting the document you can specify your own id for a document by using the field name **"\_id"**.
For specifying your own vector use the suffix (ends with) **"\_vector\_"** for the field name.
e.g. "product\_description\_vector\_"
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
document: A Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
insert_date: Whether to include insert date as a field 'insert_date_'.
overwrite: Whether to overwrite document if it exists.
update_schema: Whether the api should check the documents for vector datatype to update the schema.
quick: This will run the quickest insertion possible, which means there will be no schema checks or collection checks.
pipeline: This will run pipelines for the insert. example: pipeline=["encoders"]
"""
return requests.post(
url=self.url+'/collection/insert',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
document=document,
insert_date=insert_date,
overwrite=overwrite,
update_schema=update_schema,
quick=quick,
pipeline=pipeline,
))
@retry()
@return_curl_or_response('json')
def insert_and_encode(self, encoders, collection_name, document={}, insert_date=True, overwrite=True, update_schema=True, quick=False, store_to_pipeline=True, **kwargs):
"""Insert and encode document into a Collection
Insert a document and encode specified fields into vectors with provided model urls or model names.
{
"thumbnail" : {"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url"},
"short_description" : {"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text"},
"short_description" : {"model_url" : "bert", "alias" : "bert"},
}
This primarily uses deployed models.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
encoders: An array structure of models to encode fields with.
Encoders can be a `model_url` or a `model_name`.
For model_name, the options are: `image_text`, `image`, `text`. `text_multi`, `text_image`.
Note: image_text encodes images for text to image search whereas text_image encodes texts
for text to image search (text to image search/image to text search works both ways).
For model_url, you are free to deploy your own model and specify the required body as such.
[
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_name" : "text", "body" : "text", "field": "short_description", "alias":"bert"},
{"model_name" : "image_text", "body" : "url", "field" : "thumbnail"},
]
collection_name: Name of Collection
document: A Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
insert_date: Whether to include insert date as a field 'insert_date_'.
overwrite: Whether to overwrite document if it exists.
update_schema: Whether the api should check the documents for vector datatype to update the schema.
quick: This will run the quickest insertion possible, which means there will be no schema checks or collection checks.
store_to_pipeline: Whether to store the encoders to pipeline
"""
return requests.post(
url=self.url+'/collection/insert_and_encode',
json=dict(
username=self.username,
api_key=self.api_key,
encoders=encoders,
collection_name=collection_name,
document=document,
insert_date=insert_date,
overwrite=overwrite,
update_schema=update_schema,
quick=quick,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def bulk_insert(self, collection_name, documents={}, insert_date=True, overwrite=True, update_schema=True, quick=False, pipeline=[], **kwargs):
"""Insert multiple documents into a Collection
When inserting the document you can specify your own id for a document by using the field name **"\_id"**.
For specifying your own vector use the suffix (ends with) **"\_vector\_"** for the field name.
e.g. "product\_description\_vector\_"
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
documents: A list of documents. Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
insert_date: Whether to include insert date as a field 'insert_date_'.
overwrite: Whether to overwrite document if it exists.
update_schema: Whether the api should check the documents for vector datatype to update the schema.
quick: This will run the quickest insertion possible, which means there will be no schema checks or collection checks.
pipeline: This will run pipelines for the insert. example: pipeline=["encoders"]
"""
return requests.post(
url=self.url+'/collection/bulk_insert',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
documents=documents,
insert_date=insert_date,
overwrite=overwrite,
update_schema=update_schema,
quick=quick,
pipeline=pipeline,
))
@retry()
@return_curl_or_response('json')
def bulk_insert_and_encode(self, encoders, collection_name, documents={}, insert_date=True, overwrite=True, update_schema=True, quick=False, store_to_pipeline=True, **kwargs):
"""Insert and encode multiple documents into a Collection
Insert multiple document and encode specified fields into vectors with provided model urls or model names. [
{"model_url" : ""https://a_vector_model_url.com/encode_image_url"", "body" : "url", "field" : "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field" : "short_description"},
{"model_url" : "text", "alias" : "bert", "field" : "short_description"},
]
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
encoders: An array structure of models to encode fields with.
Encoders can be a `model_url` or a `model_name`.
For model_name, the options are: `image_text`, `image`, `text`. `text_multi`, `text_image`.
Note: image_text encodes images for text to image search whereas text_image encodes texts
for text to image search (text to image search/image to text search works both ways).
For model_url, you are free to deploy your own model and specify the required body as such.
[
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_name" : "text", "body" : "text", "field": "short_description", "alias":"bert"},
{"model_name" : "image_text", "body" : "url", "field" : "thumbnail"},
]
collection_name: Name of Collection
documents: A list of documents. Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
insert_date: Whether to include insert date as a field 'insert_date_'.
overwrite: Whether to overwrite document if it exists.
update_schema: Whether the api should check the documents for vector datatype to update the schema.
quick: This will run the quickest insertion possible, which means there will be no schema checks or collection checks.
store_to_pipeline: Whether to store the encoders to pipeline
"""
return requests.post(
url=self.url+'/collection/bulk_insert_and_encode',
json=dict(
username=self.username,
api_key=self.api_key,
encoders=encoders,
collection_name=collection_name,
documents=documents,
insert_date=insert_date,
overwrite=overwrite,
update_schema=update_schema,
quick=quick,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def store_encoders_pipeline(self, encoders, collection_name, **kwargs):
"""Store encoder to the collection's pipeline
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
encoders: An array structure of models to encode fields with.
Encoders can be a `model_url` or a `model_name`.
For model_name, the options are: `image_text`, `image`, `text`. `text_multi`, `text_image`.
Note: image_text encodes images for text to image search whereas text_image encodes texts
for text to image search (text to image search/image to text search works both ways).
For model_url, you are free to deploy your own model and specify the required body as such.
[
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_name" : "text", "body" : "text", "field": "short_description", "alias":"bert"},
{"model_name" : "image_text", "body" : "url", "field" : "thumbnail"},
]
collection_name: Name of Collection
"""
return requests.post(
url=self.url+'/collection/store_encoders_pipeline',
json=dict(
username=self.username,
api_key=self.api_key,
encoders=encoders,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def remove_encoder_from_pipeline(self, collection_name, vector_fields, **kwargs):
"""Remove an encoder from the collection's encoders pipeline
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vector_fields: Vector fields that identifies an encoder to remove from pipeline
"""
return requests.post(
url=self.url+'/collection/remove_encoder_from_pipeline',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vector_fields=vector_fields,
))
@retry()
@return_curl_or_response('json')
def delete_by_id(self,document_id, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/delete_by_id',
params=dict(
document_id=document_id,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def bulk_delete_by_id(self, collection_name, document_ids, **kwargs):
"""Delete multiple documents in a Collection by ids
Delete multiple document by its ids.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
document_ids: IDs of documents
"""
return requests.post(
url=self.url+'/collection/bulk_delete_by_id',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
document_ids=document_ids,
))
@retry()
@return_curl_or_response('json')
def edit_document(self, collection_name, document_id, edits, insert_date=True, **kwargs):
"""Edit a document in a Collection by its id
Edit by providing a key value pair of fields you are adding or changing.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
document_id: ID of a document
edits: A dictionary to edit and add fields to a document.
insert_date: Whether to include insert date as a field 'insert_date_'.
"""
return requests.post(
url=self.url+'/collection/edit_document',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
document_id=document_id,
edits=edits,
insert_date=insert_date,
))
@retry()
@return_curl_or_response('json')
def bulk_edit_document(self, collection_name, documents={}, insert_date=True, **kwargs):
"""Edits multiple documents in a Collection by its ids
Edits documents by providing a key value pair of fields you are adding or changing, make sure to include the "_id" in the documents.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
documents: A list of documents. Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
insert_date: Whether to include insert date as a field 'insert_date_'.
"""
return requests.post(
url=self.url+'/collection/bulk_edit_document',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
documents=documents,
insert_date=insert_date,
))
@retry()
@return_curl_or_response('json')
def delete_document_fields(self,document_id, fields_to_delete, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/delete_document_fields',
params=dict(
document_id=document_id,
fields_to_delete=fields_to_delete,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def update_by_filters(self, collection_name, updates, filters=[], **kwargs):
"""Updates documents by filters
Updates documents by filters. The updates to make to the documents that is returned by a filter. The updates should be specified in a format of {"field_name": "value"}. e.g. {"item.status" : "Sold Out"}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
updates: Updates to make to the documents. It should be specified in a format of {"field_name": "value"}. e.g. {"item.status" : "Sold Out"}
filters: Query for filtering the search results
"""
return requests.post(
url=self.url+'/collection/update_by_filters',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
updates=updates,
filters=filters,
))
@retry()
@return_curl_or_response('json')
def delete_by_filters(self, collection_name, filters=[], **kwargs):
"""Delete documents by filters
Delete documents by filters.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
filters: Query for filtering the search results
"""
return requests.post(
url=self.url+'/collection/delete_by_filters',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
filters=filters,
))
@retry()
@return_curl_or_response('json')
def edit_search_history(self, collection_name, search_history_id, edits, **kwargs):
"""Edit search history by its id
Edit search history by providing a key value pair of fields you are adding or changing.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
search_history_id: Search history ID of the collection.
edits: A dictionary to edit and add fields to a document.
"""
return requests.post(
url=self.url+'/collection/edit_search_history',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_history_id=search_history_id,
edits=edits,
))
@retry()
@return_curl_or_response('json')
def id(self,document_id, collection_name, include_vector=True, **kwargs):
return requests.get(
url=self.url+'/collection/id',
params=dict(
document_id=document_id,
include_vector=include_vector,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def bulk_id(self,document_ids, collection_name, include_vector=True, **kwargs):
return requests.get(
url=self.url+'/collection/bulk_id',
params=dict(
document_ids=document_ids,
include_vector=include_vector,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def bulk_missing_id(self, collection_name, document_ids, **kwargs):
"""Look up in bulk if the ids exists in the collection, returns all the missing one as a list
Look up in bulk if the ids exists in the collection, returns all the missing one as a list.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
document_ids: IDs of documents
"""
return requests.post(
url=self.url+'/collection/bulk_missing_id',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
document_ids=document_ids,
))
@retry()
@return_curl_or_response('json')
def retrieve_documents(self,collection_name, include_fields=[], cursor=None, page_size=20, sort=[], asc=False, include_vector=True, **kwargs):
return requests.get(
url=self.url+'/collection/retrieve_documents',
params=dict(
include_fields=include_fields,
cursor=cursor,
page_size=page_size,
sort=sort,
asc=asc,
include_vector=include_vector,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def random_documents(self,collection_name, seed=10, include_fields=[], page_size=20, include_vector=True, **kwargs):
return requests.get(
url=self.url+'/collection/random_documents',
params=dict(
seed=seed,
include_fields=include_fields,
page_size=page_size,
include_vector=include_vector,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def retrieve_documents_with_filters(self, collection_name, include_fields=[], cursor=None, page_size=20, sort=[], asc=False, include_vector=True, filters=[], **kwargs):
"""Retrieve some documents with filters
Retrieve documents with filters.
Cursor is provided to retrieve even more documents. Loop through it to retrieve all documents in the database.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
include_fields: Fields to include in the search results, empty array/list means all fields.
cursor: Cursor to paginate the document retrieval
page_size: Size of each page of results
sort: Fields to sort by
asc: Whether to sort results by ascending or descending order
include_vector: Include vectors in the search results
filters: Query for filtering the search results
"""
return requests.post(
url=self.url+'/collection/retrieve_documents_with_filters',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
include_fields=include_fields,
cursor=cursor,
page_size=page_size,
sort=sort,
asc=asc,
include_vector=include_vector,
filters=filters,
))
@retry()
@return_curl_or_response('json')
def random_documents_with_filters(self, collection_name, seed=10, include_fields=[], page_size=20, include_vector=True, filters=[], **kwargs):
"""Retrieve some documents randomly with filters
Mainly for testing purposes.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
seed: Random Seed for retrieving random documents.
include_fields: Fields to include in the search results, empty array/list means all fields.
page_size: Size of each page of results
include_vector: Include vectors in the search results
filters: Query for filtering the search results
"""
return requests.post(
url=self.url+'/collection/random_documents_with_filters',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
seed=seed,
include_fields=include_fields,
page_size=page_size,
include_vector=include_vector,
filters=filters,
))
@retry()
@return_curl_or_response('json')
def compare_documents(self, doc, docs_to_compare, difference_fields=[], **kwargs):
"""Compare the differences between a document against multiple other documents
Compare the differences between a document and multiple other documents.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
doc: Main document to compare other documents against.
docs_to_compare: Other documents to compare against the main document.
difference_fields: Fields to compare. Defaults to [], which compares all fields.
"""
return requests.post(
url=self.url+'/collection/compare_documents',
json=dict(
username=self.username,
api_key=self.api_key,
doc=doc,
docs_to_compare=docs_to_compare,
difference_fields=difference_fields,
))
@retry()
@return_curl_or_response('json')
def retrieve_search_history(self,**kwargs):
return requests.get(
url=self.url+'/collection/retrieve_search_history',
params=dict(
))
@retry()
@return_curl_or_response('json')
def id_search_history(self,**kwargs):
return requests.get(
url=self.url+'/collection/id_search_history',
params=dict(
))
@retry()
@return_curl_or_response('json')
def _search(self,vector, collection_name, search_fields, search_history_id, approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, asc=False, keep_search_history=True, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, **kwargs):
return requests.get(
url=self.url+'/collection/search',
params=dict(
vector=vector,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_fields=search_fields,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
asc=asc,
keep_search_history=keep_search_history,
search_history_id=search_history_id,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
))
@retry()
@return_curl_or_response('json')
def search_by_id(self,document_id, collection_name, search_field, approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, **kwargs):
return requests.get(
url=self.url+'/collection/search_by_id',
params=dict(
document_id=document_id,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_field=search_field,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def search_by_ids(self,document_ids, collection_name, search_field, vector_operation="sum", approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, **kwargs):
return requests.get(
url=self.url+'/collection/search_by_ids',
params=dict(
document_ids=document_ids,
vector_operation=vector_operation,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_field=search_field,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def search_by_positive_negative_ids(self,positive_document_ids, negative_document_ids, collection_name, search_field, vector_operation="sum", approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, **kwargs):
return requests.get(
url=self.url+'/collection/search_by_positive_negative_ids',
params=dict(
positive_document_ids=positive_document_ids,
negative_document_ids=negative_document_ids,
vector_operation=vector_operation,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_field=search_field,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def search_with_positive_negative_ids_as_history(self,vector, positive_document_ids, negative_document_ids, collection_name, search_field, vector_operation="sum", approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, **kwargs):
return requests.get(
url=self.url+'/collection/search_with_positive_negative_ids_as_history',
params=dict(
vector=vector,
positive_document_ids=positive_document_ids,
negative_document_ids=negative_document_ids,
vector_operation=vector_operation,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_field=search_field,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def encode(self, encoders, document, **kwargs):
"""Encode document into vectors
Get a document and encode specified fields into vectors with provided model urls or model names. [
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_url" : "bert", "body" : "text", "field": "short_description", "alias":"bert"},
]
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
encoders: An array structure of models to encode fields with.
Encoders can be a `model_url` or a `model_name`.
For model_name, the options are: `image_text`, `image`, `text`. `text_multi`, `text_image`.
Note: image_text encodes images for text to image search whereas text_image encodes texts
for text to image search (text to image search/image to text search works both ways).
For model_url, you are free to deploy your own model and specify the required body as such.
[
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_name" : "text", "body" : "text", "field": "short_description", "alias":"bert"},
{"model_name" : "image_text", "body" : "url", "field" : "thumbnail"},
]
document: A json document to encode.
"""
return requests.post(
url=self.url+'/collection/encode',
json=dict(
username=self.username,
api_key=self.api_key,
encoders=encoders,
document=document,
))
@retry()
@return_curl_or_response('json')
def bulk_encode(self, encoders, documents, **kwargs):
"""Bulk encode document into vectors
Get a document and encode specified fields into vectors with provided model urls or model names. {
[
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_url" : "bert", "body" : "text", "field": "short_description", "alias":"bert"},
]
}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
encoders: An array structure of models to encode fields with.
Encoders can be a `model_url` or a `model_name`.
For model_name, the options are: `image_text`, `image`, `text`. `text_multi`, `text_image`.
Note: image_text encodes images for text to image search whereas text_image encodes texts
for text to image search (text to image search/image to text search works both ways).
For model_url, you are free to deploy your own model and specify the required body as such.
[
{"model_url" : "https://a_vector_model_url.com/encode_image_url", "body" : "url", "field": "thumbnail"},
{"model_url" : "https://a_vector_model_url.com/encode_text", "body" : "text", "field": "short_description"},
{"model_name" : "text", "body" : "text", "field": "short_description", "alias":"bert"},
{"model_name" : "image_text", "body" : "url", "field" : "thumbnail"},
]
documents: Json documents to encode.
"""
return requests.post(
url=self.url+'/collection/bulk_encode',
json=dict(
username=self.username,
api_key=self.api_key,
encoders=encoders,
documents=documents,
))
@retry()
@return_curl_or_response('json')
def predict_knn_regression(self, collection_name, vector, search_field, target_field, impute_value, k=5, weighting=True, predict_operation="mean", **kwargs):
"""Predict KNN regression.
Predict with KNN regression using normal search.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vector: Vector, a list/array of floats that represents a piece of data
search_field: The field to search with.
target_field: The field to perform regression on.
k: The number of results for KNN.
weighting: weighting
impute_value: What value to fill if target field is missing.
predict_operation: How to predict using the vectors.
"""
return requests.post(
url=self.url+'/collection/predict_knn_regression',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vector=vector,
search_field=search_field,
target_field=target_field,
k=k,
weighting=weighting,
impute_value=impute_value,
predict_operation=predict_operation,
))
@retry()
@return_curl_or_response('json')
def predict_knn_regression_from_results(self, field, results, impute_value, weighting=True, predict_operation="mean", **kwargs):
"""Predict KNN regression from search results
Predict using KNN regression from search results
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
field: Field in results to weigh on.
results: List of results in a dictionary
weighting: weighting of the actual vectors
impute_value: The impute value
predict_operation: How to predict using the vectors.
"""
return requests.post(
url=self.url+'/collection/predict_knn_regression_from_results',
json=dict(
username=self.username,
api_key=self.api_key,
field=field,
results=results,
weighting=weighting,
impute_value=impute_value,
predict_operation=predict_operation,
))
@retry()
@return_curl_or_response('json')
def facets(self,collection_name, facets_fields=[], date_interval="monthly", page_size=1000, page=1, asc=False, **kwargs):
return requests.get(
url=self.url+'/collection/facets',
params=dict(
facets_fields=facets_fields,
date_interval=date_interval,
page_size=page_size,
page=page,
asc=asc,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def filters(self, collection_name, filters=[], page=1, page_size=20, asc=False, include_vector=False, sort=[], **kwargs):
"""Filters a collection
Filter is used to retrieve documents that match the conditions set in a filter query. This is used in advance search to filter the documents that are searched.
The filters query is a json body that follows the schema of:
[
{'field' : <field to filter>, 'filter_type' : <type of filter>, "condition":"==", "condition_value":"america"},
{'field' : <field to filter>, 'filter_type' : <type of filter>, "condition":">=", "condition_value":90},
]
These are the available filter_type types:
1. "contains": for filtering documents that contains a string.
{'field' : 'item_brand', 'filter_type' : 'contains', "condition":"==", "condition_value": "samsu"}
2. "exact_match"/"category": for filtering documents that matches a string or list of strings exactly.
{'field' : 'item_brand', 'filter_type' : 'category', "condition":"==", "condition_value": "sumsung"}
3. "categories": for filtering documents that contains any of a category from a list of categories.
{'field' : 'item_category_tags', 'filter_type' : 'categories', "condition":"==", "condition_value": ["tv", "smart", "bluetooth_compatible"]}
4. "exists": for filtering documents that contains a field.
{'field' : 'purchased', 'filter_type' : 'exists', "condition":"==", "condition_value":" "}
If you are looking to filter for documents where a field doesn't exist, run this:
{'field' : 'purchased', 'filter_type' : 'exists', "condition":"!=", "condition_value":" "}
5. "date": for filtering date by date range.
{'field' : 'insert_date_', 'filter_type' : 'date', "condition":">=", "condition_value":"2020-01-01"}
6. "numeric": for filtering by numeric range.
{'field' : 'price', 'filter_type' : 'numeric', "condition":">=", "condition_value":90}
7. "ids": for filtering by document ids.
{'field' : 'ids', 'filter_type' : 'ids', "condition":"==", "condition_value":["1", "10"]}
These are the available conditions:
"==", "!=", ">=", ">", "<", "<="
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
filters: Query for filtering the search results
page: Page of the results
page_size: Size of each page of results
asc: Whether to sort results by ascending or descending order
include_vector: Include vectors in the search results
sort: Fields to sort by
"""
return requests.post(
url=self.url+'/collection/filters',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
filters=filters,
page=page,
page_size=page_size,
asc=asc,
include_vector=include_vector,
sort=sort,
))
@retry()
@return_curl_or_response('json')
def advanced_search(self, collection_name, multivector_query, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Vector Similarity Search. Support for multiple vectors, vector weightings, facets and filtering
Advanced Vector Similarity Search, enables machine learning search with vector search. Search with a multiple vectors for the most similar documents.
For example: Search with a product image and description vectors to find the most similar products by what it looks like and what its described to do.
You can also give weightings of each vector field towards the search, e.g. image\_vector\_ weights 100%, whilst description\_vector\_ 50%.
Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
multivector_query: Query for advance search that allows for multiple vector and field querying
"""
return requests.post(
url=self.url+'/collection/advanced_search',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
multivector_query=multivector_query,
))
@retry()
@return_curl_or_response('json')
def advanced_search_by_id(self, collection_name, document_id, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Single Product Recommendations
Single Product Recommendations (Search by an id).
For example: Search with id of a product in the database, and using the product's image and description vectors to find the most similar products by what it looks like and what its described to do.
You can also give weightings of each vector field towards the search, e.g. image\_vector\_ weights 100%, whilst description\_vector\_ 50%.
Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
document_id: ID of a document
search_fields: Vector fields to search against, and the weightings for them.
"""
return requests.post(
url=self.url+'/collection/advanced_search_by_id',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
document_id=document_id,
search_fields=search_fields,
))
@retry()
@return_curl_or_response('json')
def advanced_search_by_ids(self, collection_name, document_ids, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, vector_operation="sum", **kwargs):
"""Advanced Multi Product Recommendations
Advanced Multi Product Recommendations (Search by ids).
For example: Search with multiple ids of products in the database, and using the product's image and description vectors to find the most similar products by what it looks like and what its described to do.
You can also give weightings of each vector field towards the search, e.g. image\_vector\_ weights 100%, whilst description\_vector\_ 50%.
You can also give weightings of on each product as well e.g. product ID-A weights 100% whilst product ID-B 50%.
Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
document_ids: Document IDs to get recommendations for, and the weightings of each document
search_fields: Vector fields to search against, and the weightings for them.
vector_operation: Aggregation for the vectors, choose from ['mean', 'sum', 'min', 'max']
"""
return requests.post(
url=self.url+'/collection/advanced_search_by_ids',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
document_ids=document_ids,
search_fields=search_fields,
vector_operation=vector_operation,
))
@retry()
@return_curl_or_response('json')
def advanced_search_by_positive_negative_ids(self, collection_name, positive_document_ids, negative_document_ids, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, vector_operation="sum", **kwargs):
"""Advanced Multi Product Recommendations with likes and dislikes
Advanced Multi Product Recommendations with Likes and Dislikes (Search by ids).
For example: Search with multiple ids of liked and dislike products in the database. Then using the product's image and description vectors to find the most similar products by what it looks like and what its described to do against the positives and most disimilar products for the negatives.
You can also give weightings of each vector field towards the search, e.g. image\_vector\_ weights 100%, whilst description\_vector\_ 50%.
You can also give weightings of on each product as well e.g. product ID-A weights 100% whilst product ID-B 50%.
Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
positive_document_ids: Positive Document IDs to get recommendations for, and the weightings of each document
negative_document_ids: Negative Document IDs to get recommendations for, and the weightings of each document
search_fields: Vector fields to search against, and the weightings for them.
vector_operation: Aggregation for the vectors, choose from ['mean', 'sum', 'min', 'max']
"""
return requests.post(
url=self.url+'/collection/advanced_search_by_positive_negative_ids',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
positive_document_ids=positive_document_ids,
negative_document_ids=negative_document_ids,
search_fields=search_fields,
vector_operation=vector_operation,
))
@retry()
@return_curl_or_response('json')
def advanced_search_with_positive_negative_ids_as_history(self, collection_name, multivector_query, positive_document_ids, negative_document_ids, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, vector_operation="sum", **kwargs):
"""Advanced Search with Likes and Dislikes as history
For example: Vector search of a query vector with multiple ids of liked and dislike products in the database. Then using the product's image and description vectors to find the most similar products by what it looks like and what its described to do against the positives and most disimilar products for the negatives.
You can also give weightings of each vector field towards the search, e.g. image\_vector\_ weights 100%, whilst description\_vector\_ 50%.
You can also give weightings of on each product as well e.g. product ID-A weights 100% whilst product ID-B 50%.
Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
multivector_query: Query for advance search that allows for multiple vector and field querying
positive_document_ids: Positive Document IDs to get recommendations for, and the weightings of each document
negative_document_ids: Negative Document IDs to get recommendations for, and the weightings of each document
vector_operation: Aggregation for the vectors, choose from ['mean', 'sum', 'min', 'max']
"""
return requests.post(
url=self.url+'/collection/advanced_search_with_positive_negative_ids_as_history',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
multivector_query=multivector_query,
positive_document_ids=positive_document_ids,
negative_document_ids=negative_document_ids,
vector_operation=vector_operation,
))
@retry()
@return_curl_or_response('json')
def aggregate(self, collection_name, aggregation_query, filters=[], page_size=20, page=1, asc=False, flatten=True, **kwargs):
"""Aggregate a collection
Aggregation/Groupby of a collection using an aggregation query.
The aggregation query is a json body that follows the schema of:
{
"groupby" : [
{"name": <alias>, "field": <field in the collection>, "agg": "category"},
{"name": <alias>, "field": <another groupby field in the collection>, "agg": "numeric"}
],
"metrics" : [
{"name": <alias>, "field": <numeric field in the collection>, "agg": "avg"}
{"name": <alias>, "field": <another numeric field in the collection>, "agg": "max"}
]
}
For example, one can use the following aggregations to group score based on region and player name.
{
"groupby" : [
{"name": "region", "field": "player_region", "agg": "category"},
{"name": "player_name", "field": "name", "agg": "category"}
],
"metrics" : [
{"name": "average_score", "field": "final_score", "agg": "avg"},
{"name": "max_score", "field": "final_score", "agg": "max"},
{'name':'total_score','field':"final_score", 'agg':'sum'},
{'name':'average_deaths','field':"final_deaths", 'agg':'avg'},
{'name':'highest_deaths','field':"final_deaths", 'agg':'max'},
]
}
- "groupby" is the fields you want to split the data into. These are the available groupby types:
- category" : groupby a field that is a category
- numeric: groupby a field that is a numeric
- "metrics" is the fields you want to metrics you want to calculate in each of those, every aggregation includes a frequency metric. These are the available metric types:
- "avg", "max", "min", "sum", "cardinality"
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
aggregation_query: Aggregation query to aggregate data
filters: Query for filtering the search results
page_size: Size of each page of results
page: Page of the results
asc: Whether to sort results by ascending or descending order
flatten:
"""
return requests.post(
url=self.url+'/collection/aggregate',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
aggregation_query=aggregation_query,
filters=filters,
page_size=page_size,
page=page,
asc=asc,
flatten=flatten,
))
@retry()
@return_curl_or_response('json')
def aggregate_fetch(self, collection_name, aggregation_query, filters=[], page_size=20, page=1, asc=False, flatten=True, **kwargs):
"""Aggregate a collection and fetch the documents
Perform an aggregation and then a Bulk ID Lookup using IDs of the aggregated results to get the documents alongside the aggregations.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
aggregation_query: Aggregation query to aggregate data
filters: Query for filtering the search results
page_size: Size of each page of results
page: Page of the results
asc: Whether to sort results by ascending or descending order
flatten:
"""
return requests.post(
url=self.url+'/collection/aggregate_fetch',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
aggregation_query=aggregation_query,
filters=filters,
page_size=page_size,
page=page,
asc=asc,
flatten=flatten,
))
@retry()
@return_curl_or_response('json')
def traditional_search(self,collection_name, text, text_fields, search_history_id, fuzzy=-1, join=True, page_size=20, page=1, include_fields=[], include_vector=False, include_count=True, asc=False, keep_search_history=True, **kwargs):
return requests.get(
url=self.url+'/collection/traditional_search',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
text=text,
text_fields=text_fields,
fuzzy=fuzzy,
join=join,
page_size=page_size,
page=page,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
asc=asc,
keep_search_history=keep_search_history,
search_history_id=search_history_id,
))
@retry()
@return_curl_or_response('json')
def hybrid_search(self,text, vector, text_fields, collection_name, search_fields, search_history_id, traditional_weight=0.075, fuzzy=-1, join=True, approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, asc=False, keep_search_history=True, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, **kwargs):
return requests.get(
url=self.url+'/collection/hybrid_search',
params=dict(
text=text,
vector=vector,
text_fields=text_fields,
traditional_weight=traditional_weight,
fuzzy=fuzzy,
join=join,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_fields=search_fields,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
asc=asc,
keep_search_history=keep_search_history,
search_history_id=search_history_id,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
))
@retry()
@return_curl_or_response('json')
def advanced_hybrid_search(self, collection_name, multivector_query, text, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, text_fields=[], traditional_weight=0.075, fuzzy=-1, join=True, **kwargs):
"""Advanced Search a text field with vector and text using Vector Search and Traditional Search
Advanced Vector similarity search + Traditional Fuzzy Search with text and vector.
You can also give weightings of each vector field towards the search, e.g. image\_vector\_ weights 100%, whilst description\_vector\_ 50%.
Advanced search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
multivector_query: Query for advance search that allows for multiple vector and field querying
text: Text Search Query (not encoded as vector)
text_fields: Text fields to search against
traditional_weight: Multiplier of traditional search. A value of 0.025~0.1 is good.
fuzzy: Fuzziness of the search. A value of 1-3 is good. For automated fuzzines use -1.
join: Whether to consider cases where there is a space in the word. E.g. Go Pro vs GoPro.
"""
return requests.post(
url=self.url+'/collection/advanced_hybrid_search',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
multivector_query=multivector_query,
text=text,
text_fields=text_fields,
traditional_weight=traditional_weight,
fuzzy=fuzzy,
join=join,
))
@retry()
@return_curl_or_response('json')
def job_status(self,job_id, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/job/job_status',
params=dict(
job_id=job_id,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def list_collection_jobs(self,collection_name, show_active_only=True, **kwargs):
return requests.get(
url=self.url+'/collection/job/list_collection_jobs',
params=dict(
show_active_only=show_active_only,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_image_field(self, collection_name, image_field, task="image", alias="", refresh=False, store_to_pipeline=True, **kwargs):
"""Start job to encode image field
Encode an image field with a model to add vectors to the collection, you can specify the task of "image" or "image_text".
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
image_field: The document image field to encode.
task: The name of the task for the job. "image" for encoding image fields with image based models, "image_text" for encoding images that can be searched against text
alias: Alias is used to uniquely identify vector fields
refresh: If True, overwrite all encoded vectors, otherwise it just encodes the fields that don't have vectors.
store_to_pipeline: Whether to store the encoder to the encoders pipeline
"""
return requests.post(
url=self.url+'/collection/job/encode_image_field',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
image_field=image_field,
task=task,
alias=alias,
refresh=refresh,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def encode_text_field(self, collection_name, text_field, task="text", alias="", refresh=False, store_to_pipeline=True, **kwargs):
"""Start job to encode text field
Encode a text field with a model to add vectors to the collection, you can specify the task of "text", "text_image" or "text_multi".
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
text_field: The document text field to encode.
task: The name of the task for the job. "text" for encoding english models, "text_multi" for encoding multilanguage models, "text_image" for encoding text that can be searched with images
alias: Alias is used to uniquely identify vector fields
refresh: If True, overwrite all encoded vectors, otherwise it just encodes the fields that don't have vectors.
store_to_pipeline: Whether to store the encoder to the encoders pipeline
"""
return requests.post(
url=self.url+'/collection/job/encode_text_field',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
text_field=text_field,
task=task,
alias=alias,
refresh=refresh,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster(self, collection_name, vector_field, n_clusters, alias="default", task="cluster", n_iter=10, n_init=5, refresh=False, store_to_pipeline=True, **kwargs):
"""Start job to cluster a vector field
Clusters a collection into groups using unsupervised machine learning. Clusters can then be aggregated to understand whats in them and how vectors are seperating data into different groups.
Advanced cluster allows for more parameters to tune and alias to name each differently trained clusters.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vector_field: Vector field to perform clustering on
alias: Alias is used to name a cluster
task: The name of the task for the job.
n_clusters: Number of clusters
n_iter: Number of iterations in each run
n_init: Number of runs to run with different centroid seeds
refresh: If True, overwrite all labelled clusters, otherwise it just labels the fields that don't have clusters.
store_to_pipeline: Whether to store the cluster model to the clusters pipeline
"""
return requests.post(
url=self.url+'/collection/job/advanced_cluster',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vector_field=vector_field,
alias=alias,
task=task,
n_clusters=n_clusters,
n_iter=n_iter,
n_init=n_init,
refresh=refresh,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def dimensionality_reduction(self, collection_name, vector_field, n_components, alias="default", task="dimensionality_reduction", refresh=False, store_to_pipeline=True, **kwargs):
"""Start job to dimensionality reduce a vector field
Dimensionality reduction allows your vectors to be reduced down to any dimensions greater than 0 using unsupervised machine learning.
This is useful for even faster search and visualising the vectors.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vector_field: Vector field to perform dimensionality reduction on
alias: Alias is used to name a dimensionality reduced vector field
task: The name of the task for the job.
n_components: The size/length to reduce the vector down to. If 0 is set then highest possible is of components is set, when this is done you can get reduction on demand of any length.
refresh: If True, overwrite all labelled dimensionality reduced fields, otherwise it just adds the fields that don't have dimensionality reduced fields.
store_to_pipeline: Whether to store the dimensionality reduction model to the dimensionality reductions pipeline
"""
return requests.post(
url=self.url+'/collection/job/dimensionality_reduction',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vector_field=vector_field,
alias=alias,
task=task,
n_components=n_components,
refresh=refresh,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def tag_vector_job(self, collection_name, tag_collection_name, vector_field, hub_username, hub_api_key, tag_field="tag", tag_vector_field="tag_vector_", field="", alias="default", metric="cosine", number_of_tags=5, include_tag_vector=True, refresh=False, store_to_pipeline=True, **kwargs):
"""Start job for tagging vectors
Search for a tag and then encode
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
tag_collection_name: Name of the collection you are retrieving the tags from
vector_field: vector field from the source collection to tag on
tag_field: The field in the tag collection to use for tagging.
tag_vector_field: vector field in the tag collection, used for matching the vectors to tag.
field: The field in the source collection to be tagged.
alias: The alias of the tags. Defaults to 'default'
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
number_of_tags: The number of tags to retrieve.
include_tag_vector: Whether to include the one hot encoded tag vector.
refresh: If True, Re-tags from scratch.
hub_username: The username of the hub account.
hub_api_key: The API key of the hub account.
store_to_pipeline: Whether to store the encoders to pipeline
"""
return requests.post(
url=self.url+'/collection/job/tag_vector_job',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
tag_collection_name=tag_collection_name,
vector_field=vector_field,
tag_field=tag_field,
tag_vector_field=tag_vector_field,
field=field,
alias=alias,
metric=metric,
number_of_tags=number_of_tags,
include_tag_vector=include_tag_vector,
refresh=refresh,
hub_username=hub_username,
hub_api_key=hub_api_key,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def tag_job(self, collection_name, tag_collection_name, hub_username, hub_api_key, field="", encoder_task="text", tag_field="tag", tag_vector_field="tag_vector_", alias="default", metric="cosine", number_of_tags=5, include_tag_vector=True, refresh=False, store_to_pipeline=True, **kwargs):
"""Start a job for encoding a field and then tagging
Encode using an encoder and tag
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
tag_collection_name: Name of the collection you are retrieving the tags from
field: The field in the source collection to be tagged.
encoder_task: Name of the task to run an encoding job on. This can be one of text, text-image, text-multi, image-text.
tag_field: The field in the tag collection to use for tagging.
tag_vector_field: vector field in the tag collection, used for matching the vectors to tag.
alias: The alias of the tags. Defaults to 'default'
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
number_of_tags: The number of tags to retrieve.
include_tag_vector: Whether to include the one hot encoded tag vector.
refresh: If True, Re-tags from scratch.
hub_username: The username of the hub account.
hub_api_key: The API key of the hub account.
store_to_pipeline: Whether to store the encoders to pipeline
"""
return requests.post(
url=self.url+'/collection/job/tag_job',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
tag_collection_name=tag_collection_name,
field=field,
encoder_task=encoder_task,
tag_field=tag_field,
tag_vector_field=tag_vector_field,
alias=alias,
metric=metric,
number_of_tags=number_of_tags,
include_tag_vector=include_tag_vector,
refresh=refresh,
hub_username=hub_username,
hub_api_key=hub_api_key,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def text_chunking(self, collection_name, text_field, chunk_field, insert_results_to_seperate_collection_name, refresh=True, store_to_pipeline=True, **kwargs):
"""Chunk a text field
Split text into separate sentences. Encode each sentence to create chunkvectors.
These are stored as _chunkvector_. The chunk field created is `field` + _chunk_.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
text_field: Text field to chunk
chunk_field: Whats the field that the text chunks will belong in
refresh: Whether to refresh the whole collection and re-encode all to vectors
insert_results_to_seperate_collection_name: If specified the chunks will be inserted into a seperate collection. Default is None which means no seperate collection.
store_to_pipeline: Whether to store the encoder to the chunking pipeline
"""
return requests.post(
url=self.url+'/collection/job/text_chunking',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
text_field=text_field,
chunk_field=chunk_field,
refresh=refresh,
insert_results_to_seperate_collection_name=insert_results_to_seperate_collection_name,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def text_chunking_encoder(self, collection_name, text_field, chunk_field, insert_results_to_seperate_collection_name, encoder_task="text", refresh=True, store_to_pipeline=True, **kwargs):
"""Chunk a text field and encode the chunks
Split text into separate sentences. Encode each sentence to create chunkvectors.
These are stored as \_chunkvector\_. The chunk field created is `field` + \_chunk\_.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
text_field: Text field to chunk
chunk_field: Whats the field that the text chunks will belong in
encoder_task: Encoder that is used to turn the text chunks into vectors
refresh: Whether to refresh the whole collection and re-encode all to vectors
insert_results_to_seperate_collection_name: If specified the chunks will be inserted into a seperate collection. Default is None which means no seperate collection.
store_to_pipeline: Whether to store the encoder to the chunking pipeline
"""
return requests.post(
url=self.url+'/collection/job/text_chunking_encoder',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
text_field=text_field,
chunk_field=chunk_field,
encoder_task=encoder_task,
refresh=refresh,
insert_results_to_seperate_collection_name=insert_results_to_seperate_collection_name,
store_to_pipeline=store_to_pipeline,
))
@retry()
@return_curl_or_response('json')
def process_pdf(self, collection_name, file_url, filename, **kwargs):
"""Process pdf
Insert a PDF into Vector AI.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: What collection to insert the PDF into
file_url: The file url blob
filename: The name of the PDF file.
"""
return requests.post(
url=self.url+'/collection/job/process_pdf',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
file_url=file_url,
filename=filename,
))
@retry()
@return_curl_or_response('json')
def process_doc(self, collection_name, file_url, filename, **kwargs):
"""Process doc or docx
Insert a word doc or docx file into Vector AI
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: What collection to insert the word doc into
file_url: The file url blob
filename: The name of the Doc or DocX file
"""
return requests.post(
url=self.url+'/collection/job/process_doc',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
file_url=file_url,
filename=filename,
))
@retry()
@return_curl_or_response('json')
def copy_collection_from_another_user(self, collection_name, source_collection_name, source_username, source_api_key, **kwargs):
"""Copy a collection from another user's projects into your project
Copy a collection from another user's projects into your project. This is considered a project job
Args
========
collection_name: Collection to copy into
username: Your username
api_key: Your api key to access the username
source_collection_name: Collection to copy frpm
source_username: Source username of whom the collection belongs to
source_api_key: Api key to access the source username
"""
return requests.post(
url=self.url+'/project/copy_collection_from_another_user',
json=dict(
collection_name=collection_name,
username=self.username,
api_key=self.api_key,
source_collection_name=source_collection_name,
source_username=source_username,
source_api_key=source_api_key,
))
@retry()
@return_curl_or_response('json')
def chunk_search(self, collection_name, chunk_field, vector, search_fields, chunk_scoring="max", page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_vector=False, include_count=True, include_facets=False, hundred_scale=False, asc=False, chunk_page=1, chunk_page_size=3, **kwargs):
"""Vector Similarity Search on Chunks.
Chunk Search allows one to search through chunks inside a document. The major difference
between chunk search and normal search in Vector AI is that it relies on the `_chunkvector_`
field.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
chunk_field: Field that the array of chunked documents are.
chunk_scoring: Scoring method for determining for ranking between document chunks.
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
asc: Whether to sort results by ascending or descending order
vector: Vector, a list/array of floats that represents a piece of data
search_fields: Vector fields to search against
chunk_page: Page of the chunk results
chunk_page_size: Size of each page of chunk results
"""
return requests.post(
url=self.url+'/collection/chunk_search',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
chunk_field=chunk_field,
chunk_scoring=chunk_scoring,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
asc=asc,
vector=vector,
search_fields=search_fields,
chunk_page=chunk_page,
chunk_page_size=chunk_page_size,
))
@retry()
@return_curl_or_response('json')
def advanced_chunk_search(self, collection_name, chunk_field, multivector_query, chunk_scoring="max", page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_vector=False, include_count=True, include_facets=False, hundred_scale=False, asc=False, chunk_page=1, chunk_page_size=3, **kwargs):
"""Advanced Vector Similarity Search on Chunks. Support for multiple vectors, vector weightings, facets and filtering
Advanced Chunk Vector Search. Search with a multiple chunkvectors for the most similar documents.
Advanced chunk search also supports filtering to only search through filtered results and facets to get the overview of products available when a minimum score is set.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
chunk_field: Field that the array of chunked documents are.
chunk_scoring: Scoring method for determining for ranking between document chunks.
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
asc: Whether to sort results by ascending or descending order
multivector_query: Query for advance search that allows for multiple vector and field querying
chunk_page: Page of the chunk results
chunk_page_size: Size of each page of chunk results
"""
return requests.post(
url=self.url+'/collection/advanced_chunk_search',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
chunk_field=chunk_field,
chunk_scoring=chunk_scoring,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
asc=asc,
multivector_query=multivector_query,
chunk_page=chunk_page,
chunk_page_size=chunk_page_size,
))
@retry()
@return_curl_or_response('json')
def advanced_multistep_chunk_search(self, collection_name, chunk_field, first_step_multivector_query, chunk_step_multivector_query, chunk_scoring="max", page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_vector=False, include_count=True, include_facets=False, hundred_scale=False, asc=False, first_step_page=1, first_step_page_size=20, **kwargs):
"""Vector Similarity Search on Chunks.
Advanced Multistep chunk search involves a simple search followed by chunk search.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
chunk_field: Field that the array of chunked documents are.
chunk_scoring: Scoring method for determining for ranking between document chunks.
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
asc: Whether to sort results by ascending or descending order
first_step_multivector_query: Query for advance search that allows for multiple vector and field querying
chunk_step_multivector_query: Query for advance search that allows for multiple vector and field querying
first_step_page: Page of the results
first_step_page_size: Size of each page of results
"""
return requests.post(
url=self.url+'/collection/advanced_multistep_chunk_search',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
chunk_field=chunk_field,
chunk_scoring=chunk_scoring,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
asc=asc,
first_step_multivector_query=first_step_multivector_query,
chunk_step_multivector_query=chunk_step_multivector_query,
first_step_page=first_step_page,
first_step_page_size=first_step_page_size,
))
@retry()
@return_curl_or_response('json')
def id_lookup_joined(self, doc_id, join_query={}, **kwargs):
"""Look up a document by its id with joins
Look up a document by its id with joins.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
join_query: Join query
doc_id: ID of a Document
"""
return requests.post(
url=self.url+'/collection/id_lookup_joined',
json=dict(
username=self.username,
api_key=self.api_key,
join_query=join_query,
doc_id=doc_id,
))
@retry()
@return_curl_or_response('json')
def join_collections(self, joined_collection_name, join_query={}, **kwargs):
"""Join collections with a query
Perform a join query on a whole collection and write the results to a new collection. We currently only support left joins.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
join_query: Join query
joined_collection_name: Name of the new collection that contains the joined results
"""
return requests.post(
url=self.url+'/collection/join_collections',
json=dict(
username=self.username,
api_key=self.api_key,
join_query=join_query,
joined_collection_name=joined_collection_name,
))
@retry()
@return_curl_or_response('json')
def publish_aggregation(self, source_collection, dest_collection, aggregation_name, description, aggregation_query, date_field="insert_date_", refresh_time="160s", start_immediately=True, **kwargs):
"""Publishes your aggregation query to a new collection
Publish and schedules your aggregation query and saves it to a new collection.
This new collection is just like any other collection and you can read, filter and aggregate it.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
source_collection: The collection where the data to aggregate comes from
dest_collection: The name of collection of where the data will be aggregated to
aggregation_name: The name for the published scheduled aggregation
description: The description for the published scheduled aggregation
aggregation_query: The aggregation query to schedule
date_field: The date field to check whether there is new data coming in
refresh_time: How often should the aggregation check for new data
start_immediately: Whether to start the published aggregation immediately
"""
return requests.post(
url=self.url+'/collection/publish_aggregation',
json=dict(
username=self.username,
api_key=self.api_key,
source_collection=source_collection,
dest_collection=dest_collection,
aggregation_name=aggregation_name,
description=description,
aggregation_query=aggregation_query,
date_field=date_field,
refresh_time=refresh_time,
start_immediately=start_immediately,
))
@retry()
@return_curl_or_response('json')
def delete_published_aggregation(self,aggregation_name, **kwargs):
return requests.get(
url=self.url+'/collection/delete_published_aggregation',
params=dict(
aggregation_name=aggregation_name,
username=self.username,
api_key=self.api_key,
))
@retry()
@return_curl_or_response('json')
def start_aggregation(self,aggregation_name, **kwargs):
return requests.get(
url=self.url+'/collection/start_aggregation',
params=dict(
aggregation_name=aggregation_name,
username=self.username,
api_key=self.api_key,
))
@retry()
@return_curl_or_response('json')
def stop_aggregation(self,aggregation_name, **kwargs):
return requests.get(
url=self.url+'/collection/stop_aggregation',
params=dict(
aggregation_name=aggregation_name,
username=self.username,
api_key=self.api_key,
))
@retry()
@return_curl_or_response('json')
def vector_aggregation(self, source_collection, dest_collection, source_to_dest_fields_mapping, vector_fields, aggregation_type="mean", refresh=True, **kwargs):
"""Aggregate vectors from one collection into another published aggregation collection
This is useful for getting vectors of a category. e.g. You have "product\_description\_vector\_" and you want the vector for a brand samsung. The "samsung" brand's vector can be the aggregate of all the samsung "product\_description\_vector\_".
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
source_collection: The collection where the data to aggregate comes from
dest_collection: The collection that a scheduled aggregation is creating
source_to_dest_fields_mapping: The collection that a scheduled aggregation is creating
vector_fields: Vector fields to aggregate to form 1 aggregated vector for each split the groupby creates
aggregation_type: Aggregation for the vectors, choose from ['mean', 'sum', 'min', 'max']
refresh: Whether to refresh the aggregation and recalculate the vectors for every single groupby
"""
return requests.post(
url=self.url+'/collection/vector_aggregation',
json=dict(
username=self.username,
api_key=self.api_key,
source_collection=source_collection,
dest_collection=dest_collection,
source_to_dest_fields_mapping=source_to_dest_fields_mapping,
vector_fields=vector_fields,
aggregation_type=aggregation_type,
refresh=refresh,
))
@retry()
@return_curl_or_response('json')
def encode_array_field(self,array_fields, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/encode_array_field',
params=dict(
array_fields=array_fields,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_array(self,array_field, array, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/encode_array',
params=dict(
array_field=array_field,
array=array,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_multiple_arrays(self, collection_name, multiarray_query, **kwargs):
"""Encode multiple arrays into vectors.
Encode Multiple arrays
Multiarray query is in the format:
{
"array_1": {"array": ["YES"], "field": "sample_array"},
"array_2": {"array": ["NO"], "field": "sample_array_2"},
}
This will then return
{
"array_1": [1e-7, 1],
"array_2": [1, 1e-7]
}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
multiarray_query: List of array fields
"""
return requests.post(
url=self.url+'/collection/encode_multiple_arrays',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
multiarray_query=multiarray_query,
))
@retry()
@return_curl_or_response('json')
def search_with_array(self,array_field, array, collection_name, search_fields, search_history_id, approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, asc=False, keep_search_history=True, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, **kwargs):
return requests.get(
url=self.url+'/collection/search_with_array',
params=dict(
array_field=array_field,
array=array,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_fields=search_fields,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
asc=asc,
keep_search_history=keep_search_history,
search_history_id=search_history_id,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
))
@retry()
@return_curl_or_response('json')
def encode_dictionary_field(self,dictionary_fields, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/encode_dictionary_field',
params=dict(
dictionary_fields=dictionary_fields,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_dictionary(self, collection_name, dictionary, dictionary_field, **kwargs):
"""Encode an dictionary into a vector
For example: a dictionary that represents a **person's characteristics visiting a store, field "person_characteristics"**:
{"height":180, "age":40, "weight":70}
-> <Encode the dictionary to vector> ->
| height | age | weight | purchases | visits |
|--------|-----|--------|-----------|--------|
| 180 | 40 | 70 | 0 | 0 |
dictionary vector: [180, 40, 70, 0, 0]
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
dictionary: A dictionary to encode into vectors
dictionary_field: The dictionary field that encoding of the dictionary is trained on
"""
return requests.post(
url=self.url+'/collection/encode_dictionary',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
dictionary=dictionary,
dictionary_field=dictionary_field,
))
@retry()
@return_curl_or_response('json')
def search_with_dictionary(self, collection_name, search_fields, search_history_id, dictionary, dictionary_field, page_size=20, page=1, approx=0, sum_fields=True, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, asc=False, keep_search_history=False, **kwargs):
"""Search a dictionary field with a dictionary using Vector Search
Vector similarity search with a dictionary directly.
For example: a dictionary that represents a **person's characteristics visiting a store, field "person_characteristics"**:
{"height":180, "age":40, "weight":70}
-> <Encode the dictionary to vector> ->
| height | age | weight | purchases | visits |
|--------|-----|--------|-----------|--------|
| 180 | 40 | 70 | 0 | 0 |
dictionary vector: [180, 40, 70, 0, 0]
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
search_fields: Vector fields to search against
page_size: Size of each page of results
page: Page of the results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
hundred_scale: Whether to scale up the metric by 100
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
search_history_id: Search history ID of the collection.
dictionary: A dictionary to encode into vectors
dictionary_field: The dictionary field that encoding of the dictionary is trained on
"""
return requests.post(
url=self.url+'/collection/search_with_dictionary',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_fields=search_fields,
page_size=page_size,
page=page,
approx=approx,
sum_fields=sum_fields,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
asc=asc,
keep_search_history=keep_search_history,
search_history_id=search_history_id,
dictionary=dictionary,
dictionary_field=dictionary_field,
))
@retry()
@return_curl_or_response('json')
def encode_fields_to_vector(self, collection_name, vector_name, selected_fields, **kwargs):
"""Encode all selected fields for a collection into vectors
Within a collection encode the specified fields in every document into vectors.
For example: we choose the fields ["height", "age", "weight"]
document 1 field: {"height":180, "age":40, "weight":70, "purchases":20, "visits": 12}
document 2 field: {"height":160, "age":32, "weight":50, "purchases":10, "visits": 24}
-> <Encode the fields to vectors> ->
| height | age | weight |
|--------|-----|--------|
| 180 | 40 | 70 |
| 160 | 32 | 50 |
document 1 vector: {"person_characteristics_vector_": [180, 40, 70]}
document 2 vector: {"person_characteristics_vector_": [160, 32, 50]}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vector_name: The name of the vector that the fields turn into
selected_fields: The fields to turn into vectors
"""
return requests.post(
url=self.url+'/collection/encode_fields_to_vector',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vector_name=vector_name,
selected_fields=selected_fields,
))
@retry()
@return_curl_or_response('json')
def encode_fields(self, collection_name, document, vector_name, **kwargs):
"""Encode fields into a vector
For example: we choose the fields ["height", "age", "weight"]
document field: {"height":180, "age":40, "weight":70, "purchases":20, "visits": 12}
-> <Encode the fields to vectors> ->
| height | age | weight |
|--------|-----|--------|
| 180 | 40 | 70 |
document vector: {"person_characteristics_vector_": [180, 40, 70]}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
document: A document to encode into vectors
vector_name: The name of the vector that the fields turn into
"""
return requests.post(
url=self.url+'/collection/encode_fields',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
document=document,
vector_name=vector_name,
))
@retry()
@return_curl_or_response('json')
def search_with_fields(self, collection_name, search_fields, search_history_id, document, selected_fields, vector_name, page_size=20, page=1, approx=0, sum_fields=True, metric="cosine", min_score=None, include_fields=[], include_vector=False, include_count=True, hundred_scale=False, asc=False, keep_search_history=False, **kwargs):
"""Search with fields with a document using Vector Search
Vector similarity search with fields directly.
For example: we choose the fields ["height", "age", "weight"]
document field: {"height":180, "age":40, "weight":70, "purchases":20, "visits": 12}
-> <Encode the fields to vectors> ->
| height | age | weight |
|--------|-----|--------|
| 180 | 40 | 70 |
document dictionary vector: {"person_characteristics_vector_": [180, 40, 70]}
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
search_fields: Vector fields to search against
page_size: Size of each page of results
page: Page of the results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
hundred_scale: Whether to scale up the metric by 100
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
search_history_id: Search history ID of the collection.
document: A document to encode into vectors
selected_fields: The fields to turn into vectors
vector_name: A name to call the vector that the fields turn into
"""
return requests.post(
url=self.url+'/collection/search_with_fields',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
search_fields=search_fields,
page_size=page_size,
page=page,
approx=approx,
sum_fields=sum_fields,
metric=metric,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
hundred_scale=hundred_scale,
asc=asc,
keep_search_history=keep_search_history,
search_history_id=search_history_id,
document=document,
selected_fields=selected_fields,
vector_name=vector_name,
))
@retry()
@return_curl_or_response('json')
def combine_vectors(self,vector_fields, vector_name, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/combine_vectors',
params=dict(
vector_fields=vector_fields,
vector_name=vector_name,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def collection_vector_mappings(self,collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/collection_vector_mappings',
params=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def cluster(self,vector_field, collection_name, n_clusters=0, gpu=True, refresh=True, **kwargs):
return requests.get(
url=self.url+'/collection/cluster',
params=dict(
vector_field=vector_field,
n_clusters=n_clusters,
gpu=gpu,
refresh=refresh,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def cluster_aggregate(self, collection_name, aggregation_query, filters=[], page_size=20, page=1, asc=False, flatten=True, **kwargs):
"""Aggregate every cluster in a collection
Takes an aggregation query and gets the aggregate of each cluster in a collection. This helps you interpret each cluster and what is in them.
Only can be used after a vector field has been clustered with /cluster.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
aggregation_query: Aggregation query to aggregate data
filters: Query for filtering the search results
page_size: Size of each page of results
page: Page of the results
asc: Whether to sort results by ascending or descending order
flatten:
"""
return requests.post(
url=self.url+'/collection/cluster_aggregate',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
aggregation_query=aggregation_query,
filters=filters,
page_size=page_size,
page=page,
asc=asc,
flatten=flatten,
))
@retry()
@return_curl_or_response('json')
def cluster_facets(self,collection_name, facets_fields=[], page_size=1000, page=1, asc=False, date_interval="monthly", **kwargs):
return requests.get(
url=self.url+'/collection/cluster_facets',
params=dict(
facets_fields=facets_fields,
page_size=page_size,
page=page,
asc=asc,
date_interval=date_interval,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def cluster_centroids(self,vector_field, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/cluster_centroids',
params=dict(
vector_field=vector_field,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def cluster_centroid_documents(self,vector_field, collection_name, metric="cosine", include_vector=False, page=1, page_size=20, **kwargs):
return requests.get(
url=self.url+'/collection/cluster_centroid_documents',
params=dict(
vector_field=vector_field,
metric=metric,
include_vector=include_vector,
page=page,
page_size=page_size,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster(self,vector_field, collection_name, alias="default", n_clusters=0, n_iter=10, n_init=5, gpu=True, refresh=True, **kwargs):
return requests.get(
url=self.url+'/collection/advanced_cluster',
params=dict(
vector_field=vector_field,
alias=alias,
n_clusters=n_clusters,
n_iter=n_iter,
n_init=n_init,
gpu=gpu,
refresh=refresh,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster_aggregate(self, collection_name, aggregation_query, vector_field, alias, filters=[], page_size=20, page=1, asc=False, flatten=True, **kwargs):
"""Aggregate every cluster in a collection
Takes an aggregation query and gets the aggregate of each cluster in a collection. This helps you interpret each cluster and what is in them.
Only can be used after a vector field has been clustered with /advanced_cluster.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
aggregation_query: Aggregation query to aggregate data
filters: Query for filtering the search results
page_size: Size of each page of results
page: Page of the results
asc: Whether to sort results by ascending or descending order
flatten:
vector_field: Clustered vector field
alias: Alias of a cluster
"""
return requests.post(
url=self.url+'/collection/advanced_cluster_aggregate',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
aggregation_query=aggregation_query,
filters=filters,
page_size=page_size,
page=page,
asc=asc,
flatten=flatten,
vector_field=vector_field,
alias=alias,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster_facets(self,vector_field, collection_name, alias="default", facets_fields=[], page_size=1000, page=1, asc=False, date_interval="monthly", **kwargs):
return requests.get(
url=self.url+'/collection/advanced_cluster_facets',
params=dict(
vector_field=vector_field,
alias=alias,
facets_fields=facets_fields,
page_size=page_size,
page=page,
asc=asc,
date_interval=date_interval,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster_centroids(self,vector_field, collection_name, alias="default", **kwargs):
return requests.get(
url=self.url+'/collection/advanced_cluster_centroids',
params=dict(
vector_field=vector_field,
alias=alias,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster_centroid_documents(self,vector_field, collection_name, alias="default", metric="cosine", include_vector=False, page=1, page_size=20, **kwargs):
return requests.get(
url=self.url+'/collection/advanced_cluster_centroid_documents',
params=dict(
vector_field=vector_field,
alias=alias,
metric=metric,
include_vector=include_vector,
page=page,
page_size=page_size,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def advanced_cluster_search(self, collection_name, multivector_query, vector_field, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, alias="default", **kwargs):
"""Advanced Vector Similarity Search on Clusters.
Only can be used after a vector field has been clustered with /advanced_cluster. Perform advanced_search on each cluster
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
multivector_query: Query for advance search that allows for multiple vector and field querying
vector_field: Vector field to perform clustering on
alias: Alias is used to name a cluster
"""
return requests.post(
url=self.url+'/collection/advanced_cluster_search',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
multivector_query=multivector_query,
vector_field=vector_field,
alias=alias,
))
@retry()
@return_curl_or_response('json')
def advanced_search_post_cluster(self, collection_name, multivector_query, cluster_vector_field, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, n_clusters=0, n_init=5, n_iter=10, return_as_clusters=False, **kwargs):
"""Performs Clustering on Top X search results
This will first perform an advanced search and then cluster the top X (page_size) search results.
Results are returned as such:
Once you have the clusters:
```
Cluster 0: [A, B, C]
Cluster 1: [D, E]
Cluster 2: [F, G]
Cluster 3: [H, I]
```
(Note, each cluster is ordered by highest to lowest search score.
This intermediately returns:
```
results_batch_1: [A, H, F, D] (ordered by highest search score)
results_batch_2: [G, E, B, I] (ordered by highest search score)
results_batch_3: [C]
```
This then returns the final results:
```
results: [A, H, F, D, G, E, B, I, C]
```
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
multivector_query: Query for advance search that allows for multiple vector and field querying
cluster_vector_field: Vector field to perform clustering on
n_clusters: Number of clusters
n_init: Number of runs to run with different centroid seeds
n_iter: Number of iterations in each run
return_as_clusters: If True, return as clusters as opposed to results list
"""
return requests.post(
url=self.url+'/collection/advanced_search_post_cluster',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
multivector_query=multivector_query,
cluster_vector_field=cluster_vector_field,
n_clusters=n_clusters,
n_init=n_init,
n_iter=n_iter,
return_as_clusters=return_as_clusters,
))
@retry()
@return_curl_or_response('json')
def insert_cluster_centroids(self, collection_name, cluster_centers, vector_field, alias="default", job=False, job_metric="cosine", **kwargs):
"""Insert cluster centroids
Insert your own cluster centroids for it to be used in approximate search settings and cluster aggregations.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
cluster_centers: Cluster centers with the key being the index number
vector_field: Clustered vector field
alias: Alias is used to name a cluster
job: Whether to run a job where each document is assigned a cluster from the cluster_center
job_metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
"""
return requests.post(
url=self.url+'/collection/insert_cluster_centroids',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
cluster_centers=cluster_centers,
vector_field=vector_field,
alias=alias,
job=job,
job_metric=job_metric,
))
@retry()
@return_curl_or_response('json')
def dimensionality_reduce(self, collection_name, vectors, vector_field, alias="default", n_components=1, **kwargs):
"""Reduces the dimension of a list of vectors
Reduce the dimensions of a list of vectors you input into a desired dimension.
This can only reduce to dimensions less than or equal to the n_components that the dimensionality reduction model is trained on.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vectors: Vectors to perform dimensionality reduction on
vector_field: Vector field to perform dimensionality reduction on
alias: Alias of the dimensionality reduced vectors
n_components: The size/length to reduce the vector down to.
"""
return requests.post(
url=self.url+'/collection/dimensionality_reduce',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vectors=vectors,
vector_field=vector_field,
alias=alias,
n_components=n_components,
))
@retry()
@return_curl_or_response('json')
def encode_text_field(self,text_field, collection_name, refresh=True, alias="default", **kwargs):
return requests.get(
url=self.url+'/collection/encode_text_field',
params=dict(
text_field=text_field,
refresh=refresh,
alias=alias,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_text(self,text, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/encode_text',
params=dict(
text=text,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def bulk_encode_text(self,texts, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/bulk_encode_text',
params=dict(
texts=texts,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def search_with_text(self, collection_name, text, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Search text fields with text using Vector Search
Vector similarity search with text directly.
For example: "product_description" represents the description of a product:
"AirPods deliver effortless, all-day audio on the go. And AirPods Pro bring Active Noise Cancellation to an in-ear headphone — with a customisable fit"
-> <Encode the text to vector> ->
i.e. text vector, "product_description_vector_": [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
text: Text to encode into vector and vector search with
search_fields: Vector fields to search against
"""
return requests.post(
url=self.url+'/collection/search_with_text',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
text=text,
search_fields=search_fields,
))
@retry()
@return_curl_or_response('json')
def encode_image_field(self,image_field, collection_name, alias="default", refresh=True, **kwargs):
return requests.get(
url=self.url+'/collection/encode_image_field',
params=dict(
image_field=image_field,
alias=alias,
refresh=refresh,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_image(self,image_url, model_url, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/encode_image',
params=dict(
image_url=image_url,
model_url=model_url,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def bulk_encode_image(self,image_urls, model_url, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/bulk_encode_image',
params=dict(
image_urls=image_urls,
model_url=model_url,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def search_with_image(self, collection_name, image_url, model_url, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Search an image field with image using Vector Search
Vector similarity search with an image directly.
_note: image has to be stored somewhere and be provided as image_url, a url that stores the image_
For example: an image_url represents an image of a celebrity:
"https://www.celebrity_images.com/brad_pitt.png"
-> <Encode the image to vector> ->
image vector: [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
image_url: The image url of an image to encode into a vector
model_url: The model url of a deployed vectorhub model
search_fields: Vector fields to search against
"""
return requests.post(
url=self.url+'/collection/search_with_image',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
image_url=image_url,
model_url=model_url,
search_fields=search_fields,
))
@retry()
@return_curl_or_response('json')
def search_with_image_upload(self, collection_name, image, model_url, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Search an image field with uploaded image using Vector Search
Vector similarity search with an uploaded image directly.
_note: image has to be sent as a base64 encoded string_
For example: an image represents an image of a celebrity:
"https://www.celebrity_images.com/brad_pitt.png"
-> <Encode the image to vector> ->
image vector: [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
image: Image represented as a base64 encoded string
model_url: The model url of a deployed vectorhub model
search_fields: Vector fields to search against
"""
return requests.post(
url=self.url+'/collection/search_with_image_upload',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
image=image,
model_url=model_url,
search_fields=search_fields,
))
@retry()
@return_curl_or_response('json')
def encode_audio_field(self,audio_field, collection_name, refresh=True, **kwargs):
return requests.get(
url=self.url+'/collection/encode_audio_field',
params=dict(
audio_field=audio_field,
refresh=refresh,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def encode_audio(self,audio_url, model_url, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/encode_audio',
params=dict(
audio_url=audio_url,
model_url=model_url,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def bulk_encode_audio(self,audio_urls, collection_name, **kwargs):
return requests.get(
url=self.url+'/collection/bulk_encode_audio',
params=dict(
audio_urls=audio_urls,
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
))
@retry()
@return_curl_or_response('json')
def search_with_audio(self, collection_name, audio_url, model_url, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Search an audio field with audio using Vector Search
Vector similarity search with an audio directly.
_note: audio has to be stored somewhere and be provided as audio_url, a url that stores the audio_
For example: an audio_url represents sounds that a pokemon make:
"https://play.pokemonshowdown.com/audio/cries/pikachu.mp3"
-> <Encode the audio to vector> ->
audio vector: [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
audio_url: The audio url of an audio to encode into a vector
model_url: The model url of a deployed vectorhub model
search_fields: Vector fields to search against
"""
return requests.post(
url=self.url+'/collection/search_with_audio',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
audio_url=audio_url,
model_url=model_url,
search_fields=search_fields,
))
@retry()
@return_curl_or_response('json')
def search_with_audio_upload(self, collection_name, audio, model_url, search_fields, page=1, page_size=20, approx=0, sum_fields=True, metric="cosine", filters=[], facets=[], min_score=None, include_fields=[], include_vector=False, include_count=True, include_facets=False, hundred_scale=False, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, keep_search_history=False, **kwargs):
"""Advanced Search audio fields with uploaded audio using Vector Search
Vector similarity search with an uploaded audio directly.
_note: audio has to be sent as a base64 encoded string_
For example: an audio represents sounds that a pokemon make:
"https://play.pokemonshowdown.com/audio/cries/pikachu.mp3"
-> <Encode the audio to vector> ->
audio vector: [0.794617772102356, 0.3581121861934662, 0.21113917231559753, 0.24878688156604767, 0.9741804003715515 ...]
-> <Vector Search> ->
Search Results: {...}
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
page: Page of the results
page_size: Size of each page of results
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
facets: Fields to include in the facets, if [] then all
min_score: Minimum score for similarity metric
include_fields: Fields to include in the search results, empty array/list means all fields.
include_vector: Include vectors in the search results
include_count: Include count in the search results
include_facets: Include facets in the search results
hundred_scale: Whether to scale up the metric by 100
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
keep_search_history: Whether to store the history of search or not
audio: Audio represented as a base64 encoded string
model_url: The model url of a deployed vectorhub model
search_fields: Vector fields to search against
"""
return requests.post(
url=self.url+'/collection/search_with_audio_upload',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
page=page,
page_size=page_size,
approx=approx,
sum_fields=sum_fields,
metric=metric,
filters=filters,
facets=facets,
min_score=min_score,
include_fields=include_fields,
include_vector=include_vector,
include_count=include_count,
include_facets=include_facets,
hundred_scale=hundred_scale,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
keep_search_history=keep_search_history,
audio=audio,
model_url=model_url,
search_fields=search_fields,
))
@retry()
@return_curl_or_response('json')
def text_chunking(self, collection_name, text_field, chunk_field, insert_results_to_seperate_collection_name, refresh=True, insert_results=True, return_processed_documents=False, **kwargs):
"""Chunking a text field in a collection.
Chunking a text field in a collection. e.g. a paragraph text field to sentence chunks
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
text_field: Text field to chunk
chunk_field: Whats the field that the text chunks will belong in
refresh: Whether to refresh the whole collection and re-encode all to vectors
insert_results: Whether to insert the processed document chunks into the collection.
insert_results_to_seperate_collection_name: If specified the chunks will be inserted into a seperate collection. Default is None which means no seperate collection.
return_processed_documents: Whether to return the processed documents.
"""
return requests.post(
url=self.url+'/collection/text_chunking',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
text_field=text_field,
chunk_field=chunk_field,
refresh=refresh,
insert_results=insert_results,
insert_results_to_seperate_collection_name=insert_results_to_seperate_collection_name,
return_processed_documents=return_processed_documents,
))
@retry()
@return_curl_or_response('json')
def tag(self, data, tag_collection_name, encoder, tag_field, approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", filters=[], min_score=None, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, **kwargs):
"""Add tagging
Tag
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
data: Image Url or text or any data suited for the encoder
tag_collection_name: Name of the collection you want to tag
encoder: Which encoder to use.
tag_field: The field used to tag in a collection. If None, automatically uses the one stated in the encoder.
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
page_size: Size of each page of results
page: Page of the results
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
min_score: Minimum score for similarity metric
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
"""
return requests.post(
url=self.url+'/collection/tag',
json=dict(
username=self.username,
api_key=self.api_key,
data=data,
tag_collection_name=tag_collection_name,
encoder=encoder,
tag_field=tag_field,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
filters=filters,
min_score=min_score,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
))
@retry()
@return_curl_or_response('json')
def post_cluster_tag(self, data, tag_collection_name, encoder, tag_field, cluster_vector_field, n_clusters, approx=0, sum_fields=True, page_size=20, page=1, metric="cosine", filters=[], min_score=None, include_search_relevance=False, search_relevance_cutoff_aggressiveness=1, asc=False, n_iter=10, n_init=5, **kwargs):
"""Post cluster tag.
Post cluster tag.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
data: Image Url or text or any data suited for the encoder
tag_collection_name: Name of the collection you want to tag
encoder: Which encoder to use.
tag_field: The field used to tag in a collection. If None, automatically uses the one stated in the encoder.
approx: Used for approximate search
sum_fields: Whether to sum the multiple vectors similarity search score as 1 or seperate
page_size: Size of each page of results
page: Page of the results
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
filters: Query for filtering the search results
min_score: Minimum score for similarity metric
include_search_relevance: Whether to calculate a search_relevance cutoff score to flag relevant and less relevant results
search_relevance_cutoff_aggressiveness: How aggressive the search_relevance cutoff score is (higher value the less results will be relevant)
asc: Whether to sort results by ascending or descending order
cluster_vector_field: The field to cluster on.
n_clusters: Number of clusters to be specified.
n_iter: Number of iterations in each run
n_init: Number of runs to run with different centroid seeds
"""
return requests.post(
url=self.url+'/collection/post_cluster_tag',
json=dict(
username=self.username,
api_key=self.api_key,
data=data,
tag_collection_name=tag_collection_name,
encoder=encoder,
tag_field=tag_field,
approx=approx,
sum_fields=sum_fields,
page_size=page_size,
page=page,
metric=metric,
filters=filters,
min_score=min_score,
include_search_relevance=include_search_relevance,
search_relevance_cutoff_aggressiveness=search_relevance_cutoff_aggressiveness,
asc=asc,
cluster_vector_field=cluster_vector_field,
n_clusters=n_clusters,
n_iter=n_iter,
n_init=n_init,
))
@retry()
@return_curl_or_response('json')
def tag_collection_from_vectors(self, collection_name, vector_field, tag_collection_name, tag_field="tag", tag_vector_field="tag_vector_", metric="cosine", alias="default", number_of_tags=5, include_tag_vector=True, pad_vector_length=100, refresh=True, return_tagged_documents=True, **kwargs):
"""Add tagging
Tag vectors
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
vector_field: vector field from the source collection to tag on
tag_collection_name: Name of the collection you are retrieving the tags from
tag_field: The field in the tag collection to use for tagging.
tag_vector_field: vector field in the tag collection, used for matching the vectors to tag.
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
alias: The alias of the tags. Defaults to 'default'
number_of_tags: The number of tags to retrieve.
include_tag_vector: Whether to include the one hot encoded tag vector.
pad_vector_length: Whether to pad the vector length of the one hot encoded array.
refresh: If True, retags the whole collection.
return_tagged_documents: If True, returns the original documents with tags.
"""
return requests.post(
url=self.url+'/collection/tag_collection_from_vectors',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
vector_field=vector_field,
tag_collection_name=tag_collection_name,
tag_field=tag_field,
tag_vector_field=tag_vector_field,
metric=metric,
alias=alias,
number_of_tags=number_of_tags,
include_tag_vector=include_tag_vector,
pad_vector_length=pad_vector_length,
refresh=refresh,
return_tagged_documents=return_tagged_documents,
))
@retry()
@return_curl_or_response('json')
def tag_documents(self, tag_collection_name, vector_field, documents={}, tag_field="tag", tag_vector_field="tag_vector_", alias="default", metric="cosine", number_of_tags=5, field="", include_tag_vector=True, pad_vector_length=100, **kwargs):
"""Tag documents.
Tag documents
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
documents: A list of documents. Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
tag_collection_name: Name of the collection you are retrieving the tags from
tag_field: The field in the tag collection to use for tagging.
tag_vector_field: vector field in the tag collection, used for matching the vectors to tag.
alias: The alias of the tags. Defaults to 'default'
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
number_of_tags: The number of tags to retrieve.
field: The field in the source collection to be tagged.
vector_field: vector field from the source collection to tag on
include_tag_vector: Whether to include the one hot encoded tag vector.
pad_vector_length: Whether to pad the vector length of the one hot encoded array.
"""
return requests.post(
url=self.url+'/collection/tag_documents',
json=dict(
username=self.username,
api_key=self.api_key,
documents=documents,
tag_collection_name=tag_collection_name,
tag_field=tag_field,
tag_vector_field=tag_vector_field,
alias=alias,
metric=metric,
number_of_tags=number_of_tags,
field=field,
vector_field=vector_field,
include_tag_vector=include_tag_vector,
pad_vector_length=pad_vector_length,
))
@retry()
@return_curl_or_response('json')
def store_taggers_pipeline(self, collection_name, taggers, **kwargs):
"""Store multiple tagger pipelines
Store pipeline for taggers.
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: Name of Collection
taggers: Taggers contain metadata to
encode and then use a specific collection to get data.
{"field": field, "vector_field": vector_field, "tag_field": tag_field,
"tag_vector_field": tag_vector_field, "number_of_tags": number_of_tags,
"alias": alias, "metric": metric}
"""
return requests.post(
url=self.url+'/collection/store_taggers_pipeline',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
taggers=taggers,
))
@retry()
@return_curl_or_response('json')
def tag_documents_from_hub(self, tag_collection_name, vector_field, hub_username, hub_api_key, documents={}, tag_field="tag", tag_vector_field="tag_vector_", alias="default", metric="cosine", number_of_tags=5, field="", include_tag_vector=True, pad_vector_length=100, **kwargs):
"""Add tagging from the Tag Hub.
Tag documents from hub API
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
documents: A list of documents. Document is a JSON-like data that we store our metadata and vectors with. For specifying id of the document use the field '\_id', for specifying vector field use the suffix of '\_vector\_'
tag_collection_name: Name of the collection you are retrieving the tags from
tag_field: The field in the tag collection to use for tagging.
tag_vector_field: vector field in the tag collection, used for matching the vectors to tag.
alias: The alias of the tags. Defaults to 'default'
metric: Similarity Metric, choose from ['cosine', 'l1', 'l2', 'dp']
number_of_tags: The number of tags to retrieve.
field: The field in the source collection to be tagged.
vector_field: vector field from the source collection to tag on
include_tag_vector: Whether to include the one hot encoded tag vector.
pad_vector_length: Whether to pad the vector length of the one hot encoded array.
hub_username: The username of the hub for the tag.
hub_api_key: The api key of the hub for the tag.
"""
return requests.post(
url=self.url+'/collection/tag_documents_from_hub',
json=dict(
username=self.username,
api_key=self.api_key,
documents=documents,
tag_collection_name=tag_collection_name,
tag_field=tag_field,
tag_vector_field=tag_vector_field,
alias=alias,
metric=metric,
number_of_tags=number_of_tags,
field=field,
vector_field=vector_field,
include_tag_vector=include_tag_vector,
pad_vector_length=pad_vector_length,
hub_username=hub_username,
hub_api_key=hub_api_key,
))
@retry()
@return_curl_or_response('json')
def rank_comparator(self, ranked_list_1, ranked_list_2, **kwargs):
"""Compare ranks between 2 results list.
Compare the ranks between 2 results list in VecDB
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
ranked_list_1: First ranked List
ranked_list_2: Second ranked list
"""
return requests.post(
url=self.url+'/experimentation/rank_comparator',
json=dict(
username=self.username,
api_key=self.api_key,
ranked_list_1=ranked_list_1,
ranked_list_2=ranked_list_2,
))
@retry()
@return_curl_or_response('json')
def bias_indicator(self, anchor_documents, documents, metadata_field, vector_field, **kwargs):
"""Compare bias of documents against anchor documents
Compare bias of documents against anchor documents
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
anchor_documents: Anchor documents to compare other documents against.
documents: Documents to compare against the anchor documents
metadata_field: Field from which the vector was derived
vector_field: Vector field to compare against
"""
return requests.post(
url=self.url+'/experimentation/bias_indicator',
json=dict(
username=self.username,
api_key=self.api_key,
anchor_documents=anchor_documents,
documents=documents,
metadata_field=metadata_field,
vector_field=vector_field,
))
@retry()
@return_curl_or_response('json')
def cluster_comparator(self, collection_name, cluster_field, cluster_value, vector_field, alias, **kwargs):
"""Compare clusters
Compare the clusters for cluster comparator
Args
========
username: Username
api_key: Api Key, you can request it from request_api_key
collection_name: the name of the collection
cluster_field: the cluster field
cluster_value: the cluster values by which to compare on
vector_field: The vector field that has been clustered
alias: The alias of the vector field
"""
return requests.post(
url=self.url+'/experimentation/cluster_comparator',
json=dict(
username=self.username,
api_key=self.api_key,
collection_name=collection_name,
cluster_field=cluster_field,
cluster_value=cluster_value,
vector_field=vector_field,
alias=alias,
))
| 39.007848 | 502 | 0.755091 | 23,242 | 164,028 | 5.109801 | 0.031839 | 0.027079 | 0.025109 | 0.027787 | 0.86686 | 0.846954 | 0.82864 | 0.80812 | 0.782169 | 0.75573 | 0 | 0.007751 | 0.156089 | 164,028 | 4,204 | 503 | 39.017127 | 0.850193 | 0.50164 | 0 | 0.848335 | 1 | 0 | 0.063898 | 0.048869 | 0 | 0 | 0 | 0 | 0 | 1 | 0.061206 | false | 0 | 0.00135 | 0.024302 | 0.123762 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
00e65e87c9df5e117a22654a740761e23d18e6c8 | 201 | py | Python | env/Lib/site-packages/libarchive-0.4.7/libarchive/adapters/archive_write_set_format.py | dondemonz/RestApi | 0459d2b8079b9f2abc50bf5e206625427c4a2dcf | [
"Apache-2.0"
] | 94 | 2015-03-13T07:38:51.000Z | 2022-03-18T02:28:04.000Z | env/Lib/site-packages/libarchive-0.4.7/libarchive/adapters/archive_write_set_format.py | dondemonz/RestApi | 0459d2b8079b9f2abc50bf5e206625427c4a2dcf | [
"Apache-2.0"
] | 45 | 2015-04-25T09:19:08.000Z | 2022-03-18T18:07:05.000Z | env/Lib/site-packages/libarchive-0.4.7/libarchive/adapters/archive_write_set_format.py | dondemonz/RestApi | 0459d2b8079b9f2abc50bf5e206625427c4a2dcf | [
"Apache-2.0"
] | 29 | 2015-03-13T07:38:43.000Z | 2021-10-10T18:23:50.000Z | import libarchive.calls.archive_write_set_format
def archive_write_set_format(archive, code):
libarchive.calls.archive_write_set_format.c_archive_write_set_format(
archive,
code)
| 28.714286 | 73 | 0.791045 | 27 | 201 | 5.407407 | 0.37037 | 0.328767 | 0.410959 | 0.575342 | 0.931507 | 0.931507 | 0 | 0 | 0 | 0 | 0 | 0 | 0.149254 | 201 | 6 | 74 | 33.5 | 0.853801 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.2 | false | 0 | 0.2 | 0 | 0.4 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
dab2af9d77d07901fb567e16987961e9bc1ba127 | 145 | py | Python | loldib/getratings/models/NA/na_akali/__init__.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_akali/__init__.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | loldib/getratings/models/NA/na_akali/__init__.py | koliupy/loldib | c9ab94deb07213cdc42b5a7c26467cdafaf81b7f | [
"Apache-2.0"
] | null | null | null | from .na_akali_top import *
from .na_akali_jng import *
from .na_akali_mid import *
from .na_akali_bot import *
from .na_akali_sup import *
| 24.166667 | 28 | 0.758621 | 25 | 145 | 4 | 0.36 | 0.3 | 0.55 | 0.68 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.172414 | 145 | 5 | 29 | 29 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
979f14343e7667c0a8774fb08cd269d7cb68cfe5 | 23,140 | py | Python | sdk/python/pulumi_vault/raft_autopilot.py | pulumi/pulumi-vault | 1682875f4a5d7d508f36e166529ad2b8aec34090 | [
"ECL-2.0",
"Apache-2.0"
] | 10 | 2019-10-07T17:44:18.000Z | 2022-03-30T20:46:33.000Z | sdk/python/pulumi_vault/raft_autopilot.py | pulumi/pulumi-vault | 1682875f4a5d7d508f36e166529ad2b8aec34090 | [
"ECL-2.0",
"Apache-2.0"
] | 79 | 2019-10-11T18:13:07.000Z | 2022-03-31T21:09:41.000Z | sdk/python/pulumi_vault/raft_autopilot.py | pulumi/pulumi-vault | 1682875f4a5d7d508f36e166529ad2b8aec34090 | [
"ECL-2.0",
"Apache-2.0"
] | 2 | 2019-10-28T10:08:40.000Z | 2020-03-17T14:20:55.000Z | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['RaftAutopilotArgs', 'RaftAutopilot']
@pulumi.input_type
class RaftAutopilotArgs:
def __init__(__self__, *,
cleanup_dead_servers: Optional[pulumi.Input[bool]] = None,
dead_server_last_contact_threshold: Optional[pulumi.Input[str]] = None,
last_contact_threshold: Optional[pulumi.Input[str]] = None,
max_trailing_logs: Optional[pulumi.Input[int]] = None,
min_quorum: Optional[pulumi.Input[int]] = None,
server_stabilization_time: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a RaftAutopilot resource.
:param pulumi.Input[bool] cleanup_dead_servers: Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
:param pulumi.Input[str] dead_server_last_contact_threshold: Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
:param pulumi.Input[str] last_contact_threshold: Limit the amount of time a server can go
without leader contact before being considered unhealthy.
:param pulumi.Input[int] max_trailing_logs: Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
:param pulumi.Input[int] min_quorum: Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
:param pulumi.Input[str] server_stabilization_time: Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
if cleanup_dead_servers is not None:
pulumi.set(__self__, "cleanup_dead_servers", cleanup_dead_servers)
if dead_server_last_contact_threshold is not None:
pulumi.set(__self__, "dead_server_last_contact_threshold", dead_server_last_contact_threshold)
if last_contact_threshold is not None:
pulumi.set(__self__, "last_contact_threshold", last_contact_threshold)
if max_trailing_logs is not None:
pulumi.set(__self__, "max_trailing_logs", max_trailing_logs)
if min_quorum is not None:
pulumi.set(__self__, "min_quorum", min_quorum)
if server_stabilization_time is not None:
pulumi.set(__self__, "server_stabilization_time", server_stabilization_time)
@property
@pulumi.getter(name="cleanupDeadServers")
def cleanup_dead_servers(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
"""
return pulumi.get(self, "cleanup_dead_servers")
@cleanup_dead_servers.setter
def cleanup_dead_servers(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "cleanup_dead_servers", value)
@property
@pulumi.getter(name="deadServerLastContactThreshold")
def dead_server_last_contact_threshold(self) -> Optional[pulumi.Input[str]]:
"""
Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
"""
return pulumi.get(self, "dead_server_last_contact_threshold")
@dead_server_last_contact_threshold.setter
def dead_server_last_contact_threshold(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_server_last_contact_threshold", value)
@property
@pulumi.getter(name="lastContactThreshold")
def last_contact_threshold(self) -> Optional[pulumi.Input[str]]:
"""
Limit the amount of time a server can go
without leader contact before being considered unhealthy.
"""
return pulumi.get(self, "last_contact_threshold")
@last_contact_threshold.setter
def last_contact_threshold(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "last_contact_threshold", value)
@property
@pulumi.getter(name="maxTrailingLogs")
def max_trailing_logs(self) -> Optional[pulumi.Input[int]]:
"""
Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
"""
return pulumi.get(self, "max_trailing_logs")
@max_trailing_logs.setter
def max_trailing_logs(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_trailing_logs", value)
@property
@pulumi.getter(name="minQuorum")
def min_quorum(self) -> Optional[pulumi.Input[int]]:
"""
Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
"""
return pulumi.get(self, "min_quorum")
@min_quorum.setter
def min_quorum(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_quorum", value)
@property
@pulumi.getter(name="serverStabilizationTime")
def server_stabilization_time(self) -> Optional[pulumi.Input[str]]:
"""
Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
return pulumi.get(self, "server_stabilization_time")
@server_stabilization_time.setter
def server_stabilization_time(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "server_stabilization_time", value)
@pulumi.input_type
class _RaftAutopilotState:
def __init__(__self__, *,
cleanup_dead_servers: Optional[pulumi.Input[bool]] = None,
dead_server_last_contact_threshold: Optional[pulumi.Input[str]] = None,
last_contact_threshold: Optional[pulumi.Input[str]] = None,
max_trailing_logs: Optional[pulumi.Input[int]] = None,
min_quorum: Optional[pulumi.Input[int]] = None,
server_stabilization_time: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering RaftAutopilot resources.
:param pulumi.Input[bool] cleanup_dead_servers: Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
:param pulumi.Input[str] dead_server_last_contact_threshold: Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
:param pulumi.Input[str] last_contact_threshold: Limit the amount of time a server can go
without leader contact before being considered unhealthy.
:param pulumi.Input[int] max_trailing_logs: Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
:param pulumi.Input[int] min_quorum: Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
:param pulumi.Input[str] server_stabilization_time: Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
if cleanup_dead_servers is not None:
pulumi.set(__self__, "cleanup_dead_servers", cleanup_dead_servers)
if dead_server_last_contact_threshold is not None:
pulumi.set(__self__, "dead_server_last_contact_threshold", dead_server_last_contact_threshold)
if last_contact_threshold is not None:
pulumi.set(__self__, "last_contact_threshold", last_contact_threshold)
if max_trailing_logs is not None:
pulumi.set(__self__, "max_trailing_logs", max_trailing_logs)
if min_quorum is not None:
pulumi.set(__self__, "min_quorum", min_quorum)
if server_stabilization_time is not None:
pulumi.set(__self__, "server_stabilization_time", server_stabilization_time)
@property
@pulumi.getter(name="cleanupDeadServers")
def cleanup_dead_servers(self) -> Optional[pulumi.Input[bool]]:
"""
Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
"""
return pulumi.get(self, "cleanup_dead_servers")
@cleanup_dead_servers.setter
def cleanup_dead_servers(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "cleanup_dead_servers", value)
@property
@pulumi.getter(name="deadServerLastContactThreshold")
def dead_server_last_contact_threshold(self) -> Optional[pulumi.Input[str]]:
"""
Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
"""
return pulumi.get(self, "dead_server_last_contact_threshold")
@dead_server_last_contact_threshold.setter
def dead_server_last_contact_threshold(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "dead_server_last_contact_threshold", value)
@property
@pulumi.getter(name="lastContactThreshold")
def last_contact_threshold(self) -> Optional[pulumi.Input[str]]:
"""
Limit the amount of time a server can go
without leader contact before being considered unhealthy.
"""
return pulumi.get(self, "last_contact_threshold")
@last_contact_threshold.setter
def last_contact_threshold(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "last_contact_threshold", value)
@property
@pulumi.getter(name="maxTrailingLogs")
def max_trailing_logs(self) -> Optional[pulumi.Input[int]]:
"""
Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
"""
return pulumi.get(self, "max_trailing_logs")
@max_trailing_logs.setter
def max_trailing_logs(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_trailing_logs", value)
@property
@pulumi.getter(name="minQuorum")
def min_quorum(self) -> Optional[pulumi.Input[int]]:
"""
Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
"""
return pulumi.get(self, "min_quorum")
@min_quorum.setter
def min_quorum(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "min_quorum", value)
@property
@pulumi.getter(name="serverStabilizationTime")
def server_stabilization_time(self) -> Optional[pulumi.Input[str]]:
"""
Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
return pulumi.get(self, "server_stabilization_time")
@server_stabilization_time.setter
def server_stabilization_time(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "server_stabilization_time", value)
class RaftAutopilot(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
cleanup_dead_servers: Optional[pulumi.Input[bool]] = None,
dead_server_last_contact_threshold: Optional[pulumi.Input[str]] = None,
last_contact_threshold: Optional[pulumi.Input[str]] = None,
max_trailing_logs: Optional[pulumi.Input[int]] = None,
min_quorum: Optional[pulumi.Input[int]] = None,
server_stabilization_time: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Autopilot enables automated workflows for managing Raft clusters. The
current feature set includes 3 main features: Server Stabilization, Dead
Server Cleanup and State API. **These three features are introduced in
Vault 1.7.**
## Example Usage
```python
import pulumi
import pulumi_vault as vault
autopilot = vault.RaftAutopilot("autopilot",
cleanup_dead_servers=True,
dead_server_last_contact_threshold="24h0m0s",
last_contact_threshold="10s",
max_trailing_logs=1000,
min_quorum=3,
server_stabilization_time="10s")
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] cleanup_dead_servers: Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
:param pulumi.Input[str] dead_server_last_contact_threshold: Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
:param pulumi.Input[str] last_contact_threshold: Limit the amount of time a server can go
without leader contact before being considered unhealthy.
:param pulumi.Input[int] max_trailing_logs: Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
:param pulumi.Input[int] min_quorum: Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
:param pulumi.Input[str] server_stabilization_time: Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: Optional[RaftAutopilotArgs] = None,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Autopilot enables automated workflows for managing Raft clusters. The
current feature set includes 3 main features: Server Stabilization, Dead
Server Cleanup and State API. **These three features are introduced in
Vault 1.7.**
## Example Usage
```python
import pulumi
import pulumi_vault as vault
autopilot = vault.RaftAutopilot("autopilot",
cleanup_dead_servers=True,
dead_server_last_contact_threshold="24h0m0s",
last_contact_threshold="10s",
max_trailing_logs=1000,
min_quorum=3,
server_stabilization_time="10s")
```
:param str resource_name: The name of the resource.
:param RaftAutopilotArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(RaftAutopilotArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
cleanup_dead_servers: Optional[pulumi.Input[bool]] = None,
dead_server_last_contact_threshold: Optional[pulumi.Input[str]] = None,
last_contact_threshold: Optional[pulumi.Input[str]] = None,
max_trailing_logs: Optional[pulumi.Input[int]] = None,
min_quorum: Optional[pulumi.Input[int]] = None,
server_stabilization_time: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = RaftAutopilotArgs.__new__(RaftAutopilotArgs)
__props__.__dict__["cleanup_dead_servers"] = cleanup_dead_servers
__props__.__dict__["dead_server_last_contact_threshold"] = dead_server_last_contact_threshold
__props__.__dict__["last_contact_threshold"] = last_contact_threshold
__props__.__dict__["max_trailing_logs"] = max_trailing_logs
__props__.__dict__["min_quorum"] = min_quorum
__props__.__dict__["server_stabilization_time"] = server_stabilization_time
super(RaftAutopilot, __self__).__init__(
'vault:index/raftAutopilot:RaftAutopilot',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
cleanup_dead_servers: Optional[pulumi.Input[bool]] = None,
dead_server_last_contact_threshold: Optional[pulumi.Input[str]] = None,
last_contact_threshold: Optional[pulumi.Input[str]] = None,
max_trailing_logs: Optional[pulumi.Input[int]] = None,
min_quorum: Optional[pulumi.Input[int]] = None,
server_stabilization_time: Optional[pulumi.Input[str]] = None) -> 'RaftAutopilot':
"""
Get an existing RaftAutopilot resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[bool] cleanup_dead_servers: Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
:param pulumi.Input[str] dead_server_last_contact_threshold: Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
:param pulumi.Input[str] last_contact_threshold: Limit the amount of time a server can go
without leader contact before being considered unhealthy.
:param pulumi.Input[int] max_trailing_logs: Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
:param pulumi.Input[int] min_quorum: Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
:param pulumi.Input[str] server_stabilization_time: Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _RaftAutopilotState.__new__(_RaftAutopilotState)
__props__.__dict__["cleanup_dead_servers"] = cleanup_dead_servers
__props__.__dict__["dead_server_last_contact_threshold"] = dead_server_last_contact_threshold
__props__.__dict__["last_contact_threshold"] = last_contact_threshold
__props__.__dict__["max_trailing_logs"] = max_trailing_logs
__props__.__dict__["min_quorum"] = min_quorum
__props__.__dict__["server_stabilization_time"] = server_stabilization_time
return RaftAutopilot(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="cleanupDeadServers")
def cleanup_dead_servers(self) -> pulumi.Output[Optional[bool]]:
"""
Specifies whether to remove dead server nodes
periodically or when a new server joins. This requires that `min-quorum` is also set.
"""
return pulumi.get(self, "cleanup_dead_servers")
@property
@pulumi.getter(name="deadServerLastContactThreshold")
def dead_server_last_contact_threshold(self) -> pulumi.Output[Optional[str]]:
"""
Limit the amount of time a
server can go without leader contact before being considered failed. This only takes
effect when `cleanup_dead_servers` is set.
"""
return pulumi.get(self, "dead_server_last_contact_threshold")
@property
@pulumi.getter(name="lastContactThreshold")
def last_contact_threshold(self) -> pulumi.Output[Optional[str]]:
"""
Limit the amount of time a server can go
without leader contact before being considered unhealthy.
"""
return pulumi.get(self, "last_contact_threshold")
@property
@pulumi.getter(name="maxTrailingLogs")
def max_trailing_logs(self) -> pulumi.Output[Optional[int]]:
"""
Maximum number of log entries in the Raft log
that a server can be behind its leader before being considered unhealthy.
"""
return pulumi.get(self, "max_trailing_logs")
@property
@pulumi.getter(name="minQuorum")
def min_quorum(self) -> pulumi.Output[Optional[int]]:
"""
Minimum number of servers allowed in a cluster before
autopilot can prune dead servers. This should at least be 3. Applicable only for
voting nodes.
"""
return pulumi.get(self, "min_quorum")
@property
@pulumi.getter(name="serverStabilizationTime")
def server_stabilization_time(self) -> pulumi.Output[Optional[str]]:
"""
Minimum amount of time a server must be
stable in the 'healthy' state before being added to the cluster.
"""
return pulumi.get(self, "server_stabilization_time")
| 48.715789 | 134 | 0.677398 | 2,800 | 23,140 | 5.336071 | 0.075714 | 0.060371 | 0.088348 | 0.046382 | 0.890436 | 0.878924 | 0.878924 | 0.873636 | 0.870223 | 0.866943 | 0 | 0.002289 | 0.244771 | 23,140 | 474 | 135 | 48.818565 | 0.852655 | 0.381072 | 0 | 0.800866 | 1 | 0 | 0.135234 | 0.072213 | 0 | 0 | 0 | 0 | 0 | 1 | 0.160173 | false | 0.004329 | 0.021645 | 0 | 0.277056 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
97b15b39d748390f9785113329fa6bb671eb2914 | 39,435 | py | Python | Assignments/Assignment_04/task_5/particlefilter/solution/SIR_PF_pendulum.py | chrstrom/TTK4250 | f453c3a59597d3fe6cff7d35b790689919798b94 | [
"Unlicense"
] | null | null | null | Assignments/Assignment_04/task_5/particlefilter/solution/SIR_PF_pendulum.py | chrstrom/TTK4250 | f453c3a59597d3fe6cff7d35b790689919798b94 | [
"Unlicense"
] | null | null | null | Assignments/Assignment_04/task_5/particlefilter/solution/SIR_PF_pendulum.py | chrstrom/TTK4250 | f453c3a59597d3fe6cff7d35b790689919798b94 | [
"Unlicense"
] | null | null | null | __pyarmor__(__name__, __file__, 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2) | 39,435 | 39,435 | 0.749994 | 9,854 | 39,435 | 3.000203 | 0.026487 | 0.003856 | 0.003958 | 0.003247 | 0.001319 | 0.000812 | 0.000812 | 0 | 0 | 0 | 0 | 0.313147 | 0.000076 | 39,435 | 1 | 39,435 | 39,435 | 0.4366 | 0 | 0 | 0 | 0 | 1 | 0.998986 | 0.998986 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
8ae056f127eb291d055f2c1f45a757b1d29ae09f | 5,102 | py | Python | nn.py | sbarratt/nn-py | db1218a5a0623444913f15e05a860eafb9d7e791 | [
"MIT",
"Unlicense"
] | null | null | null | nn.py | sbarratt/nn-py | db1218a5a0623444913f15e05a860eafb9d7e791 | [
"MIT",
"Unlicense"
] | null | null | null | nn.py | sbarratt/nn-py | db1218a5a0623444913f15e05a860eafb9d7e791 | [
"MIT",
"Unlicense"
] | null | null | null | import numpy as np
from scipy.special import expit
import IPython as ipy
import pickle
class TwoLayerNeuralNetwork:
def __init__(self, n_in, n_hid, n_out, eta, epochs, bin_size=None):
self.n_in = n_in
self.n_hid = n_hid
self.n_out = n_out
self.w_ih = .001*np.random.randn(self.n_in+1,self.n_hid)
self.w_ho = .001*np.random.randn(self.n_hid+1,self.n_out)
self.eta = eta
self.epochs = epochs
self.bin_size = bin_size
def feedforward(self, x):
x = np.r_[np.array([1]),x]
h = np.tanh(self.w_ih.T.dot(x))
h = np.r_[np.array([1]),h]
o = expit(self.w_ho.T.dot(h))
return x,h,o
def backpropogate(self, x, h, o, y):
Eo = (o-y)*o*(1-o)
Eo, h = Eo.reshape(Eo.shape[0],1), h.reshape(h.shape[0],1)
dwho = h.dot(Eo.T)
Eh = (1-h[1:]**2) * self.w_ho[1:,:].dot(Eo)
x, Eh = x.reshape(x.shape[0],1), Eh.reshape(Eh.shape[0],1)
dwih = x.dot(Eh.T)
return dwih, dwho
def calc_error(self, data, labels):
e = 0.0
for i in range(data.shape[0]):
y = np.zeros(self.n_out)
y[labels[i]] = 1
_,_, o = self.feedforward(data[i,:])
e += (0.5)*np.linalg.norm(o-y)**2
return e
def train(self, data, labels, test_data, test_labels, save_weights=False):
datapoints = data.shape[0]
for j in range(self.epochs):
for i in range(datapoints):
x, h, o = self.feedforward(data[i,:])
y = np.zeros(self.n_out)
y[labels[i]] = 1
dwih, dwho = self.backpropogate(x,h,o,y)
ipy.embed()
self.w_ih -= self.eta*dwih
self.w_ho -= self.eta*dwho
if save_weights:
pickle.dump(self.w_ih,open("w_ih.pickle","wb"))
pickle.dump(self.w_ho,open("w_ho.pickle","wb"))
print np.sum(self.predict(test_data).ravel() == test_labels), "/", test_data.shape[0]
def predict(self, data):
pred = []
for i in range(data.shape[0]):
_,_,o = self.feedforward(data[i,:])
pred.append(np.argmax(o))
return np.array(pred)
class TwoLayerNeuralNetworkSoftMax:
def __init__(self, n_in, n_hid, n_out, eta, epochs, bin_size=None):
self.n_in = n_in
self.n_hid = n_hid
self.n_out = n_out
self.w_ih = .001*np.random.randn(self.n_in+1,self.n_hid)
self.w_ho = .001*np.random.randn(self.n_hid+1,self.n_out)
self.eta = eta
self.epochs = epochs
self.bin_size = bin_size
def feedforward(self, x):
x = np.r_[np.array([1]),x]
h = np.tanh(self.w_ih.T.dot(x))
h = np.r_[np.array([1]),h]
o_prime = self.w_ho.T.dot(h)
o = np.exp(o_prime)*1.0/np.sum(np.exp(o_prime))
return x,h,o
def backpropogate(self, x, h, o, y):
Eo = (o-y)
Eo, h = Eo.reshape(Eo.shape[0],1), h.reshape(h.shape[0],1)
dwho = h.dot(Eo.T)
Eh = (1-h[1:]**2) * self.w_ho[1:,:].dot(Eo)
x, Eh = x.reshape(x.shape[0],1), Eh.reshape(Eh.shape[0],1)
dwih = x.dot(Eh.T)
return dwih, dwho
def calc_error(self, data, labels):
e = 0.0
for i in range(data.shape[0]):
y = np.zeros(self.n_out)
y[labels[i]] = 1
_,_, o = self.feedforward(data[i,:])
e -= np.log(o).dot(y)
return e
def train(self, data, labels, test_data, test_labels, save_weights=False):
datapoints = data.shape[0]
for j in range(self.epochs):
for i in range(datapoints):
x, h, o = self.feedforward(data[i,:])
y = np.zeros(self.n_out)
y[labels[i]] = 1
dwih, dwho = self.backpropogate(x,h,o,y)
self.w_ih -= self.eta*dwih
self.w_ho -= self.eta*dwho
if save_weights:
pickle.dump(self.w_ih,open("w_ih.pickle","wb"))
pickle.dump(self.w_ho,open("w_ho.pickle","wb"))
print np.sum(self.predict(test_data).ravel() == test_labels), "/", test_data.shape[0]
def predict(self, data):
pred = []
for i in range(data.shape[0]):
_,_,o = self.feedforward(data[i,:])
pred.append(np.argmax(o))
return np.array(pred)
"""
def numerical_grad(self, x, label):
e = 1e-5
y = np.zeros(self.n_out)
y[label] = 1
dwih = np.zeros(self.w_ih.shape)
for i in range(self.w_ih.shape[0]):
for j in range(self.w_ih.shape[1]):
self.w_ih[i,j] += e
f_plus = 0.5*np.linalg.norm(self.feedforward(x)[2]-y)**2
self.w_ih[i,j] -= 2*e
f_minus = 0.5*np.linalg.norm(self.feedforward(x)[2]-y)**2
self.w_ih[i,j] += e
dwih[i,j] = (f_plus-f_minus)*1.0/(2*e)
dwho = np.zeros(self.w_ho.shape)
for i in range(self.w_ho.shape[0]):
for j in range(self.w_ho.shape[1]):
self.w_ho[i,j] += e
f_plus = 0.5*np.linalg.norm(self.feedforward(x)[2]-y)**2
self.w_ho[i,j] -= 2*e
f_minus = 0.5*np.linalg.norm(self.feedforward(x)[2]-y)**2
self.w_ho[i,j] += e
dwho[i,j] = (f_plus-f_minus)*1.0/(2*e)
return dwih, dwho
def train_batch(self, data, labels, test_data, test_labels):
for _ in range(self.epochs):
for j in range(data.shape[0]/self.bin_size):
dwih, dwho = np.zeros(self.w_ih.shape),np.zeros(self.w_ho.shape)
for i in range(j*self.bin_size,(j+1)*self.bin_size):
x, h, o = self.feedforward(data[i,:])
y = np.zeros(self.n_out)
y[labels[i]] = 1
dwih1, dwho1 = self.backpropogate(x,h,o,y)
dwih, dwho = dwih + dwih1, dwho + dwho1
self.w_ih -= self.eta*dwih*1.0/self.bin_size
self.w_ho -= self.eta*dwho*1.0/self.bin_size
print np.sum(self.predict(test_data).ravel() == test_labels), "/", test_data.shape[0]
"""
| 30.369048 | 88 | 0.635045 | 1,012 | 5,102 | 3.067194 | 0.09585 | 0.054768 | 0.040593 | 0.031894 | 0.886598 | 0.859214 | 0.811534 | 0.787371 | 0.773196 | 0.773196 | 0 | 0.025659 | 0.167385 | 5,102 | 167 | 89 | 30.550898 | 0.705038 | 0 | 0 | 0.872727 | 0 | 0 | 0.014702 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.036364 | null | null | 0.018182 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
c121a1b53574595b8a0a55bb0bbf64156eeb6a42 | 6,387 | py | Python | lesson9.py | IvanGanin/Second_lesson | 3f4188ba7edcfba8fa513e0e3e5b5e57d4ad9eeb | [
"MIT"
] | 1 | 2020-02-04T07:59:10.000Z | 2020-02-04T07:59:10.000Z | lesson9.py | IvanGanin/Lessons | 3f4188ba7edcfba8fa513e0e3e5b5e57d4ad9eeb | [
"MIT"
] | null | null | null | lesson9.py | IvanGanin/Lessons | 3f4188ba7edcfba8fa513e0e3e5b5e57d4ad9eeb | [
"MIT"
] | null | null | null | import random
class Cards:
cards_list = ['6♠', '6♥', '6♣', '6♦', '7♠', '7♥', '7♣', '7♦',
'8♠', '8♥', '8♣', '8♦', '9♠', '9♥', '9♣', '9♦',
'10♠', '10♥', '10♣', '10♦', 'A♠', 'A♥', 'A♣', 'A♦',
'B♠', 'B♥', 'B♣', 'B♦', 'C♠', 'C♥', 'C♣', 'C♦',
'D♠', 'D♥', 'D♣', 'D♦'] #колода - 36 карт
# выдаем карты на руки по 6 штук
def on_hand(self):
self.hand = []
while len(self.hand) < 6:
card = random.choice(self.cards_list)
self.cards_list.remove(card)
self.hand.append(card)
return self.hand
# ходит игрок
def player_try(self):
global card
print("ход игрока")
counter = None
while counter is None:
card = random.choice(player.hand)
player.hand.remove(card)
print(card)
box = [item for item in computer.hand if card[-1] in item]
print(box)
if len(box) == 0:
computer.hand.append(card)
print("компьютеру нечем бить, ход игрока")
print(computer.hand)
card_add = random.choice(player.cards_list)
player.cards_list.remove(card_add)
player.hand.append(card_add)
print(player.hand)
else:
play = []
for item in box:
if len(card) == 3 and len(item) == 3:
if card[:2] < item[:2]:
computer.hand.remove(item)
play.append(item)
break
elif len(card) == 3 and len(item) == 2:
if card[:2] < item[0]:
computer.hand.remove(item)
play.append(item)
break
elif len(card) == 2 and len(item) == 3:
if card[0] < item[:2]:
computer.hand.remove(item)
play.append(item)
break
elif len(card) == 2 and len(item) == 2:
if card[0] < item[0]:
computer.hand.remove(item)
play.append(item)
break
if len(play) != 0:
counter = 1
print("ваша карта бита, ход компьютера")
card_add = random.choice(player.cards_list)
player.cards_list.remove(card_add)
player.hand.append(card_add)
card_add = random.choice(computer.cards_list)
computer.cards_list.remove(card_add)
computer.hand.append(card_add)
print(player.hand, len(player.cards_list))
print(computer.hand, len(computer.cards_list))
else:
computer.hand.append(card)
print("компьютеру нечем бить, ход игрока")
print(computer.hand, len(computer.cards_list))
card_add = random.choice(player.cards_list)
player.cards_list.remove(card_add)
player.hand.append(card_add)
print(player.hand, len(player.cards_list))
return card
# ход компьютера
def computer_try(self):
print("ход компьютера")
counter = None
while counter is None:
card = random.choice(computer.hand)
computer.hand.remove(card)
print(card)
box = [item for item in player.hand if card[-1] in item]
print(box)
if len(box) == 0:
player.hand.append(card)
print("игроку нечем бить, ход компьютера")
print(player.hand)
card_add = random.choice(computer.cards_list)
computer.cards_list.remove(card_add)
computer.hand.append(card_add)
print(computer.hand)
else:
play = []
for item in box:
if len(card) == 3 and len(item) == 3:
if card[:2] < item[:2]:
player.hand.remove(item)
play.append(item)
break
elif len(card) == 3 and len(item) == 2:
if card[:2] < item[0]:
player.hand.remove(item)
play.append(item)
break
elif len(card) == 2 and len(item) == 3:
if card[0] < item[:2]:
player.hand.remove(item)
play.append(item)
break
elif len(card) == 2 and len(item) == 2:
if card[0] < item[0]:
player.hand.remove(item)
play.append(item)
break
if len(play) != 0:
counter = 1
print("ваша карта бита, ход игрока")
card_add = random.choice(player.cards_list)
player.cards_list.remove(card_add)
player.hand.append(card_add)
card_add = random.choice(computer.cards_list)
computer.cards_list.remove(card_add)
computer.hand.append(card_add)
print(player.hand, len(player.cards_list))
print(computer.hand, len(computer.cards_list))
else:
player.hand.append(card)
print("игроку нечем бить, ход компьютера")
print(computer.hand, len(computer.cards_list))
card_add = random.choice(computer.cards_list)
computer.cards_list.remove(card_add)
computer.hand.append(card_add)
print(player.hand, len(player.cards_list))
return card
if __name__ == "__main__":
computer = Cards()
player = Cards()
computer.on_hand()
player.on_hand()
computer.computer_try()
player.player_try() | 42.019737 | 70 | 0.437764 | 688 | 6,387 | 4.020349 | 0.109012 | 0.087852 | 0.065799 | 0.061822 | 0.805495 | 0.804049 | 0.804049 | 0.804049 | 0.804049 | 0.771511 | 0 | 0.019479 | 0.453421 | 6,387 | 152 | 71 | 42.019737 | 0.762532 | 0.011586 | 0 | 0.760563 | 0 | 0 | 0.047227 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.021127 | false | 0 | 0.007042 | 0 | 0.06338 | 0.169014 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c127c9c465fef44485751d77a78b2c58d16f53ef | 10,093 | py | Python | v6.0.5/system/test_fortios_system_api_user.py | fortinet-solutions-cse/ansible_fgt_modules | c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719 | [
"Apache-2.0"
] | 14 | 2018-09-25T20:35:25.000Z | 2021-07-14T04:30:54.000Z | v6.0.6/system/test_fortios_system_api_user.py | fortinet-solutions-cse/ansible_fgt_modules | c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719 | [
"Apache-2.0"
] | 32 | 2018-10-09T04:13:42.000Z | 2020-05-11T07:20:28.000Z | v6.0.5/system/test_fortios_system_api_user.py | fortinet-solutions-cse/ansible_fgt_modules | c45fba49258d7c9705e7a8fd9c2a09ea4c8a4719 | [
"Apache-2.0"
] | 11 | 2018-10-09T00:14:53.000Z | 2021-11-03T10:54:09.000Z | # Copyright 2019 Fortinet, Inc.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Ansible. If not, see <https://www.gnu.org/licenses/>.
# Make coding more python3-ish
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import os
import json
import pytest
from mock import ANY
from ansible.module_utils.network.fortios.fortios import FortiOSHandler
try:
from ansible.modules.network.fortios import fortios_system_api_user
except ImportError:
pytest.skip("Could not load required modules for testing", allow_module_level=True)
@pytest.fixture(autouse=True)
def connection_mock(mocker):
connection_class_mock = mocker.patch('ansible.modules.network.fortios.fortios_system_api_user.Connection')
return connection_class_mock
fos_instance = FortiOSHandler(connection_mock)
def test_system_api_user_creation(mocker):
schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema')
set_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200}
set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result)
input_data = {
'username': 'admin',
'state': 'present',
'system_api_user': {
'accprofile': 'test_value_3',
'api_key': 'test_value_4',
'comments': 'test_value_5',
'cors_allow_origin': 'test_value_6',
'name': 'default_name_7',
'peer_auth': 'enable',
'peer_group': 'test_value_9',
'schedule': 'test_value_10',
},
'vdom': 'root'}
is_error, changed, response = fortios_system_api_user.fortios_system(input_data, fos_instance)
expected_data = {
'accprofile': 'test_value_3',
'api-key': 'test_value_4',
'comments': 'test_value_5',
'cors-allow-origin': 'test_value_6',
'name': 'default_name_7',
'peer-auth': 'enable',
'peer-group': 'test_value_9',
'schedule': 'test_value_10',
}
set_method_mock.assert_called_with('system', 'api-user', data=expected_data, vdom='root')
schema_method_mock.assert_not_called()
assert not is_error
assert changed
assert response['status'] == 'success'
assert response['http_status'] == 200
def test_system_api_user_creation_fails(mocker):
schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema')
set_method_result = {'status': 'error', 'http_method': 'POST', 'http_status': 500}
set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result)
input_data = {
'username': 'admin',
'state': 'present',
'system_api_user': {
'accprofile': 'test_value_3',
'api_key': 'test_value_4',
'comments': 'test_value_5',
'cors_allow_origin': 'test_value_6',
'name': 'default_name_7',
'peer_auth': 'enable',
'peer_group': 'test_value_9',
'schedule': 'test_value_10',
},
'vdom': 'root'}
is_error, changed, response = fortios_system_api_user.fortios_system(input_data, fos_instance)
expected_data = {
'accprofile': 'test_value_3',
'api-key': 'test_value_4',
'comments': 'test_value_5',
'cors-allow-origin': 'test_value_6',
'name': 'default_name_7',
'peer-auth': 'enable',
'peer-group': 'test_value_9',
'schedule': 'test_value_10',
}
set_method_mock.assert_called_with('system', 'api-user', data=expected_data, vdom='root')
schema_method_mock.assert_not_called()
assert is_error
assert not changed
assert response['status'] == 'error'
assert response['http_status'] == 500
def test_system_api_user_removal(mocker):
schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema')
delete_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200}
delete_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.delete', return_value=delete_method_result)
input_data = {
'username': 'admin',
'state': 'absent',
'system_api_user': {
'accprofile': 'test_value_3',
'api_key': 'test_value_4',
'comments': 'test_value_5',
'cors_allow_origin': 'test_value_6',
'name': 'default_name_7',
'peer_auth': 'enable',
'peer_group': 'test_value_9',
'schedule': 'test_value_10',
},
'vdom': 'root'}
is_error, changed, response = fortios_system_api_user.fortios_system(input_data, fos_instance)
delete_method_mock.assert_called_with('system', 'api-user', mkey=ANY, vdom='root')
schema_method_mock.assert_not_called()
assert not is_error
assert changed
assert response['status'] == 'success'
assert response['http_status'] == 200
def test_system_api_user_deletion_fails(mocker):
schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema')
delete_method_result = {'status': 'error', 'http_method': 'POST', 'http_status': 500}
delete_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.delete', return_value=delete_method_result)
input_data = {
'username': 'admin',
'state': 'absent',
'system_api_user': {
'accprofile': 'test_value_3',
'api_key': 'test_value_4',
'comments': 'test_value_5',
'cors_allow_origin': 'test_value_6',
'name': 'default_name_7',
'peer_auth': 'enable',
'peer_group': 'test_value_9',
'schedule': 'test_value_10',
},
'vdom': 'root'}
is_error, changed, response = fortios_system_api_user.fortios_system(input_data, fos_instance)
delete_method_mock.assert_called_with('system', 'api-user', mkey=ANY, vdom='root')
schema_method_mock.assert_not_called()
assert is_error
assert not changed
assert response['status'] == 'error'
assert response['http_status'] == 500
def test_system_api_user_idempotent(mocker):
schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema')
set_method_result = {'status': 'error', 'http_method': 'DELETE', 'http_status': 404}
set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result)
input_data = {
'username': 'admin',
'state': 'present',
'system_api_user': {
'accprofile': 'test_value_3',
'api_key': 'test_value_4',
'comments': 'test_value_5',
'cors_allow_origin': 'test_value_6',
'name': 'default_name_7',
'peer_auth': 'enable',
'peer_group': 'test_value_9',
'schedule': 'test_value_10',
},
'vdom': 'root'}
is_error, changed, response = fortios_system_api_user.fortios_system(input_data, fos_instance)
expected_data = {
'accprofile': 'test_value_3',
'api-key': 'test_value_4',
'comments': 'test_value_5',
'cors-allow-origin': 'test_value_6',
'name': 'default_name_7',
'peer-auth': 'enable',
'peer-group': 'test_value_9',
'schedule': 'test_value_10',
}
set_method_mock.assert_called_with('system', 'api-user', data=expected_data, vdom='root')
schema_method_mock.assert_not_called()
assert not is_error
assert not changed
assert response['status'] == 'error'
assert response['http_status'] == 404
def test_system_api_user_filter_foreign_attributes(mocker):
schema_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.schema')
set_method_result = {'status': 'success', 'http_method': 'POST', 'http_status': 200}
set_method_mock = mocker.patch('ansible.module_utils.network.fortios.fortios.FortiOSHandler.set', return_value=set_method_result)
input_data = {
'username': 'admin',
'state': 'present',
'system_api_user': {
'random_attribute_not_valid': 'tag',
'accprofile': 'test_value_3',
'api_key': 'test_value_4',
'comments': 'test_value_5',
'cors_allow_origin': 'test_value_6',
'name': 'default_name_7',
'peer_auth': 'enable',
'peer_group': 'test_value_9',
'schedule': 'test_value_10',
},
'vdom': 'root'}
is_error, changed, response = fortios_system_api_user.fortios_system(input_data, fos_instance)
expected_data = {
'accprofile': 'test_value_3',
'api-key': 'test_value_4',
'comments': 'test_value_5',
'cors-allow-origin': 'test_value_6',
'name': 'default_name_7',
'peer-auth': 'enable',
'peer-group': 'test_value_9',
'schedule': 'test_value_10',
}
set_method_mock.assert_called_with('system', 'api-user', data=expected_data, vdom='root')
schema_method_mock.assert_not_called()
assert not is_error
assert changed
assert response['status'] == 'success'
assert response['http_status'] == 200
| 36.046429 | 142 | 0.654018 | 1,218 | 10,093 | 5.064039 | 0.14532 | 0.087549 | 0.054799 | 0.052691 | 0.833982 | 0.826524 | 0.805285 | 0.805285 | 0.805285 | 0.805285 | 0 | 0.015492 | 0.219756 | 10,093 | 279 | 143 | 36.175627 | 0.767746 | 0.065788 | 0 | 0.816425 | 0 | 0 | 0.354828 | 0.092638 | 0 | 0 | 0 | 0 | 0.173913 | 1 | 0.033816 | false | 0 | 0.038647 | 0 | 0.077295 | 0.004831 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c14af92ede3e1e7a9d29a43cdf92dd6bcb697157 | 37,803 | py | Python | unit_tests/test_whetlab.py | schevalier/Whetlab-Python-Client | e934424e9ca6f06617a6421509d13c0cc87777e7 | [
"BSD-4-Clause"
] | 4 | 2015-07-31T12:47:36.000Z | 2021-04-14T16:37:55.000Z | unit_tests/test_whetlab.py | schevalier/Whetlab-Python-Client | e934424e9ca6f06617a6421509d13c0cc87777e7 | [
"BSD-4-Clause"
] | null | null | null | unit_tests/test_whetlab.py | schevalier/Whetlab-Python-Client | e934424e9ca6f06617a6421509d13c0cc87777e7 | [
"BSD-4-Clause"
] | 3 | 2016-03-25T02:23:09.000Z | 2018-05-04T03:09:02.000Z | from nose.tools import *
import whetlab, whetlab.server
from time import time, sleep
from nose.tools import with_setup, assert_equals
import numpy as np
import numpy.random as npr
whetlab.RETRY_TIMES = [] # So that it doesn't wait forever for tests that raise errors
default_access_token = None
default_description = ''
default_parameters = { 'p1':{'type':'float', 'min':0, 'max':10.0, 'size':1},
'p2':{'type':'integer', 'min':0, 'max':10, 'size':1}}
default_outcome = {'name':'Dummy outcome'}
last_created_experiment = ""
def test_required_prop_are_supported():
""" All required properties should be supported, for parameters and outcome. """
# Parameters
for props in whetlab.required_properties.values():
for x in props:
assert( x in whetlab.supported_properties )
# Outcome
for x in whetlab.outcome_required_properties:
assert( x in whetlab.outcome_supported_properties )
def test_default_values_are_legal():
""" All default values for properties should be legal, for parameters and outcome. """
#Parameters
for k,v in whetlab.default_values.items():
if k in whetlab.legal_values:
assert( v in whetlab.legal_values[k] )
# Outcome
for k,v in whetlab.outcome_default_values.items():
if k in whetlab.outcome_legal_values:
assert( v in whetlab.outcome_legal_values[k] )
def test_delete_experiment():
""" Delete experiment should remove the experiment from the server. """
name = 'test ' + str(time())
scientist = whetlab.Experiment(access_token=default_access_token,
name=name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':5.,'p2':1},5)
# Delete experiment
whetlab.delete_experiment(name,default_access_token)
# Should now be possible to create an experiment with the same name
scientist = whetlab.Experiment(access_token=default_access_token,
name=name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
# Re-deleting it
whetlab.delete_experiment(name,default_access_token)
def setup_function():
try:
whetlab.delete_experiment('test_experiment',default_access_token)
except:
pass
def teardown_function():
whetlab.delete_experiment('test_experiment',default_access_token)
class TestExperiment:
def __init__(self):
self.name = 'test ' + str(time())
# Before running each test make sure that there is no experiment
# with this name
def setup(self):
try:
whetlab.delete_experiment(self.name,default_access_token)
except:
pass
# Make sure to clean up any created experiments with this name
def teardown(self):
try:
whetlab.delete_experiment(self.name,default_access_token)
except:
pass
@raises(whetlab.server.error.client_error.ClientError)
def test_same_name(self):
""" Can't create two experiments with same name (when resume is False). """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
# Repeat Experiment creation to raise error, with resume set to False
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description+'2',
parameters=default_parameters,
outcome=default_outcome,
resume = False)
def test_resume_false(self):
""" If resume is False and experiment's name is unique, can create an experiment. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome,
resume = False)
def test_resume(self):
""" Resume correctly loads previous results. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':2.1,'p2':1},3)
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description)
# Make sure result is still there
assert( cmp(scientist._ids_to_param_values.values()[0],{'p1':2.1,'p2':1}) == 0 )
assert( cmp(scientist._ids_to_outcome_values.values()[0],3) == 0 )
@raises(ValueError)
def test_empty_name(self):
""" Experiment's name can't be empty. """
name = ''
scientist = whetlab.Experiment(access_token=default_access_token,
name=name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
@raises(whetlab.server.error.client_error.ClientError)
def test_name_too_long(self):
""" Experiment's name must have at most 500 caracters. """
name = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
scientist = whetlab.Experiment(access_token=default_access_token,
name=name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
# @raises(whetlab.server.error.client_error.ClientError)
# def test_description_too_long(self):
# """ Experiment's description must have at most 500 caracters. """
#
# name = 'test ' + str(time())
# description = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
# scientist = whetlab.Experiment(access_token=default_access_token,
# name=name,
# description=description,
# parameters=default_parameters,
# outcome=default_outcome)
@raises(ValueError)
def test_empty_parameters(self):
""" Experiment's parameters can't be empty. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters={},
outcome=default_outcome)
@raises(ValueError)
def test_empty_outcome(self):
""" Experiment's outcome can't be empty. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome={})
@raises(ValueError)
def test_unknown_parameter_properties(self):
""" Parameter properties must be valid. """
bad_parameters = { 'p1':{'type':'float', 'min':0, 'max':10.0, 'size':1, 'fake_property':10}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_min_max_properties(self):
""" Parameter property 'min' must be smaller than 'max'. """
bad_parameters = { 'p1':{'type':'float', 'min':10., 'max':1., 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_float_for_int_bounds(self):
""" Parameter properties 'min' and 'max' must be integers if the parameter is an integer. """
bad_parameters = { 'p1':{'type':'integer', 'min':0.0, 'max':0.5, 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_nan_bounds(self):
""" Parameter properties 'min' and 'max' must be finite. """
bad_parameters = { 'p1':{'type':'float', 'min':np.nan, 'max':0.5, 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_infinite_bounds(self):
""" Parameter properties 'min' and 'max' must be finite. """
bad_parameters = { 'p1':{'type':'float', 'min':-np.inf, 'max':np.inf, 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_large_neg_bounds(self):
""" Parameter properties 'min' and 'max' must be greater than -1e32. """
bad_parameters = { 'p1':{'type':'integer', 'min':-1e35, 'max':1.0, 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_large_bounds(self):
""" Parameter properties 'min' and 'max' must be less than 1e32. """
bad_parameters = { 'p1':{'type':'integer', 'min':1.0, 'max':1e33, 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_vector_bounds(self):
""" Parameter properties 'min' and 'max' must be finite numbers. """
bad_parameters = { 'p1':{'type':'float', 'min':[0.1, 0.2], 'max':0.5, 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_legal_property_value(self):
""" Parameter property must take a legal value. """
bad_parameters = { 'p1':{'type':'BAD_VALUE', 'min':1., 'max':10., 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_enum_not_supported(self):
""" Parameter type 'enum' not yet supported. """
bad_parameters = { 'p1':{'type':'enum', 'min':1., 'max':10., 'size':1}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
def test_good_enum_options(self):
""" Enum options with a legal name. """
bad_parameters = { 'p1':{'type':'enum', 'options':['one', 'two', 'three']}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
def test_enum_with_two_options(self):
""" Enums should work with just two options. """
bad_parameters = { 'p1':{'type':'enum', 'options':['one', 'two']}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
def test_enum_update(self):
""" Update supports enum. """
parameters = { 'p1':{'type':'enum', 'options':['one', 'two']}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
scientist.update({'p1':'one'},10)
@raises(ValueError)
def test_bad_enum_options(self):
""" Enum options must take a legal name. """
bad_parameters = { 'p1':{'type':'enum', 'options':['1','2','3']}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
@raises(ValueError)
def test_bad_enum_update(self):
""" Enum can't update with value not in options. """
bad_parameters = { 'p1':{'type':'enum', 'options':['one', 'two', 'three']}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
scientist.update({'p1':'four'},10.)
def test_list_enum_update(self):
""" Update supports list of enums (size > 1). """
parameters = { 'p1':{'type':'enum', 'options':['one', 'two', 'three'], 'size':3}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
job = {u'p1':['three','one','one']}
scientist.update(job,10.)
def test_list_enum_suggest(self):
""" Suggest supports list of enums (size > 1). """
parameters = { 'p1':{'type':'enum', 'options':['one', 'two'], 'size':3}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
job = scientist.suggest()
assert(len(job['p1']) == 3)
assert(job['p1'][0] in {'one','two'})
assert(job['p1'][1] in {'one','two'})
assert(job['p1'][2] in {'one','two'})
@raises(ValueError)
def test_list_bad_enum_update(self):
""" Enum can't update when one value in list (size>1) not in options. """
bad_parameters = { 'p1':{'type':'enum', 'options':['one', 'two', 'three'],'size':4}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=bad_parameters,
outcome=default_outcome)
scientist.update({'p1':['one','two','one','four']},10.)
@raises(whetlab.server.error.client_error.ClientError)
def test_access_token(self):
""" Valid access token must be provided. """
scientist = whetlab.Experiment(access_token='',
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
def test_cancel(self):
""" Cancel removes a result. """
name = 'test test_cancel ' + str(time())
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':5.1,'p2':5},10)
scientist.cancel({'p1':5.1,'p2':5})
# Make sure result was removed
scientist._sync_with_server()
assert( len(scientist._ids_to_param_values) == 0 )
assert( len(scientist._ids_to_outcome_values) == 0 )
def test_get_by_result_id(self):
""" Get a result by the id. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
jobs = []
for i in xrange(5):
jobs.append(scientist.suggest())
for i in xrange(5):
scientist.update(jobs[i], np.random.randn())
# Make sure result was removed
scientist._sync_with_server()
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
job = scientist.get_by_result_id(result_id)
assert_equals(job, jobs[i])
def test_get_result_id(self):
""" Get the id associated with a set of parameters. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
jobs = []
for i in xrange(5):
jobs.append(scientist.suggest())
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
j = dict(jobs[i]) # Throw away the id
assert_equals(result_id, scientist.get_id(j))
assert_equals(scientist.get_id(jobs[i]), scientist.get_id(j))
scientist.update_by_result_id(result_id, npr.randn())
assert_equals(result_id, scientist.get_id(j))
assert_equals(scientist.get_id(jobs[i]), scientist.get_id(j))
def test_cancel_by_result_id(self):
""" Cancel removes a result. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
jobs = []
for i in xrange(5):
jobs.append(scientist.suggest())
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
scientist.update_by_result_id(result_id, npr.randn())
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
scientist.cancel_by_result_id(result_id)
# Make sure result was removed
scientist._sync_with_server()
assert_equals(len(scientist._ids_to_param_values), 0 )
assert_equals(len(scientist._ids_to_outcome_values), 0 )
def test_get_all_results(self):
""" Cancel removes a result. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
def count_in_list(j, jobs):
hits = 0
for job in jobs:
if scientist.get_id(job) == scientist.get_id(j):
hits += 1
return hits
jobs = []
for i in xrange(5):
jobs.append(scientist.suggest())
j, o = scientist.get_all_results()
assert_equals(len(j), len(o))
assert_equals(len(j), i+1)
assert_equals(o[i], None)
assert_equals(count_in_list(jobs[i], j), 1)
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
outcome = npr.randn()
scientist.update_by_result_id(result_id, outcome)
j, o = scientist.get_all_results()
assert_equals(len(j), len(o))
assert_equals(count_in_list(jobs[i], j), 1)
assert(outcome in o)
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
scientist.cancel_by_result_id(result_id)
j, o = scientist.get_all_results()
assert_equals(len(j), len(o))
assert_equals(count_in_list(jobs[i], j), 0)
assert_equals(len(j), 5-i-1)
def test_update_by_result_id(self):
""" Update adds and can overwrite a result. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
jobs = []
for i in xrange(5):
jobs.append(scientist.suggest())
outcomes = []
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
outcomes.append(npr.randn())
scientist.update_by_result_id(result_id, outcomes[-1])
# Make sure result was added
scientist._sync_with_server()
for i in xrange(5):
result_id = scientist.get_id(jobs[i])
assert_equals(scientist._ids_to_outcome_values[result_id], outcomes[i])
def test_update(self):
""" Update adds and can overwrite a result. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':5.1,'p2':5},10)
# Make sure result was added
scientist._sync_with_server()
assert( cmp(scientist._ids_to_param_values.values()[0],{'p1':5.1,'p2':5}) == 0 )
assert( cmp(scientist._ids_to_outcome_values.values()[0],10) == 0 )
# Make sure result was overwritten
scientist.update({'p1':5.1,'p2':5},20)
scientist._sync_with_server()
assert( len(scientist._ids_to_param_values.values()) == 1 )
assert( len(scientist._ids_to_outcome_values.values()) == 1 )
assert( cmp(scientist._ids_to_param_values.values()[0],{'p1':5.1,'p2':5}) == 0 )
assert( cmp(scientist._ids_to_outcome_values.values()[0],20) == 0 )
def test_suggest_twice(self):
""" Calling suggest twice returns two different jobs. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
a = scientist.suggest()
sleep(2)
b = scientist.suggest()
# Two suggested jobs are different
assert( cmp(a,b) != 0 )
def test_suggest(self):
""" Suggest return a valid job. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
a = scientist.suggest()
# Check all parameter names are valid in suggestion
for k in a.keys():
assert(k in default_parameters.keys())
# Check if all parameters were assigned a value
for k in default_parameters.keys():
assert(k in a)
# Check parameter values are within the min/max bounds
for k,v in a.items():
assert(v >= default_parameters[k]['min'])
assert(v <= default_parameters[k]['max'])
# Check parameter values are of right type
for k,v in a.items():
if default_parameters[k]['type'] == 'integer':
assert(type(v) == int)
if default_parameters[k]['type'] == 'float':
assert(type(v) == float)
def test_best(self):
""" Best returns the best job. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':1.,'p2':4},1)
scientist.update({'p1':4.,'p2':2},2)
scientist.update({'p1':5.,'p2':1},1000)
scientist.update({'p1':9.,'p2':9},3)
scientist.update({'p1':1.,'p2':1},4)
scientist.update({'p1':5.,'p2':5},5)
assert(cmp(scientist.best(),{'p1':5.,'p2':1})==0)
def test_best_with_nan(self):
""" Best returns the best job. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':1.,'p2':4},1.0)
scientist.update({'p1':4.,'p2':2},2.0)
scientist.update({'p1':5.,'p2':1},1000)
scientist.update({'p1':9.,'p2':9},3)
scientist.update({'p1':1.,'p2':1},4)
scientist.update({'p1':5.,'p2':5},5)
scientist.update({'p1':5.,'p2':2}, np.nan)
scientist.update({'p1':5.,'p2':7}, np.nan)
assert(cmp(scientist.best(),{'p1':5.,'p2':1})==0)
def test_pending(self):
""" Pending returns jobs that have not been updated. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
a = scientist.suggest()
b = scientist.suggest()
c = scientist.suggest()
scientist.update(b,10)
l = scientist.pending()
assert(a in l)
assert(b not in l)
assert(c in l)
assert(len(l) == 2)
def test_clear_pending(self):
""" Should remove pending jobs only. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
a = scientist.suggest()
b = scientist.suggest()
c = scientist.suggest()
scientist.update(b,10)
scientist.clear_pending()
assert( len(scientist.pending()) == 0 )
# Make sure only result left is "b"
scientist._sync_with_server()
print scientist._ids_to_param_values
assert( cmp(scientist._ids_to_param_values.values()[0],b) == 0 )
assert( cmp(scientist._ids_to_outcome_values.values()[0],10) == 0 )
@raises(ValueError)
def test_update_parameter_too_small(self):
""" Update should raise error if parameter smaller than minimum. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':-5.,'p2':5},5)
@raises(ValueError)
def test_update_parameter_too_big(self):
""" Update should raise error if parameter larger than maximum. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':5.,'p2':50},5)
@raises(TypeError)
def test_update_parameter_not_integer(self):
""" Update should raise error if an integer parameter has a non-integer value. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':5.,'p2':1.},5)
@raises(TypeError)
def test_update_parameter_not_float(self):
""" Update should raise error if a float parameter has a non-float value. """
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=default_parameters,
outcome=default_outcome)
scientist.update({'p1':5,'p2':1},5)
def test_create_experiment_with_defaults(self):
""" Can create experiment with floats/integers simply by specifying min/max."""
minimal_parameter_description = {'p1':{'min':0,'max':10}, 'p2':{'type':'integer', 'min':1, 'max':4}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=minimal_parameters_description,
outcome=default_outcome)
def test_create_experiment_with_defaults(self):
""" Can create experiment with floats/integers simply by specifying min/max."""
minimal_parameters_description = {'p1':{'min':0,'max':10}, 'p2':{'type':'integer', 'min':1, 'max':4}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=minimal_parameters_description,
outcome=default_outcome)
def test_int_instead_of_integer(self):
""" Can use 'int' as type, instead of 'integer'."""
parameters = {'p1':{'type':'int', 'min':1, 'max':4}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
def test_multidimensional_parameters(self):
""" Can use multidimensional parameters."""
parameters = { 'p1':{'type':'float', 'min':0, 'max':10.0, 'size':3},
'p2':{'type':'integer', 'min':0, 'max':10, 'size':5}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
job = scientist.suggest()
assert(len(job['p1']) == 3)
assert(len(job['p2']) == 5)
scientist.update({'p1':[1.2,2.3,3.4],'p2':[4,2,5,2,1]},10.2)
@raises(ValueError)
def test_multidimensional_correct_size(self):
""" Parameters must have correct size."""
parameters = { 'p1':{'type':'float', 'min':0, 'max':10.0, 'size':3},
'p2':{'type':'integer', 'min':0, 'max':10, 'size':5}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
scientist.update({'p1':[1.2,2.3,3.4],'p2':[4,2,5]},10.2)
@raises(ValueError)
def test_multidimensional_min_max(self):
""" All dimensions must be within min/max bounds."""
parameters = { 'p1':{'type':'float', 'min':0, 'max':10.0, 'size':3},
'p2':{'type':'integer', 'min':0, 'max':10, 'size':5}}
scientist = whetlab.Experiment(access_token=default_access_token,
name=self.name,
description=default_description,
parameters=parameters,
outcome=default_outcome)
scientist.update({'p1':[1.2,2.3,3.4],'p2':[4,2,5,12,3]},10.2)
| 44.265808 | 526 | 0.531624 | 3,570 | 37,803 | 5.429412 | 0.07507 | 0.065831 | 0.055719 | 0.08915 | 0.809214 | 0.777021 | 0.758087 | 0.727906 | 0.705773 | 0.667956 | 0 | 0.018366 | 0.369151 | 37,803 | 853 | 527 | 44.317702 | 0.794406 | 0.051107 | 0 | 0.681507 | 0 | 0 | 0.045154 | 0.015193 | 0 | 0 | 0 | 0 | 0.094178 | 0 | null | null | 0.005137 | 0.010274 | null | null | 0.001712 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c1ab05bd0daed312b03733d3fca63f7c6830fd62 | 15,374 | py | Python | com/vmware/nsx_policy/infra/tier_1s/segments_client.py | vishal-12/vsphere-automation-sdk-python | 9cf363971db77ea5a12928eecd5cf5170a7fcd8a | [
"MIT"
] | null | null | null | com/vmware/nsx_policy/infra/tier_1s/segments_client.py | vishal-12/vsphere-automation-sdk-python | 9cf363971db77ea5a12928eecd5cf5170a7fcd8a | [
"MIT"
] | null | null | null | com/vmware/nsx_policy/infra/tier_1s/segments_client.py | vishal-12/vsphere-automation-sdk-python | 9cf363971db77ea5a12928eecd5cf5170a7fcd8a | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
#---------------------------------------------------------------------------
# Copyright 2019 VMware, Inc. All rights reserved.
# AUTO GENERATED FILE -- DO NOT MODIFY!
#
# vAPI stub file for package com.vmware.nsx_policy.infra.tier_1s.segments.
#---------------------------------------------------------------------------
"""
"""
__author__ = 'VMware, Inc.'
__docformat__ = 'restructuredtext en'
import sys
from vmware.vapi.bindings import type
from vmware.vapi.bindings.converter import TypeConverter
from vmware.vapi.bindings.enum import Enum
from vmware.vapi.bindings.error import VapiError
from vmware.vapi.bindings.struct import VapiStruct
from vmware.vapi.bindings.stub import (
ApiInterfaceStub, StubFactoryBase, VapiInterface)
from vmware.vapi.bindings.common import raise_core_exception
from vmware.vapi.data.validator import (UnionValidator, HasFieldsOfValidator)
from vmware.vapi.exception import CoreException
from vmware.vapi.lib.constants import TaskType
from vmware.vapi.lib.rest import OperationRestMetadata
class StaticArp(VapiInterface):
"""
"""
_VAPI_SERVICE_ID = 'com.vmware.nsx_policy.infra.tier_1s.segments.static_arp'
"""
Identifier of the service in canonical form.
"""
def __init__(self, config):
"""
:type config: :class:`vmware.vapi.bindings.stub.StubConfiguration`
:param config: Configuration to be used for creating the stub.
"""
VapiInterface.__init__(self, config, _StaticArpStub)
def delete(self,
tier1_id,
segment_id,
):
"""
Delete static ARP config
:type tier1_id: :class:`str`
:param tier1_id: Tier-1 ID (required)
:type segment_id: :class:`str`
:param segment_id: Segment ID (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('delete',
{
'tier1_id': tier1_id,
'segment_id': segment_id,
})
def get(self,
tier1_id,
segment_id,
):
"""
Read static ARP config
:type tier1_id: :class:`str`
:param tier1_id: Tier-1 ID (required)
:type segment_id: :class:`str`
:param segment_id: Segment ID (required)
:rtype: :class:`com.vmware.nsx_policy.model_client.StaticARPConfig`
:return: com.vmware.nsx_policy.model.StaticARPConfig
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('get',
{
'tier1_id': tier1_id,
'segment_id': segment_id,
})
def patch(self,
tier1_id,
segment_id,
static_arp_config,
):
"""
Create static ARP config with tier-1 and segment IDs provided if it
doesn't exist, update with provided config if it's already created.
:type tier1_id: :class:`str`
:param tier1_id: Tier-1 ID (required)
:type segment_id: :class:`str`
:param segment_id: Segment ID (required)
:type static_arp_config: :class:`com.vmware.nsx_policy.model_client.StaticARPConfig`
:param static_arp_config: (required)
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('patch',
{
'tier1_id': tier1_id,
'segment_id': segment_id,
'static_arp_config': static_arp_config,
})
def update(self,
tier1_id,
segment_id,
static_arp_config,
):
"""
Create static ARP config with tier-1 and segment IDs provided if it
doesn't exist, update with provided config if it's already created.
:type tier1_id: :class:`str`
:param tier1_id: Tier-1 ID (required)
:type segment_id: :class:`str`
:param segment_id: Segment ID (required)
:type static_arp_config: :class:`com.vmware.nsx_policy.model_client.StaticARPConfig`
:param static_arp_config: (required)
:rtype: :class:`com.vmware.nsx_policy.model_client.StaticARPConfig`
:return: com.vmware.nsx_policy.model.StaticARPConfig
:raise: :class:`com.vmware.vapi.std.errors_client.ServiceUnavailable`
Service Unavailable
:raise: :class:`com.vmware.vapi.std.errors_client.InvalidRequest`
Bad Request, Precondition Failed
:raise: :class:`com.vmware.vapi.std.errors_client.InternalServerError`
Internal Server Error
:raise: :class:`com.vmware.vapi.std.errors_client.Unauthorized`
Forbidden
:raise: :class:`com.vmware.vapi.std.errors_client.NotFound`
Not Found
"""
return self._invoke('update',
{
'tier1_id': tier1_id,
'segment_id': segment_id,
'static_arp_config': static_arp_config,
})
class _StaticArpStub(ApiInterfaceStub):
def __init__(self, config):
# properties for delete operation
delete_input_type = type.StructType('operation-input', {
'tier1_id': type.StringType(),
'segment_id': type.StringType(),
})
delete_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
delete_input_value_validator_list = [
]
delete_output_validator_list = [
]
delete_rest_metadata = OperationRestMetadata(
http_method='DELETE',
url_template='/policy/api/v1/infra/tier-1s/{tier-1-id}/segments/{segment-id}/static-arp',
path_variables={
'tier1_id': 'tier-1-id',
'segment_id': 'segment-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for get operation
get_input_type = type.StructType('operation-input', {
'tier1_id': type.StringType(),
'segment_id': type.StringType(),
})
get_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
get_input_value_validator_list = [
]
get_output_validator_list = [
]
get_rest_metadata = OperationRestMetadata(
http_method='GET',
url_template='/policy/api/v1/infra/tier-1s/{tier-1-id}/segments/{segment-id}/static-arp',
path_variables={
'tier1_id': 'tier-1-id',
'segment_id': 'segment-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for patch operation
patch_input_type = type.StructType('operation-input', {
'tier1_id': type.StringType(),
'segment_id': type.StringType(),
'static_ARP_config': type.ReferenceType('com.vmware.nsx_policy.model_client', 'StaticARPConfig'),
})
patch_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
patch_input_value_validator_list = [
]
patch_output_validator_list = [
]
patch_rest_metadata = OperationRestMetadata(
http_method='PATCH',
url_template='/policy/api/v1/infra/tier-1s/{tier-1-id}/segments/{segment-id}/static-arp',
request_body_parameter='static_ARP_config',
path_variables={
'tier1_id': 'tier-1-id',
'segment_id': 'segment-id',
},
query_parameters={
},
content_type='application/json'
)
# properties for update operation
update_input_type = type.StructType('operation-input', {
'tier1_id': type.StringType(),
'segment_id': type.StringType(),
'static_ARP_config': type.ReferenceType('com.vmware.nsx_policy.model_client', 'StaticARPConfig'),
})
update_error_dict = {
'com.vmware.vapi.std.errors.service_unavailable':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'ServiceUnavailable'),
'com.vmware.vapi.std.errors.invalid_request':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InvalidRequest'),
'com.vmware.vapi.std.errors.internal_server_error':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'InternalServerError'),
'com.vmware.vapi.std.errors.unauthorized':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'Unauthorized'),
'com.vmware.vapi.std.errors.not_found':
type.ReferenceType('com.vmware.vapi.std.errors_client', 'NotFound'),
}
update_input_value_validator_list = [
]
update_output_validator_list = [
]
update_rest_metadata = OperationRestMetadata(
http_method='PUT',
url_template='/policy/api/v1/infra/tier-1s/{tier-1-id}/segments/{segment-id}/static-arp',
request_body_parameter='static_ARP_config',
path_variables={
'tier1_id': 'tier-1-id',
'segment_id': 'segment-id',
},
query_parameters={
},
content_type='application/json'
)
operations = {
'delete': {
'input_type': delete_input_type,
'output_type': type.VoidType(),
'errors': delete_error_dict,
'input_value_validator_list': delete_input_value_validator_list,
'output_validator_list': delete_output_validator_list,
'task_type': TaskType.NONE,
},
'get': {
'input_type': get_input_type,
'output_type': type.ReferenceType('com.vmware.nsx_policy.model_client', 'StaticARPConfig'),
'errors': get_error_dict,
'input_value_validator_list': get_input_value_validator_list,
'output_validator_list': get_output_validator_list,
'task_type': TaskType.NONE,
},
'patch': {
'input_type': patch_input_type,
'output_type': type.VoidType(),
'errors': patch_error_dict,
'input_value_validator_list': patch_input_value_validator_list,
'output_validator_list': patch_output_validator_list,
'task_type': TaskType.NONE,
},
'update': {
'input_type': update_input_type,
'output_type': type.ReferenceType('com.vmware.nsx_policy.model_client', 'StaticARPConfig'),
'errors': update_error_dict,
'input_value_validator_list': update_input_value_validator_list,
'output_validator_list': update_output_validator_list,
'task_type': TaskType.NONE,
},
}
rest_metadata = {
'delete': delete_rest_metadata,
'get': get_rest_metadata,
'patch': patch_rest_metadata,
'update': update_rest_metadata,
}
ApiInterfaceStub.__init__(
self, iface_name='com.vmware.nsx_policy.infra.tier_1s.segments.static_arp',
config=config, operations=operations, rest_metadata=rest_metadata,
is_vapi_rest=False)
class StubFactory(StubFactoryBase):
_attrs = {
'StaticArp': StaticArp,
}
| 41.663957 | 109 | 0.589177 | 1,554 | 15,374 | 5.589447 | 0.107465 | 0.075639 | 0.0898 | 0.110523 | 0.823394 | 0.789777 | 0.755699 | 0.709878 | 0.705618 | 0.697559 | 0 | 0.005336 | 0.292962 | 15,374 | 368 | 110 | 41.777174 | 0.793744 | 0.24639 | 0 | 0.49789 | 1 | 0.016878 | 0.309374 | 0.205758 | 0 | 0 | 0 | 0 | 0 | 1 | 0.025316 | false | 0 | 0.050633 | 0 | 0.113924 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
c1ab20ec5e753d9dd9a1b1f3e6c89541b4c5b763 | 63 | py | Python | bob/store/__init__.py | intergalactic-software/bob | 223558be6657d910488704850f5b8db65aeb1295 | [
"MIT"
] | null | null | null | bob/store/__init__.py | intergalactic-software/bob | 223558be6657d910488704850f5b8db65aeb1295 | [
"MIT"
] | null | null | null | bob/store/__init__.py | intergalactic-software/bob | 223558be6657d910488704850f5b8db65aeb1295 | [
"MIT"
] | 1 | 2020-07-03T16:23:03.000Z | 2020-07-03T16:23:03.000Z | from bob.store.Store import *
from bob.store.MockStore import * | 31.5 | 33 | 0.793651 | 10 | 63 | 5 | 0.5 | 0.28 | 0.48 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 63 | 2 | 33 | 31.5 | 0.892857 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
c1c21e82456ad8a2d23d6e1dd7b1cf35db126ad5 | 199 | py | Python | chemtrails/contrib/permissions/forms/__init__.py | inonit/django-chemtrails | e8bd97dc68852902b57d314250e616b505db0e16 | [
"MIT"
] | 13 | 2017-06-15T11:14:02.000Z | 2021-05-10T03:52:26.000Z | chemtrails/contrib/permissions/forms/__init__.py | inonit/django-chemtrails | e8bd97dc68852902b57d314250e616b505db0e16 | [
"MIT"
] | 50 | 2017-02-21T20:02:50.000Z | 2017-12-04T13:44:29.000Z | chemtrails/contrib/permissions/forms/__init__.py | inonit/django-chemtrails | e8bd97dc68852902b57d314250e616b505db0e16 | [
"MIT"
] | 2 | 2017-04-17T19:41:23.000Z | 2020-02-06T21:06:08.000Z | # -*- coding: utf-8 -*-
from chemtrails.contrib.permissions.forms.fields import *
from chemtrails.contrib.permissions.forms.forms import *
from chemtrails.contrib.permissions.forms.widgets import *
| 33.166667 | 58 | 0.788945 | 24 | 199 | 6.541667 | 0.458333 | 0.267516 | 0.401274 | 0.611465 | 0.783439 | 0.547771 | 0 | 0 | 0 | 0 | 0 | 0.005525 | 0.090452 | 199 | 5 | 59 | 39.8 | 0.861878 | 0.105528 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
e71a6d1455dfdb19463508930b4e8fdd3d39d21b | 5,923 | py | Python | data/projects/flutils/tests/unit/moduleutils/test_lazy_import_module.py | se2p/artifact-pynguin-ssbse2020 | 32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6 | [
"CC-BY-4.0"
] | 3 | 2020-08-20T10:27:13.000Z | 2021-11-02T20:28:16.000Z | data/projects/flutils/tests/unit/moduleutils/test_lazy_import_module.py | se2p/artifact-pynguin-ssbse2020 | 32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6 | [
"CC-BY-4.0"
] | null | null | null | data/projects/flutils/tests/unit/moduleutils/test_lazy_import_module.py | se2p/artifact-pynguin-ssbse2020 | 32b5f4d27ef1b81e5c541471e98fa6e50f5ce8a6 | [
"CC-BY-4.0"
] | null | null | null | import keyword
import types
import unittest
from importlib.machinery import ModuleSpec
from unittest.mock import (
MagicMock,
call,
patch,
sentinel,
)
from flutils.moduleutils import lazy_import_module
class TestOne(unittest.TestCase):
def setUp(self):
# Mock isinstance
patcher = patch(
'__main__.isinstance',
return_value=True
)
self.isinstance = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'importlib.util.resolve_name',
return_value='foo'
)
self.resolve_name = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'sys.modules',
dict(bar=True)
)
self.modules = patcher.start()
self.addCleanup(patcher.stop)
spec = types.SimpleNamespace()
spec.loader = types.SimpleNamespace()
patcher = patch(
'importlib.util.find_spec',
return_value=spec
)
self.find_spec = patcher.start()
self.addCleanup(patcher.stop)
lazy_loader = types.SimpleNamespace()
lazy_loader.exec_module = MagicMock(return_value=None)
patcher = patch(
'flutils.moduleutils._LazyLoader',
return_value=lazy_loader
)
self.lazy_loader = patcher.start()
self.addCleanup(patcher.stop)
def test_lazy_import_module(self):
mod = lazy_import_module('foo')
self.find_spec.assert_called_once_with('foo')
self.lazy_loader.assert_called_once()
def test_lazy_import_module_with_package(self):
lazy_import_module('foo', package='bar')
self.find_spec.assert_called_once_with('foo')
self.lazy_loader.assert_called_once()
class TestTwo(unittest.TestCase):
def setUp(self):
# Mock isinstance
patcher = patch(
'__main__.isinstance',
return_value=True
)
self.isinstance = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'importlib.util.resolve_name',
return_value='foo'
)
self.resolve_name = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'sys.modules',
dict(foo=True)
)
self.modules = patcher.start()
self.addCleanup(patcher.stop)
spec = types.SimpleNamespace()
spec.loader = types.SimpleNamespace()
patcher = patch(
'importlib.util.find_spec',
return_value=spec
)
self.find_spec = patcher.start()
self.addCleanup(patcher.stop)
lazy_loader = types.SimpleNamespace()
lazy_loader.exec_module = MagicMock(return_value=None)
patcher = patch(
'flutils.moduleutils._LazyLoader',
return_value=lazy_loader
)
self.lazy_loader = patcher.start()
self.addCleanup(patcher.stop)
def test_lazy_import_module_already_loaded(self):
_ = lazy_import_module('foo')
self.find_spec.assert_not_called()
self.lazy_loader.assert_not_called()
class TestThree(unittest.TestCase):
def setUp(self):
# Mock isinstance
patcher = patch(
'__main__.isinstance',
return_value=True
)
self.isinstance = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'importlib.util.resolve_name',
return_value='foo'
)
self.resolve_name = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'sys.modules',
dict()
)
self.modules = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'importlib.util.find_spec',
return_value=None
)
self.find_spec = patcher.start()
self.addCleanup(patcher.stop)
def test_lazy_import_module_no_spec(self):
with self.assertRaises(ImportError):
lazy_import_module('foo')
self.find_spec.assert_called_once()
class TestFour(unittest.TestCase):
def setUp(self):
# Mock isinstance
patcher = patch(
'__main__.isinstance',
return_value=True
)
self.isinstance = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'importlib.util.resolve_name',
return_value='foo'
)
self.resolve_name = patcher.start()
self.addCleanup(patcher.stop)
patcher = patch(
'sys.modules',
dict(bar=True)
)
self.modules = patcher.start()
self.addCleanup(patcher.stop)
spec = types.SimpleNamespace()
spec.loader = types.SimpleNamespace()
spec.loader.create_module = MagicMock(
return_value=types.SimpleNamespace()
)
patcher = patch(
'importlib.util.find_spec',
return_value=spec
)
self.find_spec = patcher.start()
self.addCleanup(patcher.stop)
lazy_loader = types.SimpleNamespace()
lazy_loader.create_module = MagicMock(return_value=None)
lazy_loader.exec_module = MagicMock(return_value=None)
patcher = patch(
'flutils.moduleutils._LazyLoader',
return_value=lazy_loader
)
self.lazy_loader = patcher.start()
self.addCleanup(patcher.stop)
def test_lazy_import_module(self):
_ = lazy_import_module('foo')
self.find_spec.assert_called_once_with('foo')
self.lazy_loader.assert_called_once()
def test_lazy_import_module_with_package(self):
lazy_import_module('foo', package='bar')
self.find_spec.assert_called_once_with('foo')
self.lazy_loader.assert_called_once()
| 27.548837 | 64 | 0.601553 | 603 | 5,923 | 5.656716 | 0.106136 | 0.064497 | 0.089123 | 0.144826 | 0.90472 | 0.903547 | 0.883026 | 0.883026 | 0.87423 | 0.858692 | 0 | 0 | 0.302718 | 5,923 | 214 | 65 | 27.67757 | 0.825908 | 0.010637 | 0 | 0.716763 | 0 | 0 | 0.079419 | 0.050726 | 0 | 0 | 0 | 0 | 0.069364 | 1 | 0.057803 | false | 0 | 0.156069 | 0 | 0.236994 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
e796c0c058ac3b3717571aa13b9ff288fbbe0a16 | 199 | py | Python | apps/api_test/__init__.py | JackieyQi/AmfFlask | 989c50181eeaa1402ecf12bf2694f2378bd07d0b | [
"MIT"
] | null | null | null | apps/api_test/__init__.py | JackieyQi/AmfFlask | 989c50181eeaa1402ecf12bf2694f2378bd07d0b | [
"MIT"
] | null | null | null | apps/api_test/__init__.py | JackieyQi/AmfFlask | 989c50181eeaa1402ecf12bf2694f2378bd07d0b | [
"MIT"
] | null | null | null | #! /usr/bin/env python
# coding:utf8
from .test import test_bp
def api_test_configure_blueprint(app):
# app.register_blueprint(test_bp, url_prefix="/url/prefix")
app.register_blueprint(test_bp)
| 19.9 | 60 | 0.778894 | 31 | 199 | 4.709677 | 0.580645 | 0.123288 | 0.273973 | 0.328767 | 0.356164 | 0 | 0 | 0 | 0 | 0 | 0 | 0.005587 | 0.100503 | 199 | 9 | 61 | 22.111111 | 0.810056 | 0.457286 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0.333333 | 0 | 0.666667 | 0.666667 | 0 | 0 | 0 | null | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 7 |
e7cba95505acc1fc0f4c65e6d3b4636d0ea6a3ef | 12,203 | py | Python | tests/test_CEDNWBConverter.py | ssciwr/mease-lab-to-nwb | 69e7ead9cd4853bafdff0b540218166338063248 | [
"MIT"
] | null | null | null | tests/test_CEDNWBConverter.py | ssciwr/mease-lab-to-nwb | 69e7ead9cd4853bafdff0b540218166338063248 | [
"MIT"
] | null | null | null | tests/test_CEDNWBConverter.py | ssciwr/mease-lab-to-nwb | 69e7ead9cd4853bafdff0b540218166338063248 | [
"MIT"
] | null | null | null | from mease_lab_to_nwb import CEDNWBConverter
import pynwb
from pathlib import Path
import numpy as np
import pytest
import os
import mease_elabftw
def to_nwbfile(converter, filename):
metadata = converter.get_metadata()
conversion_options = converter.get_conversion_options()
converter.run_conversion(
metadata=metadata,
nwbfile_path=filename,
save_to_file=True,
overwrite=False,
conversion_options=conversion_options,
)
@pytest.fixture
def mock_get_nwb_metadata(monkeypatch):
monkeypatch.setattr(
mease_elabftw, "get_nwb_metadata", mease_elabftw.nwb.get_sample_nwb_metadata
)
def test_cednwbconverter_ttltest(tmp_path, mock_get_nwb_metadata):
# read smrx file
file_recording = str(
(Path(__file__).parent / "data" / "TTLtest_17mW.smrx").resolve()
)
source_data = dict(
CEDRecording=dict(file_path=file_recording),
CEDStimulus=dict(file_path=file_recording),
Elabftw=dict(experiment_id=1),
)
converter = CEDNWBConverter(source_data=source_data)
rec_ids = source_data["CEDRecording"]["smrx_channel_ids"]
assert len(rec_ids) == 128
stim_ids = source_data["CEDStimulus"]["smrx_channel_ids"]
assert len(stim_ids) == 3
# convert to nwb
file_nwb = str(tmp_path / "out.nwb")
to_nwbfile(converter, file_nwb)
# read nwb file
io = pynwb.NWBHDF5IO(file_nwb, "r")
nwbfile = io.read()
# metadata
assert nwbfile.fields["experimenter"] == ("Liam Keegan",)
assert (
nwbfile.fields["identifier"]
== "20211001-8b6f100d66f4312d539c52620f79d6a503c1e2d1"
)
assert (
nwbfile.fields["session_description"]
== "test fake experiment with json metadata"
)
assert nwbfile.subject.fields["description"] == "test mouse"
assert nwbfile.subject.fields["genotype"] == "Nt1Cre-ChR2-EYFP"
assert nwbfile.subject.fields["subject_id"] == "xy1"
assert nwbfile.subject.fields["weight"] == "0.002 kg"
# laser trace
assert len(nwbfile.stimulus) == 4
laser = nwbfile.stimulus["17mW Laser"]
assert type(laser) == pynwb.ogen.OptogeneticSeries
assert len(laser.data) == 180180
assert laser.starting_time == 0
assert laser.rate == 30030.030030030033
assert np.allclose(np.min(laser.data), 0, atol=1e-3)
assert np.allclose(np.max(laser.data), 0.017)
# laser stim (on/off for each individual pulse)
laser_stim = nwbfile.stimulus["17mW LaserStimulus"]
assert type(laser_stim) == pynwb.misc.IntervalSeries
assert len(laser_stim.data) == 102
assert len(laser_stim.timestamps) == 102
assert np.allclose(laser_stim.data[0:4], [1, -1, 1, -1])
assert np.allclose(laser_stim.data[-4:], [1, -1, 1, -1])
assert np.allclose(
laser_stim.timestamps[0:4], [0.6022971, 0.6123204, 0.7023303, 0.7123203]
)
assert np.allclose(
laser_stim.timestamps[-4:], [5.5023921, 5.5123821, 5.602392, 5.612382]
)
# mech trace
mech = nwbfile.stimulus["MechanicalPressure"]
assert type(mech) == pynwb.base.TimeSeries
assert len(mech.data) == 180180
assert mech.rate == 30030.030030030033
assert mech.starting_time == 0
# mech stim (none in this smrx file)
mech_stim = nwbfile.stimulus["MechanicalStimulus"]
assert type(mech_stim) == pynwb.misc.IntervalSeries
assert len(mech_stim.data) == 0
assert len(mech_stim.timestamps) == 0
# traces
assert len(nwbfile.acquisition) == 1
traces = nwbfile.acquisition["ElectricalSeries_raw"]
assert type(traces) == pynwb.ecephys.ElectricalSeries
assert traces.data.shape == (180180, 128)
io.close()
def test_cednwbconverter_m365(tmp_path, mock_get_nwb_metadata):
# read smrx file
file_recording = str(
(Path(__file__).parent / "data" / "m365_5.5mW_1sec.smrx").resolve()
)
source_data = dict(
CEDRecording=dict(file_path=file_recording),
CEDStimulus=dict(file_path=file_recording),
Elabftw=dict(experiment_id=1),
)
converter = CEDNWBConverter(source_data=source_data)
rec_ids = source_data["CEDRecording"]["smrx_channel_ids"]
assert len(rec_ids) == 64
stim_ids = source_data["CEDStimulus"]["smrx_channel_ids"]
assert len(stim_ids) == 3
# convert to nwb
file_nwb = str(tmp_path / "out.nwb")
to_nwbfile(converter, file_nwb)
# read nwb file
io = pynwb.NWBHDF5IO(file_nwb, "r")
nwbfile = io.read()
assert len(nwbfile.stimulus) == 4
# laser trace
laser = nwbfile.stimulus["5.5mW Laser"]
assert type(laser) == pynwb.ogen.OptogeneticSeries
assert len(laser.data) == 30030
assert laser.starting_time == 0
assert laser.rate == 30030.030030030033
assert np.allclose(np.min(laser.data), 0, atol=5e-3)
assert np.allclose(np.max(laser.data), 0.0055)
# laser stim (on/off for each individual pulse)
laser_stim = nwbfile.stimulus["5.5mW LaserStimulus"]
assert type(laser_stim) == pynwb.misc.IntervalSeries
assert len(laser_stim.data) == 0
assert len(laser_stim.timestamps) == 0
def test_cednwbconverter_mech_laser(tmp_path, mock_get_nwb_metadata):
# read smrx file
file_recording = str(
(Path(__file__).parent / "data" / "RhdD_H5_Mech+Laser.smrx").resolve()
)
source_data = dict(
CEDRecording=dict(file_path=file_recording),
CEDStimulus=dict(file_path=file_recording),
Elabftw=dict(experiment_id=1),
)
converter = CEDNWBConverter(source_data=source_data)
rec_ids = source_data["CEDRecording"]["smrx_channel_ids"]
assert len(rec_ids) == 64
stim_ids = source_data["CEDStimulus"]["smrx_channel_ids"]
assert len(stim_ids) == 3
# convert to nwb
file_nwb = str(tmp_path / "out.nwb")
to_nwbfile(converter, file_nwb)
# read nwb file
io = pynwb.NWBHDF5IO(file_nwb, "r")
nwbfile = io.read()
assert len(nwbfile.stimulus) == 4
# laser trace
laser = nwbfile.stimulus["?mW Laser"]
assert type(laser) == pynwb.ogen.OptogeneticSeries
assert len(laser.data) == 60060
assert laser.starting_time == 0
assert laser.rate == 30030.030030030033
# laser stim (none in this smrx file)
laser_stim = nwbfile.stimulus["?mW LaserStimulus"]
assert type(laser_stim) == pynwb.misc.IntervalSeries
assert len(laser_stim.data) == 0
assert len(laser_stim.timestamps) == 0
# mech trace
mech = nwbfile.stimulus["MechanicalPressure"]
assert type(mech) == pynwb.base.TimeSeries
assert len(mech.data) == 60060
assert mech.rate == 30030.030030030033
assert mech.starting_time == 0
# mech stim (none in this smrx file)
mech_stim = nwbfile.stimulus["MechanicalStimulus"]
assert type(mech_stim) == pynwb.misc.IntervalSeries
assert len(mech_stim.data) == 0
assert len(mech_stim.timestamps) == 0
# traces
assert len(nwbfile.acquisition) == 1
traces = nwbfile.acquisition["ElectricalSeries_raw"]
assert type(traces) == pynwb.ecephys.ElectricalSeries
assert traces.data.shape == (60060, 64)
io.close()
def test_cednwbconverter_dual_laser(tmp_path, mock_get_nwb_metadata):
# read smrx file
file_recording = str(
(
Path(__file__).parent / "data" / "Dual_RhdD_H3__RhdC_H5_LaserOnly_99mW.smrx"
).resolve()
)
source_data = dict(
CEDRecording=dict(file_path=file_recording),
CEDStimulus=dict(file_path=file_recording),
Elabftw=dict(experiment_id=1),
)
converter = CEDNWBConverter(source_data=source_data)
rec_ids = source_data["CEDRecording"]["smrx_channel_ids"]
# recording contains two probes
assert len(rec_ids) == 128
stim_ids = source_data["CEDStimulus"]["smrx_channel_ids"]
assert len(stim_ids) == 3
# convert to nwb
file_nwb = str(tmp_path / "out.nwb")
to_nwbfile(converter, file_nwb)
# read nwb file
io = pynwb.NWBHDF5IO(file_nwb, "r")
nwbfile = io.read()
assert len(nwbfile.stimulus) == 4
# laser trace
laser = nwbfile.stimulus["99mW Laser"]
assert type(laser) == pynwb.ogen.OptogeneticSeries
assert len(laser.data) == 30030
assert laser.starting_time == 0
assert laser.rate == 30030.030030030033
assert np.allclose(np.min(laser.data), 0, atol=5e-3)
assert np.allclose(np.max(laser.data), 0.099)
# laser stim
laser_stim = nwbfile.stimulus["99mW LaserStimulus"]
assert type(laser_stim) == pynwb.misc.IntervalSeries
assert len(laser_stim.data) == 20
assert len(laser_stim.timestamps) == 20
assert np.allclose(laser_stim.data[0:4], [1, -1, 1, -1])
assert np.allclose(laser_stim.data[-4:], [1, -1, 1, -1])
assert np.allclose(
laser_stim.timestamps[0:4], [0.0021312, 0.0121545, 0.1021311, 0.1121544]
)
assert np.allclose(
laser_stim.timestamps[-4:], [0.8021637, 0.8121537, 0.9021636, 0.9121536]
)
# mech trace
mech = nwbfile.stimulus["MechanicalPressure"]
assert type(mech) == pynwb.base.TimeSeries
assert len(mech.data) == 30030
assert mech.rate == 30030.030030030033
assert mech.starting_time == 0
# mech stim (none in this smrx file)
mech_stim = nwbfile.stimulus["MechanicalStimulus"]
assert type(mech_stim) == pynwb.misc.IntervalSeries
assert len(mech_stim.data) == 0
assert len(mech_stim.timestamps) == 0
# traces
assert len(nwbfile.acquisition) == 1
traces = nwbfile.acquisition["ElectricalSeries_raw"]
assert type(traces) == pynwb.ecephys.ElectricalSeries
assert traces.data.shape == (30030, 128)
io.close()
def test_cednwbconverter_mech_laser_bifreq(tmp_path, mock_get_nwb_metadata):
# read smrx file
file_recording = str(
(Path(__file__).parent / "data" / "H5_Mech_1Hz_10Hz.smrx").resolve()
)
source_data = dict(
CEDRecording=dict(file_path=file_recording),
CEDStimulus=dict(file_path=file_recording),
Elabftw=dict(experiment_id=1),
)
converter = CEDNWBConverter(source_data=source_data)
rec_ids = source_data["CEDRecording"]["smrx_channel_ids"]
assert len(rec_ids) == 64
stim_ids = source_data["CEDStimulus"]["smrx_channel_ids"]
assert len(stim_ids) == 3
# convert to nwb
file_nwb = str(tmp_path / "out.nwb")
to_nwbfile(converter, file_nwb)
# read nwb file
io = pynwb.NWBHDF5IO(file_nwb, "r")
nwbfile = io.read()
assert len(nwbfile.stimulus) == 4
# laser trace
laser = nwbfile.stimulus["?mW Laser"]
assert type(laser) == pynwb.ogen.OptogeneticSeries
assert len(laser.data) == 5555238 # note: actual length in smrx file is 5555556
assert laser.starting_time == 0
assert laser.rate == 30030.030030030033
# laser stim
laser_stim = nwbfile.stimulus["?mW LaserStimulus"]
assert type(laser_stim) == pynwb.misc.IntervalSeries
assert len(laser_stim.data) == 216
assert len(laser_stim.timestamps) == 216
assert np.allclose(laser_stim.data[0:4], [1, -1, 1, -1])
assert np.allclose(laser_stim.data[-4:], [1, -1, 1, -1])
assert np.allclose(
laser_stim.timestamps[0:4], [64.9925424, 64.9975374, 65.9925414, 65.9975697]
)
assert np.allclose(
laser_stim.timestamps[-4:], [174.8920995, 174.8970945, 174.9920994, 174.9970944]
)
# mech trace
mech = nwbfile.stimulus["MechanicalPressure"]
assert type(mech) == pynwb.base.TimeSeries
assert len(mech.data) == 5555556
assert mech.rate == 30030.030030030033
assert mech.starting_time == 0
# mech stim
mech_stim = nwbfile.stimulus["MechanicalStimulus"]
assert type(mech_stim) == pynwb.misc.IntervalSeries
assert len(mech_stim.data) == 6
assert len(mech_stim.timestamps) == 6
assert np.allclose(mech_stim.data[:], [1, -1, 1, -1, 1, -1])
assert np.allclose(
mech_stim.timestamps[:],
[4.9914369, 9.9914985, 64.9925424, 69.9925374, 124.99155, 129.9915117],
)
# traces
assert len(nwbfile.acquisition) == 1
traces = nwbfile.acquisition["ElectricalSeries_raw"]
assert type(traces) == pynwb.ecephys.ElectricalSeries
assert traces.data.shape == (
5555238,
64,
) # note: actual length in smrx file is 5555556
io.close()
| 37.547692 | 88 | 0.68098 | 1,581 | 12,203 | 5.079696 | 0.127135 | 0.05155 | 0.039846 | 0.031378 | 0.825053 | 0.799029 | 0.780974 | 0.765035 | 0.756319 | 0.752335 | 0 | 0.073828 | 0.197492 | 12,203 | 324 | 89 | 37.66358 | 0.746247 | 0.060887 | 0 | 0.619926 | 0 | 0 | 0.091697 | 0.011736 | 0 | 0 | 0 | 0 | 0.431734 | 1 | 0.02583 | false | 0 | 0.02583 | 0 | 0.051661 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
82321af76ae53efa3b6db09ea1089b1505e02fc1 | 6,468 | py | Python | dbdaora/geospatial/repositories/_tests/test_integration_repository_geospatial_aioredis_get.py | dutradda/sqldataclass | 5c87a3818e9d736bbf5e1438edc5929a2f5acd3f | [
"MIT"
] | 21 | 2019-10-14T14:33:33.000Z | 2022-02-11T04:43:07.000Z | dbdaora/geospatial/repositories/_tests/test_integration_repository_geospatial_aioredis_get.py | dutradda/sqldataclass | 5c87a3818e9d736bbf5e1438edc5929a2f5acd3f | [
"MIT"
] | null | null | null | dbdaora/geospatial/repositories/_tests/test_integration_repository_geospatial_aioredis_get.py | dutradda/sqldataclass | 5c87a3818e9d736bbf5e1438edc5929a2f5acd3f | [
"MIT"
] | 1 | 2019-09-29T23:51:44.000Z | 2019-09-29T23:51:44.000Z | import itertools
import asynctest
import pytest
from dbdaora import GeoSpatialQuery
from dbdaora.exceptions import EntityNotFoundError
@pytest.mark.asyncio
async def test_should_get_from_memory(
repository, serialized_fake_entity, fake_entity
):
await repository.memory_data_source.geoadd(
'fake:fake2:fake', *itertools.chain(*serialized_fake_entity)
)
entity = await repository.query(
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=5,
longitude=6,
max_distance=1,
).entity
assert entity == fake_entity
@pytest.mark.asyncio
async def test_should_raise_not_found_error(repository, fake_entity, mocker):
fake_query = GeoSpatialQuery(
repository,
memory=True,
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=1,
longitude=1,
max_distance=1,
)
with pytest.raises(EntityNotFoundError) as exc_info:
await repository.query(
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=1,
longitude=1,
max_distance=1,
).entity
assert exc_info.value.args == (fake_query,)
@pytest.mark.asyncio
async def test_should_raise_not_found_error_when_already_raised_before(
repository, mocker, fake_entity
):
expected_query = GeoSpatialQuery(
repository,
memory=True,
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=1,
longitude=1,
max_distance=1,
)
repository.memory_data_source.georadius = asynctest.CoroutineMock(
side_effect=[[]]
)
repository.memory_data_source.exists = asynctest.CoroutineMock(
side_effect=[True]
)
repository.memory_data_source.geoadd = asynctest.CoroutineMock()
with pytest.raises(EntityNotFoundError) as exc_info:
await repository.query(
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=1,
longitude=1,
max_distance=1,
).entity
assert exc_info.value.args == (expected_query,)
assert repository.memory_data_source.georadius.call_args_list == [
mocker.call(
key='fake:fake2:fake',
longitude=1,
latitude=1,
radius=1,
unit='km',
with_dist=True,
with_coord=True,
count=None,
),
]
assert repository.memory_data_source.exists.call_args_list == [
mocker.call('fake:fake2:fake')
]
assert not repository.memory_data_source.geoadd.called
@pytest.mark.asyncio
async def test_should_set_already_not_found_error(
repository, mocker, fake_entity
):
expected_query = GeoSpatialQuery(
repository,
memory=True,
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=1,
longitude=1,
max_distance=1,
)
repository.memory_data_source.georadius = asynctest.CoroutineMock(
side_effect=[[]]
)
repository.memory_data_source.exists = asynctest.CoroutineMock(
side_effect=[False]
)
repository.fallback_data_source.query = asynctest.CoroutineMock(
return_value=[]
)
repository.memory_data_source.geoadd = asynctest.CoroutineMock()
with pytest.raises(EntityNotFoundError) as exc_info:
await repository.query(
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=1,
longitude=1,
max_distance=1,
).entity
assert exc_info.value.args == (expected_query,)
assert repository.memory_data_source.georadius.call_args_list == [
mocker.call(
key='fake:fake2:fake',
longitude=1,
latitude=1,
radius=1,
unit='km',
with_dist=True,
with_coord=True,
count=None,
),
]
assert repository.memory_data_source.exists.call_args_list == [
mocker.call('fake:fake2:fake')
]
assert repository.fallback_data_source.query.call_args_list == [
mocker.call('fake:fake2:fake')
]
assert not repository.memory_data_source.geoadd.called
@pytest.mark.asyncio
async def test_should_get_from_fallback(
repository,
fake_entity,
fake_fallback_data_entity,
fake_fallback_data_entity2,
):
await repository.memory_data_source.delete('fake:fake2:fake')
repository.fallback_data_source.db[
'fake:fake2:m1'
] = fake_fallback_data_entity
repository.fallback_data_source.db[
'fake:fake2:m2'
] = fake_fallback_data_entity2
entity = await repository.query(
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=5,
longitude=6,
max_distance=1,
).entity
assert entity == fake_entity
assert repository.memory_data_source.exists('fake:fake2:fake')
@pytest.mark.asyncio
async def test_should_set_memory_after_got_fallback(
repository,
fake_entity,
mocker,
fake_fallback_data_entity,
fake_fallback_data_entity2,
):
repository.memory_data_source.georadius = asynctest.CoroutineMock(
side_effect=[[], fake_entity.data]
)
repository.memory_data_source.exists = asynctest.CoroutineMock(
side_effect=[False]
)
repository.fallback_data_source.db[
'fake:fake2:m1'
] = fake_fallback_data_entity
repository.fallback_data_source.db[
'fake:fake2:m2'
] = fake_fallback_data_entity2
repository.memory_data_source.geoadd = asynctest.CoroutineMock()
entity = await repository.query(
fake_id=fake_entity.fake_id,
fake2_id=fake_entity.fake2_id,
latitude=5,
longitude=6,
max_distance=1,
).entity
assert repository.memory_data_source.georadius.called
assert repository.memory_data_source.exists.called
assert repository.memory_data_source.geoadd.call_args_list == [
mocker.call(
'fake:fake2:fake',
longitude=6.000002324581146,
latitude=4.999999830436074,
member=b'm1',
),
mocker.call(
'fake:fake2:fake',
longitude=6.000002324581146,
latitude=4.999999830436074,
member=b'm2',
),
]
assert entity == fake_entity
| 28.368421 | 77 | 0.656153 | 737 | 6,468 | 5.454545 | 0.118046 | 0.074627 | 0.104478 | 0.135821 | 0.894527 | 0.852736 | 0.814179 | 0.807214 | 0.765174 | 0.736318 | 0 | 0.028898 | 0.256339 | 6,468 | 227 | 78 | 28.493392 | 0.806861 | 0 | 0 | 0.77451 | 0 | 0 | 0.032468 | 0 | 0 | 0 | 0 | 0 | 0.083333 | 1 | 0 | false | 0 | 0.02451 | 0 | 0.02451 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
413ec4a15ddd83d51cff50971dead324d5fbb8ac | 12,488 | py | Python | test_vert/test_stores/_base.py | hosford42/vert | 145af17fd9c10732cfd4811397c34586b8215e2a | [
"MIT"
] | 2 | 2017-03-09T22:59:20.000Z | 2020-08-29T20:40:32.000Z | test_vert/test_stores/_base.py | hosford42/vert | 145af17fd9c10732cfd4811397c34586b8215e2a | [
"MIT"
] | 1 | 2017-02-27T20:29:00.000Z | 2017-02-27T20:29:00.000Z | test_vert/test_stores/_base.py | hosford42/vert | 145af17fd9c10732cfd4811397c34586b8215e2a | [
"MIT"
] | 1 | 2022-01-08T19:54:42.000Z | 2022-01-08T19:54:42.000Z | # -*- coding: utf-8 -*-
# Copyright 2017 Aaron M. Hosford
# See LICENSE.txt for licensing information.
from typing import Type
import unittest
from vert import Graph, GraphStore, Vertex, Edge, DirectedEdge
class TestGraphStore(unittest.TestCase):
def createStore(self) -> GraphStore:
raise NotImplementedError()
def expectedStoreClass(self) -> Type[GraphStore]:
raise NotImplementedError()
def setUp(self):
# The failure messages don't work properly when they're done in a base class, making this necessary.
print("Setting up %s" % self)
super().setUp()
self._graph = Graph(self.createStore())
def tearDown(self):
# The failure messages don't work properly when they're done in a base class, making this necessary.
print("Tearing down %s" % self)
super().tearDown()
@property
def graph(self):
return self._graph
def testDefaultStoreClass(self):
self.assertIsInstance(self.graph._graph_store, self.expectedStoreClass())
def testVertexCreationAndDeletion(self):
vertex_name = 'v1'
vertex = self.graph.vertices[vertex_name]
self.assertEqual(vertex.vid, vertex_name)
self.assertIsInstance(vertex, Vertex)
self.assert_(not vertex.exists)
self.assertEqual(len(self.graph.vertices), 0)
vertex.add()
self.assertEqual(len(self.graph.vertices), 1)
self.assert_(vertex.exists)
vertex.remove()
self.assertEqual(len(self.graph.vertices), 0)
self.assert_(not vertex.exists)
vertex2 = self.graph.vertices.add(vertex_name)
self.assert_(vertex.exists)
self.assert_(vertex2.exists)
self.assertEqual(vertex, vertex2)
self.assertEqual(vertex.vid, vertex2.vid)
vertex2.remove()
self.assert_(not vertex.exists)
self.assert_(not vertex2.exists)
def testVertexLabels(self):
vertex_name = 'v2'
label1_name = 'l1'
label2_name = 'l2'
vertex = self.graph.vertices[vertex_name]
self.assertIsInstance(vertex, Vertex)
self.assert_(not vertex.exists)
self.assertEqual(len(vertex.labels), 0)
self.assert_(not vertex.labels)
vertex.labels.add(label1_name)
self.assert_(vertex.exists)
self.assert_(label1_name in vertex.labels)
self.assert_(label2_name not in vertex.labels)
self.assertEqual(len(vertex.labels), 1)
self.assert_(vertex.labels)
vertex.remove()
self.assert_(not vertex.exists)
self.assert_(not vertex.labels)
self.assert_(label1_name not in vertex.labels)
self.assert_(label2_name not in vertex.labels)
self.graph.vertices.add(vertex_name)
self.assert_(vertex.exists)
self.assert_(label1_name not in vertex.labels)
self.assert_(label2_name not in vertex.labels)
vertex.labels.add(label2_name)
self.assert_(label1_name not in vertex.labels)
self.assert_(label2_name in vertex.labels)
self.assertEqual(len(vertex.labels), 1)
vertex.labels.add(label1_name)
self.assert_(label1_name in vertex.labels)
self.assert_(label2_name in vertex.labels)
self.assertEqual(len(vertex.labels), 2)
vertex.labels.remove(label2_name)
self.assert_(label1_name in vertex.labels)
self.assert_(label2_name not in vertex.labels)
self.assertEqual(len(vertex.labels), 1)
vertex.labels.discard(label2_name)
self.assert_(label1_name in vertex.labels)
self.assert_(label2_name not in vertex.labels)
self.assertEqual(len(vertex.labels), 1)
vertex.labels.discard(label1_name)
self.assert_(label1_name not in vertex.labels)
self.assert_(label2_name not in vertex.labels)
self.assertEqual(len(vertex.labels), 0)
vertex.remove()
self.assertEqual(len(vertex.labels), 0)
self.assert_(not vertex.labels)
self.assert_(not vertex.exists)
def testVertexData(self):
vertex_name = 'v3'
key1 = 'k1'
key2 = 'k2'
value1 = 'val1'
value2 = 2
value3 = {value1: value2}
vertex = self.graph.vertices[vertex_name].add()
self.assertIsInstance(vertex, Vertex)
self.assert_(not vertex.data)
with self.assertRaises(KeyError):
_ = vertex.data[key1]
with self.assertRaises(KeyError):
_ = vertex.data[key2]
self.assertEqual(len(vertex.data), 0)
self.assertNotIn(key1, vertex.data)
self.assertNotIn(key2, vertex.data)
vertex.data[key1] = value1
self.assert_(vertex.data)
self.assertEqual(vertex.data[key1], value1)
with self.assertRaises(KeyError):
_ = vertex.data[key2]
self.assertEqual(len(vertex.data), 1)
self.assertIn(key1, vertex.data)
self.assertNotIn(key2, vertex.data)
vertex.data[key2] = value2
self.assert_(vertex.data)
self.assertEqual(vertex.data[key1], value1)
self.assertEqual(vertex.data[key2], value2)
self.assertEqual(len(vertex.data), 2)
self.assertIn(key1, vertex.data)
self.assertIn(key2, vertex.data)
del vertex.data[key1]
self.assert_(vertex.data)
with self.assertRaises(KeyError):
_ = vertex.data[key1]
self.assertEqual(vertex.data[key2], value2)
self.assertEqual(len(vertex.data), 1)
self.assertNotIn(key1, vertex.data)
self.assertIn(key2, vertex.data)
vertex.data[key1] = value3
self.assert_(vertex.data)
self.assertEqual(vertex.data[key1], value3)
self.assertEqual(vertex.data[key2], value2)
self.assertEqual(len(vertex.data), 2)
self.assertIn(key1, vertex.data)
self.assertIn(key2, vertex.data)
vertex.data[key2] = value3
self.assert_(vertex.data)
self.assertEqual(vertex.data[key1], value3)
self.assertEqual(vertex.data[key2], value3)
self.assertEqual(len(vertex.data), 2)
self.assertIn(key1, vertex.data)
self.assertIn(key2, vertex.data)
vertex.data.clear()
self.assert_(not vertex.data)
with self.assertRaises(KeyError):
_ = vertex.data[key1]
with self.assertRaises(KeyError):
_ = vertex.data[key2]
self.assertEqual(len(vertex.data), 0)
self.assertNotIn(key1, vertex.data)
self.assertNotIn(key2, vertex.data)
def testEdgeCreationAndDeletion(self):
vid1 = 'v4'
vid2 = 'v5'
eid = (vid1, vid2)
edge = self.graph.edges[eid]
self.assertIsInstance(edge, DirectedEdge)
self.assertIsInstance(edge.source, Vertex)
self.assertIsInstance(edge.sink, Vertex)
self.assertEqual(edge.eid, eid)
self.assertEqual(edge.source.vid, vid1)
self.assertEqual(edge.sink.vid, vid2)
self.assert_(not edge.exists)
self.assert_(not edge.source.exists)
self.assert_(not edge.sink.exists)
self.assertEqual(len(self.graph.vertices), 0)
self.assertEqual(len(self.graph.edges), 0)
edge.add()
self.assertEqual(len(self.graph.edges), 1)
self.assertEqual(len(self.graph.vertices), 2)
self.assert_(edge.exists)
self.assert_(edge.source.exists)
self.assert_(edge.sink.exists)
edge.remove()
self.assertEqual(len(self.graph.edges), 0)
self.assertEqual(len(self.graph.vertices), 2)
self.assert_(not edge.exists)
self.assert_(edge.source.exists)
self.assert_(edge.sink.exists)
edge2 = self.graph.edges.add(eid)
self.assert_(edge.exists)
self.assert_(edge2.exists)
self.assertEqual(edge, edge2)
self.assertEqual(edge.eid, edge2.eid)
edge2.remove()
self.assert_(not edge.exists)
self.assert_(not edge2.exists)
def testEdgeLabels(self):
vid1 = 'v6'
vid2 = 'v7'
eid = (vid1, vid2)
label1_name = 'l1'
label2_name = 'l2'
edge = self.graph.edges[eid]
self.assertIsInstance(edge, Edge)
self.assert_(not edge.exists)
self.assertEqual(len(edge.labels), 0)
self.assert_(not edge.labels)
edge.labels.add(label1_name)
self.assert_(edge.exists)
self.assert_(label1_name in edge.labels)
self.assert_(label2_name not in edge.labels)
self.assertEqual(len(edge.labels), 1)
self.assert_(edge.labels)
edge.remove()
self.assert_(not edge.exists)
self.assert_(not edge.labels)
self.assert_(label1_name not in edge.labels)
self.assert_(label2_name not in edge.labels)
self.graph.edges.add(eid)
self.assert_(edge.exists)
self.assert_(label1_name not in edge.labels)
self.assert_(label2_name not in edge.labels)
edge.labels.add(label2_name)
self.assert_(label1_name not in edge.labels)
self.assert_(label2_name in edge.labels)
self.assertEqual(len(edge.labels), 1)
edge.labels.add(label1_name)
self.assert_(label1_name in edge.labels)
self.assert_(label2_name in edge.labels)
self.assertEqual(len(edge.labels), 2)
edge.labels.remove(label2_name)
self.assert_(label1_name in edge.labels)
self.assert_(label2_name not in edge.labels)
self.assertEqual(len(edge.labels), 1)
edge.labels.discard(label2_name)
self.assert_(label1_name in edge.labels)
self.assert_(label2_name not in edge.labels)
self.assertEqual(len(edge.labels), 1)
edge.labels.discard(label1_name)
self.assert_(label1_name not in edge.labels)
self.assert_(label2_name not in edge.labels)
self.assertEqual(len(edge.labels), 0)
edge.remove()
self.assertEqual(len(edge.labels), 0)
self.assert_(not edge.labels)
self.assert_(not edge.exists)
def testEdgeData(self):
v1 = 'v8'
v2 = 'v9'
eid = (v1, v2)
key1 = 'k1'
key2 = 'k2'
value1 = 'val1'
value2 = 2
value3 = {value1: value2}
edge = self.graph.edges[eid].add()
self.assertIsInstance(edge, Edge)
self.assert_(not edge.data)
with self.assertRaises(KeyError):
_ = edge.data[key1]
with self.assertRaises(KeyError):
_ = edge.data[key2]
self.assertEqual(len(edge.data), 0)
self.assertNotIn(key1, edge.data)
self.assertNotIn(key2, edge.data)
edge.data[key1] = value1
self.assert_(edge.data)
self.assertEqual(edge.data[key1], value1)
with self.assertRaises(KeyError):
_ = edge.data[key2]
self.assertEqual(len(edge.data), 1)
self.assertIn(key1, edge.data)
self.assertNotIn(key2, edge.data)
edge.data[key2] = value2
self.assert_(edge.data)
self.assertEqual(edge.data[key1], value1)
self.assertEqual(edge.data[key2], value2)
self.assertEqual(len(edge.data), 2)
self.assertIn(key1, edge.data)
self.assertIn(key2, edge.data)
del edge.data[key1]
self.assert_(edge.data)
with self.assertRaises(KeyError):
_ = edge.data[key1]
self.assertEqual(edge.data[key2], value2)
self.assertEqual(len(edge.data), 1)
self.assertNotIn(key1, edge.data)
self.assertIn(key2, edge.data)
edge.data[key1] = value3
self.assert_(edge.data)
self.assertEqual(edge.data[key1], value3)
self.assertEqual(edge.data[key2], value2)
self.assertEqual(len(edge.data), 2)
self.assertIn(key1, edge.data)
self.assertIn(key2, edge.data)
edge.data[key2] = value3
self.assert_(edge.data)
self.assertEqual(edge.data[key1], value3)
self.assertEqual(edge.data[key2], value3)
self.assertEqual(len(edge.data), 2)
self.assertIn(key1, edge.data)
self.assertIn(key2, edge.data)
edge.data.clear()
self.assert_(not edge.data)
with self.assertRaises(KeyError):
_ = edge.data[key1]
with self.assertRaises(KeyError):
_ = edge.data[key2]
self.assertEqual(len(edge.data), 0)
self.assertNotIn(key1, edge.data)
self.assertNotIn(key2, edge.data)
| 38.073171 | 108 | 0.633088 | 1,515 | 12,488 | 5.114851 | 0.081188 | 0.108401 | 0.090592 | 0.041296 | 0.829655 | 0.807072 | 0.774035 | 0.744741 | 0.706672 | 0.645245 | 0 | 0.026037 | 0.252643 | 12,488 | 327 | 109 | 38.189602 | 0.804243 | 0.023543 | 0 | 0.706271 | 0 | 0 | 0.005743 | 0 | 0 | 0 | 0 | 0 | 0.646865 | 1 | 0.039604 | false | 0 | 0.009901 | 0.0033 | 0.056106 | 0.006601 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
4142da629a7ebd9ce528537fb6d8c46b1d0829d1 | 261,371 | py | Python | assessments/Assignments-2/model_v0.py | linksdl/acs-project-krr | c7ee4af3faaf89e31f8c2763008ae2ccbdf6de04 | [
"Apache-2.0"
] | null | null | null | assessments/Assignments-2/model_v0.py | linksdl/acs-project-krr | c7ee4af3faaf89e31f8c2763008ae2ccbdf6de04 | [
"Apache-2.0"
] | null | null | null | assessments/Assignments-2/model_v0.py | linksdl/acs-project-krr | c7ee4af3faaf89e31f8c2763008ae2ccbdf6de04 | [
"Apache-2.0"
] | 1 | 2020-11-22T18:05:33.000Z | 2020-11-22T18:05:33.000Z | # !/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Project : acs-project-krr
@File : ddd.py
@Author : Billy Sheng
@Contact : shengdl999links@gmail.com
@Date : 2020/11/21 10:29 上午
@Version : 1.0.0
@License : Apache License 2.0
@Desc : None
"""
MODELS = [
{
'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_3'},
'City': {'c_1'},
'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_3', 'v_2'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'),
('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_3'},
'City': {'c_1'},
'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_3', 'v_2'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'),
('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_3', 'v_2'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('v_4', 't_1'),
('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'),
('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_3', 'v_2'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'),
('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_3', 'v_2'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'),
('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_3', 'v_2'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'),
('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'),
('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_4', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_4', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'),
('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 'v_2'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_4', 't_2'),
('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 'v_2'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 'v_2'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'), ('v_2', 't_1'), ('v_1', 't_1'),
('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'),
('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'), ('v_3', 'c_1'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'),
('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('v_3', 'c_1'), ('v_4', 't_2'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'), ('v_1', 't_2'),
('v_2', 't_1'), ('v_3', 'c_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'),
('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'),
('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'),
('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_3', 'c_1'),
('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_2', 't_1'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('t_2', 'c_1'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'),
('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'),
('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_2'),
('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'),
('v_4', 't_2'), ('v_1', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'),
('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'),
('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'),
('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'),
('t_2', 'v_4'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'),
('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_2', 't_1'), ('v_4', 't_1')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'),
('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'),
('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_2'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'),
('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_5', 'v_3'}, 'City': {'c_1'}, 'Town': {'t_2', 't_1'},
'Village': {'v_4', 'v_1', 'v_2', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('c_1', 'v_1'), ('c_1', 'v_4'),
('t_2', 'v_3'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'),
('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'v_5'), ('t_1', 'v_3'), ('t_2', 'v_2')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'),
('v_1', 'c_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'),
('v_1', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'),
('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'),
('v_6', 't_2'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'),
('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_3'), ('c_1', 'v_1'), ('v_1', 'c_1'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 'v_2'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 'v_2'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('t_2', 'v_3'),
('v_5', 't_1'), ('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('v_4', 't_1'), ('c_1', 't_2'),
('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_6'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_6', 't_1'),
('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('t_2', 'v_3'),
('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 'v_2'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 'v_2'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_5'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('c_1', 'v_2'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_2', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('c_1', 'v_2'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 'v_2'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('v_2', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 'v_2'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'),
('v_6', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'),
('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('t_2', 'v_5'), ('v_2', 't_1'), ('v_1', 't_1'), ('v_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_3', 'c_1'), ('v_1', 't_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'),
('v_2', 't_1'), ('v_1', 't_1'), ('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'),
('v_2', 't_1'), ('v_1', 't_1'), ('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'),
('t_2', 'c_1'), ('v_3', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('v_4', 't_1'),
('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('t_2', 'v_5'), ('v_3', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'),
('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('t_2', 'v_5'), ('v_3', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_3', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_3', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'),
('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'),
('v_3', 'c_1'), ('v_1', 't_1'), ('v_4', 't_2'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('v_4', 't_1'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('v_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('v_4', 't_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('t_2', 'v_5'), ('v_1', 't_2'), ('v_2', 't_1'), ('v_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('t_2', 'v_5'), ('v_1', 't_2'), ('v_2', 't_1'), ('v_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'),
('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'), ('t_2', 'v_5'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_3', 'c_1'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_3', 'c_1'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_3', 'c_1'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('v_4', 't_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('t_2', 'v_5'), ('v_1', 't_2'), ('v_3', 'c_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('v_6', 't_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_2'), ('c_1', 'v_3'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_3', 'c_1'), ('v_4', 't_2'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'),
('v_4', 'c_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'),
('v_4', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'),
('c_1', 't_1'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'),
('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'),
('v_6', 't_2'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'),
('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_1', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_3'), ('c_1', 'v_4'), ('v_4', 'c_1'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_6', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('c_1', 'v_5'),
('t_2', 'v_3'), ('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_6', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('v_5', 'c_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'),
('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_6', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'),
('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_1'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_5', 'c_1'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'),
('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_6', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_2'), ('v_6', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_2'), ('v_6', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('v_5', 'c_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'),
('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_6', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('v_6', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('c_1', 'v_5'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'),
('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_2'), ('v_5', 'c_1'), ('t_2', 'c_1'), ('v_1', 't_2'),
('v_4', 't_2'), ('v_6', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_5', 'c_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_6', 'c_1'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('v_6', 'c_1'), ('t_1', 'v_2'), ('t_2', 'v_3'),
('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_6', 'c_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_6', 'c_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('v_3', 't_1'), ('v_6', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_6', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_6', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('v_6', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('c_1', 'v_6'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('v_6', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_6', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_6', 'c_1'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_6', 'c_1'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_6', 'c_1'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_6', 'c_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('c_1', 'v_6'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_6', 'c_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('v_6', 'c_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_6', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_6', 'c_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_6', 'c_1'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('c_1', 'v_6'), ('v_2', 't_2'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_6', 'c_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('c_1', 'v_6'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'),
('v_5', 't_2'), ('t_1', 'c_1'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_2'), ('v_1', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'),
('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'),
('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_1'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_2'), ('t_1', 'v_3')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_1', 'c_1'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_1')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_1', 'v_6'), ('c_1', 't_2'),
('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_2', 't_2'), ('v_6', 't_1'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_1', 'v_3'), ('t_1', 'v_4'), ('t_2', 'v_6'),
('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'), ('t_1', 'v_2'), ('c_1', 'v_4'),
('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 'v_6'), ('t_2', 'v_2'), ('t_1', 'v_1'), ('c_1', 'v_2'),
('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_3'), ('c_1', 't_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_3', 'v_4'),
('t_1', 'c_1'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_3', 'v_4'),
('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('t_3', 'v_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_3', 'v_6'), ('t_1', 'v_1'), ('v_3', 't_1'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_3'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_3', 't_1'), ('t_3', 'c_1'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('c_1', 't_3'), ('c_1', 't_2'), ('v_6', 't_3'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('v_5', 't_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_2', 't_3'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_2', 't_3'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('t_3', 'c_1'), ('t_1', 'c_1'),
('v_5', 't_3'), ('c_1', 't_3'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_6', 't_3'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_3', 'c_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_3', 'v_3'),
('c_1', 't_3'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_6', 't_3'), ('v_2', 't_2'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('t_3', 'v_3'),
('v_5', 't_3'), ('c_1', 't_3'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('v_2', 't_3'), ('t_2', 'v_3'), ('v_5', 't_2'),
('t_1', 'c_1'), ('c_1', 't_3'), ('v_4', 't_1'), ('c_1', 't_2'), ('v_6', 't_3'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_2', 'v_3'), ('t_1', 'c_1'),
('v_5', 't_3'), ('c_1', 't_3'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_3', 'v_3'),
('c_1', 't_3'), ('v_4', 't_1'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_4'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_6', 't_3'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_3'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('t_3', 'v_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('v_2', 't_3'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_3'), ('c_1', 't_1'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_6'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_4'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('v_5', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('v_5', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('t_3', 'c_1'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_3', 'v_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_1'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_4', 't_3'), ('v_1', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('c_1', 't_3'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_4', 't_2'), ('v_1', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_3', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_3'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('v_4', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_2', 't_1'), ('v_1', 't_3'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_3', 'v_4'), ('v_4', 't_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_3'), ('t_1', 'v_3'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_3'), ('c_1', 't_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('v_1', 't_2'), ('v_2', 't_1'), ('v_4', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_3', 'v_1'),
('t_1', 'c_1'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_3', 'v_1'),
('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_3', 'v_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_4'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_3'), ('c_1', 't_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_3'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_3', 'c_1'), ('t_3', 'v_6'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_3', 'v_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_3'), ('c_1', 't_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_6'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_4'), ('c_1', 't_3'), ('v_4', 't_3'), ('c_1', 't_2'),
('c_1', 't_1'), ('v_6', 't_2'), ('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_3'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('t_1', 'v_2'), ('v_5', 't_1'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('t_3', 'v_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_3', 'v_5'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('v_5', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('v_2', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('v_4', 't_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_2', 't_1'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'),
('v_2', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_3', 'v_5'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_2'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_3', 'v_4'), ('v_4', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'),
('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_3'), ('t_3', 'c_1')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_2'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_2', 't_1'), ('v_1', 't_3'), ('v_4', 't_2'), ('t_3', 'c_1'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_3', 'v_6'), ('t_1', 'v_4'), ('v_3', 't_1'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_3'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_2', 't_3'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_1', 'c_1'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_3'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_2', 't_3'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_1', 'c_1'), ('v_5', 't_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_3', 't_1'), ('t_3', 'c_1'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('c_1', 't_3'), ('c_1', 't_2'), ('v_6', 't_3'), ('v_2', 't_2'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('v_5', 't_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_3'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_2', 't_3'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_3'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_3'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('v_4', 't_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_3'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('t_3', 'v_4'), ('c_1', 't_3'), ('v_4', 't_3'), ('c_1', 't_2'), ('v_2', 't_2'),
('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_3'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_2', 'v_6'), ('v_3', 't_1'), ('v_5', 't_1'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_3'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_2', 't_3'), ('v_3', 't_1'), ('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_2'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('v_4', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_1', 'v_3'), ('t_2', 'v_2'), ('t_3', 'v_5')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('t_3', 'c_1'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_3', 'v_4'),
('v_4', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_3'), ('t_1', 'v_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_1'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('c_1', 't_3'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_3'), ('v_4', 't_2'), ('t_1', 'v_3'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_3'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('v_2', 't_3'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_3'), ('c_1', 't_1'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 't_2'), ('t_2', 'v_1'), ('c_1', 't_1'), ('v_6', 't_2'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_6', 't_3'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_2', 'v_2'), ('t_3', 'v_6')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_2', 'v_6'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('t_3', 'v_3'), ('c_1', 't_3'), ('c_1', 't_2'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_2'), ('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_6'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_1'),
('t_1', 'c_1'), ('t_1', 'v_5'), ('c_1', 't_3'), ('c_1', 't_2'), ('c_1', 't_1'), ('v_6', 't_2'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_3', 'c_1'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('v_5', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'),
('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'v_5'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('t_1', 'c_1'), ('v_5', 't_3'), ('c_1', 't_3'),
('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'v_5'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('v_5', 't_2'), ('t_1', 'c_1'), ('t_3', 'v_3'), ('c_1', 't_3'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('v_5', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'),
('c_1', 't_1'), ('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_3', 'v_5'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('t_1', 'v_4'), ('t_3', 'c_1'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_3', 'v_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('t_2', 'v_5'), ('t_2', 'c_1'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_1', 'v_4'), ('t_2', 'v_3'), ('v_5', 't_2'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_6', 't_1'), ('c_1', 't_1'), ('t_2', 'v_5'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_3'), ('v_4', 't_1'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_1'), ('t_2', 'c_1'), ('v_1', 't_2'), ('v_4', 't_3'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_4'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('v_4', 't_3'), ('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_1'), ('v_6', 't_1'),
('c_1', 't_1'), ('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_2'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_5', 't_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'), ('c_1', 't_3'), ('t_1', 'v_6'),
('c_1', 't_2'), ('t_2', 'v_1'), ('t_2', 'v_4'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'c_1'),
('t_3', 'v_2'), ('v_1', 't_2'), ('v_4', 't_2'), ('t_3', 'c_1'), ('v_2', 't_3'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'),
('t_1', 'v_5'), ('t_3', 'v_4'), ('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('v_4', 't_3'),
('v_2', 't_2'), ('c_1', 't_1'), ('v_6', 't_1'), ('t_2', 'c_1'), ('v_1', 't_3'), ('t_2', 'v_2')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_5', 't_1'), ('t_3', 'c_1'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_1', 'v_5'), ('t_3', 'v_3'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_2', 't_2'), ('c_1', 't_1'),
('v_6', 't_1'), ('t_2', 'c_1'), ('v_4', 't_2'), ('v_1', 't_3'), ('t_2', 'v_2'), ('v_3', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}},
{'DOMAIN': {'v_1', 'v_4', 't_2', 't_1', 'c_1', 'v_2', 't_3', 'v_6', 'v_5', 'v_3'}, 'City': {'c_1'},
'Town': {'t_3', 't_2', 't_1'}, 'Village': {'v_4', 'v_1', 'v_2', 'v_6', 'v_5', 'v_3'},
'Road': {('v_3', 't_2'), ('t_2', 'v_3'), ('v_5', 't_1'), ('t_3', 'v_1'), ('t_1', 'c_1'), ('t_1', 'v_5'),
('c_1', 't_3'), ('t_1', 'v_6'), ('c_1', 't_2'), ('t_2', 'v_4'), ('v_6', 't_1'), ('c_1', 't_1'),
('t_2', 'c_1'), ('t_3', 'v_2'), ('v_1', 't_3'), ('v_4', 't_2'), ('t_3', 'c_1'), ('v_2', 't_3')},
'>': {('c_1', 'v_1'), ('t_3', 'v_4'), ('t_1', 'v_5'), ('t_2', 'v_1'), ('t_3', 'v_2'), ('t_1', 'v_3'),
('t_1', 'v_4'), ('t_2', 'v_6'), ('c_1', 'v_5'), ('t_2', 'v_3'), ('t_1', 'v_6'), ('c_1', 't_1'),
('t_3', 'v_5'), ('t_1', 'v_2'), ('c_1', 'v_4'), ('t_3', 'v_1'), ('c_1', 't_2'), ('t_2', 'v_4'),
('c_1', 'v_6'), ('t_2', 'v_2'), ('t_3', 'v_6'), ('t_1', 'v_1'), ('c_1', 'v_2'), ('t_3', 'v_3'),
('c_1', 't_3'), ('c_1', 'v_3'), ('t_2', 'v_5')}}]
| 99.569905 | 117 | 0.335546 | 55,062 | 261,371 | 1.106135 | 0.000872 | 0.190622 | 0.12846 | 0.062654 | 0.997307 | 0.997307 | 0.997307 | 0.997258 | 0.997209 | 0.997209 | 0 | 0.134644 | 0.237984 | 261,371 | 2,624 | 118 | 99.607851 | 0.171156 | 0.000964 | 0 | 0.798696 | 0 | 0 | 0.336351 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
414a7f3268f46a27646656037c93663817960262 | 13 | py | Python | Python/euler1.py | shujanpannag/Random_Programs | 77b7a8197e154926411d9939ef1e4effbc6eabfe | [
"MIT"
] | null | null | null | Python/euler1.py | shujanpannag/Random_Programs | 77b7a8197e154926411d9939ef1e4effbc6eabfe | [
"MIT"
] | null | null | null | Python/euler1.py | shujanpannag/Random_Programs | 77b7a8197e154926411d9939ef1e4effbc6eabfe | [
"MIT"
] | null | null | null | 1000/(3+4+5)
| 6.5 | 12 | 0.538462 | 4 | 13 | 1.75 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.583333 | 0.076923 | 13 | 1 | 13 | 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
417189d5b1e3743596b0aca0de9fa1e6ec8a5e2d | 1,911 | py | Python | SO2MI/Shop.py | Qman11010101/SO2MarketInfoBotForTwitter | 4d4c911792ffcba9253bb84cc7395b129bbb8bcd | [
"MIT"
] | 2 | 2019-10-21T05:39:23.000Z | 2019-10-26T14:39:26.000Z | SO2MI/Shop.py | Qman11010101/SO2MarketInfoBotForTwitter | 4d4c911792ffcba9253bb84cc7395b129bbb8bcd | [
"MIT"
] | 18 | 2019-10-21T06:33:33.000Z | 2021-06-02T00:41:11.000Z | SO2MI/Shop.py | Qman11010101/SO2MarketInfoBotForTwitter | 4d4c911792ffcba9253bb84cc7395b129bbb8bcd | [
"MIT"
] | null | null | null | from .getApi import getApi
from .Exceptions import NoShopError
def funcShopFromName(shopName):
allshop = getApi("allshop", "https://so2-api.mutoys.com/json/shop/all.json")
town = getApi("town", "https://so2-api.mutoys.com/master/area.json")
# 店名→店データ変換
dl = []
for shop in allshop:
if shop["shop_name"] == shopName:
shopID = shop["user_id"]
areaID = shop["area_id"]
posX, posY = shop["pos_x"], shop["pos_y"]
foundationDays = shop["foundation_days"]
honor = shop["title"]
dl = [shopID, areaID, [posX, posY], foundationDays, honor]
break
if dl == []:
raise NoShopError("such shop does not exists")
# エリアID→地域名変換
areaID_temp = dl[1]
for col in town:
if town[col]["area_id"] == areaID_temp:
dl[1] = town[col]["name"]
break
return f"オーナー番号: {dl[0]}\n所在地: {dl[1]}({dl[2][0]}, {dl[2][1]})\n創業: {dl[3]}日\n称号: {dl[4]}"
def funcShopFromID(shopID):
allshop = getApi("allshop", "https://so2-api.mutoys.com/json/shop/all.json")
town = getApi("town", "https://so2-api.mutoys.com/master/area.json")
# ID→店データ変換
dl = []
for shop in allshop:
if shop["user_id"] == shopID:
shopName = shop["shop_name"]
areaID = shop["area_id"]
posX, posY = shop["pos_x"], shop["pos_y"]
foundationDays = shop["foundation_days"]
honor = shop["title"]
dl = [shopName, areaID, [posX, posY], foundationDays, honor]
break
if dl == []:
raise NoShopError("such shop does not exists")
# エリアID→地域名変換
areaID_temp = dl[1]
for col in town:
if town[col]["area_id"] == areaID_temp:
dl[1] = town[col]["name"]
break
return f"店名: {dl[0]}\n所在地: {dl[1]}({dl[2][0]}, {dl[2][1]})\n創業: {dl[3]}日\n称号: {dl[4]}"
| 33.526316 | 94 | 0.546834 | 256 | 1,911 | 4.027344 | 0.253906 | 0.017459 | 0.042677 | 0.065955 | 0.812803 | 0.812803 | 0.812803 | 0.812803 | 0.812803 | 0.752667 | 0 | 0.017429 | 0.279435 | 1,911 | 56 | 95 | 34.125 | 0.728395 | 0.022501 | 0 | 0.727273 | 0 | 0.045455 | 0.285561 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.045455 | false | 0 | 0.045455 | 0 | 0.136364 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
41ca2fe2aefa118ce75e74b3f24ecdc3b7fc9b2f | 331,604 | py | Python | intersight/api/software_api.py | CiscoDevNet/intersight-python | 04b721f37c3044646a91c185c7259edfb991557a | [
"Apache-2.0"
] | 5 | 2021-12-16T15:13:32.000Z | 2022-03-29T16:09:54.000Z | intersight/api/software_api.py | CiscoDevNet/intersight-python | 04b721f37c3044646a91c185c7259edfb991557a | [
"Apache-2.0"
] | 4 | 2022-01-25T19:05:51.000Z | 2022-03-29T20:18:37.000Z | intersight/api/software_api.py | CiscoDevNet/intersight-python | 04b721f37c3044646a91c185c7259edfb991557a | [
"Apache-2.0"
] | 2 | 2020-07-07T15:01:08.000Z | 2022-01-31T04:27:35.000Z | """
Cisco Intersight
Cisco Intersight is a management platform delivered as a service with embedded analytics for your Cisco and 3rd party IT infrastructure. This platform offers an intelligent level of management that enables IT organizations to analyze, simplify, and automate their environments in more advanced ways than the prior generations of tools. Cisco Intersight provides an integrated and intuitive management experience for resources in the traditional data center as well as at the edge. With flexible deployment options to address complex security needs, getting started with Intersight is quick and easy. Cisco Intersight has deep integration with Cisco UCS and HyperFlex systems allowing for remote deployment, configuration, and ongoing maintenance. The model-based deployment works for a single system in a remote location or hundreds of systems in a data center and enables rapid, standardized configuration and deployment. It also streamlines maintaining those systems whether you are working with small or very large configurations. The Intersight OpenAPI document defines the complete set of properties that are returned in the HTTP response. From that perspective, a client can expect that no additional properties are returned, unless these properties are explicitly defined in the OpenAPI document. However, when a client uses an older version of the Intersight OpenAPI document, the server may send additional properties because the software is more recent than the client. In that case, the client may receive properties that it does not know about. Some generated SDKs perform a strict validation of the HTTP response body against the OpenAPI document. # noqa: E501
The version of the OpenAPI document: 1.0.9-4950
Contact: intersight@cisco.com
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from intersight.api_client import ApiClient, Endpoint as _Endpoint
from intersight.model_utils import ( # noqa: F401
check_allowed_values,
check_validations,
date,
datetime,
file_type,
none_type,
validate_and_convert_types
)
from intersight.model.error import Error
from intersight.model.software_appliance_distributable import SoftwareApplianceDistributable
from intersight.model.software_appliance_distributable_response import SoftwareApplianceDistributableResponse
from intersight.model.software_download_history import SoftwareDownloadHistory
from intersight.model.software_download_history_response import SoftwareDownloadHistoryResponse
from intersight.model.software_hcl_meta import SoftwareHclMeta
from intersight.model.software_hcl_meta_response import SoftwareHclMetaResponse
from intersight.model.software_hyperflex_bundle_distributable import SoftwareHyperflexBundleDistributable
from intersight.model.software_hyperflex_bundle_distributable_response import SoftwareHyperflexBundleDistributableResponse
from intersight.model.software_hyperflex_distributable import SoftwareHyperflexDistributable
from intersight.model.software_hyperflex_distributable_response import SoftwareHyperflexDistributableResponse
from intersight.model.software_release_meta import SoftwareReleaseMeta
from intersight.model.software_release_meta_response import SoftwareReleaseMetaResponse
from intersight.model.software_solution_distributable import SoftwareSolutionDistributable
from intersight.model.software_solution_distributable_response import SoftwareSolutionDistributableResponse
from intersight.model.software_ucsd_bundle_distributable import SoftwareUcsdBundleDistributable
from intersight.model.software_ucsd_bundle_distributable_response import SoftwareUcsdBundleDistributableResponse
from intersight.model.software_ucsd_distributable import SoftwareUcsdDistributable
from intersight.model.software_ucsd_distributable_response import SoftwareUcsdDistributableResponse
class SoftwareApi(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 __create_software_appliance_distributable(
self,
software_appliance_distributable,
**kwargs
):
"""Create a 'software.ApplianceDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_appliance_distributable(software_appliance_distributable, async_req=True)
>>> result = thread.get()
Args:
software_appliance_distributable (SoftwareApplianceDistributable): The 'software.ApplianceDistributable' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareApplianceDistributable
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['software_appliance_distributable'] = \
software_appliance_distributable
return self.call_with_http_info(**kwargs)
self.create_software_appliance_distributable = _Endpoint(
settings={
'response_type': (SoftwareApplianceDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ApplianceDistributables',
'operation_id': 'create_software_appliance_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_appliance_distributable',
'if_match',
'if_none_match',
],
'required': [
'software_appliance_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_appliance_distributable':
(SoftwareApplianceDistributable,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_appliance_distributable': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_appliance_distributable
)
def __create_software_hcl_meta(
self,
software_hcl_meta,
**kwargs
):
"""Create a 'software.HclMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_hcl_meta(software_hcl_meta, async_req=True)
>>> result = thread.get()
Args:
software_hcl_meta (SoftwareHclMeta): The 'software.HclMeta' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareHclMeta
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['software_hcl_meta'] = \
software_hcl_meta
return self.call_with_http_info(**kwargs)
self.create_software_hcl_meta = _Endpoint(
settings={
'response_type': (SoftwareHclMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HclMeta',
'operation_id': 'create_software_hcl_meta',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_hcl_meta',
'if_match',
'if_none_match',
],
'required': [
'software_hcl_meta',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_hcl_meta':
(SoftwareHclMeta,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_hcl_meta': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_hcl_meta
)
def __create_software_hyperflex_bundle_distributable(
self,
software_hyperflex_bundle_distributable,
**kwargs
):
"""Create a 'software.HyperflexBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_hyperflex_bundle_distributable(software_hyperflex_bundle_distributable, async_req=True)
>>> result = thread.get()
Args:
software_hyperflex_bundle_distributable (SoftwareHyperflexBundleDistributable): The 'software.HyperflexBundleDistributable' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareHyperflexBundleDistributable
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['software_hyperflex_bundle_distributable'] = \
software_hyperflex_bundle_distributable
return self.call_with_http_info(**kwargs)
self.create_software_hyperflex_bundle_distributable = _Endpoint(
settings={
'response_type': (SoftwareHyperflexBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexBundleDistributables',
'operation_id': 'create_software_hyperflex_bundle_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_hyperflex_bundle_distributable',
'if_match',
'if_none_match',
],
'required': [
'software_hyperflex_bundle_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_hyperflex_bundle_distributable':
(SoftwareHyperflexBundleDistributable,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_hyperflex_bundle_distributable': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_hyperflex_bundle_distributable
)
def __create_software_hyperflex_distributable(
self,
software_hyperflex_distributable,
**kwargs
):
"""Create a 'software.HyperflexDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_hyperflex_distributable(software_hyperflex_distributable, async_req=True)
>>> result = thread.get()
Args:
software_hyperflex_distributable (SoftwareHyperflexDistributable): The 'software.HyperflexDistributable' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareHyperflexDistributable
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['software_hyperflex_distributable'] = \
software_hyperflex_distributable
return self.call_with_http_info(**kwargs)
self.create_software_hyperflex_distributable = _Endpoint(
settings={
'response_type': (SoftwareHyperflexDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexDistributables',
'operation_id': 'create_software_hyperflex_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_hyperflex_distributable',
'if_match',
'if_none_match',
],
'required': [
'software_hyperflex_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_hyperflex_distributable':
(SoftwareHyperflexDistributable,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_hyperflex_distributable': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_hyperflex_distributable
)
def __create_software_release_meta(
self,
software_release_meta,
**kwargs
):
"""Create a 'software.ReleaseMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_release_meta(software_release_meta, async_req=True)
>>> result = thread.get()
Args:
software_release_meta (SoftwareReleaseMeta): The 'software.ReleaseMeta' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareReleaseMeta
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['software_release_meta'] = \
software_release_meta
return self.call_with_http_info(**kwargs)
self.create_software_release_meta = _Endpoint(
settings={
'response_type': (SoftwareReleaseMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ReleaseMeta',
'operation_id': 'create_software_release_meta',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_release_meta',
'if_match',
'if_none_match',
],
'required': [
'software_release_meta',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_release_meta':
(SoftwareReleaseMeta,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_release_meta': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_release_meta
)
def __create_software_solution_distributable(
self,
software_solution_distributable,
**kwargs
):
"""Create a 'software.SolutionDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_solution_distributable(software_solution_distributable, async_req=True)
>>> result = thread.get()
Args:
software_solution_distributable (SoftwareSolutionDistributable): The 'software.SolutionDistributable' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareSolutionDistributable
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['software_solution_distributable'] = \
software_solution_distributable
return self.call_with_http_info(**kwargs)
self.create_software_solution_distributable = _Endpoint(
settings={
'response_type': (SoftwareSolutionDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/SolutionDistributables',
'operation_id': 'create_software_solution_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_solution_distributable',
'if_match',
'if_none_match',
],
'required': [
'software_solution_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_solution_distributable':
(SoftwareSolutionDistributable,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_solution_distributable': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_solution_distributable
)
def __create_software_ucsd_bundle_distributable(
self,
software_ucsd_bundle_distributable,
**kwargs
):
"""Create a 'software.UcsdBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_ucsd_bundle_distributable(software_ucsd_bundle_distributable, async_req=True)
>>> result = thread.get()
Args:
software_ucsd_bundle_distributable (SoftwareUcsdBundleDistributable): The 'software.UcsdBundleDistributable' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareUcsdBundleDistributable
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['software_ucsd_bundle_distributable'] = \
software_ucsd_bundle_distributable
return self.call_with_http_info(**kwargs)
self.create_software_ucsd_bundle_distributable = _Endpoint(
settings={
'response_type': (SoftwareUcsdBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdBundleDistributables',
'operation_id': 'create_software_ucsd_bundle_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_ucsd_bundle_distributable',
'if_match',
'if_none_match',
],
'required': [
'software_ucsd_bundle_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_ucsd_bundle_distributable':
(SoftwareUcsdBundleDistributable,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_ucsd_bundle_distributable': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_ucsd_bundle_distributable
)
def __create_software_ucsd_distributable(
self,
software_ucsd_distributable,
**kwargs
):
"""Create a 'software.UcsdDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.create_software_ucsd_distributable(software_ucsd_distributable, async_req=True)
>>> result = thread.get()
Args:
software_ucsd_distributable (SoftwareUcsdDistributable): The 'software.UcsdDistributable' resource to create.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [optional]
if_none_match (str): For methods that apply server-side changes, If-None-Match used with the * value can be used to create a resource not known to exist, guaranteeing that another resource creation didn't happen before, losing the data of the previous put. The request will be processed only if the eventually existing resource's ETag doesn't match any of the values listed. Otherwise, the status code 412 (Precondition Failed) is used. The asterisk is a special value representing any resource. It is only useful when creating a resource, usually with PUT, to check if another resource with the identity has already been created before. The comparison with the stored ETag uses the weak comparison algorithm, meaning two resources are considered identical if the content is equivalent - they don't have to be identical byte for byte.. [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:
SoftwareUcsdDistributable
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['software_ucsd_distributable'] = \
software_ucsd_distributable
return self.call_with_http_info(**kwargs)
self.create_software_ucsd_distributable = _Endpoint(
settings={
'response_type': (SoftwareUcsdDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdDistributables',
'operation_id': 'create_software_ucsd_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'software_ucsd_distributable',
'if_match',
'if_none_match',
],
'required': [
'software_ucsd_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'software_ucsd_distributable':
(SoftwareUcsdDistributable,),
'if_match':
(str,),
'if_none_match':
(str,),
},
'attribute_map': {
'if_match': 'If-Match',
'if_none_match': 'If-None-Match',
},
'location_map': {
'software_ucsd_distributable': 'body',
'if_match': 'header',
'if_none_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json'
]
},
api_client=api_client,
callable=__create_software_ucsd_distributable
)
def __delete_software_appliance_distributable(
self,
moid,
**kwargs
):
"""Delete a 'software.ApplianceDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_appliance_distributable(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_appliance_distributable = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ApplianceDistributables/{Moid}',
'operation_id': 'delete_software_appliance_distributable',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_appliance_distributable
)
def __delete_software_hcl_meta(
self,
moid,
**kwargs
):
"""Delete a 'software.HclMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_hcl_meta(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_hcl_meta = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HclMeta/{Moid}',
'operation_id': 'delete_software_hcl_meta',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_hcl_meta
)
def __delete_software_hyperflex_bundle_distributable(
self,
moid,
**kwargs
):
"""Delete a 'software.HyperflexBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_hyperflex_bundle_distributable(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_hyperflex_bundle_distributable = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexBundleDistributables/{Moid}',
'operation_id': 'delete_software_hyperflex_bundle_distributable',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_hyperflex_bundle_distributable
)
def __delete_software_hyperflex_distributable(
self,
moid,
**kwargs
):
"""Delete a 'software.HyperflexDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_hyperflex_distributable(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_hyperflex_distributable = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexDistributables/{Moid}',
'operation_id': 'delete_software_hyperflex_distributable',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_hyperflex_distributable
)
def __delete_software_release_meta(
self,
moid,
**kwargs
):
"""Delete a 'software.ReleaseMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_release_meta(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_release_meta = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ReleaseMeta/{Moid}',
'operation_id': 'delete_software_release_meta',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_release_meta
)
def __delete_software_solution_distributable(
self,
moid,
**kwargs
):
"""Delete a 'software.SolutionDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_solution_distributable(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_solution_distributable = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/SolutionDistributables/{Moid}',
'operation_id': 'delete_software_solution_distributable',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_solution_distributable
)
def __delete_software_ucsd_bundle_distributable(
self,
moid,
**kwargs
):
"""Delete a 'software.UcsdBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_ucsd_bundle_distributable(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_ucsd_bundle_distributable = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdBundleDistributables/{Moid}',
'operation_id': 'delete_software_ucsd_bundle_distributable',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_ucsd_bundle_distributable
)
def __delete_software_ucsd_distributable(
self,
moid,
**kwargs
):
"""Delete a 'software.UcsdDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.delete_software_ucsd_distributable(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.delete_software_ucsd_distributable = _Endpoint(
settings={
'response_type': None,
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdDistributables/{Moid}',
'operation_id': 'delete_software_ucsd_distributable',
'http_method': 'DELETE',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [],
},
api_client=api_client,
callable=__delete_software_ucsd_distributable
)
def __get_software_appliance_distributable_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.ApplianceDistributable' resource. # 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_software_appliance_distributable_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareApplianceDistributable
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_appliance_distributable_by_moid = _Endpoint(
settings={
'response_type': (SoftwareApplianceDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ApplianceDistributables/{Moid}',
'operation_id': 'get_software_appliance_distributable_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_appliance_distributable_by_moid
)
def __get_software_appliance_distributable_list(
self,
**kwargs
):
"""Read a 'software.ApplianceDistributable' resource. # 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_software_appliance_distributable_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareApplianceDistributableResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_appliance_distributable_list = _Endpoint(
settings={
'response_type': (SoftwareApplianceDistributableResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ApplianceDistributables',
'operation_id': 'get_software_appliance_distributable_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_appliance_distributable_list
)
def __get_software_download_history_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.DownloadHistory' resource. # 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_software_download_history_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareDownloadHistory
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_download_history_by_moid = _Endpoint(
settings={
'response_type': (SoftwareDownloadHistory,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/DownloadHistories/{Moid}',
'operation_id': 'get_software_download_history_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_download_history_by_moid
)
def __get_software_download_history_list(
self,
**kwargs
):
"""Read a 'software.DownloadHistory' resource. # 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_software_download_history_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareDownloadHistoryResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_download_history_list = _Endpoint(
settings={
'response_type': (SoftwareDownloadHistoryResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/DownloadHistories',
'operation_id': 'get_software_download_history_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_download_history_list
)
def __get_software_hcl_meta_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.HclMeta' resource. # 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_software_hcl_meta_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareHclMeta
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_hcl_meta_by_moid = _Endpoint(
settings={
'response_type': (SoftwareHclMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HclMeta/{Moid}',
'operation_id': 'get_software_hcl_meta_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_hcl_meta_by_moid
)
def __get_software_hcl_meta_list(
self,
**kwargs
):
"""Read a 'software.HclMeta' resource. # 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_software_hcl_meta_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareHclMetaResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_hcl_meta_list = _Endpoint(
settings={
'response_type': (SoftwareHclMetaResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HclMeta',
'operation_id': 'get_software_hcl_meta_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_hcl_meta_list
)
def __get_software_hyperflex_bundle_distributable_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.HyperflexBundleDistributable' resource. # 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_software_hyperflex_bundle_distributable_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareHyperflexBundleDistributable
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_hyperflex_bundle_distributable_by_moid = _Endpoint(
settings={
'response_type': (SoftwareHyperflexBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexBundleDistributables/{Moid}',
'operation_id': 'get_software_hyperflex_bundle_distributable_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_hyperflex_bundle_distributable_by_moid
)
def __get_software_hyperflex_bundle_distributable_list(
self,
**kwargs
):
"""Read a 'software.HyperflexBundleDistributable' resource. # 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_software_hyperflex_bundle_distributable_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareHyperflexBundleDistributableResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_hyperflex_bundle_distributable_list = _Endpoint(
settings={
'response_type': (SoftwareHyperflexBundleDistributableResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexBundleDistributables',
'operation_id': 'get_software_hyperflex_bundle_distributable_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_hyperflex_bundle_distributable_list
)
def __get_software_hyperflex_distributable_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.HyperflexDistributable' resource. # 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_software_hyperflex_distributable_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareHyperflexDistributable
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_hyperflex_distributable_by_moid = _Endpoint(
settings={
'response_type': (SoftwareHyperflexDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexDistributables/{Moid}',
'operation_id': 'get_software_hyperflex_distributable_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_hyperflex_distributable_by_moid
)
def __get_software_hyperflex_distributable_list(
self,
**kwargs
):
"""Read a 'software.HyperflexDistributable' resource. # 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_software_hyperflex_distributable_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareHyperflexDistributableResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_hyperflex_distributable_list = _Endpoint(
settings={
'response_type': (SoftwareHyperflexDistributableResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexDistributables',
'operation_id': 'get_software_hyperflex_distributable_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_hyperflex_distributable_list
)
def __get_software_release_meta_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.ReleaseMeta' resource. # 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_software_release_meta_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareReleaseMeta
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_release_meta_by_moid = _Endpoint(
settings={
'response_type': (SoftwareReleaseMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ReleaseMeta/{Moid}',
'operation_id': 'get_software_release_meta_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_release_meta_by_moid
)
def __get_software_release_meta_list(
self,
**kwargs
):
"""Read a 'software.ReleaseMeta' resource. # 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_software_release_meta_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareReleaseMetaResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_release_meta_list = _Endpoint(
settings={
'response_type': (SoftwareReleaseMetaResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ReleaseMeta',
'operation_id': 'get_software_release_meta_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_release_meta_list
)
def __get_software_solution_distributable_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.SolutionDistributable' resource. # 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_software_solution_distributable_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareSolutionDistributable
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_solution_distributable_by_moid = _Endpoint(
settings={
'response_type': (SoftwareSolutionDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/SolutionDistributables/{Moid}',
'operation_id': 'get_software_solution_distributable_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_solution_distributable_by_moid
)
def __get_software_solution_distributable_list(
self,
**kwargs
):
"""Read a 'software.SolutionDistributable' resource. # 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_software_solution_distributable_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareSolutionDistributableResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_solution_distributable_list = _Endpoint(
settings={
'response_type': (SoftwareSolutionDistributableResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/SolutionDistributables',
'operation_id': 'get_software_solution_distributable_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_solution_distributable_list
)
def __get_software_ucsd_bundle_distributable_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.UcsdBundleDistributable' resource. # 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_software_ucsd_bundle_distributable_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareUcsdBundleDistributable
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_ucsd_bundle_distributable_by_moid = _Endpoint(
settings={
'response_type': (SoftwareUcsdBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdBundleDistributables/{Moid}',
'operation_id': 'get_software_ucsd_bundle_distributable_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_ucsd_bundle_distributable_by_moid
)
def __get_software_ucsd_bundle_distributable_list(
self,
**kwargs
):
"""Read a 'software.UcsdBundleDistributable' resource. # 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_software_ucsd_bundle_distributable_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareUcsdBundleDistributableResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_ucsd_bundle_distributable_list = _Endpoint(
settings={
'response_type': (SoftwareUcsdBundleDistributableResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdBundleDistributables',
'operation_id': 'get_software_ucsd_bundle_distributable_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_ucsd_bundle_distributable_list
)
def __get_software_ucsd_distributable_by_moid(
self,
moid,
**kwargs
):
"""Read a 'software.UcsdDistributable' resource. # 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_software_ucsd_distributable_by_moid(moid, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
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:
SoftwareUcsdDistributable
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['moid'] = \
moid
return self.call_with_http_info(**kwargs)
self.get_software_ucsd_distributable_by_moid = _Endpoint(
settings={
'response_type': (SoftwareUcsdDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdDistributables/{Moid}',
'operation_id': 'get_software_ucsd_distributable_by_moid',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'moid',
],
'required': [
'moid',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
},
'attribute_map': {
'moid': 'Moid',
},
'location_map': {
'moid': 'path',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_ucsd_distributable_by_moid
)
def __get_software_ucsd_distributable_list(
self,
**kwargs
):
"""Read a 'software.UcsdDistributable' resource. # 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_software_ucsd_distributable_list(async_req=True)
>>> result = thread.get()
Keyword Args:
filter (str): Filter criteria for the resources to return. A URI with a $filter query option identifies a subset of the entries from the Collection of Entries. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the $filter option. The expression language that is used in $filter queries supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false).. [optional] if omitted the server will use the default value of ""
orderby (str): Determines what properties are used to sort the collection of resources.. [optional]
top (int): Specifies the maximum number of resources to return in the response.. [optional] if omitted the server will use the default value of 100
skip (int): Specifies the number of resources to skip in the response.. [optional] if omitted the server will use the default value of 0
select (str): Specifies a subset of properties to return.. [optional] if omitted the server will use the default value of ""
expand (str): Specify additional attributes or related resources to return in addition to the primary resources.. [optional]
apply (str): Specify one or more transformation operations to perform aggregation on the resources. The transformations are processed in order with the output from a transformation being used as input for the subsequent transformation. The \"$apply\" query takes a sequence of set transformations, separated by forward slashes to express that they are consecutively applied, i.e. the result of each transformation is the input to the next transformation. Supported aggregation methods are \"aggregate\" and \"groupby\". The **aggregate** transformation takes a comma-separated list of one or more aggregate expressions as parameters and returns a result set with a single instance, representing the aggregated value for all instances in the input set. The **groupby** transformation takes one or two parameters and 1. Splits the initial set into subsets where all instances in a subset have the same values for the grouping properties specified in the first parameter, 2. Applies set transformations to each subset according to the second parameter, resulting in a new set of potentially different structure and cardinality, 3. Ensures that the instances in the result set contain all grouping properties with the correct values for the group, 4. Concatenates the intermediate result sets into one result set. A groupby transformation affects the structure of the result set.. [optional]
count (bool): The $count query specifies the service should return the count of the matching resources, instead of returning the resources.. [optional]
inlinecount (str): The $inlinecount query option allows clients to request an inline count of the matching resources included with the resources in the response.. [optional] if omitted the server will use the default value of "allpages"
at (str): Similar to \"$filter\", but \"at\" is specifically used to filter versioning information properties for resources to return. A URI with an \"at\" Query Option identifies a subset of the Entries from the Collection of Entries identified by the Resource Path section of the URI. The subset is determined by selecting only the Entries that satisfy the predicate expression specified by the query option. The expression language that is used in at operators supports references to properties and literals. The literal values can be strings enclosed in single quotes, numbers and boolean values (true or false) or any of the additional literal representations shown in the Abstract Type System section.. [optional]
tags (str): The 'tags' parameter is used to request a summary of the Tag utilization for this resource. When the 'tags' parameter is specified, the response provides a list of tag keys, the number of times the key has been used across all documents, and the tag values that have been assigned to the tag key.. [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:
SoftwareUcsdDistributableResponse
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')
return self.call_with_http_info(**kwargs)
self.get_software_ucsd_distributable_list = _Endpoint(
settings={
'response_type': (SoftwareUcsdDistributableResponse,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdDistributables',
'operation_id': 'get_software_ucsd_distributable_list',
'http_method': 'GET',
'servers': None,
},
params_map={
'all': [
'filter',
'orderby',
'top',
'skip',
'select',
'expand',
'apply',
'count',
'inlinecount',
'at',
'tags',
],
'required': [],
'nullable': [
],
'enum': [
'inlinecount',
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
('inlinecount',): {
"ALLPAGES": "allpages",
"NONE": "none"
},
},
'openapi_types': {
'filter':
(str,),
'orderby':
(str,),
'top':
(int,),
'skip':
(int,),
'select':
(str,),
'expand':
(str,),
'apply':
(str,),
'count':
(bool,),
'inlinecount':
(str,),
'at':
(str,),
'tags':
(str,),
},
'attribute_map': {
'filter': '$filter',
'orderby': '$orderby',
'top': '$top',
'skip': '$skip',
'select': '$select',
'expand': '$expand',
'apply': '$apply',
'count': '$count',
'inlinecount': '$inlinecount',
'at': 'at',
'tags': 'tags',
},
'location_map': {
'filter': 'query',
'orderby': 'query',
'top': 'query',
'skip': 'query',
'select': 'query',
'expand': 'query',
'apply': 'query',
'count': 'query',
'inlinecount': 'query',
'at': 'query',
'tags': 'query',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json',
'text/csv',
'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
],
'content_type': [],
},
api_client=api_client,
callable=__get_software_ucsd_distributable_list
)
def __patch_software_appliance_distributable(
self,
moid,
software_appliance_distributable,
**kwargs
):
"""Update a 'software.ApplianceDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_appliance_distributable(moid, software_appliance_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_appliance_distributable (SoftwareApplianceDistributable): The 'software.ApplianceDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareApplianceDistributable
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['moid'] = \
moid
kwargs['software_appliance_distributable'] = \
software_appliance_distributable
return self.call_with_http_info(**kwargs)
self.patch_software_appliance_distributable = _Endpoint(
settings={
'response_type': (SoftwareApplianceDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ApplianceDistributables/{Moid}',
'operation_id': 'patch_software_appliance_distributable',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_appliance_distributable',
'if_match',
],
'required': [
'moid',
'software_appliance_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_appliance_distributable':
(SoftwareApplianceDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_appliance_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_appliance_distributable
)
def __patch_software_hcl_meta(
self,
moid,
software_hcl_meta,
**kwargs
):
"""Update a 'software.HclMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_hcl_meta(moid, software_hcl_meta, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_hcl_meta (SoftwareHclMeta): The 'software.HclMeta' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareHclMeta
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['moid'] = \
moid
kwargs['software_hcl_meta'] = \
software_hcl_meta
return self.call_with_http_info(**kwargs)
self.patch_software_hcl_meta = _Endpoint(
settings={
'response_type': (SoftwareHclMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HclMeta/{Moid}',
'operation_id': 'patch_software_hcl_meta',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_hcl_meta',
'if_match',
],
'required': [
'moid',
'software_hcl_meta',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_hcl_meta':
(SoftwareHclMeta,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_hcl_meta': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_hcl_meta
)
def __patch_software_hyperflex_bundle_distributable(
self,
moid,
software_hyperflex_bundle_distributable,
**kwargs
):
"""Update a 'software.HyperflexBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_hyperflex_bundle_distributable(moid, software_hyperflex_bundle_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_hyperflex_bundle_distributable (SoftwareHyperflexBundleDistributable): The 'software.HyperflexBundleDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareHyperflexBundleDistributable
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['moid'] = \
moid
kwargs['software_hyperflex_bundle_distributable'] = \
software_hyperflex_bundle_distributable
return self.call_with_http_info(**kwargs)
self.patch_software_hyperflex_bundle_distributable = _Endpoint(
settings={
'response_type': (SoftwareHyperflexBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexBundleDistributables/{Moid}',
'operation_id': 'patch_software_hyperflex_bundle_distributable',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_hyperflex_bundle_distributable',
'if_match',
],
'required': [
'moid',
'software_hyperflex_bundle_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_hyperflex_bundle_distributable':
(SoftwareHyperflexBundleDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_hyperflex_bundle_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_hyperflex_bundle_distributable
)
def __patch_software_hyperflex_distributable(
self,
moid,
software_hyperflex_distributable,
**kwargs
):
"""Update a 'software.HyperflexDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_hyperflex_distributable(moid, software_hyperflex_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_hyperflex_distributable (SoftwareHyperflexDistributable): The 'software.HyperflexDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareHyperflexDistributable
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['moid'] = \
moid
kwargs['software_hyperflex_distributable'] = \
software_hyperflex_distributable
return self.call_with_http_info(**kwargs)
self.patch_software_hyperflex_distributable = _Endpoint(
settings={
'response_type': (SoftwareHyperflexDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexDistributables/{Moid}',
'operation_id': 'patch_software_hyperflex_distributable',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_hyperflex_distributable',
'if_match',
],
'required': [
'moid',
'software_hyperflex_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_hyperflex_distributable':
(SoftwareHyperflexDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_hyperflex_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_hyperflex_distributable
)
def __patch_software_release_meta(
self,
moid,
software_release_meta,
**kwargs
):
"""Update a 'software.ReleaseMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_release_meta(moid, software_release_meta, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_release_meta (SoftwareReleaseMeta): The 'software.ReleaseMeta' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareReleaseMeta
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['moid'] = \
moid
kwargs['software_release_meta'] = \
software_release_meta
return self.call_with_http_info(**kwargs)
self.patch_software_release_meta = _Endpoint(
settings={
'response_type': (SoftwareReleaseMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ReleaseMeta/{Moid}',
'operation_id': 'patch_software_release_meta',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_release_meta',
'if_match',
],
'required': [
'moid',
'software_release_meta',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_release_meta':
(SoftwareReleaseMeta,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_release_meta': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_release_meta
)
def __patch_software_solution_distributable(
self,
moid,
software_solution_distributable,
**kwargs
):
"""Update a 'software.SolutionDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_solution_distributable(moid, software_solution_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_solution_distributable (SoftwareSolutionDistributable): The 'software.SolutionDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareSolutionDistributable
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['moid'] = \
moid
kwargs['software_solution_distributable'] = \
software_solution_distributable
return self.call_with_http_info(**kwargs)
self.patch_software_solution_distributable = _Endpoint(
settings={
'response_type': (SoftwareSolutionDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/SolutionDistributables/{Moid}',
'operation_id': 'patch_software_solution_distributable',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_solution_distributable',
'if_match',
],
'required': [
'moid',
'software_solution_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_solution_distributable':
(SoftwareSolutionDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_solution_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_solution_distributable
)
def __patch_software_ucsd_bundle_distributable(
self,
moid,
software_ucsd_bundle_distributable,
**kwargs
):
"""Update a 'software.UcsdBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_ucsd_bundle_distributable(moid, software_ucsd_bundle_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_ucsd_bundle_distributable (SoftwareUcsdBundleDistributable): The 'software.UcsdBundleDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareUcsdBundleDistributable
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['moid'] = \
moid
kwargs['software_ucsd_bundle_distributable'] = \
software_ucsd_bundle_distributable
return self.call_with_http_info(**kwargs)
self.patch_software_ucsd_bundle_distributable = _Endpoint(
settings={
'response_type': (SoftwareUcsdBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdBundleDistributables/{Moid}',
'operation_id': 'patch_software_ucsd_bundle_distributable',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_ucsd_bundle_distributable',
'if_match',
],
'required': [
'moid',
'software_ucsd_bundle_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_ucsd_bundle_distributable':
(SoftwareUcsdBundleDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_ucsd_bundle_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_ucsd_bundle_distributable
)
def __patch_software_ucsd_distributable(
self,
moid,
software_ucsd_distributable,
**kwargs
):
"""Update a 'software.UcsdDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.patch_software_ucsd_distributable(moid, software_ucsd_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_ucsd_distributable (SoftwareUcsdDistributable): The 'software.UcsdDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareUcsdDistributable
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['moid'] = \
moid
kwargs['software_ucsd_distributable'] = \
software_ucsd_distributable
return self.call_with_http_info(**kwargs)
self.patch_software_ucsd_distributable = _Endpoint(
settings={
'response_type': (SoftwareUcsdDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdDistributables/{Moid}',
'operation_id': 'patch_software_ucsd_distributable',
'http_method': 'PATCH',
'servers': None,
},
params_map={
'all': [
'moid',
'software_ucsd_distributable',
'if_match',
],
'required': [
'moid',
'software_ucsd_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_ucsd_distributable':
(SoftwareUcsdDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_ucsd_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__patch_software_ucsd_distributable
)
def __update_software_appliance_distributable(
self,
moid,
software_appliance_distributable,
**kwargs
):
"""Update a 'software.ApplianceDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_appliance_distributable(moid, software_appliance_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_appliance_distributable (SoftwareApplianceDistributable): The 'software.ApplianceDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareApplianceDistributable
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['moid'] = \
moid
kwargs['software_appliance_distributable'] = \
software_appliance_distributable
return self.call_with_http_info(**kwargs)
self.update_software_appliance_distributable = _Endpoint(
settings={
'response_type': (SoftwareApplianceDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ApplianceDistributables/{Moid}',
'operation_id': 'update_software_appliance_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_appliance_distributable',
'if_match',
],
'required': [
'moid',
'software_appliance_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_appliance_distributable':
(SoftwareApplianceDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_appliance_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_appliance_distributable
)
def __update_software_hcl_meta(
self,
moid,
software_hcl_meta,
**kwargs
):
"""Update a 'software.HclMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_hcl_meta(moid, software_hcl_meta, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_hcl_meta (SoftwareHclMeta): The 'software.HclMeta' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareHclMeta
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['moid'] = \
moid
kwargs['software_hcl_meta'] = \
software_hcl_meta
return self.call_with_http_info(**kwargs)
self.update_software_hcl_meta = _Endpoint(
settings={
'response_type': (SoftwareHclMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HclMeta/{Moid}',
'operation_id': 'update_software_hcl_meta',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_hcl_meta',
'if_match',
],
'required': [
'moid',
'software_hcl_meta',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_hcl_meta':
(SoftwareHclMeta,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_hcl_meta': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_hcl_meta
)
def __update_software_hyperflex_bundle_distributable(
self,
moid,
software_hyperflex_bundle_distributable,
**kwargs
):
"""Update a 'software.HyperflexBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_hyperflex_bundle_distributable(moid, software_hyperflex_bundle_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_hyperflex_bundle_distributable (SoftwareHyperflexBundleDistributable): The 'software.HyperflexBundleDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareHyperflexBundleDistributable
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['moid'] = \
moid
kwargs['software_hyperflex_bundle_distributable'] = \
software_hyperflex_bundle_distributable
return self.call_with_http_info(**kwargs)
self.update_software_hyperflex_bundle_distributable = _Endpoint(
settings={
'response_type': (SoftwareHyperflexBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexBundleDistributables/{Moid}',
'operation_id': 'update_software_hyperflex_bundle_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_hyperflex_bundle_distributable',
'if_match',
],
'required': [
'moid',
'software_hyperflex_bundle_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_hyperflex_bundle_distributable':
(SoftwareHyperflexBundleDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_hyperflex_bundle_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_hyperflex_bundle_distributable
)
def __update_software_hyperflex_distributable(
self,
moid,
software_hyperflex_distributable,
**kwargs
):
"""Update a 'software.HyperflexDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_hyperflex_distributable(moid, software_hyperflex_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_hyperflex_distributable (SoftwareHyperflexDistributable): The 'software.HyperflexDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareHyperflexDistributable
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['moid'] = \
moid
kwargs['software_hyperflex_distributable'] = \
software_hyperflex_distributable
return self.call_with_http_info(**kwargs)
self.update_software_hyperflex_distributable = _Endpoint(
settings={
'response_type': (SoftwareHyperflexDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/HyperflexDistributables/{Moid}',
'operation_id': 'update_software_hyperflex_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_hyperflex_distributable',
'if_match',
],
'required': [
'moid',
'software_hyperflex_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_hyperflex_distributable':
(SoftwareHyperflexDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_hyperflex_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_hyperflex_distributable
)
def __update_software_release_meta(
self,
moid,
software_release_meta,
**kwargs
):
"""Update a 'software.ReleaseMeta' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_release_meta(moid, software_release_meta, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_release_meta (SoftwareReleaseMeta): The 'software.ReleaseMeta' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareReleaseMeta
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['moid'] = \
moid
kwargs['software_release_meta'] = \
software_release_meta
return self.call_with_http_info(**kwargs)
self.update_software_release_meta = _Endpoint(
settings={
'response_type': (SoftwareReleaseMeta,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/ReleaseMeta/{Moid}',
'operation_id': 'update_software_release_meta',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_release_meta',
'if_match',
],
'required': [
'moid',
'software_release_meta',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_release_meta':
(SoftwareReleaseMeta,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_release_meta': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_release_meta
)
def __update_software_solution_distributable(
self,
moid,
software_solution_distributable,
**kwargs
):
"""Update a 'software.SolutionDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_solution_distributable(moid, software_solution_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_solution_distributable (SoftwareSolutionDistributable): The 'software.SolutionDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareSolutionDistributable
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['moid'] = \
moid
kwargs['software_solution_distributable'] = \
software_solution_distributable
return self.call_with_http_info(**kwargs)
self.update_software_solution_distributable = _Endpoint(
settings={
'response_type': (SoftwareSolutionDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/SolutionDistributables/{Moid}',
'operation_id': 'update_software_solution_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_solution_distributable',
'if_match',
],
'required': [
'moid',
'software_solution_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_solution_distributable':
(SoftwareSolutionDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_solution_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_solution_distributable
)
def __update_software_ucsd_bundle_distributable(
self,
moid,
software_ucsd_bundle_distributable,
**kwargs
):
"""Update a 'software.UcsdBundleDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_ucsd_bundle_distributable(moid, software_ucsd_bundle_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_ucsd_bundle_distributable (SoftwareUcsdBundleDistributable): The 'software.UcsdBundleDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareUcsdBundleDistributable
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['moid'] = \
moid
kwargs['software_ucsd_bundle_distributable'] = \
software_ucsd_bundle_distributable
return self.call_with_http_info(**kwargs)
self.update_software_ucsd_bundle_distributable = _Endpoint(
settings={
'response_type': (SoftwareUcsdBundleDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdBundleDistributables/{Moid}',
'operation_id': 'update_software_ucsd_bundle_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_ucsd_bundle_distributable',
'if_match',
],
'required': [
'moid',
'software_ucsd_bundle_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_ucsd_bundle_distributable':
(SoftwareUcsdBundleDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_ucsd_bundle_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_ucsd_bundle_distributable
)
def __update_software_ucsd_distributable(
self,
moid,
software_ucsd_distributable,
**kwargs
):
"""Update a 'software.UcsdDistributable' resource. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.update_software_ucsd_distributable(moid, software_ucsd_distributable, async_req=True)
>>> result = thread.get()
Args:
moid (str): The unique Moid identifier of a resource instance.
software_ucsd_distributable (SoftwareUcsdDistributable): The 'software.UcsdDistributable' resource to update.
Keyword Args:
if_match (str): For methods that apply server-side changes, and in particular for PUT, If-Match can be used to prevent the lost update problem. It can check if the modification of a resource that the user wants to upload will not override another change that has been done since the original resource was fetched. If the request cannot be fulfilled, the 412 (Precondition Failed) response is returned. When modifying a resource using POST or PUT, the If-Match header must be set to the value of the resource ModTime property after which no lost update problem should occur. For example, a client send a GET request to obtain a resource, which includes the ModTime property. The ModTime indicates the last time the resource was created or modified. The client then sends a POST or PUT request with the If-Match header set to the ModTime property of the resource as obtained in the GET request.. [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:
SoftwareUcsdDistributable
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['moid'] = \
moid
kwargs['software_ucsd_distributable'] = \
software_ucsd_distributable
return self.call_with_http_info(**kwargs)
self.update_software_ucsd_distributable = _Endpoint(
settings={
'response_type': (SoftwareUcsdDistributable,),
'auth': [
'cookieAuth',
'http_signature',
'oAuth2',
'oAuth2'
],
'endpoint_path': '/api/v1/software/UcsdDistributables/{Moid}',
'operation_id': 'update_software_ucsd_distributable',
'http_method': 'POST',
'servers': None,
},
params_map={
'all': [
'moid',
'software_ucsd_distributable',
'if_match',
],
'required': [
'moid',
'software_ucsd_distributable',
],
'nullable': [
],
'enum': [
],
'validation': [
]
},
root_map={
'validations': {
},
'allowed_values': {
},
'openapi_types': {
'moid':
(str,),
'software_ucsd_distributable':
(SoftwareUcsdDistributable,),
'if_match':
(str,),
},
'attribute_map': {
'moid': 'Moid',
'if_match': 'If-Match',
},
'location_map': {
'moid': 'path',
'software_ucsd_distributable': 'body',
'if_match': 'header',
},
'collection_format_map': {
}
},
headers_map={
'accept': [
'application/json'
],
'content_type': [
'application/json',
'application/json-patch+json'
]
},
api_client=api_client,
callable=__update_software_ucsd_distributable
)
| 46.869823 | 1,678 | 0.521468 | 31,369 | 331,604 | 5.33396 | 0.017916 | 0.018826 | 0.015539 | 0.016137 | 0.97555 | 0.96931 | 0.958959 | 0.952397 | 0.949773 | 0.945321 | 0 | 0.00273 | 0.405749 | 331,604 | 7,074 | 1,679 | 46.876449 | 0.846374 | 0.432531 | 0 | 0.762183 | 0 | 0 | 0.235482 | 0.071623 | 0 | 0 | 0 | 0 | 0 | 1 | 0.01027 | false | 0 | 0.004631 | 0 | 0.025171 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
6bbd9b40e26e2c242dea0aaaaa1ebca842ee24c9 | 154 | py | Python | main_lesson1.py | Dmitrii388444/lesson1 | eedc559332cf6a703f81511e4f091d162fd642dc | [
"MIT"
] | null | null | null | main_lesson1.py | Dmitrii388444/lesson1 | eedc559332cf6a703f81511e4f091d162fd642dc | [
"MIT"
] | null | null | null | main_lesson1.py | Dmitrii388444/lesson1 | eedc559332cf6a703f81511e4f091d162fd642dc | [
"MIT"
] | null | null | null | import flask
import request
print('hello world')
print('hello world')
print('hello world')
print('hello world')
print('hello world')
print('hello world')
| 17.111111 | 20 | 0.74026 | 22 | 154 | 5.181818 | 0.272727 | 0.526316 | 0.789474 | 0.877193 | 0.789474 | 0.789474 | 0.789474 | 0.789474 | 0.789474 | 0.789474 | 0 | 0 | 0.103896 | 154 | 8 | 21 | 19.25 | 0.826087 | 0 | 0 | 0.75 | 0 | 0 | 0.428571 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.25 | 0 | 0.25 | 0.75 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 13 |
6bf2961b0870bd4d0b5b05251a467c0f0f631ca2 | 4,028 | py | Python | crypto/close_encounter/src/gen.py | tghack/tg17hack | ecf9371b71414b91b0e04696fe75400cc9e38146 | [
"MIT"
] | 21 | 2017-04-15T19:05:41.000Z | 2021-01-17T01:53:59.000Z | crypto/close_encounter/src/gen.py | tghack/tg17hack | ecf9371b71414b91b0e04696fe75400cc9e38146 | [
"MIT"
] | 1 | 2017-04-18T18:58:48.000Z | 2017-04-20T19:34:40.000Z | crypto/close_encounter/src/gen.py | tghack/tg17hack | ecf9371b71414b91b0e04696fe75400cc9e38146 | [
"MIT"
] | 2 | 2017-04-17T13:55:00.000Z | 2017-04-20T13:31:32.000Z | import gmpy2
from Crypto.Util.number import bytes_to_long, long_to_bytes
p = 25447604191089399379159931601501080444223112829447986750317096149517867285845804966271224749129184435412473271409883249120061873968316995978986208798525834614270065406921367971958806946394627934176702490478759393791972348471983612822611594685811932943967049526367295051671357595632868487646701478129716574546371021473575044516208082932409236271197813454910887142831892249406409490190907510457662553380813454668746280863597454887291739055374694568403834521593202067598993705171014838010676808696015931234887447378008864624871506336318997332815619367547971665857733583462023685385857017492039424076060074104480283422433
q = 25447604191089399379159931601501080444223112829447986750317096149517867285845804966271224749129184435412473271409883249120061873968316995978986208798525834614270065406921367971958806946394627934176702490478759393791972348471983612822611594685811932943967049526367295051671357595632868487646701478129716574546371021473575044516208082932409236271197813454910887142831892249406409490190907510457662553380813454668746280863597454887291739055374694568403834521593202067598993705171014838010676808696015931234887447378008864624871506336318997332815619367547971665857733583462023685385857017492039424076060074104480283423261
n = 647580559066350764512574139212258654419619377791696792933162647847016182264786947024323483613445156926567953673211035865775396889491136493021394983865794529477091872229948860215774356455333826515720143910504339782998354975787558272036453370109286642626999091302012693562994565167763421804705602666440082138719409120664293876204553614511882382199434919365954997082834057119072868066696185230896273866269308019520562618860020221768639470528300841159704088859374399286986458302241337120121430937058280863472916553328255990461105886678126859657320418919609691089677744300138990131567897731188913573095161012997609872471436323858576012929455006921387033623358406327954425701649820631580290471076284227572599505740170534938538303275799383217459413578897504327360744415747939112249827320135553789275968404788584789483305709222531189435482276931098037440920811828069795012367922134191531348046137386595744872323763751900274523135348273825558806037832123609238684829315071013742451094194097286985647180549444633529585827947422469423955056342229557961062943802754823402381380268562309956425582717559687406317915547051766896665477764999756658000565784673316779579644624160889528487904071313946671365415309957045804828537561845474437630201414013
d = 431720372710900509675049426141505769613079585194464528622108431898010788176524631349548989075630104617711969115474023910516931259660757662014263322577196352984727914819965906810516237636889217677146762607002893188665569983858372181357635580072857761751332727534675129041996376778508947869803735110960054759146272747109529250803035743007921588132956612910636664721889371412715245377797456820597515910846205346347041745906680147845759647018867227439802725906249599524657638868160891413414287291372187242315277702218837326974070591118751239771546945946406460726451829533425993421045265154125942382063440675331739914980923619100129222753797791372122687641646640068197019818766124292854169824336395078426704704161274777378475571155319744479479526553973913556935181332100591628680858126214474035554700527263863689076634869494382447098598888156102713476850392425798780764319992023425864505295196447602986090232313565962676726657503687855074437299199804962249244237848450257888419546605622334991222907712708545672447001893773895009745042520334908700858906883096049342163048399679415701526923153432896917761262795622916576535338660070000426847544027773762761056652661948102955029714903897853164878696358828673880499792939816884152446423045547
e = 3
flag = "TG17{t00_cl0se_f0r_g00d_crypt0}"
m = bytes_to_long(flag)
print "plain: {}".format(m)
c = gmpy2.powmod(m, e, n)
print "ciphertext: {}".format(c)
p = gmpy2.powmod(c, d, n)
print "decrypted: {}".format(long_to_bytes(p))
| 212 | 1,237 | 0.973932 | 62 | 4,028 | 63.080645 | 0.548387 | 0.00358 | 0.005625 | 0.006137 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.933618 | 0.012661 | 4,028 | 18 | 1,238 | 223.777778 | 0.049786 | 0 | 0 | 0 | 0 | 0 | 0.016634 | 0.007696 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0.142857 | null | null | 0.214286 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2e2419c3ddce84a2c603172123b7b0afe86cf349 | 21,469 | py | Python | tests/test_configurator.py | cloudblue/django-telegram | a034cbef87cc132834e145d027a3275699fb474a | [
"Apache-2.0"
] | 1 | 2021-07-06T02:30:15.000Z | 2021-07-06T02:30:15.000Z | tests/test_configurator.py | cloudblue/django-telegram | a034cbef87cc132834e145d027a3275699fb474a | [
"Apache-2.0"
] | 1 | 2021-03-29T13:48:21.000Z | 2021-03-29T13:48:21.000Z | tests/test_configurator.py | cloudblue/django-telegram | a034cbef87cc132834e145d027a3275699fb474a | [
"Apache-2.0"
] | 2 | 2021-12-29T08:45:17.000Z | 2021-12-29T08:47:03.000Z | import pytest
from django.core.exceptions import ImproperlyConfigured
from django_telegram.configurator import TelegramBotConfigurator
MW_DEF = 'django_telegram.bot.middleware.TelegramMiddleware'
def cond_fn(data):
return True
def test_condition_config_value_ok():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'value',
'field': 'f1',
'field_value': 'f1_value',
}
assert c._check_mw_config_rule_condition(condition) is None
def test_condition_config_function_ok():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'function',
'function': 'tests.test_configurator.cond_fn',
}
assert c._check_mw_config_rule_condition(condition) is None
def test_condition_config_function_no_function():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'function',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert str((
'Condition "function" key must be set and have value. ',
'Or specified function could be found.',
)) == str(err.value)
def test_condition_config_function_empty_function():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'function',
'function': '',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert str((
'Condition "function" key must be set and have value. ',
'Or specified function could be found.',
)) == str(err.value)
def test_condition_config_function_unknown_function():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'function',
'function': 'unknown',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert str((
'Condition "function" key must be set and have value. ',
'Or specified function could be found.',
)) == str(err.value)
def test_condition_config_no_type():
c = TelegramBotConfigurator({}, [])
condition = {
'field': 'f1',
'field_value': 'f1_value',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert 'Condition "type" key has not been set' == str(err.value)
def test_condition_config_type_empty():
c = TelegramBotConfigurator({}, [])
condition = {
'type': '',
'field': 'f1',
'field_value': 'f1_value',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert 'Condition "type" key must be one of "[\'function\', \'value\']"' == str(err.value)
def test_condition_config_type_value_no_field():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'value',
'field_value': 'f1_value',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert 'Condition "field" key must be set' == str(err.value)
def test_condition_config_type_value_field_empty():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'value',
'field': '',
'field_value': 'f1_value',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert 'Condition "field" key is empty' == str(err.value)
def test_condition_config_type_value_no_field_value():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'value',
'field': 'f',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert 'Condition "field_value" key must be set' == str(err.value)
def test_condition_config_type_value_field_value_empty():
c = TelegramBotConfigurator({}, [])
condition = {
'type': 'value',
'field': 'f',
'field_value': '',
}
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_config_rule_condition(condition)
assert 'Condition "field_value" key is empty' == str(err.value)
def test_mw_config_not_enabled():
mw_config = {
'TOKEN': 'token',
'CHAT_ID': -1001339325227,
'CONFIG': [{
'view': 'reports-fail',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
}
c = TelegramBotConfigurator(mw_config, [])
assert c._check_mw_settings() is None
def test_mw_config_ok():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'reports-fail',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
assert c._check_mw_settings() is None
def test_mw_config_no_chat_id():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'RULES': [{
'view': 'reports-fail',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert '"MIDDLEWARE[CHAT_ID]" key has not been set.' == str(err.value)
def test_mw_config_chat_id_empty():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': '',
'RULES': [{
'view': 'reports-fail',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert '"MIDDLEWARE[CHAT_ID]" key has no value set.' == str(err.value)
def test_mw_config_no_rules():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': 123,
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert '"MIDDLEWARE[RULES]" key has not been set.' == str(err.value)
def test_mw_config_rules_not_list():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': 123,
'RULES': {},
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert '"MIDDLEWARE[RULES]" object must be a list.' == str(err.value)
def test_mw_config_no_mw_key():
mw_config = {
'TOKEN': 'token',
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert f'"{MW_DEF}" is enabled, however "MIDDLEWARE" key has not been set.' == str(err.value)
def test_mw_config_mw_empty():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert f'"{MW_DEF}" is enabled, however "MIDDLEWARE" is empty.' == str(
err.value)
def test_mw_config_wrong_rule_condition():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'reports-fail',
'trigger_codes': [204],
'conditions': {
'type': '',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = (
'"MIDDLEWARE[RULES]" position "0" error: Condition "type"'
' key must be one of "[\'function\', \'value\']"'
)
assert err_expected == str(err.value)
def test_mw_config_wrong_rule_no_view():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert '"MIDDLEWARE[RULES]" position "0" error: "view" key has not been set' == str(err.value)
def test_mw_config_wrong_rule_view_empty():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': '',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'Template is blocked due to report failed',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
assert '"MIDDLEWARE[RULES]" position "0" error: "view" key has not been set' == str(err.value)
def test_mw_config_wrong_rule_no_message():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = '"MIDDLEWARE[RULES]" position "0" error: "message" key has not been set'
assert err_expected == str(err.value)
def test_mw_config_wrong_rule_message_empty():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [204],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': '',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = '"MIDDLEWARE[RULES]" position "0" error: "message" key has not been set'
assert err_expected == str(err.value)
def test_mw_config_wrong_rule_no_trigger_codes():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = '"MIDDLEWARE[RULES]" position "0" error: "trigger_codes" key has not been set'
assert err_expected == str(err.value)
def test_mw_config_wrong_rule_trigger_codes_empty():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = '"MIDDLEWARE[RULES]" position "0" error: "trigger_codes" key has not been set'
assert err_expected == str(err.value)
def test_mw_config_wrong_rule_trigger_codes_not_list():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': {3: 4},
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = '"MIDDLEWARE[RULES]" position "0" error: "trigger_codes" object must be a list.'
assert err_expected == str(err.value)
def test_mw_config_wrong_rule_trigger_codes_not_all_integers():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2, "3"],
'conditions': {
'type': 'value',
'field': 'template.status',
'field_value': 'blocked',
},
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c._check_mw_settings()
err_expected = (
'"MIDDLEWARE[RULES]" position "0" error: '
'"trigger_codes" contains non-integer values'
)
assert err_expected == str(err.value)
def test_mw_config_ok_rule_no_conditions():
mw_config = {
'TOKEN': 'token',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
assert c._check_mw_settings() is None
def test_global_config_ok():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': 'local',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
assert c.run_check() is None
def test_global_config_no_token():
mw_config = {
'COMMANDS_SUFFIX': 'local',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"TOKEN" key has not been set.' == str(err.value)
def test_global_config_token_empty():
mw_config = {
'TOKEN': '',
'COMMANDS_SUFFIX': 'local',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"TOKEN" key has no value set.' == str(err.value)
def test_global_config_no_suffix():
mw_config = {
'TOKEN': 'token',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"COMMANDS_SUFFIX" key has not been set.' == str(err.value)
def test_global_config_empty_suffix():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': '',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
assert c.run_check() is None
def test_global_config_no_prop():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': '',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"HISTORY_LOOKUP_MODEL_PROPERTY" key has not been set.' == str(err.value)
def test_global_config_prop_empty():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': '',
'HISTORY_LOOKUP_MODEL_PROPERTY': '',
'CONVERSATIONS': [],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"HISTORY_LOOKUP_MODEL_PROPERTY" key has no value set.' == str(err.value)
def test_global_config_no_conversations():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': '',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"CONVERSATIONS" key has not been set.' == str(err.value)
def test_global_config_conversations_not_list():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': '',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': {3: 4},
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
with pytest.raises(ImproperlyConfigured) as err:
c.run_check()
assert '"CONVERSATIONS" must be a list of strings.' == str(err.value)
def test_global_config_conversations_not_empty():
mw_config = {
'TOKEN': 'token',
'COMMANDS_SUFFIX': '',
'HISTORY_LOOKUP_MODEL_PROPERTY': 'prop',
'CONVERSATIONS': ['test'],
'MIDDLEWARE': {
'CHAT_ID': -1001339325227,
'RULES': [{
'view': 'view',
'trigger_codes': [1, 2],
'message': 'msg',
}],
},
}
c = TelegramBotConfigurator(mw_config, [MW_DEF])
assert c.run_check() is None
| 26.439655 | 99 | 0.531138 | 2,045 | 21,469 | 5.309046 | 0.056235 | 0.062632 | 0.045685 | 0.102791 | 0.94271 | 0.929631 | 0.90163 | 0.877038 | 0.865617 | 0.836419 | 0 | 0.025508 | 0.333504 | 21,469 | 811 | 100 | 26.472256 | 0.733245 | 0 | 0 | 0.730769 | 0 | 0 | 0.242815 | 0.020728 | 0 | 0 | 0 | 0 | 0.0625 | 1 | 0.064103 | false | 0 | 0.004808 | 0.001603 | 0.070513 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
2e3bfc833e977d034a0b15c15dba5cb89df15429 | 585,027 | py | Python | Genetic-Algorithm_temp/abcdef.py | Machine-Learning/Genetic-Algorithm | b5006b3fcae89fc4838eaa20fe288633743dd42a | [
"MIT"
] | null | null | null | Genetic-Algorithm_temp/abcdef.py | Machine-Learning/Genetic-Algorithm | b5006b3fcae89fc4838eaa20fe288633743dd42a | [
"MIT"
] | null | null | null | Genetic-Algorithm_temp/abcdef.py | Machine-Learning/Genetic-Algorithm | b5006b3fcae89fc4838eaa20fe288633743dd42a | [
"MIT"
] | 1 | 2022-03-13T06:09:37.000Z | 2022-03-13T06:09:37.000Z | # generations = [[[-6.60253963e-07, -7.89743317e-13, -5.21154029e-13, 3.13877049e-11,
# -8.07357446e-11, -9.81414244e-16, 6.14538476e-16, 6.06474128e-06,
# -1.17242218e-06, -5.13649190e-09, 5.20233422e-10], [-9.95015856e-07, -1.14042627e-12, -4.16916180e-13, 3.13877049e-11,
# -6.17212330e-11, -8.36332548e-16, 4.24735332e-16, 6.76873255e-06,
# -1.44130303e-06, -5.72374144e-09, 6.39497189e-10], [-9.63363805e-07, -5.73999491e-13, -4.81930092e-13, 3.13877049e-11,
# -6.48654462e-11, -9.50076122e-16, 6.14538476e-16, 6.06474128e-06,
# -1.03285037e-06, -5.72374144e-09, 5.20233422e-10], [-9.95015856e-07, -1.08803576e-12, -4.18455857e-13, 3.41298481e-11,
# -6.94153490e-11, -9.01707662e-16, 7.59148451e-16, 8.62254226e-06,
# -1.16797407e-06, -5.21468760e-09, 4.69509583e-10], [-7.95821174e-07, -1.09412145e-12, -5.12464776e-13, 1.88619147e-11,
# -6.94153490e-11, -1.17314358e-15, 6.58292750e-16, 6.06474128e-06,
# -1.17242218e-06, -5.72374144e-09, 5.20233422e-10], [-1.26523346e-06, -7.91424898e-13, -5.52431562e-13, 3.13877049e-11,
# -7.04324791e-11, -9.50076122e-16, 6.14538476e-16, 7.09518315e-06,
# -1.17242218e-06, -5.30274656e-09, 6.32038632e-10], [-9.95015856e-07, -1.08803576e-12, -4.48177124e-13, 3.13877049e-11,
# -8.97756506e-11, -9.50076122e-16, 6.14538476e-16, 6.06474128e-06,
# -1.17242218e-06, -5.72374144e-09, 6.39497189e-10], [-6.34737870e-07, -8.32617383e-13, -5.21154029e-13, 3.13877049e-11,
# -7.31337876e-11, -9.50076122e-16, 4.24735332e-16, 6.76873255e-06,
# -1.44130303e-06, -5.72374144e-09, 5.20233422e-10], [-1.23752535e-06, -7.42118101e-13, -4.97200822e-13, 3.21629430e-11,
# -6.55535323e-11, -9.01707662e-16, 9.83287199e-16, 8.58004727e-06,
# -1.16797407e-06, -5.21468760e-09, 3.45204359e-10], [-5.43197906e-07, -1.01958291e-12, -4.48177124e-13, 3.27754575e-11,
# -9.95718880e-11, -6.20166356e-16, 7.40213902e-16, 8.58004727e-06,
# -1.16797407e-06, -2.72117691e-09, 4.69509583e-10]],
# [[-7.95821174e-07, -1.09412145e-12, -5.12464776e-13, 1.88619147e-11,
# -6.94153490e-11, -1.17314358e-15, 6.83679990e-16, 8.62254226e-06,
# -1.16797407e-06, -5.21468760e-09, 4.69509583e-10], [-9.95015856e-07, -9.03756039e-13, -4.18455857e-13, 3.01039866e-11,
# -8.00976688e-11, -9.21318930e-16, 7.36056083e-16, 6.06474128e-06,
# -8.77868745e-07, -5.72374144e-09, 5.20233422e-10], [-7.95821174e-07, -1.09412145e-12, -5.12464776e-13, 1.97761323e-11,
# -6.94153490e-11, -9.01707662e-16, 7.59148451e-16, 8.62254226e-06,
# -1.16797407e-06, -4.14038344e-09, 3.70097585e-10], [-9.95015856e-07, -1.08803576e-12, -4.18455857e-13, 3.41298481e-11,
# -6.94153490e-11, -1.17314358e-15, 6.58292750e-16, 7.32104098e-06,
# -1.17242218e-06, -5.72374144e-09, 5.20233422e-10], [-9.95015856e-07, -1.14042627e-12, -5.21154029e-13, 3.13877049e-11,
# -8.01142083e-11, -9.81414244e-16, 6.14538476e-16, 6.06474128e-06,
# -1.17242218e-06, -5.13649190e-09, 5.20233422e-10], [-6.60253963e-07, -8.02731517e-13, -4.16916180e-13, 3.13877049e-11,
# -6.17212330e-11, -8.36332548e-16, 3.03273166e-16, 5.69115079e-06,
# -1.29857054e-06, -7.22088257e-09, 6.39497189e-10], [-6.60253963e-07, -7.07740257e-13, -5.21154029e-13, 3.13877049e-11,
# -8.07357446e-11, -9.48804393e-16, 4.68185369e-16, 5.65200263e-06,
# -1.28444451e-06, -5.72374144e-09, 5.20233422e-10], [-1.13392269e-06, -6.08437675e-13, -4.81930092e-13, 3.13877049e-11,
# -5.54207921e-11, -9.81414244e-16, 6.14538476e-16, 7.02002340e-06,
# -1.17242218e-06, -4.69566821e-09, 5.78577098e-10], [-6.60253963e-07, -9.99189577e-13, -4.16916180e-13, 3.65465585e-11,
# -5.17398338e-11, -8.56061360e-16, 4.80194012e-16, 7.54631137e-06,
# -1.24616637e-06, -5.72374144e-09, 6.39497189e-10], [-7.51845443e-07, -1.21558416e-12, -5.21154029e-13, 3.13877049e-11,
# -8.07357446e-11, -9.81414244e-16, 5.99709694e-16, 6.06474128e-06,
# -1.40181527e-06, -5.13649190e-09, 5.20233422e-10]],
# [[-9.95015856e-07, -1.08803576e-12, -4.18455857e-13, 3.13877049e-11,
# -8.01142083e-11, -9.81414244e-16, 6.14538476e-16, 6.06474128e-06,
# -1.17242218e-06, -4.68672639e-09, 5.20233422e-10], [-9.95015856e-07, -1.18969925e-12, -5.21154029e-13, 2.68597242e-11,
# -7.11549124e-11, -1.22636943e-15, 6.58292750e-16, 7.50651569e-06,
# -1.17242218e-06, -5.72374144e-09, 6.44702767e-10], [-6.71097205e-07, -8.83057691e-13, -4.16916180e-13, 3.41298481e-11,
# -6.94153490e-11, -1.50596664e-15, 6.58292750e-16, 5.28450192e-06,
# -1.39708965e-06, -5.72374144e-09, 5.20233422e-10], [-9.50757822e-07, -1.09802569e-12, -5.25221435e-13, 3.13877049e-11,
# -6.49056677e-11, -7.11840793e-16, 3.03273166e-16, 5.69115079e-06,
# -1.29857054e-06, -7.22088257e-09, 6.39497189e-10], [-6.60253963e-07, -8.02731517e-13, -5.32442780e-13, 3.13877049e-11,
# -6.94153490e-11, -1.05854600e-15, 7.59148451e-16, 8.62254226e-06,
# -1.16797407e-06, -4.14038344e-09, 3.70097585e-10], [-7.95821174e-07, -1.36660519e-12, -4.84735269e-13, 1.97761323e-11,
# -5.31994037e-11, -8.36332548e-16, 2.90235405e-16, 5.84235653e-06,
# -1.29857054e-06, -7.22088257e-09, 6.39497189e-10], [-6.60253963e-07, -8.02731517e-13, -4.16916180e-13, 3.13877049e-11,
# -6.17212330e-11, -8.36332548e-16, 3.03273166e-16, 5.69115079e-06,
# -1.11511681e-06, -5.80740660e-09, 4.18187361e-10], [-9.95015856e-07, -1.14042627e-12, -5.21154029e-13, 3.75835534e-11,
# -8.01142083e-11, -1.08549119e-15, 6.14538476e-16, 6.06474128e-06,
# -1.17242218e-06, -5.13649190e-09, 6.39497189e-10], [-6.60253963e-07, -8.02731517e-13, -4.16916180e-13, 3.13877049e-11,
# -6.17212330e-11, -8.36332548e-16, 2.81064888e-16, 6.06474128e-06,
# -1.17242218e-06, -5.13649190e-09, 5.20233422e-10], [-9.95015856e-07, -8.66220480e-13, -5.53619661e-13, 3.13877049e-11,
# -8.01142083e-11, -1.08852788e-15, 6.14538476e-16, 5.69115079e-06,
# -1.16311673e-06, -7.95212955e-09, 7.04401625e-10]],
# [[-7.95821174e-07, -1.36660519e-12, -4.84735269e-13, 2.41048677e-11,
# -5.31994037e-11, -9.81414244e-16, 6.14538476e-16, 6.06474128e-06,
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# -2.02626479e-11, -7.20890889e-17, 1.12543204e-15, 7.81479722e-06,
# -1.09723154e-06, -2.05340037e-09, 4.10108058e-10], [-4.79299960e-08, -3.08083941e-12, -1.09599925e-15, 2.02475298e-11,
# -1.60029272e-11, -7.20890889e-17, 9.83188595e-16, 7.81479722e-06,
# -1.31487176e-06, -2.05340037e-09, 3.98034444e-10]],
# [[-4.79299960e-08, -2.47715980e-12, -1.15002740e-15, 2.07182073e-11,
# -1.60029272e-11, -7.20890889e-17, 6.90346218e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 3.98034444e-10], [-2.77320682e-08, -2.47715980e-12, -1.09599925e-15, 1.79982577e-11,
# -1.76287861e-11, -8.65072639e-17, 9.83188595e-16, 7.81479722e-06,
# -1.37371938e-06, -2.09095144e-09, 3.98034444e-10], [-4.95013727e-08, -2.98608879e-12, -1.09599925e-15, 1.91744794e-11,
# -1.30702535e-11, -7.20890889e-17, 1.13783530e-15, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 3.03165218e-10], [-4.79299960e-08, -2.47715980e-12, -1.15002740e-15, 2.04373427e-11,
# -1.60029272e-11, -7.20890889e-17, 9.83188595e-16, 8.07649903e-06,
# -1.10806420e-06, -1.97783129e-09, 4.07999600e-10], [-4.75710844e-08, -2.47715980e-12, -1.16249535e-15, 2.18696604e-11,
# -1.77354884e-11, -5.23521586e-17, 9.83188595e-16, 7.81479722e-06,
# -1.10806420e-06, -1.38505048e-09, 4.07999600e-10], [-4.95013727e-08, -2.98608879e-12, -7.78770805e-16, 1.94422004e-11,
# -1.75287081e-11, -7.20890889e-17, 1.13783530e-15, 7.81479722e-06,
# -1.25588126e-06, -1.51487051e-09, 3.98034444e-10], [-4.75710844e-08, -2.47715980e-12, -1.16249535e-15, 2.18696604e-11,
# -1.77354884e-11, -6.52086918e-17, 9.83188595e-16, 7.81479722e-06,
# -1.10806420e-06, -1.97783129e-09, 3.14705112e-10], [-4.95013727e-08, -2.79532095e-12, -1.09599925e-15, 1.53763448e-11,
# -1.69079042e-11, -6.99572490e-17, 8.20133362e-16, 6.83324660e-06,
# -1.16196170e-06, -1.97783129e-09, 3.98034444e-10], [-3.40799954e-08, -2.47715980e-12, -1.15002740e-15, 1.61159188e-11,
# -1.60029272e-11, -5.81327672e-17, 9.83188595e-16, 5.59990538e-06,
# -1.10806420e-06, -1.97783129e-09, 3.98034444e-10], [-3.50278743e-08, -2.47715980e-12, -1.09599925e-15, 1.26877952e-11,
# -2.08681608e-11, -9.02915791e-17, 7.74041635e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 3.98034444e-10]],
# [[-3.51216102e-08, -2.47715980e-12, -1.09599925e-15, 1.65748762e-11,
# -1.80582876e-11, -1.06223468e-16, 9.83188595e-16, 6.66918769e-06,
# -9.91802173e-07, -2.05340037e-09, 2.89546036e-10], [-2.64398880e-08, -2.47715980e-12, -1.09599925e-15, 1.26877952e-11,
# -2.08681608e-11, -6.73850300e-17, 7.74041635e-16, 7.81479722e-06,
# -1.52600793e-06, -2.09095144e-09, 3.98034444e-10], [-4.79299960e-08, -2.47715980e-12, -1.15002740e-15, 2.07182073e-11,
# -1.60029272e-11, -9.95205603e-17, 7.74041635e-16, 4.85880173e-06,
# -1.10806420e-06, -2.05340037e-09, 3.98034444e-10], [-3.50278743e-08, -2.47715980e-12, -8.81212887e-16, 1.26877952e-11,
# -1.85997587e-11, -6.14961903e-17, 6.90346218e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 3.98034444e-10], [-3.40799954e-08, -2.47715980e-12, -1.15002740e-15, 1.61159188e-11,
# -1.60029272e-11, -5.81327672e-17, 9.83188595e-16, 5.59990538e-06,
# -1.10806420e-06, -1.97783129e-09, 3.98034444e-10], [-4.20857531e-08, -2.47715980e-12, -8.11933780e-16, 2.03224928e-11,
# -1.60029272e-11, -5.81327672e-17, 8.58570149e-16, 5.59990538e-06,
# -1.10806420e-06, -2.35174337e-09, 3.98034444e-10], [-3.42258014e-08, -2.94899550e-12, -1.09599925e-15, 1.26877952e-11,
# -2.08681608e-11, -9.02915791e-17, 7.09570835e-16, 7.40642744e-06,
# -1.25588126e-06, -1.51487051e-09, 3.98034444e-10], [-5.32764346e-08, -3.48152979e-12, -7.78770805e-16, 1.94422004e-11,
# -1.67541301e-11, -7.20890889e-17, 1.13783530e-15, 5.37094047e-06,
# -1.10806420e-06, -1.67968788e-09, 3.98034444e-10], [-4.79299960e-08, -2.47715980e-12, -1.15002740e-15, 2.07182073e-11,
# -1.98561010e-11, -7.67375026e-17, 5.30039789e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 3.98034444e-10], [-4.79299960e-08, -2.47715980e-12, -1.15002740e-15, 2.07182073e-11,
# -1.60029272e-11, -7.20890889e-17, 6.90346218e-16, 7.14581588e-06,
# -1.10806420e-06, -2.05340037e-09, 3.98034444e-10]],
# [[-4.20857531e-08, -2.47715980e-12, -8.11933780e-16, 1.60682813e-11,
# -1.24866093e-11, -7.20890889e-17, 6.90346218e-16, 7.14581588e-06,
# -9.19276537e-07, -2.05340037e-09, 3.75078067e-10], [-4.79299960e-08, -2.47715980e-12, -1.15002740e-15, 2.07182073e-11,
# -1.60029272e-11, -5.81327672e-17, 8.58570149e-16, 5.59990538e-06,
# -1.25036049e-06, -2.35174337e-09, 3.75783217e-10], [-3.25156341e-08, -2.47715980e-12, -7.78770805e-16, 1.94422004e-11,
# -1.67541301e-11, -7.20890889e-17, 1.13783530e-15, 5.76150195e-06,
# -1.10806420e-06, -1.67968788e-09, 4.95423266e-10], [-5.32764346e-08, -4.17710614e-12, -8.81212887e-16, 1.26877952e-11,
# -1.59340051e-11, -6.14961903e-17, 8.61366288e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-3.71696237e-08, -2.47715980e-12, -8.81212887e-16, 1.26877952e-11,
# -1.85997587e-11, -6.14961903e-17, 6.90346218e-16, 6.66918769e-06,
# -8.58069675e-07, -2.05340037e-09, 3.98034444e-10], [-4.20857531e-08, -2.47715980e-12, -8.11933780e-16, 2.03224928e-11,
# -1.60029272e-11, -5.81327672e-17, 8.58570149e-16, 5.59990538e-06,
# -1.10806420e-06, -2.35174337e-09, 3.98034444e-10], [-4.85827527e-08, -2.98954049e-12, -1.15002740e-15, 2.56842833e-11,
# -1.98561010e-11, -7.67375026e-17, 1.47850782e-15, 3.82834471e-06,
# -7.78229155e-07, -1.67968788e-09, 3.98034444e-10], [-5.47201278e-08, -3.48152979e-12, -7.78770805e-16, 2.39756246e-11,
# -1.67541301e-11, -7.20890889e-17, 5.30039789e-16, 6.67885111e-06,
# -1.32543321e-06, -2.05340037e-09, 4.22892615e-10], [-4.79299960e-08, -2.47715980e-12, -1.22484290e-15, 2.07182073e-11,
# -1.60029272e-11, -8.62311970e-17, 7.74041635e-16, 5.37094047e-06,
# -1.23691007e-06, -1.34290895e-09, 4.53866466e-10], [-5.32764346e-08, -4.33647019e-12, -7.78770805e-16, 1.61258466e-11,
# -1.67541301e-11, -7.20890889e-17, 1.37939665e-15, 4.85880173e-06,
# -1.10806420e-06, -2.05340037e-09, 4.51744786e-10]],
# [[-4.79299960e-08, -2.47715980e-12, -1.02761697e-15, 1.26877952e-11,
# -1.59340051e-11, -5.17171803e-17, 1.11899432e-15, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-5.32764346e-08, -4.74463599e-12, -8.81212887e-16, 1.67578253e-11,
# -1.60029272e-11, -6.03946164e-17, 7.74041635e-16, 5.37094047e-06,
# -1.23691007e-06, -1.34290895e-09, 3.39280261e-10], [-3.87972028e-08, -2.47715980e-12, -1.11229293e-15, 1.98452014e-11,
# -1.60029272e-11, -8.62311970e-17, 6.19724224e-16, 5.37094047e-06,
# -1.23691007e-06, -1.10425451e-09, 4.53866466e-10], [-4.79299960e-08, -2.47715980e-12, -8.11933780e-16, 2.03224928e-11,
# -1.60029272e-11, -5.81327672e-17, 8.58570149e-16, 5.59990538e-06,
# -8.70797688e-07, -1.97843286e-09, 3.98034444e-10], [-6.15447310e-08, -4.17710614e-12, -8.81212887e-16, 1.26877952e-11,
# -1.59340051e-11, -6.14961903e-17, 9.78085615e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-5.32764346e-08, -5.29338212e-12, -8.81212887e-16, 9.63490509e-12,
# -1.47029574e-11, -6.14961903e-17, 7.11009688e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-5.32764346e-08, -4.50142432e-12, -8.81212887e-16, 1.26877952e-11,
# -1.25735064e-11, -5.26782173e-17, 6.73968636e-16, 6.66918769e-06,
# -1.10806420e-06, -2.47753701e-09, 3.04333616e-10], [-6.40431139e-08, -4.17710614e-12, -8.81212887e-16, 1.26877952e-11,
# -1.59340051e-11, -4.39386468e-17, 8.28413802e-16, 6.66918769e-06,
# -1.10806420e-06, -1.88846174e-09, 4.04195021e-10], [-4.79299960e-08, -2.74086441e-12, -7.93689427e-16, 1.26877952e-11,
# -1.59340051e-11, -7.75189728e-17, 8.61366288e-16, 6.43912773e-06,
# -1.10806420e-06, -2.05340037e-09, 4.41017473e-10], [-5.90127941e-08, -4.17710614e-12, -1.27239955e-15, 1.56816452e-11,
# -1.60029272e-11, -8.62311970e-17, 7.74041635e-16, 5.37094047e-06,
# -1.23691007e-06, -1.25616234e-09, 4.53866466e-10]],
# [[-5.32764346e-08, -5.29338212e-12, -8.81212887e-16, 9.63490509e-12,
# -1.47029574e-11, -6.14961903e-17, 8.54924928e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-4.79299960e-08, -2.47715980e-12, -1.02761697e-15, 1.26877952e-11,
# -1.59340051e-11, -5.11718533e-17, 1.11899432e-15, 8.59758765e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-5.90127941e-08, -4.17710614e-12, -1.27239955e-15, 1.56816452e-11,
# -1.60029272e-11, -4.37640459e-17, 7.11009688e-16, 6.66918769e-06,
# -1.14381504e-06, -2.05340037e-09, 4.05847411e-10], [-5.32764346e-08, -5.29338212e-12, -8.81212887e-16, 9.63490509e-12,
# -1.38312388e-11, -8.62311970e-17, 7.74041635e-16, 5.37094047e-06,
# -1.53596357e-06, -1.49757670e-09, 4.53866466e-10], [-6.15447310e-08, -4.72730230e-12, -8.81212887e-16, 1.65507847e-11,
# -1.91486871e-11, -7.63479690e-17, 7.74041635e-16, 5.37094047e-06,
# -1.23691007e-06, -1.59056057e-09, 3.94153351e-10], [-5.32764346e-08, -4.74463599e-12, -8.81212887e-16, 1.50231776e-11,
# -1.59340051e-11, -6.14961903e-17, 9.78085615e-16, 6.66918769e-06,
# -9.65750746e-07, -2.30847822e-09, 4.04195021e-10], [-3.87972028e-08, -2.49607375e-12, -1.39359218e-15, 1.62940254e-11,
# -1.60029272e-11, -1.07478184e-16, 6.19724224e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-4.79299960e-08, -1.96606090e-12, -1.04294667e-15, 1.26877952e-11,
# -1.59340051e-11, -5.17171803e-17, 1.11899432e-15, 6.93293076e-06,
# -1.43562380e-06, -1.27130196e-09, 4.37210889e-10], [-5.18288561e-08, -4.25208421e-12, -1.01233637e-15, 1.26877952e-11,
# -1.94086428e-11, -4.39386468e-17, 8.28413802e-16, 6.66918769e-06,
# -1.10806420e-06, -2.05340037e-09, 4.04195021e-10], [-5.32764346e-08, -5.29338212e-12, -8.81212887e-16, 9.63490509e-12,
# -1.47029574e-11, -6.14961903e-17, 7.11009688e-16, 6.66918769e-06,
# -1.10806420e-06, -1.88846174e-09, 4.04195021e-10]]]
# # print(len(generations
generations = [[[-4.96100716e-08, -3.96966395e-12, -1.12458314e-15, 1.21380599e-11,-1.47029574e-11, -6.95901668e-17, 6.19724224e-16, 8.06510978e-06,-1.10806420e-06, -2.16897394e-09, 4.10935941e-10],
[-4.91781705e-08, -4.25208421e-12, -1.29034071e-15, 1.42126425e-11,-1.97502120e-11, -1.09505998e-16, 7.86598507e-16, 7.07451898e-06,-1.32763335e-06, -2.10833428e-09, 4.04195021e-10],
[-3.87202760e-08, -2.70027562e-12, -1.01233637e-15, 1.43162775e-11,-1.63506213e-11, -6.14961903e-17, 9.45040922e-16, 4.69273223e-06,-1.25455288e-06, -2.00876912e-09, 4.50222790e-10],
[-4.91781705e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.47029574e-11, -5.33612381e-17, 6.19724224e-16, 7.07451898e-06,-1.10806420e-06, -2.20837342e-09, 4.04195021e-10],
[-4.91781705e-08, -2.49607375e-12, -1.29657792e-15, 1.38840310e-11,-1.55699035e-11, -5.37103693e-17, 7.11009688e-16, 7.30220274e-06,-8.73195529e-07, -2.05340037e-09, 2.56768108e-10],
[-2.77246885e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.47029574e-11, -6.14961903e-17, 6.19724224e-16, 6.12556162e-06,-1.10806420e-06, -2.30185588e-09, 3.42888150e-10],
[-2.73280261e-08, -3.04939256e-12, -1.01233637e-15, 1.43162775e-11,-1.94289971e-11, -6.14961903e-17, 7.90970352e-16, 6.66918769e-06,-1.10806420e-06, -2.34667965e-09, 4.98060614e-10],
[-4.81338329e-08, -3.04939256e-12, -1.01233637e-15, 1.43162775e-11,-1.94289971e-11, -6.14961903e-17, 9.41339911e-16, 6.66918769e-06,-1.04637694e-06, -2.20837342e-09, 4.04195021e-10],
[-4.96100716e-08, -5.03008184e-12, -1.16052428e-15, 1.26442745e-11,-1.49912356e-11, -1.38668932e-16, 7.72000444e-16, 7.07451898e-06,-1.10806420e-06, -2.30185588e-09, 4.37131804e-10],
[-4.15568920e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.47029574e-11, -6.14961903e-17, 7.86598507e-16, 7.07451898e-06,-1.10806420e-06, -2.10833428e-09, 4.04195021e-10]],
[[-3.78200955e-08, -4.25208421e-12, -1.03823656e-15, 1.30671579e-11,-1.47029574e-11, -4.56623263e-17, 6.35580896e-16, 7.07451898e-06,-1.10806420e-06, -2.20837342e-09, 4.04195021e-10],
[-5.84456524e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.47029574e-11, -5.33612381e-17, 6.86751553e-16, 5.47186294e-06,-1.10806420e-06, -2.10833428e-09, 3.39056629e-10],
[-4.91781705e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.47029574e-11, -5.33612381e-17, 5.34153848e-16, 8.36664143e-06,-1.31288010e-06, -2.20837342e-09, 3.23288919e-10],
[-4.21059474e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.73268564e-11, -5.33612381e-17, 6.19724224e-16, 7.07451898e-06,-7.93570577e-07, -2.69921022e-09, 4.04195021e-10],
[-4.91781705e-08, -2.49607375e-12, -1.29657792e-15, 1.38840310e-11,-2.02043959e-11, -5.37103693e-17, 7.11009688e-16, 7.30220274e-06,-8.73195529e-07, -2.00527737e-09, 4.60097224e-10],
[-4.91781705e-08, -4.25208421e-12, -1.06223797e-15, 1.21380599e-11,-1.47029574e-11, -5.43169307e-17, 7.05309885e-16, 7.83789039e-06,-1.10806420e-06, -2.20837342e-09, 2.56768108e-10],
[-4.15568920e-08, -4.03543159e-12, -1.16730966e-15, 1.30823714e-11,-1.63506213e-11, -6.14961903e-17, 9.45040922e-16, 4.69273223e-06,-1.25455288e-06, -2.23689899e-09, 3.52242193e-10],
[-3.87202760e-08, -3.10917966e-12, -1.01233637e-15, 1.43162775e-11,-1.47029574e-11, -6.14961903e-17, 7.86598507e-16, 7.90329631e-06,-1.10806420e-06, -2.46723797e-09, 4.04195021e-10],
[-3.87202760e-08, -2.70027562e-12, -1.01233637e-15, 1.05116306e-11,-1.63506213e-11, -5.29933473e-17, 7.86598507e-16, 7.07451898e-06,-1.14523755e-06, -2.42805199e-09, 4.04195021e-10],
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[-2.95158505e-08, -4.81315891e-12, -1.16413143e-15, 1.63887269e-11,-1.24354018e-11, -1.29592932e-16, 5.39973644e-16, 6.70552595e-06,-1.42989611e-06, -2.52154849e-09, 4.04195021e-10],
[-3.73478878e-08, -4.81315891e-12, -2.00460291e-15, 1.82757828e-11,-1.24354018e-11, -7.96859479e-17, 6.78683934e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 4.04195021e-10],
[-4.49521077e-08, -4.81315891e-12, -1.59702183e-15, 1.63887269e-11,-1.24354018e-11, -7.96859479e-17, 8.73194258e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 5.24656505e-10],
[-4.49521077e-08, -4.81315891e-12, -1.59702183e-15, 1.21171884e-11,-1.24354018e-11, -7.96859479e-17, 7.69734830e-16, 7.79311104e-06,-1.10806420e-06, -2.20113143e-09, 4.04195021e-10],
[-4.49521077e-08, -3.86995587e-12, -1.59702183e-15, 1.63887269e-11,-1.24354018e-11, -5.84603340e-17, 7.64137220e-16, 7.79311104e-06,-1.06223517e-06, -2.97802056e-09, 4.04195021e-10],
[-4.49521077e-08, -4.54406806e-12, -1.59702183e-15, 1.54276139e-11,-1.24354018e-11, -6.61062909e-17, 6.78683934e-16, 9.37754434e-06,-1.10806420e-06, -2.52154849e-09, 3.07261793e-10],
[-4.49521077e-08, -4.81315891e-12, -1.22479671e-15, 1.63887269e-11,-1.24354018e-11, -9.60685640e-17, 8.09450430e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 4.70494940e-10],
[-4.49521077e-08, -4.65664742e-12, -1.59702183e-15, 1.63887269e-11,-1.09854434e-11, -7.40191648e-17, 6.78683934e-16, 7.33223346e-06,-1.11415069e-06, -2.52154849e-09, 4.20045306e-10]],
[[-3.50370742e-08, -4.81315891e-12, -2.00460291e-15, 1.82757828e-11,-1.24354018e-11, -9.02060697e-17, 6.78683934e-16, 7.33223346e-06,-1.11415069e-06, -2.52154849e-09, 4.20045306e-10],
[-4.49521077e-08, -5.93312736e-12, -1.59702183e-15, 1.63887269e-11,-1.09854434e-11, -7.40191648e-17, 6.78683934e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 4.04195021e-10],
[-3.44052380e-08, -4.54076115e-12, -1.35605626e-15, 1.63887269e-11,-9.10161846e-12, -1.25685965e-16, 6.46946153e-16, 9.67000448e-06,-1.10806420e-06, -2.38715453e-09, 4.04195021e-10],
[-3.30274803e-08, -4.20659836e-12, -1.59702183e-15, 1.21171884e-11,-1.23809477e-11, -6.22523908e-17, 7.69734830e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 3.24303772e-10],
[-2.95158505e-08, -3.96950110e-12, -1.16413143e-15, 1.63887269e-11,-1.24354018e-11, -1.23006438e-16, 5.39973644e-16, 9.25499050e-06,-1.10806420e-06, -2.52154849e-09, 4.04195021e-10],
[-3.55091072e-08, -4.54076115e-12, -1.39810421e-15, 1.63887269e-11,-9.10161846e-12, -1.25685965e-16, 6.46946153e-16, 7.79311104e-06,-1.10806420e-06, -3.20216846e-09, 4.04195021e-10],
[-3.66036252e-08, -4.81315891e-12, -2.00460291e-15, 2.01964257e-11,-1.54404432e-11, -7.96859479e-17, 6.78683934e-16, 7.79311104e-06,-1.10806420e-06, -3.27008428e-09, 3.67954260e-10],
[-2.95158505e-08, -4.78481920e-12, -1.16413143e-15, 1.63887269e-11,-1.24354018e-11, -1.14109502e-16, 5.39527448e-16, 9.57628892e-06,-1.43809403e-06, -2.56884677e-09, 4.04195021e-10],
[-4.49521077e-08, -5.40883280e-12, -1.42777336e-15, 1.63887269e-11,-9.10161846e-12, -1.25685965e-16, 6.46946153e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 3.79407089e-10],
[-3.55091072e-08, -4.81315891e-12, -1.59702183e-15, 1.53625012e-11,-1.24354018e-11, -7.96859479e-17, 7.83029877e-16, 7.79311104e-06,-1.10806420e-06, -2.20113143e-09, 4.04195021e-10]],
[[-2.95158505e-08, -4.81315891e-12, -1.59702183e-15, 1.56618112e-11,-1.24315447e-11, -7.96859479e-17, 7.83029877e-16, 7.79311104e-06,-1.10806420e-06, -2.20113143e-09, 4.04195021e-10],
[-3.33785482e-08, -3.96950110e-12, -1.23320894e-15, 1.63857578e-11,-1.24354018e-11, -1.23006438e-16, 5.39973644e-16, 9.25499050e-06,-8.67804584e-07, -2.09460103e-09, 3.25194261e-10],
[-4.39755982e-08, -4.81315891e-12, -1.59702183e-15, 1.53625012e-11,-1.24354018e-11, -7.96859479e-17, 6.78683934e-16, 7.33223346e-06,-9.72914681e-07, -2.52154849e-09, 4.20045306e-10],
[-2.99952076e-08, -4.08300132e-12, -2.03324167e-15, 1.82757828e-11,-1.24354018e-11, -9.02060697e-17, 7.83029877e-16, 7.79311104e-06,-1.15201684e-06, -2.20113143e-09, 4.04195021e-10],
[-3.29854083e-08, -5.40883280e-12, -1.42777336e-15, 1.63887269e-11,-1.03968520e-11, -1.23006438e-16, 6.16179670e-16, 9.25499050e-06,-1.10806420e-06, -2.52154849e-09, 4.04195021e-10],
[-2.95158505e-08, -3.96950110e-12, -1.16413143e-15, 1.63887269e-11,-1.14572161e-11, -1.25685965e-16, 5.49020411e-16, 9.13372382e-06,-1.10806420e-06, -2.52154849e-09, 2.94325581e-10],
[-3.89223210e-08, -5.70820319e-12, -1.71606156e-15, 1.53625012e-11,-1.24354018e-11, -8.12802825e-17, 6.46946153e-16, 7.79311104e-06,-1.10806420e-06, -3.20216846e-09, 4.04195021e-10],
[-4.15828873e-08, -4.54076115e-12, -1.29900459e-15, 1.63887269e-11,-9.10161846e-12, -1.25685965e-16, 7.31697776e-16, 7.79311104e-06,-1.10806420e-06, -1.71898539e-09, 4.04195021e-10],
[-4.49521077e-08, -4.28889603e-12, -1.42777336e-15, 1.63887269e-11,-9.10161846e-12, -1.21897709e-16, 6.46946153e-16, 7.79311104e-06,-1.10806420e-06, -2.52154849e-09, 3.53078523e-10],
[-4.49521077e-08, -5.93312736e-12, -1.59702183e-15, 1.27116583e-11,-1.09854434e-11, -7.40191648e-17, 6.78683934e-16, 7.79311104e-06,-1.10806420e-06, -2.10441398e-09, 3.79407089e-10]],
[[-2.99952076e-08, -4.08300132e-12, -2.03324167e-15, 1.82757828e-11,-1.09854434e-11, -7.40191648e-17, 6.77166939e-16, 7.79311104e-06,-1.29754790e-06, -2.10441398e-09, 3.79407089e-10],
[-4.49521077e-08, -5.93312736e-12, -1.22774484e-15, 1.27116583e-11,-1.24354018e-11, -9.02060697e-17, 7.83029877e-16, 8.63029039e-06,-1.15201684e-06, -2.20113143e-09, 4.32571294e-10],
[-4.33823837e-08, -5.93312736e-12, -1.93299850e-15, 1.27116583e-11,-1.09854434e-11, -9.33566248e-17, 5.50823418e-16, 7.28912498e-06,-1.14295161e-06, -1.71898539e-09, 4.04195021e-10],
[-4.06868968e-08, -4.54076115e-12, -1.29900459e-15, 1.63887269e-11,-9.10161846e-12, -1.34548917e-16, 8.41119089e-16, 7.79311104e-06,-9.32242718e-07, -2.28597338e-09, 3.46514713e-10],
[-2.95158505e-08, -4.81315891e-12, -2.04883127e-15, 1.74845111e-11,-1.24315447e-11, -7.96859479e-17, 7.83029877e-16, 7.79311104e-06,-1.10806420e-06, -2.21000670e-09, 4.04195021e-10],
[-2.95158505e-08, -5.16779642e-12, -1.59702183e-15, 1.55448661e-11,-1.24315447e-11, -8.03666500e-17, 7.83029877e-16, 7.79311104e-06,-1.10806420e-06, -2.20113143e-09, 3.21088236e-10],
[-3.29854083e-08, -4.54076115e-12, -1.11278316e-15, 1.63887269e-11,-9.10161846e-12, -1.25685965e-16, 7.31697776e-16, 9.43902382e-06,-1.10806420e-06, -1.34483441e-09, 3.98381095e-10],
[-4.15828873e-08, -5.40883280e-12, -1.42777336e-15, 1.63887269e-11,-1.06177496e-11, -1.23006438e-16, 6.00068425e-16, 9.25499050e-06,-1.10806420e-06, -2.52154849e-09, 4.04195021e-10],
[-3.29854083e-08, -5.40883280e-12, -1.42777336e-15, 1.63887269e-11,-1.24315447e-11, -7.96859479e-17, 7.83029877e-16, 7.79311104e-06,-1.10806420e-06, -1.83503268e-09, 4.85768590e-10],
[-3.69796080e-08, -4.81315891e-12, -1.73166081e-15, 1.51666790e-11,-1.03968520e-11, -1.23006438e-16, 6.92486070e-16, 9.25499050e-06,-1.32288766e-06, -2.21723313e-09, 4.01067051e-10]]]
# print(len(generations)) | 95.172767 | 205 | 0.683763 | 99,998 | 585,027 | 4.00028 | 0.102152 | 0.028499 | 0.014549 | 0.016939 | 0.811587 | 0.762784 | 0.760122 | 0.716589 | 0.648245 | 0.645383 | 0 | 0.686776 | 0.087492 | 585,027 | 6,147 | 206 | 95.172767 | 0.062547 | 0.915337 | 0 | 0.02 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
2e879ce160414fd260263c3e5e8883464c2bd714 | 165 | py | Python | calendartools/views/__init__.py | chrischambers/django-calendartools | 7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe | [
"BSD-3-Clause"
] | 1 | 2015-12-15T19:12:14.000Z | 2015-12-15T19:12:14.000Z | calendartools/views/__init__.py | chrischambers/django-calendartools | 7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe | [
"BSD-3-Clause"
] | null | null | null | calendartools/views/__init__.py | chrischambers/django-calendartools | 7fb2cb88a5913df1c01f4f92bcbd0d4a2d2f98fe | [
"BSD-3-Clause"
] | null | null | null | from calendartools.views.agenda import *
from calendartools.views.calendars import *
from calendartools.views.events import *
from calendartools.views.ical import *
| 33 | 43 | 0.830303 | 20 | 165 | 6.85 | 0.4 | 0.49635 | 0.642336 | 0.613139 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.09697 | 165 | 4 | 44 | 41.25 | 0.919463 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
5cf35e2862503626dddd9722a7388a4b6c6197fd | 26 | py | Python | src/gae_flask_boilerplate/main.py | euri10/python-gae_flask_boilerplate | 408ed2d9b718b34e73a9a4654b65a0c0d00f2117 | [
"BSD-2-Clause"
] | null | null | null | src/gae_flask_boilerplate/main.py | euri10/python-gae_flask_boilerplate | 408ed2d9b718b34e73a9a4654b65a0c0d00f2117 | [
"BSD-2-Clause"
] | null | null | null | src/gae_flask_boilerplate/main.py | euri10/python-gae_flask_boilerplate | 408ed2d9b718b34e73a9a4654b65a0c0d00f2117 | [
"BSD-2-Clause"
] | null | null | null |
from app import app
| 3.25 | 19 | 0.615385 | 4 | 26 | 4 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.384615 | 26 | 7 | 20 | 3.714286 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
cf073ee36b6b50adf9328ca76c64d414eb4e0786 | 180 | py | Python | app/controllers/category/__init__.py | Brunoro811/api_dangels | 21c064eaa4f5009412dddc9676044d6cc08a5b65 | [
"MIT"
] | null | null | null | app/controllers/category/__init__.py | Brunoro811/api_dangels | 21c064eaa4f5009412dddc9676044d6cc08a5b65 | [
"MIT"
] | null | null | null | app/controllers/category/__init__.py | Brunoro811/api_dangels | 21c064eaa4f5009412dddc9676044d6cc08a5b65 | [
"MIT"
] | null | null | null | from .create import create_category
from .get_all import get_all_category
from .get_one import get_category
from .delete import delete_category
from .update import update_category
| 30 | 37 | 0.861111 | 28 | 180 | 5.25 | 0.321429 | 0.326531 | 0.204082 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 180 | 5 | 38 | 36 | 0.91875 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
cf4114693e82c7810f046f899f9459e0ca494920 | 31,415 | py | Python | hanibal/ans_reporte/crear_informe_estadocuenta_excel.py | Christian-Castro/castro_odoo8 | 8247fdb20aa39e043b6fa0c4d0af509462ab3e00 | [
"Unlicense"
] | null | null | null | hanibal/ans_reporte/crear_informe_estadocuenta_excel.py | Christian-Castro/castro_odoo8 | 8247fdb20aa39e043b6fa0c4d0af509462ab3e00 | [
"Unlicense"
] | null | null | null | hanibal/ans_reporte/crear_informe_estadocuenta_excel.py | Christian-Castro/castro_odoo8 | 8247fdb20aa39e043b6fa0c4d0af509462ab3e00 | [
"Unlicense"
] | null | null | null | # -*- coding: utf-8 -*-
import openpyxl
from openpyxl import Workbook
import openpyxl.worksheet
import unicodedata
from copy import deepcopy
from openpyxl.chart import (
Reference,
Series,
BarChart
)
from openpyxl.chart.marker import DataPoint
from openpyxl.drawing.fill import PatternFillProperties, ColorChoice
from openpyxl import Workbook
from openpyxl.styles import PatternFill, Border, Side, Alignment, Protection, Font
from openpyxl.styles.borders import Border, Side
from openpyxl.drawing.image import Image
from datetime import datetime, date, timedelta
import time
import locale
global root
def crear_wb_informe():
wb = openpyxl.Workbook()
return wb
def unicodeText(text):
try:
text = unicodedata.unicode(text, 'utf-8')
except TypeError:
return text
def crea_hoja_info(wb, title, flag):
sheet = wb.active
if(flag == 0):
#sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL
#sheet.print_options.scale = 100
sheet.page_margins.left = 0.1
sheet.page_margins.right = 0.1
sheet.page_margins.top = 0.5
sheet.page_margins.bottom = 0.5
#sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT
#sheet.sheet_properties.pageSetUpPr.fitToPage = True
sheet.page_setup.fitToWidht = False
#sheet.print_options.horizontalCentered = True
if(flag == 1):
#sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL
#sheet.print_options.scale = 100
#sheet.sheet_properties.pageSetUpPr.fitToPage = True
sheet.page_setup.fitToWidth = False
sheet.page_margins.left = 0.1
sheet.page_margins.right = 0.1
sheet.page_margins.top = 0.5
sheet.page_margins.bottom = 0.5
#sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT
#sheet.print_options.horizontalCentered = True
sheet.title = title
return sheet
def crea_hoja_info_pdf(wb, title, flag):
sheet = wb.active
if(flag == 0):
#sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL
#sheet.print_options.scale = 100
sheet.page_margins.left = 0.1
sheet.page_margins.right = 0.1
sheet.page_margins.top = 0.5
sheet.page_margins.bottom = 0.5
#sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT
#sheet.sheet_properties.pageSetUpPr.fitToPage = True
sheet.page_setup.fitToWidht = False
#sheet.print_options.horizontalCentered = True
if(flag == 1):
#sheet.page_setup.paperSize = sheet.PAPERSIZE_A4_SMALL
#sheet.print_options.scale = 100
#sheet.sheet_properties.pageSetUpPr.fitToPage = True
sheet.page_setup.fitToWidth = False
sheet.page_margins.left = 0.1
sheet.page_margins.right = 0.1
sheet.page_margins.top = 0.5
sheet.page_margins.bottom = 0.5
#sheet.page_setup.orientation = sheet.ORIENTATION_PORTRAIT
#sheet.print_options.horizontalCentered = True
sheet.title = title
return sheet
def border_tabla(sheet, col, colfin, fil, filfin, styleleft, styletop, styleright, stylebottom):
colfin=colfin+1
filfin=filfin+2
border_cell = Border(left=Side(style=styleleft), top=Side(style=styletop), right=Side(style=styleright), bottom=Side(style=stylebottom))
for i in range(fil, filfin-1):
for j in range(col, colfin):
sheet.cell(row=i, column=j).border = border_cell
def columnas_filas(sheet, flag, celda, value):
if (flag == 0):
sheet.column_dimensions[celda].width = value
if (flag == 1):
sheet.row_dimensions[int(celda)].height = value
def poner_border(sheet, fil, col, styleleft, styletop, styleright, stylebottom):
border_cell = Border(left=Side(style=styleleft), top=Side(style=styletop), right=Side(style=styleright), bottom=Side(style=stylebottom))
sheet.cell(row=fil, column=col).border = border_cell
def Informe(sheet, dic,lista_alumnos,cant_alumno):
columnas_filas(sheet, 0, 'A', 10.00)
columnas_filas(sheet, 0, 'B', 15.00)
columnas_filas(sheet, 0, 'C', 15.00)
columnas_filas(sheet, 0, 'D', 10.00)
columnas_filas(sheet, 0, 'E', 12.00)
columnas_filas(sheet, 0, 'F', 18.00)
columnas_filas(sheet, 0, 'G', 1.00)
alignment_title = Alignment(horizontal='center', vertical='center')
fuente = Font(bold=False, size=7, name='arial')
fuente3 = Font(bold=True, size=10, name='arial')
fuente2 = Font(bold=True, size=7, name='arial')
fila = 3
fila1 = 2
acum=1
cont=0
col=2
col1=4
fil=4
coli=2
colf=2
sheet.merge_cells('A2:F2')
sheet['A2'].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['A2'].font = fuente3
sheet['A2']= 'ESTADO DE CUENTA'
sheet['E1'].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E1'].font = fuente2
sheet['E1']= 'Usuario'
sheet['F1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['F1'].font = fuente
sheet['F1']= str(dic['usuario_id'].encode('utf-8'))
sheet['A1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A1'].font = fuente2
sheet['A1']= 'Cia'
sheet.merge_cells('B1:C1')
sheet['B1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B1'].font = fuente
sheet['B1']= str(dic['company_id'].encode('utf-8'))
sheet['A3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A3'].font = fuente2
sheet['A3']= 'Fecha Emision:'
#fecha_actual = datetime.strftime(datetime.now(), '%d-%m-%Y %H:%M:%S')
fecha_actual = dic['fecha_corte']
sheet.merge_cells('B3:C3')
sheet['B3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B3'].font = fuente
sheet['B3']= fecha_actual
poner_border(sheet,1,1,'medium','medium','none','none')
poner_border(sheet,1,2,'none','medium','none','none')
poner_border(sheet,1,3,'none','medium','none','none')
poner_border(sheet,1,4,'none','medium','none','none')
poner_border(sheet,1,5,'none','medium','none','none')
poner_border(sheet,1,6,'none','medium','medium','none')
poner_border(sheet,2,1,'medium','none','none','none')
poner_border(sheet,2,6,'none','none','medium','none')
poner_border(sheet,3,1,'medium','none','none','medium')
poner_border(sheet,3,2,'none','none','none','medium')
poner_border(sheet,3,3,'none','none','none','medium')
poner_border(sheet,3,4,'none','none','none','medium')
poner_border(sheet,3,5,'none','none','none','medium')
poner_border(sheet,3,6,'none','none','medium','medium')
fila=2
total_general=0.0
saldo_general=0.0
for recorrer in lista_alumnos:
sheet['A'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+2)].font = fuente2
sheet['A'+str(fila+2)]= 'Alumno:'
sheet.merge_cells('B'+str(fila+2)+':C'+str(fila+2))
sheet['B'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B'+str(fila+2)].font = fuente
if recorrer['alumno']!=False:
sheet['B'+str(fila+2)]= str(recorrer['alumno'])
sheet['A'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+3)].font = fuente2
sheet['A'+str(fila+3)]= 'Direccion'
sheet.merge_cells('B'+str(fila+3)+':C'+str(fila+3))
sheet['B'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B'+str(fila+3)].font = fuente
sheet['B'+str(fila+3)]= str(recorrer['direccion'].encode('utf-8'))
sheet['A'+str(fila+4)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+4)].font = fuente2
sheet['A'+str(fila+4)]= 'Telefono'
sheet.merge_cells('B'+str(fila+4)+':C'+str(fila+4))
sheet['B'+str(fila+4)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B'+str(fila+4)].font = fuente
sheet['B'+str(fila+4)]= (recorrer['telefono'])
sheet['D'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+2)].font = fuente2
sheet['D'+str(fila+2)]= 'Representante'
sheet.merge_cells('E'+str(fila+2)+':F'+str(fila+2))
sheet['E'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['E'+str(fila+2)].font = fuente
if recorrer['representante']!=False:
sheet['E'+str(fila+2)]= str(recorrer['representante'].encode('utf-8'))
sheet['D'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+3)].font = fuente2
sheet['D'+str(fila+3)]= 'Cedula o RUC'
sheet.merge_cells('E'+str(fila+3)+':F'+str(fila+3))
sheet['E'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['E'+str(fila+3)].font = fuente
sheet['E'+str(fila+3)]= (recorrer['cedula'])
sheet.merge_cells('D'+str(fila+5)+':E'+str(fila+5))
sheet['D'+str(fila+5)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+5)].font = fuente
sheet['D'+str(fila+5)]= 'Jornada: '+(recorrer['jornada'])
sheet['A'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+6)].font = fuente
sheet['A'+str(fila+6)]= 'STATUS A'
sheet.merge_cells('D'+str(fila+6)+':E'+str(fila+6))
sheet['D'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+6)].font = fuente
sheet['D'+str(fila+6)]= 'Curso: '+(recorrer['curso'])
sheet['F'+str(fila+5)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['F'+str(fila+5)].font = fuente
sheet['F'+str(fila+5)]= 'Seccion: '+(recorrer['seccion'])
sheet['F'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['F'+str(fila+6)].font = fuente
sheet['F'+str(fila+6)]= 'Paralelo: '+(recorrer['paralelo'])
poner_border(sheet,fila+7,6,'none','medium','medium','none')
poner_border(sheet,fila+7,1,'medium','medium','none','none')
poner_border(sheet,fila+7,2,'none','medium','none','none')
poner_border(sheet,fila+7,3,'none','medium','none','none')
poner_border(sheet,fila+7,4,'none','medium','none','none')
poner_border(sheet,fila+7,5,'none','medium','none','none')
sheet.merge_cells('A'+str(fila+7)+':B'+str(fila+7))
sheet['A'+str(fila+7)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['A'+str(fila+7)].font = fuente2
sheet['A'+str(fila+7)]= 'Documento'
sheet['A'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['A'+str(fila+8)].font = fuente2
sheet['A'+str(fila+8)]= 'Tipo'
sheet['B'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['B'+str(fila+8)].font = fuente2
sheet['B'+str(fila+8)]= 'Numero'
sheet['C'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['C'+str(fila+8)].font = fuente2
sheet['C'+str(fila+8)]= 'Emision'
sheet['D'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['D'+str(fila+8)].font = fuente2
sheet['D'+str(fila+8)]= 'Cargos'
sheet['E'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['E'+str(fila+8)].font = fuente2
sheet['E'+str(fila+8)]= 'Abonos'
sheet['F'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['F'+str(fila+8)].font = fuente2
sheet['F'+str(fila+8)]= 'Comentario'
poner_border(sheet,fila+8,6,'none','none','medium','medium')
poner_border(sheet,fila+8,1,'medium','none','none','medium')
poner_border(sheet,fila+8,2,'none','none','none','medium')
poner_border(sheet,fila+8,3,'none','none','none','medium')
poner_border(sheet,fila+8,4,'none','none','none','medium')
poner_border(sheet,fila+8,5,'none','none','none','medium')
sheet.merge_cells('A'+str(fila+9)+':B'+str(fila+9))
sheet['A'+str(fila+9)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+9)].font = fuente2
sheet['A'+str(fila+9)]= 'Saldo al '+str(dic['fecha_desde'])
sheet['D'+str(fila+9)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila+9)].font = fuente2
sheet['D'+str(fila+9)]= "{:,}".format(float(recorrer['cargo'])).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila+9)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila+9)].font = fuente2
sheet['E'+str(fila+9)]= 0.00
fila=fila+10
saldo=0.0
total=0.0
for det in recorrer['detalle']:
sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila)].font = fuente
sheet['A'+str(fila)]= det['tipo']
sheet['B'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B'+str(fila)].font = fuente
sheet['B'+str(fila)]= det['numero']
sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['C'+str(fila)].font = fuente
sheet['C'+str(fila)]= det['emision']
sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila)].font = fuente
sheet['D'+str(fila)]= "{:,}".format(float(det['cargos'])).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila)].font = fuente
sheet['E'+str(fila)]= "{:,}".format(float(det['total'])).replace(',','~').replace('.',',').replace('~','.')
sheet['F'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top')
sheet['F'+str(fila)].font = fuente
sheet['F'+str(fila)]= det['comentario']
saldo=saldo+float(det['cargos'])
total=total+float(det['total'])
fila=fila+1
sheet.merge_cells('A'+str(fila)+':B'+str(fila))
sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila)].font = fuente2
sheet['A'+str(fila)]= 'Saldo al '+str(dic['fecha_hasta'])
sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila)].font = fuente2
sheet['D'+str(fila)]= "{:,}".format(float(saldo)).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila)].font = fuente2
sheet['E'+str(fila)]= "{:,}".format(float(total)).replace(',','~').replace('.',',').replace('~','.')
resta=saldo-total
sheet['F'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['F'+str(fila)].font = fuente2
sheet['F'+str(fila)]= "{:,}".format(float(resta)).replace(',','~').replace('.',',').replace('~','.')
poner_border(sheet,fila,4,'none','medium','none','none')
poner_border(sheet,fila,5,'none','medium','none','none')
total_general = total_general + total
saldo_general = saldo_general + saldo
fila= fila + 2
sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['C'+str(fila)].font = fuente2
sheet['C'+str(fila)]= 'Total General'
sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila)].font = fuente2
sheet['D'+str(fila)]= "{:,}".format(float(saldo_general)).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila)].font = fuente2
sheet['E'+str(fila)]= "{:,}".format(float(total_general)).replace(',','~').replace('.',',').replace('~','.')
def Informe_pdf(sheet, dic,lista_alumnos,cant_alumno):
columnas_filas(sheet, 0, 'A', 8.00)
columnas_filas(sheet, 0, 'B', 15.00)
columnas_filas(sheet, 0, 'C', 15.00)
columnas_filas(sheet, 0, 'D', 10.00)
columnas_filas(sheet, 0, 'E', 12.00)
columnas_filas(sheet, 0, 'F', 18.00)
columnas_filas(sheet, 0, 'G', 1.00)
alignment_title = Alignment(horizontal='center', vertical='center')
fuente = Font(bold=False, size=7, name='arial')
fuente3 = Font(bold=True, size=10, name='arial')
fuente2 = Font(bold=True, size=7, name='arial')
fila = 3
fila1 = 2
acum=1
cont=0
col=2
col1=4
fil=4
coli=2
colf=2
sheet.merge_cells('A2:F2')
sheet['A2'].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['A2'].font = fuente3
sheet['A2']= 'ESTADO DE CUENTA'
sheet['E1'].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E1'].font = fuente2
sheet['E1']= 'Usuario'
sheet['F1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['F1'].font = fuente
sheet['F1']= str(dic['usuario_id'].encode('utf-8'))
sheet['A1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A1'].font = fuente2
sheet['A1']= 'Cia'
sheet.merge_cells('B1:C1')
sheet['B1'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B1'].font = fuente
sheet['B1']= str(dic['company_id'].encode('utf-8'))
sheet['A3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A3'].font = fuente2
sheet['A3']= 'Fecha Emision:'
#fecha_actual = datetime.strftime(datetime.now(), '%d-%m-%Y %H:%M:%S')
fecha_actual = dic['fecha_corte']
sheet.merge_cells('B3:C3')
sheet['B3'].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B3'].font = fuente
sheet['B3']= fecha_actual
poner_border(sheet,1,1,'medium','medium','none','none')
poner_border(sheet,1,2,'none','medium','none','none')
poner_border(sheet,1,3,'none','medium','none','none')
poner_border(sheet,1,4,'none','medium','none','none')
poner_border(sheet,1,5,'none','medium','none','none')
poner_border(sheet,1,6,'none','medium','medium','none')
poner_border(sheet,2,1,'medium','none','none','none')
poner_border(sheet,2,6,'none','none','medium','none')
poner_border(sheet,3,1,'medium','none','none','medium')
poner_border(sheet,3,2,'none','none','none','medium')
poner_border(sheet,3,3,'none','none','none','medium')
poner_border(sheet,3,4,'none','none','none','medium')
poner_border(sheet,3,5,'none','none','none','medium')
poner_border(sheet,3,6,'none','none','medium','medium')
fila=2
total_general=0.0
saldo_general=0.0
for recorrer in lista_alumnos:
columnas_filas(sheet, 1, str(fila+2), 10.00)
columnas_filas(sheet, 1, str(fila+3), 15.00)
columnas_filas(sheet, 1, str(fila+4), 10.00)
columnas_filas(sheet, 1, str(fila+5), 10.00)
columnas_filas(sheet, 1, str(fila+6), 10.00)
columnas_filas(sheet, 1, str(fila+7), 10.00)
columnas_filas(sheet, 1, str(fila+8), 10.00)
columnas_filas(sheet, 1, str(fila+9), 10.00)
sheet['A'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+2)].font = fuente2
sheet['A'+str(fila+2)]= 'Alumno:'
sheet.merge_cells('B'+str(fila+2)+':C'+str(fila+2))
sheet['B'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top')
sheet['B'+str(fila+2)].font = fuente
if recorrer['alumno']!=False:
sheet['B'+str(fila+2)]= str(recorrer['alumno'])
sheet['A'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+3)].font = fuente2
sheet['A'+str(fila+3)]= 'Direccion'
sheet.merge_cells('B'+str(fila+3)+':C'+str(fila+3))
sheet['B'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='justify')
sheet['B'+str(fila+3)].font = fuente
sheet['B'+str(fila+3)]= str(recorrer['direccion'].encode('utf-8'))
sheet['A'+str(fila+4)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+4)].font = fuente2
sheet['A'+str(fila+4)]= 'Telefono'
sheet.merge_cells('B'+str(fila+4)+':C'+str(fila+4))
sheet['B'+str(fila+4)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top')
sheet['B'+str(fila+4)].font = fuente
sheet['B'+str(fila+4)]= (recorrer['telefono'])
sheet['D'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+2)].font = fuente2
sheet['D'+str(fila+2)]= 'Representante'
sheet.merge_cells('E'+str(fila+2)+':F'+str(fila+2))
sheet['E'+str(fila+2)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top')
sheet['E'+str(fila+2)].font = fuente
if recorrer['representante']!=False:
sheet['E'+str(fila+2)]= str(recorrer['representante'].encode('utf-8'))
sheet['D'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+3)].font = fuente2
sheet['D'+str(fila+3)]= 'Cedula o RUC'
sheet.merge_cells('E'+str(fila+3)+':F'+str(fila+3))
sheet['E'+str(fila+3)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top')
sheet['E'+str(fila+3)].font = fuente
sheet['E'+str(fila+3)]= (recorrer['cedula'])
sheet.merge_cells('D'+str(fila+5)+':E'+str(fila+5))
sheet['D'+str(fila+5)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+5)].font = fuente
sheet['D'+str(fila+5)]= 'Jornada: '+(recorrer['jornada'])
sheet['A'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+6)].font = fuente
sheet['A'+str(fila+6)]= 'STATUS A'
sheet.merge_cells('D'+str(fila+6)+':E'+str(fila+6))
sheet['D'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['D'+str(fila+6)].font = fuente
sheet['D'+str(fila+6)]= 'Curso: '+(recorrer['curso'])
sheet['F'+str(fila+5)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['F'+str(fila+5)].font = fuente
sheet['F'+str(fila+5)]= 'Seccion: '+(recorrer['seccion'])
sheet['F'+str(fila+6)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['F'+str(fila+6)].font = fuente
sheet['F'+str(fila+6)]= 'Paralelo: '+(recorrer['paralelo'])
poner_border(sheet,fila+7,6,'none','medium','medium','none')
poner_border(sheet,fila+7,1,'medium','medium','none','none')
poner_border(sheet,fila+7,2,'none','medium','none','none')
poner_border(sheet,fila+7,3,'none','medium','none','none')
poner_border(sheet,fila+7,4,'none','medium','none','none')
poner_border(sheet,fila+7,5,'none','medium','none','none')
sheet.merge_cells('A'+str(fila+7)+':B'+str(fila+7))
sheet['A'+str(fila+7)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['A'+str(fila+7)].font = fuente2
sheet['A'+str(fila+7)]= 'Documento'
sheet['A'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['A'+str(fila+8)].font = fuente2
sheet['A'+str(fila+8)]= 'Tipo'
sheet['B'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['B'+str(fila+8)].font = fuente2
sheet['B'+str(fila+8)]= 'Numero'
sheet['C'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['C'+str(fila+8)].font = fuente2
sheet['C'+str(fila+8)]= 'Emision'
sheet['D'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['D'+str(fila+8)].font = fuente2
sheet['D'+str(fila+8)]= 'Cargos'
sheet['E'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['E'+str(fila+8)].font = fuente2
sheet['E'+str(fila+8)]= 'Abonos'
sheet['F'+str(fila+8)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['F'+str(fila+8)].font = fuente2
sheet['F'+str(fila+8)]= 'Comentario'
poner_border(sheet,fila+8,6,'none','none','medium','medium')
poner_border(sheet,fila+8,1,'medium','none','none','medium')
poner_border(sheet,fila+8,2,'none','none','none','medium')
poner_border(sheet,fila+8,3,'none','none','none','medium')
poner_border(sheet,fila+8,4,'none','none','none','medium')
poner_border(sheet,fila+8,5,'none','none','none','medium')
sheet.merge_cells('A'+str(fila+9)+':B'+str(fila+9))
sheet['A'+str(fila+9)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila+9)].font = fuente2
sheet['A'+str(fila+9)]= 'Saldo al '+str(dic['fecha_desde'])
sheet['D'+str(fila+9)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila+9)].font = fuente2
sheet['D'+str(fila+9)]= "{:,}".format(float(recorrer['cargo'])).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila+9)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila+9)].font = fuente2
sheet['E'+str(fila+9)]= 0.00
fila=fila+10
saldo=0.0
total=0.0
for det in recorrer['detalle']:
columnas_filas(sheet, 1, str(fila), 10.00)
sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila)].font = fuente
sheet['A'+str(fila)]= det['tipo']
sheet['B'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['B'+str(fila)].font = fuente
sheet['B'+str(fila)]= det['numero']
sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['C'+str(fila)].font = fuente
sheet['C'+str(fila)]= det['emision']
sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila)].font = fuente
sheet['D'+str(fila)]= "{:,}".format(float(det['cargos'])).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila)].font = fuente
sheet['E'+str(fila)]= "{:,}".format(float(det['total'])).replace(',','~').replace('.',',').replace('~','.')
sheet['F'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='justify', vertical='top')
sheet['F'+str(fila)].font = fuente
sheet['F'+str(fila)]= det['comentario']
saldo=saldo+float(det['cargos'])
total=total+float(det['total'])
fila=fila+1
sheet.merge_cells('A'+str(fila)+':B'+str(fila))
sheet['A'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='left', vertical='top')
sheet['A'+str(fila)].font = fuente2
sheet['A'+str(fila)]= 'Saldo al '+str(dic['fecha_hasta'])
sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila)].font = fuente2
sheet['D'+str(fila)]= "{:,}".format(float(saldo)).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila)].font = fuente2
sheet['E'+str(fila)]= "{:,}".format(float(total)).replace(',','~').replace('.',',').replace('~','.')
resta=saldo-total
sheet['F'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['F'+str(fila)].font = fuente2
sheet['F'+str(fila)]= "{:,}".format(float(resta)).replace(',','~').replace('.',',').replace('~','.')
poner_border(sheet,fila,4,'none','medium','none','none')
poner_border(sheet,fila,5,'none','medium','none','none')
total_general = total_general + total
saldo_general = saldo_general + saldo
fila= fila + 2
sheet['C'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='center', vertical='top')
sheet['C'+str(fila)].font = fuente2
sheet['C'+str(fila)]= 'Total General'
sheet['D'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['D'+str(fila)].font = fuente2
sheet['D'+str(fila)]= "{:,}".format(float(saldo_general)).replace(',','~').replace('.',',').replace('~','.')
sheet['E'+str(fila)].alignment = alignment_title.copy(wrapText=True,horizontal='right', vertical='top')
sheet['E'+str(fila)].font = fuente2
sheet['E'+str(fila)]= "{:,}".format(float(total_general)).replace(',','~').replace('.',',').replace('~','.') | 46.818182 | 140 | 0.622633 | 4,259 | 31,415 | 4.521249 | 0.050951 | 0.100696 | 0.107499 | 0.126194 | 0.943602 | 0.943498 | 0.940278 | 0.938824 | 0.929477 | 0.929477 | 0 | 0.024941 | 0.166576 | 31,415 | 671 | 141 | 46.818182 | 0.710526 | 0.035238 | 0 | 0.879377 | 0 | 0 | 0.107512 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.01751 | false | 0 | 0.029183 | 0 | 0.054475 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
cf82c845459b40c650e0a5a4d21cb74396b8f5b5 | 283 | py | Python | materials/ch_03/assign_var.py | epsilonxe/RMUTT_09090016 | 863dd8a6471b560831b742da4aec27209c294df5 | [
"MIT"
] | null | null | null | materials/ch_03/assign_var.py | epsilonxe/RMUTT_09090016 | 863dd8a6471b560831b742da4aec27209c294df5 | [
"MIT"
] | null | null | null | materials/ch_03/assign_var.py | epsilonxe/RMUTT_09090016 | 863dd8a6471b560831b742da4aec27209c294df5 | [
"MIT"
] | null | null | null | x = 10
a = 2
print('x = ', x)
x = x + a
print('x = ', x)
x += a # มีค่าเท่ากับนิพจน์ x = x + a
print('x = ', x)
x = x - a
print('x = ', x)
x -= a # มีค่าเท่ากับนิพจน์ x = x - a
print('x = ', x)
x = x * a
print('x = ', x)
x *= a # มีค่าเท่ากับนิพจน์ x = x * a
print('x = ', x) | 16.647059 | 39 | 0.402827 | 85 | 283 | 1.552941 | 0.188235 | 0.287879 | 0.204545 | 0.363636 | 0.962121 | 0.962121 | 0.962121 | 0.962121 | 0.962121 | 0.962121 | 0 | 0.015228 | 0.303887 | 283 | 17 | 40 | 16.647059 | 0.563452 | 0.310954 | 0 | 0.466667 | 0 | 0 | 0.146597 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.466667 | 0 | 0 | 1 | null | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 11 |
cf87b1f3f0f3383fec59dd8ef6c4222f039f22b6 | 28,302 | py | Python | plot/all_plots.py | biomac-lab/COVID_schools_dashboard | 173d1b9df5735e65ec0bf2594cf942ccbbad9f5e | [
"Apache-2.0"
] | null | null | null | plot/all_plots.py | biomac-lab/COVID_schools_dashboard | 173d1b9df5735e65ec0bf2594cf942ccbbad9f5e | [
"Apache-2.0"
] | null | null | null | plot/all_plots.py | biomac-lab/COVID_schools_dashboard | 173d1b9df5735e65ec0bf2594cf942ccbbad9f5e | [
"Apache-2.0"
] | 2 | 2021-09-25T15:37:45.000Z | 2021-10-01T17:48:28.000Z | import sys
sys.path.append('../')
from matplotlib import figure
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
from tqdm import tqdm
from models import model
### Config folders
config_data = pd.read_csv('configlin.csv', sep=',', header=None, index_col=0)
figures_path = config_data.loc['figures_dir'][1]
results_path = config_data.loc['results_dir'][1]
ages_data_path = config_data.loc['bogota_age_data_dir'][1]
houses_data_path = config_data.loc['bogota_houses_data_dir'][1]
### Arguments
import argparse
parser = argparse.ArgumentParser(description='Dynamics visualization.')
parser.add_argument('--population', default=10000, type=int,
help='Speficy the number of individials')
parser.add_argument('--type_sim', default='intervention', type=str,
help='Speficy the type of simulation to plot')
args = parser.parse_args()
number_nodes = args.population
pop = number_nodes
### Read functions
def load_results_dyn(type_res,path=results_path,n=pop):
read_path = os.path.join(path,'{}_{}.csv'.format(str(n),str(type_res)))
read_file = pd.read_csv(read_path)
return read_file
def load_results_int(type_res,path=results_path,n=pop):
read_path = os.path.join(path,'{}_inter_{}_schoolcap_{}_{}.csv'.format(str(n),str(args.intervention),
str(args.school_occupation),type_res))
read_file = pd.read_csv(read_path)
return read_file
def load_results_ints(type_res,n,int_effec,schl_occup,path=results_path):
read_path = os.path.join(path,'{}_inter_{}_schoolcap_{}_mask_N95_peopleMasked_1.0_ventilation_3_ID_ND_{}.csv'.format(str(n),str(int_effec),
str(schl_occup),type_res))
read_file = pd.read_csv(read_path)
return read_file
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
### Plot new cases
results_path = os.path.join(results_path,'intervention',str(pop))
intervention_effcs = [0.0,0.2,0.4,0.6] #,1.0]
interv_legend_label = [r'$0\%$ intervention efficiency',r'$20\%$ intervention efficiency',r'$40\%$ intervention efficiency',r'$60\%$ intervention efficiency'] #,r'No intervention, schools $100\%$ occupation']
interv_color_label = ['k','tab:red','tab:purple','tab:orange']
school_caps = [0.35] #[0.15,0.25,0.35,0.55,1.0]
states_ = ['S', 'E', 'I1', 'I2', 'I3', 'D', 'R']
plot_state = 'E'
alpha = 0.05
# lineal
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
plt.figure(figsize=(6,4)) # create figure
for i, inter_ in enumerate(intervention_effcs):
# read results
# if inter_ < 1.0:
res_read = load_results_ints('soln',args.population,inter_,cap_,path=results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# read results with no intervention
# elif inter_ == 1.0:
# res_read = load_results_dyn('soln',os.path.join('results','no_intervention',str(pop)))
# res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
# res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
# res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# plot
plt.plot(res_median['tvec'],res_median[plot_state]*pop,color=interv_color_label[i])
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median['tvec'],res_loCI[plot_state]*pop,res_upCI[plot_state]*pop,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='gray',alpha=0.05)
plt.annotate('Schools \n closed',(0,500),size=9)
plt.annotate('Schools \n open',(22,500),size=9)
plt.xlim([0,max(res_median['tvec'])])
plt.ylim([0,0.065*pop])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"New cases per 10,000 ind",size=12)
if args.type_sim == 'intervention':
plt.title(r'New cases with schools opening ${}\%$ occupation'.format(int(cap_*100)))
elif args.type_sim == 'school_alternancy':
plt.title(r'New cases with schools alterning ${}\%$ occupation'.format(int(cap_*100)))
if not os.path.isdir( os.path.join(figures_path,'cases_evolution') ):
os.makedirs( os.path.join(figures_path,'cases_evolution') )
save_path = os.path.join(figures_path,'cases_evolution','{}_lin_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
#plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
plt.show()
school_caps = [0.35] #[0.15,0.25,0.35,0.55,1.0]
plot_state = 'D'
alpha = 0.05
# lineal
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
plt.figure(figsize=(6,4)) # create figure
for i, inter_ in enumerate(intervention_effcs):
# read results
if inter_ < 1.0:
res_read = load_results_ints('soln',args.population,inter_,cap_,path=results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# read results with no intervention
elif inter_ == 1.0:
res_read = load_results_dyn('soln',os.path.join('results','no_intervention',str(pop)))
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# plot
plt.plot(res_median['tvec'],res_median[plot_state]*pop,color=interv_color_label[i])
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median['tvec'],res_loCI[plot_state]*pop,res_upCI[plot_state]*pop,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='gray',alpha=0.05)
plt.annotate('Schools \n closed',(0,150),size=9)
plt.annotate('Schools \n open',(22,150),size=9)
plt.xlim([0,max(res_median['tvec'])])
plt.ylim([0,0.02*pop])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"Deaths per 10,000 ind",size=12)
if args.type_sim == 'intervention':
plt.title(r'Deaths with schools opening ${}\%$ occupation'.format(int(cap_*100)))
elif args.type_sim == 'school_alternancy':
plt.title(r'Deaths with schools alterning ${}\%$ occupation'.format(int(cap_*100)))
if not os.path.isdir( os.path.join(figures_path,'cases_evolution') ):
os.makedirs( os.path.join(figures_path,'cases_evolution') )
save_path = os.path.join(figures_path,'cases_evolution','{}_lin_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
#plt.show()
# logaritmic
plot_state = 'E'
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
plt.figure(figsize=(6,4)) # create figure
for i, inter_ in enumerate(intervention_effcs):
# read results
if inter_ < 1.0:
res_read = load_results_ints('soln',args.population,inter_,cap_,path=results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# read results with no intervention
elif inter_ == 1.0:
res_read = load_results_dyn('soln',os.path.join('results','no_intervention',str(pop)))
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# plot
plt.plot(res_median['tvec'],res_median['E']*100,color=interv_color_label[i])
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median['tvec'],res_loCI['E']*100,res_upCI['E']*100,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='gray',alpha=0.05)
plt.annotate('Schools \n closed',(0,1.2),size=9)
plt.annotate('Schools \n open',(22,1.2),size=9)
plt.xlim([0,max(res_median['tvec'])])
plt.ylim([1/pop*200,1*100])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"$\%$ new cases per 100,000 ind",size=12)
plt.semilogy()
plt.tight_layout()
if args.type_sim == 'intervention':
plt.title(r'New cases with schools opening ${}\%$ occupation'.format(int(cap_*100)))
elif args.type_sim == 'school_alternancy':
plt.title(r'New cases with schools alterning ${}\%$ occupation'.format(int(cap_*100)))
if not os.path.isdir( os.path.join(figures_path,'cases_evolution') ):
os.makedirs( os.path.join(figures_path,'cases_evolution') )
save_path = os.path.join(figures_path,'cases_evolution','{}_log_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
#plt.show()
# # logaritmic
# plot_state = 'D'
# for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
# plt.figure(figsize=(6,4)) # create figure
# for i, inter_ in enumerate(intervention_effcs):
# # read results
# if inter_ < 1.0:
# res_read = load_results_ints('soln',args.population,inter_,cap_,path=results_path)
# res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
# res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
# res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# # read results with no intervention
# elif inter_ == 1.0:
# res_read = load_results_dyn('soln',os.path.join('results','no_intervention',str(pop)))
# res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
# res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
# res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# # plot
# plt.plot(res_median['tvec'],res_median[plot_state]*100,color=interv_color_label[i])
# plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
# plt.gca().set_prop_cycle(None)
# plt.fill_between(res_median['tvec'],res_loCI[plot_state]*100,res_upCI[plot_state]*100,color=interv_color_label[i],alpha=0.3)
# plt.axvspan(0,20,color='gray',alpha=0.05)
# plt.annotate('Schools \n closed',(0,1.2),size=9)
# plt.annotate('Schools \n open',(22,1.2),size=9)
# plt.xlim([0,max(res_median['tvec'])])
# plt.ylim([1/pop*200,2*100])
# plt.xticks(size=12)
# plt.yticks(size=12)
# plt.xlabel("Time (days)",size=12)
# plt.ylabel(r"$\%$ Deaths per 100,000 ind",size=12)
# plt.semilogy()
# plt.tight_layout()
# if args.type_sim == 'intervention':
# plt.title(r'Deaths with schools opening ${}\%$ occupation'.format(int(cap_*100)))
# elif args.type_sim == 'school_alternancy':
# plt.title(r'Deaths with schools alterning ${}\%$ occupation'.format(int(cap_*100)))
# if not os.path.isdir( os.path.join(figures_path,'cases_evolution') ):
# os.makedirs( os.path.join(figures_path,'cases_evolution') )
# save_path = os.path.join(figures_path,'cases_evolution','{}_log_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
# #plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
# plt.show()
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
### Plot dayly incidence
intervention_effcs = [0.2,0.4,0.6,1.0]
interv_legend_label = [r'$20\%$ intervention efficiency',r'$40\%$ intervention efficiency',r'$60\%$ intervention efficiency',r'No intervention, schools $100\%$ occupation']
interv_color_label = ['tab:red','tab:purple','tab:orange','k']
states_ = ['S', 'E', 'I1', 'I2', 'I3', 'D', 'R']
plot_state = 'E'
school_caps = [0.35]#[0.15,0.25,0.35,0.55,1.0]
alpha = 0.05
# lineal
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
plt.figure(figsize=(6,4)) # create figure
for i, inter_ in enumerate(intervention_effcs):
# read results
if inter_ < 1.0:
res_read = load_results_ints('soln_cum',args.population,inter_,cap_,results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
res_tvec = list(res_median['tvec'])
res_inc = model.get_daily_iter(res_read,res_tvec)
res_median_inc = res_inc.groupby('tvec').median(); res_median_inc = res_median_inc.reset_index()
res_loCI_inc = res_inc.groupby('tvec').quantile(alpha/2); res_loCI_inc = res_loCI_inc.reset_index()
res_upCI_inc = res_inc.groupby('tvec').quantile(1-alpha/2); res_upCI_inc = res_upCI_inc.reset_index()
# read results with no intervention
elif inter_ == 1.0:
res_read = load_results_dyn('soln',os.path.join('results','no_intervention',str(pop)))
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
res_tvec = list(res_median['tvec'])
res_inc = model.get_daily_iter(res_read,res_tvec)
res_median_inc = res_inc.groupby('tvec').median(); res_median_inc = res_median_inc.reset_index()
res_loCI_inc = res_inc.groupby('tvec').quantile(alpha/2); res_loCI_inc = res_loCI_inc.reset_index()
res_upCI_inc = res_inc.groupby('tvec').quantile(1-alpha/2); res_upCI_inc = res_upCI_inc.reset_index()
# plot
plt.plot(res_median_inc['tvec'],res_median_inc[plot_state]*pop,color=interv_color_label[i],alpha=0.6)
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median_inc['tvec'],res_loCI_inc[plot_state]*pop,res_upCI_inc[plot_state]*pop,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='k',alpha=0.035)
plt.annotate('Schools \n closed',(0,8))
plt.xlim([0,max(res_median_inc['tvec'])])
plt.ylim([0,0.2*pop])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"Daily incidence per 100,000 ind",size=12)
if args.type_sim == 'intervention':
plt.title(r'Daily incidence with schools opening ${:.2f}\%$ occupation'.format(cap_*100))
elif args.type_sim == 'school_alternancy':
plt.title(r'Daily incidence with schools alterning ${:.2f}\%$ occupation'.format(cap_*100))
plt.semilogy()
plt.tight_layout()
if not os.path.isdir( os.path.join(figures_path,'daily_incidence') ):
os.makedirs( os.path.join(figures_path,'daily_incidence') )
save_path = os.path.join(figures_path,'daily_incidence','{}_lin_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
#plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
plt.show()
# log
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
plt.figure(figsize=(6,4)) # create figure
for i, inter_ in enumerate(intervention_effcs):
# read results
if inter_ < 1.0:
res_read = load_results_ints('soln_cum',args.population,inter_,cap_,results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
res_tvec = list(res_median['tvec'])
res_inc = model.get_daily_iter(res_read,res_tvec)
res_median_inc = res_inc.groupby('tvec').median(); res_median_inc = res_median_inc.reset_index()
res_loCI_inc = res_inc.groupby('tvec').quantile(alpha/2); res_loCI_inc = res_loCI_inc.reset_index()
res_upCI_inc = res_inc.groupby('tvec').quantile(1-alpha/2); res_upCI_inc = res_upCI_inc.reset_index()
# read results with no intervention
elif inter_ == 1.0:
res_read = load_results_dyn('soln_cum',os.path.join('results','no_intervention',str(pop)))
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
res_tvec = list(res_median['tvec'])
res_inc = model.get_daily_iter(res_read,res_tvec)
res_median_inc = res_inc.groupby('tvec').median(); res_median_inc = res_median_inc.reset_index()
res_loCI_inc = res_inc.groupby('tvec').quantile(alpha/2); res_loCI_inc = res_loCI_inc.reset_index()
res_upCI_inc = res_inc.groupby('tvec').quantile(1-alpha/2); res_upCI_inc = res_upCI_inc.reset_index()
# plot
plt.plot(res_median_inc['tvec'],res_median_inc[plot_state]*pop,color=interv_color_label[i],alpha=0.6)
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median_inc['tvec'],res_loCI_inc[plot_state]*pop,res_upCI_inc[plot_state]*pop,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='k',alpha=0.035)
plt.annotate('Schools \n closed',(0,8))
plt.xlim([0,max(res_median_inc['tvec'])])
plt.ylim([pop/pop,0.01*pop])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"Daily incidence per 100,000 ind",size=12)
if args.type_sim == 'intervention':
plt.title(r'Daily incidence with schools opening ${:.2f}\%$ occupation'.format(cap_*100))
elif args.type_sim == 'school_alternancy':
plt.title(r'Daily incidence with schools alterning ${:.2f}\%$ occupation'.format(cap_*100))
plt.semilogy()
plt.tight_layout()
if not os.path.isdir( os.path.join(figures_path,'daily_incidence') ):
os.makedirs( os.path.join(figures_path,'daily_incidence') )
save_path = os.path.join(figures_path,'daily_incidence','{}_log_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
#plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
plt.show()
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
########################################################################################################################################
### PLot comulative number
intervention_effcs = [0.0,0.2,0.4,0.6] #,1.0]
interv_legend_label = [r'$0\%$ intervention efficiency',r'$20\%$ intervention efficiency',r'$40\%$ intervention efficiency',r'$60\%$ intervention efficiency'] #,r'No intervention, schools $100\%$ occupation']
interv_color_label = ['k','tab:red','tab:purple','tab:orange']
school_caps = [0.35] #[0.15,0.25,0.35,0.55,1.0]
states_ = ['S', 'E', 'I1', 'I2', 'I3', 'D', 'R']
plot_state = 'E'
plt.figure(figsize=(6,4)) # create figure
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
for i, inter_ in enumerate(intervention_effcs):
# read results
if inter_ < 1.0:
res_read = load_results_ints('soln_cum',args.population,inter_,cap_,results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# read results with no intervention
elif inter_ == 1.0:
res_read = load_results_dyn('soln_cum',os.path.join('results','no_intervention',str(pop)))
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
res_tvec = list(res_median['tvec'])
# plot
plt.plot(res_median['tvec'],res_median[plot_state]*pop,color=interv_color_label[i],alpha=0.6)
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median['tvec'],res_loCI[plot_state]*pop,res_upCI[plot_state]*pop,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='k',alpha=0.035)
plt.annotate('Schools \n closed',(0,6000),size=9)
plt.annotate('Schools \n open',(22,6000),size=9)
plt.xlim([0,max(res_median['tvec'])])
plt.ylim([0,0.9*pop])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"Cumulative cases per 10,000 ind",size=12)
if args.type_sim == 'intervention':
plt.title(r'Cumulative cases with schools opening ${}\%$ occupation'.format(int(cap_*100)))
elif args.type_sim == 'school_alternancy':
plt.title(r'Cumulative cases with schools alterning ${}\%$ occupation'.format(int(cap_*100)))
plt.tight_layout()
if not os.path.isdir( os.path.join(figures_path,'comulative_cases') ):
os.makedirs( os.path.join(figures_path,'comulative_cases') )
save_path = os.path.join(figures_path,'comulative_cases','{}_lin_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
#plt.show()
states_ = ['S', 'E', 'I1', 'I2', 'I3', 'D', 'R']
plot_state = 'D'
plt.figure(figsize=(6,4)) # create figure
for c, cap_ in tqdm(enumerate(school_caps), total=len(school_caps)):
for i, inter_ in enumerate(intervention_effcs):
# read results
if inter_ < 1.0:
res_read = load_results_ints('soln_cum',args.population,inter_,cap_,results_path)
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
# read results with no intervention
elif inter_ == 1.0:
res_read = load_results_dyn('soln_cum',os.path.join('results','no_intervention',str(pop)))
res_median = res_read.groupby('tvec').median(); res_median = res_median.reset_index()
res_loCI = res_read.groupby('tvec').quantile(alpha/2); res_loCI = res_loCI.reset_index()
res_upCI = res_read.groupby('tvec').quantile(1-alpha/2); res_upCI = res_upCI.reset_index()
res_tvec = list(res_median['tvec'])
# plot
plt.plot(res_median['tvec'],res_median[plot_state]*pop,color=interv_color_label[i],alpha=0.6)
plt.legend(interv_legend_label,frameon=False,framealpha=0.0,bbox_to_anchor=(1,1), loc="best")
plt.gca().set_prop_cycle(None)
plt.fill_between(res_median['tvec'],res_loCI[plot_state]*pop,res_upCI[plot_state]*pop,color=interv_color_label[i],alpha=0.3)
plt.axvspan(0,20,color='gray',alpha=0.05)
plt.annotate('Schools \n closed',(0,20000),size=9)
plt.annotate('Schools \n open',(22,20000),size=9)
plt.xlim([0,max(res_median['tvec'])])
plt.ylim([0,0.02*pop])
plt.xticks(size=12)
plt.yticks(size=12)
plt.xlabel("Time (days)",size=12)
plt.ylabel(r"Comulative deaths per 100,000 ind",size=12)
if args.type_sim == 'intervention':
plt.title(r'Comulative deaths with schools opening ${}\%$ occupation'.format(int(cap_*100)))
elif args.type_sim == 'school_alternancy':
plt.title(r'Comulative deaths with schools alterning ${}\%$ occupation'.format(int(cap_*100)))
if not os.path.isdir( os.path.join(figures_path,'comulative_cases') ):
os.makedirs( os.path.join(figures_path,'comulative_cases') )
save_path = os.path.join(figures_path,'comulative_cases','{}_lin_{}_dynamics_schoolcap_{}_n_{}.png'.format(plot_state,args.type_sim,cap_,str(pop)))
plt.savefig(save_path,dpi=400, transparent=True, bbox_inches='tight', pad_inches=0.1 )
plt.show() | 57.291498 | 208 | 0.614197 | 3,915 | 28,302 | 4.185185 | 0.05977 | 0.05383 | 0.041013 | 0.052731 | 0.937809 | 0.934757 | 0.929997 | 0.927067 | 0.916143 | 0.913091 | 0 | 0.030177 | 0.165183 | 28,302 | 494 | 209 | 57.291498 | 0.663309 | 0.145573 | 0 | 0.796296 | 0 | 0 | 0.157018 | 0.018617 | 0 | 0 | 0 | 0 | 0 | 1 | 0.009259 | false | 0 | 0.027778 | 0 | 0.046296 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
d85997fad30746c4d0a0a3751286e0e259f8386f | 159 | py | Python | src/tidyxbrl/__init__.py | cowboycodeman/tidyxbrl | b669184815a293c5415d259b9edb57cdc95088c3 | [
"MIT"
] | null | null | null | src/tidyxbrl/__init__.py | cowboycodeman/tidyxbrl | b669184815a293c5415d259b9edb57cdc95088c3 | [
"MIT"
] | null | null | null | src/tidyxbrl/__init__.py | cowboycodeman/tidyxbrl | b669184815a293c5415d259b9edb57cdc95088c3 | [
"MIT"
] | null | null | null | from .xbrl_apikey import *
from .xbrl_query import *
from .xbrl_parse import *
from .edgar_query import *
from .edgar_cik import *
from .edgar_frames import *
| 22.714286 | 27 | 0.773585 | 24 | 159 | 4.875 | 0.375 | 0.42735 | 0.384615 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.150943 | 159 | 6 | 28 | 26.5 | 0.866667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
d8b36145baa7c04dfe634ea75b001dff404905ef | 17,360 | py | Python | tests/test_lin_rg.py | dafrog/ml_algorithms | 232f7e3031191be4bcb3780466d79f510f07fad5 | [
"MIT"
] | null | null | null | tests/test_lin_rg.py | dafrog/ml_algorithms | 232f7e3031191be4bcb3780466d79f510f07fad5 | [
"MIT"
] | null | null | null | tests/test_lin_rg.py | dafrog/ml_algorithms | 232f7e3031191be4bcb3780466d79f510f07fad5 | [
"MIT"
] | null | null | null | import unittest
import os
from numpy.testing import assert_allclose
from numpy import ones, zeros, float64, array, append, genfromtxt
from ml_algorithms.lin_rg import (normal_eqn, cost_func,
reg_cost_func, grad,
reg_grad, predict, h)
from ml_algorithms.utils import numerical_grad
TESTDATA1 = os.path.join(os.path.dirname(__file__), 'data1.csv')
TESTDATA2 = os.path.join(os.path.dirname(__file__), 'data2.csv')
class TestLinearRegression(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.data1 = genfromtxt(TESTDATA1, delimiter=',')
cls.data2 = genfromtxt(TESTDATA2, delimiter=',')
cls.err = 1e-4
# NORMAL EQUATION
def test_normal_eqn_data1(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=int)
X = append(intercept, X, axis=1)
assert_allclose([[-3.896], [1.193]],
normal_eqn(X, y),
rtol=0, atol=0.001)
def test_normal_eqn_data2(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=int)
X = append(intercept, X, axis=1)
assert_allclose([[89597.909], [139.210], [-8738.019]],
normal_eqn(X, y),
rtol=0, atol=0.001)
# COST FUNCTION
def test_cost_func_data1_1(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[32.073]],
cost_func(X, y, theta),
rtol=0, atol=0.001)
def test_cost_func_data1_2(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[10.266]],
cost_func(X, y, theta),
rtol=0, atol=0.001)
def test_cost_func_data1_3(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-1], [2]])
assert_allclose([[54.242]],
cost_func(X, y, theta),
rtol=0, atol=0.001)
def test_cost_func_data2_1(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[65591548106.457]],
cost_func(X, y, theta),
rtol=0, atol=0.001)
def test_cost_func_data2_2(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[64828197300.798]],
cost_func(X, y, theta),
rtol=0, atol=0.001)
def test_cost_func_data2_3(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-25.3], [32], [7.8]])
assert_allclose([[43502644952.311]],
cost_func(X, y, theta),
rtol=0, atol=0.001)
# REGULARIZED COST FUNCTION
def test_reg_cost_func_data1_1(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 0
assert_allclose([[10.266]],
reg_cost_func(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_cost_func_data1_2(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 100
assert_allclose([[10.781984]],
reg_cost_func(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_cost_func_data1_3(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-1], [2]])
_lambda = 750
assert_allclose([[69.706373]],
reg_cost_func(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_cost_func_data2_1(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 0
assert_allclose([[64828197300.798]],
reg_cost_func(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_cost_func_data2_2(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 1000000
assert_allclose([[64828218577.393623]],
reg_cost_func(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_cost_func_data2_3(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-25.3], [32], [7.8]])
_lambda = 1000000
assert_allclose([[43514185803.375198]],
reg_cost_func(X, y, theta, _lambda),
rtol=0, atol=0.001)
# GRADIENT
def test_grad_data1_1(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[-5.839], [-65.329]],
grad(X, y, theta),
rtol=0, atol=0.001)
def test_grad_data1_2(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[3.321], [24.235]],
grad(X, y, theta),
rtol=0, atol=0.001)
def test_grad_data1_3(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-1], [2]])
assert_allclose([[9.480], [89.319]],
grad(X, y, theta),
rtol=0, atol=0.001)
def test_grad_data1_4(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = (1 / 3) * ones((n + 1, 1), dtype=float64)
def J(theta):
return cost_func(X, y, theta)
assert_allclose(grad(X, y, theta),
numerical_grad(J, theta, self.err),
rtol=0, atol=0.001)
def test_grad_data1_5(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = - 7.43 * ones((n + 1, 1), dtype=float64)
def J(theta):
return cost_func(X, y, theta)
assert_allclose(grad(X, y, theta),
numerical_grad(J, theta, self.err),
rtol=0, atol=0.001)
def test_grad_data1_6(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[3.46], [-2.76]])
def J(theta):
return cost_func(X, y, theta)
assert_allclose(grad(X, y, theta),
numerical_grad(J, theta, self.err),
rtol=0, atol=0.001)
def test_grad_data2_1(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[-340412.659], [-764209128.191], [-1120367.702]],
grad(X, y, theta),
rtol=0, atol=0.001)
def test_grad_data2_2(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[-338407.808], [-759579615.064], [-1113679.894]],
grad(X, y, theta),
rtol=0, atol=0.001)
def test_grad_data2_3(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-25.3], [32], [7.8]])
assert_allclose([[-276391.445], [-616340858.434], [-906796.414]],
grad(X, y, theta),
rtol=0, atol=0.001)
# REGULARIZED GRADIENT
def test_reg_grad_data1_1(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 0
assert_allclose([[3.321], [24.235]],
reg_grad(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_grad_data1_2(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 100
assert_allclose([[3.320665], [25.265821]],
reg_grad(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_grad_data1_3(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-1], [2]])
_lambda = 750
assert_allclose([[9.480465], [104.783153]],
reg_grad(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_grad_data1_4(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = -8.4 * ones((n + 1, 1), dtype=float64)
_lambda = 0.762
def J(theta):
return reg_cost_func(X, y, theta, _lambda)
assert_allclose(reg_grad(X, y, theta, _lambda),
numerical_grad(J, theta, self.err),
rtol=0, atol=0.001)
def test_reg_grad_data1_5(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = 3.2 * ones((n + 1, 1), dtype=float64)
_lambda = 154
def J(theta):
return reg_cost_func(X, y, theta, _lambda)
assert_allclose(reg_grad(X, y, theta, _lambda),
numerical_grad(J, theta, self.err),
rtol=0, atol=0.001)
def test_reg_grad_data1_6(self):
y = self.data1[:, -1:]
X = self.data1[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-12.4], [23.56]])
_lambda = 943
def J(theta):
return reg_cost_func(X, y, theta, _lambda)
assert_allclose(reg_grad(X, y, theta, _lambda),
numerical_grad(J, theta, self.err),
rtol=0, atol=0.001)
def test_reg_grad_data2_1(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 0
assert_allclose([[-338407.808], [-759579615.064], [-1113679.894]],
reg_grad(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_grad_data2_2(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
_lambda = 1000000
assert_allclose([[-338407.808], [-759558338.468], [-1092403.298]],
reg_grad(X, y, theta, _lambda),
rtol=0, atol=0.001)
def test_reg_grad_data2_3(self):
y = self.data2[:, -1:]
X = self.data2[:, :-1]
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-25.3], [32], [7.8]])
_lambda = 1000000
assert_allclose([[-276391.444681],
[-615660007.370213],
[-740838.968085]],
reg_grad(X, y, theta, _lambda),
rtol=0, atol=0.001)
# PREDICT
def test_predict_1(self):
X = array([[3.5]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-3.6303], [1.1664]])
assert_allclose([[0.4521]],
predict(X, theta),
rtol=0, atol=0.001)
def test_predict_2(self):
X = array([[3.5]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[0]],
predict(X, theta),
rtol=0, atol=0.001)
def test_predict_3(self):
X = array([[-3.5, 2.7]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[0.2]],
predict(X, theta),
rtol=0, atol=0.001)
def test_predict_4(self):
X = array([[-3.5, 2.7]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = -1 * ones((n + 1, 1), dtype=float64)
assert_allclose([[-0.2]],
predict(X, theta),
rtol=0, atol=0.001)
# HYPOTHESYS
def test_h_1(self):
X = array([[3.5]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = array([[-3.6303], [1.1664]])
assert_allclose([[0.4521]],
h(X, theta),
rtol=0, atol=0.001)
def test_h_2(self):
X = array([[3.5]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = zeros((n + 1, 1), dtype=float64)
assert_allclose([[0]],
h(X, theta),
rtol=0, atol=0.001)
def test_h_3(self):
X = array([[-3.5, 2.7]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = ones((n + 1, 1), dtype=float64)
assert_allclose([[0.2]],
h(X, theta),
rtol=0, atol=0.001)
def test_h_4(self):
X = array([[-3.5, 2.7]])
m, n = X.shape
intercept = ones((m, 1), dtype=float64)
X = append(intercept, X, axis=1)
theta = -1 * ones((n + 1, 1), dtype=float64)
assert_allclose([[-0.2]],
h(X, theta),
rtol=0, atol=0.001)
if __name__ == '__main__':
unittest.main()
| 31.736746 | 74 | 0.476613 | 2,267 | 17,360 | 3.525364 | 0.073666 | 0.04955 | 0.104104 | 0.04004 | 0.883133 | 0.881006 | 0.877503 | 0.866742 | 0.855731 | 0.843969 | 0 | 0.114714 | 0.365783 | 17,360 | 546 | 75 | 31.794872 | 0.611172 | 0.005991 | 0 | 0.800915 | 0 | 0 | 0.001623 | 0 | 0 | 0 | 0 | 0 | 0.093822 | 1 | 0.107551 | false | 0 | 0.01373 | 0.01373 | 0.1373 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
d8cb92d2440e78854bccded907d171737d327f7d | 882 | py | Python | lib/apiclient/ext/django_orm.py | motord/Motorcycle-Diaries | bb5e5e2d4d79573b4231e760d7662db26c03a55e | [
"BSD-3-Clause"
] | 1 | 2022-01-10T03:07:22.000Z | 2022-01-10T03:07:22.000Z | lib/apiclient/ext/django_orm.py | motord/Motorcycle-Diaries | bb5e5e2d4d79573b4231e760d7662db26c03a55e | [
"BSD-3-Clause"
] | null | null | null | lib/apiclient/ext/django_orm.py | motord/Motorcycle-Diaries | bb5e5e2d4d79573b4231e760d7662db26c03a55e | [
"BSD-3-Clause"
] | null | null | null | from django.db import models
class OAuthCredentialsField(models.Field):
__metaclass__ = models.SubfieldBase
def db_type(self):
return 'VARCHAR'
def to_python(self, value):
if value is None:
return None
if isinstance(value, apiclient.oauth.Credentials):
return value
return pickle.loads(base64.b64decode(value))
def get_db_prep_value(self, value):
return base64.b64encode(pickle.dumps(value))
class FlowThreeLeggedField(models.Field):
__metaclass__ = models.SubfieldBase
def db_type(self):
return 'VARCHAR'
def to_python(self, value):
print "In to_python", value
if value is None:
return None
if isinstance(value, apiclient.oauth.FlowThreeLegged):
return value
return pickle.loads(base64.b64decode(value))
def get_db_prep_value(self, value):
return base64.b64encode(pickle.dumps(value))
| 22.615385 | 58 | 0.723356 | 112 | 882 | 5.526786 | 0.330357 | 0.058158 | 0.06462 | 0.084006 | 0.812601 | 0.812601 | 0.812601 | 0.812601 | 0.812601 | 0.812601 | 0 | 0.022284 | 0.185941 | 882 | 38 | 59 | 23.210526 | 0.839833 | 0 | 0 | 0.769231 | 0 | 0 | 0.029478 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.038462 | null | null | 0.038462 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
d8eabd3f6fa32778426bb72dc2ae4530e774e03d | 3,426 | py | Python | accelbyte_py_sdk/api/social/__init__.py | encyphered/accelbyte-python-sdk | 09c1e989d7251de308150fdcd3119d662ca2d205 | [
"MIT"
] | null | null | null | accelbyte_py_sdk/api/social/__init__.py | encyphered/accelbyte-python-sdk | 09c1e989d7251de308150fdcd3119d662ca2d205 | [
"MIT"
] | null | null | null | accelbyte_py_sdk/api/social/__init__.py | encyphered/accelbyte-python-sdk | 09c1e989d7251de308150fdcd3119d662ca2d205 | [
"MIT"
] | null | null | null | """Auto-generated top-level package for the social API."""
__version__ = "1.17.1"
__author__ = "AccelByte"
__email__ = "dev@accelbyte.net"
# game_profile
from .wrappers import get_user_profiles
from .wrappers import get_profile
from .wrappers import public_get_user_profiles
from .wrappers import public_create_profile
from .wrappers import public_get_profile_attribute
from .wrappers import public_update_attribute
from .wrappers import public_get_profile
from .wrappers import public_update_profile
from .wrappers import public_delete_profile
from .wrappers import public_get_user_game_profiles
# global_statistic
from .wrappers import get_global_stat_items
# user_statistic
from .wrappers import bulk_inc_user_stat_item
from .wrappers import bulk_inc_user_stat_item_value
from .wrappers import bulk_fetch_stat_items
from .wrappers import bulk_reset_user_stat_item
from .wrappers import bulk_inc_user_stat_item_1
from .wrappers import bulk_inc_user_stat_item_value_1
from .wrappers import get_user_stat_items
from .wrappers import bulk_create_user_stat_items
from .wrappers import bulk_reset_user_stat_item_1
from .wrappers import create_user_stat_item
from .wrappers import delete_user_stat_items
from .wrappers import reset_user_stat_item_value
from .wrappers import inc_user_stat_item_value
from .wrappers import public_bulk_inc_user_stat_item
from .wrappers import public_bulk_inc_user_stat_item_value
from .wrappers import bulk_fetch_stat_items_1
from .wrappers import bulk_reset_user_stat_item_2
from .wrappers import public_bulk_inc_user_stat_item_1
from .wrappers import bulk_inc_user_stat_item_value_2
from .wrappers import public_query_user_stat_items
from .wrappers import public_bulk_create_user_stat_items
from .wrappers import bulk_reset_user_stat_item_3
from .wrappers import public_inc_user_stat_item
from .wrappers import public_inc_user_stat_item_value
from .wrappers import public_create_user_stat_item
from .wrappers import delete_user_stat_items_1
from .wrappers import reset_user_stat_item_value_1
from .wrappers import bulk_update_user_stat_item_v2
from .wrappers import bulk_update_user_stat_item
from .wrappers import update_user_stat_item_value
from .wrappers import delete_user_stat_items_2
from .wrappers import bulk_update_user_stat_item_1
from .wrappers import bulk_update_user_stat_item_2
from .wrappers import update_user_stat_item_value_1
# stat_configuration
from .wrappers import get_stat
from .wrappers import delete_stat
from .wrappers import update_stat
from .wrappers import get_stats
from .wrappers import create_stat
from .wrappers import query_stats
from .wrappers import export_stats
from .wrappers import import_stats
from .wrappers import create_stat_1
# slot_config
from .wrappers import get_namespace_slot_config
from .wrappers import update_namespace_slot_config
from .wrappers import delete_namespace_slot_config
from .wrappers import get_user_slot_config
from .wrappers import update_user_slot_config
from .wrappers import delete_user_slot_config
# slot
from .wrappers import get_slot_data
from .wrappers import get_user_namespace_slots
from .wrappers import public_get_slot_data
from .wrappers import public_update_user_namespace_slot
from .wrappers import public_delete_user_namespace_slot
from .wrappers import public_get_user_namespace_slots
from .wrappers import public_create_user_namespace_slot
from .wrappers import public_update_user_namespace_slot_metadata
| 39.837209 | 64 | 0.88237 | 538 | 3,426 | 5.148699 | 0.098513 | 0.294585 | 0.441877 | 0.190614 | 0.846209 | 0.786282 | 0.591697 | 0.496029 | 0.289531 | 0.203971 | 0 | 0.006412 | 0.089609 | 3,426 | 85 | 65 | 40.305882 | 0.881693 | 0.039113 | 0 | 0 | 1 | 0 | 0.009753 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.957746 | 0 | 0.957746 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
d8ef3aacd365114173d35a25b611bf87fec0cee2 | 245 | py | Python | PiCN/Layers/ICNLayer/PendingInterestTable/__init__.py | tairun/PiCN | be73b23caef8bac726e340668e34ed194efe8485 | [
"BSD-3-Clause"
] | null | null | null | PiCN/Layers/ICNLayer/PendingInterestTable/__init__.py | tairun/PiCN | be73b23caef8bac726e340668e34ed194efe8485 | [
"BSD-3-Clause"
] | 5 | 2020-07-15T09:01:42.000Z | 2020-09-28T08:45:21.000Z | PiCN/Layers/ICNLayer/PendingInterestTable/__init__.py | tairun/PiCN | be73b23caef8bac726e340668e34ed194efe8485 | [
"BSD-3-Clause"
] | null | null | null | """The Pending Interest Table"""
from .BasePendingInterestTable import BasePendingInterestTable
from .BasePendingInterestTable import PendingInterestTableEntry
from .PendingInterestTableMemoryExact import PendingInterestTableMemoryExact
| 35 | 77 | 0.865306 | 16 | 245 | 13.25 | 0.5625 | 0.264151 | 0.320755 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097959 | 245 | 6 | 78 | 40.833333 | 0.959276 | 0.106122 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | null | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
2b2cc5132aa40d800700da5fb722ff6523d6daa7 | 19,157 | py | Python | python/tests/test_hsv.py | tttom/MacroMax | e5f66252befb11e9fd906eb6e1a8a8c5eacf1451 | [
"MIT"
] | 11 | 2019-04-15T19:04:33.000Z | 2021-10-17T16:14:57.000Z | python/tests/test_hsv.py | tttom/MacroMax | e5f66252befb11e9fd906eb6e1a8a8c5eacf1451 | [
"MIT"
] | null | null | null | python/tests/test_hsv.py | tttom/MacroMax | e5f66252befb11e9fd906eb6e1a8a8c5eacf1451 | [
"MIT"
] | 2 | 2019-05-10T10:51:09.000Z | 2020-06-09T13:31:03.000Z | import unittest
import numpy.testing as npt
import numpy as np
from macromax.utils.display.hsv import hsv2rgb, rgb2hsv
class TestHsv2Rgb(unittest.TestCase):
def test_scalar(self):
npt.assert_array_equal(hsv2rgb(0, 0, 0), [0, 0, 0])
npt.assert_array_equal(hsv2rgb(0, 1, 0), [0, 0, 0])
npt.assert_array_equal(hsv2rgb(1, 0, 0), [0, 0, 0])
npt.assert_array_equal(hsv2rgb(0, 0, 1), [1, 1, 1])
npt.assert_array_equal(hsv2rgb(0, 0, 0.5), [0.5, 0.5, 0.5])
npt.assert_array_equal(hsv2rgb(0, 0, 1.5), [1.5, 1.5, 1.5])
npt.assert_array_equal(hsv2rgb(0, 1, 1), [1, 0, 0])
npt.assert_array_equal(hsv2rgb(0, 0.5, 1), [1, 0.5, 0.5])
npt.assert_array_equal(hsv2rgb(0, 1, 1), [1, 0, 0])
npt.assert_array_equal(hsv2rgb(1, 1, 1), [1, 0, 0])
npt.assert_array_equal(hsv2rgb(1/6, 1, 1), [1, 1, 0])
npt.assert_array_equal(hsv2rgb(2/6, 1, 1), [0, 1, 0])
npt.assert_array_equal(hsv2rgb(3/6, 1, 1), [0, 1, 1])
npt.assert_array_equal(hsv2rgb(4/6, 1, 1), [0, 0, 1])
npt.assert_array_equal(hsv2rgb(5/6, 1, 1), [1, 0, 1])
def test_vector(self):
npt.assert_array_equal(hsv2rgb(np.ones(4), np.ones(4), np.ones(4)),
np.concatenate((np.ones((4, 1)), np.zeros((4, 1)), np.zeros((4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb([0], [0], [0]), [[0, 0, 0]])
npt.assert_array_equal(hsv2rgb([0], [0], [1]), [[1, 1, 1]])
npt.assert_array_equal(hsv2rgb([0], [0], [0.5]), [[0.5, 0.5, 0.5]])
npt.assert_array_equal(hsv2rgb([0], [0], [1.5]), [[1.5, 1.5, 1.5]])
npt.assert_array_equal(hsv2rgb([0], [1], [1]), [[1, 0, 0]])
npt.assert_array_equal(hsv2rgb([0], [0.5], [1]), [[1, 0.5, 0.5]])
npt.assert_array_equal(hsv2rgb([0], [1], [1]), [[1, 0, 0]])
npt.assert_array_equal(hsv2rgb([1], [1], [1]), [[1, 0, 0]])
npt.assert_array_equal(hsv2rgb([1/6], [1], [1]), [[1, 1, 0]])
npt.assert_array_equal(hsv2rgb([2/6], [1], [1]), [[0, 1, 0]])
npt.assert_array_equal(hsv2rgb([3/6], [1], [1]), [[0, 1, 1]])
npt.assert_array_equal(hsv2rgb([4/6], [1], [1]), [[0, 0, 1]])
npt.assert_array_equal(hsv2rgb([5/6], [1], [1]), [[1, 0, 1]])
npt.assert_array_equal(hsv2rgb([0], [0], [0]), [[0, 0, 0]])
def test_matrix(self):
npt.assert_array_equal(hsv2rgb(np.ones((5, 4)), np.ones((5, 4)), np.ones((5, 4))),
np.concatenate((np.ones((5, 4, 1)), np.zeros((5, 4, 1)), np.zeros((5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb([[0]], [[0]], [[0]]), [[[0, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[0]], [[0]], [[1]]), [[[1, 1, 1]]])
npt.assert_array_equal(hsv2rgb([[0]], [[0]], [[0.5]]), [[[0.5, 0.5, 0.5]]])
npt.assert_array_equal(hsv2rgb([[0]], [[0]], [[1.5]]), [[[1.5, 1.5, 1.5]]])
npt.assert_array_equal(hsv2rgb([[0]], [[1]], [[1]]), [[[1, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[0]], [[0.5]], [[1]]), [[[1, 0.5, 0.5]]])
npt.assert_array_equal(hsv2rgb([[0]], [[1]], [[1]]), [[[1, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[1]], [[1]], [[1]]), [[[1, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[1/6]], [[1]], [[1]]), [[[1, 1, 0]]])
npt.assert_array_equal(hsv2rgb([[2/6]], [[1]], [[1]]), [[[0, 1, 0]]])
npt.assert_array_equal(hsv2rgb([[3/6]], [[1]], [[1]]), [[[0, 1, 1]]])
npt.assert_array_equal(hsv2rgb([[4/6]], [[1]], [[1]]), [[[0, 0, 1]]])
npt.assert_array_equal(hsv2rgb([[5/6]], [[1]], [[1]]), [[[1, 0, 1]]])
npt.assert_array_equal(hsv2rgb([[0]], [[0]], [[0]]), [[[0, 0, 0]]])
def test_tensor(self):
npt.assert_array_equal(hsv2rgb(np.ones((6, 5, 4)), np.ones((6, 5, 4)), np.ones((6, 5, 4))),
np.concatenate((np.ones((6, 5, 4, 1)), np.zeros((6, 5, 4, 1)), np.zeros((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb(np.ones((6, 5, 4)), 1, np.ones((6, 5, 4))),
np.concatenate((np.ones((6, 5, 4, 1)), np.zeros((6, 5, 4, 1)), np.zeros((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb(np.ones((6, 5, 4)), np.ones((6, 5, 4)), 1),
np.concatenate((np.ones((6, 5, 4, 1)), np.zeros((6, 5, 4, 1)), np.zeros((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb(1, np.ones((6, 5, 4)), 1),
np.concatenate((np.ones((6, 5, 4, 1)), np.zeros((6, 5, 4, 1)), np.zeros((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb([[[0]]], [[[0]]], [[[0]]]), [[[[0, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[0]]], [[[1]]]), [[[[1, 1, 1]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[0]]], [[[0.5]]]), [[[[0.5, 0.5, 0.5]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[0]]], [[[1.5]]]), [[[[1.5, 1.5, 1.5]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[1]]], [[[1]]]), [[[[1, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[0.5]]], [[[1]]]), [[[[1, 0.5, 0.5]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[1]]], [[[1]]]), [[[[1, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[1]]], [[[1]]], [[[1]]]), [[[[1, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[1/6]]], [[[1]]], [[[1]]]), [[[[1, 1, 0]]]])
npt.assert_array_equal(hsv2rgb([[[2/6]]], [[[1]]], [[[1]]]), [[[[0, 1, 0]]]])
npt.assert_array_equal(hsv2rgb([[[3/6]]], [[[1]]], [[[1]]]), [[[[0, 1, 1]]]])
npt.assert_array_equal(hsv2rgb([[[4/6]]], [[[1]]], [[[1]]]), [[[[0, 0, 1]]]])
npt.assert_array_equal(hsv2rgb([[[5/6]]], [[[1]]], [[[1]]]), [[[[1, 0, 1]]]])
npt.assert_array_equal(hsv2rgb([[[0]]], [[[0]]], [[[0]]]), [[[[0, 0, 0]]]])
class TestRgb2Hsv(unittest.TestCase):
def test_scalar(self):
npt.assert_array_equal(rgb2hsv(0, 0, 0), [0, 0, 0])
npt.assert_array_equal(rgb2hsv(1, 1, 1), [0, 0, 1])
npt.assert_array_equal(rgb2hsv(0.5, 0.5, 0.5), [0, 0, 0.5])
npt.assert_array_equal(rgb2hsv(1.5, 1.5, 1.5), [0, 0, 1.5])
npt.assert_array_equal(rgb2hsv(1, 0, 0), [0, 1, 1])
npt.assert_array_equal(rgb2hsv(1, 0.5, 0.5), [0, 0.5, 1])
npt.assert_array_equal(rgb2hsv(1, 0, 0), [0, 1, 1])
npt.assert_array_equal(rgb2hsv(1, 1, 0), [1/6, 1, 1])
npt.assert_array_equal(rgb2hsv(0, 1, 0), [2/6, 1, 1])
npt.assert_array_equal(rgb2hsv(0, 1, 1), [3/6, 1, 1])
npt.assert_array_equal(rgb2hsv(0, 0, 1), [4/6, 1, 1])
npt.assert_array_equal(rgb2hsv(1, 0, 1), [5/6, 1, 1])
def test_vector(self):
npt.assert_array_equal(rgb2hsv(np.ones(4), np.zeros(4), np.zeros(4)),
np.concatenate((np.zeros((4, 1)), np.ones((4, 1)), np.ones((4, 1))), axis=-1))
npt.assert_array_equal(rgb2hsv([0], [0], [0]), [[0, 0, 0]])
npt.assert_array_equal(rgb2hsv([1], [1], [1]), [[0, 0, 1]])
npt.assert_array_equal(rgb2hsv([0.5], [0.5], [0.5]), [[0, 0, 0.5]])
npt.assert_array_equal(rgb2hsv([1.5], [1.5], [1.5]), [[0, 0, 1.5]])
npt.assert_array_equal(rgb2hsv([1], [0], [0]), [[0, 1, 1]])
npt.assert_array_equal(rgb2hsv([1], [0.5], [0.5]), [[0, 0.5, 1]])
npt.assert_array_equal(rgb2hsv([1], [0], [0]), [[0, 1, 1]])
npt.assert_array_equal(rgb2hsv([1], [1], [0]), [[1/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([0], [1], [0]), [[2/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([0], [1], [1]), [[3/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([0], [0], [1]), [[4/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([1], [0], [1]), [[5/6, 1, 1]])
def test_matrix(self):
npt.assert_array_equal(rgb2hsv(np.ones((5, 4)), np.zeros((5, 4)), np.zeros((5, 4))),
np.concatenate((np.zeros((5, 4, 1)), np.ones((5, 4, 1)), np.ones((5, 4, 1))), axis=-1))
npt.assert_array_equal(rgb2hsv([[0]], [[0]], [[0]]), [[[0, 0, 0]]])
npt.assert_array_equal(rgb2hsv([[1]], [[1]], [[1]]), [[[0, 0, 1]]])
npt.assert_array_equal(rgb2hsv([[0.5]], [[0.5]], [[0.5]]), [[[0, 0, 0.5]]])
npt.assert_array_equal(rgb2hsv([[1.5]], [[1.5]], [[1.5]]), [[[0, 0, 1.5]]])
npt.assert_array_equal(rgb2hsv([[1]], [[0]], [[0]]), [[[0, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[1]], [[0.5]], [[0.5]]), [[[0, 0.5, 1]]])
npt.assert_array_equal(rgb2hsv([[1]], [[0]], [[0]]), [[[0, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[1]], [[1]], [[0]]), [[[1/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[0]], [[1]], [[0]]), [[[2/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[0]], [[1]], [[1]]), [[[3/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[0]], [[0]], [[1]]), [[[4/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[1]], [[0]], [[1]]), [[[5/6, 1, 1]]])
def test_tensor(self):
npt.assert_array_equal(
rgb2hsv(np.ones((6, 5, 4)), np.zeros((6, 5, 4)), np.zeros((6, 5, 4))),
np.concatenate((np.zeros((6, 5, 4, 1)), np.ones((6, 5, 4, 1)), np.ones((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(rgb2hsv([[[0]]], [[[0]]], [[[0]]]), [[[[0, 0, 0]]]])
npt.assert_array_equal(rgb2hsv([[[1]]], [[[1]]], [[[1]]]), [[[[0, 0, 1]]]])
npt.assert_array_equal(rgb2hsv([[[0.5]]], [[[0.5]]], [[[0.5]]]), [[[[0, 0, 0.5]]]])
npt.assert_array_equal(rgb2hsv([[[1.5]]], [[[1.5]]], [[[1.5]]]), [[[[0, 0, 1.5]]]])
npt.assert_array_equal(rgb2hsv([[[1]]], [[[0]]], [[[0]]]), [[[[0, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[1]]], [[[0.5]]], [[[0.5]]]), [[[[0, 0.5, 1]]]])
npt.assert_array_equal(rgb2hsv([[[1]]], [[[0]]], [[[0]]]), [[[[0, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[1]]], [[[1]]], [[[0]]]), [[[[1/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[0]]], [[[1]]], [[[0]]]), [[[[2/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[0]]], [[[1]]], [[[1]]]), [[[[3/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[0]]], [[[0]]], [[[1]]]), [[[[4/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[1]]], [[[0]]], [[[1]]]), [[[[5/6, 1, 1]]]])
class TestHsv2RgbSingleArgument(unittest.TestCase):
def test_scalar(self):
npt.assert_array_equal(hsv2rgb([0, 0, 0]), [0, 0, 0])
npt.assert_array_equal(hsv2rgb([0, 1, 0]), [0, 0, 0])
npt.assert_array_equal(hsv2rgb([1, 0, 0]), [0, 0, 0])
npt.assert_array_equal(hsv2rgb([0, 0, 1]), [1, 1, 1])
npt.assert_array_equal(hsv2rgb([0, 0, 0.5]), [0.5, 0.5, 0.5])
npt.assert_array_equal(hsv2rgb([0, 0, 1.5]), [1.5, 1.5, 1.5])
npt.assert_array_equal(hsv2rgb([0, 1, 1]), [1, 0, 0])
npt.assert_array_equal(hsv2rgb([0, 0.5, 1]), [1, 0.5, 0.5])
npt.assert_array_equal(hsv2rgb([0, 1, 1]), [1, 0, 0])
npt.assert_array_equal(hsv2rgb([1, 1, 1]), [1, 0, 0])
npt.assert_array_equal(hsv2rgb([1/6, 1, 1]), [1, 1, 0])
npt.assert_array_equal(hsv2rgb([2/6, 1, 1]), [0, 1, 0])
npt.assert_array_equal(hsv2rgb([3/6, 1, 1]), [0, 1, 1])
npt.assert_array_equal(hsv2rgb([4/6, 1, 1]), [0, 0, 1])
npt.assert_array_equal(hsv2rgb([5/6, 1, 1]), [1, 0, 1])
def test_vector(self):
npt.assert_array_equal(hsv2rgb(np.ones((4, 3))),
np.concatenate((np.ones((4, 1)), np.zeros((4, 1)), np.zeros((4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb([[0, 0, 0]]), [[0, 0, 0]])
npt.assert_array_equal(hsv2rgb([[0, 0, 1]]), [[1, 1, 1]])
npt.assert_array_equal(hsv2rgb([[0, 0, 0.5]]), [[0.5, 0.5, 0.5]])
npt.assert_array_equal(hsv2rgb([[0, 0, 1.5]]), [[1.5, 1.5, 1.5]])
npt.assert_array_equal(hsv2rgb([[0, 1, 1]]), [[1, 0, 0]])
npt.assert_array_equal(hsv2rgb([[0, 0.5, 1]]), [[1, 0.5, 0.5]])
npt.assert_array_equal(hsv2rgb([[0, 1, 1]]), [[1, 0, 0]])
npt.assert_array_equal(hsv2rgb([[1, 1, 1]]), [[1, 0, 0]])
npt.assert_array_equal(hsv2rgb([[1/6, 1, 1]]), [[1, 1, 0]])
npt.assert_array_equal(hsv2rgb([[2/6, 1, 1]]), [[0, 1, 0]])
npt.assert_array_equal(hsv2rgb([[3/6, 1, 1]]), [[0, 1, 1]])
npt.assert_array_equal(hsv2rgb([[4/6, 1, 1]]), [[0, 0, 1]])
npt.assert_array_equal(hsv2rgb([[5/6, 1, 1]]), [[1, 0, 1]])
npt.assert_array_equal(hsv2rgb([[0, 0, 0]]), [[0, 0, 0]])
def test_matrix(self):
npt.assert_array_equal(hsv2rgb(np.ones((5, 4, 3))),
np.concatenate((np.ones((5, 4, 1)), np.zeros((5, 4, 1)), np.zeros((5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb([[[0, 0, 0]]]), [[[0, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[[0, 0, 1]]]), [[[1, 1, 1]]])
npt.assert_array_equal(hsv2rgb([[[0, 0, 0.5]]]), [[[0.5, 0.5, 0.5]]])
npt.assert_array_equal(hsv2rgb([[[0, 0, 1.5]]]), [[[1.5, 1.5, 1.5]]])
npt.assert_array_equal(hsv2rgb([[[0, 1, 1]]]), [[[1, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[[0, 0.5, 1]]]), [[[1, 0.5, 0.5]]])
npt.assert_array_equal(hsv2rgb([[[0, 1, 1]]]), [[[1, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[[1, 1, 1]]]), [[[1, 0, 0]]])
npt.assert_array_equal(hsv2rgb([[[1/6, 1, 1]]]), [[[1, 1, 0]]])
npt.assert_array_equal(hsv2rgb([[[2/6, 1, 1]]]), [[[0, 1, 0]]])
npt.assert_array_equal(hsv2rgb([[[3/6, 1, 1]]]), [[[0, 1, 1]]])
npt.assert_array_equal(hsv2rgb([[[4/6, 1, 1]]]), [[[0, 0, 1]]])
npt.assert_array_equal(hsv2rgb([[[5/6, 1, 1]]]), [[[1, 0, 1]]])
npt.assert_array_equal(hsv2rgb([[0, 0, 0]]), [[0, 0, 0]])
def test_tensor(self):
npt.assert_array_equal(hsv2rgb(np.ones((6, 5, 4, 3))),
np.concatenate((np.ones((6, 5, 4, 1)), np.zeros((6, 5, 4, 1)), np.zeros((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(hsv2rgb([[[[0, 0, 0]]]]), [[[[0, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 0, 1]]]]), [[[[1, 1, 1]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 0, 0.5]]]]), [[[[0.5, 0.5, 0.5]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 0, 1.5]]]]), [[[[1.5, 1.5, 1.5]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 1, 1]]]]), [[[[1, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 0.5, 1]]]]), [[[[1, 0.5, 0.5]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 1, 1]]]]), [[[[1, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[[1, 1, 1]]]]), [[[[1, 0, 0]]]])
npt.assert_array_equal(hsv2rgb([[[[1/6, 1, 1]]]]), [[[[1, 1, 0]]]])
npt.assert_array_equal(hsv2rgb([[[[2/6, 1, 1]]]]), [[[[0, 1, 0]]]])
npt.assert_array_equal(hsv2rgb([[[[3/6, 1, 1]]]]), [[[[0, 1, 1]]]])
npt.assert_array_equal(hsv2rgb([[[[4/6, 1, 1]]]]), [[[[0, 0, 1]]]])
npt.assert_array_equal(hsv2rgb([[[[5/6, 1, 1]]]]), [[[[1, 0, 1]]]])
npt.assert_array_equal(hsv2rgb([[[[0, 0, 0]]]]), [[[[0, 0, 0]]]])
class TestRgb2HsvSingleArgument(unittest.TestCase):
def test_scalar(self):
npt.assert_array_equal(rgb2hsv([0, 0, 0]), [0, 0, 0])
npt.assert_array_equal(rgb2hsv([1, 1, 1]), [0, 0, 1])
npt.assert_array_equal(rgb2hsv([0.5, 0.5, 0.5]), [0, 0, 0.5])
npt.assert_array_equal(rgb2hsv([1.5, 1.5, 1.5]), [0, 0, 1.5])
npt.assert_array_equal(rgb2hsv([1, 0, 0]), [0, 1, 1])
npt.assert_array_equal(rgb2hsv([1, 0.5, 0.5]), [0, 0.5, 1])
npt.assert_array_equal(rgb2hsv([1, 0, 0]), [0, 1, 1])
npt.assert_array_equal(rgb2hsv([1, 1, 0]), [1/6, 1, 1])
npt.assert_array_equal(rgb2hsv([0, 1, 0]), [2/6, 1, 1])
npt.assert_array_equal(rgb2hsv([0, 1, 1]), [3/6, 1, 1])
npt.assert_array_equal(rgb2hsv([0, 0, 1]), [4/6, 1, 1])
npt.assert_array_equal(rgb2hsv([1, 0, 1]), [5/6, 1, 1])
def test_vector(self):
npt.assert_array_equal(rgb2hsv(np.concatenate((np.ones((4, 1)), np.zeros((4, 1)), np.zeros((4, 1))), axis=-1)),
np.concatenate((np.zeros((4, 1)), np.ones((4, 1)), np.ones((4, 1))), axis=-1))
npt.assert_array_equal(rgb2hsv([[0, 0, 0]]), [[0, 0, 0]])
npt.assert_array_equal(rgb2hsv([[1, 1, 1]]), [[0, 0, 1]])
npt.assert_array_equal(rgb2hsv([[0.5, 0.5, 0.5]]), [[0, 0, 0.5]])
npt.assert_array_equal(rgb2hsv([[1.5, 1.5, 1.5]]), [[0, 0, 1.5]])
npt.assert_array_equal(rgb2hsv([[1, 0, 0]]), [[0, 1, 1]])
npt.assert_array_equal(rgb2hsv([[1, 0.5, 0.5]]), [[0, 0.5, 1]])
npt.assert_array_equal(rgb2hsv([[1, 0, 0]]), [[0, 1, 1]])
npt.assert_array_equal(rgb2hsv([[1, 1, 0]]), [[1/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([[0, 1, 0]]), [[2/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([[0, 1, 1]]), [[3/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([[0, 0, 1]]), [[4/6, 1, 1]])
npt.assert_array_equal(rgb2hsv([[1, 0, 1]]), [[5/6, 1, 1]])
def test_matrix(self):
npt.assert_array_equal(rgb2hsv(np.concatenate((np.ones((5, 4, 1)), np.zeros((5, 4, 1)), np.zeros((5, 4, 1))), axis=-1)),
np.concatenate((np.zeros((5, 4, 1)), np.ones((5, 4, 1)), np.ones((5, 4, 1))), axis=-1))
npt.assert_array_equal(rgb2hsv([[[0, 0, 0]]]), [[[0, 0, 0]]])
npt.assert_array_equal(rgb2hsv([[[1, 1, 1]]]), [[[0, 0, 1]]])
npt.assert_array_equal(rgb2hsv([[[0.5, 0.5, 0.5]]]), [[[0, 0, 0.5]]])
npt.assert_array_equal(rgb2hsv([[[1.5, 1.5, 1.5]]]), [[[0, 0, 1.5]]])
npt.assert_array_equal(rgb2hsv([[[1, 0, 0]]]), [[[0, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[[1, 0.5, 0.5]]]), [[[0, 0.5, 1]]])
npt.assert_array_equal(rgb2hsv([[[1, 0, 0]]]), [[[0, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[[1, 1, 0]]]), [[[1/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[[0, 1, 0]]]), [[[2/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[[0, 1, 1]]]), [[[3/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[[0, 0, 1]]]), [[[4/6, 1, 1]]])
npt.assert_array_equal(rgb2hsv([[[1, 0, 1]]]), [[[5/6, 1, 1]]])
def test_tensor(self):
npt.assert_array_equal(
rgb2hsv(np.concatenate((np.ones((6, 5, 4, 1)), np.zeros((6, 5, 4, 1)), np.zeros((6, 5, 4, 1))), axis=-1)),
np.concatenate((np.zeros((6, 5, 4, 1)), np.ones((6, 5, 4, 1)), np.ones((6, 5, 4, 1))), axis=-1))
npt.assert_array_equal(rgb2hsv([[[[0, 0, 0]]]]), [[[[0, 0, 0]]]])
npt.assert_array_equal(rgb2hsv([[[[1, 1, 1]]]]), [[[[0, 0, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[0.5, 0.5, 0.5]]]]), [[[[0, 0, 0.5]]]])
npt.assert_array_equal(rgb2hsv([[[[1.5, 1.5, 1.5]]]]), [[[[0, 0, 1.5]]]])
npt.assert_array_equal(rgb2hsv([[[[1, 0, 0]]]]), [[[[0, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[1, 0.5, 0.5]]]]), [[[[0, 0.5, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[1, 0, 0]]]]), [[[[0, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[1, 1, 0]]]]), [[[[1/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[0, 1, 0]]]]), [[[[2/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[0, 1, 1]]]]), [[[[3/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[0, 0, 1]]]]), [[[[4/6, 1, 1]]]])
npt.assert_array_equal(rgb2hsv([[[[1, 0, 1]]]]), [[[[5/6, 1, 1]]]])
if __name__ == '__main__':
unittest.main()
| 61.796774 | 128 | 0.494023 | 3,212 | 19,157 | 2.798879 | 0.013699 | 0.061402 | 0.350389 | 0.475528 | 0.977419 | 0.975528 | 0.975417 | 0.971413 | 0.969967 | 0.965628 | 0 | 0.131389 | 0.205408 | 19,157 | 309 | 129 | 61.996764 | 0.459204 | 0 | 0 | 0.283582 | 0 | 0 | 0.000418 | 0 | 0 | 0 | 0 | 0 | 0.839552 | 1 | 0.059701 | false | 0 | 0.014925 | 0 | 0.089552 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
2b35f6d914961b86118a445a771ac60e668a3a12 | 960 | py | Python | updwntrgnl.py | coder1912/Programs | 6f9875462bea99fe403e6a8568122a0d8720ed75 | [
"MIT"
] | null | null | null | updwntrgnl.py | coder1912/Programs | 6f9875462bea99fe403e6a8568122a0d8720ed75 | [
"MIT"
] | null | null | null | updwntrgnl.py | coder1912/Programs | 6f9875462bea99fe403e6a8568122a0d8720ed75 | [
"MIT"
] | 11 | 2020-10-05T02:25:19.000Z | 2020-10-29T18:55:17.000Z | n = int(input("Please Enter the number of steps: "))
m = 1
for i in range(n):
for j in range(n-i-1):
print(" ",end="")
for k in range(1,m+1):
print("*",end="")
m=m+2
print("\r")
print("-----------------------------------------------")
m = 1
for i in range(1,n):
m = m+2
for i in range(n):
for k in range(i):
print(" ",end="")
for j in range(1,m+1):
print("*",end="")
m = m-2
print("\r")
print("-----------------------------------------------")
m = 1
for i in range(n):
for j in range(n-i-1):
print(" ",end="")
for k in range(1,m+1):
print("*",end="")
m=m+2
print("\r")
m = 1
for i in range(1,n-1):
m = m+2
for i in range(n-1):
for k in range(i+1):
print(" ",end="")
for j in range(1,m+1):
print("*",end="")
m = m-2
print("\r")
print("-----------------------------------------------")
| 23.414634 | 57 | 0.364583 | 149 | 960 | 2.348993 | 0.14094 | 0.28 | 0.18 | 0.188571 | 0.891429 | 0.828571 | 0.82 | 0.82 | 0.685714 | 0.685714 | 0 | 0.037092 | 0.297917 | 960 | 40 | 58 | 24 | 0.482196 | 0 | 0 | 0.85 | 0 | 0 | 0.207609 | 0.153261 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0.375 | 0 | 0 | 0 | null | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
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