xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

1e9c781b419e835c7eced196ef491e5128f9bbdb

62

py

Python

ACM-Solution/powerof2.py

wasi0013/Python-CodeBase

4a7a36395162f68f84ded9085fa34cc7c9b19233

[ "MIT" ]

2

2016-04-26T15:40:40.000Z

2018-07-18T10:16:42.000Z

ACM-Solution/powerof2.py

wasi0013/Python-CodeBase

4a7a36395162f68f84ded9085fa34cc7c9b19233

[ "MIT" ]

1

2016-04-26T15:44:15.000Z

2016-04-29T14:44:40.000Z

ACM-Solution/powerof2.py

wasi0013/Python-CodeBase

4a7a36395162f68f84ded9085fa34cc7c9b19233

[ "MIT" ]

1

2018-10-02T16:12:19.000Z

exec("x=0;N=int(input());print(2**(sum()+1));"*int(input()))

31

61

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2.75

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1

null

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7

94ab75228b05a2963c183365296413528d246a91

92

py

Python

parameters_8000.py

ayusharora99/E-CollegeBooks

bc9897215a9ed4d17e372a0371318967e3d480eb

[ "BSD-3-Clause" ]

2

2018-12-08T23:59:12.000Z

2019-02-13T23:04:36.000Z

parameters_8000.py

ayusharora99/E-CollegeBooks

bc9897215a9ed4d17e372a0371318967e3d480eb

[ "BSD-3-Clause" ]

null

parameters_8000.py

ayusharora99/E-CollegeBooks

bc9897215a9ed4d17e372a0371318967e3d480eb

[ "BSD-3-Clause" ]

3

2018-12-08T23:59:17.000Z

2019-02-13T23:04:38.000Z

password="pbkdf2(1000,20,sha512)$ba0f5757f7b448ec$d9c2e5887e65f45311047c8ff518f200ee11b98f"

46

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0.891304

7

92

11.714286

1

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0.01087

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92

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1

null

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1

0

8

94ac6044959d983d47c287f915e389ed8d95187d

328

py

Python

src/omfgp/cli.py

miketlk/omfgp

6e5a0f52f2688d81bde3e5169a37311c9517fe1d

[ "MIT" ]

null

src/omfgp/cli.py

miketlk/omfgp

6e5a0f52f2688d81bde3e5169a37311c9517fe1d

[ "MIT" ]

null

src/omfgp/cli.py

miketlk/omfgp

6e5a0f52f2688d81bde3e5169a37311c9517fe1d

[ "MIT" ]

1

2021-08-16T10:19:52.000Z

DEFAULT_KEY = { "ENC": b"\x40\x41\x42\x43\x44\x45\x46\x47\x48\x49\x4A\x4B\x4C\x4D\x4E\x4F", "MAC": b"\x40\x41\x42\x43\x44\x45\x46\x47\x48\x49\x4A\x4B\x4C\x4D\x4E\x4F", "DEK": b"\x40\x41\x42\x43\x44\x45\x46\x47\x48\x49\x4A\x4B\x4C\x4D\x4E\x4F", } def list(key=DEFAULT_KEY): """Lists all applets""" print(key)

36.444444

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0.643293

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328

3.166667

0.424242

0.057416

0.100478

0.143541

0.703349

0

0.267123

0.109756

328

9

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36.444444

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0.051829

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0.428571

0.656863

0.627451

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0.142857

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null

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12

94f3b18944a11c75445cef1a85dda665ee609119

46,746

py

Python

lib/jnpr/healthbot/swagger/api/organization_api.py

Juniper/healthbot-py-client

49f0884b5d01ac8430aa7ed4c9acb4e7a2b717a6

[ "Apache-2.0" ]

10

2019-10-23T12:54:37.000Z

2022-02-07T19:24:30.000Z

lib/jnpr/healthbot/swagger/api/organization_api.py

Juniper/healthbot-py-client

49f0884b5d01ac8430aa7ed4c9acb4e7a2b717a6

[ "Apache-2.0" ]

5

2019-09-30T04:29:25.000Z

2022-02-16T12:21:06.000Z

lib/jnpr/healthbot/swagger/api/organization_api.py

Juniper/healthbot-py-client

49f0884b5d01ac8430aa7ed4c9acb4e7a2b717a6

[ "Apache-2.0" ]

4

2019-09-30T01:17:48.000Z

2020-08-25T07:27:54.000Z

# coding: utf-8 """ Paragon Insights APIs API interface for PI application # noqa: E501 OpenAPI spec version: 4.0.0 Contact: healthbot-feedback@juniper.net 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 jnpr.healthbot.swagger.api_client import ApiClient class OrganizationApi(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_healthbot_organization_site_edge_edge_by_id(self, organization_name, site_name, edge_name, edge, **kwargs): # noqa: E501 """Create edge by ID # noqa: E501 Create operation of resource: edge # 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_healthbot_organization_site_edge_edge_by_id(organization_name, site_name, edge_name, edge, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param EdgeSchema edge: edgebody object (required) :param str x_iam_token: authentication header object :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.create_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, edge, **kwargs) # noqa: E501 else: (data) = self.create_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, edge, **kwargs) # noqa: E501 return data def create_healthbot_organization_site_edge_edge_by_id_with_http_info(self, organization_name, site_name, edge_name, edge, **kwargs): # noqa: E501 """Create edge by ID # noqa: E501 Create operation of resource: edge # 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_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, edge, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param EdgeSchema edge: edgebody object (required) :param str x_iam_token: authentication header object :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'edge_name', 'edge', 'x_iam_token'] # 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_healthbot_organization_site_edge_edge_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `create_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `create_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'edge_name' is set if ('edge_name' not in params or params['edge_name'] is None): raise ValueError("Missing the required parameter `edge_name` when calling `create_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'edge' is set if ('edge' not in params or params['edge'] is None): raise ValueError("Missing the required parameter `edge` when calling `create_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 if 'edge_name' in params: path_params['edge_name'] = params['edge_name'] # noqa: E501 query_params = [] header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'edge' in params: body_params = params['edge'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/edge/{edge_name}/', '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 create_healthbot_organization_site_site_by_id(self, organization_name, site_name, site, **kwargs): # noqa: E501 """Create site by ID # noqa: E501 Create operation of resource: site # 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_healthbot_organization_site_site_by_id(organization_name, site_name, site, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param SiteSchema site: sitebody object (required) :param str x_iam_token: authentication header object :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.create_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, site, **kwargs) # noqa: E501 else: (data) = self.create_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, site, **kwargs) # noqa: E501 return data def create_healthbot_organization_site_site_by_id_with_http_info(self, organization_name, site_name, site, **kwargs): # noqa: E501 """Create site by ID # noqa: E501 Create operation of resource: site # 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_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, site, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param SiteSchema site: sitebody object (required) :param str x_iam_token: authentication header object :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'site', 'x_iam_token'] # 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_healthbot_organization_site_site_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `create_healthbot_organization_site_site_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `create_healthbot_organization_site_site_by_id`") # noqa: E501 # verify the required parameter 'site' is set if ('site' not in params or params['site'] is None): raise ValueError("Missing the required parameter `site` when calling `create_healthbot_organization_site_site_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 query_params = [] header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'site' in params: body_params = params['site'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/', '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 delete_healthbot_organization_site_edge_edge_by_id(self, organization_name, site_name, edge_name, **kwargs): # noqa: E501 """Delete edge by ID # noqa: E501 Delete operation of resource: edge # 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_healthbot_organization_site_edge_edge_by_id(organization_name, site_name, edge_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param str x_iam_token: authentication header object :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_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, **kwargs) # noqa: E501 else: (data) = self.delete_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, **kwargs) # noqa: E501 return data def delete_healthbot_organization_site_edge_edge_by_id_with_http_info(self, organization_name, site_name, edge_name, **kwargs): # noqa: E501 """Delete edge by ID # noqa: E501 Delete operation of resource: edge # 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_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param str x_iam_token: authentication header object :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'edge_name', 'x_iam_token'] # 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_healthbot_organization_site_edge_edge_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `delete_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `delete_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'edge_name' is set if ('edge_name' not in params or params['edge_name'] is None): raise ValueError("Missing the required parameter `edge_name` when calling `delete_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 if 'edge_name' in params: path_params['edge_name'] = params['edge_name'] # noqa: E501 query_params = [] header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/edge/{edge_name}/', '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 delete_healthbot_organization_site_site_by_id(self, organization_name, site_name, **kwargs): # noqa: E501 """Delete site by ID # noqa: E501 Delete operation of resource: site # 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_healthbot_organization_site_site_by_id(organization_name, site_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str x_iam_token: authentication header object :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_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, **kwargs) # noqa: E501 else: (data) = self.delete_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, **kwargs) # noqa: E501 return data def delete_healthbot_organization_site_site_by_id_with_http_info(self, organization_name, site_name, **kwargs): # noqa: E501 """Delete site by ID # noqa: E501 Delete operation of resource: site # 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_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str x_iam_token: authentication header object :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'x_iam_token'] # 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_healthbot_organization_site_site_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `delete_healthbot_organization_site_site_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `delete_healthbot_organization_site_site_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 query_params = [] header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/', '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 retrieve_healthbot_organization_site_edge_edge_by_id(self, organization_name, site_name, edge_name, **kwargs): # noqa: E501 """Retrieve edge by ID # noqa: E501 Retrieve operation of resource: edge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.retrieve_healthbot_organization_site_edge_edge_by_id(organization_name, site_name, edge_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param str x_iam_token: authentication header object :param bool working: true queries undeployed configuration :return: EdgeSchema If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.retrieve_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, **kwargs) # noqa: E501 else: (data) = self.retrieve_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, **kwargs) # noqa: E501 return data def retrieve_healthbot_organization_site_edge_edge_by_id_with_http_info(self, organization_name, site_name, edge_name, **kwargs): # noqa: E501 """Retrieve edge by ID # noqa: E501 Retrieve operation of resource: edge # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.retrieve_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param str x_iam_token: authentication header object :param bool working: true queries undeployed configuration :return: EdgeSchema If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'edge_name', 'x_iam_token', 'working'] # 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 retrieve_healthbot_organization_site_edge_edge_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `retrieve_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `retrieve_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'edge_name' is set if ('edge_name' not in params or params['edge_name'] is None): raise ValueError("Missing the required parameter `edge_name` when calling `retrieve_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 if 'edge_name' in params: path_params['edge_name'] = params['edge_name'] # noqa: E501 query_params = [] if 'working' in params: query_params.append(('working', params['working'])) # noqa: E501 header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/edge/{edge_name}/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='EdgeSchema', # 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 retrieve_healthbot_organization_site_site_by_id(self, organization_name, site_name, **kwargs): # noqa: E501 """Retrieve site by ID # noqa: E501 Retrieve operation of resource: site # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.retrieve_healthbot_organization_site_site_by_id(organization_name, site_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str x_iam_token: authentication header object :param bool working: true queries undeployed configuration :return: SiteSchema If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.retrieve_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, **kwargs) # noqa: E501 else: (data) = self.retrieve_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, **kwargs) # noqa: E501 return data def retrieve_healthbot_organization_site_site_by_id_with_http_info(self, organization_name, site_name, **kwargs): # noqa: E501 """Retrieve site by ID # noqa: E501 Retrieve operation of resource: site # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.retrieve_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str x_iam_token: authentication header object :param bool working: true queries undeployed configuration :return: SiteSchema If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'x_iam_token', 'working'] # 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 retrieve_healthbot_organization_site_site_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `retrieve_healthbot_organization_site_site_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `retrieve_healthbot_organization_site_site_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 query_params = [] if 'working' in params: query_params.append(('working', params['working'])) # noqa: E501 header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='SiteSchema', # 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_healthbot_organization_site_edge_edge_by_id(self, organization_name, site_name, edge_name, edge, **kwargs): # noqa: E501 """Update edge by ID # noqa: E501 Update operation of resource: edge # 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_healthbot_organization_site_edge_edge_by_id(organization_name, site_name, edge_name, edge, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param EdgeSchema edge: edgebody object (required) :param str x_iam_token: authentication header object :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.update_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, edge, **kwargs) # noqa: E501 else: (data) = self.update_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, edge, **kwargs) # noqa: E501 return data def update_healthbot_organization_site_edge_edge_by_id_with_http_info(self, organization_name, site_name, edge_name, edge, **kwargs): # noqa: E501 """Update edge by ID # noqa: E501 Update operation of resource: edge # 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_healthbot_organization_site_edge_edge_by_id_with_http_info(organization_name, site_name, edge_name, edge, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param str edge_name: ID of edge-name (required) :param EdgeSchema edge: edgebody object (required) :param str x_iam_token: authentication header object :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'edge_name', 'edge', 'x_iam_token'] # 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_healthbot_organization_site_edge_edge_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `update_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `update_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'edge_name' is set if ('edge_name' not in params or params['edge_name'] is None): raise ValueError("Missing the required parameter `edge_name` when calling `update_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 # verify the required parameter 'edge' is set if ('edge' not in params or params['edge'] is None): raise ValueError("Missing the required parameter `edge` when calling `update_healthbot_organization_site_edge_edge_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 if 'edge_name' in params: path_params['edge_name'] = params['edge_name'] # noqa: E501 query_params = [] header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'edge' in params: body_params = params['edge'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/edge/{edge_name}/', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def update_healthbot_organization_site_site_by_id(self, organization_name, site_name, site, **kwargs): # noqa: E501 """Update site by ID # noqa: E501 Update operation of resource: site # 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_healthbot_organization_site_site_by_id(organization_name, site_name, site, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param SiteSchema site: sitebody object (required) :param str x_iam_token: authentication header object :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.update_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, site, **kwargs) # noqa: E501 else: (data) = self.update_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, site, **kwargs) # noqa: E501 return data def update_healthbot_organization_site_site_by_id_with_http_info(self, organization_name, site_name, site, **kwargs): # noqa: E501 """Update site by ID # noqa: E501 Update operation of resource: site # 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_healthbot_organization_site_site_by_id_with_http_info(organization_name, site_name, site, async_req=True) >>> result = thread.get() :param async_req bool :param str organization_name: ID of organization-name (required) :param str site_name: ID of site-name (required) :param SiteSchema site: sitebody object (required) :param str x_iam_token: authentication header object :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['organization_name', 'site_name', 'site', 'x_iam_token'] # 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_healthbot_organization_site_site_by_id" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'organization_name' is set if ('organization_name' not in params or params['organization_name'] is None): raise ValueError("Missing the required parameter `organization_name` when calling `update_healthbot_organization_site_site_by_id`") # noqa: E501 # verify the required parameter 'site_name' is set if ('site_name' not in params or params['site_name'] is None): raise ValueError("Missing the required parameter `site_name` when calling `update_healthbot_organization_site_site_by_id`") # noqa: E501 # verify the required parameter 'site' is set if ('site' not in params or params['site'] is None): raise ValueError("Missing the required parameter `site` when calling `update_healthbot_organization_site_site_by_id`") # noqa: E501 collection_formats = {} path_params = {} if 'organization_name' in params: path_params['organization_name'] = params['organization_name'] # noqa: E501 if 'site_name' in params: path_params['site_name'] = params['site_name'] # noqa: E501 query_params = [] header_params = {} if 'x_iam_token' in params: header_params['x-iam-token'] = params['x_iam_token'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'site' in params: body_params = params['site'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/config/organization/{organization_name}/site/{site_name}/', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

46.980905

164

0.648013

5,652

46,746

5.058209

0.030786

0.046731

0.069957

0.047011

0.982896

0.980237

0.980027

0.979712

0

0.014873

0.266461

46,746

994

165

47.028169

0.818868

0.323835

0

0.824399

1

0

0.260303

0.090026

0

1

0.031423

false

0

0.007394

0

0.085028

0

null

0

1

0

1

0

null

0

8

bf5d47be37cf903e8580c7840db1fffd47cb12b1

86

py

Python

berth/builder/backends/__init__.py

joealcorn/berth.cc

9cba1355d49705a13ae58cfdffa26ee6a3fb9e31

[ "MIT" ]

null

berth/builder/backends/__init__.py

joealcorn/berth.cc

9cba1355d49705a13ae58cfdffa26ee6a3fb9e31

[ "MIT" ]

null

berth/builder/backends/__init__.py

joealcorn/berth.cc

9cba1355d49705a13ae58cfdffa26ee6a3fb9e31

[ "MIT" ]

null

from berth.builder.backends.base import * from berth.builder.backends.sphinx import *

28.666667

43

0.813953

12

86

5.833333

0.583333

0.257143

0.457143

0.685714

0

0.093023

86

2

44

43

0.897436

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

8

44c253f81c7a3412e5a62fbd60011c97050af7d2

2,720

py

Python

tests/test_year_2014.py

l0pht511/jpholiday

083145737b61fad3420c066968c4329d17dc3baf

[ "MIT" ]

179

2017-10-05T12:41:10.000Z

2022-03-24T22:18:25.000Z

tests/test_year_2014.py

l0pht511/jpholiday

083145737b61fad3420c066968c4329d17dc3baf

[ "MIT" ]

17

2018-10-23T00:51:13.000Z

2021-11-22T11:40:06.000Z

tests/test_year_2014.py

l0pht511/jpholiday

083145737b61fad3420c066968c4329d17dc3baf

[ "MIT" ]

17

2018-10-19T11:13:07.000Z

2022-01-29T08:05:56.000Z

# coding: utf-8 import datetime import unittest import jpholiday class TestYear2014(unittest.TestCase): def test_holiday(self): """ 2014年祝日 """ self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 1, 1)), '元日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 1, 13)), '成人の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 2, 11)), '建国記念の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 3, 21)), '春分の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 4, 29)), '昭和の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 5, 3)), '憲法記念日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 5, 4)), 'みどりの日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 5, 5)), 'こどもの日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 5, 6)), 'みどりの日振替休日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 7, 21)), '海の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 9, 15)), '敬老の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 9, 23)), '秋分の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 10, 13)), '体育の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 11, 3)), '文化の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 11, 23)), '勤労感謝の日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 11, 24)), '勤労感謝の日振替休日') self.assertEqual(jpholiday.is_holiday_name(datetime.date(2014, 12, 23)), '天皇誕生日') def test_count_month(self): """ 2014年月祝日数 """ self.assertEqual(len(jpholiday.month_holidays(2014, 1)), 2) self.assertEqual(len(jpholiday.month_holidays(2014, 2)), 1) self.assertEqual(len(jpholiday.month_holidays(2014, 3)), 1) self.assertEqual(len(jpholiday.month_holidays(2014, 4)), 1) self.assertEqual(len(jpholiday.month_holidays(2014, 5)), 4) self.assertEqual(len(jpholiday.month_holidays(2014, 6)), 0) self.assertEqual(len(jpholiday.month_holidays(2014, 7)), 1) self.assertEqual(len(jpholiday.month_holidays(2014, 8)), 0) self.assertEqual(len(jpholiday.month_holidays(2014, 9)), 2) self.assertEqual(len(jpholiday.month_holidays(2014, 10)), 1) self.assertEqual(len(jpholiday.month_holidays(2014, 11)), 3) self.assertEqual(len(jpholiday.month_holidays(2014, 12)), 1) def test_count_year(self): """ 2014年祝日数 """ self.assertEqual(len(jpholiday.year_holidays(2014)), 17)

51.320755

95

0.682353

352

2,720

5.125

0.176136

0.249446

0.226164

0.245011

0.809313

0.736142

0.511641

0.394124

0

0.095702

0.170221

2,720

52

96

52.307692

0.703589

0.015074

0

0.032963

0

0.810811

1

0.081081

false

0

0.081081

0

0.189189

0

null

1

0

null

0

1

0

8

7852458867e37b272e2b2586e35216cc3c5811b9

137

py

Python

module.py

supperhappie/web_scraper

334cc877d06fe0083f52fca84de7fb906a0ff5f7

[ "MIT" ]

null

module.py

supperhappie/web_scraper

334cc877d06fe0083f52fca84de7fb906a0ff5f7

[ "MIT" ]

null

module.py

supperhappie/web_scraper

334cc877d06fe0083f52fca84de7fb906a0ff5f7

[ "MIT" ]

null

from math import * print(ceil(4.2)) print(sum([0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1])) print(fsum([0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1]))

27.4

50

0.569343

47

137

1.659574

0.234043

0.461538

0.615385

0.820513

0.461538

0

0.292308

0.051095

137

5

50

27.4

0.307692

0

1

0

true

0

0.25

0

0.25

0.75

0

1

null

1

0

1

0

1

0

1

0

null

0

1

0

1

0

9

78758a785c0289c32fa159acab530e62fdfcc65f

24,304

py

Python

mayan/apps/documents/tests/test_document_file_views.py

nattangwiwat/Mayan-EDMS-recitation

fcf16afb56eae812fb99144d65ae1ae6749de0b7

[ "Apache-2.0" ]

343

2015-01-05T14:19:35.000Z

2018-12-10T19:07:48.000Z

mayan/apps/documents/tests/test_document_file_views.py

nattangwiwat/Mayan-EDMS-recitation

fcf16afb56eae812fb99144d65ae1ae6749de0b7

[ "Apache-2.0" ]

191

2015-01-03T00:48:19.000Z

2018-11-30T09:10:25.000Z

mayan/apps/documents/tests/test_document_file_views.py

nattangwiwat/Mayan-EDMS-recitation

fcf16afb56eae812fb99144d65ae1ae6749de0b7

[ "Apache-2.0" ]

257

2019-05-14T10:26:37.000Z

2022-03-30T03:37:36.000Z

from mayan.apps.converter.layers import layer_saved_transformations from mayan.apps.converter.permissions import ( permission_transformation_delete, permission_transformation_edit ) from mayan.apps.converter.tests.mixins import LayerTestMixin from mayan.apps.documents.tests.literals import TEST_MULTI_PAGE_TIFF from mayan.apps.file_caching.events import event_cache_partition_purged from mayan.apps.file_caching.models import CachePartitionFile from mayan.apps.file_caching.permissions import permission_cache_partition_purge from mayan.apps.file_caching.tests.mixins import CachePartitionViewTestMixin from ..events import ( event_document_file_deleted, event_document_file_downloaded, event_document_file_edited, ) from ..permissions import ( permission_document_file_delete, permission_document_file_download, permission_document_file_edit, permission_document_file_print, permission_document_file_view ) from .base import GenericDocumentViewTestCase from .mixins.document_file_mixins import ( DocumentFileTestMixin, DocumentFileTransformationTestMixin, DocumentFileTransformationViewTestMixin, DocumentFileViewTestMixin ) class DocumentFileViewTestCase( DocumentFileTestMixin, DocumentFileViewTestMixin, GenericDocumentViewTestCase ): def test_document_file_delete_no_permission(self): first_file = self.test_document.file_latest self._upload_new_file() test_document_file_count = self.test_document.files.count() self._clear_events() response = self._request_test_document_file_delete_view( document_file=first_file ) self.assertEqual(response.status_code, 404) self.assertEqual( self.test_document.files.count(), test_document_file_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_delete_with_access(self): first_file = self.test_document.file_latest self._upload_new_file() self.grant_access( obj=self.test_document, permission=permission_document_file_delete ) test_document_file_count = self.test_document.files.count() self._clear_events() response = self._request_test_document_file_delete_view( document_file=first_file ) self.assertEqual(response.status_code, 302) self.assertEqual( self.test_document.files.count(), test_document_file_count - 1 ) events = self._get_test_events() self.assertEqual(events.count(), 1) self.assertEqual(events[0].action_object, None) self.assertEqual(events[0].actor, self._test_case_user) self.assertEqual(events[0].target, self.test_document) self.assertEqual(events[0].verb, event_document_file_deleted.id) def test_trashed_document_file_delete_with_access(self): first_file = self.test_document.file_latest self._upload_new_file() self.grant_access( obj=self.test_document, permission=permission_document_file_delete ) test_document_file_count = self.test_document.files.count() self.test_document.delete() self._clear_events() response = self._request_test_document_file_delete_view( document_file=first_file ) self.assertEqual(response.status_code, 404) self.assertEqual( self.test_document.files.count(), test_document_file_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_delete_multiple_no_permission(self): self._upload_new_file() test_document_file_count = self.test_document.files.count() self._clear_events() response = self._request_test_document_file_delete_multiple_view() self.assertEqual(response.status_code, 404) self.assertEqual( self.test_document.files.count(), test_document_file_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_delete_multiple_with_access(self): self._upload_new_file() self.grant_access( obj=self.test_document, permission=permission_document_file_delete ) test_document_file_count = self.test_document.files.count() self._clear_events() response = self._request_test_document_file_delete_multiple_view() self.assertEqual(response.status_code, 302) self.assertEqual( self.test_document.files.count(), test_document_file_count - 1 ) events = self._get_test_events() self.assertEqual(events.count(), 1) self.assertEqual(events[0].action_object, None) self.assertEqual(events[0].actor, self._test_case_user) self.assertEqual(events[0].target, self.test_document) self.assertEqual(events[0].verb, event_document_file_deleted.id) def test_document_file_edit_view_no_permission(self): document_file_comment = self.test_document_file.comment self._clear_events() response = self._request_test_document_file_edit_view() self.assertEqual(response.status_code, 404) self.test_document_file.refresh_from_db() self.assertEqual( self.test_document_file.comment, document_file_comment ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_edit_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_edit ) document_file_comment = self.test_document_file.comment document_file_filename = self.test_document_file.filename self._clear_events() response = self._request_test_document_file_edit_view() self.assertEqual(response.status_code, 302) self.test_document_file.refresh_from_db() self.assertNotEqual( self.test_document_file.comment, document_file_comment ) self.assertNotEqual( self.test_document_file.filename, document_file_filename ) events = self._get_test_events() self.assertEqual(events.count(), 1) self.assertEqual(events[0].action_object, self.test_document) self.assertEqual(events[0].actor, self._test_case_user) self.assertEqual(events[0].target, self.test_document_file) self.assertEqual(events[0].verb, event_document_file_edited.id) def test_trashed_document_file_edit_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_edit ) document_file_comment = self.test_document_file.comment document_file_filename = self.test_document_file.filename self.test_document.delete() self._clear_events() response = self._request_test_document_file_edit_view() self.assertEqual(response.status_code, 404) self.test_document_file.refresh_from_db() self.assertEqual( self.test_document_file.comment, document_file_comment ) self.assertEqual( self.test_document_file.filename, document_file_filename ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_list_no_permission(self): self._clear_events() response = self._request_test_document_file_list_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_list_with_access(self): self.grant_access( obj=self.test_document, permission=permission_document_file_view ) self._clear_events() response = self._request_test_document_file_list_view() self.assertContains( response=response, status_code=200, text=str(self.test_document_file) ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_trashed_document_file_list_with_access(self): self.grant_access( obj=self.test_document, permission=permission_document_file_view ) self.test_document.delete() self._clear_events() response = self._request_test_document_file_list_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_print_form_view_no_permission(self): self._clear_events() response = self._request_test_document_file_print_form_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_print_form_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_print ) self._clear_events() response = self._request_test_document_file_print_form_view() self.assertEqual(response.status_code, 200) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_trashed_document_file_print_form_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_print ) self.test_document.delete() self._clear_events() response = self._request_test_document_file_print_form_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_print_view_no_permission(self): self._clear_events() response = self._request_test_document_file_print_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_print_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_print ) self._clear_events() response = self._request_test_document_file_print_view() self.assertEqual(response.status_code, 200) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_trashed_document_file_print_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_print ) self.test_document.delete() self._clear_events() response = self._request_test_document_file_print_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_properties_view_no_permission(self): self._clear_events() response = self._request_test_document_file_properties_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_properties_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_view ) self._clear_events() response = self._request_test_document_file_properties_view() self.assertContains( response=response, text=self.test_document_file.filename, status_code=200 ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_trashed_document_file_properties_view_with_access(self): self.grant_access( obj=self.test_document_file, permission=permission_document_file_view ) self.test_document.delete() self._clear_events() response = self._request_test_document_file_properties_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) class DocumentFileDownloadViewTestCase( DocumentFileViewTestMixin, GenericDocumentViewTestCase ): def test_document_file_download_view_no_permission(self): self._clear_events() response = self._request_test_document_file_download_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_download_view_with_permission(self): # Set the expected_content_types for # common.tests.mixins.ContentTypeCheckMixin self.expected_content_types = ( self.test_document.file_latest.mimetype, ) self.grant_access( obj=self.test_document, permission=permission_document_file_download ) self._clear_events() response = self._request_test_document_file_download_view() self.assertEqual(response.status_code, 200) with self.test_document.file_latest.open() as file_object: self.assert_download_response( response=response, content=file_object.read(), filename=self.test_document.file_latest.filename, mime_type=self.test_document.file_latest.mimetype ) events = self._get_test_events() self.assertEqual(events.count(), 1) self.assertEqual(events[0].action_object, self.test_document) self.assertEqual(events[0].actor, self._test_case_user) self.assertEqual(events[0].target, self.test_document_file) self.assertEqual(events[0].verb, event_document_file_downloaded.id) def test_trashed_document_file_download_view_with_permission(self): self.grant_access( obj=self.test_document, permission=permission_document_file_download ) self.test_document.delete() self._clear_events() response = self._request_test_document_file_download_view() self.assertEqual(response.status_code, 404) events = self._get_test_events() self.assertEqual(events.count(), 0) class DocumentFileTransformationViewTestCase( LayerTestMixin, DocumentFileTransformationTestMixin, DocumentFileTransformationViewTestMixin, GenericDocumentViewTestCase ): test_document_filename = TEST_MULTI_PAGE_TIFF def test_document_file_transformations_clear_view_no_permission(self): self._create_document_file_transformation() transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() self._clear_events() response = self._request_test_document_file_transformations_clear_view() self.assertEqual(response.status_code, 404) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), transformation_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_transformations_clear_view_with_access(self): self._create_document_file_transformation() transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() self.grant_access( obj=self.test_document_file, permission=permission_transformation_delete ) self._clear_events() response = self._request_test_document_file_transformations_clear_view() self.assertEqual(response.status_code, 302) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), transformation_count - 1 ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_trashed_document_file_transformations_clear_view_with_access(self): self._create_document_file_transformation() transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() self.grant_access( obj=self.test_document_file, permission=permission_transformation_delete ) self.test_document.delete() self._clear_events() response = self._request_test_document_file_transformations_clear_view() self.assertEqual(response.status_code, 404) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), transformation_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_multiple_transformations_clear_view_no_permission(self): self._create_document_file_transformation() transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() self._clear_events() response = self._request_test_document_file_multiple_transformations_clear_view() self.assertEqual(response.status_code, 404) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), transformation_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_multiple_transformations_clear_view_with_access(self): self._create_document_file_transformation() transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() self.grant_access( obj=self.test_document_file, permission=permission_document_file_view ) self.grant_access( obj=self.test_document_file, permission=permission_transformation_delete ) self._clear_events() response = self._request_test_document_file_multiple_transformations_clear_view() self.assertEqual(response.status_code, 302) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), transformation_count - 1, ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_transformations_clone_view_no_permission(self): self._create_document_file_transformation() page_first_transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() page_last_transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.last() ).count() self._clear_events() response = self._request_test_document_file_transformations_clone_view() self.assertEqual(response.status_code, 404) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), page_first_transformation_count ) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.last() ).count(), page_last_transformation_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_transformations_clone_view_with_access(self): self._create_document_file_transformation() page_first_transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count() page_last_transformation_count = layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.last() ).count() self.grant_access( obj=self.test_document_file, permission=permission_transformation_edit ) self._clear_events() response = self._request_test_document_file_transformations_clone_view() self.assertEqual(response.status_code, 302) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.first() ).count(), page_first_transformation_count ) self.assertEqual( layer_saved_transformations.get_transformations_for( obj=self.test_document_file.pages.last() ).count(), page_last_transformation_count + 1 ) events = self._get_test_events() self.assertEqual(events.count(), 0) class DocumentFileCachePurgeViewTestCase( CachePartitionViewTestMixin, GenericDocumentViewTestCase ): def test_document_file_cache_purge_no_permission(self): self.test_object = self.test_document_file self._inject_test_object_content_type() self.test_document_file.file_pages.first().generate_image() test_document_file_cache_partitions = self.test_document_file.get_cache_partitions() cache_partition_file_count = CachePartitionFile.objects.filter( partition__in=test_document_file_cache_partitions ).count() self._clear_events() response = self._request_test_object_file_cache_partition_purge_view() self.assertEqual(response.status_code, 404) self.assertEqual( CachePartitionFile.objects.filter( partition__in=test_document_file_cache_partitions ).count(), cache_partition_file_count ) events = self._get_test_events() self.assertEqual(events.count(), 0) def test_document_file_cache_purge_with_access(self): self.test_object = self.test_document_file self._inject_test_object_content_type() self.grant_access( obj=self.test_document_file, permission=permission_cache_partition_purge ) self.test_document_file.file_pages.first().generate_image() test_document_file_cache_partitions = self.test_document_file.get_cache_partitions() cache_partition_file_count = CachePartitionFile.objects.filter( partition__in=test_document_file_cache_partitions ).count() self._clear_events() cache_partitions = self.test_document_file.get_cache_partitions() response = self._request_test_object_file_cache_partition_purge_view() self.assertEqual(response.status_code, 302) self.assertNotEqual( CachePartitionFile.objects.filter( partition__in=test_document_file_cache_partitions ).count(), cache_partition_file_count ) events = self._get_test_events() self.assertEqual(events.count(), 2) self.assertEqual(events[0].action_object, self.test_document_file) self.assertEqual(events[0].actor, self._test_case_user) self.assertEqual(events[0].target, cache_partitions[0]) self.assertEqual(events[0].verb, event_cache_partition_purged.id) self.assertEqual(events[1].action_object, self.test_document_file) self.assertEqual(events[1].actor, self._test_case_user) self.assertEqual(events[1].target, cache_partitions[1]) self.assertEqual(events[1].verb, event_cache_partition_purged.id)

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94

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5.852624

0.046892

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159e0993e20c2d1a01b6aa2da9021b6c99e89aa2

4,417

py

Python

xndtools/kernel_generator/tests/test_test_array.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

3

2019-11-12T16:01:26.000Z

2020-06-27T19:27:27.000Z

xndtools/kernel_generator/tests/test_test_array.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

4

2018-04-25T17:12:43.000Z

2018-08-23T18:17:24.000Z

xndtools/kernel_generator/tests/test_test_array.py

xnd-project/xndtools

9478f31954091d861ce538ba278f7f888e23d19b

[ "BSD-3-Clause" ]

6

2018-05-04T08:10:40.000Z

2019-03-19T10:00:21.000Z

import pytest from xndtools.kernel_generator.utils import NormalizedTypeMap from xnd import xnd import test_array as m long_t = NormalizedTypeMap()('long') def assert_equal(x, y): assert x == y and x.dtype == y.dtype def test_array_range_input(): # C kernel a = xnd([1, 2, 3], dtype=long_t) r = m.test_array_range_input(a) assert_equal(r, xnd(6, type=long_t)) assert_equal(a, xnd([0, 1, 2], dtype=long_t)) # because `a` matches exactly # F kernel # TODO # Xnd kernel a = xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t) x = a[1::2] r = m.test_array_range_input(x) assert_equal(r, xnd(12, type=long_t)) assert_equal(x, xnd([2, 4, 6], dtype=long_t)) assert_equal(a, xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t)) # Strided kernel # TODO def test_array_range_inplace(): # C kernel a = xnd([1, 2, 3], dtype=long_t) r = m.test_array_range_inplace(a) assert_equal(r, xnd(6, type=long_t)) assert_equal(a, xnd([0, 1, 2], dtype=long_t)) # F kernel # TODO # Xnd kernel a = xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t) x = a[1::2] assert_equal(x, xnd([2, 4, 6], dtype=long_t)) r = m.test_array_range_inplace(x) assert_equal(r, xnd(12, type=long_t)) assert_equal(x, xnd([0, 1, 2], dtype=long_t)) assert_equal(a, xnd([1, 0, 3, 1, 5, 2, 7], dtype=long_t)) # Strided kernel # TODO def test_array_range_inout(): # C kernel a = xnd([1, 2, 3], dtype=long_t) r = m.test_array_range_inout(a) assert_equal(r, xnd(6, type=long_t)) assert_equal(a, xnd([0, 1, 2], dtype=long_t)) # F kernel # TODO # Xnd kernel a = xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t) x = a[1::2] assert_equal(x, xnd([2, 4, 6], dtype=long_t)) with pytest.raises(ValueError, match=r'.* must be C-contiguous .*'): r = m.test_array_range_inout(x) # Strided kernel # TODO def test_array_range_input_output(): # C kernel a = xnd([1, 2, 3], dtype=long_t) o, r = m.test_array_range_input_output(a) assert_equal(r, xnd(6, type=long_t)) assert_equal(o, xnd([0, 1, 2], dtype=long_t)) assert_equal(a, xnd([1, 2, 3], dtype=long_t)) # F kernel # TODO # Xnd kernel a = xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t) x = a[1::2] assert_equal(x, xnd([2, 4, 6], dtype=long_t)) o, r = m.test_array_range_input_output(x) assert_equal(r, xnd(12, type=long_t)) assert_equal(o, xnd([0, 1, 2], dtype=long_t)) assert_equal(x, xnd([2, 4, 6], dtype=long_t)) assert_equal(a, xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t)) # Strided kernel # TODO def test_array_range_inplace_output(): # C kernel a = xnd([1, 2, 3], dtype=long_t) o, r = m.test_array_range_inplace_output(a) assert_equal(r, xnd(6, type=long_t)) assert_equal(o, xnd([0, 1, 2], dtype=long_t)) assert_equal(a, xnd([0, 1, 2], dtype=long_t)) # F kernel # TODO # Xnd kernel a = xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t) x = a[1::2] assert_equal(x, xnd([2, 4, 6], dtype=long_t)) o, r = m.test_array_range_inplace_output(x) assert_equal(r, xnd(12, type=long_t)) assert_equal(o, xnd([0, 1, 2], dtype=long_t)) assert_equal(x, xnd([0, 1, 2], dtype=long_t)) assert_equal(a, xnd([1, 0, 3, 1, 5, 2, 7], dtype=long_t)) # Strided kernel # TODO def test_array_range_inout_output(): # C kernel a = xnd([1, 2, 3], dtype=long_t) o, r = m.test_array_range_inout_output(a) assert_equal(r, xnd(6, type=long_t)) assert_equal(o, xnd([0, 1, 2], dtype=long_t)) assert_equal(a, xnd([0, 1, 2], dtype=long_t)) # F kernel # TODO # Xnd kernel a = xnd([1, 2, 3, 4, 5, 6, 7], dtype=long_t) x = a[1::2] assert_equal(x, xnd([2, 4, 6], dtype=long_t)) with pytest.raises(ValueError, match=r'.* must be C-contiguous .*'): o, r = m.test_array_range_inout_output(x) # Strided kernel # TODO def test_array_range_output(): # using C, F, or Xnd kernel if defined o, r = m.test_array_range_output(xnd(3, type=long_t)) assert_equal(r, xnd(0, type=long_t)) # could be random assert_equal(o, xnd([0, 1, 2], dtype=long_t)) def test_array_range_hide(): # using C, F, or Xnd kernel if defined r = m.test_array_range_hide(xnd(3, type=long_t)) assert r.type == xnd(0, type=long_t).type # r value is random

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3.066584

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0.758745

0.746683

0

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0.235228

4,417

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null

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null

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1

0

7

eca386f51bbe9df459d3def8a91f74e99caeab92

28

py

Python

test_data/src-mini-project/src/mini/example.py

ajeetraina/pycograph

a1bcf8e0f62a605a798e82373ec2279add83cf16

[ "BSD-3-Clause" ]

346

2016-02-22T20:21:10.000Z

2022-01-27T20:55:53.000Z

Language Skills/Python/Unit 4/2-Taking a Vacation/Review of Functions/1-Before we begin.py

vpstudios/Codecademy-Exercise-Answers

ebd0ee8197a8001465636f52c69592ea6745aa0c

[ "MIT" ]

55

2016-04-07T13:58:44.000Z

2020-06-25T12:20:24.000Z

Language Skills/Python/Unit 4/2-Taking a Vacation/Review of Functions/1-Before we begin.py

vpstudios/Codecademy-Exercise-Answers

ebd0ee8197a8001465636f52c69592ea6745aa0c

[ "MIT" ]

477

2016-02-21T06:17:02.000Z

2021-12-22T10:08:01.000Z

def answer(): return 42

9.333333

13

0.607143

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0

1

0

7

ecabd3f4bdd9f064663b14463a5ce941ba9d7e6b

20,935

py

Python

abeja/notebook/api/client.py

abeja-inc/abeja-platform-sdk

97cfc99b11ffc1fccb3f527435277bc89e18b8c3

[ "Apache-2.0" ]

2

2020-10-20T18:38:16.000Z

2020-10-20T20:12:35.000Z

abeja/notebook/api/client.py

abeja-inc/abeja-platform-sdk

97cfc99b11ffc1fccb3f527435277bc89e18b8c3

[ "Apache-2.0" ]

30

2020-04-07T01:15:47.000Z

2020-11-18T03:25:19.000Z

abeja/notebook/api/client.py

abeja-inc/abeja-platform-sdk

97cfc99b11ffc1fccb3f527435277bc89e18b8c3

[ "Apache-2.0" ]

null

from typing import Optional from abeja.common.api_client import BaseAPIClient from abeja.notebook.types import InstanceType, ImageType, NotebookType class APIClient(BaseAPIClient): """A Low-Level client for Notebook API .. code-block:: python from abeja.notebook import APIClient api_client = APIClient() """ def create_notebook( self, organization_id: str, job_definition_name: str, instance_type: Optional[str] = None, image: Optional[str] = None, notebook_type: Optional[str] = None) -> dict: """create a notebook. API reference: POST /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" instance_type = 'cpu-1' image = 'abeja-inc/all-cpu:19.10' notebook_type = 'lab' response = api_client.create_notebook( organization_id, job_definition_name, instance_type, image, notebook_type ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **instance_type** (str): **[optional]** instance type (ex. cpu-1) - **image** (str): **[optional]** runtime environment (ex. abeja-inc/all-cpu:19.10) - **notebook_type** (str): **[optional]** notebook type (notebook or lab) Return type: dict Returns: Response Syntax: .. code-block:: python { "job_definition_id": "1234567890123", "training_notebook_id": "1410000000000", "name": "notebook-3", "description": None, "status": "Pending", "status_message": None, "instance_type": "cpu-1", "image": "abeja-inc/all-cpu:18.10", "creator": { "updated_at": "2018-01-04T03:02:12Z", "role": "admin", "is_registered": True, "id": "1122334455660", "email": "test@abeja.asia", "display_name": None, "created_at": "2017-05-26T01:38:46Z" }, "created_at": "2018-06-07T04:42:34.913644Z", "modified_at": "2018-06-07T04:42:34.913726Z" } Raises: - NotFound - BadRequest - Unauthorized: Authentication failed - InternalServerError """ params = {} if instance_type is not None and InstanceType.to_enum(instance_type): params['instance_type'] = instance_type if image is not None and ImageType.to_enum(image): params['image'] = image if notebook_type is not None and NotebookType.to_enum(notebook_type): params['notebook_type'] = notebook_type path = '/organizations/{}/training/definitions/{}/notebooks'.format( organization_id, job_definition_name) return self._connection.api_request( method='POST', path=path, json=params) def get_notebooks( self, organization_id: str, job_definition_name: str) -> dict: """get notebooks. API reference: GET /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" response = api_client.get_notebooks( organization_id, job_definition_name ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name Return type: dict Returns: Response Syntax: .. code-block:: python { "total": 1, "offset": 0, "limit": 10, "entries": [ { "job_definition_id": "1234567890123", "training_notebook_id": "1410000000000", "name": "notebook-3", "description": None, "status": "Pending", "status_message": None, "instance_type": "cpu-1", "image": "abeja-inc/all-cpu:18.10", "creator": { "updated_at": "2018-01-04T03:02:12Z", "role": "admin", "is_registered": True, "id": "1122334455660", "email": "test@abeja.asia", "display_name": None, "created_at": "2017-05-26T01:38:46Z" }, "created_at": "2018-06-07T04:42:34.913644Z", "modified_at": "2018-06-07T04:42:34.913726Z" } ] } Raises: - NotFound - Unauthorized: Authentication failed - InternalServerError """ path = '/organizations/{}/training/definitions/{}/notebooks'.format( organization_id, job_definition_name) return self._connection.api_request(method='GET', path=path) def get_notebook( self, organization_id: str, job_definition_name: str, notebook_id: str=None) -> dict: """get a notebook. API reference: GET /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks/{notebook_id} Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" notebook_id = "1230000000000" response = api_client.get_notebook( organization_id, job_definition_name, notebook_id ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **notebook_id** (str): notebook id Return type: dict Returns: Response Syntax: .. code-block:: python { "job_definition_id": "1234567890123", "training_notebook_id": "1410000000000", "name": "notebook-3", "description": None, "status": "Pending", "status_message": None, "instance_type": "cpu-1", "image": "abeja-inc/all-cpu:18.10", "creator": { "updated_at": "2018-01-04T03:02:12Z", "role": "admin", "is_registered": True, "id": "1122334455660", "email": "test@abeja.asia", "display_name": None, "created_at": "2017-05-26T01:38:46Z" }, "created_at": "2018-06-07T04:42:34.913644Z", "modified_at": "2018-06-07T04:42:34.913726Z" } Raises: - NotFound - Unauthorized: Authentication failed - InternalServerError """ path = '/organizations/{}/training/definitions/{}/notebooks/{}'.format( organization_id, job_definition_name, notebook_id) return self._connection.api_request(method='GET', path=path) def update_notebook( self, organization_id: str, job_definition_name: str, notebook_id: str, instance_type: Optional[str] = None, image: Optional[str] = None, notebook_type: Optional[str] = None) -> dict: """update a notebook. API reference: PUT /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks/{notebook_id} Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" notebook_id = "1230000000000" instance_type = 'cpu-1' image = 'abeja-inc/all-cpu:19.10' response = api_client.update_notebook( organization_id, job_definition_name, notebook_id, instance_type=instance_type, image=image ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **notebook_id** (str): notebook id - **instance_type** (str): **[optional]** instance type (ex. cpu-1) - **image** (str): **[optional]** runtime environment (ex. abeja-inc/all-cpu:19.10) - **notebook_type** (str): **[optional]** notebook type (notebook or lab) Return type: dict Returns: Response Syntax: .. code-block:: python { "job_definition_id": "1234567890123", "training_notebook_id": 0, "name": "notebook-3", "description": None, "status": "Pending", "status_message": None, "instance_type": "cpu-1", "image": "abeja-inc/all-cpu:18.10", "creator": { "updated_at": "2018-01-04T03:02:12Z", "role": "admin", "is_registered": True, "id": "1122334455660", "email": "test@abeja.asia", "display_name": None, "created_at": "2017-05-26T01:38:46Z" }, "created_at": "2018-06-07T04:42:34.913644Z", "modified_at": "2018-06-07T04:42:34.913726Z" } Raises: - NotFound - BadRequest - Unauthorized: Authentication failed - InternalServerError """ params = {} if instance_type is not None and InstanceType.to_enum(instance_type): params['instance_type'] = instance_type if image is not None and ImageType.to_enum(image): params['image'] = image if notebook_type is not None and NotebookType.to_enum(notebook_type): params['notebook_type'] = notebook_type path = '/organizations/{}/training/definitions/{}/notebooks/{}'.format( organization_id, job_definition_name, notebook_id) return self._connection.api_request( method='PUT', path=path, json=params) def delete_notebook( self, organization_id: str, job_definition_name: str, notebook_id: str) -> dict: """delete a notebook. API reference: DELETE /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks/{notebook_id} Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" notebook_id = "1230000000000" response = api_client.delete_notebook( organization_id, job_definition_name, notebook_id ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **notebook_id** (str): notebook id Return type: dict Returns: Response Syntax: .. code-block:: python { "value": { "message": "1111111111111 deleted" } } Raises: - NotFound - Unauthorized: Authentication failed - InternalServerError """ path = '/organizations/{}/training/definitions/{}/notebooks/{}'.format( organization_id, job_definition_name, notebook_id) return self._connection.api_request(method='DELETE', path=path) def start_notebook( self, organization_id: str, job_definition_name: str, notebook_id: str, notebook_type: Optional[str] = None) -> dict: """start a notebook. API reference: POST /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks/{notebook_id}/start Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" notebook_id = "1230000000000" response = api_client.start_notebook( organization_id, job_definition_name, notebook_id ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **notebook_id** (str): notebook id - **notebook_type** (str): **[optional]** notebook type (notebook or lab) Return type: dict Returns: Response Syntax: .. code-block:: python { "job_definition_id": "1234567890123", "training_notebook_id": 0, "name": "notebook-3", "description": None, "status": "Pending", "status_message": None, "instance_type": "cpu-1", "image": "abeja-inc/all-cpu:18.10", "creator": { "updated_at": "2018-01-04T03:02:12Z", "role": "admin", "is_registered": True, "id": "1122334455660", "email": "test@abeja.asia", "display_name": None, "created_at": "2017-05-26T01:38:46Z" }, "created_at": "2018-06-07T04:42:34.913644Z", "modified_at": "2018-06-07T04:42:34.913726Z" } Raises: - NotFound - Unauthorized: Authentication failed - InternalServerError """ params = {} if notebook_type is not None and NotebookType.to_enum(notebook_type): params['notebook_type'] = notebook_type path = '/organizations/{}/training/definitions/{}/notebooks/{}/start'.format( organization_id, job_definition_name, notebook_id) return self._connection.api_request( method='POST', path=path, json=params) def stop_notebook( self, organization_id: str, job_definition_name: str, notebook_id: str) -> dict: """stop a notebook. API reference: POST /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks/{notebook_id}/stop Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" notebook_id = "1230000000000" response = api_client.stop_notebook( organization_id, job_definition_name, notebook_id ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **notebook_id** (str): notebook id Return type: dict Returns: Response Syntax: .. code-block:: python { "job_definition_id": "1234567890123", "training_notebook_id": 0, "name": "notebook-3", "description": None, "status": "Pending", "status_message": None, "instance_type": "cpu-1", "image": "abeja-inc/all-cpu:18.10", "creator": { "updated_at": "2018-01-04T03:02:12Z", "role": "admin", "is_registered": True, "id": "1122334455660", "email": "test@abeja.asia", "display_name": None, "created_at": "2017-05-26T01:38:46Z" }, "created_at": "2018-06-07T04:42:34.913644Z", "modified_at": "2018-06-07T04:42:34.913726Z" } Raises: - NotFound - Unauthorized: Authentication failed - InternalServerError """ path = '/organizations/{}/training/definitions/{}/notebooks/{}/stop'.format( organization_id, job_definition_name, notebook_id) return self._connection.api_request(method='POST', path=path, json={}) def get_notebook_recent_logs( self, organization_id: str, job_definition_name: str, notebook_id: str, next_forward_token: Optional[str]=None, next_backward_token: Optional[str]=None, ) -> dict: """get recent logs of the notebook. API reference: GET /organizations/{organization_id}/training/definitions/{job_definition_name}/notebooks/{notebook_id}/recentlogs Request Syntax: .. code-block:: python organization_id = "1410000000000" job_definition_name = "test_job_definition" notebook_id = "1230000000000" response = api_client.get_notebook_recent_logs( organization_id, job_definition_name, notebook_id ) Params: - **organization_id** (str): organization id - **job_definition_name** (str): training job definition name - **notebook_id** (str): notebook id - **next_forward_token** (str): **[optional]** token for the next page of logs - **next_backward_token** (str): **[optional]** token for the next previous of logs Return type: dict Returns: Response Syntax: .. code-block:: python { "events": [ { "message": "start executing model with abeja-runtime-python36 (version: 0.X.X)", "timestamp": "2019-10-16T00:00:00.000Z" } ], "next_backward_token": "...", "next_forward_token": "..." } Raises: - NotFound - Unauthorized: Authentication failed - InternalServerError """ params = {} if next_forward_token: params['next_forward_token'] = next_forward_token if next_backward_token: params['next_backward_token'] = next_backward_token path = '/organizations/{}/training/definitions/{}/notebooks/{}/recentlogs'.format( organization_id, job_definition_name, notebook_id) return self._connection.api_request( method='GET', path=path, params=params)

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7

ecb49bb21b35339cb73ab865c201f6495e6361cb

29

py

Python

taskwiki/__init__.py

PtitCaius/taskwiki

8c4da3a744fd1aa22bfb0369658cecc925e77fd0

[ "MIT" ]

465

2015-03-27T09:42:18.000Z

2020-07-18T20:35:19.000Z

taskwiki/__init__.py

PtitCaius/taskwiki

8c4da3a744fd1aa22bfb0369658cecc925e77fd0

[ "MIT" ]

272

2015-01-10T20:38:02.000Z

2020-07-16T12:55:15.000Z

taskwiki/__init__.py

PtitCaius/taskwiki

8c4da3a744fd1aa22bfb0369658cecc925e77fd0

[ "MIT" ]

66

2015-03-21T16:33:39.000Z

2020-07-12T09:20:29.000Z

14.5

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Python

admin/tools/crawler_db/PS3-PHPtoMYSQL.py

gorpoorko/TCXS-Project-Store-Web-PlayStation3-V3

58bf3ba08723d775e8b42e6dfa16ea5b79594d26

[ "MIT" ]

null

admin/tools/crawler_db/PS3-PHPtoMYSQL.py

gorpoorko/TCXS-Project-Store-Web-PlayStation3-V3

58bf3ba08723d775e8b42e6dfa16ea5b79594d26

[ "MIT" ]

null

admin/tools/crawler_db/PS3-PHPtoMYSQL.py

gorpoorko/TCXS-Project-Store-Web-PlayStation3-V3

58bf3ba08723d775e8b42e6dfa16ea5b79594d26

[ "MIT" ]

null

import os import pathlib import sqlite3 from bs4 import BeautifulSoup import pymysql.cursors from datetime import datetime #faz a conexao com o banco de dados conexao = pymysql.connect(host = 'localhost', user = 'root', password = '', db = 'tcxs_store', charset = 'utf8mb4', cursorclass = pymysql.cursors.DictCursor) #variavies iniciais dados = open('base.html', 'r', encoding="utf-8").read() dados= BeautifulSoup(dados, 'html5lib') key_titulo = dados.find_all('h2', {'class':'titulo_jogo'}) key_desc = dados.find_all('p', {'class':'textoJogo'}) key_contentid = dados.find_all('a', href=True) key_imagem = dados.find_all('img',{'class':'caixa_imagem'}) key_links = dados.find_all('a', href=True) titulos = [] for titulo in key_titulo: titulo = str(titulo).split('"titulo_jogo">')[1].split('</h')[0].replace("'","").replace('</h2>','').replace(':','') titulos.append(titulo) #print(titulo) descricoes = [] for desc in key_desc: desc = str(desc).split('textoJogo">')[1].replace('</p>','') descricoes.append(desc) #print(desc) ids = [] invalidar = ['index.php','psp.php','ps1.php','ps2.php','ps3.php','emuladores.php','https://tcxsproject.com.br/doadores/','https://tcxsproject.com.br/dev/ps3xploit.com/'] for id in key_contentid: id = id['href'] if id in invalidar: pass else: try: id = id.split('/')[5].split('.pkg')[0] ids.append(id) #print(id) except: id = 'FALTA CONTENT_ID' ids.append(id) #print(id) imagens = [] for imagem in key_imagem: imagem = str(imagem).split('ps3/')[1].split('"/>')[0].replace('" width="170','') imagens.append(imagem) print(imagem) links = [] invalidar = ['index.php','psp.php','ps1.php','ps2.php','ps3.php','emuladores.php','https://tcxsproject.com.br/doadores/','https://tcxsproject.com.br/dev/ps3xploit.com/'] for link in key_links: link = link['href'] if link in invalidar: #print(f'Pulando o {link}') pass else: links.append(link) #print(f'gravando o {link}') print(len(titulos), len(descricoes), len(imagens), len(links)) dicionario_jogos = list(zip(list(titulos), list(imagens), list(links)))#-- #print(dicionario_jogos) now = datetime.now() hoje = now.strftime('%Y-%m-%d %H:%M:%S') if len(links) == 30: print('==== 30 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','{links[24]}','{links[25]}','{links[26]}','{links[27]}', '{links[28]}','{links[29]}') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 29: print('==== 29 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','{links[24]}','{links[25]}','{links[26]}','{links[27]}', '{links[28]}','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 28: print('==== 28 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','{links[24]}','{links[25]}','{links[26]}','{links[27]}', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 27: print('==== 27 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','{links[24]}','{links[25]}','{links[26]}','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 26: print('==== 26 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','{links[24]}','{links[25]}','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 25: print('==== 25 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','{links[24]}','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 24: print('==== 24 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','{links[23]}','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 23: print('==== 23 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','{links[22]}','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 22: print('==== 22 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '{links[21]}','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 21: print('==== 21 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','{links[20]}', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 20: print('==== 20 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','{links[19]}','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 19: print('==== 19 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','{links[18]}','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 18: print('==== 18LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','{links[17]}','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 17: print('==== 17 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','{links[16]}','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 16: print('==== 16 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','{links[15]}','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 15: print('==== 15 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '{links[14]}','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 14: print('==== 14 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','{links[13]}', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 13: print('==== 13 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','{links[12]}','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 12: print('==== 12 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','{links[11]}','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 11: print('==== 11 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','{links[10]}','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 10: print('==== 10 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','{links[9]}','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 9: print('==== 9 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','{links[8]}','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 8: print('==== 8 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '{links[7]}','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 7: print('==== 7 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','{links[6]}', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 6: print('==== 6 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','{links[5]}','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 5: print('==== 5 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','{links[4]}','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 4: print('==== 4 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','{links[3]}','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 3: print('==== 3 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','{links[2]}','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close() if len(links) == 2: print('==== 2 LINKS ENCONTRADOS ======') print(f'Titulo: {titulos[0]}') print(f'Descrição: {descricoes[0]}') print(f'ContentID: {ids[0]}') print(f'Link:{links[0:]}') with conexao.cursor() as cursor: tabela = f"""INSERT INTO playstation_ps3 (titulo,descricao,content_id,imagem,cadastro, link1,link2,link3,link4,link5,link6,link7,link8,link9,link10,link11,link12,link13,link14, link15,link16,link17,link18,link19,link20,link21,link22,link23,link24,link25,link26,link27, link28,link29,link30) VALUES ('{titulos[0]}','{descricoes[0]}','{ids[0]}','{imagens[0]}','{hoje}', '{links[0]}','{links[1]}','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---','---','---','---','---','---', '---','---') """ cursor.execute(tabela) conexao.commit() conexao.close()

42.430905

169

0.533412

3,910

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0.048082

0.039474

0.033954

0.037188

0.922558

0.920551

0.918209

0.915756

0

0.089469

0.159112

33,775

795

170

42.484277

0.542058

0.005093

0

0.782895

0

0.245066

0.73919

0.521292

0

1

0

false

0.004934

0.009868

0

0.009868

0.241776

0

null

0

1

0

1

0

1

null

0

10

01d12b115e20541f808963ebd17fccca6ce7f41b

188

py

Python

largescale/src/neuron/neuron/__init__.py

cosmozhang-lab/motion-illusion-model

32a5ccab920095818b220642bae491429ff71f27

[ "MIT" ]

null

largescale/src/neuron/neuron/__init__.py

cosmozhang-lab/motion-illusion-model

32a5ccab920095818b220642bae491429ff71f27

[ "MIT" ]

null

largescale/src/neuron/neuron/__init__.py

cosmozhang-lab/motion-illusion-model

32a5ccab920095818b220642bae491429ff71f27

[ "MIT" ]

null

# Package: largescale.src.neuron.neuron from neuron import NeuronGroup from neuron import T_EXCITATORY, T_INHIBITORY, T_EXC, T_E, T_INH, T_I from neuron import T_ON, T_OFF, T_O, T_F

31.333333

70

0.776596

35

188

3.885714

0.514286

0.220588

0.352941

0.25

0

0.154255

188

5

71

37.6

0.855346

0.196809

0

1

0

true

0

1

0

1

0

null

1

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1

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null

0

1

0

1

0

1

0

7

01e8d97a7e29b1604d1107707c9dd60bfb163ffc

43,415

py

Python

AlignmentPracticableIORepa.py

caiks/AlignmentRepaPy

7b67e5e1ed7a40fc0c9588b92d72536b12edaf11

[ "MIT" ]

null

AlignmentPracticableIORepa.py

caiks/AlignmentRepaPy

7b67e5e1ed7a40fc0c9588b92d72536b12edaf11

[ "MIT" ]

null

AlignmentPracticableIORepa.py

caiks/AlignmentRepaPy

7b67e5e1ed7a40fc0c9588b92d72536b12edaf11

[ "MIT" ]

null

from AlignmentPracticableRepa import * import logging from timeit import default_timer as timer from sys import stdout # logging.basicConfig(format='%(asctime)s : %(name)s : %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO) # logging.basicConfig(format='%(message)s', level=logging.INFO) logging.basicConfig(format='%(message)s') layerer_log = logging.getLogger('layerer') layerer_log.setLevel(logging.INFO) tupler_log = logging.getLogger('tupler') tupler_log.setLevel(logging.INFO) parter_log = logging.getLogger('parter') parter_log.setLevel(logging.INFO) roller_log = logging.getLogger('roller') roller_log.setLevel(logging.INFO) applier_log = logging.getLogger('applier') applier_log.setLevel(logging.INFO) dervarser_log = logging.getLogger('dervarser') dervarser_log.setLevel(logging.INFO) decomper_log = logging.getLogger('decomper') decomper_log.setLevel(logging.INFO) # parametersSystemsLayererMaxRollByMExcludedSelfHighestIORepa_u :: # Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> # System -> Set.Set Variable -> HistoryRepa -> HistogramRepaRed -> HistoryRepa -> HistogramRepaRed -> Integer -> # IO (System, Fud, [(Set.Set Variable, Double)]) def parametersSystemsLayererMaxRollByMExcludedSelfHighestIORepa_u(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,uu,vv,xx,xxp,xxrr,xxrrp,f): repaRounding = 1e-6 def sgl(x): return sset([x]) def maxr(mm): if len(mm) > 0: return list(sset([b for (_,b) in mm]))[-1:][0] return 0 uvars = systemsSetVar cart = systemsSetVarsSetStateCartesian_u lluu = listsSystem_u uunion = pairSystemsUnion sunion = pairStatesUnionLeft ssgl = stateSingleton llaa = listsHistogram_u hhvvr = historyRepasVectorVar apvvr = histogramRepaRedsVectorVar hrhx = historyRepasRed def unit(qq): return llaa([(ss,1) for ss in qq]) tttr = systemsTransformsTransformRepa_u apply = historyRepasListTransformRepasApply_u trans = histogramsSetVarsTransform_u ttpp = transformsPartition und = transformsUnderlying qqff = setTransformsFud_u ffqq = fudsSetTransform def funion(ff,gg): return qqff(ffqq(ff) | ffqq(gg)) def buildfftup(uu,vv,ff,hh,hhp,hhrr,hhrrp): return parametersSystemsBuilderTupleNoSumlayerMultiEffectiveRepa_ui(xmax,omax,bmax,mmax,uu,vv,ff,hh,hhp,hhrr,hhrrp) def parter(uu,kk,bb,y1): return parametersSystemsPartitionerMaxRollByMRepa_ui(mmax,umax,pmax,uu,kk,bb,y1) def roller(qq): return parametersRollerMaximumRollExcludedSelfRepa_i(qq) def buildffdervar(uu,vv,ff,xx,xxp,xxrr,xxrrp): (x1,s1) = parametersSystemsBuilderDerivedVarsHighestNoSumlayerRepa_ui(wmax,omax,uu,vv,ff,xx,xxp,xxrr,xxrrp) return ([(kk,a) for ((kk,_,_),a) in x1],s1) def layer(vv,uu,ff,mm,xx,xxp,xxrr,xxrrp,f,l): if l > lmax: return (uu,ff,mm) layerer_log.info(">>> layer\tfud: %d\tlayer: %d" % (f,l)) t1 = timer() tupler_log.info(">>> tupler") tupler_log.info("substrate cardinality: %d" % len(vv)) tupler_log.info("fud cardinality: %d" % len(ffqq(ff))) stdout.flush() (x2,s2) = buildfftup(uu,vv,ff,xx,xxp,xxrr,xxrrp) if len(x2) > 0: tupler_log.info("tuple cardinality: %d" % len(x2)) tupler_log.info("max tuple algn: %.2f" % max([b for (a,b) in x2])) else: tupler_log.info("no tuples") t2 = timer() tupler_log.info("tupler\tsearched: %d\trate: %.2f" % (s2,s2/(t2-t1))) tupler_log.info("<<< tupler %.3fs" % (t2-t1)) parter_log.info(">>> parter") stdout.flush() y3 = [parter(uu,kk,bb,y1) for ((kk,bb),y1) in x2] x3 = [x for (ll,_) in y3 for x in ll] s3 = sum([s for (_,s) in y3]) if len(x3) > 0: parter_log.info("partitions cardinality: %d" % len(x3)) else: parter_log.info("no tuple partitions") t3 = timer() parter_log.info("parter\tsearched: %d\trate: %.2f" % (s3,s3/(t3-t2))) parter_log.info("<<< parter %.3fs" % (t3-t2)) roller_log.info(">>> roller") stdout.flush() y4 = [roller(qq) for qq in x3] x4 = [x for (ll,_) in y4 for x in ll] s4 = sum([s for (_,s) in y4]) if len(x4) > 0: roller_log.info("roll cardinality: %d" % len(x4)) else: roller_log.info("no rolls") t4 = timer() roller_log.info("roller\tsearched: %d\trate: %.2f" % (s4,s4/(t4-t3))) roller_log.info("<<< roller %.3fs" % (t4-t3)) applier_log.info(">>> application") stdout.flush() ll0 = [] for (yy,pp) in x4: for (jj,p) in zip(yy,pp): if max(p) + 1 < len(p): ii = list(zip(cart(uu,jj),p)) ll0.append(ii) ll = [] for (b,ii) in enumerate(ll0): w = VarPair((VarPair((VarInt(f),VarInt(l))),VarInt(b+1))) ww = sset([ValInt(u) for (_,u) in ii]) tt = trans(unit([sunion(ss,ssgl(w,ValInt(u))) for (ss,u) in ii]),sgl(w)) ll.append((tt,(w,ww))) ll1 = [] for (tt,(w,ww)) in ll: if all([len(ww) != len(ww1) or und(tt) != und(tt1) or ttpp(tt) != ttpp(tt1) for (tt1,(w1,ww1)) in ll if w > w1]): ll1.append((tt,(w,ww))) if len(ll1) > 0: hh = qqff(sset([tt for (tt,_) in ll1])) uu1 = uunion(uu,lluu([(w,ww) for (_,(w,ww)) in ll1])) ffr = [tttr(uu1,tt) for (tt,_) in ll1] xx1 = apply(xx,ffr) xxp1 = hrhx(xx1) xxrr1 = apply(xxrr,ffr) xxrrp1 = hrhx(xxrr1) gg = funion(ff,hh) applier_log.info("fud cardinality: %d" % len(ffqq(gg))) t5 = timer() applier_log.info("<<< application %.3fs" % (t5-t4)) dervarser_log.info( ">>> dervarser") stdout.flush() (mm1,s5) = buildffdervar(uu1,vv,gg,xx1,xxp1,xxrr1,xxrrp1) if len(mm1) > 0: dervarser_log.info("der vars algn density: %.2f" % maxr(mm1)) else: dervarser_log.info("no der vars sets") t6 = timer() dervarser_log.info("dervarser\tsearched: %d\trate: %.2f" % (s5,s5/(t6-t5))) dervarser_log.info("<<< dervarser %.3fs" % (t6-t5)) layerer_log.info( "<<< layer %.3fs" % (t6-t1)) stdout.flush() if l <= lmax and (len(mm) == 0 or maxr(mm1) > maxr(mm) + repaRounding): (ffr,ll0,ll,ll1) = (None,None,None,None) (x2,x3,x4) = (None,None,None) return layer(vv,uu1,gg,mm1,xx1,xxp1,xxrr1,xxrrp1,f,l+1) else: t5 = timer() applier_log.info("<<< application %.3fs" % (t5-t4)) layerer_log.info( "<<< layer %.3fs" % (t5-t1)) stdout.flush() return (uu,ff,mm) layerer_log.info(">>> layerer") t1 = timer() x1 = layer(vv,uu,fudEmpty(),[],xx,xxp,xxrr,xxrrp,f,1) t2 = timer() layerer_log.info("<<< layerer %.3fs" % (t2-t1)) stdout.flush() return x1 # parametersSystemsHistoryRepasDecomperMaxRollByMExcludedSelfHighestFmaxIORepa :: # Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> # Integer -> Integer -> # System -> Set.Set Variable -> HistoryRepa -> # IO (Maybe (System, DecompFud)) def parametersSystemsHistoryRepasDecomperMaxRollByMExcludedSelfHighestFmaxIORepa(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed,uu,vv,aa): repaRounding = 1e-6 dom = relationsDomain def maxd(mm): if len(mm) > 0: return list(sset([(b,a) for (a,b) in mm]))[-1] return (0,sset()) def tsgl(r): return sdict([(r,sdict())]) uvars = systemsSetVar acard = histogramsCardinality trim = histogramsTrim aall = histogramsList def red(aa,vv): return setVarsHistogramsReduce(vv,aa) def unit(ss): return setStatesHistogramUnit(sset([ss])) aahh = histogramsHistory hhhr = systemsHistoriesHistoryRepa def vars(hr): return sset(historyRepasVectorVar(hr)) size = historyRepasSize rraa = systemsHistogramRepasHistogram hrhx = historyRepasRed def hrhrred(hr,vv): return setVarsHistoryRepasHistoryRepaReduced(vv,hr) def hrred(hr,vv): return setVarsHistoryRepasReduce(1,vv,hr) def reduce(uu,ww,hh): return rraa(uu,hrred(hh,ww)) def select(uu,ss,hh): return historyRepasHistoryRepasHistoryRepaSelection_u(hhhr(uu,aahh(unit(ss))),hh) hrconcat = vectorHistoryRepasConcat_u hrshuffle = historyRepasShuffle_u ffqq = fudsSetTransform fder = fudsDerived tttr = systemsTransformsTransformRepa_u def apply(uu,ff,hh): return historyRepasListTransformRepasApply(hh,[tttr(uu,tt) for tt in ffqq(ff)]) depends = fudsSetVarsDepends zzdf = treePairStateFudsDecompFud dfzz = decompFudsTreePairStateFud def zztrim(df): pp = [] for ll in treesPaths(df): (_,ff) = ll[-1] if len(ff) == 0: pp.append(ll[:-1]) else: pp.append(ll) return pathsTree(pp) def layerer(uu,xx,f): decomper_log.info(">>> repa shuffle") stdout.flush() t1 = timer() z = size(xx) xxrr = hrconcat([hrshuffle(xx,seed+i*z) for i in range(1,mult+1)]) t2 = timer() decomper_log.info("<<< repa shuffle %.3fs" % (t2-t1)) decomper_log.info(">>> repa perimeters") stdout.flush() t1 = timer() xxp = hrhx(xx) xxrrp = hrhx(xxrr) t2 = timer() decomper_log.info("<<< repa perimeters %.3fs" % (t2-t1)) return parametersSystemsLayererMaxRollByMExcludedSelfHighestIORepa_u(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,uu,vv,xx,xxp,xxrr,xxrrp,f) def decomp(uu,zz,qq,f): if len(zz) == 0: (uur,ffr,nnr) = layerer(uu,aa,f) if len(ffr) == 0 or len(nnr) == 0: return (uu, decompFudEmpty()) (ar,kkr) = maxd(nnr) if ar <= repaRounding: return (uu, decompFudEmpty()) decomper_log.info(">>> slicing") stdout.flush() t3 = timer() ffr1 = depends(ffr,kkr) decomper_log.info("dependent fud cardinality : %d" % len(ffqq(ffr1))) aar = apply(uur,ffr1,aa) aa1 = trim(reduce(uur,fder(ffr1),aar)) decomper_log.info("derived cardinality : %d" % acard(aa1)) zzr = tsgl((stateEmpty(),ffr1)) qq[(stateEmpty(),ffr1)] = (aar,aa1) (ffr,nnr,kkr) = (None,None,None) t4 = timer() decomper_log.info("<<< slicing %.3fs" % (t4-t3)) stdout.flush() return decomp(uur,zzr,qq,f+1) if fmax > 0 and f > fmax: return (uu,zzdf(zztrim(zz))) decomper_log.info(">>> slice selection") stdout.flush() t1 = timer() mm = [] for (nn,yy) in treesPlaces(zz): (rr,ff) = nn[-1] if len(ff) > 0: (bb,bb1) = qq[(rr,ff)] tt = dom(treesRoots(yy)) for (ss,a) in aall(red(bb1,fder(ff))): if a > 0 and ss not in tt: mm.append((a,(nn,ss,bb))) decomper_log.info("slices: %d" % len(mm)) if len(mm) == 0: t2 = timer() decomper_log.info("<<< slice selection %.3fs" % (t2-t1)) stdout.flush() return (uu,zzdf(zztrim(zz))) mm.sort(key = lambda x: x[0]) (a,(nn,ss,bb)) = mm[-1] cc = hrhrred(select(uu,ss,bb),vars(aa)) decomper_log.info("decomp path length: %d" % len(nn)) decomper_log.info("slice size: %d" % a) t2 = timer() decomper_log.info("<<< slice selection %.3fs" % (t2-t1)) stdout.flush() (uuc,ffc,nnc) = layerer(uu,cc,f) decomper_log.info(">>> slicing") stdout.flush() t3 = timer() (ac,kkc) = maxd(nnc) ffc1 = fudEmpty() if ac > repaRounding: ffc1 = depends(ffc,kkc) decomper_log.info("dependent fud cardinality : %d" % len(ffqq(ffc1))) ccc = apply(uuc,ffc1,cc) cc1 = trim(reduce(uuc,fder(ffc1),ccc)) decomper_log.info("derived cardinality : %d" % acard(cc1)) qq[(ss,ffc1)] = (ccc,cc1) zzc = pathsTree(treesPaths(zz) + [nn+[(ss,ffc1)]]) (mm,cc,ffc,nnc,kkc) = (None,None,None,None,None) t4 = timer() decomper_log.info("<<< slicing %.3fs" % (t4-t3)) stdout.flush() return decomp(uuc,zzc,qq,f+1) if wmax < 0 or lmax < 0 or xmax < 0 or omax < 0 or bmax < 0 or mmax < 1 or umax < 0 or pmax < 0: return None if size(aa) == 0 or mult < 1: return None if not (vars(aa).issubset(uvars(uu)) and vv.issubset(vars(aa))): return None decomper_log.info(">>> decomper") t1 = timer() x1 = decomp(uu,emptyTree(),sdict(),1) decomper_log.info("nodes: %d" % len(treesNodes(dfzz(x1[1])))) t2 = timer() decomper_log.info("<<< decomper repa %.3fs" % (t2 - t1)) stdout.flush() return x1 # parametersSystemsLayererLevelMaxRollByMExcludedSelfHighestIORepa_u :: # Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> # System -> Set.Set Variable -> Fud -> # HistoryRepa -> HistogramRepaRed -> HistoryRepa -> HistogramRepaRed -> Integer -> Integer -> # IO (System, Fud, [(Set.Set Variable, Double)]) def parametersSystemsLayererLevelMaxRollByMExcludedSelfHighestIORepa_u(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,uu,vvg,ffg,xx,xxp,xxrr,xxrrp,f,g): repaRounding = 1e-6 def sgl(x): return sset([x]) def maxr(mm): if len(mm) > 0: return list(sset([b for (_,b) in mm]))[-1:][0] return 0 uvars = systemsSetVar cart = systemsSetVarsSetStateCartesian_u lluu = listsSystem_u uunion = pairSystemsUnion sunion = pairStatesUnionLeft ssgl = stateSingleton llaa = listsHistogram_u hhvvr = historyRepasVectorVar apvvr = histogramRepaRedsVectorVar hrhx = historyRepasRed def unit(qq): return llaa([(ss,1) for ss in qq]) tttr = systemsTransformsTransformRepa_u apply = historyRepasListTransformRepasApply_u trans = histogramsSetVarsTransform_u ttpp = transformsPartition und = transformsUnderlying qqff = setTransformsFud_u ffqq = fudsSetTransform def fder(ff): und = transformsUnderlying vv = set() for (aa,ww) in ff: vv |= ww for tt in ff: vv -= und(tt) return vv def fund(ff): und = transformsUnderlying vv = set() for tt in ff: vv |= und(tt) for (aa,ww) in ff: vv -= ww return vv def depends(ff,vv): und = transformsUnderlying dd = dict([(v,(xx,ww)) for (xx,ww) in ff for v in ww]) yy = set(dd.keys()) def deps(uu,xx): ff = [] for w in uu & yy - xx: tt = dd[w] ff.append(tt) zz = xx.copy() zz.add(w) ff = ff + deps(und(tt),zz) return ff return set(deps(vv,set())) def funion(ff,gg): return qqff(ffqq(ff) | ffqq(gg)) def buildfftup(uu,vvg,ffg,ff,hh,hhp,hhrr,hhrrp): return parametersSystemsBuilderTupleLevelNoSumlayerMultiEffectiveRepa_ui(xmax,omax,bmax,mmax,uu,vvg,ffg,ff,hh,hhp,hhrr,hhrrp) def parter(uu,kk,bb,y1): return parametersSystemsPartitionerMaxRollByMRepa_ui(mmax,umax,pmax,uu,kk,bb,y1) def roller(qq): return parametersRollerMaximumRollExcludedSelfRepa_i(qq) def buildffdervar(uu,vv,ffg,ff,xx,xxp,xxrr,xxrrp): (x1,s1) = parametersSystemsBuilderDerivedVarsLevelHighestNoSumlayerRepa_ui(wmax,omax,uu,vv,ffg,ff,xx,xxp,xxrr,xxrrp) return ([(kk,a) for ((kk,_,_),a) in x1],s1) def layer(uu,ff,mm,xx,xxp,xxrr,xxrrp,l): if l > lmax: return (uu,ff,mm) layerer_log.info(">>> layer\tfud: %d\tlevel node: %d\tlayer: %d" % (f,g,l)) t1 = timer() tupler_log.info(">>> tupler") tupler_log.info("level substrate cardinality: %d" % len(vvg)) tupler_log.info("level fud derived cardinality: %d" % len(fder(ffg))) tupler_log.info("fud cardinality: %d" % len(ffqq(ff))) tupler_log.info("level excluded fud cardinality: %d" % len(ffqq(ff)-ffqq(ffg))) stdout.flush() (x2,s2) = buildfftup(uu,vvg,ffg,ff,xx,xxp,xxrr,xxrrp) if len(x2) > 0: tupler_log.info("tuple cardinality: %d" % len(x2)) tupler_log.info("max tuple algn: %.2f" % max([b for (a,b) in x2])) else: tupler_log.info("no tuples") t2 = timer() tupler_log.info("tupler\tsearched: %d\trate: %.2f" % (s2,s2/(t2-t1))) tupler_log.info("<<< tupler %.3fs" % (t2-t1)) parter_log.info(">>> parter") stdout.flush() y3 = [parter(uu,kk,bb,y1) for ((kk,bb),y1) in x2] x3 = [x for (ll,_) in y3 for x in ll] s3 = sum([s for (_,s) in y3]) if len(x3) > 0: parter_log.info("partitions cardinality: %d" % len(x3)) else: parter_log.info("no tuple partitions") t3 = timer() parter_log.info("parter\tsearched: %d\trate: %.2f" % (s3,s3/(t3-t2))) parter_log.info("<<< parter %.3fs" % (t3-t2)) roller_log.info(">>> roller") stdout.flush() y4 = [roller(qq) for qq in x3] x4 = [x for (ll,_) in y4 for x in ll] s4 = sum([s for (_,s) in y4]) if len(x4) > 0: roller_log.info("roll cardinality: %d" % len(x4)) else: roller_log.info("no rolls") t4 = timer() roller_log.info("roller\tsearched: %d\trate: %.2f" % (s4,s4/(t4-t3))) roller_log.info("<<< roller %.3fs" % (t4-t3)) applier_log.info(">>> application") stdout.flush() ll0 = [] for (yy,pp) in x4: for (jj,p) in zip(yy,pp): if max(p) + 1 < len(p): ii = list(zip(cart(uu,jj),p)) ll0.append(ii) ll = [] for (b,ii) in enumerate(ll0): w = VarPair((VarPair((VarPair((VarInt(f),VarInt(g))),VarInt(l))),VarInt(b+1))) ww = sset([ValInt(u) for (_,u) in ii]) tt = trans(unit([sunion(ss,ssgl(w,ValInt(u))) for (ss,u) in ii]),sgl(w)) ll.append((tt,(w,ww))) ll1 = [] for (tt,(w,ww)) in ll: if all([len(ww) != len(ww1) or und(tt) != und(tt1) or ttpp(tt) != ttpp(tt1) for (tt1,(w1,ww1)) in ll if w > w1]): ll1.append((tt,(w,ww))) if len(ll1) > 0: hh = qqff(sset([tt for (tt,_) in ll1])) uu1 = uunion(uu,lluu([(w,ww) for (_,(w,ww)) in ll1])) ffr = [tttr(uu1,tt) for (tt,_) in ll1] xx1 = apply(xx,ffr) xxp1 = hrhx(xx1) xxrr1 = apply(xxrr,ffr) xxrrp1 = hrhx(xxrr1) gg = funion(funion(ff,hh),depends(ffg,fund(hh))) applier_log.info("fud cardinality: %d" % len(ffqq(gg))) t5 = timer() applier_log.info("<<< application %.3fs" % (t5-t4)) dervarser_log.info( ">>> dervarser") stdout.flush() (mm1,s5) = buildffdervar(uu1,vvg,ffg,gg,xx1,xxp1,xxrr1,xxrrp1) if len(mm1) > 0: dervarser_log.info("der vars algn density: %.2f" % maxr(mm1)) else: dervarser_log.info("no der vars sets") t6 = timer() dervarser_log.info("dervarser\tsearched: %d\trate: %.2f" % (s5,s5/(t6-t5))) dervarser_log.info("<<< dervarser %.3fs" % (t6-t5)) layerer_log.info( "<<< layer %.3fs" % (t6-t1)) stdout.flush() if l <= lmax and (len(mm) == 0 or maxr(mm1) > maxr(mm) + repaRounding): (ffr,ll0,ll,ll1) = (None,None,None,None) (x2,x3,x4) = (None,None,None) return layer(uu1,gg,mm1,xx1,xxp1,xxrr1,xxrrp1,l+1) else: t5 = timer() applier_log.info("<<< application %.3fs" % (t5-t4)) layerer_log.info( "<<< layer %.3fs" % (t5-t1)) stdout.flush() return (uu,ff,mm) layerer_log.info(">>> layerer") t1 = timer() x1 = layer(uu,fudEmpty(),[],xx,xxp,xxrr,xxrrp,1) t2 = timer() layerer_log.info("<<< layerer %.3fs" % (t2-t1)) stdout.flush() return x1 # parametersSystemsLayererLevelMaxRollByMExcludedSelfHighestIORepa_u_1 :: # Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> # System -> Set.Set Variable -> Fud -> # HistoryRepa -> HistogramRepaRed -> HistoryRepa -> HistogramRepaRed -> Integer -> Integer -> # IO (System, Fud, [(Set.Set Variable, Double)]) def parametersSystemsLayererLevelMaxRollByMExcludedSelfHighestIORepa_u_1(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,uu,vvg,ffg,xx,xxp,xxrr,xxrrp,f,g): repaRounding = 1e-6 def sgl(x): return sset([x]) def maxr(mm): if len(mm) > 0: return list(sset([b for (_,b) in mm]))[-1:][0] return 0 uvars = systemsSetVar cart = systemsSetVarsSetStateCartesian_u lluu = listsSystem_u uunion = pairSystemsUnion sunion = pairStatesUnionLeft ssgl = stateSingleton llaa = listsHistogram_u hhvvr = historyRepasVectorVar apvvr = histogramRepaRedsVectorVar hrhx = historyRepasRed def unit(qq): return llaa([(ss,1) for ss in qq]) tttr = systemsTransformsTransformRepa_u apply = historyRepasListTransformRepasApply_u trans = histogramsSetVarsTransform_u ttpp = transformsPartition und = transformsUnderlying qqff = setTransformsFud_u ffqq = fudsSetTransform fund = fudsUnderlying fder = fudsDerived depends = fudsSetVarsDepends def funion(ff,gg): return qqff(ffqq(ff) | ffqq(gg)) def buildfftup(uu,vvg,ffg,ff,hh,hhp,hhrr,hhrrp): return parametersSystemsBuilderTupleLevelNoSumlayerMultiEffectiveRepa_ui(xmax,omax,bmax,mmax,uu,vvg,ffg,ff,hh,hhp,hhrr,hhrrp) def parter(uu,kk,bb,y1): return parametersSystemsPartitionerMaxRollByMRepa_ui(mmax,umax,pmax,uu,kk,bb,y1) def roller(qq): return parametersRollerMaximumRollExcludedSelfRepa_i(qq) def buildffdervar(uu,vv,ffg,ff,xx,xxp,xxrr,xxrrp): (x1,s1) = parametersSystemsBuilderDerivedVarsLevelHighestNoSumlayerRepa_ui(wmax,omax,uu,vv,ffg,ff,xx,xxp,xxrr,xxrrp) return ([(kk,a) for ((kk,_,_),a) in x1],s1) def layer(uu,ff,mm,xx,xxp,xxrr,xxrrp,l): if l > lmax: return (uu,ff,mm) layerer_log.info(">>> layer\tfud: %d\tlevel node: %d\tlayer: %d" % (f,g,l)) t1 = timer() tupler_log.info(">>> tupler") tupler_log.info("level substrate cardinality: %d" % len(vvg)) tupler_log.info("level fud derived cardinality: %d" % len(fder(ffg))) tupler_log.info("fud cardinality: %d" % len(ffqq(ff))) tupler_log.info("level excluded fud cardinality: %d" % len(ffqq(ff)-ffqq(ffg))) stdout.flush() (x2,s2) = buildfftup(uu,vvg,ffg,ff,xx,xxp,xxrr,xxrrp) if len(x2) > 0: tupler_log.info("tuple cardinality: %d" % len(x2)) tupler_log.info("max tuple algn: %.2f" % max([b for (a,b) in x2])) else: tupler_log.info("no tuples") t2 = timer() tupler_log.info("tupler\tsearched: %d\trate: %.2f" % (s2,s2/(t2-t1))) tupler_log.info("<<< tupler %.3fs" % (t2-t1)) parter_log.info(">>> parter") stdout.flush() y3 = [parter(uu,kk,bb,y1) for ((kk,bb),y1) in x2] x3 = [x for (ll,_) in y3 for x in ll] s3 = sum([s for (_,s) in y3]) if len(x3) > 0: parter_log.info("partitions cardinality: %d" % len(x3)) else: parter_log.info("no tuple partitions") t3 = timer() parter_log.info("parter\tsearched: %d\trate: %.2f" % (s3,s3/(t3-t2))) parter_log.info("<<< parter %.3fs" % (t3-t2)) roller_log.info(">>> roller") stdout.flush() y4 = [roller(qq) for qq in x3] x4 = [x for (ll,_) in y4 for x in ll] s4 = sum([s for (_,s) in y4]) if len(x4) > 0: roller_log.info("roll cardinality: %d" % len(x4)) else: roller_log.info("no rolls") t4 = timer() roller_log.info("roller\tsearched: %d\trate: %.2f" % (s4,s4/(t4-t3))) roller_log.info("<<< roller %.3fs" % (t4-t3)) applier_log.info(">>> application") stdout.flush() ll0 = [] for (yy,pp) in x4: for (jj,p) in zip(yy,pp): if max(p) + 1 < len(p): ii = list(zip(cart(uu,jj),p)) ll0.append(ii) ll = [] for (b,ii) in enumerate(ll0): w = VarPair((VarPair((VarPair((VarInt(f),VarInt(g))),VarInt(l))),VarInt(b+1))) ww = sset([ValInt(u) for (_,u) in ii]) tt = trans(unit([sunion(ss,ssgl(w,ValInt(u))) for (ss,u) in ii]),sgl(w)) ll.append((tt,(w,ww))) ll1 = [] for (tt,(w,ww)) in ll: if all([len(ww) != len(ww1) or und(tt) != und(tt1) or ttpp(tt) != ttpp(tt1) for (tt1,(w1,ww1)) in ll if w > w1]): ll1.append((tt,(w,ww))) if len(ll1) > 0: hh = qqff(sset([tt for (tt,_) in ll1])) uu1 = uunion(uu,lluu([(w,ww) for (_,(w,ww)) in ll1])) ffr = [tttr(uu1,tt) for (tt,_) in ll1] xx1 = apply(xx,ffr) xxp1 = hrhx(xx1) xxrr1 = apply(xxrr,ffr) xxrrp1 = hrhx(xxrr1) gg = funion(funion(ff,hh),depends(ffg,fund(hh))) applier_log.info("fud cardinality: %d" % len(ffqq(gg))) t5 = timer() applier_log.info("<<< application %.3fs" % (t5-t4)) dervarser_log.info( ">>> dervarser") stdout.flush() (mm1,s5) = buildffdervar(uu1,vvg,ffg,gg,xx1,xxp1,xxrr1,xxrrp1) if len(mm1) > 0: dervarser_log.info("der vars algn density: %.2f" % maxr(mm1)) else: dervarser_log.info("no der vars sets") t6 = timer() dervarser_log.info("dervarser\tsearched: %d\trate: %.2f" % (s5,s5/(t6-t5))) dervarser_log.info("<<< dervarser %.3fs" % (t6-t5)) layerer_log.info( "<<< layer %.3fs" % (t6-t1)) stdout.flush() if l <= lmax and (len(mm) == 0 or maxr(mm1) > maxr(mm) + repaRounding): (ffr,ll0,ll,ll1) = (None,None,None,None) (x2,x3,x4) = (None,None,None) return layer(uu1,gg,mm1,xx1,xxp1,xxrr1,xxrrp1,l+1) else: t5 = timer() applier_log.info("<<< application %.3fs" % (t5-t4)) layerer_log.info( "<<< layer %.3fs" % (t5-t1)) stdout.flush() return (uu,ff,mm) layerer_log.info(">>> layerer") t1 = timer() x1 = layer(uu,fudEmpty(),[],xx,xxp,xxrr,xxrrp,1) t2 = timer() layerer_log.info("<<< layerer %.3fs" % (t2-t1)) stdout.flush() return x1 # parametersSystemsHistoryRepasDecomperLevelMaxRollByMExcludedSelfHighestFmaxIORepa :: # Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> # Integer -> Integer -> # System -> Tree (Integer, Set.Set Variable, Fud) -> HistoryRepa -> # IO (Maybe (System, DecompFud)) def parametersSystemsHistoryRepasDecomperLevelMaxRollByMExcludedSelfHighestFmaxIORepa(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed,uu,zzg,aa): repaRounding = 1e-6 dom = relationsDomain def maxd(mm): if len(mm) > 0: return list(sset([(b,a) for (a,b) in mm]))[-1] return (0,sset()) def tsgl(r): return sdict([(r,sdict())]) uvars = systemsSetVar acard = histogramsCardinality trim = histogramsTrim aall = histogramsList def red(aa,vv): return setVarsHistogramsReduce(vv,aa) def unit(ss): return setStatesHistogramUnit(sset([ss])) qqff = setTransformsFud_u ffqq = fudsSetTransform def fder(ff): und = transformsUnderlying vv = set() for (aa,ww) in ff: vv |= ww for tt in ff: vv -= und(tt) return vv def fvars(ff): vars = histogramsSetVar vv = set() for (aa,ww) in ff: vv |= vars(aa) return vv def fund(ff): und = transformsUnderlying vv = set() for tt in ff: vv |= und(tt) for (aa,ww) in ff: vv -= ww return vv def funion(ff,gg): return qqff(ffqq(ff) | ffqq(gg)) aahh = histogramsHistory hhhr = systemsHistoriesHistoryRepa def vars(hr): return sset(historyRepasVectorVar(hr)) size = historyRepasSize rraa = systemsHistogramRepasHistogram hrhx = historyRepasRed def hrhrred(hr,vv): return setVarsHistoryRepasHistoryRepaReduced(vv,hr) def hrred(hr,vv): return setVarsHistoryRepasReduce(1,vv,hr) def reduce(uu,ww,hh): return rraa(uu,hrred(hh,ww)) def select(uu,ss,hh): return historyRepasHistoryRepasHistoryRepaSelection_u(hhhr(uu,aahh(unit(ss))),hh) hrconcat = vectorHistoryRepasConcat_u hrshuffle = historyRepasShuffle_u ffqq = fudsSetTransform tttr = systemsTransformsTransformRepa_u def ltrsort(uu,ff,hr): vars = historyRepasVectorVar return listVariablesListTransformRepasSort(vars(hr),[tttr(uu,tt) for tt in ffqq(ff)]) ltrmul = historyRepasListTransformRepasApply_u def apply(uu,ff,hr): return historyRepasListTransformRepasApply(hr,[tttr(uu,tt) for tt in ffqq(ff)]) depends = fudsSetVarsDepends zzdf = treePairStateFudsDecompFud dfzz = decompFudsTreePairStateFud def zztrim(df): pp = [] for ll in treesPaths(df): (_,ff) = ll[-1] if len(ff) == 0: pp.append(ll[:-1]) else: pp.append(ll) return pathsTree(pp) def okLevel(zzg): for (wmaxg,vvg,ffg) in treesElements(zzg): if wmaxg < 0: return False if not vvg.issubset(vars(aa)): return False if not fvars(ffg).issubset(uvars(uu)): return False if not fund(ffg).issubset(vars(aa)): return False return True def layerer(wmax,uu,vvg,ffg,xx,f,g): decomper_log.info(">>> repa shuffle") stdout.flush() t1 = timer() z = size(xx) xxrr = hrconcat([hrshuffle(xx,seed+i*z) for i in range(1,mult+1)]) t2 = timer() decomper_log.info("<<< repa shuffle %.3fs" % (t2-t1)) decomper_log.info(">>> repa perimeters") stdout.flush() t1 = timer() vv1 = fder(ffg) | vvg frg = ltrsort(uu,ffg,xx) xx1 = hrhrred(ltrmul(xx,frg),vv1) xxp = hrhx(xx1) xxrr1 = hrhrred(ltrmul(xxrr,frg),vv1) xxrrp = hrhx(xxrr1) t2 = timer() decomper_log.info("<<< repa perimeters %.3fs" % (t2-t1)) return parametersSystemsLayererLevelMaxRollByMExcludedSelfHighestIORepa_u(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,uu,vvg,ffg,xx1,xxp,xxrr1,xxrrp,f,g) def level(uu,aa,ttg,f,g): (uu0,ff0,g0) = (uu,fudEmpty(),g) for ((wmaxg,vvg,ffg),xxg) in ttg.items(): (uuh,ffh,gh) = level(uu0,aa,xxg,f,g0) (uu1,gg,nn) = layerer(wmaxg,uuh,vvg,funion(ffg,ffh),aa,f,gh) (a,kk) = maxd(nn) gg1 = fudEmpty() if a > repaRounding: gg1 = depends(gg,kk) (uu0,ff0,g0) = (uu1,funion(ff0,gg1),gh+1) return (uu0,ff0,g0) def decomp(uu,zz,qq,f): if len(zz) == 0: (uur,ffr,_) = level(uu,aa,zzg,f,1) if len(ffr) == 0: return (uu, decompFudEmpty()) decomper_log.info(">>> slicing") stdout.flush() t3 = timer() decomper_log.info("dependent fud cardinality : %d" % len(ffqq(ffr))) aar = apply(uur,ffr,aa) wwr = sset(fder(ffr)) aa1 = trim(reduce(uur,wwr,aar)) decomper_log.info("derived cardinality : %d" % acard(red(aa1,wwr))) zzr = tsgl((stateEmpty(),ffr)) qq[(stateEmpty(),ffr)] = (aar,aa1) (ffr,nnr,kkr) = (None,None,None) t4 = timer() decomper_log.info("<<< slicing %.3fs" % (t4-t3)) stdout.flush() return decomp(uur,zzr,qq,f+1) if fmax > 0 and f > fmax: return (uu,zzdf(zztrim(zz))) decomper_log.info(">>> slice selection") stdout.flush() t1 = timer() mm = [] for (nn,yy) in treesPlaces(zz): (rr,ff) = nn[-1] if len(ff) > 0: (bb,bb1) = qq[(rr,ff)] tt = dom(treesRoots(yy)) for (ss,a) in aall(red(bb1,fder(ff))): if a > 0 and ss not in tt: mm.append((a,(nn,ss,bb))) decomper_log.info("slices: %d" % len(mm)) if len(mm) == 0: t2 = timer() decomper_log.info("<<< slice selection %.3fs" % (t2-t1)) stdout.flush() return (uu,zzdf(zztrim(zz))) mm.sort(key = lambda x: x[0]) (a,(nn,ss,bb)) = mm[-1] cc = hrhrred(select(uu,ss,bb),vars(aa)) decomper_log.info("decomp path length: %d" % len(nn)) decomper_log.info("slice size: %d" % a) t2 = timer() decomper_log.info("<<< slice selection %.3fs" % (t2-t1)) stdout.flush() (uuc,ffc,_) = level(uu,cc,zzg,f,1) decomper_log.info(">>> slicing") stdout.flush() t3 = timer() decomper_log.info("dependent fud cardinality : %d" % len(ffqq(ffc))) wwc = sset(fder(ffc)) ccc = apply(uuc,ffc,cc) cc1 = trim(reduce(uuc,wwc,ccc)) decomper_log.info("derived cardinality : %d" % acard(red(cc1,wwc))) qq[(ss,ffc)] = (ccc,cc1) zzc = pathsTree(treesPaths(zz) + [nn+[(ss,ffc)]]) (mm,cc,ffc,nnc,kkc) = (None,None,None,None,None) t4 = timer() decomper_log.info("<<< slicing %.3fs" % (t4-t3)) stdout.flush() return decomp(uuc,zzc,qq,f+1) if wmax < 0 or lmax < 0 or xmax < 0 or omax < 0 or bmax < 0 or mmax < 1 or umax < 0 or pmax < 0: return None if size(aa) == 0 or mult < 1: return None if not vars(aa).issubset(uvars(uu)): return None if not okLevel(zzg): return None decomper_log.info(">>> decomper") t1 = timer() x1 = decomp(uu,emptyTree(),sdict(),1) decomper_log.info("nodes: %d" % len(treesNodes(dfzz(x1[1])))) t2 = timer() decomper_log.info("<<< decomper repa %.3fs" % (t2 - t1)) stdout.flush() return x1 # parametersSystemsHistoryRepasDecomperLevelMaxRollByMExcludedSelfHighestFmaxIORepa_1 :: # Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> Integer -> # Integer -> Integer -> # System -> Tree (Integer, Set.Set Variable, Fud) -> HistoryRepa -> # IO (Maybe (System, DecompFud)) def parametersSystemsHistoryRepasDecomperLevelMaxRollByMExcludedSelfHighestFmaxIORepa_1(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,fmax,mult,seed,uu,zzg,aa): repaRounding = 1e-6 dom = relationsDomain def maxd(mm): if len(mm) > 0: return list(sset([(b,a) for (a,b) in mm]))[-1] return (0,sset()) def tsgl(r): return sdict([(r,sdict())]) uvars = systemsSetVar acard = histogramsCardinality trim = histogramsTrim aall = histogramsList def red(aa,vv): return setVarsHistogramsReduce(vv,aa) def unit(ss): return setStatesHistogramUnit(sset([ss])) qqff = setTransformsFud_u ffqq = fudsSetTransform fvars = fudsVars fder = fudsDerived fund = fudsUnderlying def funion(ff,gg): return qqff(ffqq(ff) | ffqq(gg)) aahh = histogramsHistory hhhr = systemsHistoriesHistoryRepa def vars(hr): return sset(historyRepasVectorVar(hr)) size = historyRepasSize rraa = systemsHistogramRepasHistogram hrhx = historyRepasRed def hrhrred(hr,vv): return setVarsHistoryRepasHistoryRepaReduced(vv,hr) def hrred(hr,vv): return setVarsHistoryRepasReduce(1,vv,hr) def reduce(uu,ww,hh): return rraa(uu,hrred(hh,ww)) def select(uu,ss,hh): return historyRepasHistoryRepasHistoryRepaSelection_u(hhhr(uu,aahh(unit(ss))),hh) hrconcat = vectorHistoryRepasConcat_u hrshuffle = historyRepasShuffle_u ffqq = fudsSetTransform fder = fudsDerived tttr = systemsTransformsTransformRepa_u def apply(uu,ff,hh): return historyRepasListTransformRepasApply(hh,[tttr(uu,tt) for tt in ffqq(ff)]) depends = fudsSetVarsDepends zzdf = treePairStateFudsDecompFud dfzz = decompFudsTreePairStateFud def zztrim(df): pp = [] for ll in treesPaths(df): (_,ff) = ll[-1] if len(ff) == 0: pp.append(ll[:-1]) else: pp.append(ll) return pathsTree(pp) def okLevel(zzg): for (wmaxg,vvg,ffg) in treesElements(zzg): if wmaxg < 0: return False if not vvg.issubset(vars(aa)): return False if not fvars(ffg).issubset(uvars(uu)): return False if not fund(ffg).issubset(vars(aa)): return False return True def layerer(wmax,uu,vvg,ffg,xx,f,g): decomper_log.info(">>> repa shuffle") stdout.flush() t1 = timer() z = size(xx) xxrr = hrconcat([hrshuffle(xx,seed+i*z) for i in range(1,mult+1)]) t2 = timer() decomper_log.info("<<< repa shuffle %.3fs" % (t2-t1)) decomper_log.info(">>> repa perimeters") stdout.flush() t1 = timer() xx1 = apply(uu,ffg,xx) xxp = hrhx(xx1) xxrr1 = apply(uu,ffg,xxrr) xxrrp = hrhx(xxrr1) t2 = timer() decomper_log.info("<<< repa perimeters %.3fs" % (t2-t1)) return parametersSystemsLayererLevelMaxRollByMExcludedSelfHighestIORepa_u(wmax,lmax,xmax,omax,bmax,mmax,umax,pmax,uu,vvg,ffg,xx1,xxp,xxrr1,xxrrp,f,g) def level(uu,aa,ttg,f,g): (uu0,ff0,g0) = (uu,fudEmpty(),g) for ((wmaxg,vvg,ffg),xxg) in ttg.items(): (uuh,ffh,gh) = level(uu0,aa,xxg,f,g0) (uu1,gg,nn) = layerer(wmaxg,uuh,vvg,funion(ffg,ffh),aa,f,gh) (a,kk) = maxd(nn) gg1 = fudEmpty() if a > repaRounding: gg1 = depends(gg,kk) (uu0,ff0,g0) = (uu1,funion(ff0,gg1),gh+1) return (uu0,ff0,g0) def decomp(uu,zz,qq,f): if len(zz) == 0: (uur,ffr,_) = level(uu,aa,zzg,f,1) if len(ffr) == 0: return (uu, decompFudEmpty()) decomper_log.info(">>> slicing") stdout.flush() t3 = timer() decomper_log.info("dependent fud cardinality : %d" % len(ffqq(ffr))) aar = apply(uur,ffr,aa) wwr = fder(ffr) aa1 = trim(reduce(uur,wwr,aar)) decomper_log.info("derived cardinality : %d" % acard(red(aa1,wwr))) zzr = tsgl((stateEmpty(),ffr)) qq[(stateEmpty(),ffr)] = (aar,aa1) (ffr,nnr,kkr) = (None,None,None) t4 = timer() decomper_log.info("<<< slicing %.3fs" % (t4-t3)) stdout.flush() return decomp(uur,zzr,qq,f+1) if fmax > 0 and f > fmax: return (uu,zzdf(zztrim(zz))) decomper_log.info(">>> slice selection") stdout.flush() t1 = timer() mm = [] for (nn,yy) in treesPlaces(zz): (rr,ff) = nn[-1] if len(ff) > 0: (bb,bb1) = qq[(rr,ff)] tt = dom(treesRoots(yy)) for (ss,a) in aall(red(bb1,fder(ff))): if a > 0 and ss not in tt: mm.append((a,(nn,ss,bb))) decomper_log.info("slices: %d" % len(mm)) if len(mm) == 0: t2 = timer() decomper_log.info("<<< slice selection %.3fs" % (t2-t1)) stdout.flush() return (uu,zzdf(zztrim(zz))) mm.sort(key = lambda x: x[0]) (a,(nn,ss,bb)) = mm[-1] cc = hrhrred(select(uu,ss,bb),vars(aa)) decomper_log.info("decomp path length: %d" % len(nn)) decomper_log.info("slice size: %d" % a) t2 = timer() decomper_log.info("<<< slice selection %.3fs" % (t2-t1)) stdout.flush() (uuc,ffc,_) = level(uu,cc,zzg,f,1) decomper_log.info(">>> slicing") stdout.flush() t3 = timer() decomper_log.info("dependent fud cardinality : %d" % len(ffqq(ffc))) wwc = fder(ffc) ccc = apply(uuc,ffc,cc) cc1 = trim(reduce(uuc,wwc,ccc)) decomper_log.info("derived cardinality : %d" % acard(red(cc1,wwc))) qq[(ss,ffc)] = (ccc,cc1) zzc = pathsTree(treesPaths(zz) + [nn+[(ss,ffc)]]) (mm,cc,ffc,nnc,kkc) = (None,None,None,None,None) t4 = timer() decomper_log.info("<<< slicing %.3fs" % (t4-t3)) stdout.flush() return decomp(uuc,zzc,qq,f+1) if wmax < 0 or lmax < 0 or xmax < 0 or omax < 0 or bmax < 0 or mmax < 1 or umax < 0 or pmax < 0: return None if size(aa) == 0 or mult < 1: return None if not vars(aa).issubset(uvars(uu)): return None if not okLevel(zzg): return None decomper_log.info(">>> decomper") t1 = timer() x1 = decomp(uu,emptyTree(),sdict(),1) decomper_log.info("nodes: %d" % len(treesNodes(dfzz(x1[1])))) t2 = timer() decomper_log.info("<<< decomper repa %.3fs" % (t2 - t1)) stdout.flush() return x1

41.151659

158

0.54548

5,339

43,415

4.383592

0.066866

0.048752

0.040378

0.046659

0.915741

0.911767

0.90758

0.903478

0.889036

0.870877

0

0.025468

0.305424

43,415

1,054

159

41.190702

0.750622

0.048509

0

0.90239

0

0.081924

0

null

0.006972

0.003984

null

0

null

0

1

0

1

0

null

0

1

0

8

17111a7abf53a035de98baa91e71385ab4317ae0

7,123

py

Python

pygcn/models.py

coquid/pygcn

a11788468514cce47bd4262849456895def13714

[ "MIT" ]

null

pygcn/models.py

coquid/pygcn

a11788468514cce47bd4262849456895def13714

[ "MIT" ]

null

pygcn/models.py

coquid/pygcn

a11788468514cce47bd4262849456895def13714

[ "MIT" ]

null

import torch.nn as nn import torch.nn.functional as F from pygcn.layers import GraphConvolution, MyGraphConvolution class GCN(nn.Module): def __init__(self, nfeat, nhid, nclass, dropout): super(GCN, self).__init__() self.gc1 = GraphConvolution(nfeat, nhid) self.gc2 = GraphConvolution(nhid, nclass) self.dropout = dropout def forward(self, x, adj): x = F.relu(self.gc1(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc2(x, adj) return F.log_softmax(x, dim=1) class MyGCN_v1(nn.Module): def __init__(self, nfeat, nhid, nout, dropout): super(MyGCN_v1, self).__init__() self.gc1 = MyGraphConvolution(nfeat, nhid) self.gc2 = MyGraphConvolution(nhid, nhid) self.gc3 = MyGraphConvolution(nhid, nhid) self.gc4 = MyGraphConvolution(nhid, nhid) self.gc5 = MyGraphConvolution(nhid, nhid) self.gc6 = MyGraphConvolution(nhid, nout) self.dropout = dropout def forward(self, x, adj): x = F.relu(self.gc1(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.relu(self.gc2(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.relu(self.gc3(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.relu(self.gc4(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.relu(self.gc5(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc6(x, adj) return x class MyGCN_v2(nn.Module): def __init__(self, nfeat, nhid, nout, dropout): super(MyGCN_v2, self).__init__() self.gc1 = MyGraphConvolution(nfeat, 12) self.gc2 = MyGraphConvolution(12, 10) self.gc3 = MyGraphConvolution(10, 8) self.gc4 = MyGraphConvolution(8, 6) self.gc5 = MyGraphConvolution(6, 4) self.gc6 = MyGraphConvolution(4, nout) self.dropout = dropout def forward(self, x, adj): x = F.tanhshrink(self.gc1(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc2(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc3(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc4(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc5(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc6(x, adj) return x class MyGCN_v3(nn.Module): def __init__(self, nfeat, nhid, nout, dropout): super(MyGCN_v3, self).__init__() self.gc1 = MyGraphConvolution(nfeat, 12) self.gc2 = MyGraphConvolution(12, 10) self.gc3 = MyGraphConvolution(10, 8) self.gc4 = MyGraphConvolution(8, 6) self.gc5 = MyGraphConvolution(6, 4) self.gc6 = MyGraphConvolution(4, nout) self.dropout = dropout def forward(self, x, adj): x = (self.gc1(x, adj)) x = F.dropout(x, p=0, training=self.training) x = (self.gc2(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc3(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc4(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc5(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc6(x, adj) return x class MyGCN_v4(nn.Module): def __init__(self, nfeat, nhid, nout, dropout): super(MyGCN_v4, self).__init__() self.gc1 = MyGraphConvolution(nfeat, 12) self.gc2 = MyGraphConvolution(12, 10) self.gc3 = MyGraphConvolution(10, 8) self.gc4 = MyGraphConvolution(8, 6) self.gc5 = MyGraphConvolution(6, 4) self.gc6 = MyGraphConvolution(4, nout) self.dropout = dropout def forward(self, x, adj): x = (self.gc1(x, adj)) x = F.dropout(x, p=0, training=self.training) x = F.relu(self.gc2(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc3(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc4(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc5(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc6(x, adj) return x class MyGCN_v5(nn.Module): def __init__(self, nfeat, nhid, nout, dropout): super(MyGCN_v5, self).__init__() self.gc1 = MyGraphConvolution(nfeat, 12) self.gc2 = MyGraphConvolution(12, 10) self.gc3 = MyGraphConvolution(10, 8) self.gc4 = MyGraphConvolution(8, 6) self.gc5 = MyGraphConvolution(6, 4) self.gc6 = MyGraphConvolution(4, nout) self.dropout = dropout def forward(self, x, adj): x = (self.gc1(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.relu(self.gc2(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc3(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = F.tanhshrink(self.gc4(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc5(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc6(x, adj) return x class MyGCN_v6(nn.Module): def __init__(self, nfeat, nhid, nout, dropout): super(MyGCN_v6, self).__init__() self.gc1 = MyGraphConvolution(nfeat, 12) self.gc2 = MyGraphConvolution(12, 11) self.gc3 = MyGraphConvolution(11, 10) self.gc4 = MyGraphConvolution(10, 9) self.gc5 = MyGraphConvolution(9, 8) self.gc6 = MyGraphConvolution(8, 7) self.gc7 = MyGraphConvolution(7, 6) self.gc8 = MyGraphConvolution(6, 5) self.gc9 = MyGraphConvolution(5, 4) self.gc10 = MyGraphConvolution(4, nout) self.dropout = dropout def forward(self, x, adj): x = (self.gc1(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc2(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc3(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc4(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = (self.gc5(x, adj)) x = F.dropout(x, self.dropout, training=self.training) x = self.gc6(x, adj) x = F.dropout(x, self.dropout, training=self.training) x = self.gc7(x, adj) x = F.dropout(x, self.dropout, training=self.training) x = self.gc8(x, adj) x = F.dropout(x, self.dropout, training=self.training) x = self.gc9(x, adj) x = F.dropout(x, self.dropout, training=self.training) x = self.gc10(x, adj) return x

38.090909

62

0.600168

978

7,123

4.300614

0.063395

0.068949

0.049929

0.054208

0.843795

0.83476

0.828103

0

0.034365

0.260564

7,123

186

63

38.295699

0.764192

0

0.713415

0

1

0.085366

false

0

0.018293

0

0.189024

0

null

0

1

0

1

0

null

0

7

17435a67c44ac869cb192970c53155e65fc347af

8,912

py

Python

WN.py

neyudin/wavenetglow

3261dd8163709b2204b1c9ba90bc544755439fa5

[ "BSD-3-Clause" ]

null

WN.py

neyudin/wavenetglow

3261dd8163709b2204b1c9ba90bc544755439fa5

[ "BSD-3-Clause" ]

2

2020-01-28T22:48:08.000Z

2020-03-03T16:25:33.000Z

WN.py

neyudin/wavenetglow

3261dd8163709b2204b1c9ba90bc544755439fa5

[ "BSD-3-Clause" ]

null

import torch import torch.nn as nn class WN(torch.nn.Module): """ WN block for affine coupling layer. Actual version """ def __init__(self, num_channels, mel_channels, n_layers=8, residual_channels=512, gate_channels=256, skip_channels=256): """ Parameters ---------- num_channels : int Number of x_a channels mel_channels : int Number of spectrogram (condition c) channels ---------- Parameters from original paper ---------- n_layers : int The depth of WN (default : 8) residual_channels : int Number of chanels used by residual connections (default : 512) gate_channels : int Number of filters and gates channels (default : 256) skip_channels : int Number of chanels used by skip connections """ super(WN, self).__init__() self.n_layers = n_layers self.num_channels = num_channels self.residual_channels = residual_channels self.gate_channels = gate_channels self.skip_channels = skip_channels self.mel_channels = mel_channels self.dilations_list = [2**i for i in range(n_layers)] self.conv_input = nn.Conv1d(in_channels=num_channels, out_channels=residual_channels, kernel_size=1) self.conv_filter = nn.ModuleList([ torch.nn.utils.weight_norm(nn.Conv1d( in_channels=residual_channels, out_channels=gate_channels, kernel_size=3, dilation=d, padding=(2 * d // 2) ), name='weight') for d in self.dilations_list]) self.conv_gate = nn.ModuleList([ torch.nn.utils.weight_norm(nn.Conv1d( in_channels=residual_channels, out_channels=gate_channels, kernel_size=3, dilation=d, padding=(2 * d // 2) ), name='weight') for d in self.dilations_list]) self.conv_mel = nn.ModuleList([ torch.nn.utils.weight_norm(nn.Conv1d( in_channels=mel_channels, out_channels=gate_channels * 2, kernel_size=1 ), name='weight') for _ in range(len(self.dilations_list))]) self.conv_residual = nn.ModuleList([ torch.nn.utils.weight_norm(nn.Conv1d( in_channels=gate_channels, out_channels=residual_channels, kernel_size=1 ), name='weight') for _ in range(len(self.dilations_list) - 1)]) self.conv_skip = nn.ModuleList([ torch.nn.utils.weight_norm(nn.Conv1d( in_channels=gate_channels, out_channels=skip_channels, kernel_size=1 ), name='weight') for _ in range(len(self.dilations_list))]) self.conv_out = nn.Conv1d( in_channels=skip_channels, out_channels=2 * num_channels, # log s, t kernel_size=1) self.conv_out.weight.data.uniform_(-0.0001, 0.0001) self.conv_out.bias.data.uniform_(-0.0001, 0.0001) def forward(self, x_a, c): """ Parameters ---------- x_a : FloatTensor of size batch_size * num_channels * T Unchangable part of embedding c : FloatTensor of size batch_size * mel_channels * T Upsampled mel-spectrogram """ assert x_a.size(2) == c.size(2) # Проверить, что спектрограмме не забыли сделать upsampling x_acc = 0 x = self.conv_input(x_a) for i in range(len(self.dilations_list)): x_filter = self.conv_filter[i](x) x_gate = self.conv_gate[i](x) c_proj = self.conv_mel[i](c) x_filter = x_filter + c_proj[:, :self.gate_channels] x_gate = x_gate + c_proj[:, self.gate_channels:] x_gate = torch.sigmoid(x_gate) x_filter = torch.tanh(x_filter) x_filter_gate = x_gate * x_filter x_skip = self.conv_skip[i](x_filter_gate) if i != len(self.dilations_list) - 1: x_res = self.conv_residual[i](x_filter_gate) x = x + x_res x_acc = x_acc + x_skip return self.conv_out(x_acc) class VanillaWN(torch.nn.Module): """ WN block for affine coupling layer. """ def __init__(self, num_channels, mel_channels, n_layers=4, residual_channels=128, gate_channels=64, skip_channels=64, pre_channels=32): """ Parameters ---------- num_channels : int Number of x_a channels mel_channels : int Number of spectrogram (condition c) channels ---------- Parameters from original paper ---------- n_layers : int The depth of WN (default : 8) residual_channels : int Number of chanels used by residual connections (default : 512) gate_channels : int Number of filters and gates channels (default : 256) skip_channels : int Number of chanels used by skip connections pre_channels : int Number of channels in final non-linearity """ super(VanillaWN, self).__init__() self.n_layers = n_layers self.num_channels = num_channels self.residual_channels = residual_channels self.gate_channels = gate_channels self.skip_channels = skip_channels self.mel_channels = mel_channels self.dilations_list = [2**i for i in range(n_layers)] self.conv_input = nn.Conv1d(in_channels=num_channels, out_channels=residual_channels, kernel_size=1) self.conv_filter = nn.ModuleList([ nn.Conv1d( in_channels=residual_channels, out_channels=gate_channels, kernel_size=3, dilation=d, padding=(2 * d // 2) ) for d in self.dilations_list]) self.conv_gate = nn.ModuleList([ nn.Conv1d( in_channels=residual_channels, out_channels=gate_channels, kernel_size=3, dilation=d, padding=(2 * d // 2) ) for d in self.dilations_list]) self.conv_mel = nn.ModuleList([ nn.Conv1d( in_channels=mel_channels, out_channels=gate_channels * 2, kernel_size=1 ) for _ in range(len(self.dilations_list))]) self.conv_residual = nn.ModuleList([ nn.Conv1d( in_channels=gate_channels, out_channels=residual_channels, kernel_size=1 ) for _ in range(len(self.dilations_list) - 1)]) self.conv_skip = nn.ModuleList([ nn.Conv1d( in_channels=gate_channels, out_channels=skip_channels, kernel_size=1 ) for _ in range(len(self.dilations_list))]) self.conv_out_1 = nn.Conv1d( in_channels=skip_channels, out_channels=pre_channels, kernel_size=1) self.conv_out_2 = nn.Conv1d( in_channels=pre_channels, out_channels=2 * num_channels, kernel_size=1) def forward(self, x_a, c): """ Parameters ---------- x_a : FloatTensor of size batch_size * num_channels * T Unchangable part of embedding c : FloatTensor of size batch_size * mel_channels * T Upsampled mel-spectrogram """ assert x_a.size(2) == c.size(2) # Проверить, что спектрограмме не забыли сделать upsampling x_acc = 0 x = self.conv_input(x_a) for i in range(len(self.dilations_list)): x_filter = self.conv_filter[i](x) x_gate = self.conv_gate[i](x) c_proj = self.conv_mel[i](c) x_filter = x_filter + c_proj[:, :self.gate_channels] x_gate = x_gate + c_proj[:, self.gate_channels:] x_gate = torch.sigmoid(x_gate) x_filter = torch.tanh(x_filter) x_filter_gate = x_gate * x_filter x_skip = self.conv_skip[i](x_filter_gate) if i != len(self.dilations_list) - 1: x_res = self.conv_residual[i](x_filter_gate) x = x + x_res x = x * 0.5**0.5 x_acc = x_acc + x_skip return self.conv_out_2(torch.relu(self.conv_out_1(x_acc)))

36.080972

108

0.546118

1,057

8,912

4.332072

0.109745

0.055907

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0.363443

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0

1

0

null

0

7

1757cb3adbb9ba3e3de2859b8f9845a3c7c5f97a

12,676

py

Python

utils/yolo.py

Cuda-Chen/fish-yolo-grabcut

359da92815d49a7d238fe8de7bd51e5de68f0d40

[ "MIT" ]

7

2020-01-20T00:33:29.000Z

2022-01-01T04:36:06.000Z

utils/yolo.py

ZurMaD/fish-yolo-grabcut

c2570691143df36d528d2b3c115bf2bc29cddfd6

[ "MIT" ]

1

2020-01-09T09:18:29.000Z

2020-01-16T13:22:43.000Z

utils/yolo.py

Cuda-Chen/fish-yolo-grabcut

359da92815d49a7d238fe8de7bd51e5de68f0d40

[ "MIT" ]

5

2020-06-20T01:50:10.000Z

2020-12-24T09:13:10.000Z

#!/usr/bin/python import numpy as np import argparse import time import cv2 as cv import os def runYOLODetection(args): # load my fish class labels that my YOLO model was trained on labelsPath = os.path.sep.join([args["yolo"], "fish.names"]) #labelsPath = os.path.sep.join([args["yolo"], "coco.names"]) LABELS = open(labelsPath).read().strip().split("\n") # initialize a list of colors to represent each possible class label np.random.seed(0) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") print(COLORS) #COLORS = np.array([255, 0, 0], dtype="uint8") # derive the paths to the YOLO weights and model configuration weightsPath = os.path.sep.join([args["yolo"], "fish.weights"]) configPath = os.path.sep.join([args["yolo"], "fish_test.cfg"]) #weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"]) #configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"]) # load my YOLO object detector trained on my fish dataset (1 class) print("[INFO] loading YOLO from disk ...") net = cv.dnn.readNetFromDarknet(configPath, weightsPath) # load input image and grab its spatial dimensions image = cv.imread(args["image"]) (H, W) = image.shape[:2] # determine only the *output* layer names that we need from YOLO ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # construct a blob from the input image and then perform a forward # pass of the YOLO object detector, giving us our bounding boxes and # associated probabilities # NOTE: (608, 608) is my YOLO input image size. However, using # (416, 416) results in much accutate result. Pretty interesting. blob = cv.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() # show execution time information of YOLO print("[INFO] YOLO took {:.6f} seconds.".format(end - start)) # initialize out lists of detected bounding boxes, confidences, and # class IDs, respectively boxes = [] confidences = [] classIDs = [] # loop over each of the layer outputs for output in layerOutputs: # loop over each of the detections for detection in output: # extract the class ID and confidence (i.e., probability) of # the current object detection scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # filter out weak predictions by ensuring the detected # probability is greater then the minimum probability if confidence > args["confidence"]: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update out list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weark and overlapping bounding # boxes idxs = cv.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"]) # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # draw a bounding box rectangle and label on the image color = [int(c) for c in COLORS[classIDs[i]]] cv.rectangle(image, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv.putText(image, text, (x, y - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) return image def runYOLOBoundingBoxes(args): # load my fish class labels that my YOLO model was trained on labelsPath = os.path.sep.join([args["yolo"], "fish.names"]) #labelsPath = os.path.sep.join([args["yolo"], "coco.names"]) LABELS = open(labelsPath).read().strip().split("\n") # initialize a list of colors to represent each possible class label np.random.seed(0) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") print(COLORS) #COLORS = np.array([255, 0, 0], dtype="uint8") # derive the paths to the YOLO weights and model configuration weightsPath = os.path.sep.join([args["yolo"], "fish.weights"]) configPath = os.path.sep.join([args["yolo"], "fish_test.cfg"]) #weightsPath = os.path.sep.join([args["yolo"], "yolov3.weights"]) #configPath = os.path.sep.join([args["yolo"], "yolov3.cfg"]) # load my YOLO object detector trained on my fish dataset (1 class) print("[INFO] loading YOLO from disk ...") net = cv.dnn.readNetFromDarknet(configPath, weightsPath) # load input image and grab its spatial dimensions image = cv.imread(args["image"]) (H, W) = image.shape[:2] # determine only the *output* layer names that we need from YOLO ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # construct a blob from the input image and then perform a forward # pass of the YOLO object detector, giving us our bounding boxes and # associated probabilities # NOTE: (608, 608) is my YOLO input image size. However, using # (416, 416) results in much accutate result. Pretty interesting. blob = cv.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() # show execution time information of YOLO print("[INFO] YOLO took {:.6f} seconds.".format(end - start)) # initialize out lists of detected bounding boxes, confidences, and # class IDs, respectively boxes = [] confidences = [] classIDs = [] # loop over each of the layer outputs for output in layerOutputs: # loop over each of the detections for detection in output: # extract the class ID and confidence (i.e., probability) of # the current object detection scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # filter out weak predictions by ensuring the detected # probability is greater then the minimum probability if confidence > args["confidence"]: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update out list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weark and overlapping bounding # boxes idxs = cv.dnn.NMSBoxes(boxes, confidences, args["confidence"], args["threshold"]) return image, boxes, idxs def runYOLOBoundingBoxes_streamlit(image, yolopath, _confidence, _threshold): # load my fish class labels that my YOLO model was trained on labelsPath = os.path.sep.join([yolopath, "fish.names"]) LABELS = open(labelsPath).read().strip().split("\n") # initialize a list of colors to represent each possible class label np.random.seed(0) COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") print(COLORS) #COLORS = np.array([255, 0, 0], dtype="uint8") # derive the paths to the YOLO weights and model configuration weightsPath = os.path.sep.join([yolopath, "fish.weights"]) configPath = os.path.sep.join([yolopath, "fish_test.cfg"]) # load my YOLO object detector trained on my fish dataset (1 class) print("[INFO] loading YOLO model ...") net = cv.dnn.readNetFromDarknet(configPath, weightsPath) # grab input image's spatial dimensions (H, W) = image.shape[:2] # determine only the *output* layer names that we need from YOLO ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # construct a blob from the input image and then perform a forward # pass of the YOLO object detector, giving us our bounding boxes and # associated probabilities # NOTE: (608, 608) is my YOLO input image size. However, using # (416, 416) results in much accutate result. Pretty interesting. blob = cv.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.time() layerOutputs = net.forward(ln) end = time.time() # show execution time information of YOLO print("[INFO] YOLO took {:.6f} seconds.".format(end - start)) # initialize out lists of detected bounding boxes, confidences, and # class IDs, respectively boxes = [] confidences = [] classIDs = [] # loop over each of the layer outputs for output in layerOutputs: # loop over each of the detections for detection in output: # extract the class ID and confidence (i.e., probability) of # the current object detection scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # filter out weak predictions by ensuring the detected # probability is greater then the minimum probability if confidence > _confidence: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update out list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # apply non-maxima suppression to suppress weark and overlapping bounding # boxes idxs = cv.dnn.NMSBoxes(boxes, confidences, _confidence, _threshold) return boxes, idxs if __name__ == '__main__': ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="path to input image") ap.add_argument("-y", "--yolo", required=True, help="base path to YOLO directory") ap.add_argument("-c", "--confidence", type=float, default=0.25, help="minimum probability to filter weak detections") ap.add_argument("-t", "--threshold", type=float, default=0.45, help="threshold when applying non-maxima suppression") args = vars(ap.parse_args()) image = runYOLODetection(args) # show the output image #cv.namedWindow("Image", cv.WINDOW_NORMAL) #cv.resizeWindow("image", 1920, 1080) cv.imshow("Image", image) #cv.imwrite("predictions.jpg", image) cv.waitKey(0)

40.758842

77

0.620227

1,637

12,676

4.787416

0.161271

0.013398

0.017226

0.024882

0.857343

0.847263

0.842925

0

0.018249

0.269407

12,676

310

78

40.890323

0.827988

0.415273

0

0.726667

0

0.085159

0

1

0.02

false

0

0.033333

0

0.073333

0.06

0

null

0

1

0

1

0

null

0

7

1791323b17a576d78ae9d6ff260342ba69a97bc9

919

py

Python

tests/test_half_wildcard.py

oqwa/gwh

9399a9b0fd5815e81d68a4d52215c273f9c0d49b

[ "MIT" ]

null

tests/test_half_wildcard.py

oqwa/gwh

9399a9b0fd5815e81d68a4d52215c273f9c0d49b

[ "MIT" ]

null

tests/test_half_wildcard.py

oqwa/gwh

9399a9b0fd5815e81d68a4d52215c273f9c0d49b

[ "MIT" ]

null

from gwh import * from tests.utils import * app = GitWebhook() app.add_handler(lambda: None, repository=KNOWN_REPO) app.add_handler(lambda: None, type=KNOWN_TYPE) def test_bitbucket(): check_bitbucket_webhook(app, KNOWN_TYPE, KNOWN_REPO, "master", hit_expected=True) check_bitbucket_webhook(app, UNKNOWN_TYPE, KNOWN_REPO, "master", hit_expected=True) check_bitbucket_webhook(app, KNOWN_TYPE, UNKNOWN_REPO, "master", hit_expected=True) check_bitbucket_webhook(app, UNKNOWN_TYPE, UNKNOWN_REPO, "master", hit_expected=False) def test_gitlab(): check_gitlab_webhook(app, KNOWN_TYPE, KNOWN_REPO, "master", hit_expected=True) check_gitlab_webhook(app, UNKNOWN_TYPE, KNOWN_REPO, "master", hit_expected=True) check_gitlab_webhook(app, KNOWN_TYPE, UNKNOWN_REPO, "master", hit_expected=True) check_gitlab_webhook(app, UNKNOWN_TYPE, UNKNOWN_REPO, "master", hit_expected=False)

43.761905

90

0.771491

127

919

5.220472

0.212598

0.120664

0.156863

0.253394

0.838612

0.769231

0.731523

0

0.124048

919

20

91

45.95

0.823602

0

0.052231

0

1

0.133333

false

0

0.133333

0

0.266667

0

null

0

1

0

null

0

7

bd85a5016f4253e00cc44d9424523f4276b499b6

128

py

Python

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_1/_pkg0_1_0_1_0/_mod0_1_0_1_0_4.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_1/_pkg0_1_0_1_0/_mod0_1_0_1_0_4.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/completion/heavyStarPropagation/lib/_pkg0/_pkg0_1/_pkg0_1_0/_pkg0_1_0_1/_pkg0_1_0_1_0/_mod0_1_0_1_0_4.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

name0_1_0_1_0_4_0 = None name0_1_0_1_0_4_1 = None name0_1_0_1_0_4_2 = None name0_1_0_1_0_4_3 = None name0_1_0_1_0_4_4 = None

14.222222

24

0.820313

40

128

1.875

0.175

0.266667

0.466667

0.533333

0.88

0.746667

0

0.318182

0.140625

128

9

25

14.222222

0.363636

0

1

0

false

0

1

null

1

0

1

0

1

0

1

0

null

0

10

bda5541d8519516c1c651c498c62fb368ec8e19e

2,796

py

Python

Decorator Foundation.py

Enthuisasticpessimist/Small-exercise

b169000023c3863f9e30d2cfc7c0f6e228f612f7

[ "MIT" ]

null

Decorator Foundation.py

Enthuisasticpessimist/Small-exercise

b169000023c3863f9e30d2cfc7c0f6e228f612f7

[ "MIT" ]

null

Decorator Foundation.py

Enthuisasticpessimist/Small-exercise

b169000023c3863f9e30d2cfc7c0f6e228f612f7

[ "MIT" ]

null

##-----------Non parametric decorator---------------- ####initialization ##name = 'a' ##password = '1' ##user_status = False ## ####decorator ##def login(func): ## def inner(): ## global name,password,user_status ## if user_status == True: ## pass ## else: ## n = input('name:') ## p = input('password:') ## if n == name and p == password: ## user_status = True ## if user_status: ## func() ## return inner ## ##@login ##def webpage1(): ## print('webpage---1') ##@login ##def webpage2(): ## print('webpage---2') ## ####webpage1 = login(webpage1)##original method1 ####webpage2 = login(webpage2)##original method2 ##webpage1() ##webpage2() ##-----------Non parametric decorator---------------- ####-----------Parametric decorator-------------------- ####initialization ##name = 'a' ##password = '1' ##user_status = False ## ####decorator ##def login(func): ## def inner(*args,**kwargs):##arbitrary parameters can be passed in ## global name,password,user_status ## if user_status == True: ## pass ## else: ## n = input('name:') ## p = input('password:') ## if n == name and p == password: ## user_status = True ## if user_status: ## func(*args,**kwargs)##arbitrary parameters can be passed in ## return inner ## ##@login ##def webpage1(arg): ## print('webpage---1',arg) ##@login ##def webpage2(): ## print('webpage---2') ## ####webpage1 = login(webpage1)##original method1 ####webpage2 = login(webpage2)##original method2 ##webpage1('111') ##webpage2() ####-----------Parametric decorator-------------------- ##-----------Multi-layer decorator-------------------- ##initialization name = 'a' password = '1' user_status = False ##decorator def login(auth_type): def outer(func): def inner(*args,**kwargs):##arbitrary parameters can be passed in global name,password,user_status if auth_type == 'qq': if user_status == True: pass else: n = input('name:') p = input('password:') if n == name and p == password: user_status = True if user_status: func(*args,**kwargs)##arbitrary parameters can be passed in else: print('auth_type is wrong!') return inner return outer @login('qq') def webpage1(arg): print('webpage---1',arg) @login('weixin') def webpage2(): print('webpage---2') ##temp = login("qq")##original method1 ##webpage1 = temp(webpage1) webpage1('111') ##-----------Multi-layer decorator--------------------

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bdd6f392de5f9a7829a76f27096203708e20bfc0

79

py

Python

2017/12.12/python/lsh-get-github-user.py

mksweetlife/study

0786a4bd7901ac0d1aa5efdae5b755693eee5cd3

[ "MIT" ]

1

2017-10-24T08:19:15.000Z

2017/12.12/python/lsh-get-github-user.py

mksweetlife/study

0786a4bd7901ac0d1aa5efdae5b755693eee5cd3

[ "MIT" ]

31

2017-10-31T11:09:44.000Z

2018-12-04T07:47:46.000Z

2017/12.12/python/lsh-get-github-user.py

mksweetlife/study

0786a4bd7901ac0d1aa5efdae5b755693eee5cd3

[ "MIT" ]

5

2017-10-26T02:13:08.000Z

2018-07-05T04:58:47.000Z

def getUser(): return "Sanghak,Lee / http://sanghaklee.tistory.com" #FIXME:

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bddd1a20f636344c19e0119a658dd15a4909a0a2

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py

Python

match_rcnn/mmdetection/mmdet/pretrained_models/cocopth.py

201419/taobao-live-product-recognition

1f5de5917b43b2b58f4387a77272fc7c587a1051

[ "Apache-2.0" ]

null

match_rcnn/mmdetection/mmdet/pretrained_models/cocopth.py

201419/taobao-live-product-recognition

1f5de5917b43b2b58f4387a77272fc7c587a1051

[ "Apache-2.0" ]

null

match_rcnn/mmdetection/mmdet/pretrained_models/cocopth.py

201419/taobao-live-product-recognition

1f5de5917b43b2b58f4387a77272fc7c587a1051

[ "Apache-2.0" ]

1

2021-05-14T03:30:29.000Z

import torch import numpy as np num_classes = 25 model_coco = torch.load(r"/media/alvinai/Documents/model/faster_rcnn_r50_fpn_1x_20190610-bf0ea559.pth") # print(model_coco) # print(model_coco["state_dict"]["rpn_head.rpn_cls.weight"].shape) # a = model_coco["state_dict"]["rpn_head.rpn_cls.weight"][0] # model_coco["state_dict"]["rpn_head.rpn_cls.weight"]=np.insert(model_coco["state_dict"]["rpn_head.rpn_cls.weight"], 0, values=a, axis=0) # print(model_coco["state_dict"]["rpn_head.rpn_cls.weight"].shape) # b=model_coco["state_dict"]["rpn_head.rpn_cls.bias"][0] # model_coco["state_dict"]["rpn_head.rpn_cls.bias"] = np.insert(model_coco["state_dict"]["rpn_head.rpn_cls.bias"], 0, values=b, axis=0) # print(model_coco["state_dict"]["rpn_head.rpn_cls.bias"].shape) # c= model_coco["state_dict"]["rpn_head.rpn_reg.weight"][0].repeat(4,1,1,1) # model_coco["state_dict"]["rpn_head.rpn_reg.weight"]=np.insert(model_coco["state_dict"]["rpn_head.rpn_reg.weight"], 0, values=c, axis=0) # # c= model_coco["state_dict"]["rpn_head.rpn_reg.weight"][1] # # model_coco["state_dict"]["rpn_head.rpn_reg.weight"]=np.insert(model_coco["state_dict"]["rpn_head.rpn_reg.weight"], 0, values=c, axis=0) # # c= model_coco["state_dict"]["rpn_head.rpn_reg.weight"][2] # # model_coco["state_dict"]["rpn_head.rpn_reg.weight"]=np.insert(model_coco["state_dict"]["rpn_head.rpn_reg.weight"], 0, values=c, axis=0) # # c= model_coco["state_dict"]["rpn_head.rpn_reg.weight"][3] # # model_coco["state_dict"]["rpn_head.rpn_reg.weight"]=np.insert(model_coco["state_dict"]["rpn_head.rpn_reg.weight"], 0, values=c, axis=0) # print(model_coco["state_dict"]["rpn_head.rpn_reg.weight"].shape) # d=model_coco["state_dict"]["rpn_head.rpn_reg.bias"][0].repeat(4,) # model_coco["state_dict"]["rpn_head.rpn_reg.bias"] = np.insert(model_coco["state_dict"]["rpn_head.rpn_reg.bias"], 0, values=d, axis=0) # print(model_coco["state_dict"]["rpn_head.rpn_reg.bias"].shape) # # model_coco["state_dict"]["rpn_head.rpn_reg.weight"] = model_coco["state_dict"]["rpn_head.rpn_reg.weight"].repeat(2,1,1,1) # # model_coco["state_dict"]["rpn_head.rpn_reg.bias"] = model_coco["state_dict"]["rpn_head.rpn_reg.bias"].repeat(2,) # weight model_coco["state_dict"]["bbox_head.fc_cls.weight"] = model_coco["state_dict"]["bbox_head.fc_cls.weight"][ :num_classes, :] model_coco["state_dict"]["bbox_head.fc_reg.weight"] = model_coco["state_dict"]["bbox_head.fc_reg.weight"][ :num_classes*4, :] # bias model_coco["state_dict"]["bbox_head.fc_cls.bias"] = model_coco["state_dict"]["bbox_head.fc_cls.bias"][:num_classes] model_coco["state_dict"]["bbox_head.fc_reg.bias"] = model_coco["state_dict"]["bbox_head.fc_reg.bias"][:num_classes*4] # save new model torch.save(model_coco, r"/media/alvinai/Documents/underwater/model/libra_faster_rcnn_r50_fpn_1x_cls_%d.pth" % num_classes)

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bdf4b884e53e55033540d679eaf6e95f48c085d7

118

py

Python

tests/test_crawler.py

Yotamho/nba-analytics

13174040198d44aab035de58cf785bce6926958a

[ "MIT" ]

null

tests/test_crawler.py

Yotamho/nba-analytics

13174040198d44aab035de58cf785bce6926958a

[ "MIT" ]

null

tests/test_crawler.py

Yotamho/nba-analytics

13174040198d44aab035de58cf785bce6926958a

[ "MIT" ]

null

from nba_analytics.crawler import pbp_for_range def test_crawler(): assert pbp_for_range(3, 2008, 2009) != None

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bdfb84bba555606efcd2d3ca97385378284beca7

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py

Python

tests/test_levdistresult.py

ZenulAbidin/bip39validator

b78f2db6f46b56b408eef3a51e921e96247a9b46

[ "MIT" ]

3

2021-02-11T20:37:56.000Z

2021-06-11T03:29:15.000Z

tests/test_levdistresult.py

ZenulAbidin/bip39validator

b78f2db6f46b56b408eef3a51e921e96247a9b46

[ "MIT" ]

4

2020-10-04T23:11:08.000Z

2020-12-23T00:32:52.000Z

tests/test_levdistresult.py

ZenulAbidin/bip39validator

b78f2db6f46b56b408eef3a51e921e96247a9b46

[ "MIT" ]

null

from unittest import TestCase from bip39validator import ValidationFailed from bip39validator.BIP39WordList import BIP39WordList levdist_gt2 = """brown brpyt""" levdist_le2 = """brow brol""" # Expected results *must* be in word alphabetical order. class TestLevDistResult(TestCase): def test_getwordpairs_eq(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [("brol", "brow")] self.assertEqual(expected_res, res.getwordpairs_eq(1)) try: res.getwordpairs_eq(2) self.fail() except AssertionError as e: pass def test_getlinepairs_eq(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(2,1)] self.assertEqual(expected_res, res.getlinepairs_eq(1)) try: res.getwordpairs_eq(0) self.fail() except AssertionError as e: pass def test_getwordpairs_lt(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [("brol", "brow")] self.assertEqual(expected_res, res.getwordpairs_lt(2)) try: res.getwordpairs_lt(0) self.fail() except AssertionError as e: pass def test_getlinepairs_lt(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(2, 1)] self.assertEqual(expected_res, res.getlinepairs_lt(2)) try: res.getlinepairs_lt(0) self.fail() except AssertionError as e: pass def test_getwordpairs_gt(self): bip39 = BIP39WordList("levdist_gt2", string=levdist_gt2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [("brown", "brpyt")] self.assertEqual(expected_res, res.getwordpairs_gt(2)) try: res.getwordpairs_gt(0) self.fail() except AssertionError as e: pass def test_getlinepairs_gt(self): bip39 = BIP39WordList("levdist_gt2", string=levdist_gt2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(1, 2)] self.assertEqual(expected_res, res.getlinepairs_gt(2)) try: res.getlinepairs_gt(0) self.fail() except AssertionError as e: pass def test_getwordpairs_list(self): concat = "\n".join([levdist_le2]+["zzyzx"]) bip39 = BIP39WordList("levdist_concat", string=concat) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [("brol", "brow")] self.assertEqual(expected_res, res.getwordpairs_list([1,2])) for t in ["abc", [], ["a"], 0]: try: res.getwordpairs_list(t) self.fail() except AssertionError as e: pass def test_getlinepairs_list(self): concat = "\n".join([levdist_le2]+["zzyzx"]) bip39 = BIP39WordList("levdist_concat", string=concat) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(2, 1)] self.assertEqual(expected_res, res.getlinepairs_list([1,2])) for t in ["abc", [], ["a"], 0]: try: res.getlinepairs_list(t) self.fail() except AssertionError as e: pass def test_getdist(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = 1 self.assertEqual(expected_res, res.getdist("brow", "brol")) for t in [(1, "abc"), ("", "abc"), ("ABC", "abc"), ("abc", 1), ("abc", ""), ("abc", "ABC")]: try: res.getdist(*t) self.fail() except AssertionError as e: pass def test_getdist_all(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(("brol", "brow"), (2, 1), 1)] self.assertEqual(expected_res, res.getdist_all("brow")) for t in [1, "", "ABC"]: try: res.getdist_all(t) self.fail() except AssertionError as e: pass def test_getdist_all_eq(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(("brol", "brow"), (2, 1), 1)] self.assertEqual(expected_res, res.getdist_all_eq("brow", 1)) for t in [1, "", "ABC"]: try: res.getdist_all_eq(t, 1) self.fail() except AssertionError as e: pass except KeyError as e: pass try: res.getdist_all_eq("abc", 0) self.fail() except AssertionError as e: pass except KeyError as e: pass def test_getdist_all_lt(self): bip39 = BIP39WordList("levdist_le2", string=levdist_le2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(("brol", "brow"), (2, 1), 1)] self.assertEqual(expected_res, res.getdist_all_lt("brow", 2)) for t in [1, "", "ABC"]: try: res.getdist_all_lt(t, 1) self.fail() except AssertionError as e: pass except KeyError as e: pass try: res.getdist_all_lt("abc", 0) self.fail() except AssertionError as e: pass except KeyError as e: pass def test_getdist_all_gt(self): bip39 = BIP39WordList("levdist_gt2", string=levdist_gt2) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(("brpyt", "brown"), (2, 1), 3)] self.assertEqual(expected_res, res.getdist_all_gt("brown", 2)) for t in [1, "", "ABC"]: try: res.getdist_all_gt(t, 1) self.fail() except AssertionError as e: pass try: res.getdist_all_gt("abc", 0) self.fail() except AssertionError as e: pass except KeyError as e: pass def test_getdist_all_list(self): concat = "\n".join([levdist_le2]+["zzyzx"]) bip39 = BIP39WordList("concat", string=concat) try: res = bip39.test_lev_distance(2) except ValidationFailed as e: res = e.status_obj expected_res = [(("brol", "brow"), (2, 1), 1)] self.assertEqual(expected_res, res.getdist_all_list("brow", [1])) for t in [1, "", "ABC"]: for u in ["abc", [], ["a"], 0]: try: res.getdist_all_list(t, u) self.fail() except AssertionError as e: pass except KeyError as e: pass

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8

da804ba481b451cc0ca78dfe3274c111f94eaf58

16,539

py

Python

data-processing/utils/__init__.py

mark-andrews/BayesianAccountMemoryText

28609a4d3d3924c5082af81359ffc3f78f6eb6da

[ "CC-BY-4.0" ]

2

2020-04-10T17:14:19.000Z

2020-04-10T17:14:26.000Z

data-processing/utils/__init__.py

mark-andrews/BayesianAccountMemoryText

28609a4d3d3924c5082af81359ffc3f78f6eb6da

[ "CC-BY-4.0" ]

18

2020-03-24T17:07:23.000Z

2021-12-13T20:01:11.000Z

data-processing/utils/__init__.py

mark-andrews/BayesianAccountMemoryText

28609a4d3d3924c5082af81359ffc3f78f6eb6da

[ "CC-BY-4.0" ]

null

""" Some general utils. """ ##============================================================================= ## Standard library imports ##============================================================================= #import string #import re #import os #import errno #import hashlib # ##================================ End Imports ================================ # #def deletechars(s, exclude_chars): # ''' Fast deletion of characters from string. # It uses a dummy translation table, and so no mapping is applied, and we # just delete the exclude_chars characters. # ''' # phony_translate_table = string.maketrans("","") # return s.translate(phony_translate_table, exclude_chars) # # #def deletepunctuation(s): # ''' Fast deletion of punctuation from string''' # return deletechars(s,string.punctuation) # # #def tokenize(text, foldcase=True): # ''' # A very cheap and easy tokenization. # First, remove "'s". For example, "dog's" becomes "dog". # Second, zap utf-8 chars. # Then, remove all punctuation and, by default, fold upper and lower case words # and then split by whitespace. # ''' # # text = re.sub(r'\'s','', text) # s = ''.join([s for s in text if s in string.printable]) # # s = str(s) # Got to convert it to str. # s = deletepunctuation(s) # # if foldcase: # s = s.lower() # return s.split() # # #def mkdir_p(path): # ''' # Make a directory, making parents if necessary. # Taken verbatim from # http://stackoverflow.com/a/600612 # ''' # try: # os.makedirs(path) # except OSError as exc: # Python >2.5 # if exc.errno == errno.EEXIST and os.path.isdir(path): # pass # else: raise # # #def checksum(argument, algorithm='sha256'): # ''' # Returns the hash checksum of `argument'. # If `argument' is a name of a file, then perform the checksum on the file. # Otherwise, the checksum is of the string `argument'. # By default, it will be the sha1 checksum (and so equivalent to linux's # sha1sum). Alternatively, the algorithm could be md5 (equivalent to linux's # md5sum), or else sha224, sha256, sha384, sha512. # ''' # # h = hashlib.new(algorithm) # # if os.path.exists(argument) and os.path.isfile(argument): # argument = open(argument,'rb').read() # # h.update(argument) # # return h.hexdigest() # I didn't have anywhere better to put these. hdptm_170617202450_6333_state_checksums = ''' hdptm_170617202450_6333_state_19000.npz 2dadab2c09f54f4d03a1187c8d5db49a8ec0a2bfe7bd5f5630448958ba4f21ac hdptm_170617202450_6333_state_19010.npz 0edcb069ab3e559f62728d372f98fb5c047ca8a47ede262dad05ef236d29615f hdptm_170617202450_6333_state_19020.npz 395b4214a753d811f18f24c6665665bdfc201928c7e661294ab0e991b993b1c5 hdptm_170617202450_6333_state_19030.npz 469e9742d4a508c4b34e5283254041ec34b58ebf1f82a15f845949d7367708d5 hdptm_170617202450_6333_state_19040.npz 036d02b36964f24b4a49465769eb51f46ccbc6f52255797b32207c398c6a31f5 hdptm_170617202450_6333_state_19050.npz 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hdptm_201117172636_2290_state_12030.npz fa447583f242ffe45bc441cee34dc9ecb78da33b18a480d98026e70e262981ee hdptm_201117172636_2290_state_12040.npz 4ee93667dd3662032d00263628c37980d02c6682a0ba75050e2f96a1fb54e612 hdptm_201117172636_2290_state_12050.npz d6fa8eb2340534f90710c81d32b385be158d8b20d96923eaac1fe0f7e8ce958e hdptm_201117172636_2290_state_12060.npz 52c02cce5f6dead3c57c1bbf849caaa3c25e879905ce713cf0a1adc025182213 hdptm_201117172636_2290_state_12070.npz aac843fc16c7af66aadbb38e589842d37c28369910bad13604615fc4d7ea2c8d hdptm_201117172636_2290_state_12080.npz 96214e1648a7480c7c635950c978e0109934c5b6390260476f1f02c47bf00249 hdptm_201117172636_2290_state_12090.npz 1814613561f524275a6991f7157c7d17b470d4c6df5801ebd4bb910990f90f17 hdptm_201117172636_2290_state_12100.npz 1fdf6bba764eb3650a530a3b3f8f4378a602463c48edaa741e811ec9f4b40547 hdptm_201117172636_2290_state_12110.npz 55e80b139763c636b9b1df998095867334e1328a35c826fbf19b3cedef921da3 hdptm_201117172636_2290_state_12120.npz 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73.834821

104

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16,539

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7

da8e99c09aabca3db1bc0e1af11da10e940286d6

2,549

py

Python

tests/fixtures/dict_list/docket_list_with_homicide.py

SimmonsRitchie/court_docket_scraper

f467d59c4ea8dbddb4fd7545dc36656a4b30e46d

[ "MIT" ]

1

2021-10-29T20:12:44.000Z

tests/fixtures/dict_list/docket_list_with_homicide.py

SimmonsRitchie/court_docket_scraper

f467d59c4ea8dbddb4fd7545dc36656a4b30e46d

[ "MIT" ]

2

2019-07-19T20:13:16.000Z

tests/fixtures/dict_list/docket_list_with_homicide.py

SimmonsRitchie/court_docket_scraper

f467d59c4ea8dbddb4fd7545dc36656a4b30e46d

[ "MIT" ]

null

docket_list = [ { "county": "Dauphin", "docketnum": 1, "case_caption": "Commonwealth V. Smith, John A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "John A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; Driving W/O A License", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", }, { "county": "Dauphin", "docketnum": 2, "case_caption": "Commonwealth V. Smith, Duke A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "Duke A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; Driving W/O A License", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", }, { "county": "Dauphin", "docketnum": 3, "case_caption": "Commonwealth V. Smith, John A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "John A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; homicide; Driving W/O A " "License", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", }, { "county": "Dauphin", "docketnum": 4, "case_caption": "Commonwealth V. Smith, John A.", "arresting_agency": "Harrisburg PD", "municipality": "Harrisburg", "defendant": "John A. Smith", "defendant_race": "white", "defendant_gender": "Male", "dob": "01/01/1986", "filing_date": "03/03/2019", "charges": "Receiving Stolen Property; Driving W/O A License; Murder", "bail": 25000, "url": "https://ujsportal.pacourts.us/DocketSheets/MDJReport.ashx?docketNumber=MJ-12302-CR-0000110-2019&dnh=zj8BkxXzkOi23xMzscQ6hw%3d%3d", } ]

39.828125

146

0.582581

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2,549

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null

0

7

16fee0714125b907c565a7460bda1a63c75c9808

3,682

py

Python

boundlexx/boundless/migrations/0002_create_item_timeseries.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

1

2021-04-23T11:49:50.000Z

boundlexx/boundless/migrations/0002_create_item_timeseries.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

1

2021-04-17T18:17:12.000Z

boundlexx/boundless/migrations/0002_create_item_timeseries.py

AngellusMortis/boundlexx

407f5e38e8e0f067cbcb358787fc9af6a9be9b2a

[ "MIT" ]

null

# Generated by Django 3.0.8 on 2020-07-21 17:44 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('boundless', '0001_initial'), ] operations = [ migrations.CreateModel( name='ItemShopStandPrice', fields=[ ('time', models.DateTimeField(auto_now=True, primary_key=True, serialize=False)), ('location_x', models.IntegerField()), ('location_y', models.IntegerField()), ('location_z', models.IntegerField()), ('price', models.DecimalField(decimal_places=2, max_digits=10)), ('item_count', models.IntegerField()), ('beacon_name', models.CharField(db_index=True, max_length=64)), ('guild_tag', models.CharField(max_length=8)), ('shop_activity', models.IntegerField()), ('active', models.BooleanField(db_index=True, default=True)), ('world', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.World')), ('item', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.Item')), ], options={ 'abstract': False, 'unique_together': {('time', 'world', 'location_x', 'location_y', 'item', 'price', 'item_count')}, }, ), migrations.CreateModel( name='ItemRequestBasketPrice', fields=[ ('time', models.DateTimeField(auto_now=True, primary_key=True, serialize=False)), ('location_x', models.IntegerField()), ('location_y', models.IntegerField()), ('location_z', models.IntegerField()), ('price', models.DecimalField(decimal_places=2, max_digits=10)), ('item_count', models.IntegerField()), ('beacon_name', models.CharField(db_index=True, max_length=64)), ('guild_tag', models.CharField(max_length=8)), ('shop_activity', models.IntegerField()), ('active', models.BooleanField(db_index=True, default=True)), ('world', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.World')), ('item', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='boundless.Item')), ], options={ 'abstract': False, 'unique_together': {('time', 'world', 'location_x', 'location_y', 'item', 'price', 'item_count')}, }, ), migrations.RunSQL( "CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE", reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( 'ALTER TABLE "boundless_itemshopstandprice" DROP CONSTRAINT "boundless_itemshopstandprice_pkey"', reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( "SELECT create_hypertable('boundless_itemshopstandprice', 'time', chunk_time_interval => 86400000000, migrate_data => true, create_default_indexes => false)", reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( 'ALTER TABLE "boundless_itemrequestbasketprice" DROP CONSTRAINT "boundless_itemrequestbasketprice_pkey"', reverse_sql=migrations.RunSQL.noop ), migrations.RunSQL( "SELECT create_hypertable('boundless_itemrequestbasketprice', 'time', chunk_time_interval => 86400000000, migrate_data => true, create_default_indexes => false)", reverse_sql=migrations.RunSQL.noop ), ]

51.138889

209

0.605921

348

3,682

6.215517

0.281609

0.083218

0.032362

0.050855

0.784096

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0.0194

0.258012

3,682

71

210

51.859155

0.772328

0.012222

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0

0.27923

0.084182

0

1

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0

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0

1

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1

0

null

0

7

e541c66e26b2dcd462e9c9a22b50ce0b746cca85

7,028

py

Python

process_azure_roles.py

noamsdahan/iam-dataset

da640ba65906f3f6091c6cbfdfdc0ca03df83f8f

[ "MIT" ]

58

2021-06-23T07:12:19.000Z

2022-03-26T14:55:00.000Z

process_azure_roles.py

noamsdahan/iam-dataset

da640ba65906f3f6091c6cbfdfdc0ca03df83f8f

[ "MIT" ]

8

2021-11-01T15:41:19.000Z

2022-02-08T08:04:05.000Z

process_azure_roles.py

noamsdahan/iam-dataset

da640ba65906f3f6091c6cbfdfdc0ca03df83f8f

[ "MIT" ]

4

2021-07-31T03:13:12.000Z

2022-03-22T08:28:08.000Z

import os import json import time import requests import re result = { 'roles': [] } raw_roles = [] with open("azure/built-in-roles-raw.json", "r") as f: raw_roles = json.loads(f.read()) provider_ops = [] with open("azure/provider-operations.json", "r") as f: provider_ops = json.loads(f.read()) for raw_role in raw_roles: if raw_role['roleType'] != "BuiltInRole": continue permitted_actions = [] permitted_data_actions = [] has_unknown = False has_external = False for permission in raw_role['permissions']: for action in permission['actions']: matched = False matchexpression = "^" + action.replace(".", "\\.").replace("*", ".*").replace("?", ".{{1}}") + "$" for provider in provider_ops: for operation in provider['operations']: if not operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_actions.append({ 'name': operation['name'], 'description': operation['description'], 'displayName': operation['displayName'], 'providerName': provider['name'], 'providerDisplayName': provider['displayName'] }) matched = True for resource_type in provider['resourceTypes']: for operation in resource_type['operations']: if not operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_actions.append({ 'name': operation['name'], 'description': operation['description'], 'displayName': operation['displayName'], 'providerName': provider['name'], 'providerDisplayName': provider['displayName'] }) matched = True if not action.lower().startswith("microsoft."): has_external = True if not matched: has_unknown = True for permission in raw_role['permissions']: for action in permission['dataActions']: matched = False matchexpression = "^" + action.replace(".", "\\.").replace("*", ".*").replace("?", ".{{1}}") + "$" for provider in provider_ops: for operation in provider['operations']: if operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_data_actions.append({ 'name': operation['name'], 'description': operation['description'], 'displayName': operation['displayName'], 'providerName': provider['name'], 'providerDisplayName': provider['displayName'] }) matched = True for resource_type in provider['resourceTypes']: for operation in resource_type['operations']: if operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_data_actions.append({ 'name': operation['name'], 'description': operation['description'], 'displayName': operation['displayName'], 'providerName': provider['name'], 'providerDisplayName': provider['displayName'] }) matched = True if not action.lower().startswith("microsoft."): has_external = True if not matched: has_unknown = True for permission in raw_role['permissions']: for action in permission['notActions']: matched = False matchexpression = "^" + action.replace(".", "\\.").replace("*", ".*").replace("?", ".{{1}}") + "$" for provider in provider_ops: for operation in provider['operations']: if not operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_actions = list(filter(lambda x: x['name'].lower() != operation['name'].lower(), permitted_actions)) matched = True for resource_type in provider['resourceTypes']: for operation in resource_type['operations']: if not operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_actions = list(filter(lambda x: x['name'].lower() != operation['name'].lower(), permitted_actions)) matched = True if not action.lower().startswith("microsoft."): has_external = True if not matched: has_unknown = True for permission in raw_role['permissions']: for action in permission['notDataActions']: matched = False matchexpression = "^" + action.replace(".", "\\.").replace("*", ".*").replace("?", ".{{1}}") + "$" for provider in provider_ops: for operation in provider['operations']: if operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_data_actions = list(filter(lambda x: x['name'].lower() != operation['name'].lower(), permitted_data_actions)) matched = True for resource_type in provider['resourceTypes']: for operation in resource_type['operations']: if operation['isDataAction'] and re.search(matchexpression.lower(), operation['name'].lower()): permitted_data_actions = list(filter(lambda x: x['name'].lower() != operation['name'].lower(), permitted_data_actions)) matched = True if not action.lower().startswith("microsoft."): has_external = True if not matched: has_unknown = True result['roles'].append({ 'name': raw_role['roleName'], 'description': raw_role['description'], 'permittedActions': permitted_actions, 'permittedDataActions': permitted_data_actions, 'rawPermissions': raw_role['permissions'], 'hasUnknown': has_unknown, 'hasExternal': has_external }) with open("azure/built-in-roles.json", "w") as f: f.write(json.dumps(result, indent=2, sort_keys=True))

50.2

147

0.517075

585

7,028

6.104274

0.136752

0.058247

0.060487

0.077289

0.8317

0.817698

0

0.0011

0.353159

7,028

139

148

50.561151

0.784426

0

0.726563

0

0.157371

0.011952

0

1

0

false

0

0.039063

0

0.039063

0

null

0

1

0

1

0

null

0

7

e56946e13d2d2d1c51e541739f896030848cdd8a

76,894

py

Python

utils/utils_df_nn.py

Lifelong-ML/LASEM

c4ec052c850e37f54bc3e6faf6b988a4c5239f10

[ "MIT" ]

8

2021-07-06T14:35:50.000Z

2022-03-03T08:45:13.000Z

utils/utils_df_nn.py

Lifelong-ML/LASEM

c4ec052c850e37f54bc3e6faf6b988a4c5239f10

[ "MIT" ]

null

utils/utils_df_nn.py

Lifelong-ML/LASEM

c4ec052c850e37f54bc3e6faf6b988a4c5239f10

[ "MIT" ]

1

2021-07-09T09:26:11.000Z

import numpy as np import tensorflow as tf from utils.utils import * from utils.utils_nn import * ########################################################### ##### functions to generate parameter ##### ########################################################### #### function to generate knowledge-base parameters for ELLA_tensorfactor layer def new_ELLA_KB_param(shape, layer_number, task_number, reg_type, init_tensor=None, trainable=True): #kb_name = 'KB_'+str(layer_number)+'_'+str(task_number) kb_name = 'KB_'+str(layer_number) if init_tensor is None: param_to_return = tf.get_variable(name=kb_name, shape=shape, dtype=tf.float32, regularizer=reg_type, trainable=trainable) elif type(init_tensor) == np.ndarray: param_to_return = tf.get_variable(name=kb_name, shape=shape, dtype=tf.float32, regularizer=reg_type, initializer=tf.constant_initializer(init_tensor), trainable=trainable) else: param_to_return = init_tensor return param_to_return #### function to generate task-specific parameters for ELLA_tensorfactor layer def new_ELLA_cnn_deconv_TS_param(shape, layer_number, task_number, reg_type): ts_w_name, ts_b_name, ts_p_name = 'TS_DeconvW0_'+str(layer_number)+'_'+str(task_number), 'TS_Deconvb0_'+str(layer_number)+'_'+str(task_number), 'TS_Convb0_'+str(layer_number)+'_'+str(task_number) return [tf.get_variable(name=ts_w_name, shape=shape[0], dtype=tf.float32, regularizer=reg_type), tf.get_variable(name=ts_b_name, shape=shape[1], dtype=tf.float32, regularizer=reg_type), tf.get_variable(name=ts_p_name, shape=shape[2], dtype=tf.float32, regularizer=reg_type)] #### function to generate task-specific parameters for ELLA_tensorfactor layer def new_ELLA_cnn_deconv_tensordot_TS_param(shape, layer_number, task_number, reg_type, init_tensor, trainable): ts_w_name, ts_b_name, ts_k_name, ts_p_name = 'TS_DeconvW0_'+str(layer_number)+'_'+str(task_number), 'TS_Deconvb0_'+str(layer_number)+'_'+str(task_number), 'TS_ConvW1_'+str(layer_number)+'_'+str(task_number), 'TS_Convb0_'+str(layer_number)+'_'+str(task_number) params_to_return, params_name = [], [ts_w_name, ts_b_name, ts_k_name, ts_p_name] for i, (t, n) in enumerate(zip(init_tensor, params_name)): if t is None: params_to_return.append(tf.get_variable(name=n, shape=shape[i], dtype=tf.float32, regularizer=reg_type if trainable and i<3 else None, trainable=trainable)) elif type(t) == np.ndarray: params_to_return.append(tf.get_variable(name=n, shape=shape[i], dtype=tf.float32, regularizer=reg_type if trainable and i<3 else None, trainable=trainable, initializer=tf.constant_initializer(t))) else: params_to_return.append(t) return params_to_return #### function to generate task-specific parameters for ELLA_tensorfactor layer def new_ELLA_cnn_deconv_tensordot_TS_param2(shape, layer_number, task_number, reg_type): ts_w_name, ts_b_name, ts_k_name, ts_k_name2, ts_p_name = 'TS_DeconvW0_'+str(layer_number)+'_'+str(task_number), 'TS_Deconvb0_'+str(layer_number)+'_'+str(task_number), 'TS_tdot_W1_'+str(layer_number)+'_'+str(task_number), 'TS_tdot_W2_'+str(layer_number)+'_'+str(task_number), 'TS_tdot_b0_'+str(layer_number)+'_'+str(task_number) return [tf.get_variable(name=ts_w_name, shape=shape[0], dtype=tf.float32, regularizer=reg_type), tf.get_variable(name=ts_b_name, shape=shape[1], dtype=tf.float32, regularizer=reg_type), tf.get_variable(name=ts_k_name, shape=shape[2], dtype=tf.float32, regularizer=reg_type), tf.get_variable(name=ts_k_name2, shape=shape[3], dtype=tf.float32, regularizer=reg_type), tf.get_variable(name=ts_p_name, shape=shape[4], dtype=tf.float32, regularizer=reg_type)] ############################################################### ##### functions for adding ELLA network (CNN/Deconv ver) ##### ############################################################### #### function to generate convolutional layer with shared knowledge base #### KB_size : [filter_height(and width), num_of_channel] #### TS_size : deconv_filter_height(and width) #### TS_stride_size : [stride_in_height, stride_in_width] def new_ELLA_cnn_deconv_layer(layer_input, k_size, ch_size, stride_size, KB_size, TS_size, TS_stride_size, layer_num, task_num, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_param=None, TS_param=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_size=None, skip_connect_input=None): assert (k_size[0] == k_size[1] and k_size[0] == (KB_size[0]-1)*TS_stride_size[0]+1), "CNN kernel size does not match the output size of Deconv from KB" with tf.name_scope('ELLA_cdnn_KB'): if KB_param is None: ## KB \in R^{1 \times h \times w \times c} KB_param = new_ELLA_KB_param([1, KB_size[0], KB_size[0], KB_size[1]], layer_num, task_num, KB_reg_type) if TS_param is None: ## TS1 : Deconv W \in R^{h \times w \times ch_in*ch_out \times c} ## TS2 : Deconv bias \in R^{ch_out} TS_param = new_ELLA_cnn_deconv_TS_param([[TS_size, TS_size, ch_size[0]*ch_size[1], KB_size[1]], [1, 1, 1, ch_size[0]*ch_size[1]], [ch_size[1]]], layer_num, task_num, TS_reg_type) with tf.name_scope('ELLA_cdnn_TS'): para_tmp = tf.add(tf.nn.conv2d_transpose(KB_param, TS_param[0], [1, k_size[0], k_size[1], ch_size[0]*ch_size[1]], strides=[1, TS_stride_size[0], TS_stride_size[1], 1]), TS_param[1]) if para_activation_fn is not None: para_tmp = para_activation_fn(para_tmp) W, b = tf.reshape(para_tmp, k_size+ch_size), TS_param[2] layer_eqn, _ = new_cnn_layer(layer_input, k_size+ch_size, stride_size=stride_size, activation_fn=activation_fn, weight=W, bias=b, padding_type=padding_type, max_pooling=max_pool, pool_size=pool_size, skip_connect_input=skip_connect_input) return layer_eqn, [KB_param], TS_param, [W, b] #### function to generate network of convolutional layers with shared knowledge base def new_ELLA_cnn_deconv_net(net_input, k_sizes, ch_sizes, stride_sizes, KB_sizes, TS_sizes, TS_stride_sizes, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_params=None, TS_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, flat_output=False, input_size=[0, 0], task_index=0, skip_connections=[]): _num_TS_param_per_layer = 3 ## first element : make new KB&TS / second element : make new TS / third element : not make new para control_flag = [(KB_params is None and TS_params is None), (not (KB_params is None) and (TS_params is None)), not (KB_params is None or TS_params is None)] if control_flag[1]: TS_params = [] elif control_flag[0]: KB_params, TS_params = [], [] cnn_gen_params=[] layers_for_skip, next_skip_connect = [net_input], None with tf.name_scope('ELLA_cdnn_net'): layers = [] for layer_cnt in range(len(k_sizes)//2): next_skip_connect = skip_connections.pop(0) if (len(skip_connections) > 0 and next_skip_connect is None) else next_skip_connect if next_skip_connect is not None: skip_connect_in, skip_connect_out = next_skip_connect assert (skip_connect_in > -1 and skip_connect_out > -1), "Given skip connection has error (try connecting non-existing layer)" else: skip_connect_in, skip_connect_out = -1, -1 if layer_cnt == skip_connect_out: processed_skip_connect_input = layers_for_skip[skip_connect_in] for layer_cnt_tmp in range(skip_connect_in, skip_connect_out): if max_pool and (pool_sizes[2*layer_cnt_tmp]>1 or pool_sizes[2*layer_cnt_tmp+1]>1): processed_skip_connect_input = tf.nn.max_pool(processed_skip_connect_input, ksize=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], strides=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], padding=padding_type) else: processed_skip_connect_input = None if layer_cnt == 0 and control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[layer_cnt], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[1]: layer_tmp, _, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[layer_cnt], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[2]: layer_tmp, _, _, cnn_gen_para_tmp = new_ELLA_cnn_deconv_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[layer_cnt], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[layer_cnt], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[1]: layer_tmp, _, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[layer_cnt], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[2]: layer_tmp, _, _, cnn_gen_para_tmp = new_ELLA_cnn_deconv_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[layer_cnt], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) layers.append(layer_tmp) layers_for_skip.append(layer_tmp) cnn_gen_params = cnn_gen_params + cnn_gen_para_tmp if control_flag[1]: TS_params = TS_params + TS_para_tmp elif control_flag[0]: KB_params = KB_params + KB_para_tmp TS_params = TS_params + TS_para_tmp if layer_cnt == skip_connect_out: next_skip_connect = None #### flattening output if flat_output: output_dim = [int(layers[-1].shape[1]*layers[-1].shape[2]*layers[-1].shape[3])] layers.append(tf.reshape(layers[-1], [-1, output_dim[0]])) else: output_dim = layers[-1].shape[1:] #### add dropout layer if dropout: layers.append(tf.nn.dropout(layers[-1], dropout_prob)) return (layers, KB_params, TS_params, cnn_gen_params, output_dim) #### function to generate network of cnn->ffnn def new_ELLA_cnn_deconv_fc_net(net_input, k_sizes, ch_sizes, stride_sizes, fc_sizes, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, cnn_activation_fn=tf.nn.relu, cnn_para_activation_fn=tf.nn.relu, cnn_KB_params=None, cnn_TS_params=None, fc_activation_fn=tf.nn.relu, fc_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, input_size=[0, 0], output_type=None, task_index=0, skip_connections=[]): ## add CNN layers cnn_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, cnn_output_dim = new_ELLA_cnn_deconv_net(net_input, k_sizes, ch_sizes, stride_sizes, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_params=cnn_KB_params, TS_params=cnn_TS_params, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_sizes=pool_sizes, dropout=dropout, dropout_prob=dropout_prob, flat_output=True, input_size=input_size, task_index=task_index, skip_connections=skip_connections) ## add fc layers #fc_model, fc_params = new_fc_net(cnn_model[-1], [cnn_output_dim[0]]+fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net') fc_model, fc_params = new_fc_net(cnn_model[-1], fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net') return (cnn_model+fc_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, fc_params) ########################################################################### ##### functions for adding ELLA network (CNN/Deconv & Tensordot ver) ##### ########################################################################### #### KB_size : [filter_height(and width), num_of_channel] #### TS_size : [deconv_filter_height(and width), deconv_filter_channel] #### TS_stride_size : [stride_in_height, stride_in_width] def new_ELLA_cnn_deconv_tensordot_layer(layer_input, k_size, ch_size, stride_size, KB_size, TS_size, TS_stride_size, layer_num, task_num, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_param=None, TS_param=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_size=None, skip_connect_input=None, highway_connect_type=0, highway_W=None, highway_b=None, trainable=True, trainable_KB=True): assert (k_size[0] == k_size[1] and k_size[0] == (KB_size[0]-1)*TS_stride_size[0]+1), "CNN kernel size does not match the output size of Deconv from KB" with tf.name_scope('ELLA_cdnn_KB'): ## KB \in R^{1 \times h \times w \times c} KB_param = new_ELLA_KB_param([1, KB_size[0], KB_size[0], KB_size[1]], layer_num, task_num, KB_reg_type, KB_param, trainable=trainable_KB) ## TS1 : Deconv W \in R^{h \times w \times kb_c_out \times c} ## TS2 : Deconv bias \in R^{kb_c_out} ## TS3 : tensor W \in R^{kb_c_out \times ch_in \times ch_out} ## TS4 : Conv bias \in R^{ch_out} TS_param = new_ELLA_cnn_deconv_tensordot_TS_param([[TS_size[0], TS_size[0], TS_size[1], KB_size[1]], [1, 1, 1, TS_size[1]], [TS_size[1], ch_size[0], ch_size[1]], [ch_size[1]]], layer_num, task_num, TS_reg_type, [None, None, None, None] if TS_param is None else TS_param, trainable=trainable) with tf.name_scope('DFCNN_param_gen'): para_tmp = tf.add(tf.nn.conv2d_transpose(KB_param, TS_param[0], [1, k_size[0], k_size[1], TS_size[1]], strides=[1, TS_stride_size[0], TS_stride_size[1], 1]), TS_param[1]) para_tmp = tf.reshape(para_tmp, [k_size[0], k_size[1], TS_size[1]]) if para_activation_fn is not None: para_tmp = para_activation_fn(para_tmp) W = tf.tensordot(para_tmp, TS_param[2], [[2], [0]]) b = TS_param[3] ## HighwayNet's skip connection highway_params, gate = [], None if highway_connect_type > 0: with tf.name_scope('highway_connection'): if highway_connect_type == 1: x = layer_input if highway_W is None: highway_W = new_weight([k_size[0], k_size[1], ch_size[0], ch_size[1]]) if highway_b is None: highway_b = new_bias([ch_size[1]], init_val=-2.0) gate, _ = new_cnn_layer(x, k_size+ch_size, stride_size=stride_size, activation_fn=None, weight=highway_W, bias=highway_b, padding_type=padding_type, max_pooling=False) elif highway_connect_type == 2: x = tf.reshape(layer_input, [-1, int(layer_input.shape[1]*layer_input.shape[2]*layer_input.shape[3])]) if highway_W is None: highway_W = new_weight([int(x.shape[1]), 1]) if highway_b is None: highway_b = new_bias([1], init_val=-2.0) gate = tf.broadcast_to(tf.stack([tf.stack([tf.matmul(x, highway_W) + highway_b], axis=2)], axis=3), layer_input.get_shape()) gate = tf.nn.sigmoid(gate) highway_params = [highway_W, highway_b] layer_eqn, _ = new_cnn_layer(layer_input, k_size+ch_size, stride_size=stride_size, activation_fn=activation_fn, weight=W, bias=b, padding_type=padding_type, max_pooling=max_pool, pool_size=pool_size, skip_connect_input=skip_connect_input, highway_connect_type=highway_connect_type, highway_gate=gate) return layer_eqn, [KB_param], TS_param, [W, b], highway_params #### function to generate network of convolutional layers with shared knowledge base def new_ELLA_cnn_deconv_tensordot_net(net_input, k_sizes, ch_sizes, stride_sizes, KB_sizes, TS_sizes, TS_stride_sizes, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_params=None, TS_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, flat_output=False, input_size=[0, 0], task_index=0, skip_connections=[]): _num_TS_param_per_layer = 4 ## first element : make new KB&TS / second element : make new TS / third element : not make new para / fourth element : make new KB control_flag = [(KB_params is None and TS_params is None), (not (KB_params is None) and (TS_params is None)), not (KB_params is None or TS_params is None), ((KB_params is None) and not (TS_params is None))] if control_flag[1]: TS_params = [] elif control_flag[3]: KB_params = [] elif control_flag[0]: KB_params, TS_params = [], [] cnn_gen_params = [] layers_for_skip, next_skip_connect = [net_input], None with tf.name_scope('ELLA_cdnn_net'): layers = [] for layer_cnt in range(len(k_sizes)//2): next_skip_connect = skip_connections.pop(0) if (len(skip_connections) > 0 and next_skip_connect is None) else next_skip_connect if next_skip_connect is not None: skip_connect_in, skip_connect_out = next_skip_connect assert (skip_connect_in > -1 and skip_connect_out > -1), "Given skip connection has error (try connecting non-existing layer)" else: skip_connect_in, skip_connect_out = -1, -1 if layer_cnt == skip_connect_out: processed_skip_connect_input = layers_for_skip[skip_connect_in] for layer_cnt_tmp in range(skip_connect_in, skip_connect_out): if max_pool and (pool_sizes[2*layer_cnt_tmp]>1 or pool_sizes[2*layer_cnt_tmp+1]>1): processed_skip_connect_input = tf.nn.max_pool(processed_skip_connect_input, ksize=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], strides=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], padding=padding_type) else: processed_skip_connect_input = None if layer_cnt == 0 and control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[1]: layer_tmp, _, TS_para_tmp, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[2]: layer_tmp, _, _, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[3]: layer_tmp, KB_para_tmp, _, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[1]: layer_tmp, _, TS_para_tmp, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[2]: layer_tmp, _, _, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[3]: layer_tmp, KB_para_tmp, _, cnn_gen_para_tmp, _ = new_ELLA_cnn_deconv_tensordot_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) layers.append(layer_tmp) layers_for_skip.append(layer_tmp) cnn_gen_params = cnn_gen_params + cnn_gen_para_tmp if control_flag[1]: TS_params = TS_params + TS_para_tmp elif control_flag[3]: KB_params = KB_params + KB_para_tmp elif control_flag[0]: KB_params = KB_params + KB_para_tmp TS_params = TS_params + TS_para_tmp if layer_cnt == skip_connect_out: next_skip_connect = None #### flattening output if flat_output: output_dim = [int(layers[-1].shape[1]*layers[-1].shape[2]*layers[-1].shape[3])] layers.append(tf.reshape(layers[-1], [-1, output_dim[0]])) else: output_dim = layers[-1].shape[1:] #### add dropout layer if dropout: layers.append(tf.nn.dropout(layers[-1], dropout_prob)) return (layers, KB_params, TS_params, cnn_gen_params, output_dim) #### function to generate network of cnn (with shared KB through deconv)-> simple ffnn def new_ELLA_cnn_deconv_tensordot_fc_net(net_input, k_sizes, ch_sizes, stride_sizes, fc_sizes, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, cnn_activation_fn=tf.nn.relu, cnn_para_activation_fn=tf.nn.relu, cnn_KB_params=None, cnn_TS_params=None, fc_activation_fn=tf.nn.relu, fc_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, input_size=[0, 0], output_type=None, task_index=0, skip_connections=[]): ## add CNN layers cnn_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, cnn_output_dim = new_ELLA_cnn_deconv_tensordot_net(net_input, k_sizes, ch_sizes, stride_sizes, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_params=cnn_KB_params, TS_params=cnn_TS_params, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_sizes=pool_sizes, dropout=dropout, dropout_prob=dropout_prob, flat_output=True, input_size=input_size, task_index=task_index, skip_connections=skip_connections) ## add fc layers #fc_model, fc_params = new_fc_net(cnn_model[-1], [cnn_output_dim[0]]+fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net') fc_model, fc_params = new_fc_net(cnn_model[-1], fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net') return (cnn_model+fc_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, fc_params) ########################################################################### ##### functions for adding ELLA network (CNN/Deconv & Tensordot ver2) ##### ########################################################################### #### KB_size : [filter_height(and width), num_of_channel0, num_of_channel1] #### TS_size : [deconv_filter_height(and width), deconv_filter_channel] #### TS_stride_size : [stride_in_height, stride_in_width] def new_ELLA_cnn_deconv_tensordot_layer2(layer_input, k_size, ch_size, stride_size, KB_size, TS_size, TS_stride_size, layer_num, task_num, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_param=None, TS_param=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_size=None, skip_connect_input=None): assert (k_size[0] == k_size[1] and k_size[0] == (KB_size[0]-1)*TS_stride_size[0]+1), "CNN kernel size does not match the output size of Deconv from KB" with tf.name_scope('ELLA_cdnn_KB'): if KB_param is None: ## KB \in R^{d \times h \times w \times c} KB_param = new_ELLA_KB_param([KB_size[1], KB_size[0], KB_size[0], KB_size[2]], layer_num, task_num, KB_reg_type) if TS_param is None: ## TS1 : Deconv W \in R^{h \times w \times kb_c_out \times c} ## TS2 : Deconv bias \in R^{kb_c_out} ## TS3 : tensor W \in R^{d \times ch_in} ## TS4 : tensor W \in R^{kb_c_out \times ch_out} ## TS5 : Conv bias \in R^{ch_out} TS_param = new_ELLA_cnn_deconv_tensordot_TS_param2([[TS_size[0], TS_size[0], TS_size[1], KB_size[2]], [1, 1, 1, TS_size[1]], [KB_size[1], ch_size[0]], [TS_size[1], ch_size[1]], [1, 1, 1, ch_size[1]]], layer_num, task_num, TS_reg_type) with tf.name_scope('ELLA_cdnn_TS'): para_tmp = tf.add(tf.nn.conv2d_transpose(KB_param, TS_param[0], [KB_size[1], k_size[0], k_size[1], TS_size[1]], strides=[1, TS_stride_size[0], TS_stride_size[1], 1]), TS_param[1]) if para_activation_fn is not None: para_tmp = para_activation_fn(para_tmp) para_tmp = tf.tensordot(para_tmp, TS_param[2], [[0], [0]]) W = tf.tensordot(para_tmp, TS_param[3], [[2], [0]]) b = TS_param[4] layer_eqn, _ = new_cnn_layer(layer_input, k_size+ch_size, stride_size=stride_size, activation_fn=activation_fn, weight=W, bias=b, padding_type=padding_type, max_pooling=max_pool, pool_size=pool_size, skip_connect_input=skip_connect_input) return layer_eqn, [KB_param], TS_param, [W, b] #### function to generate network of convolutional layers with shared knowledge base def new_ELLA_cnn_deconv_tensordot_net2(net_input, k_sizes, ch_sizes, stride_sizes, KB_sizes, TS_sizes, TS_stride_sizes, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_params=None, TS_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, flat_output=False, input_size=[0, 0], task_index=0, skip_connections=[]): _num_TS_param_per_layer = 5 ## first element : make new KB&TS / second element : make new TS / third element : not make new para / fourth element : make new KB control_flag = [(KB_params is None and TS_params is None), (not (KB_params is None) and (TS_params is None)), not (KB_params is None or TS_params is None), ((KB_params is None) and not (TS_params is None))] if control_flag[1]: TS_params = [] elif control_flag[3]: KB_params = [] elif control_flag[0]: KB_params, TS_params = [], [] cnn_gen_params = [] layers_for_skip, next_skip_connect = [net_input], None with tf.name_scope('ELLA_cdnn_net'): layers = [] for layer_cnt in range(len(k_sizes)//2): next_skip_connect = skip_connections.pop(0) if (len(skip_connections) > 0 and next_skip_connect is None) else next_skip_connect if next_skip_connect is not None: skip_connect_in, skip_connect_out = next_skip_connect assert (skip_connect_in > -1 and skip_connect_out > -1), "Given skip connection has error (try connecting non-existing layer)" else: skip_connect_in, skip_connect_out = -1, -1 if layer_cnt == skip_connect_out: processed_skip_connect_input = layers_for_skip[skip_connect_in] for layer_cnt_tmp in range(skip_connect_in, skip_connect_out): if max_pool and (pool_sizes[2*layer_cnt_tmp]>1 or pool_sizes[2*layer_cnt_tmp+1]>1): processed_skip_connect_input = tf.nn.max_pool(processed_skip_connect_input, ksize=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], strides=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], padding=padding_type) else: processed_skip_connect_input = None if layer_cnt == 0 and control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[1]: layer_tmp, _, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[2]: layer_tmp, _, _, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif layer_cnt == 0 and control_flag[3]: layer_tmp, KB_para_tmp, _, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[1]: layer_tmp, _, TS_para_tmp, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[2]: layer_tmp, _, _, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=KB_params[layer_cnt], TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) elif control_flag[3]: layer_tmp, KB_para_tmp, _, cnn_gen_para_tmp = new_ELLA_cnn_deconv_tensordot_layer2(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], KB_sizes[3*layer_cnt:3*(layer_cnt+1)], TS_sizes[2*layer_cnt:2*(layer_cnt+1)], TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=para_activation_fn, KB_param=None, TS_param=TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input) layers.append(layer_tmp) layers_for_skip.append(layer_tmp) cnn_gen_params = cnn_gen_params + cnn_gen_para_tmp if control_flag[1]: TS_params = TS_params + TS_para_tmp elif control_flag[3]: KB_params = KB_params + KB_para_tmp elif control_flag[0]: KB_params = KB_params + KB_para_tmp TS_params = TS_params + TS_para_tmp if layer_cnt == skip_connect_out: next_skip_connect = None #### flattening output if flat_output: output_dim = [int(layers[-1].shape[1]*layers[-1].shape[2]*layers[-1].shape[3])] layers.append(tf.reshape(layers[-1], [-1, output_dim[0]])) else: output_dim = layers[-1].shape[1:] #### add dropout layer if dropout: layers.append(tf.nn.dropout(layers[-1], dropout_prob)) return (layers, KB_params, TS_params, cnn_gen_params, output_dim) #### function to generate network of cnn (with shared KB through deconv)-> simple ffnn def new_ELLA_cnn_deconv_tensordot_fc_net2(net_input, k_sizes, ch_sizes, stride_sizes, fc_sizes, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, cnn_activation_fn=tf.nn.relu, cnn_para_activation_fn=tf.nn.relu, cnn_KB_params=None, cnn_TS_params=None, fc_activation_fn=tf.nn.relu, fc_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, input_size=[0, 0], output_type=None, task_index=0, skip_connections=[]): ## add CNN layers cnn_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, cnn_output_dim = new_ELLA_cnn_deconv_tensordot_net2(net_input, k_sizes, ch_sizes, stride_sizes, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_params=cnn_KB_params, TS_params=cnn_TS_params, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_sizes=pool_sizes, dropout=dropout, dropout_prob=dropout_prob, flat_output=True, input_size=input_size, task_index=task_index, skip_connections=skip_connections) ## add fc layers #fc_model, fc_params = new_fc_net(cnn_model[-1], [cnn_output_dim[0]]+fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net') fc_model, fc_params = new_fc_net(cnn_model[-1], fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net') return (cnn_model+fc_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, fc_params) ############################################################################################################## #### functions for Conv-FC nets whose conv layers are freely set to shared across tasks by DeconvFactor #### ############################################################################################################## def new_ELLA_flexible_cnn_deconv_tensordot_fc_net(net_input, k_sizes, ch_sizes, stride_sizes, fc_sizes, cnn_sharing, cnn_KB_sizes, cnn_TS_sizes, cnn_TS_stride_sizes, cnn_activation_fn=tf.nn.relu, cnn_para_activation_fn=tf.nn.relu, cnn_KB_params=None, cnn_TS_params=None, cnn_params=None, fc_activation_fn=tf.nn.relu, fc_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, input_size=[0, 0], output_type=None, task_index=0, skip_connections=[], highway_connect_type=0, cnn_highway_params=None, trainable=True, trainable_KB=True): _num_TS_param_per_layer = 4 num_conv_layers = [len(k_sizes)//2, len(ch_sizes)-1, len(stride_sizes)//2, len(cnn_sharing), len(cnn_KB_sizes)//2, len(cnn_TS_sizes)//2, len(cnn_TS_stride_sizes)//2] assert (all([(num_conv_layers[i]==num_conv_layers[i+1]) for i in range(len(num_conv_layers)-1)])), "Parameters related to conv layers are wrong!" num_conv_layers = num_conv_layers[0] ''' if cnn_KB_params is not None: assert (len(cnn_KB_params) == 1), "Given init value of KB (last layer) is wrong!" if cnn_TS_params is not None: assert (len(cnn_TS_params) == 4), "Given init value of TS (last layer) is wrong!" ''' ## add CNN layers ## first element : make new KB&TS / second element : make new TS / third element : not make new para / fourth element : make new KB control_flag = [(cnn_KB_params is None and cnn_TS_params is None), (not (cnn_KB_params is None) and (cnn_TS_params is None)), not (cnn_KB_params is None or cnn_TS_params is None), ((cnn_KB_params is None) and not (cnn_TS_params is None))] if control_flag[1]: cnn_TS_params = [] elif control_flag[3]: cnn_KB_params = [] elif control_flag[0]: cnn_KB_params, cnn_TS_params = [], [] cnn_gen_params = [] if cnn_params is None: cnn_params = [None for _ in range(2*num_conv_layers)] layers_for_skip, next_skip_connect = [net_input], None with tf.name_scope('Hybrid_DFCNN'): cnn_model, cnn_params_to_return, cnn_highway_params_to_return = [], [], [] cnn_KB_to_return, cnn_TS_to_return = [], [] for layer_cnt in range(num_conv_layers): KB_para_tmp, TS_para_tmp, para_tmp = [None], [None for _ in range(_num_TS_param_per_layer)], [None, None] highway_para_tmp = [None, None] if cnn_highway_params is None else cnn_highway_params[2*layer_cnt:2*(layer_cnt+1)] cnn_gen_para_tmp = [None, None] next_skip_connect = skip_connections.pop(0) if (len(skip_connections) > 0 and next_skip_connect is None) else next_skip_connect if next_skip_connect is not None: skip_connect_in, skip_connect_out = next_skip_connect assert (skip_connect_in > -1 and skip_connect_out > -1), "Given skip connection has error (try connecting non-existing layer)" else: skip_connect_in, skip_connect_out = -1, -1 if layer_cnt == skip_connect_out: processed_skip_connect_input = layers_for_skip[skip_connect_in] for layer_cnt_tmp in range(skip_connect_in, skip_connect_out): if max_pool and (pool_sizes[2*layer_cnt_tmp]>1 or pool_sizes[2*layer_cnt_tmp+1]>1): processed_skip_connect_input = tf.nn.max_pool(processed_skip_connect_input, ksize=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], strides=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], padding=padding_type) else: processed_skip_connect_input = None if layer_cnt == 0: if control_flag[0] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif control_flag[1] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=cnn_KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif control_flag[2] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=cnn_KB_params[layer_cnt], TS_param=cnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif control_flag[3] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=None, TS_param=cnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif (not cnn_sharing[layer_cnt]): layer_tmp, para_tmp = new_cnn_layer(layer_input=net_input, k_size=k_sizes[2*layer_cnt:2*(layer_cnt+1)]+ch_sizes[layer_cnt:layer_cnt+2], stride_size=[1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], activation_fn=cnn_activation_fn, weight=cnn_params[2*layer_cnt], bias=cnn_params[2*layer_cnt+1], padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, trainable=trainable) else: if control_flag[0] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(cnn_model[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=None, TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif control_flag[1] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(cnn_model[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=cnn_KB_params[layer_cnt], TS_param=None, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif control_flag[2] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(cnn_model[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=cnn_KB_params[layer_cnt], TS_param=cnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif control_flag[3] and cnn_sharing[layer_cnt]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_gen_para_tmp, highway_para_tmp = new_ELLA_cnn_deconv_tensordot_layer(cnn_model[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], cnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], cnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=cnn_activation_fn, para_activation_fn=cnn_para_activation_fn, KB_param=None, TS_param=cnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, highway_connect_type=highway_connect_type, highway_W=highway_para_tmp[0], highway_b=highway_para_tmp[1], trainable=trainable, trainable_KB=trainable_KB) elif (not cnn_sharing[layer_cnt]): layer_tmp, para_tmp = new_cnn_layer(layer_input=cnn_model[layer_cnt-1], k_size=k_sizes[2*layer_cnt:2*(layer_cnt+1)]+ch_sizes[layer_cnt:layer_cnt+2], stride_size=[1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], activation_fn=cnn_activation_fn, weight=cnn_params[2*layer_cnt], bias=cnn_params[2*layer_cnt+1], padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], skip_connect_input=processed_skip_connect_input, trainable=trainable) cnn_model.append(layer_tmp) layers_for_skip.append(layer_tmp) cnn_KB_to_return = cnn_KB_to_return + KB_para_tmp cnn_TS_to_return = cnn_TS_to_return + TS_para_tmp cnn_params_to_return = cnn_params_to_return + para_tmp cnn_gen_params = cnn_gen_params + cnn_gen_para_tmp cnn_highway_params_to_return = cnn_highway_params_to_return + highway_para_tmp if layer_cnt == skip_connect_out: next_skip_connect = None #### flattening output output_dim = [int(cnn_model[-1].shape[1]*cnn_model[-1].shape[2]*cnn_model[-1].shape[3])] cnn_model.append(tf.reshape(cnn_model[-1], [-1, output_dim[0]])) #### add dropout layer if dropout: cnn_model.append(tf.nn.dropout(cnn_model[-1], dropout_prob)) ## add fc layers fc_model, fc_params = new_fc_net(cnn_model[-1], fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, tensorboard_name_scope='fc_net', trainable=trainable) #return (cnn_model+fc_model, cnn_KB_params, cnn_TS_params, cnn_gen_params, cnn_params_to_return, cnn_highway_params_to_return, fc_params) return (cnn_model+fc_model, cnn_KB_to_return, cnn_TS_to_return, cnn_gen_params, cnn_params_to_return, cnn_highway_params_to_return, fc_params) #### function to generate DARTS-based network for selective sharing on DF-CNN def new_darts_dfcnn_layer(layer_input, k_size, ch_size, stride_size, KB_size, TS_size, TS_stride_size, layer_num, task_num, activation_fn=tf.nn.relu, para_activation_fn=tf.nn.relu, KB_param=None, TS_param=None, conv_param=None, select_param=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pooling=False, pool_size=None, trainable=True, skip_connect_input=None, name_scope='darts_dfcnn_layer', use_numpy_var_in_graph=False): with tf.name_scope(name_scope): ## init DF-CNN KB params if KB_param is None or (type(KB_param) == np.ndarray and not use_numpy_var_in_graph): KB_param = new_ELLA_KB_param([1, KB_size[0], KB_size[0], KB_size[1]], layer_num, task_num, KB_reg_type, KB_param, trainable=trainable) ## init DF-CNN task-specific mapping params if TS_param is None or (type(TS_param) == np.ndarray and not use_numpy_var_in_graph): TS_param = new_ELLA_cnn_deconv_tensordot_TS_param([[TS_size[0], TS_size[0], TS_size[1], KB_size[1]], [1, 1, 1, TS_size[1]], [TS_size[1], ch_size[0], ch_size[1]], [ch_size[1]]], layer_num, task_num, TS_reg_type, [None, None, None, None] if TS_param is None else TS_param, trainable=trainable) ## init task-specific conv params if conv_param is None: conv_param = [new_weight(shape=k_size+ch_size, trainable=trainable), new_bias(shape=[ch_size[-1]], trainable=trainable)] else: if conv_param[0] is None or (type(conv_param[0]) == np.ndarray and not use_numpy_var_in_graph): conv_param[0] = new_weight(shape=k_size+ch_size, init_tensor=conv_param[0], trainable=trainable) if conv_param[1] is None or (type(conv_param[1]) == np.ndarray and not use_numpy_var_in_graph): conv_param[1] = new_bias(shape=[ch_size[-1]], init_tensor=conv_param[1], trainable=trainable) ## init DARTS-selection params if select_param is None: select_param = new_weight(shape=[2], init_tensor=np.zeros(2, dtype=np.float32), trainable=trainable) elif (type(select_param) == np.ndarray) and not use_numpy_var_in_graph: select_param = new_weight(shape=[2], init_tensor=select_param, trainable=trainable) with tf.name_scope('DFCNN_param_gen'): para_tmp = tf.add(tf.nn.conv2d_transpose(KB_param, TS_param[0], [1, k_size[0], k_size[1], TS_size[1]], strides=[1, TS_stride_size[0], TS_stride_size[1], 1]), TS_param[1]) para_tmp = tf.reshape(para_tmp, [k_size[0], k_size[1], TS_size[1]]) if para_activation_fn is not None: para_tmp = para_activation_fn(para_tmp) W = tf.tensordot(para_tmp, TS_param[2], [[2], [0]]) b = TS_param[3] mixing_weight = tf.reshape(tf.nn.softmax(select_param), [2,1]) shared_conv_layer = tf.nn.conv2d(layer_input, W, strides=stride_size, padding=padding_type) + b TS_conv_layer = tf.nn.conv2d(layer_input, conv_param[0], strides=stride_size, padding=padding_type) + conv_param[1] if skip_connect_input is not None: shape1, shape2 = shared_conv_layer.get_shape().as_list(), skip_connect_input.get_shape().as_list() assert (len(shape1) == len(shape2)), "Shape of layer's output and input of skip connection do not match!" assert (all([(x==y) for (x, y) in zip(shape1, shape2)])), "Shape of layer's output and input of skip connection do NOT match!" shared_conv_layer = shared_conv_layer + skip_connect_input TS_conv_layer = TS_conv_layer + skip_connect_input if not (activation_fn is None): shared_conv_layer = activation_fn(shared_conv_layer) TS_conv_layer = activation_fn(TS_conv_layer) mixed_conv_temp = tf.tensordot(tf.stack([TS_conv_layer, shared_conv_layer], axis=4), mixing_weight, axes=[[4], [0]]) conv_layer = tf.reshape(mixed_conv_temp, mixed_conv_temp.get_shape()[0:-1]) if max_pooling and (pool_size[1] > 1 or pool_size[2] > 1): layer = tf.nn.max_pool(conv_layer, ksize=pool_size, strides=pool_size, padding=padding_type) else: layer = conv_layer return (layer, [KB_param], TS_param, conv_param, [select_param]) def new_darts_dfcnn_net(net_input, k_sizes, ch_sizes, stride_sizes, dfcnn_KB_sizes, dfcnn_TS_sizes, dfcnn_TS_stride_sizes, activation_fn=tf.nn.relu, dfcnn_TS_activation_fn=tf.nn.relu, dfcnn_KB_params=None, dfcnn_TS_params=None, cnn_TS_params=None, select_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, flat_output=False, trainable=True, task_index=0, skip_connections=[], use_numpy_var_in_graph=False): _num_TS_param_per_layer = 4 num_conv_layers = [len(k_sizes)//2, len(ch_sizes)-1, len(stride_sizes)//2, len(dfcnn_KB_sizes)//2, len(dfcnn_TS_sizes)//2, len(dfcnn_TS_stride_sizes)//2] assert (all([(num_conv_layers[i]==num_conv_layers[i+1]) for i in range(len(num_conv_layers)-1)])), "Parameters related to conv layers are wrong!" num_conv_layers = num_conv_layers[0] ## first element : make new KB&TS / second element : make new TS / third element : not make new para / fourth element : make new KB control_flag = [(dfcnn_KB_params is None and dfcnn_TS_params is None), (not (dfcnn_KB_params is None) and (dfcnn_TS_params is None)), not (dfcnn_KB_params is None or dfcnn_TS_params is None), ((dfcnn_KB_params is None) and not (dfcnn_TS_params is None))] if cnn_TS_params is None: cnn_TS_params = [None for _ in range(2*num_conv_layers)] else: assert(len(cnn_TS_params) == 2*num_conv_layers), "Check given parameters!" if select_params is None: select_params = [None for _ in range(num_conv_layers)] layers_for_skip, next_skip_connect = [net_input], None layers, dfcnn_shared_params_return, dfcnn_TS_params_return, cnn_TS_params_return, select_params_return = [], [], [], [], [] with tf.name_scope('DARTS_DFCNN_net'): for layer_cnt in range(num_conv_layers): next_skip_connect = skip_connections.pop(0) if (len(skip_connections) > 0 and next_skip_connect is None) else None if next_skip_connect is not None: skip_connect_in, skip_connect_out = next_skip_connect assert (skip_connect_in > -1 and skip_connect_out > -1), "Given skip connection has error (try connecting non-existing layer)" else: skip_connect_in, skip_connect_out = -1, -1 if layer_cnt == skip_connect_out: processed_skip_connect_input = layers_for_skip[skip_connect_in] for layer_cnt_tmp in range(skip_connect_in, skip_connect_out): if max_pool and (pool_sizes[2*layer_cnt_tmp]>1 or pool_sizes[2*layer_cnt_tmp+1]>1): processed_skip_connect_input = tf.nn.max_pool(processed_skip_connect_input, ksize=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], strides=[1]+pool_sizes[2*layer_cnt_tmp:2*(layer_cnt_tmp+1)]+[1], padding=padding_type) else: processed_skip_connect_input = None if layer_cnt == 0: if control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=None, TS_param=None, conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) elif control_flag[1]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=dfcnn_KB_params[layer_cnt], TS_param=None, conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) elif control_flag[2]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=dfcnn_KB_params[layer_cnt], TS_param=dfcnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) elif control_flag[3]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(net_input, k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=None, TS_param=dfcnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) else: if control_flag[0]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=None, TS_param=None, conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) elif control_flag[1]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=dfcnn_KB_params[layer_cnt], TS_param=None, conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) elif control_flag[2]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=dfcnn_KB_params[layer_cnt], TS_param=dfcnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) elif control_flag[3]: layer_tmp, KB_para_tmp, TS_para_tmp, cnn_TS_para_tmp, select_para_tmp = new_darts_dfcnn_layer(layers[layer_cnt-1], k_sizes[2*layer_cnt:2*(layer_cnt+1)], ch_sizes[layer_cnt:layer_cnt+2], [1]+stride_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], dfcnn_KB_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_sizes[2*layer_cnt:2*(layer_cnt+1)], dfcnn_TS_stride_sizes[2*layer_cnt:2*(layer_cnt+1)], layer_cnt, task_index, activation_fn=activation_fn, para_activation_fn=dfcnn_TS_activation_fn, KB_param=None, TS_param=dfcnn_TS_params[_num_TS_param_per_layer*layer_cnt:_num_TS_param_per_layer*(layer_cnt+1)], conv_param=cnn_TS_params[2*layer_cnt:2*(layer_cnt+1)], select_param=select_params[layer_cnt], KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pooling=max_pool, pool_size=[1]+pool_sizes[2*layer_cnt:2*(layer_cnt+1)]+[1], trainable=trainable, skip_connect_input=processed_skip_connect_input, use_numpy_var_in_graph=use_numpy_var_in_graph) layers.append(layer_tmp) layers_for_skip.append(layer_tmp) dfcnn_shared_params_return = dfcnn_shared_params_return + KB_para_tmp dfcnn_TS_params_return = dfcnn_TS_params_return + TS_para_tmp cnn_TS_params_return = cnn_TS_params_return + cnn_TS_para_tmp select_params_return = select_params_return + select_para_tmp if layer_cnt == skip_connect_out: next_skip_connect = None #### flattening output if flat_output: output_dim = [int(layers[-1].shape[1]*layers[-1].shape[2]*layers[-1].shape[3])] layers.append(tf.reshape(layers[-1], [-1, output_dim[0]])) else: output_dim = layers[-1].shape[1:] #### add dropout layer if dropout: layers.append(tf.nn.dropout(layers[-1], dropout_prob)) return (layers, dfcnn_shared_params_return, dfcnn_TS_params_return, cnn_TS_params_return, select_params_return, output_dim) def new_darts_dfcnn_fc_net(net_input, k_sizes, ch_sizes, stride_sizes, fc_sizes, dfcnn_KB_sizes, dfcnn_TS_sizes, dfcnn_TS_stride_sizes, cnn_activation_fn=tf.nn.relu, dfcnn_TS_activation_fn=tf.nn.relu, fc_activation_fn=tf.nn.relu, dfcnn_KB_params=None, dfcnn_TS_params=None, cnn_TS_params=None, select_params=None, fc_params=None, KB_reg_type=None, TS_reg_type=None, padding_type='SAME', max_pool=False, pool_sizes=None, dropout=False, dropout_prob=None, output_type=None, trainable=True, task_index=0, skip_connections=[], use_numpy_var_in_graph=False): cnn_model, dfcnn_shared_params_return, dfcnn_TS_params_return, cnn_TS_params_return, cnn_select_params_return, cnn_output_dim = new_darts_dfcnn_net(net_input, k_sizes, ch_sizes, stride_sizes, dfcnn_KB_sizes, dfcnn_TS_sizes, dfcnn_TS_stride_sizes, activation_fn=cnn_activation_fn, dfcnn_TS_activation_fn=dfcnn_TS_activation_fn, dfcnn_KB_params=dfcnn_KB_params, dfcnn_TS_params=dfcnn_TS_params, cnn_TS_params=cnn_TS_params, select_params=select_params, KB_reg_type=KB_reg_type, TS_reg_type=TS_reg_type, padding_type=padding_type, max_pool=max_pool, pool_sizes=pool_sizes, dropout=dropout, dropout_prob=dropout_prob, flat_output=True, trainable=trainable, task_index=task_index, skip_connections=skip_connections, use_numpy_var_in_graph=use_numpy_var_in_graph) fc_model, fc_params_return = new_fc_net(cnn_model[-1], fc_sizes, activation_fn=fc_activation_fn, params=fc_params, output_type=output_type, use_numpy_var_in_graph=use_numpy_var_in_graph) return (cnn_model+fc_model, dfcnn_shared_params_return, dfcnn_TS_params_return, cnn_TS_params_return, cnn_select_params_return, fc_params_return)

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f902e32cb0c6f87b960daa97f2b7828a5ba52e29

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Python

ross/tests/test_rubbing.py

rodrigomoliveira1/ross

51f9379a8a834e1253b94e70dd9f5324acd8c78e

[ "MIT" ]

1

2021-07-20T04:24:19.000Z

ross/tests/test_rubbing.py

rodrigomoliveira1/ross

51f9379a8a834e1253b94e70dd9f5324acd8c78e

[ "MIT" ]

null

ross/tests/test_rubbing.py

rodrigomoliveira1/ross

51f9379a8a834e1253b94e70dd9f5324acd8c78e

[ "MIT" ]

null

import os from pathlib import Path from tempfile import tempdir import numpy as np import pytest from numpy.testing import assert_allclose, assert_almost_equal import ross as rs from ross.defects.misalignment import MisalignmentFlex from ross.units import Q_ steel2 = rs.Material(name="Steel", rho=7850, E=2.17e11, Poisson=0.2992610837438423) # Rotor with 6 DoFs, with internal damping, with 10 shaft elements, 2 disks and 2 bearings. i_d = 0 o_d = 0.019 n = 33 # fmt: off L = np.array( [0 , 25, 64, 104, 124, 143, 175, 207, 239, 271, 303, 335, 345, 355, 380, 408, 436, 466, 496, 526, 556, 586, 614, 647, 657, 667, 702, 737, 772, 807, 842, 862, 881, 914] )/ 1000 # fmt: on L = [L[i] - L[i - 1] for i in range(1, len(L))] shaft_elem = [ rs.ShaftElement6DoF( material=steel2, L=l, idl=i_d, odl=o_d, idr=i_d, odr=o_d, alpha=8.0501, beta=1.0e-5, rotary_inertia=True, shear_effects=True, ) for l in L ] Id = 0.003844540885417 Ip = 0.007513248437500 disk0 = rs.DiskElement6DoF(n=12, m=2.6375, Id=Id, Ip=Ip) disk1 = rs.DiskElement6DoF(n=24, m=2.6375, Id=Id, Ip=Ip) kxx1 = 4.40e5 kyy1 = 4.6114e5 kzz = 0 cxx1 = 27.4 cyy1 = 2.505 czz = 0 kxx2 = 9.50e5 kyy2 = 1.09e8 cxx2 = 50.4 cyy2 = 100.4553 bearing0 = rs.BearingElement6DoF( n=4, kxx=kxx1, kyy=kyy1, cxx=cxx1, cyy=cyy1, kzz=kzz, czz=czz ) bearing1 = rs.BearingElement6DoF( n=31, kxx=kxx2, kyy=kyy2, cxx=cxx2, cyy=cyy2, kzz=kzz, czz=czz ) rotor = rs.Rotor(shaft_elem, [disk0, disk1], [bearing0, bearing1]) @pytest.fixture def rub(): unbalance_magnitudet = np.array([5e-4, 0]) unbalance_phaset = np.array([-np.pi / 2, 0]) rubbing = rotor.run_rubbing( dt=0.001, tI=0, tF=0.5, deltaRUB=7.95e-5, kRUB=1.1e6, cRUB=40, miRUB=0.3, posRUB=12, speed=125.66370614359172, unbalance_magnitude=unbalance_magnitudet, unbalance_phase=unbalance_phaset, print_progress=True, ) return rubbing @pytest.fixture def rub_units(): unbalance_magnitudet = Q_(np.array([0.043398083107259365, 0]), "lb*in") unbalance_phaset = Q_(np.array([-90.0, 0.0]), "degrees") rubbing = rotor.run_rubbing( dt=0.001, tI=0, tF=0.5, deltaRUB=7.95e-5, kRUB=1.1e6, cRUB=40, miRUB=0.3, posRUB=12, speed=Q_(1200, "RPM"), unbalance_magnitude=unbalance_magnitudet, unbalance_phase=unbalance_phaset, print_progress=True, ) return rubbing def test_rub_parameters(rub): assert rub.dt == 0.001 assert rub.tI == 0 assert rub.tF == 0.5 assert rub.deltaRUB == 7.95e-5 assert rub.kRUB == 1.1e6 assert rub.cRUB == 40 assert rub.miRUB == 0.3 assert rub.posRUB == 12 assert rub.speed == 125.66370614359172 def test_rub_parameters_units(rub_units): assert rub_units.dt == 0.001 assert rub_units.tI == 0 assert rub_units.tF == 0.5 assert rub_units.deltaRUB == 7.95e-5 assert rub_units.kRUB == 1.1e6 assert rub_units.cRUB == 40 assert rub_units.miRUB == 0.3 assert rub_units.posRUB == 12 assert rub_units.speed == 125.66370614359172 def test_rub_forces(rub): assert rub.forces_rub[rub.posRUB * 6, :] == pytest.approx( # fmt: off np.array([ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1.33959978, 2.38449456, 2.49659676, 1.81196092, 0.59693967, -1.62826881, -4.24183226, -6.00328692, -6.20469077, -4.73542742, -2.72934246, -2.58572889, -6.09380802, -11.89929423, -16.28700512, -16.40580808, -12.28949661, -6.4715516 , -1.9219398 , 0.03597126, 0.1483467 , 0.06399221, 0.18405941, 0.44370982, 0.59170921, 0.55028863, 0.61661081, 1.15972902, 1.99145991, 2.2827491 , 1.21484512, 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. , -0.16254596, -1.15328767, -1.31402774, -0.57077859, 0.17589894, 0. , 0. , 0. , 0. , 1.35177422, 4.63052083, 8.26595768, 10.77229714, 11.33408396, 9.97185414, 7.43725544, 4.8091569 , 2.90279915, 1.9280518 , 1.60783411, 1.51278525, 1.30371098, 0.82428403, 0.11364267, -1.19150903, -2.28684891, -2.8371608 , -2.70293006, -1.7191864 , 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. , -0.36389397, -0.02296953, -0.58251754, -1.26899237, -1.37833025, -0.83533185, 0. , -1.54209802, -3.90213044, -5.84909057, -5.54764546, -2.82245046, 0. , 0. , -0.9813435 , -5.02620683, -7.74948602, -7.28498629, -4.21292376, -0.68113248, 0. , 0. , 0. , 0. , 0.49651344, 0.94005508, 1.20771384, 1.01602262, 0.58504824, 0.42249176, 0.74712263, 1.21263464, 1.24036134, 0.53226162, 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. , 0.81085807, 2.24845489, 2.79783564, 2.36749471, 1.48390821, 0.91606906, 1.10872277, 1.79762142, 2.27408108, 2.02409761, 1.09722613, 0.03554649, 0. , 0. , 0. , 0. , 0. , -3.20746741, -5.67109902, -5.84109266, -3.27138013, 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. , -0.94605595, -2.89024152, -2.82355527, -0.91081202, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.29208245, 0.31276202, 0.55407755, 0.65238154, 0.38599931, 0. , 0. , 0. , 0. , 0. , 0.30206142, 1.24645948, 2.17118657, 2.79095166, 2.92291644, 2.4997066 , 1.57624218, 0.3448333 , 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. , -0.25292217, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , -1.16895344, -3.13003066, -4.31801297, -3.79661585, -1.72270664, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.22784948, 0.25813886, 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. , -1.0783812 , -1.3305057 , 0.04528174, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]) # fmt: on ) assert rub.forces_rub[rub.posRUB * 6 + 1, :] == pytest.approx( # fmt: off np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -2.04052219e+00, -4.37686754e+00, -6.57561714e+00, -8.28614638e+00, -9.37724617e+00, -9.76009440e+00, -9.25692987e+00, -7.68859694e+00, -5.19404219e+00, -2.47936767e+00, -6.07948333e-01, -1.39115180e-01, -2.90615182e-01, 5.97112915e-01, 3.77091904e+00, 7.17958236e+00, 8.50566481e+00, 6.92594573e+00, 3.76719699e+00, 1.38672233e+00, 1.14491558e+00, 2.47008298e+00, 3.68895320e+00, 3.55463037e+00, 2.15169896e+00, 7.16172655e-01, 4.58130638e-01, 1.31722645e+00, 2.08017814e+00, 1.73698314e+00, 3.66421390e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.41608952e+00, 3.91872725e+00, 6.08751103e+00, 6.04879589e+00, 3.34005262e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.32257678e+00, 4.72982449e+00, 5.65624377e+00, 3.74610313e+00, 2.79267497e-01, -1.61571947e+00, -2.61760973e+00, -2.76295228e+00, -2.46754501e+00, -2.27912450e+00, -2.49834149e+00, -3.08123496e+00, -3.77081696e+00, -4.26182369e+00, -4.32814157e+00, -3.92465771e+00, -3.18784204e+00, -2.30089569e+00, -1.36585055e+00, -4.14179880e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -2.27746914e-01, -2.70148968e+00, -3.76415772e+00, -3.18400597e+00, -1.61071020e+00, -1.55937047e-01, 0.00000000e+00, -3.48135704e-01, -1.20173785e+00, -1.41164098e+00, -8.22789836e-01, 2.22972608e-02, 0.00000000e+00, 0.00000000e+00, 1.11826215e+00, 3.42190067e+00, 6.43880255e+00, 7.73939551e+00, 6.02642177e+00, 2.15551882e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.30262708e+00, 2.86790215e+00, 2.69095371e+00, 1.30596104e+00, 6.27355622e-02, -1.46409693e-01, -1.61596825e-03, 2.02368230e-01, 7.76880971e-02, -1.13943983e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -1.20894403e-01, -9.67454370e-02, -3.75553448e-01, -6.69880393e-01, -7.81524379e-01, -8.20393160e-01, -1.19541101e+00, -2.17352648e+00, -3.38963807e+00, -3.97901667e+00, -3.24152409e+00, -1.21249778e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -1.71167570e+00, -2.63258535e+00, -2.11381589e+00, -7.29071480e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.55809454e-01, -1.33550998e-01, 1.30622371e-01, 5.14772296e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.87400070e-01, 2.52352092e+00, 3.18829790e+00, 2.12154931e+00, 1.96463435e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -1.71590151e-01, 8.49398637e-02, -3.92130275e-03, -3.10907319e-01, -7.32520442e-01, -1.04949045e+00, -1.03758926e+00, -5.57304261e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -2.22986515e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.36141783e-01, -1.62089253e-01, -6.56719131e-02, 3.67462355e-01, 6.11527272e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -7.23117550e-02, -4.63871628e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.90041966e-01, 1.14524591e+00, 4.21896347e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]) # fmt: on ) def test_rub_forces_units(rub_units): assert rub_units.forces_rub[rub_units.posRUB * 6, :] == pytest.approx( # fmt: off np.array([ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1.33959978, 2.38449456, 2.49659676, 1.81196092, 0.59693967, -1.62826881, -4.24183226, -6.00328692, -6.20469077, -4.73542742, -2.72934246, -2.58572889, -6.09380802, -11.89929423, -16.28700512, -16.40580808, -12.28949661, -6.4715516 , -1.9219398 , 0.03597126, 0.1483467 , 0.06399221, 0.18405941, 0.44370982, 0.59170921, 0.55028863, 0.61661081, 1.15972902, 1.99145991, 2.2827491 , 1.21484512, 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. , -0.16254596, -1.15328767, -1.31402774, -0.57077859, 0.17589894, 0. , 0. , 0. , 0. , 1.35177422, 4.63052083, 8.26595768, 10.77229714, 11.33408396, 9.97185414, 7.43725544, 4.8091569 , 2.90279915, 1.9280518 , 1.60783411, 1.51278525, 1.30371098, 0.82428403, 0.11364267, -1.19150903, -2.28684891, -2.8371608 , -2.70293006, -1.7191864 , 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. , -0.36389397, -0.02296953, -0.58251754, -1.26899237, -1.37833025, -0.83533185, 0. , -1.54209802, -3.90213044, -5.84909057, -5.54764546, -2.82245046, 0. , 0. , -0.9813435 , -5.02620683, -7.74948602, -7.28498629, -4.21292376, -0.68113248, 0. , 0. , 0. , 0. , 0.49651344, 0.94005508, 1.20771384, 1.01602262, 0.58504824, 0.42249176, 0.74712263, 1.21263464, 1.24036134, 0.53226162, 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. , 0.81085807, 2.24845489, 2.79783564, 2.36749471, 1.48390821, 0.91606906, 1.10872277, 1.79762142, 2.27408108, 2.02409761, 1.09722613, 0.03554649, 0. , 0. , 0. , 0. , 0. , -3.20746741, -5.67109902, -5.84109266, -3.27138013, 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. , -0.94605595, -2.89024152, -2.82355527, -0.91081202, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.29208245, 0.31276202, 0.55407755, 0.65238154, 0.38599931, 0. , 0. , 0. , 0. , 0. , 0.30206142, 1.24645948, 2.17118657, 2.79095166, 2.92291644, 2.4997066 , 1.57624218, 0.3448333 , 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. , -0.25292217, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , -1.16895344, -3.13003066, -4.31801297, -3.79661585, -1.72270664, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.22784948, 0.25813886, 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. , -1.0783812 , -1.3305057 , 0.04528174, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]) # fmt: on ) assert rub_units.forces_rub[rub_units.posRUB * 6 + 1, :] == pytest.approx( # fmt: off np.array([ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, -2.04052219e+00, -4.37686754e+00, -6.57561714e+00, -8.28614638e+00, -9.37724617e+00, -9.76009440e+00, -9.25692987e+00, -7.68859694e+00, -5.19404219e+00, -2.47936767e+00, -6.07948333e-01, -1.39115180e-01, -2.90615182e-01, 5.97112915e-01, 3.77091904e+00, 7.17958236e+00, 8.50566481e+00, 6.92594573e+00, 3.76719699e+00, 1.38672233e+00, 1.14491558e+00, 2.47008298e+00, 3.68895320e+00, 3.55463037e+00, 2.15169896e+00, 7.16172655e-01, 4.58130638e-01, 1.31722645e+00, 2.08017814e+00, 1.73698314e+00, 3.66421390e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.41608952e+00, 3.91872725e+00, 6.08751103e+00, 6.04879589e+00, 3.34005262e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.32257678e+00, 4.72982449e+00, 5.65624377e+00, 3.74610313e+00, 2.79267497e-01, -1.61571947e+00, -2.61760973e+00, -2.76295228e+00, -2.46754501e+00, -2.27912450e+00, -2.49834149e+00, -3.08123496e+00, -3.77081696e+00, -4.26182369e+00, -4.32814157e+00, -3.92465771e+00, -3.18784204e+00, -2.30089569e+00, -1.36585055e+00, -4.14179880e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 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0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 8.90041966e-01, 1.14524591e+00, 4.21896347e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]) # fmt: on )

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f924a3360f6478f383a6de27cd9e66749ac25041

36,224

py

Python

tests/io/test_hdf_writer.py

hbdeng/pycroscopy

f9a6d273f6a8e6fdda1287cec82cd6da32d9e2a5

[ "MIT" ]

1

2020-02-13T20:54:47.000Z

tests/io/test_hdf_writer.py

Liambcollins/pycroscopy

fd02ac735a1194d2a5687183fafe00368ed8a3ca

[ "MIT" ]

null

tests/io/test_hdf_writer.py

Liambcollins/pycroscopy

fd02ac735a1194d2a5687183fafe00368ed8a3ca

[ "MIT" ]

1

2020-03-20T13:19:09.000Z

# -*- coding: utf-8 -*- """ Created on Tue Nov 3 15:07:16 2017 @author: Suhas Somnath """ from __future__ import division, print_function, unicode_literals, absolute_import import unittest import os import h5py import numpy as np import sys sys.path.append("../../../pycroscopy/") from pycroscopy.io.virtual_data import VirtualGroup, VirtualDataset from pycroscopy.io.hdf_writer import HDFwriter from pyUSID.io.hdf_utils import get_attr, get_h5_obj_refs # Until an elegant solution presents itself class TestHDFWriter(unittest.TestCase): @staticmethod def __delete_existing_file(file_path): if os.path.exists(file_path): os.remove(file_path) def test_init_invalid_input(self): with self.assertRaises(TypeError): _ = HDFwriter(4) def test_init_path_non_existant_file_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) writer = HDFwriter(file_path) self.assertIsInstance(writer, HDFwriter, "writer should be an HDFwriter") writer.close() os.remove(file_path) def test_init_path_existing_file_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) h5_f = h5py.File(file_path) h5_f.close() # Existing h5 file writer = HDFwriter(file_path) self.assertIsInstance(writer, HDFwriter, "writer should be an HDFwriter") writer.close() os.remove(file_path) def test_init_h5_handle_r_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) h5_f = h5py.File(file_path) h5_f.close() h5_f = h5py.File(file_path, mode='r') # hdf handle but of mode r with self.assertRaises(TypeError): _ = HDFwriter(h5_f) os.remove(file_path) def test_init_h5_handle_r_plus_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) h5_f = h5py.File(file_path) h5_f.close() h5_f = h5py.File(file_path, mode='r+') # open h5 file handle or mode r+ writer = HDFwriter(h5_f) self.assertIsInstance(writer, HDFwriter, "writer should be an HDFwriter") writer.close() os.remove(file_path) def test_init_h5_handle_w_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) h5_f = h5py.File(file_path) h5_f.close() h5_f = h5py.File(file_path, mode='w') # open h5 file handle or mode w writer = HDFwriter(h5_f) self.assertIsInstance(writer, HDFwriter, "writer should be an HDFwriter") writer.close() os.remove(file_path) def test_init_h5_handle_closed(self): file_path = 'test.h5' self.__delete_existing_file(file_path) h5_f = h5py.File(file_path) h5_f.close() # Existing h5 file but closed with self.assertRaises(ValueError): _ = HDFwriter(h5_f) os.remove(file_path) def test_simple_dset_write_success_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: dtype = np.uint16 dset_name = 'test' data = np.random.randint(0, high=15, size=5, dtype=dtype) microdset = VirtualDataset(dset_name, data) writer = HDFwriter(h5_f) h5_d = writer._create_simple_dset(h5_f, microdset) self.assertIsInstance(h5_d, h5py.Dataset) self.assertEqual(h5_d.parent, h5_f) self.assertEqual(h5_d.name, '/' + dset_name) self.assertEqual(h5_d.shape, data.shape) self.assertTrue(np.allclose(h5_d[()], data)) self.assertEqual(h5_d.dtype, dtype) os.remove(file_path) def test_simple_dset_write_success_more_options_02(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: dset_name = 'test' data = np.random.rand(16, 1024) dtype = data.dtype compression = 'gzip' chunking=(1, 1024) microdset = VirtualDataset(dset_name, data, dtype=dtype, compression=compression, chunking=chunking) writer = HDFwriter(h5_f) h5_d = writer._create_simple_dset(h5_f, microdset) self.assertIsInstance(h5_d, h5py.Dataset) self.assertEqual(h5_d.parent, h5_f) self.assertEqual(h5_d.name, '/' + dset_name) self.assertEqual(h5_d.shape, data.shape) self.assertTrue(np.allclose(h5_d[()], data)) self.assertEqual(h5_d.dtype, dtype) self.assertEqual(h5_d.compression, compression) self.assertEqual(h5_d.chunks, chunking) os.remove(file_path) def test_simple_dset_write_success_more_options_03(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: dset_name = 'test' data = np.random.rand(16, 1024) dtype = np.float16 compression = 'gzip' chunking=(1, 1024) microdset = VirtualDataset(dset_name, data, dtype=dtype, compression=compression, chunking=chunking) writer = HDFwriter(h5_f) h5_d = writer._create_simple_dset(h5_f, microdset) self.assertIsInstance(h5_d, h5py.Dataset) self.assertEqual(h5_d.parent, h5_f) self.assertEqual(h5_d.name, '/' + dset_name) self.assertEqual(h5_d.shape, data.shape) self.assertEqual(h5_d.dtype, dtype) self.assertEqual(h5_d.compression, compression) self.assertEqual(h5_d.chunks, chunking) self.assertTrue(np.all(h5_d[()] - data < 1E-3)) os.remove(file_path) def test_empty_dset_write_success_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: dset_name = 'test' maxshape = (16, 1024) microdset = VirtualDataset(dset_name, None, maxshape=maxshape) writer = HDFwriter(h5_f) h5_d = writer._create_empty_dset(h5_f, microdset) self.assertIsInstance(h5_d, h5py.Dataset) self.assertEqual(h5_d.parent, h5_f) self.assertEqual(h5_d.name, '/' + dset_name) self.assertEqual(h5_d.shape, maxshape) self.assertEqual(h5_d.maxshape, maxshape) # dtype is assigned automatically by h5py. Not to be tested here os.remove(file_path) def test_empty_dset_write_success_w_options_02(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: dset_name = 'test' maxshape = (16, 1024) chunking = (1, 1024) compression = 'gzip' dtype = np.float16 microdset = VirtualDataset(dset_name, None, maxshape=maxshape, dtype=dtype, compression=compression, chunking=chunking) writer = HDFwriter(h5_f) h5_d = writer._create_empty_dset(h5_f, microdset) self.assertIsInstance(h5_d, h5py.Dataset) self.assertEqual(h5_d.parent, h5_f) self.assertEqual(h5_d.name, '/' + dset_name) self.assertEqual(h5_d.dtype, dtype) self.assertEqual(h5_d.compression, compression) self.assertEqual(h5_d.chunks, chunking) self.assertEqual(h5_d.shape, maxshape) self.assertEqual(h5_d.maxshape, maxshape) os.remove(file_path) def test_expandable_dset_write_success_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: dset_name = 'test' maxshape = (None, 1024) data = np.random.rand(1, 1024) microdset = VirtualDataset(dset_name, data, maxshape=maxshape) writer = HDFwriter(h5_f) h5_d = writer._create_resizeable_dset(h5_f, microdset) self.assertIsInstance(h5_d, h5py.Dataset) self.assertEqual(h5_d.parent, h5_f) self.assertEqual(h5_d.name, '/' + dset_name) self.assertEqual(h5_d.shape, data.shape) self.assertEqual(h5_d.maxshape, maxshape) self.assertTrue(np.allclose(h5_d[()], data)) # Now test to make sure that the dataset can be expanded: # TODO: add this to the example! expansion_axis = 0 h5_d.resize(h5_d.shape[expansion_axis] + 1, axis=expansion_axis) self.assertEqual(h5_d.shape, (data.shape[0]+1, data.shape[1])) self.assertEqual(h5_d.maxshape, maxshape) # Finally try checking to see if this new data is also present in the file new_data = np.random.rand(1024) h5_d[1] = new_data data = np.vstack((np.squeeze(data), new_data)) self.assertTrue(np.allclose(h5_d[()], data)) os.remove(file_path) # TODO: will have to check to see if the parent is correctly declared for the group def test_group_create_non_indexed_simple_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: grp_name = 'test' micro_group = VirtualGroup(grp_name) writer = HDFwriter(h5_f) h5_grp = writer._create_group(h5_f, micro_group) self.assertIsInstance(h5_grp, h5py.Group) self.assertEqual(h5_grp.parent, h5_f) self.assertEqual(h5_grp.name, '/' + grp_name) # self.assertEqual(len(h5_grp.items), 0) os.remove(file_path) def test_group_create_indexed_simple_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: grp_name = 'test_' micro_group = VirtualGroup(grp_name) writer = HDFwriter(h5_f) h5_grp = writer._create_group(h5_f, micro_group) self.assertIsInstance(h5_grp, h5py.Group) self.assertEqual(h5_grp.parent, h5_f) self.assertEqual(h5_grp.name, '/' + grp_name + '000') # self.assertEqual(len(h5_grp.items), 0) os.remove(file_path) def test_group_create_root_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: grp_name = '' micro_group = VirtualGroup(grp_name) writer = HDFwriter(h5_f) with self.assertRaises(ValueError): _ = writer._create_group(h5_f, micro_group) os.remove(file_path) def test_group_create_indexed_nested_01(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: outer_grp_name = 'outer_' micro_group = VirtualGroup(outer_grp_name) writer = HDFwriter(h5_f) h5_outer_grp = writer._create_group(h5_f, micro_group) self.assertIsInstance(h5_outer_grp, h5py.Group) self.assertEqual(h5_outer_grp.parent, h5_f) self.assertEqual(h5_outer_grp.name, '/' + outer_grp_name + '000') inner_grp_name = 'inner_' micro_group = VirtualGroup(inner_grp_name) h5_inner_grp = writer._create_group(h5_outer_grp, micro_group) self.assertIsInstance(h5_inner_grp, h5py.Group) self.assertEqual(h5_inner_grp.parent, h5_outer_grp) self.assertEqual(h5_inner_grp.name, h5_outer_grp.name + '/' + inner_grp_name + '000') os.remove(file_path) def test_write_legal_reg_ref_multi_dim_data(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))}} writer._write_dset_attributes(h5_dset, attrs.copy()) h5_f.flush() # two atts point to region references. one for labels self.assertEqual(len(h5_dset.attrs), 1 + len(attrs['labels'])) # check if the labels attribute was written: self.assertTrue(np.all([x in list(attrs['labels'].keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:None:2], data[1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_write_legal_reg_ref_multi_dim_data_2nd_dim(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 3) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(None), slice(0, None, 2)), 'odd_rows': (slice(None), slice(1, None, 2))}} writer._write_dset_attributes(h5_dset, attrs.copy()) h5_f.flush() # two atts point to region references. one for labels self.assertEqual(len(h5_dset.attrs), 1 + len(attrs['labels'])) # check if the labels attribute was written: self.assertTrue(np.all([x in list(attrs['labels'].keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:, 0:None:2], data[:, 1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_write_legal_reg_ref_one_dim_data(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(0, None, 2)), 'odd_rows': (slice(1, None, 2))}} writer._write_dset_attributes(h5_dset, attrs.copy()) h5_f.flush() # two atts point to region references. one for labels self.assertEqual(len(h5_dset.attrs), 1 + len(attrs['labels'])) # check if the labels attribute was written: self.assertTrue(np.all([x in list(attrs['labels'].keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:None:2], data[1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_generate_and_write_reg_ref_legal(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(2, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': ['row_1', 'row_2']} if sys.version_info.major == 3: with self.assertWarns(UserWarning): writer._write_dset_attributes(h5_dset, attrs.copy()) else: writer._write_dset_attributes(h5_dset, attrs.copy()) h5_f.flush() # two atts point to region references. one for labels self.assertEqual(len(h5_dset.attrs), 1 + len(attrs['labels'])) # check if the labels attribute was written: self.assertTrue(np.all([x in list(attrs['labels']) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[0], data[1]] written_data = [h5_dset[h5_dset.attrs['row_1']], h5_dset[h5_dset.attrs['row_2']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(np.squeeze(exp), np.squeeze(act))) os.remove(file_path) def test_generate_and_write_reg_ref_illegal(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(3, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) # with self.assertWarns(UserWarning): writer._write_dset_attributes(h5_dset, {'labels': ['row_1', 'row_2']}) self.assertEqual(len(h5_dset.attrs), 0) h5_f.flush() os.remove(file_path) def test_generate_and_write_reg_ref_illegal(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(2, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) # with self.assertWarns(UserWarning): with self.assertRaises(TypeError): writer._write_dset_attributes(h5_dset, {'labels': [1, np.arange(3)]}) os.remove(file_path) def test_write_illegal_reg_ref_too_many_slices(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(0, None, 2), slice(None), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None), slice(None))}} with self.assertRaises(ValueError): writer._write_dset_attributes(h5_dset, attrs.copy()) os.remove(file_path) def test_write_illegal_reg_ref_too_few_slices(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(0, None, 2)), 'odd_rows': (slice(1, None, 2))}} with self.assertRaises(ValueError): writer._write_dset_attributes(h5_dset, attrs.copy()) os.remove(file_path) def test_write_reg_ref_slice_dim_larger_than_data(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(0, 15, 2), slice(None)), 'odd_rows': (slice(1, 15, 2), slice(None))}} writer._write_dset_attributes(h5_dset, attrs.copy()) h5_f.flush() # two atts point to region references. one for labels self.assertEqual(len(h5_dset.attrs), 1 + len(attrs['labels'])) # check if the labels attribute was written: self.assertTrue(np.all([x in list(attrs['labels'].keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:None:2], data[1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_write_illegal_reg_ref_not_slice_objs(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'labels': {'even_rows': (slice(0, None, 2), 15), 'odd_rows': (slice(1, None, 2), 'hello')}} with self.assertRaises(TypeError): writer._write_dset_attributes(h5_dset, attrs.copy()) os.remove(file_path) def test_write_simple_atts_reg_ref_to_dset(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) h5_dset = writer._create_simple_dset(h5_f, VirtualDataset('test', data)) self.assertIsInstance(h5_dset, h5py.Dataset) attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3'], 'labels': {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))} } writer._write_dset_attributes(h5_dset, attrs.copy()) reg_ref = attrs.pop('labels') self.assertEqual(len(h5_dset.attrs), len(attrs) + 1 + len(reg_ref)) for key, expected_val in attrs.items(): self.assertTrue(np.all(get_attr(h5_dset, key) == expected_val)) self.assertTrue(np.all([x in list(reg_ref.keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:None:2], data[1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_write_invalid_input(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) with self.assertRaises(TypeError): _ = writer.write(np.arange(5)) def test_write_dset_under_root(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) data = np.random.rand(5, 7) attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3'], 'labels': {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))} } micro_dset = VirtualDataset('test', data) micro_dset.attrs = attrs.copy() [h5_dset] = writer.write(micro_dset) self.assertIsInstance(h5_dset, h5py.Dataset) reg_ref = attrs.pop('labels') self.assertEqual(len(h5_dset.attrs), len(attrs) + 1 + len(reg_ref)) for key, expected_val in attrs.items(): self.assertTrue(np.all(get_attr(h5_dset, key) == expected_val)) self.assertTrue(np.all([x in list(reg_ref.keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:None:2], data[1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_write_dset_under_existing_group(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) h5_g = writer._create_group(h5_f, VirtualGroup('test_group')) self.assertIsInstance(h5_g, h5py.Group) data = np.random.rand(5, 7) attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3'], 'labels': {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))} } micro_dset = VirtualDataset('test', data, parent='/test_group') micro_dset.attrs = attrs.copy() [h5_dset] = writer.write(micro_dset) self.assertIsInstance(h5_dset, h5py.Dataset) self.assertEqual(h5_dset.parent, h5_g) reg_ref = attrs.pop('labels') self.assertEqual(len(h5_dset.attrs), len(attrs) + 1 + len(reg_ref)) for key, expected_val in attrs.items(): self.assertTrue(np.all(get_attr(h5_dset, key) == expected_val)) self.assertTrue(np.all([x in list(reg_ref.keys()) for x in get_attr(h5_dset, 'labels')])) expected_data = [data[:None:2], data[1:None:2]] written_data = [h5_dset[h5_dset.attrs['even_rows']], h5_dset[h5_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) os.remove(file_path) def test_write_dset_under_invalid_group(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) with self.assertRaises(KeyError): _ = writer.write(VirtualDataset('test', np.random.rand(5, 7), parent='/does_not_exist')) os.remove(file_path) def test_write_root(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3']} micro_group = VirtualGroup('') micro_group.attrs = attrs writer = HDFwriter(h5_f) [ret_val] = writer.write(micro_group) self.assertIsInstance(ret_val, h5py.File) self.assertEqual(h5_f, ret_val) for key, expected_val in attrs.items(): self.assertTrue(np.all(get_attr(h5_f, key) == expected_val)) os.remove(file_path) def test_write_single_group(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3']} micro_group = VirtualGroup('Test_') micro_group.attrs = attrs writer = HDFwriter(h5_f) [h5_group] = writer.write(micro_group) for key, expected_val in attrs.items(): self.assertTrue(np.all(get_attr(h5_group, key) == expected_val)) os.remove(file_path) def test_group_indexing_sequential(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: writer = HDFwriter(h5_f) micro_group_0 = VirtualGroup('Test_', attrs={'att_1': 'string_val', 'att_2': 1.2345}) [h5_group_0] = writer.write(micro_group_0) _ = writer.write(VirtualGroup('blah')) self.assertIsInstance(h5_group_0, h5py.Group) self.assertEqual(h5_group_0.name, '/Test_000') for key, expected_val in micro_group_0.attrs.items(): self.assertTrue(np.all(get_attr(h5_group_0, key) == expected_val)) micro_group_1 = VirtualGroup('Test_', attrs={'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3']}) [h5_group_1] = writer.write(micro_group_1) self.assertIsInstance(h5_group_1, h5py.Group) self.assertEqual(h5_group_1.name, '/Test_001') for key, expected_val in micro_group_1.attrs.items(): self.assertTrue(np.all(get_attr(h5_group_1, key) == expected_val)) os.remove(file_path) def test_group_indexing_simultaneous(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: micro_group_0 = VirtualGroup('Test_', attrs = {'att_1': 'string_val', 'att_2': 1.2345}) micro_group_1 = VirtualGroup('Test_', attrs={'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3']}) root_group = VirtualGroup('', children=[VirtualGroup('blah'), micro_group_0, VirtualGroup('meh'), micro_group_1]) writer = HDFwriter(h5_f) h5_refs_list = writer.write(root_group) [h5_group_1] = get_h5_obj_refs(['Test_001'], h5_refs_list) [h5_group_0] = get_h5_obj_refs(['Test_000'], h5_refs_list) self.assertIsInstance(h5_group_0, h5py.Group) self.assertEqual(h5_group_0.name, '/Test_000') for key, expected_val in micro_group_0.attrs.items(): self.assertTrue(np.all(get_attr(h5_group_0, key) == expected_val)) self.assertIsInstance(h5_group_1, h5py.Group) self.assertEqual(h5_group_1.name, '/Test_001') for key, expected_val in micro_group_1.attrs.items(): self.assertTrue(np.all(get_attr(h5_group_1, key) == expected_val)) os.remove(file_path) def test_write_simple_tree(self): file_path = 'test.h5' self.__delete_existing_file(file_path) with h5py.File(file_path) as h5_f: inner_dset_data = np.random.rand(5, 7) inner_dset_attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3'], 'labels': {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))} } inner_dset = VirtualDataset('inner_dset', inner_dset_data) inner_dset.attrs = inner_dset_attrs.copy() attrs_inner_grp = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3']} inner_group = VirtualGroup('indexed_inner_group_') inner_group.attrs = attrs_inner_grp inner_group.add_children(inner_dset) outer_dset_data = np.random.rand(5, 7) outer_dset_attrs = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3'], 'labels': {'even_rows': (slice(0, None, 2), slice(None)), 'odd_rows': (slice(1, None, 2), slice(None))} } outer_dset = VirtualDataset('test', outer_dset_data, parent='/test_group') outer_dset.attrs = outer_dset_attrs.copy() attrs_outer_grp = {'att_1': 'string_val', 'att_2': 1.2345, 'att_3': [1, 2, 3, 4], 'att_4': ['str_1', 'str_2', 'str_3']} outer_group = VirtualGroup('unindexed_outer_group') outer_group.attrs = attrs_outer_grp outer_group.add_children([inner_group, outer_dset]) writer = HDFwriter(h5_f) h5_refs_list = writer.write(outer_group) # I don't know of a more elegant way to do this: [h5_outer_dset] = get_h5_obj_refs([outer_dset.name], h5_refs_list) [h5_inner_dset] = get_h5_obj_refs([inner_dset.name], h5_refs_list) [h5_outer_group] = get_h5_obj_refs([outer_group.name], h5_refs_list) [h5_inner_group] = get_h5_obj_refs(['indexed_inner_group_000'], h5_refs_list) self.assertIsInstance(h5_outer_dset, h5py.Dataset) self.assertIsInstance(h5_inner_dset, h5py.Dataset) self.assertIsInstance(h5_outer_group, h5py.Group) self.assertIsInstance(h5_inner_group, h5py.Group) # check assertions for the inner dataset first self.assertEqual(h5_inner_dset.parent, h5_inner_group) reg_ref = inner_dset_attrs.pop('labels') self.assertEqual(len(h5_inner_dset.attrs), len(inner_dset_attrs) + 1 + len(reg_ref)) for key, expected_val in inner_dset_attrs.items(): self.assertTrue(np.all(get_attr(h5_inner_dset, key) == expected_val)) self.assertTrue(np.all([x in list(reg_ref.keys()) for x in get_attr(h5_inner_dset, 'labels')])) expected_data = [inner_dset_data[:None:2], inner_dset_data[1:None:2]] written_data = [h5_inner_dset[h5_inner_dset.attrs['even_rows']], h5_inner_dset[h5_inner_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) # check assertions for the inner data group next: self.assertEqual(h5_inner_group.parent, h5_outer_group) for key, expected_val in attrs_inner_grp.items(): self.assertTrue(np.all(get_attr(h5_inner_group, key) == expected_val)) # check the outer dataset next: self.assertEqual(h5_outer_dset.parent, h5_outer_group) reg_ref = outer_dset_attrs.pop('labels') self.assertEqual(len(h5_outer_dset.attrs), len(outer_dset_attrs) + 1 + len(reg_ref)) for key, expected_val in outer_dset_attrs.items(): self.assertTrue(np.all(get_attr(h5_outer_dset, key) == expected_val)) self.assertTrue(np.all([x in list(reg_ref.keys()) for x in get_attr(h5_outer_dset, 'labels')])) expected_data = [outer_dset_data[:None:2], outer_dset_data[1:None:2]] written_data = [h5_outer_dset[h5_outer_dset.attrs['even_rows']], h5_outer_dset[h5_outer_dset.attrs['odd_rows']]] for exp, act in zip(expected_data, written_data): self.assertTrue(np.allclose(exp, act)) # Finally check the outer group: self.assertEqual(h5_outer_group.parent, h5_f) for key, expected_val in attrs_outer_grp.items(): self.assertTrue(np.all(get_attr(h5_outer_group, key) == expected_val)) os.remove(file_path) if __name__ == '__main__': unittest.main()

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CodeForces/EducationalRound73/B.py

takaaki82/Java-Lessons

c4f11462bf84c091527dde5f25068498bfb2cc49

[ "MIT" ]

1

2018-11-25T04:15:45.000Z

CodeForces/EducationalRound73/B.py

takaaki82/Java-Lessons

c4f11462bf84c091527dde5f25068498bfb2cc49

[ "MIT" ]

null

CodeForces/EducationalRound73/B.py

takaaki82/Java-Lessons

c4f11462bf84c091527dde5f25068498bfb2cc49

[ "MIT" ]

2

2018-08-08T13:01:14.000Z

2018-11-25T12:38:36.000Z

N = int(input()) grid = [["*"] * N for _ in range(N)] grid[0][0] = "W" for i in range(N): for j in range(N): if grid[i][j] == "*": if j == 0: if grid[i - 1][j] == "W": grid[i][j] = "B" else: grid[i][j] = "W" else: if grid[i][j - 1] == "W": grid[i][j] = "B" else: grid[i][j] = "W" if grid[i][j] == "W": if 0 <= i - 2 < N and 0 <= j + 1 < N: if grid[i - 2][j + 1] == "*": grid[i - 2][j + 1] = "B" if 0 <= i - 1 < N and 0 <= j + 2 < N: if grid[i - 1][j + 2] == "*": grid[i - 1][j + 2] = "B" if 0 <= i + 1 < N and 0 <= j + 2 < N: if grid[i + 1][j + 2] == "*": grid[i + 1][j + 2] = "B" if 0 <= i + 2 < N and 0 <= j + 1 < N: if grid[i + 2][j + 1] == "*": grid[i + 2][j + 1] = "B" if 0 <= i + 2 < N and 0 <= j - 1 < N: if grid[i + 2][j - 1] == "*": grid[i + 2][j - 1] = "B" if 0 <= i + 1 < N and 0 <= j - 2 < N: if grid[i + 1][j - 2] == "*": grid[i + 1][j - 2] = "B" if 0 <= i - 1 < N and 0 <= j + 2 < N: if grid[i - 1][j + 2] == "*": grid[i - 1][j + 2] = "B" if 0 <= i - 2 < N and 0 <= j - 1 < N: if grid[i - 2][j - 1] == "*": grid[i - 2][j - 1] = "B" else: if 0 <= i - 2 < N and 0 <= j + 1 < N: if grid[i - 2][j + 1] == "*": grid[i - 2][j + 1] = "W" if 0 <= i - 1 < N and 0 <= j + 2 < N: if grid[i - 1][j + 2] == "*": grid[i - 1][j + 2] = "W" if 0 <= i + 1 < N and 0 <= j + 2 < N: if grid[i + 1][j + 2] == "*": grid[i + 1][j + 2] = "W" if 0 <= i + 2 < N and 0 <= j + 1 < N: if grid[i + 2][j + 1] == "*": grid[i + 2][j + 1] = "W" if 0 <= i + 2 < N and 0 <= j - 1 < N: if grid[i + 2][j - 1] == "*": grid[i + 2][j - 1] = "W" if 0 <= i + 1 < N and 0 <= j - 2 < N: if grid[i + 1][j - 2] == "*": grid[i + 1][j - 2] = "W" if 0 <= i - 1 < N and 0 <= j + 2 < N: if grid[i - 1][j + 2] == "*": grid[i - 1][j + 2] = "W" if 0 <= i - 2 < N and 0 <= j - 1 < N: if grid[i - 2][j - 1] == "*": grid[i - 2][j - 1] = "W" for i in range(N): for j in range(N): print(grid[i][j], end="") print()

38.527027

49

0.244476

435

2,851

1.6

0.043678

0.29454

0.201149

0.195402

0.886494

0.862069

0

0.102465

0.544721

2,851

73

50

39.054795

0.433744

0

0.685714

0

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null

1

0

1

0

1

0

null

0

12

00d37eaf2e152000bf2c3d503f3ab04f61ff6ea3

36

py

Python

frappe/patches/v4_0/remove_index_sitemap.py

pawaranand/phr_frappe

d997ae7d6fbade4b2c4a2491603d988876dfd67e

[ "MIT" ]

1

2022-03-05T16:02:39.000Z

frappe/patches/v4_0/remove_index_sitemap.py

pawaranand/phr_frappe

d997ae7d6fbade4b2c4a2491603d988876dfd67e

[ "MIT" ]

1

2015-07-11T20:52:38.000Z

2019-12-06T15:00:58.000Z

frappe/patches/v4_0/remove_index_sitemap.py

pawaranand/phr_frappe

d997ae7d6fbade4b2c4a2491603d988876dfd67e

[ "MIT" ]

2

2015-09-05T05:30:23.000Z

2018-03-21T19:45:10.000Z

import frappe def execute(): pass

7.2

14

0.722222

5

36

5.2

1

0

0.194444

36

4

15

9

0.896552

0

1

0.333333

true

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

8

da9c410d602b9ec28b6c717a86a0c174065fc8dc

36

py

Python

reporter/__init__.py

eaingaran/TimeMachine

f6199827ffc358dd32f26edd8d68e2dbf7c63a90

[ "MIT" ]

null

reporter/__init__.py

eaingaran/TimeMachine

f6199827ffc358dd32f26edd8d68e2dbf7c63a90

[ "MIT" ]

null

reporter/__init__.py

eaingaran/TimeMachine

f6199827ffc358dd32f26edd8d68e2dbf7c63a90

[ "MIT" ]

null

from reporter import GenerateReport

18

35

0.888889

4

36

8

1

0

0.111111

36

1

36

1

0

1

0

1

0

true

0

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1

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1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

daaf9ad2bbd982879bb0f1d6c500eb2886d1827f

18,405

py

Python

sdk/python/pulumi_buildkite/team.py

grapl-security/pulumi-buildkite

f801ecb661d82da6b939b13f5520038e3b6e891f

[ "Apache-2.0" ]

null

sdk/python/pulumi_buildkite/team.py

grapl-security/pulumi-buildkite

f801ecb661d82da6b939b13f5520038e3b6e891f

[ "Apache-2.0" ]

null

sdk/python/pulumi_buildkite/team.py

grapl-security/pulumi-buildkite

f801ecb661d82da6b939b13f5520038e3b6e891f

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['TeamArgs', 'Team'] @pulumi.input_type class TeamArgs: def __init__(__self__, *, default_member_role: pulumi.Input[str], default_team: pulumi.Input[bool], privacy: pulumi.Input[str], description: Optional[pulumi.Input[str]] = None, members_can_create_pipelines: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a Team resource. :param pulumi.Input[str] default_member_role: Default role to assign to a team member. :param pulumi.Input[bool] default_team: Whether to assign this team to a user by default. :param pulumi.Input[str] privacy: The privacy level to set the team too. :param pulumi.Input[str] description: The description to assign to the team. :param pulumi.Input[bool] members_can_create_pipelines: Whether team members can create. :param pulumi.Input[str] name: The name of the team. """ pulumi.set(__self__, "default_member_role", default_member_role) pulumi.set(__self__, "default_team", default_team) pulumi.set(__self__, "privacy", privacy) if description is not None: pulumi.set(__self__, "description", description) if members_can_create_pipelines is not None: pulumi.set(__self__, "members_can_create_pipelines", members_can_create_pipelines) if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter(name="defaultMemberRole") def default_member_role(self) -> pulumi.Input[str]: """ Default role to assign to a team member. """ return pulumi.get(self, "default_member_role") @default_member_role.setter def default_member_role(self, value: pulumi.Input[str]): pulumi.set(self, "default_member_role", value) @property @pulumi.getter(name="defaultTeam") def default_team(self) -> pulumi.Input[bool]: """ Whether to assign this team to a user by default. """ return pulumi.get(self, "default_team") @default_team.setter def default_team(self, value: pulumi.Input[bool]): pulumi.set(self, "default_team", value) @property @pulumi.getter def privacy(self) -> pulumi.Input[str]: """ The privacy level to set the team too. """ return pulumi.get(self, "privacy") @privacy.setter def privacy(self, value: pulumi.Input[str]): pulumi.set(self, "privacy", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description to assign to the team. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="membersCanCreatePipelines") def members_can_create_pipelines(self) -> Optional[pulumi.Input[bool]]: """ Whether team members can create. """ return pulumi.get(self, "members_can_create_pipelines") @members_can_create_pipelines.setter def members_can_create_pipelines(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "members_can_create_pipelines", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the team. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @pulumi.input_type class _TeamState: def __init__(__self__, *, default_member_role: Optional[pulumi.Input[str]] = None, default_team: Optional[pulumi.Input[bool]] = None, description: Optional[pulumi.Input[str]] = None, members_can_create_pipelines: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, privacy: Optional[pulumi.Input[str]] = None, slug: Optional[pulumi.Input[str]] = None, uuid: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering Team resources. :param pulumi.Input[str] default_member_role: Default role to assign to a team member. :param pulumi.Input[bool] default_team: Whether to assign this team to a user by default. :param pulumi.Input[str] description: The description to assign to the team. :param pulumi.Input[bool] members_can_create_pipelines: Whether team members can create. :param pulumi.Input[str] name: The name of the team. :param pulumi.Input[str] privacy: The privacy level to set the team too. :param pulumi.Input[str] slug: The name of the team. :param pulumi.Input[str] uuid: The UUID for the team. """ if default_member_role is not None: pulumi.set(__self__, "default_member_role", default_member_role) if default_team is not None: pulumi.set(__self__, "default_team", default_team) if description is not None: pulumi.set(__self__, "description", description) if members_can_create_pipelines is not None: pulumi.set(__self__, "members_can_create_pipelines", members_can_create_pipelines) if name is not None: pulumi.set(__self__, "name", name) if privacy is not None: pulumi.set(__self__, "privacy", privacy) if slug is not None: pulumi.set(__self__, "slug", slug) if uuid is not None: pulumi.set(__self__, "uuid", uuid) @property @pulumi.getter(name="defaultMemberRole") def default_member_role(self) -> Optional[pulumi.Input[str]]: """ Default role to assign to a team member. """ return pulumi.get(self, "default_member_role") @default_member_role.setter def default_member_role(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "default_member_role", value) @property @pulumi.getter(name="defaultTeam") def default_team(self) -> Optional[pulumi.Input[bool]]: """ Whether to assign this team to a user by default. """ return pulumi.get(self, "default_team") @default_team.setter def default_team(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "default_team", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description to assign to the team. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="membersCanCreatePipelines") def members_can_create_pipelines(self) -> Optional[pulumi.Input[bool]]: """ Whether team members can create. """ return pulumi.get(self, "members_can_create_pipelines") @members_can_create_pipelines.setter def members_can_create_pipelines(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "members_can_create_pipelines", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name of the team. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def privacy(self) -> Optional[pulumi.Input[str]]: """ The privacy level to set the team too. """ return pulumi.get(self, "privacy") @privacy.setter def privacy(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "privacy", value) @property @pulumi.getter def slug(self) -> Optional[pulumi.Input[str]]: """ The name of the team. """ return pulumi.get(self, "slug") @slug.setter def slug(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "slug", value) @property @pulumi.getter def uuid(self) -> Optional[pulumi.Input[str]]: """ The UUID for the team. """ return pulumi.get(self, "uuid") @uuid.setter def uuid(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "uuid", value) class Team(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, default_member_role: Optional[pulumi.Input[str]] = None, default_team: Optional[pulumi.Input[bool]] = None, description: Optional[pulumi.Input[str]] = None, members_can_create_pipelines: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, privacy: Optional[pulumi.Input[str]] = None, __props__=None): """ ## # Resource: team This resource allows you to create and manage teams. Buildkite Documentation: https://buildkite.com/docs/pipelines/permissions Note: You must first enable Teams on your organization. ## Example Usage ```python import pulumi import pulumi_buildkite as buildkite team = buildkite.Team("team", default_member_role="MEMBER", default_team=True, privacy="VISIBLE") ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] default_member_role: Default role to assign to a team member. :param pulumi.Input[bool] default_team: Whether to assign this team to a user by default. :param pulumi.Input[str] description: The description to assign to the team. :param pulumi.Input[bool] members_can_create_pipelines: Whether team members can create. :param pulumi.Input[str] name: The name of the team. :param pulumi.Input[str] privacy: The privacy level to set the team too. """ ... @overload def __init__(__self__, resource_name: str, args: TeamArgs, opts: Optional[pulumi.ResourceOptions] = None): """ ## # Resource: team This resource allows you to create and manage teams. Buildkite Documentation: https://buildkite.com/docs/pipelines/permissions Note: You must first enable Teams on your organization. ## Example Usage ```python import pulumi import pulumi_buildkite as buildkite team = buildkite.Team("team", default_member_role="MEMBER", default_team=True, privacy="VISIBLE") ``` :param str resource_name: The name of the resource. :param TeamArgs 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(TeamArgs, 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, default_member_role: Optional[pulumi.Input[str]] = None, default_team: Optional[pulumi.Input[bool]] = None, description: Optional[pulumi.Input[str]] = None, members_can_create_pipelines: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, privacy: 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.plugin_download_url is None: opts.plugin_download_url = _utilities.get_plugin_download_url() 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__ = TeamArgs.__new__(TeamArgs) if default_member_role is None and not opts.urn: raise TypeError("Missing required property 'default_member_role'") __props__.__dict__["default_member_role"] = default_member_role if default_team is None and not opts.urn: raise TypeError("Missing required property 'default_team'") __props__.__dict__["default_team"] = default_team __props__.__dict__["description"] = description __props__.__dict__["members_can_create_pipelines"] = members_can_create_pipelines __props__.__dict__["name"] = name if privacy is None and not opts.urn: raise TypeError("Missing required property 'privacy'") __props__.__dict__["privacy"] = privacy __props__.__dict__["slug"] = None __props__.__dict__["uuid"] = None super(Team, __self__).__init__( 'buildkite:index/team:Team', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, default_member_role: Optional[pulumi.Input[str]] = None, default_team: Optional[pulumi.Input[bool]] = None, description: Optional[pulumi.Input[str]] = None, members_can_create_pipelines: Optional[pulumi.Input[bool]] = None, name: Optional[pulumi.Input[str]] = None, privacy: Optional[pulumi.Input[str]] = None, slug: Optional[pulumi.Input[str]] = None, uuid: Optional[pulumi.Input[str]] = None) -> 'Team': """ Get an existing Team resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] default_member_role: Default role to assign to a team member. :param pulumi.Input[bool] default_team: Whether to assign this team to a user by default. :param pulumi.Input[str] description: The description to assign to the team. :param pulumi.Input[bool] members_can_create_pipelines: Whether team members can create. :param pulumi.Input[str] name: The name of the team. :param pulumi.Input[str] privacy: The privacy level to set the team too. :param pulumi.Input[str] slug: The name of the team. :param pulumi.Input[str] uuid: The UUID for the team. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _TeamState.__new__(_TeamState) __props__.__dict__["default_member_role"] = default_member_role __props__.__dict__["default_team"] = default_team __props__.__dict__["description"] = description __props__.__dict__["members_can_create_pipelines"] = members_can_create_pipelines __props__.__dict__["name"] = name __props__.__dict__["privacy"] = privacy __props__.__dict__["slug"] = slug __props__.__dict__["uuid"] = uuid return Team(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="defaultMemberRole") def default_member_role(self) -> pulumi.Output[str]: """ Default role to assign to a team member. """ return pulumi.get(self, "default_member_role") @property @pulumi.getter(name="defaultTeam") def default_team(self) -> pulumi.Output[bool]: """ Whether to assign this team to a user by default. """ return pulumi.get(self, "default_team") @property @pulumi.getter def description(self) -> pulumi.Output[Optional[str]]: """ The description to assign to the team. """ return pulumi.get(self, "description") @property @pulumi.getter(name="membersCanCreatePipelines") def members_can_create_pipelines(self) -> pulumi.Output[Optional[bool]]: """ Whether team members can create. """ return pulumi.get(self, "members_can_create_pipelines") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name of the team. """ return pulumi.get(self, "name") @property @pulumi.getter def privacy(self) -> pulumi.Output[str]: """ The privacy level to set the team too. """ return pulumi.get(self, "privacy") @property @pulumi.getter def slug(self) -> pulumi.Output[str]: """ The name of the team. """ return pulumi.get(self, "slug") @property @pulumi.getter def uuid(self) -> pulumi.Output[str]: """ The UUID for the team. """ return pulumi.get(self, "uuid")

38.34375

134

0.629394

2,157

18,405

5.122856

0.077886

0.093575

0.08362

0.075656

0.844344

0.823891

0.792127

0.762986

0.753665

0.7381

0

0.000074

0.26721

18,405

479

135

38.4238

0.819233

0.249878

0

0.659259

1

0

0.098952

0.029386

0

1

0.159259

false

0.003704

0.018519

0

0.274074

0

null

0

1

0

1

0

null

0

7

dae7ac093bb2806c2693266b9bf2b6b2ab7584bc

9,341

py

Python

whatthefood/tests/test_ops.py

lychanl/WhatTheFood

94b6eec2c306e7e55b19395cde207d6e6beec7fe

[ "MIT" ]

null

whatthefood/tests/test_ops.py

lychanl/WhatTheFood

94b6eec2c306e7e55b19395cde207d6e6beec7fe

[ "MIT" ]

null

whatthefood/tests/test_ops.py

lychanl/WhatTheFood

94b6eec2c306e7e55b19395cde207d6e6beec7fe

[ "MIT" ]

null

import unittest import numpy as np import whatthefood.graph as graph class TestOps(unittest.TestCase): def test_matmul(self): x_arr = np.array([[1, 2], [2, 3], [3, 4]]) y_arr = np.array([[1, 2, 3, 4], [4, 5, 6, 7]]) x = graph.Constant(x_arr) y = graph.Constant(y_arr) m = graph.Matmul(x, y) np.testing.assert_array_equal(graph.run(m), np.matmul(x_arr, y_arr)) def test_matmul_grad(self): x_arr = np.array([[1, 2], [2, 3], [3, 4]]) y_arr = np.array([[1, 2, 3, 4], [4, 5, 6, 7]]) x = graph.Constant(x_arr) y = graph.Constant(y_arr) m = graph.Matmul(x, y) g = graph.Grad(m, [x, y]) mv, (g_x, g_y) = graph.run((m, g)) self.assertSequenceEqual(g_x.shape, x.shape) self.assertSequenceEqual(g_y.shape, y.shape) np.testing.assert_array_equal(g_x, np.matmul(np.ones_like(mv), y_arr.T)) np.testing.assert_array_equal(g_y, np.matmul(x_arr.T, np.ones_like(mv))) def test_matmul_vec(self): x = graph.Constant([1, 2, 3]) y = graph.Constant([[1, 2], [1, 3], [2, 4]]) m = graph.Matmul(x, y) np.testing.assert_array_equal([9, 20], graph.run(m)) def test_matmul_vec_grad(self): x = graph.Constant([1, 2, 3]) y = graph.Constant([[1, 2], [1, 3], [2, 4]]) m = graph.Matmul(x, y) g = graph.Grad(m, [x, y]) g_x, g_y = graph.run(g) np.testing.assert_array_equal([3, 4, 6], g_x) np.testing.assert_array_equal([[1, 1], [2, 2], [3, 3]], g_y) def test_reduce_sum(self): x = graph.Constant([[[1], [2]], [[3], [4]], [[5], [6]]]) y1 = graph.ReduceSum(x, axis=0) y2 = graph.ReduceSum(x, axis=(1, -1)) y3 = graph.ReduceSum(x) np.testing.assert_array_equal([[9], [12]], graph.run(y1)) np.testing.assert_array_equal([3, 7, 11], graph.run(y2)) self.assertEqual(21, graph.run(y3)) def test_reduce_sum_batched(self): x_arr = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]) y_arr = np.array([9, 12]) x = graph.Placeholder(shape=(3, 2, 1), batched=True) y1 = graph.ReduceSum(x, (0, 2), True) y2 = graph.ReduceSum(x, (0, 2), False) np.testing.assert_array_equal(y_arr * 3, graph.run(y1, {x: np.array([x_arr, 2 * x_arr])})) np.testing.assert_array_equal([y_arr, 2 * y_arr], graph.run(y2, {x: np.array([x_arr, 2 * x_arr])})) def test_reduce_sum_grad(self): x = graph.Constant([[[1], [2]], [[3], [4]], [[5], [6]]]) y1 = graph.ReduceSum(x, axis=0) y2 = graph.ReduceSum(x, axis=(1, -1)) y3 = graph.ReduceSum(x) g1 = graph.Grad(y1, x) g2 = graph.Grad(y2, x) g3 = graph.Grad(y3, x) np.testing.assert_array_equal(np.ones_like(x.value), graph.run(g1)) np.testing.assert_array_equal(np.ones_like(x.value), graph.run(g2)) np.testing.assert_array_equal(np.ones_like(x.value), graph.run(g3)) def test_reduce_sum_grad_batched(self): x_arr = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]) x = graph.Placeholder(shape=(3, 2, 1), batched=True) y1 = graph.ReduceSum(x, (0, 2), True) y2 = graph.ReduceSum(x, (0, 2), False) g1 = graph.Grad(y1, x) g2 = graph.Grad(y2, x) np.testing.assert_array_equal( [np.ones_like(x_arr), np.ones_like(x_arr)], graph.run(g1, {x: np.array([x_arr, 2 * x_arr])})) np.testing.assert_array_equal( [np.ones_like(x_arr), np.ones_like(x_arr)], graph.run(g2, {x: np.array([x_arr, 2 * x_arr])})) def test_reduce_mean(self): x = graph.Constant([[[1], [2]], [[3], [4]], [[5], [6]]]) y1 = graph.ReduceMean(x, axis=0) y2 = graph.ReduceMean(x, axis=(1, -1)) y3 = graph.ReduceMean(x) np.testing.assert_array_equal([[3], [4]], graph.run(y1)) np.testing.assert_array_equal([1.5, 3.5, 5.5], graph.run(y2)) self.assertEqual(3.5, graph.run(y3)) def test_reduce_mean_batched(self): x_arr = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]) y_arr = np.array([3, 4]) x = graph.Placeholder(shape=(3, 2, 1), batched=True) y1 = graph.ReduceMean(x, (0, 2), True) y2 = graph.ReduceMean(x, (0, 2), False) np.testing.assert_array_equal(y_arr * 1.5, graph.run(y1, {x: np.array([x_arr, 2 * x_arr])})) np.testing.assert_array_equal([y_arr, 2 * y_arr], graph.run(y2, {x: np.array([x_arr, 2 * x_arr])})) def test_reduce_mean_grad(self): x = graph.Constant([[[1], [2]], [[3], [4]], [[5], [6]]]) y1 = graph.ReduceMean(x, axis=0) y2 = graph.ReduceMean(x, axis=(1, -1)) y3 = graph.ReduceMean(x) g1 = graph.Grad(y1, x) g2 = graph.Grad(y2, x) g3 = graph.Grad(y3, x) np.testing.assert_array_equal(np.ones_like(x.value) / 3, graph.run(g1)) np.testing.assert_array_equal(np.ones_like(x.value) / 2, graph.run(g2)) np.testing.assert_array_equal(np.ones_like(x.value) / 6, graph.run(g3)) def test_reduce_mean_grad_batched(self): x_arr = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]) x = graph.Placeholder(shape=(3, 2, 1), batched=True) y1 = graph.ReduceMean(x, (0, 2), True) y2 = graph.ReduceMean(x, (0, 2), False) g1 = graph.Grad(y1, x) g2 = graph.Grad(y2, x) np.testing.assert_array_equal( [np.ones_like(x_arr) / 6, np.ones_like(x_arr) / 6], graph.run(g1, {x: np.array([x_arr, 2 * x_arr])})) np.testing.assert_array_equal( [np.ones_like(x_arr) / 3, np.ones_like(x_arr) / 3], graph.run(g2, {x: np.array([x_arr, 2 * x_arr])})) def test_slice(self): x_arr = [[1, 2, 3], [4, 5, 6]] x = graph.Constant(x_arr) y1 = graph.Slice(x, (0, 1), (2, 2)) np.testing.assert_array_equal([[2], [5]], graph.run(y1)) def test_slice_batched(self): x_arr = np.array([[1, 2, 3], [4, 5, 6]]) x = graph.Placeholder((2, 3), True) y1 = graph.Slice(x, (0, 1), (2, 2)) np.testing.assert_array_equal( [[[2], [5]], [[-2], [-5]]], graph.run(y1, {x: np.array([x_arr, -x_arr])})) def test_slice_grad(self): x_arr = [[1, 2, 3], [4, 5, 6]] x = graph.Constant(x_arr) y1 = graph.Slice(x, (0, 1), (2, 2)) g1 = graph.Grad(y1, x) np.testing.assert_array_equal([[0, 1, 0], [0, 1, 0]], graph.run(g1)) def test_slice_grad_batched(self): x_arr = np.array([[1, 2, 3], [4, 5, 6]]) x = graph.Placeholder((2, 3), True) y1 = graph.Slice(x, (0, 1), (2, 2)) g1 = graph.Grad(y1, x) np.testing.assert_array_equal( [[[0, 1, 0], [0, 1, 0]], [[0, 1, 0], [0, 1, 0]]], graph.run(g1, {x: np.array([x_arr, -x_arr])})) def test_concatenate(self): x1 = graph.Constant([[1, 2, 3], [4, 5, 6]]) x2 = graph.Constant([[7, 8], [9, 10]]) y = graph.Concatenate((x1, x2), axis=1) np.testing.assert_array_equal([[1, 2, 3, 7, 8], [4, 5, 6, 9, 10]], graph.run(y)) def test_concatenate_batched(self): x1_arr = np.array([[1, 2, 3], [4, 5, 6]]) x2_arr = np.array([[7, 8], [9, 10]]) x1 = graph.Placeholder(x1_arr.shape, batched=True) x2 = graph.Placeholder(x2_arr.shape, batched=True) y = graph.Concatenate((x1, x2), axis=-1) np.testing.assert_array_equal( [[[1, 2, 3, -7, -8], [4, 5, 6, -9, -10]], [[-1, -2, -3, 7, 8], [-4, -5, -6, 9, 10]]], graph.run(y, {x1: np.array([x1_arr, -x1_arr]), x2: np.array([-x2_arr, x2_arr])})) def test_concatenate_grad(self): x1 = graph.Constant([[1, 2, 3], [4, 5, 6]]) x2 = graph.Constant([[7, 8], [9, 10]]) y = graph.Concatenate((x1, x2), axis=1) g = graph.Grad(y, (x1, x2)) g1, g2 = graph.run(g) np.testing.assert_array_equal(np.ones_like(x1.value), g1) np.testing.assert_array_equal(np.ones_like(x2.value), g2) def test_multply(self): x1 = graph.Constant([[1], [2], [3], [4]]) x2 = graph.Constant([[1, -1], [2, -2], [3, -3], [4, -4]]) y = graph.Multiply(x1, x2) np.testing.assert_array_equal([[1, -1], [4, -4], [9, -9], [16, -16]], graph.run(y)) def test_multply_grad(self): x1 = graph.Constant([[1], [2], [3], [4]]) x2 = graph.Constant([[1, -1], [2, -2], [3, -3], [4, -4]]) y = graph.Multiply(x1, x2) g = graph.Grad(y, (x1, x2)) g1, g2 = graph.run(g) np.testing.assert_array_equal([[0], [0], [0], [0]], g1) np.testing.assert_array_equal([[1, 1], [2, 2], [3, 3], [4, 4]], g2) def test_divide(self): x1 = graph.Constant([1, 2, 3, 4]) x2 = graph.Constant([4, 3, 2, 1]) y = graph.Divide(x1, x2) np.testing.assert_array_equal([1/4, 2/3, 3/2, 4], graph.run(y)) def test_divide_grad(self): x1 = graph.Constant([1, 2, 3, 4]) x2 = graph.Constant([4, 3, 2, 1]) y = graph.Divide(x1, x2) g = graph.Grad(y, (x1, x2)) g1, g2 = graph.run(g) np.testing.assert_array_equal([1/4, 1/3, 1/2, 1], g1) np.testing.assert_array_equal([-1/16, -2/9, -3/4, -4], g2)

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970f7b10a07b98a1a06557eff5b9b16e7aa0fdb8

59,592

py

Python

sdk/python/pulumi_aws_native/elasticloadbalancingv2/outputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

29

2021-09-30T19:32:07.000Z

2022-03-22T21:06:08.000Z

sdk/python/pulumi_aws_native/elasticloadbalancingv2/outputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

232

2021-09-30T19:26:26.000Z

2022-03-31T23:22:06.000Z

sdk/python/pulumi_aws_native/elasticloadbalancingv2/outputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

4

2021-11-10T19:42:01.000Z

2022-02-05T10:15:49.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'ListenerAction', 'ListenerAuthenticateCognitoConfig', 'ListenerAuthenticateOidcConfig', 'ListenerCertificate', 'ListenerCertificateCertificate', 'ListenerFixedResponseConfig', 'ListenerForwardConfig', 'ListenerRedirectConfig', 'ListenerRuleAction', 'ListenerRuleAuthenticateCognitoConfig', 'ListenerRuleAuthenticateOidcConfig', 'ListenerRuleFixedResponseConfig', 'ListenerRuleForwardConfig', 'ListenerRuleHostHeaderConfig', 'ListenerRuleHttpHeaderConfig', 'ListenerRuleHttpRequestMethodConfig', 'ListenerRulePathPatternConfig', 'ListenerRuleQueryStringConfig', 'ListenerRuleQueryStringKeyValue', 'ListenerRuleRedirectConfig', 'ListenerRuleRuleCondition', 'ListenerRuleSourceIpConfig', 'ListenerRuleTargetGroupStickinessConfig', 'ListenerRuleTargetGroupTuple', 'ListenerTargetGroupStickinessConfig', 'ListenerTargetGroupTuple', 'LoadBalancerAttribute', 'LoadBalancerSubnetMapping', 'LoadBalancerTag', 'TargetGroupAttribute', 'TargetGroupMatcher', 'TargetGroupTag', 'TargetGroupTargetDescription', ] @pulumi.output_type class ListenerAction(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "authenticateCognitoConfig": suggest = "authenticate_cognito_config" elif key == "authenticateOidcConfig": suggest = "authenticate_oidc_config" elif key == "fixedResponseConfig": suggest = "fixed_response_config" elif key == "forwardConfig": suggest = "forward_config" elif key == "redirectConfig": suggest = "redirect_config" elif key == "targetGroupArn": suggest = "target_group_arn" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerAction. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerAction.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerAction.__key_warning(key) return super().get(key, default) def __init__(__self__, *, type: str, authenticate_cognito_config: Optional['outputs.ListenerAuthenticateCognitoConfig'] = None, authenticate_oidc_config: Optional['outputs.ListenerAuthenticateOidcConfig'] = None, fixed_response_config: Optional['outputs.ListenerFixedResponseConfig'] = None, forward_config: Optional['outputs.ListenerForwardConfig'] = None, order: Optional[int] = None, redirect_config: Optional['outputs.ListenerRedirectConfig'] = None, target_group_arn: Optional[str] = None): pulumi.set(__self__, "type", type) if authenticate_cognito_config is not None: pulumi.set(__self__, "authenticate_cognito_config", authenticate_cognito_config) if authenticate_oidc_config is not None: pulumi.set(__self__, "authenticate_oidc_config", authenticate_oidc_config) if fixed_response_config is not None: pulumi.set(__self__, "fixed_response_config", fixed_response_config) if forward_config is not None: pulumi.set(__self__, "forward_config", forward_config) if order is not None: pulumi.set(__self__, "order", order) if redirect_config is not None: pulumi.set(__self__, "redirect_config", redirect_config) if target_group_arn is not None: pulumi.set(__self__, "target_group_arn", target_group_arn) @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") @property @pulumi.getter(name="authenticateCognitoConfig") def authenticate_cognito_config(self) -> Optional['outputs.ListenerAuthenticateCognitoConfig']: return pulumi.get(self, "authenticate_cognito_config") @property @pulumi.getter(name="authenticateOidcConfig") def authenticate_oidc_config(self) -> Optional['outputs.ListenerAuthenticateOidcConfig']: return pulumi.get(self, "authenticate_oidc_config") @property @pulumi.getter(name="fixedResponseConfig") def fixed_response_config(self) -> Optional['outputs.ListenerFixedResponseConfig']: return pulumi.get(self, "fixed_response_config") @property @pulumi.getter(name="forwardConfig") def forward_config(self) -> Optional['outputs.ListenerForwardConfig']: return pulumi.get(self, "forward_config") @property @pulumi.getter def order(self) -> Optional[int]: return pulumi.get(self, "order") @property @pulumi.getter(name="redirectConfig") def redirect_config(self) -> Optional['outputs.ListenerRedirectConfig']: return pulumi.get(self, "redirect_config") @property @pulumi.getter(name="targetGroupArn") def target_group_arn(self) -> Optional[str]: return pulumi.get(self, "target_group_arn") @pulumi.output_type class ListenerAuthenticateCognitoConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "userPoolArn": suggest = "user_pool_arn" elif key == "userPoolClientId": suggest = "user_pool_client_id" elif key == "userPoolDomain": suggest = "user_pool_domain" elif key == "authenticationRequestExtraParams": suggest = "authentication_request_extra_params" elif key == "onUnauthenticatedRequest": suggest = "on_unauthenticated_request" elif key == "sessionCookieName": suggest = "session_cookie_name" elif key == "sessionTimeout": suggest = "session_timeout" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerAuthenticateCognitoConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerAuthenticateCognitoConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerAuthenticateCognitoConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, user_pool_arn: str, user_pool_client_id: str, user_pool_domain: str, authentication_request_extra_params: Optional[Any] = None, on_unauthenticated_request: Optional[str] = None, scope: Optional[str] = None, session_cookie_name: Optional[str] = None, session_timeout: Optional[str] = None): pulumi.set(__self__, "user_pool_arn", user_pool_arn) pulumi.set(__self__, "user_pool_client_id", user_pool_client_id) pulumi.set(__self__, "user_pool_domain", user_pool_domain) if authentication_request_extra_params is not None: pulumi.set(__self__, "authentication_request_extra_params", authentication_request_extra_params) if on_unauthenticated_request is not None: pulumi.set(__self__, "on_unauthenticated_request", on_unauthenticated_request) if scope is not None: pulumi.set(__self__, "scope", scope) if session_cookie_name is not None: pulumi.set(__self__, "session_cookie_name", session_cookie_name) if session_timeout is not None: pulumi.set(__self__, "session_timeout", session_timeout) @property @pulumi.getter(name="userPoolArn") def user_pool_arn(self) -> str: return pulumi.get(self, "user_pool_arn") @property @pulumi.getter(name="userPoolClientId") def user_pool_client_id(self) -> str: return pulumi.get(self, "user_pool_client_id") @property @pulumi.getter(name="userPoolDomain") def user_pool_domain(self) -> str: return pulumi.get(self, "user_pool_domain") @property @pulumi.getter(name="authenticationRequestExtraParams") def authentication_request_extra_params(self) -> Optional[Any]: return pulumi.get(self, "authentication_request_extra_params") @property @pulumi.getter(name="onUnauthenticatedRequest") def on_unauthenticated_request(self) -> Optional[str]: return pulumi.get(self, "on_unauthenticated_request") @property @pulumi.getter def scope(self) -> Optional[str]: return pulumi.get(self, "scope") @property @pulumi.getter(name="sessionCookieName") def session_cookie_name(self) -> Optional[str]: return pulumi.get(self, "session_cookie_name") @property @pulumi.getter(name="sessionTimeout") def session_timeout(self) -> Optional[str]: return pulumi.get(self, "session_timeout") @pulumi.output_type class ListenerAuthenticateOidcConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "authorizationEndpoint": suggest = "authorization_endpoint" elif key == "clientId": suggest = "client_id" elif key == "clientSecret": suggest = "client_secret" elif key == "tokenEndpoint": suggest = "token_endpoint" elif key == "userInfoEndpoint": suggest = "user_info_endpoint" elif key == "authenticationRequestExtraParams": suggest = "authentication_request_extra_params" elif key == "onUnauthenticatedRequest": suggest = "on_unauthenticated_request" elif key == "sessionCookieName": suggest = "session_cookie_name" elif key == "sessionTimeout": suggest = "session_timeout" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerAuthenticateOidcConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerAuthenticateOidcConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerAuthenticateOidcConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, authorization_endpoint: str, client_id: str, client_secret: str, issuer: str, token_endpoint: str, user_info_endpoint: str, authentication_request_extra_params: Optional[Any] = None, on_unauthenticated_request: Optional[str] = None, scope: Optional[str] = None, session_cookie_name: Optional[str] = None, session_timeout: Optional[str] = None): pulumi.set(__self__, "authorization_endpoint", authorization_endpoint) pulumi.set(__self__, "client_id", client_id) pulumi.set(__self__, "client_secret", client_secret) pulumi.set(__self__, "issuer", issuer) pulumi.set(__self__, "token_endpoint", token_endpoint) pulumi.set(__self__, "user_info_endpoint", user_info_endpoint) if authentication_request_extra_params is not None: pulumi.set(__self__, "authentication_request_extra_params", authentication_request_extra_params) if on_unauthenticated_request is not None: pulumi.set(__self__, "on_unauthenticated_request", on_unauthenticated_request) if scope is not None: pulumi.set(__self__, "scope", scope) if session_cookie_name is not None: pulumi.set(__self__, "session_cookie_name", session_cookie_name) if session_timeout is not None: pulumi.set(__self__, "session_timeout", session_timeout) @property @pulumi.getter(name="authorizationEndpoint") def authorization_endpoint(self) -> str: return pulumi.get(self, "authorization_endpoint") @property @pulumi.getter(name="clientId") def client_id(self) -> str: return pulumi.get(self, "client_id") @property @pulumi.getter(name="clientSecret") def client_secret(self) -> str: return pulumi.get(self, "client_secret") @property @pulumi.getter def issuer(self) -> str: return pulumi.get(self, "issuer") @property @pulumi.getter(name="tokenEndpoint") def token_endpoint(self) -> str: return pulumi.get(self, "token_endpoint") @property @pulumi.getter(name="userInfoEndpoint") def user_info_endpoint(self) -> str: return pulumi.get(self, "user_info_endpoint") @property @pulumi.getter(name="authenticationRequestExtraParams") def authentication_request_extra_params(self) -> Optional[Any]: return pulumi.get(self, "authentication_request_extra_params") @property @pulumi.getter(name="onUnauthenticatedRequest") def on_unauthenticated_request(self) -> Optional[str]: return pulumi.get(self, "on_unauthenticated_request") @property @pulumi.getter def scope(self) -> Optional[str]: return pulumi.get(self, "scope") @property @pulumi.getter(name="sessionCookieName") def session_cookie_name(self) -> Optional[str]: return pulumi.get(self, "session_cookie_name") @property @pulumi.getter(name="sessionTimeout") def session_timeout(self) -> Optional[str]: return pulumi.get(self, "session_timeout") @pulumi.output_type class ListenerCertificate(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "certificateArn": suggest = "certificate_arn" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerCertificate. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerCertificate.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerCertificate.__key_warning(key) return super().get(key, default) def __init__(__self__, *, certificate_arn: Optional[str] = None): if certificate_arn is not None: pulumi.set(__self__, "certificate_arn", certificate_arn) @property @pulumi.getter(name="certificateArn") def certificate_arn(self) -> Optional[str]: return pulumi.get(self, "certificate_arn") @pulumi.output_type class ListenerCertificateCertificate(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "certificateArn": suggest = "certificate_arn" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerCertificateCertificate. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerCertificateCertificate.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerCertificateCertificate.__key_warning(key) return super().get(key, default) def __init__(__self__, *, certificate_arn: Optional[str] = None): if certificate_arn is not None: pulumi.set(__self__, "certificate_arn", certificate_arn) @property @pulumi.getter(name="certificateArn") def certificate_arn(self) -> Optional[str]: return pulumi.get(self, "certificate_arn") @pulumi.output_type class ListenerFixedResponseConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "statusCode": suggest = "status_code" elif key == "contentType": suggest = "content_type" elif key == "messageBody": suggest = "message_body" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerFixedResponseConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerFixedResponseConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerFixedResponseConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, status_code: str, content_type: Optional[str] = None, message_body: Optional[str] = None): pulumi.set(__self__, "status_code", status_code) if content_type is not None: pulumi.set(__self__, "content_type", content_type) if message_body is not None: pulumi.set(__self__, "message_body", message_body) @property @pulumi.getter(name="statusCode") def status_code(self) -> str: return pulumi.get(self, "status_code") @property @pulumi.getter(name="contentType") def content_type(self) -> Optional[str]: return pulumi.get(self, "content_type") @property @pulumi.getter(name="messageBody") def message_body(self) -> Optional[str]: return pulumi.get(self, "message_body") @pulumi.output_type class ListenerForwardConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "targetGroupStickinessConfig": suggest = "target_group_stickiness_config" elif key == "targetGroups": suggest = "target_groups" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerForwardConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerForwardConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerForwardConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, target_group_stickiness_config: Optional['outputs.ListenerTargetGroupStickinessConfig'] = None, target_groups: Optional[Sequence['outputs.ListenerTargetGroupTuple']] = None): if target_group_stickiness_config is not None: pulumi.set(__self__, "target_group_stickiness_config", target_group_stickiness_config) if target_groups is not None: pulumi.set(__self__, "target_groups", target_groups) @property @pulumi.getter(name="targetGroupStickinessConfig") def target_group_stickiness_config(self) -> Optional['outputs.ListenerTargetGroupStickinessConfig']: return pulumi.get(self, "target_group_stickiness_config") @property @pulumi.getter(name="targetGroups") def target_groups(self) -> Optional[Sequence['outputs.ListenerTargetGroupTuple']]: return pulumi.get(self, "target_groups") @pulumi.output_type class ListenerRedirectConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "statusCode": suggest = "status_code" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRedirectConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRedirectConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRedirectConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, status_code: str, host: Optional[str] = None, path: Optional[str] = None, port: Optional[str] = None, protocol: Optional[str] = None, query: Optional[str] = None): pulumi.set(__self__, "status_code", status_code) if host is not None: pulumi.set(__self__, "host", host) if path is not None: pulumi.set(__self__, "path", path) if port is not None: pulumi.set(__self__, "port", port) if protocol is not None: pulumi.set(__self__, "protocol", protocol) if query is not None: pulumi.set(__self__, "query", query) @property @pulumi.getter(name="statusCode") def status_code(self) -> str: return pulumi.get(self, "status_code") @property @pulumi.getter def host(self) -> Optional[str]: return pulumi.get(self, "host") @property @pulumi.getter def path(self) -> Optional[str]: return pulumi.get(self, "path") @property @pulumi.getter def port(self) -> Optional[str]: return pulumi.get(self, "port") @property @pulumi.getter def protocol(self) -> Optional[str]: return pulumi.get(self, "protocol") @property @pulumi.getter def query(self) -> Optional[str]: return pulumi.get(self, "query") @pulumi.output_type class ListenerRuleAction(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "authenticateCognitoConfig": suggest = "authenticate_cognito_config" elif key == "authenticateOidcConfig": suggest = "authenticate_oidc_config" elif key == "fixedResponseConfig": suggest = "fixed_response_config" elif key == "forwardConfig": suggest = "forward_config" elif key == "redirectConfig": suggest = "redirect_config" elif key == "targetGroupArn": suggest = "target_group_arn" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleAction. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleAction.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleAction.__key_warning(key) return super().get(key, default) def __init__(__self__, *, type: str, authenticate_cognito_config: Optional['outputs.ListenerRuleAuthenticateCognitoConfig'] = None, authenticate_oidc_config: Optional['outputs.ListenerRuleAuthenticateOidcConfig'] = None, fixed_response_config: Optional['outputs.ListenerRuleFixedResponseConfig'] = None, forward_config: Optional['outputs.ListenerRuleForwardConfig'] = None, order: Optional[int] = None, redirect_config: Optional['outputs.ListenerRuleRedirectConfig'] = None, target_group_arn: Optional[str] = None): pulumi.set(__self__, "type", type) if authenticate_cognito_config is not None: pulumi.set(__self__, "authenticate_cognito_config", authenticate_cognito_config) if authenticate_oidc_config is not None: pulumi.set(__self__, "authenticate_oidc_config", authenticate_oidc_config) if fixed_response_config is not None: pulumi.set(__self__, "fixed_response_config", fixed_response_config) if forward_config is not None: pulumi.set(__self__, "forward_config", forward_config) if order is not None: pulumi.set(__self__, "order", order) if redirect_config is not None: pulumi.set(__self__, "redirect_config", redirect_config) if target_group_arn is not None: pulumi.set(__self__, "target_group_arn", target_group_arn) @property @pulumi.getter def type(self) -> str: return pulumi.get(self, "type") @property @pulumi.getter(name="authenticateCognitoConfig") def authenticate_cognito_config(self) -> Optional['outputs.ListenerRuleAuthenticateCognitoConfig']: return pulumi.get(self, "authenticate_cognito_config") @property @pulumi.getter(name="authenticateOidcConfig") def authenticate_oidc_config(self) -> Optional['outputs.ListenerRuleAuthenticateOidcConfig']: return pulumi.get(self, "authenticate_oidc_config") @property @pulumi.getter(name="fixedResponseConfig") def fixed_response_config(self) -> Optional['outputs.ListenerRuleFixedResponseConfig']: return pulumi.get(self, "fixed_response_config") @property @pulumi.getter(name="forwardConfig") def forward_config(self) -> Optional['outputs.ListenerRuleForwardConfig']: return pulumi.get(self, "forward_config") @property @pulumi.getter def order(self) -> Optional[int]: return pulumi.get(self, "order") @property @pulumi.getter(name="redirectConfig") def redirect_config(self) -> Optional['outputs.ListenerRuleRedirectConfig']: return pulumi.get(self, "redirect_config") @property @pulumi.getter(name="targetGroupArn") def target_group_arn(self) -> Optional[str]: return pulumi.get(self, "target_group_arn") @pulumi.output_type class ListenerRuleAuthenticateCognitoConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "userPoolArn": suggest = "user_pool_arn" elif key == "userPoolClientId": suggest = "user_pool_client_id" elif key == "userPoolDomain": suggest = "user_pool_domain" elif key == "authenticationRequestExtraParams": suggest = "authentication_request_extra_params" elif key == "onUnauthenticatedRequest": suggest = "on_unauthenticated_request" elif key == "sessionCookieName": suggest = "session_cookie_name" elif key == "sessionTimeout": suggest = "session_timeout" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleAuthenticateCognitoConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleAuthenticateCognitoConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleAuthenticateCognitoConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, user_pool_arn: str, user_pool_client_id: str, user_pool_domain: str, authentication_request_extra_params: Optional[Any] = None, on_unauthenticated_request: Optional[str] = None, scope: Optional[str] = None, session_cookie_name: Optional[str] = None, session_timeout: Optional[int] = None): pulumi.set(__self__, "user_pool_arn", user_pool_arn) pulumi.set(__self__, "user_pool_client_id", user_pool_client_id) pulumi.set(__self__, "user_pool_domain", user_pool_domain) if authentication_request_extra_params is not None: pulumi.set(__self__, "authentication_request_extra_params", authentication_request_extra_params) if on_unauthenticated_request is not None: pulumi.set(__self__, "on_unauthenticated_request", on_unauthenticated_request) if scope is not None: pulumi.set(__self__, "scope", scope) if session_cookie_name is not None: pulumi.set(__self__, "session_cookie_name", session_cookie_name) if session_timeout is not None: pulumi.set(__self__, "session_timeout", session_timeout) @property @pulumi.getter(name="userPoolArn") def user_pool_arn(self) -> str: return pulumi.get(self, "user_pool_arn") @property @pulumi.getter(name="userPoolClientId") def user_pool_client_id(self) -> str: return pulumi.get(self, "user_pool_client_id") @property @pulumi.getter(name="userPoolDomain") def user_pool_domain(self) -> str: return pulumi.get(self, "user_pool_domain") @property @pulumi.getter(name="authenticationRequestExtraParams") def authentication_request_extra_params(self) -> Optional[Any]: return pulumi.get(self, "authentication_request_extra_params") @property @pulumi.getter(name="onUnauthenticatedRequest") def on_unauthenticated_request(self) -> Optional[str]: return pulumi.get(self, "on_unauthenticated_request") @property @pulumi.getter def scope(self) -> Optional[str]: return pulumi.get(self, "scope") @property @pulumi.getter(name="sessionCookieName") def session_cookie_name(self) -> Optional[str]: return pulumi.get(self, "session_cookie_name") @property @pulumi.getter(name="sessionTimeout") def session_timeout(self) -> Optional[int]: return pulumi.get(self, "session_timeout") @pulumi.output_type class ListenerRuleAuthenticateOidcConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "authorizationEndpoint": suggest = "authorization_endpoint" elif key == "clientId": suggest = "client_id" elif key == "clientSecret": suggest = "client_secret" elif key == "tokenEndpoint": suggest = "token_endpoint" elif key == "userInfoEndpoint": suggest = "user_info_endpoint" elif key == "authenticationRequestExtraParams": suggest = "authentication_request_extra_params" elif key == "onUnauthenticatedRequest": suggest = "on_unauthenticated_request" elif key == "sessionCookieName": suggest = "session_cookie_name" elif key == "sessionTimeout": suggest = "session_timeout" elif key == "useExistingClientSecret": suggest = "use_existing_client_secret" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleAuthenticateOidcConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleAuthenticateOidcConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleAuthenticateOidcConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, authorization_endpoint: str, client_id: str, client_secret: str, issuer: str, token_endpoint: str, user_info_endpoint: str, authentication_request_extra_params: Optional[Any] = None, on_unauthenticated_request: Optional[str] = None, scope: Optional[str] = None, session_cookie_name: Optional[str] = None, session_timeout: Optional[int] = None, use_existing_client_secret: Optional[bool] = None): pulumi.set(__self__, "authorization_endpoint", authorization_endpoint) pulumi.set(__self__, "client_id", client_id) pulumi.set(__self__, "client_secret", client_secret) pulumi.set(__self__, "issuer", issuer) pulumi.set(__self__, "token_endpoint", token_endpoint) pulumi.set(__self__, "user_info_endpoint", user_info_endpoint) if authentication_request_extra_params is not None: pulumi.set(__self__, "authentication_request_extra_params", authentication_request_extra_params) if on_unauthenticated_request is not None: pulumi.set(__self__, "on_unauthenticated_request", on_unauthenticated_request) if scope is not None: pulumi.set(__self__, "scope", scope) if session_cookie_name is not None: pulumi.set(__self__, "session_cookie_name", session_cookie_name) if session_timeout is not None: pulumi.set(__self__, "session_timeout", session_timeout) if use_existing_client_secret is not None: pulumi.set(__self__, "use_existing_client_secret", use_existing_client_secret) @property @pulumi.getter(name="authorizationEndpoint") def authorization_endpoint(self) -> str: return pulumi.get(self, "authorization_endpoint") @property @pulumi.getter(name="clientId") def client_id(self) -> str: return pulumi.get(self, "client_id") @property @pulumi.getter(name="clientSecret") def client_secret(self) -> str: return pulumi.get(self, "client_secret") @property @pulumi.getter def issuer(self) -> str: return pulumi.get(self, "issuer") @property @pulumi.getter(name="tokenEndpoint") def token_endpoint(self) -> str: return pulumi.get(self, "token_endpoint") @property @pulumi.getter(name="userInfoEndpoint") def user_info_endpoint(self) -> str: return pulumi.get(self, "user_info_endpoint") @property @pulumi.getter(name="authenticationRequestExtraParams") def authentication_request_extra_params(self) -> Optional[Any]: return pulumi.get(self, "authentication_request_extra_params") @property @pulumi.getter(name="onUnauthenticatedRequest") def on_unauthenticated_request(self) -> Optional[str]: return pulumi.get(self, "on_unauthenticated_request") @property @pulumi.getter def scope(self) -> Optional[str]: return pulumi.get(self, "scope") @property @pulumi.getter(name="sessionCookieName") def session_cookie_name(self) -> Optional[str]: return pulumi.get(self, "session_cookie_name") @property @pulumi.getter(name="sessionTimeout") def session_timeout(self) -> Optional[int]: return pulumi.get(self, "session_timeout") @property @pulumi.getter(name="useExistingClientSecret") def use_existing_client_secret(self) -> Optional[bool]: return pulumi.get(self, "use_existing_client_secret") @pulumi.output_type class ListenerRuleFixedResponseConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "statusCode": suggest = "status_code" elif key == "contentType": suggest = "content_type" elif key == "messageBody": suggest = "message_body" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleFixedResponseConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleFixedResponseConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleFixedResponseConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, status_code: str, content_type: Optional[str] = None, message_body: Optional[str] = None): pulumi.set(__self__, "status_code", status_code) if content_type is not None: pulumi.set(__self__, "content_type", content_type) if message_body is not None: pulumi.set(__self__, "message_body", message_body) @property @pulumi.getter(name="statusCode") def status_code(self) -> str: return pulumi.get(self, "status_code") @property @pulumi.getter(name="contentType") def content_type(self) -> Optional[str]: return pulumi.get(self, "content_type") @property @pulumi.getter(name="messageBody") def message_body(self) -> Optional[str]: return pulumi.get(self, "message_body") @pulumi.output_type class ListenerRuleForwardConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "targetGroupStickinessConfig": suggest = "target_group_stickiness_config" elif key == "targetGroups": suggest = "target_groups" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleForwardConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleForwardConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleForwardConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, target_group_stickiness_config: Optional['outputs.ListenerRuleTargetGroupStickinessConfig'] = None, target_groups: Optional[Sequence['outputs.ListenerRuleTargetGroupTuple']] = None): if target_group_stickiness_config is not None: pulumi.set(__self__, "target_group_stickiness_config", target_group_stickiness_config) if target_groups is not None: pulumi.set(__self__, "target_groups", target_groups) @property @pulumi.getter(name="targetGroupStickinessConfig") def target_group_stickiness_config(self) -> Optional['outputs.ListenerRuleTargetGroupStickinessConfig']: return pulumi.get(self, "target_group_stickiness_config") @property @pulumi.getter(name="targetGroups") def target_groups(self) -> Optional[Sequence['outputs.ListenerRuleTargetGroupTuple']]: return pulumi.get(self, "target_groups") @pulumi.output_type class ListenerRuleHostHeaderConfig(dict): def __init__(__self__, *, values: Optional[Sequence[str]] = None): if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter def values(self) -> Optional[Sequence[str]]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRuleHttpHeaderConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "httpHeaderName": suggest = "http_header_name" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleHttpHeaderConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleHttpHeaderConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleHttpHeaderConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, http_header_name: Optional[str] = None, values: Optional[Sequence[str]] = None): if http_header_name is not None: pulumi.set(__self__, "http_header_name", http_header_name) if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter(name="httpHeaderName") def http_header_name(self) -> Optional[str]: return pulumi.get(self, "http_header_name") @property @pulumi.getter def values(self) -> Optional[Sequence[str]]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRuleHttpRequestMethodConfig(dict): def __init__(__self__, *, values: Optional[Sequence[str]] = None): if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter def values(self) -> Optional[Sequence[str]]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRulePathPatternConfig(dict): def __init__(__self__, *, values: Optional[Sequence[str]] = None): if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter def values(self) -> Optional[Sequence[str]]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRuleQueryStringConfig(dict): def __init__(__self__, *, values: Optional[Sequence['outputs.ListenerRuleQueryStringKeyValue']] = None): if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter def values(self) -> Optional[Sequence['outputs.ListenerRuleQueryStringKeyValue']]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRuleQueryStringKeyValue(dict): def __init__(__self__, *, key: Optional[str] = None, value: Optional[str] = None): if key is not None: pulumi.set(__self__, "key", key) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> Optional[str]: return pulumi.get(self, "key") @property @pulumi.getter def value(self) -> Optional[str]: return pulumi.get(self, "value") @pulumi.output_type class ListenerRuleRedirectConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "statusCode": suggest = "status_code" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleRedirectConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleRedirectConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleRedirectConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, status_code: str, host: Optional[str] = None, path: Optional[str] = None, port: Optional[str] = None, protocol: Optional[str] = None, query: Optional[str] = None): pulumi.set(__self__, "status_code", status_code) if host is not None: pulumi.set(__self__, "host", host) if path is not None: pulumi.set(__self__, "path", path) if port is not None: pulumi.set(__self__, "port", port) if protocol is not None: pulumi.set(__self__, "protocol", protocol) if query is not None: pulumi.set(__self__, "query", query) @property @pulumi.getter(name="statusCode") def status_code(self) -> str: return pulumi.get(self, "status_code") @property @pulumi.getter def host(self) -> Optional[str]: return pulumi.get(self, "host") @property @pulumi.getter def path(self) -> Optional[str]: return pulumi.get(self, "path") @property @pulumi.getter def port(self) -> Optional[str]: return pulumi.get(self, "port") @property @pulumi.getter def protocol(self) -> Optional[str]: return pulumi.get(self, "protocol") @property @pulumi.getter def query(self) -> Optional[str]: return pulumi.get(self, "query") @pulumi.output_type class ListenerRuleRuleCondition(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "hostHeaderConfig": suggest = "host_header_config" elif key == "httpHeaderConfig": suggest = "http_header_config" elif key == "httpRequestMethodConfig": suggest = "http_request_method_config" elif key == "pathPatternConfig": suggest = "path_pattern_config" elif key == "queryStringConfig": suggest = "query_string_config" elif key == "sourceIpConfig": suggest = "source_ip_config" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleRuleCondition. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleRuleCondition.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleRuleCondition.__key_warning(key) return super().get(key, default) def __init__(__self__, *, field: Optional[str] = None, host_header_config: Optional['outputs.ListenerRuleHostHeaderConfig'] = None, http_header_config: Optional['outputs.ListenerRuleHttpHeaderConfig'] = None, http_request_method_config: Optional['outputs.ListenerRuleHttpRequestMethodConfig'] = None, path_pattern_config: Optional['outputs.ListenerRulePathPatternConfig'] = None, query_string_config: Optional['outputs.ListenerRuleQueryStringConfig'] = None, source_ip_config: Optional['outputs.ListenerRuleSourceIpConfig'] = None, values: Optional[Sequence[str]] = None): if field is not None: pulumi.set(__self__, "field", field) if host_header_config is not None: pulumi.set(__self__, "host_header_config", host_header_config) if http_header_config is not None: pulumi.set(__self__, "http_header_config", http_header_config) if http_request_method_config is not None: pulumi.set(__self__, "http_request_method_config", http_request_method_config) if path_pattern_config is not None: pulumi.set(__self__, "path_pattern_config", path_pattern_config) if query_string_config is not None: pulumi.set(__self__, "query_string_config", query_string_config) if source_ip_config is not None: pulumi.set(__self__, "source_ip_config", source_ip_config) if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter def field(self) -> Optional[str]: return pulumi.get(self, "field") @property @pulumi.getter(name="hostHeaderConfig") def host_header_config(self) -> Optional['outputs.ListenerRuleHostHeaderConfig']: return pulumi.get(self, "host_header_config") @property @pulumi.getter(name="httpHeaderConfig") def http_header_config(self) -> Optional['outputs.ListenerRuleHttpHeaderConfig']: return pulumi.get(self, "http_header_config") @property @pulumi.getter(name="httpRequestMethodConfig") def http_request_method_config(self) -> Optional['outputs.ListenerRuleHttpRequestMethodConfig']: return pulumi.get(self, "http_request_method_config") @property @pulumi.getter(name="pathPatternConfig") def path_pattern_config(self) -> Optional['outputs.ListenerRulePathPatternConfig']: return pulumi.get(self, "path_pattern_config") @property @pulumi.getter(name="queryStringConfig") def query_string_config(self) -> Optional['outputs.ListenerRuleQueryStringConfig']: return pulumi.get(self, "query_string_config") @property @pulumi.getter(name="sourceIpConfig") def source_ip_config(self) -> Optional['outputs.ListenerRuleSourceIpConfig']: return pulumi.get(self, "source_ip_config") @property @pulumi.getter def values(self) -> Optional[Sequence[str]]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRuleSourceIpConfig(dict): def __init__(__self__, *, values: Optional[Sequence[str]] = None): if values is not None: pulumi.set(__self__, "values", values) @property @pulumi.getter def values(self) -> Optional[Sequence[str]]: return pulumi.get(self, "values") @pulumi.output_type class ListenerRuleTargetGroupStickinessConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "durationSeconds": suggest = "duration_seconds" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleTargetGroupStickinessConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleTargetGroupStickinessConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleTargetGroupStickinessConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, duration_seconds: Optional[int] = None, enabled: Optional[bool] = None): if duration_seconds is not None: pulumi.set(__self__, "duration_seconds", duration_seconds) if enabled is not None: pulumi.set(__self__, "enabled", enabled) @property @pulumi.getter(name="durationSeconds") def duration_seconds(self) -> Optional[int]: return pulumi.get(self, "duration_seconds") @property @pulumi.getter def enabled(self) -> Optional[bool]: return pulumi.get(self, "enabled") @pulumi.output_type class ListenerRuleTargetGroupTuple(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "targetGroupArn": suggest = "target_group_arn" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerRuleTargetGroupTuple. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerRuleTargetGroupTuple.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerRuleTargetGroupTuple.__key_warning(key) return super().get(key, default) def __init__(__self__, *, target_group_arn: Optional[str] = None, weight: Optional[int] = None): if target_group_arn is not None: pulumi.set(__self__, "target_group_arn", target_group_arn) if weight is not None: pulumi.set(__self__, "weight", weight) @property @pulumi.getter(name="targetGroupArn") def target_group_arn(self) -> Optional[str]: return pulumi.get(self, "target_group_arn") @property @pulumi.getter def weight(self) -> Optional[int]: return pulumi.get(self, "weight") @pulumi.output_type class ListenerTargetGroupStickinessConfig(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "durationSeconds": suggest = "duration_seconds" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerTargetGroupStickinessConfig. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerTargetGroupStickinessConfig.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerTargetGroupStickinessConfig.__key_warning(key) return super().get(key, default) def __init__(__self__, *, duration_seconds: Optional[int] = None, enabled: Optional[bool] = None): if duration_seconds is not None: pulumi.set(__self__, "duration_seconds", duration_seconds) if enabled is not None: pulumi.set(__self__, "enabled", enabled) @property @pulumi.getter(name="durationSeconds") def duration_seconds(self) -> Optional[int]: return pulumi.get(self, "duration_seconds") @property @pulumi.getter def enabled(self) -> Optional[bool]: return pulumi.get(self, "enabled") @pulumi.output_type class ListenerTargetGroupTuple(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "targetGroupArn": suggest = "target_group_arn" if suggest: pulumi.log.warn(f"Key '{key}' not found in ListenerTargetGroupTuple. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: ListenerTargetGroupTuple.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: ListenerTargetGroupTuple.__key_warning(key) return super().get(key, default) def __init__(__self__, *, target_group_arn: Optional[str] = None, weight: Optional[int] = None): if target_group_arn is not None: pulumi.set(__self__, "target_group_arn", target_group_arn) if weight is not None: pulumi.set(__self__, "weight", weight) @property @pulumi.getter(name="targetGroupArn") def target_group_arn(self) -> Optional[str]: return pulumi.get(self, "target_group_arn") @property @pulumi.getter def weight(self) -> Optional[int]: return pulumi.get(self, "weight") @pulumi.output_type class LoadBalancerAttribute(dict): def __init__(__self__, *, key: Optional[str] = None, value: Optional[str] = None): if key is not None: pulumi.set(__self__, "key", key) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> Optional[str]: return pulumi.get(self, "key") @property @pulumi.getter def value(self) -> Optional[str]: return pulumi.get(self, "value") @pulumi.output_type class LoadBalancerSubnetMapping(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "subnetId": suggest = "subnet_id" elif key == "allocationId": suggest = "allocation_id" elif key == "iPv6Address": suggest = "i_pv6_address" elif key == "privateIPv4Address": suggest = "private_i_pv4_address" if suggest: pulumi.log.warn(f"Key '{key}' not found in LoadBalancerSubnetMapping. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: LoadBalancerSubnetMapping.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: LoadBalancerSubnetMapping.__key_warning(key) return super().get(key, default) def __init__(__self__, *, subnet_id: str, allocation_id: Optional[str] = None, i_pv6_address: Optional[str] = None, private_i_pv4_address: Optional[str] = None): pulumi.set(__self__, "subnet_id", subnet_id) if allocation_id is not None: pulumi.set(__self__, "allocation_id", allocation_id) if i_pv6_address is not None: pulumi.set(__self__, "i_pv6_address", i_pv6_address) if private_i_pv4_address is not None: pulumi.set(__self__, "private_i_pv4_address", private_i_pv4_address) @property @pulumi.getter(name="subnetId") def subnet_id(self) -> str: return pulumi.get(self, "subnet_id") @property @pulumi.getter(name="allocationId") def allocation_id(self) -> Optional[str]: return pulumi.get(self, "allocation_id") @property @pulumi.getter(name="iPv6Address") def i_pv6_address(self) -> Optional[str]: return pulumi.get(self, "i_pv6_address") @property @pulumi.getter(name="privateIPv4Address") def private_i_pv4_address(self) -> Optional[str]: return pulumi.get(self, "private_i_pv4_address") @pulumi.output_type class LoadBalancerTag(dict): def __init__(__self__, *, key: str, value: str): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> str: return pulumi.get(self, "key") @property @pulumi.getter def value(self) -> str: return pulumi.get(self, "value") @pulumi.output_type class TargetGroupAttribute(dict): def __init__(__self__, *, key: Optional[str] = None, value: Optional[str] = None): if key is not None: pulumi.set(__self__, "key", key) if value is not None: pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> Optional[str]: return pulumi.get(self, "key") @property @pulumi.getter def value(self) -> Optional[str]: return pulumi.get(self, "value") @pulumi.output_type class TargetGroupMatcher(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "grpcCode": suggest = "grpc_code" elif key == "httpCode": suggest = "http_code" if suggest: pulumi.log.warn(f"Key '{key}' not found in TargetGroupMatcher. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: TargetGroupMatcher.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: TargetGroupMatcher.__key_warning(key) return super().get(key, default) def __init__(__self__, *, grpc_code: Optional[str] = None, http_code: Optional[str] = None): if grpc_code is not None: pulumi.set(__self__, "grpc_code", grpc_code) if http_code is not None: pulumi.set(__self__, "http_code", http_code) @property @pulumi.getter(name="grpcCode") def grpc_code(self) -> Optional[str]: return pulumi.get(self, "grpc_code") @property @pulumi.getter(name="httpCode") def http_code(self) -> Optional[str]: return pulumi.get(self, "http_code") @pulumi.output_type class TargetGroupTag(dict): def __init__(__self__, *, key: str, value: str): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> str: return pulumi.get(self, "key") @property @pulumi.getter def value(self) -> str: return pulumi.get(self, "value") @pulumi.output_type class TargetGroupTargetDescription(dict): @staticmethod def __key_warning(key: str): suggest = None if key == "availabilityZone": suggest = "availability_zone" if suggest: pulumi.log.warn(f"Key '{key}' not found in TargetGroupTargetDescription. Access the value via the '{suggest}' property getter instead.") def __getitem__(self, key: str) -> Any: TargetGroupTargetDescription.__key_warning(key) return super().__getitem__(key) def get(self, key: str, default = None) -> Any: TargetGroupTargetDescription.__key_warning(key) return super().get(key, default) def __init__(__self__, *, id: str, availability_zone: Optional[str] = None, port: Optional[int] = None): pulumi.set(__self__, "id", id) if availability_zone is not None: pulumi.set(__self__, "availability_zone", availability_zone) if port is not None: pulumi.set(__self__, "port", port) @property @pulumi.getter def id(self) -> str: return pulumi.get(self, "id") @property @pulumi.getter(name="availabilityZone") def availability_zone(self) -> Optional[str]: return pulumi.get(self, "availability_zone") @property @pulumi.getter def port(self) -> Optional[int]: return pulumi.get(self, "port")

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971ea22b4f898d7b945a3d66e55c054fd8c1a0fd

10,581

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Python

awx/main/tests/functional/api/test_webhooks.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

17

2021-04-03T01:40:17.000Z

2022-03-03T11:45:20.000Z

awx/main/tests/functional/api/test_webhooks.py

Avinesh/awx

6310a2edd890d6062a9f6bcdeb2b46c4b876c2bf

[ "Apache-2.0" ]

24

2021-05-18T21:13:35.000Z

2022-03-29T10:23:52.000Z

awx/main/tests/functional/api/test_webhooks.py

hostinger/awx

dac01b14e2c04c201a162ea03ef8386d822e3923

[ "Apache-2.0" ]

24

2020-11-27T08:37:35.000Z

2021-03-08T13:27:15.000Z

import pytest from awx.api.versioning import reverse from awx.main.models.mixins import WebhookTemplateMixin from awx.main.models.credential import Credential, CredentialType @pytest.mark.django_db @pytest.mark.parametrize( "user_role, expect", [ ('superuser', 200), ('org admin', 200), ('jt admin', 200), ('jt execute', 403), ('org member', 403), ] ) def test_get_webhook_key_jt(organization_factory, job_template_factory, get, user_role, expect): objs = organization_factory("org", superusers=['admin'], users=['user']) jt = job_template_factory("jt", organization=objs.organization, inventory='test_inv', project='test_proj').job_template if user_role == 'superuser': user = objs.superusers.admin else: user = objs.users.user grant_obj = objs.organization if user_role.startswith('org') else jt getattr(grant_obj, '{}_role'.format(user_role.split()[1])).members.add(user) url = reverse('api:webhook_key', kwargs={'model_kwarg': 'job_templates', 'pk': jt.pk}) response = get(url, user=user, expect=expect) if expect < 400: assert response.data == {'webhook_key': ''} @pytest.mark.django_db @pytest.mark.parametrize( "user_role, expect", [ ('superuser', 200), ('org admin', 200), ('jt admin', 200), ('jt execute', 403), ('org member', 403), ] ) def test_get_webhook_key_wfjt(organization_factory, workflow_job_template_factory, get, user_role, expect): objs = organization_factory("org", superusers=['admin'], users=['user']) wfjt = workflow_job_template_factory("wfjt", organization=objs.organization).workflow_job_template if user_role == 'superuser': user = objs.superusers.admin else: user = objs.users.user grant_obj = objs.organization if user_role.startswith('org') else wfjt getattr(grant_obj, '{}_role'.format(user_role.split()[1])).members.add(user) url = reverse('api:webhook_key', kwargs={'model_kwarg': 'workflow_job_templates', 'pk': wfjt.pk}) response = get(url, user=user, expect=expect) if expect < 400: assert response.data == {'webhook_key': ''} @pytest.mark.django_db @pytest.mark.parametrize( "user_role, expect", [ ('superuser', 201), ('org admin', 201), ('jt admin', 201), ('jt execute', 403), ('org member', 403), ] ) def test_post_webhook_key_jt(organization_factory, job_template_factory, post, user_role, expect): objs = organization_factory("org", superusers=['admin'], users=['user']) jt = job_template_factory("jt", organization=objs.organization, inventory='test_inv', project='test_proj').job_template if user_role == 'superuser': user = objs.superusers.admin else: user = objs.users.user grant_obj = objs.organization if user_role.startswith('org') else jt getattr(grant_obj, '{}_role'.format(user_role.split()[1])).members.add(user) url = reverse('api:webhook_key', kwargs={'model_kwarg': 'job_templates', 'pk': jt.pk}) response = post(url, {}, user=user, expect=expect) if expect < 400: assert bool(response.data.get('webhook_key')) @pytest.mark.django_db @pytest.mark.parametrize( "user_role, expect", [ ('superuser', 201), ('org admin', 201), ('jt admin', 201), ('jt execute', 403), ('org member', 403), ] ) def test_post_webhook_key_wfjt(organization_factory, workflow_job_template_factory, post, user_role, expect): objs = organization_factory("org", superusers=['admin'], users=['user']) wfjt = workflow_job_template_factory("wfjt", organization=objs.organization).workflow_job_template if user_role == 'superuser': user = objs.superusers.admin else: user = objs.users.user grant_obj = objs.organization if user_role.startswith('org') else wfjt getattr(grant_obj, '{}_role'.format(user_role.split()[1])).members.add(user) url = reverse('api:webhook_key', kwargs={'model_kwarg': 'workflow_job_templates', 'pk': wfjt.pk}) response = post(url, {}, user=user, expect=expect) if expect < 400: assert bool(response.data.get('webhook_key')) @pytest.mark.django_db @pytest.mark.parametrize( "service", [s for s, _ in WebhookTemplateMixin.SERVICES] ) def test_set_webhook_service(organization_factory, job_template_factory, patch, service): objs = organization_factory("org", superusers=['admin']) jt = job_template_factory("jt", organization=objs.organization, inventory='test_inv', project='test_proj').job_template admin = objs.superusers.admin assert (jt.webhook_service, jt.webhook_key) == ('', '') url = reverse('api:job_template_detail', kwargs={'pk': jt.pk}) patch(url, {'webhook_service': service}, user=admin, expect=200) jt.refresh_from_db() assert jt.webhook_service == service assert jt.webhook_key != '' @pytest.mark.django_db @pytest.mark.parametrize( "service", [s for s, _ in WebhookTemplateMixin.SERVICES] ) def test_unset_webhook_service(organization_factory, job_template_factory, patch, service): objs = organization_factory("org", superusers=['admin']) jt = job_template_factory("jt", organization=objs.organization, webhook_service=service, inventory='test_inv', project='test_proj').job_template admin = objs.superusers.admin assert jt.webhook_service == service assert jt.webhook_key != '' url = reverse('api:job_template_detail', kwargs={'pk': jt.pk}) patch(url, {'webhook_service': ''}, user=admin, expect=200) jt.refresh_from_db() assert (jt.webhook_service, jt.webhook_key) == ('', '') @pytest.mark.django_db @pytest.mark.parametrize( "service", [s for s, _ in WebhookTemplateMixin.SERVICES] ) def test_set_webhook_credential(organization_factory, job_template_factory, patch, service): objs = organization_factory("org", superusers=['admin']) jt = job_template_factory("jt", organization=objs.organization, webhook_service=service, inventory='test_inv', project='test_proj').job_template admin = objs.superusers.admin assert jt.webhook_service == service assert jt.webhook_key != '' cred_type = CredentialType.defaults['{}_token'.format(service)]() cred_type.save() cred = Credential.objects.create(credential_type=cred_type, name='test-cred', inputs={'token': 'secret'}) url = reverse('api:job_template_detail', kwargs={'pk': jt.pk}) patch(url, {'webhook_credential': cred.pk}, user=admin, expect=200) jt.refresh_from_db() assert jt.webhook_service == service assert jt.webhook_key != '' assert jt.webhook_credential == cred @pytest.mark.django_db @pytest.mark.parametrize( "service,token", [ (s, WebhookTemplateMixin.SERVICES[i - 1][0]) for i, (s, _) in enumerate(WebhookTemplateMixin.SERVICES) ] ) def test_set_wrong_service_webhook_credential(organization_factory, job_template_factory, patch, service, token): objs = organization_factory("org", superusers=['admin']) jt = job_template_factory("jt", organization=objs.organization, webhook_service=service, inventory='test_inv', project='test_proj').job_template admin = objs.superusers.admin assert jt.webhook_service == service assert jt.webhook_key != '' cred_type = CredentialType.defaults['{}_token'.format(token)]() cred_type.save() cred = Credential.objects.create(credential_type=cred_type, name='test-cred', inputs={'token': 'secret'}) url = reverse('api:job_template_detail', kwargs={'pk': jt.pk}) response = patch(url, {'webhook_credential': cred.pk}, user=admin, expect=400) jt.refresh_from_db() assert jt.webhook_service == service assert jt.webhook_key != '' assert jt.webhook_credential is None assert response.data == {'webhook_credential': ["Must match the selected webhook service."]} @pytest.mark.django_db @pytest.mark.parametrize( "service", [s for s, _ in WebhookTemplateMixin.SERVICES] ) def test_set_webhook_credential_without_service(organization_factory, job_template_factory, patch, service): objs = organization_factory("org", superusers=['admin']) jt = job_template_factory("jt", organization=objs.organization, inventory='test_inv', project='test_proj').job_template admin = objs.superusers.admin assert jt.webhook_service == '' assert jt.webhook_key == '' cred_type = CredentialType.defaults['{}_token'.format(service)]() cred_type.save() cred = Credential.objects.create(credential_type=cred_type, name='test-cred', inputs={'token': 'secret'}) url = reverse('api:job_template_detail', kwargs={'pk': jt.pk}) response = patch(url, {'webhook_credential': cred.pk}, user=admin, expect=400) jt.refresh_from_db() assert jt.webhook_service == '' assert jt.webhook_key == '' assert jt.webhook_credential is None assert response.data == {'webhook_credential': ["Must match the selected webhook service."]} @pytest.mark.django_db @pytest.mark.parametrize( "service", [s for s, _ in WebhookTemplateMixin.SERVICES] ) def test_unset_webhook_service_with_credential(organization_factory, job_template_factory, patch, service): objs = organization_factory("org", superusers=['admin']) jt = job_template_factory("jt", organization=objs.organization, webhook_service=service, inventory='test_inv', project='test_proj').job_template admin = objs.superusers.admin assert jt.webhook_service == service assert jt.webhook_key != '' cred_type = CredentialType.defaults['{}_token'.format(service)]() cred_type.save() cred = Credential.objects.create(credential_type=cred_type, name='test-cred', inputs={'token': 'secret'}) jt.webhook_credential = cred jt.save() url = reverse('api:job_template_detail', kwargs={'pk': jt.pk}) response = patch(url, {'webhook_service': ''}, user=admin, expect=400) jt.refresh_from_db() assert jt.webhook_service == service assert jt.webhook_key != '' assert jt.webhook_credential == cred assert response.data == {'webhook_credential': ["Must match the selected webhook service."]}

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113

0.671865

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10,581

5.369788

0.080252

0.058022

0.057143

0.038681

0.961026

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40.696154

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null

0

1

0

1

0

null

0

7

975afa8d06b3b9c7712ca708d7fed99f0a1cdb6b

8,367

py

Python

thornode_client/thornode_client/api/tx_api.py

hoodieonwho/thorchain-python-client

fccfd66552e16bdab1dbb90b68022475c7a9693d

[ "MIT" ]

null

thornode_client/thornode_client/api/tx_api.py

hoodieonwho/thorchain-python-client

fccfd66552e16bdab1dbb90b68022475c7a9693d

[ "MIT" ]

null

thornode_client/thornode_client/api/tx_api.py

hoodieonwho/thorchain-python-client

fccfd66552e16bdab1dbb90b68022475c7a9693d

[ "MIT" ]

null

# coding: utf-8 """ THORChain API This documentation outlines the API for THORChain. NOTE: This document is a **work in progress**. # noqa: E501 OpenAPI spec version: 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 thornode_client.api_client import ApiClient class TxApi(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 get_a_tx_with_given_hash(self, hash, **kwargs): # noqa: E501 """Get a tx with given hash # noqa: E501 Retrieve a tx with the given hash from THORChain # 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_a_tx_with_given_hash(hash, async_req=True) >>> result = thread.get() :param async_req bool :param str hash: Tx hash of an inbound transaction or outbound transaction (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.get_a_tx_with_given_hash_with_http_info(hash, **kwargs) # noqa: E501 else: (data) = self.get_a_tx_with_given_hash_with_http_info(hash, **kwargs) # noqa: E501 return data def get_a_tx_with_given_hash_with_http_info(self, hash, **kwargs): # noqa: E501 """Get a tx with given hash # noqa: E501 Retrieve a tx with the given hash from THORChain # 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_a_tx_with_given_hash_with_http_info(hash, async_req=True) >>> result = thread.get() :param async_req bool :param str hash: Tx hash of an inbound transaction or outbound transaction (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['hash'] # 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_a_tx_with_given_hash" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'hash' is set if self.api_client.client_side_validation and ('hash' not in params or params['hash'] is None): # noqa: E501 raise ValueError("Missing the required parameter `hash` when calling `get_a_tx_with_given_hash`") # noqa: E501 collection_formats = {} path_params = {} if 'hash' in params: path_params['hash'] = params['hash'] # 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 = [] # noqa: E501 return self.api_client.call_api( '/thorchain/tx/{hash}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tx_signers(self, hash, **kwargs): # noqa: E501 """Get tx signers # noqa: E501 Get tx signers that match the request hash # 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_tx_signers(hash, async_req=True) >>> result = thread.get() :param async_req bool :param str hash: Tx hash of an inbound transaction or outbound transaction (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.get_tx_signers_with_http_info(hash, **kwargs) # noqa: E501 else: (data) = self.get_tx_signers_with_http_info(hash, **kwargs) # noqa: E501 return data def get_tx_signers_with_http_info(self, hash, **kwargs): # noqa: E501 """Get tx signers # noqa: E501 Get tx signers that match the request hash # 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_tx_signers_with_http_info(hash, async_req=True) >>> result = thread.get() :param async_req bool :param str hash: Tx hash of an inbound transaction or outbound transaction (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['hash'] # 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_tx_signers" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'hash' is set if self.api_client.client_side_validation and ('hash' not in params or params['hash'] is None): # noqa: E501 raise ValueError("Missing the required parameter `hash` when calling `get_tx_signers`") # noqa: E501 collection_formats = {} path_params = {} if 'hash' in params: path_params['hash'] = params['hash'] # 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 = [] # noqa: E501 return self.api_client.call_api( '/thorchain/tx/{hash}/signers', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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123

0.60643

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0.020721

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1

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null

0

7

8ae9058fefadf30c0326f4f63c43379b31ae9689

9,267

py

Python

src/tests/functional/test_entry_data_endpoint.py

jroberts07/fpl-stats-api

3a7b5faacec5f83643a16143000b46bea70b5364

[ "MIT" ]

null

src/tests/functional/test_entry_data_endpoint.py

jroberts07/fpl-stats-api

3a7b5faacec5f83643a16143000b46bea70b5364

[ "MIT" ]

7

2019-09-08T21:46:14.000Z

2019-12-23T15:06:53.000Z

src/tests/functional/test_entry_data_endpoint.py

jroberts07/fpl_stats_api

3a7b5faacec5f83643a16143000b46bea70b5364

[ "MIT" ]

null

from aioresponses import aioresponses from server import app as sanic_app async def test_success_multiple_classics(test_cli): """Test entry data with an array of classic leagues. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: with open( 'tests/functional/data/' 'entry_response_multiple_classic_leagues.json' ) as f: entry_data = f.read() with open( 'tests/functional/data/' 'league_response_less_than_fifty.json' ) as f: league_data = f.read() m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=200, body=entry_data ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=1 ), status=200, body=league_data ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=2 ), status=200, body=league_data ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 200 resp_json = await resp.json() assert resp_json == { "name": "TEAM A", "leagues": [ { "id": 1, "name": "LEAGUE A", }, { "id": 2, "name": "LEAGUE B", } ] } async def test_success_multiple_classics_some_more_than_fifty(test_cli): """Test entry data with an array of classic leagues some with more than fifty entries. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: with open( 'tests/functional/data/' 'entry_response_multiple_classic_leagues.json' ) as f: entry_data = f.read() with open( 'tests/functional/data/' 'league_response_less_than_fifty.json' ) as f: league_data_less_than_fifty = f.read() with open( 'tests/functional/data/' 'league_response_more_than_fifty.json' ) as f: league_data_more_than_fifty = f.read() m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=200, body=entry_data ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=1 ), status=200, body=league_data_less_than_fifty ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=2 ), status=200, body=league_data_more_than_fifty ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 200 resp_json = await resp.json() assert resp_json == { "name": "TEAM A", "leagues": [ { "id": 1, "name": "LEAGUE A", } ] } async def test_league_api_bad_response(test_cli): """Test entry data with a bad response from league API. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: with open( 'tests/functional/data/' 'entry_response_single_classic_league.json' ) as f: entry_data = f.read() with open( 'tests/functional/data/' 'bad_response.json' ) as f: league_data = f.read() m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=200, body=entry_data ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=1 ), status=200, body=league_data ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 500 resp_json = await resp.json() assert resp_json == { "error": "THERE WAS A PROBLEM WITH THE DATA RETURNED FROM FPL" } async def test_success_single_classics(test_cli): """Test entry data with a single classic league. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: with open( 'tests/functional/data/' 'entry_response_single_classic_league.json' ) as f: entry_data = f.read() with open( 'tests/functional/data/' 'league_response_less_than_fifty.json' ) as f: league_data = f.read() m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=200, body=entry_data ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=1 ), status=200, body=league_data ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 200 resp_json = await resp.json() assert resp_json == { "name": "TEAM A", "leagues": [ { "id": 1, "name": "LEAGUE A", } ] } async def test_no_leagues(test_cli): """Test entry data with no leagues. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: with open( 'tests/functional/data/entry_response_no_leagues.json' ) as f: fpl_data = f.read() m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=200, body=fpl_data ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 200 resp_json = await resp.json() assert resp_json == { "name": "TEAM A", "leagues": [] } async def test_no_name(test_cli): """Test entry data with no name. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: with open( 'tests/functional/data/entry_response_no_name.json' ) as f: entry_data = f.read() with open( 'tests/functional/data/' 'league_response_less_than_fifty.json' ) as f: league_data = f.read() m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=200, body=entry_data ) m.get( sanic_app.config.FPL_URL + sanic_app.config.LEAGUE_DATA.format( league_id=1 ), status=200, body=league_data ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 200 resp_json = await resp.json() assert resp_json == { "name": None, "leagues": [ { "id": 1, "name": "LEAGUE A", } ] } async def test_no_player_cookie(test_cli): """Test entry data with no player_cookie. Args: test_cli (obj): The test event loop. """ resp = await test_cli.get( '/entry_data/123?player_cookie=' ) assert resp.status == 400 resp_json = await resp.json() assert resp_json == { "error": "PARAMETERS REQUIRED: player_cookie" } async def test_fpl_error_response(test_cli): """Test entry data with an error response from FPL. Args: test_cli (obj): The test event loop. """ with aioresponses(passthrough=['http://127.0.0.1:']) as m: m.get( sanic_app.config.FPL_URL + sanic_app.config.ENTRY_DATA.format( entry_id=123 ), status=500, body=None ) resp = await test_cli.get( '/entry_data/123?player_cookie=456' ) assert resp.status == 500 resp_json = await resp.json() assert resp_json == { "error": "ERROR CONNECTING TO THE FANTASY API" }

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75

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1,036

9,267

4.243243

0.082046

0.067561

0.089172

0.038217

0.893085

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0

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null

0

1

0

1

0

null

0

7

8af62ad39d558e0d5a5806871f1e4a6b7a8df43e

194,948

py

Python

src/lib/tracker/multitracker.py

DerryHub/FairMOT-attack

2678cebe939eb8e14106c1e9e07f0c44b5ba975f

[ "MIT" ]

18

2021-11-18T15:38:46.000Z

2022-03-22T07:24:27.000Z

src/lib/tracker/multitracker.py

DerryHub/FairMOT-attack

2678cebe939eb8e14106c1e9e07f0c44b5ba975f

[ "MIT" ]

null

src/lib/tracker/multitracker.py

DerryHub/FairMOT-attack

2678cebe939eb8e14106c1e9e07f0c44b5ba975f

[ "MIT" ]

1

2021-11-25T03:14:37.000Z

# ------------------------------------------------------------------------------ # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. # ------------------------------------------------------------------------------ from collections import deque import cv2 import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from models import * from models.decode import mot_decode from models.model import create_model, load_model from models.utils import _tranpose_and_gather_feat, _tranpose_and_gather_feat_expand from tracker import matching from tracking_utils.kalman_filter import KalmanFilter from tracking_utils.log import logger from tracking_utils.utils import * from utils.post_process import ctdet_post_process from cython_bbox import bbox_overlaps as bbox_ious from .basetrack import BaseTrack, TrackState from scipy.optimize import linear_sum_assignment import random import pickle import copy class GaussianBlurConv(nn.Module): def __init__(self, channels=3): super(GaussianBlurConv, self).__init__() self.channels = channels kernel = [[0.00078633, 0.00655965, 0.01330373, 0.00655965, 0.00078633], [0.00655965, 0.05472157, 0.11098164, 0.05472157, 0.00655965], [0.01330373, 0.11098164, 0.22508352, 0.11098164, 0.01330373], [0.00655965, 0.05472157, 0.11098164, 0.05472157, 0.00655965], [0.00078633, 0.00655965, 0.01330373, 0.00655965, 0.00078633]] kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0) kernel = np.repeat(kernel, self.channels, axis=0) self.weight = nn.Parameter(data=kernel, requires_grad=False) def __call__(self, x): x = F.conv2d(x, self.weight, padding=2, groups=self.channels) return x gaussianBlurConv = GaussianBlurConv().cuda() seed = 0 random.seed(seed) np.random.seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # Remove randomness (may be slower on Tesla GPUs) # https://pytorch.org/docs/stable/notes/randomness.html if seed == 0: torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False smoothL1 = torch.nn.SmoothL1Loss() mse = torch.nn.MSELoss() td_ = {} def bbox_dis(bbox1, bbox2): center1 = (bbox1[:, :2] + bbox1[:, 2:]) / 2 center2 = (bbox2[:, :2] + bbox2[:, 2:]) / 2 center1 = np.repeat(center1.reshape(-1, 1, 2), len(bbox2), axis=1) center2 = np.repeat(center2.reshape(1, -1, 2), len(bbox1), axis=0) dis = np.sqrt(np.sum((center1 - center2) ** 2, axis=-1)) return dis class STrack(BaseTrack): shared_kalman = KalmanFilter() shared_kalman_ = KalmanFilter() def __init__(self, tlwh, score, temp_feat, buffer_size=30): # wait activate self._tlwh = np.asarray(tlwh, dtype=np.float) self.kalman_filter = None self.mean, self.covariance = None, None self.is_activated = False self.score = score self.tracklet_len = 0 self.exist_len = 1 self.smooth_feat = None self.smooth_feat_ad = None self.update_features(temp_feat) self.features = deque([], maxlen=buffer_size) self.alpha = 0.9 self.curr_tlbr = self.tlwh_to_tlbr(self._tlwh) self.det_dict = {} def get_v(self): return self.mean[4:6] if self.mean is not None else None def update_features_ad(self, feat): feat /= np.linalg.norm(feat) if self.smooth_feat_ad is None: self.smooth_feat_ad = feat else: self.smooth_feat_ad = self.alpha * self.smooth_feat_ad + (1 - self.alpha) * feat self.smooth_feat_ad /= np.linalg.norm(self.smooth_feat_ad) def update_features(self, feat): feat /= np.linalg.norm(feat) self.curr_feat = feat if self.smooth_feat is None: self.smooth_feat = feat else: self.smooth_feat = self.alpha * self.smooth_feat + (1 - self.alpha) * feat self.features.append(feat) self.smooth_feat /= np.linalg.norm(self.smooth_feat) def predict(self): mean_state = self.mean.copy() if self.state != TrackState.Tracked: mean_state[7] = 0 self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance) @staticmethod def multi_predict(stracks): if len(stracks) > 0: multi_mean = np.asarray([st.mean.copy() for st in stracks]) multi_covariance = np.asarray([st.covariance for st in stracks]) for i, st in enumerate(stracks): if st.state != TrackState.Tracked: multi_mean[i][7] = 0 multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance) for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)): stracks[i].mean = mean stracks[i].covariance = cov @staticmethod def multi_predict_(stracks): if len(stracks) > 0: multi_mean = np.asarray([st.mean.copy() for st in stracks]) multi_covariance = np.asarray([st.covariance for st in stracks]) for i, st in enumerate(stracks): if st.state != TrackState.Tracked: multi_mean[i][7] = 0 multi_mean, multi_covariance = STrack.shared_kalman_.multi_predict(multi_mean, multi_covariance) for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)): stracks[i].mean = mean stracks[i].covariance = cov def activate(self, kalman_filter, frame_id, track_id=None): """Start a new tracklet""" self.kalman_filter = kalman_filter if track_id: self.track_id = track_id['track_id'] track_id['track_id'] += 1 else: self.track_id = self.next_id() self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh)) self.tracklet_len = 0 self.state = TrackState.Tracked self.is_activated = True self.frame_id = frame_id self.start_frame = frame_id def activate_(self, kalman_filter, frame_id, track_id=None): """Start a new tracklet""" self.kalman_filter = kalman_filter if track_id: self.track_id = track_id['track_id'] track_id['track_id'] += 1 else: self.track_id = self.next_id_() self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh)) self.tracklet_len = 0 self.state = TrackState.Tracked self.is_activated = True self.frame_id = frame_id self.start_frame = frame_id def re_activate(self, new_track, frame_id, new_id=False): self.curr_tlbr = self.tlwh_to_tlbr(new_track.tlwh) self.mean, self.covariance = self.kalman_filter.update( self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh) ) self.update_features(new_track.curr_feat) self.tracklet_len = 0 self.exist_len += 1 self.state = TrackState.Tracked self.is_activated = True self.frame_id = frame_id if new_id: self.track_id = self.next_id() def re_activate_(self, new_track, frame_id, new_id=False): self.curr_tlbr = self.tlwh_to_tlbr(new_track.tlwh) self.mean, self.covariance = self.kalman_filter.update( self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh) ) self.update_features(new_track.curr_feat) self.tracklet_len = 0 self.exist_len += 1 self.state = TrackState.Tracked self.is_activated = True self.frame_id = frame_id if new_id: self.track_id = self.next_id_() def update(self, new_track, frame_id, update_feature=True): """ Update a matched track :type new_track: STrack :type frame_id: int :type update_feature: bool :return: """ self.frame_id = frame_id self.tracklet_len += 1 self.exist_len += 1 self.curr_tlbr = self.tlwh_to_tlbr(new_track.tlwh) new_tlwh = new_track.tlwh self.mean, self.covariance = self.kalman_filter.update( self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh)) self.state = TrackState.Tracked self.is_activated = True self.score = new_track.score if update_feature: self.update_features(new_track.curr_feat) @property # @jit(nopython=True) def tlwh(self): """Get current position in bounding box format `(top left x, top left y, width, height)`. """ if self.mean is None: return self._tlwh.copy() ret = self.mean[:4].copy() ret[2] *= ret[3] ret[:2] -= ret[2:] / 2 return ret @property # @jit(nopython=True) def tlbr(self): """Convert bounding box to format `(min x, min y, max x, max y)`, i.e., `(top left, bottom right)`. """ ret = self.tlwh.copy() ret[2:] += ret[:2] return ret @staticmethod # @jit(nopython=True) def tlwh_to_xyah(tlwh): """Convert bounding box to format `(center x, center y, aspect ratio, height)`, where the aspect ratio is `width / height`. """ ret = np.asarray(tlwh).copy() ret[:2] += ret[2:] / 2 ret[2] /= ret[3] return ret def to_xyah(self): return self.tlwh_to_xyah(self.tlwh) @staticmethod # @jit(nopython=True) def tlbr_to_tlwh(tlbr): ret = np.asarray(tlbr).copy() ret[2:] -= ret[:2] return ret @staticmethod # @jit(nopython=True) def tlwh_to_tlbr(tlwh): ret = np.asarray(tlwh).copy() ret[2:] += ret[:2] return ret def __repr__(self): return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame) class JDETracker(object): def __init__( self, opt, frame_rate=30, tracked_stracks=[], lost_stracks=[], removed_stracks=[], frame_id=0, ad_last_info={}, model=None ): self.opt = opt print('Creating model...') if model: self.model = model else: self.model = create_model(opt.arch, opt.heads, opt.head_conv) self.model = load_model(self.model, opt.load_model).cuda() self.model.eval() self.log_index = [] self.unconfirmed_ad_iou = None self.tracked_stracks_ad_iou = None self.strack_pool_ad_iou = None self.tracked_stracks = copy.deepcopy(tracked_stracks) # type: list[STrack] self.lost_stracks = copy.deepcopy(lost_stracks) # type: list[STrack] self.removed_stracks = copy.deepcopy(removed_stracks) # type: list[STrack] self.tracked_stracks_ad = copy.deepcopy(tracked_stracks) # type: list[STrack] self.lost_stracks_ad = copy.deepcopy(lost_stracks) # type: list[STrack] self.removed_stracks_ad = copy.deepcopy(removed_stracks) # type: list[STrack] self.tracked_stracks_ = copy.deepcopy(tracked_stracks) # type: list[STrack] self.lost_stracks_ = copy.deepcopy(lost_stracks) # type: list[STrack] self.removed_stracks_ = copy.deepcopy(removed_stracks) # type: list[STrack] self.frame_id = frame_id self.frame_id_ = frame_id self.frame_id_ad = frame_id self.det_thresh = opt.conf_thres self.buffer_size = int(frame_rate / 30.0 * opt.track_buffer) self.max_time_lost = self.buffer_size self.max_per_image = 128 self.kalman_filter = KalmanFilter() self.kalman_filter_ad = KalmanFilter() self.kalman_filter_ = KalmanFilter() self.attacked_ids = set([]) self.low_iou_ids = set([]) self.ATTACK_IOU_THR = opt.iou_thr self.attack_iou_thr = self.ATTACK_IOU_THR self.ad_last_info = copy.deepcopy(ad_last_info) self.FRAME_THR = 10 self.temp_i = 0 self.multiple_ori_ids = {} self.multiple_att_ids = {} self.multiple_ori2att = {} self.multiple_att_freq = {} # hijacking attack self.ad_bbox = True self.ad_ids = set([]) def post_process(self, dets, meta): dets = dets.detach().cpu().numpy() dets = dets.reshape(1, -1, dets.shape[2]) dets = ctdet_post_process( dets.copy(), [meta['c']], [meta['s']], meta['out_height'], meta['out_width'], self.opt.num_classes) for j in range(1, self.opt.num_classes + 1): dets[0][j] = np.array(dets[0][j], dtype=np.float32).reshape(-1, 5) return dets[0] def merge_outputs(self, detections): results = {} for j in range(1, self.opt.num_classes + 1): results[j] = np.concatenate( [detection[j] for detection in detections], axis=0).astype(np.float32) scores = np.hstack( [results[j][:, 4] for j in range(1, self.opt.num_classes + 1)]) if len(scores) > self.max_per_image: kth = len(scores) - self.max_per_image thresh = np.partition(scores, kth)[kth] for j in range(1, self.opt.num_classes + 1): keep_inds = (results[j][:, 4] >= thresh) results[j] = results[j][keep_inds] return results @staticmethod def recoverImg(im_blob, img0): height = 608 width = 1088 im_blob = im_blob.cpu() * 255.0 shape = img0.shape[:2] # shape = [height, width] ratio = min(float(height) / shape[0], float(width) / shape[1]) new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height] dw = (width - new_shape[0]) / 2 # width padding dh = (height - new_shape[1]) / 2 # height padding top, bottom = round(dh - 0.1), round(dh + 0.1) left, right = round(dw - 0.1), round(dw + 0.1) im_blob = im_blob.squeeze().permute(1, 2, 0)[top:height - bottom, left:width - right, :].numpy().astype( np.uint8) im_blob = cv2.cvtColor(im_blob, cv2.COLOR_RGB2BGR) h, w, _ = img0.shape im_blob = cv2.resize(im_blob, (w, h)) return im_blob def recoverNoise(self, noise, img0): height = 608 width = 1088 shape = img0.shape[:2] # shape = [height, width] ratio = min(float(height) / shape[0], float(width) / shape[1]) new_shape = (round(shape[1] * ratio), round(shape[0] * ratio)) # new_shape = [width, height] dw = (width - new_shape[0]) / 2 # width padding dh = (height - new_shape[1]) / 2 # height padding top, bottom = round(dh - 0.1), round(dh + 0.1) left, right = round(dw - 0.1), round(dw + 0.1) noise = noise[:, :, top:height - bottom, left:width - right] h, w, _ = img0.shape # noise = self.resizeTensor(noise, h, w).cpu().squeeze().permute(1, 2, 0).numpy() noise = noise.cpu().squeeze().permute(1, 2, 0).numpy() noise = (noise[:, :, ::-1] * 255).astype(np.int) return noise @staticmethod def resizeTensor(tensor, height, width): h = torch.linspace(-1, 1, height).view(-1, 1).repeat(1, width).to(tensor.device) w = torch.linspace(-1, 1, width).repeat(height, 1).to(tensor.device) grid = torch.cat((h.unsqueeze(2), w.unsqueeze(2)), dim=2) grid = grid.unsqueeze(0) output = F.grid_sample(tensor, grid=grid, mode='bilinear', align_corners=True) return output @staticmethod def processIoUs(ious): h, w = ious.shape assert h == w ious = np.tril(ious, -1) index = np.argsort(-ious.reshape(-1)) indSet = set([]) for ind in index: i = ind // h j = ind % w if ious[i, j] == 0: break if i in indSet or j in indSet: ious[i, j] = 0 else: indSet.add(i) indSet.add(j) return ious def attack_sg_hj( self, im_blob, img0, dets, inds, remain_inds, last_info, outputs_ori, attack_id, attack_ind, ad_bbox, track_v ): noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori H, W = outputs_ori['hm'].size()[2:] hm_index = inds[0][remain_inds] hm_index_att = hm_index[attack_ind].item() index = list(range(hm_index.size(0))) index.pop(attack_ind) wh_ori = outputs['wh'].clone().data reg_ori = outputs['reg'].clone().data i = 0 while True: i += 1 loss = 0 hm_index_att_lst = [hm_index_att] loss -= ((outputs['hm'].view(-1)[hm_index_att_lst].sigmoid()) ** 2).mean() if ad_bbox: assert track_v is not None hm_index_gen = hm_index_att_lst[0] hm_index_gen += -(np.sign(track_v[0]) + W * np.sign(track_v[1])) loss -= ((1 - outputs['hm'].view(-1)[[hm_index_gen]].sigmoid()) ** 2).mean() loss -= smoothL1(outputs['wh'].view(2, -1)[:, [hm_index_gen]].T, wh_ori.view(2, -1)[:, hm_index_att_lst].T) loss -= smoothL1(outputs['reg'].view(2, -1)[:, [hm_index_gen]].T, reg_ori.view(2, -1)[:, hm_index_att_lst].T) loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad * 2 im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data outputs, suc, _ = self.forwardFeatureDet( im_blob, img0, dets, [attack_ind], thr=1 if ad_bbox else 0, vs=[track_v] if ad_bbox else [] ) if suc: break if i > 60: break return noise, i, suc def attack_sg_det( self, im_blob, img0, dets, inds, remain_inds, last_info, outputs_ori, attack_id, attack_ind ): noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori H, W = outputs_ori['hm'].size()[2:] hm_index = inds[0][remain_inds] hm_index_att = hm_index[attack_ind].item() index = list(range(hm_index.size(0))) index.pop(attack_ind) i = 0 while True: i += 1 loss = 0 hm_index_att_lst = [hm_index_att] # for n_i in range(3): # for n_j in range(3): # hm_index_att_ = hm_index_att + (n_i - 1) * W + (n_j - 1) # hm_index_att_ = max(0, min(H * W - 1, hm_index_att_)) # hm_index_att_lst.append(hm_index_att_) loss -= ((outputs['hm'].view(-1)[hm_index_att_lst].sigmoid()) ** 2).mean() # loss += ((outputs['hm'].view(-1)[hm_index_att_lst].sigmoid()) ** 2 * # torch.log(1 - outputs['hm'].view(-1)[hm_index_att_lst].sigmoid())).mean() loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad * 2 im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data outputs, suc, _ = self.forwardFeatureDet( im_blob, img0, dets, [attack_ind] ) if suc: break if i > 60: break return noise, i, suc def attack_mt_hj( self, im_blob, img0, dets, inds, remain_inds, last_info, outputs_ori, attack_ids, attack_inds, ad_ids, track_vs ): img0_h, img0_w = img0.shape[:2] H, W = outputs_ori['hm'].size()[2:] r_w, r_h = img0_w / W, img0_h / H r_max = max(r_w, r_h) noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori wh_ori = outputs['wh'].clone().data reg_ori = outputs['reg'].clone().data i = 0 hm_index = inds[0][remain_inds] hm_index_att_lst = hm_index[attack_inds].cpu().numpy().tolist() best_i = None best_noise = None best_fail = np.inf while True: i += 1 loss = 0 loss -= ((outputs['hm'].view(-1)[hm_index_att_lst].sigmoid()) ** 2).mean() hm_index_att_lst_ = [hm_index_att_lst[j] for j in range(len(hm_index_att_lst)) if attack_ids[j] not in ad_ids] if len(hm_index_att_lst_): assert len(track_vs) == len(hm_index_att_lst_) hm_index_gen_lst = [] for index in range(len(hm_index_att_lst_)): track_v = track_vs[index] hm_index_gen = hm_index_att_lst_[index] hm_index_gen += -(np.sign(track_v[0]) + W * np.sign(track_v[1])) hm_index_gen_lst.append(hm_index_gen) loss -= ((1 - outputs['hm'].view(-1)[hm_index_gen_lst].sigmoid()) ** 2).mean() loss -= smoothL1(outputs['wh'].view(2, -1)[:, hm_index_gen_lst].T, wh_ori.view(2, -1)[:, hm_index_att_lst_].T) loss -= smoothL1(outputs['reg'].view(2, -1)[:, hm_index_gen_lst].T, reg_ori.view(2, -1)[:, hm_index_att_lst_].T) loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad thrs = [0 for j in range(len(attack_inds))] for j in range(len(thrs)): if attack_ids[j] not in ad_ids: thrs[j] = 0.9 im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data outputs, suc, fail_ids = self.forwardFeatureDet( im_blob, img0, dets, attack_inds.tolist(), thr=thrs ) if fail_ids is not None: if fail_ids == 0: break elif fail_ids <= best_fail: best_fail = fail_ids best_i = i best_noise = noise.clone() if i > 60: if self.opt.no_f_noise: return None, i, False else: if best_i is not None: noise = best_noise i = best_i return noise, i, False return noise, i, True def attack_mt_det( self, im_blob, img0, dets, inds, remain_inds, last_info, outputs_ori, attack_ids, attack_inds ): img0_h, img0_w = img0.shape[:2] H, W = outputs_ori['hm'].size()[2:] r_w, r_h = img0_w / W, img0_h / H r_max = max(r_w, r_h) noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori wh_ori = outputs['wh'].clone().data reg_ori = outputs['reg'].clone().data i = 0 hm_index = inds[0][remain_inds] hm_index_att_lst = hm_index[attack_inds].cpu().numpy().tolist() best_i = None best_noise = None best_fail = np.inf while True: i += 1 loss = 0 loss -= ((outputs['hm'].view(-1)[hm_index_att_lst].sigmoid()) ** 2).mean() loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data outputs, suc, fail_ids = self.forwardFeatureDet( im_blob, img0, dets, attack_inds.tolist() ) if fail_ids is not None: if fail_ids == 0: break elif fail_ids <= best_fail: best_fail = fail_ids best_i = i best_noise = noise.clone() if i > 60: if self.opt.no_f_noise: return None, i, False else: if best_i is not None: noise = best_noise i = best_i return noise, i, False return noise, i, True def attack_sg_feat( self, im_blob, img0, id_features, dets, inds, remain_inds, last_info, outputs_ori, attack_id, attack_ind, target_id, target_ind ): noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data last_ad_id_features = [None for _ in range(len(id_features[0]))] for i in range(len(id_features)): id_features[i] = id_features[i][[attack_ind, target_ind]] i = 0 suc = True while True: i += 1 loss = 0 loss_feat = 0 for id_i, id_feature in enumerate(id_features): if last_ad_id_features[attack_ind] is not None: last_ad_id_feature = torch.from_numpy(last_ad_id_features[attack_ind]).unsqueeze(0).cuda() sim_1 = torch.mm(id_feature[0:0 + 1], last_ad_id_feature.T).squeeze() sim_2 = torch.mm(id_feature[1:1 + 1], last_ad_id_feature.T).squeeze() loss_feat += sim_2 - sim_1 if last_ad_id_features[target_ind] is not None: last_ad_id_feature = torch.from_numpy(last_ad_id_features[target_ind]).unsqueeze(0).cuda() sim_1 = torch.mm(id_feature[1:1 + 1], last_ad_id_feature.T).squeeze() sim_2 = torch.mm(id_feature[0:0 + 1], last_ad_id_feature.T).squeeze() loss_feat += sim_2 - sim_1 if last_ad_id_features[attack_ind] is None and last_ad_id_features[target_ind] is None: loss_feat += torch.mm(id_feature[0:0 + 1], id_feature[1:1 + 1].T).squeeze() loss += loss_feat / len(id_features) loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data id_features_, outputs_, ae_attack_id, ae_target_id, hm_index_ = self.forwardFeatureSg( im_blob, img0, dets, inds, remain_inds, attack_id, attack_ind, target_id, target_ind, last_info ) if id_features_ is not None: id_features = id_features_ if ae_attack_id != attack_id and ae_attack_id is not None: break if i > 60: suc = False break return noise, i, suc def attack_sg_cl( self, im_blob, img0, id_features, dets, inds, remain_inds, last_info, outputs_ori, attack_id, attack_ind, target_id, target_ind ): img0_h, img0_w = img0.shape[:2] H, W = outputs_ori['hm'].size()[2:] r_w, r_h = img0_w / W, img0_h / H r_max = max(r_w, r_h) noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori wh_ori = outputs['wh'].clone().data reg_ori = outputs['reg'].clone().data last_ad_id_features = [None for _ in range(len(id_features[0]))] strack_pool = copy.deepcopy(last_info['last_strack_pool']) last_attack_det = None last_target_det = None STrack.multi_predict(strack_pool) for strack in strack_pool: if strack.track_id == attack_id: last_ad_id_features[attack_ind] = strack.smooth_feat last_attack_det = torch.from_numpy(strack.tlbr).cuda().float() last_attack_det[[0, 2]] = (last_attack_det[[0, 2]] - 0.5 * W * (r_w - r_max)) / r_max last_attack_det[[1, 3]] = (last_attack_det[[1, 3]] - 0.5 * H * (r_h - r_max)) / r_max elif strack.track_id == target_id: last_ad_id_features[target_ind] = strack.smooth_feat last_target_det = torch.from_numpy(strack.tlbr).cuda().float() last_target_det[[0, 2]] = (last_target_det[[0, 2]] - 0.5 * W * (r_w - r_max)) / r_max last_target_det[[1, 3]] = (last_target_det[[1, 3]] - 0.5 * H * (r_h - r_max)) / r_max last_attack_det_center = torch.round( (last_attack_det[:2] + last_attack_det[2:]) / 2) if last_attack_det is not None else None last_target_det_center = torch.round( (last_target_det[:2] + last_target_det[2:]) / 2) if last_target_det is not None else None hm_index = inds[0][remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][[attack_ind, target_ind]] i = 0 j = -1 suc = True ori_hm_index = hm_index[[attack_ind, target_ind]].clone() ori_hm_index_re = hm_index[[target_ind, attack_ind]].clone() att_hm_index = None noise_0 = None i_0 = None noise_1 = None i_1 = None while True: i += 1 loss = 0 loss_feat = 0 # for id_i, id_feature in enumerate(id_features): # if last_ad_id_features[attack_ind] is not None: # last_ad_id_feature = torch.from_numpy(last_ad_id_features[attack_ind]).unsqueeze(0).cuda() # sim_1 = torch.mm(id_feature[0:0 + 1], last_ad_id_feature.T).squeeze() # sim_2 = torch.mm(id_feature[1:1 + 1], last_ad_id_feature.T).squeeze() # loss_feat += sim_2 - sim_1 # if last_ad_id_features[target_ind] is not None: # last_ad_id_feature = torch.from_numpy(last_ad_id_features[target_ind]).unsqueeze(0).cuda() # sim_1 = torch.mm(id_feature[1:1 + 1], last_ad_id_feature.T).squeeze() # sim_2 = torch.mm(id_feature[0:0 + 1], last_ad_id_feature.T).squeeze() # loss_feat += sim_2 - sim_1 # if last_ad_id_features[attack_ind] is None and last_ad_id_features[target_ind] is None: # loss_feat += torch.mm(id_feature[0:0 + 1], id_feature[1:1 + 1].T).squeeze() # loss += loss_feat / len(id_features) if i in [1, 10, 20, 30, 35, 40, 45, 50, 55]: attack_det_center = torch.stack([hm_index[attack_ind] % W, hm_index[attack_ind] // W]).float() target_det_center = torch.stack([hm_index[target_ind] % W, hm_index[target_ind] // W]).float() if last_target_det_center is not None: attack_center_delta = attack_det_center - last_target_det_center if torch.max(torch.abs(attack_center_delta)) > 1: attack_center_delta /= torch.max(torch.abs(attack_center_delta)) attack_det_center = torch.round(attack_det_center - attack_center_delta).int() hm_index[attack_ind] = attack_det_center[0] + attack_det_center[1] * W if last_attack_det_center is not None: target_center_delta = target_det_center - last_attack_det_center if torch.max(torch.abs(target_center_delta)) > 1: target_center_delta /= torch.max(torch.abs(target_center_delta)) target_det_center = torch.round(target_det_center - target_center_delta).int() hm_index[target_ind] = target_det_center[0] + target_det_center[1] * W att_hm_index = hm_index[[attack_ind, target_ind]].clone() if att_hm_index is not None: n_att_hm_index = [] n_ori_hm_index_re = [] for hm_ind in range(len(att_hm_index)): for n_i in range(3): for n_j in range(3): att_hm_ind = att_hm_index[hm_ind].item() att_hm_ind = att_hm_ind + (n_i - 1) * W + (n_j - 1) att_hm_ind = max(0, min(H*W-1, att_hm_ind)) n_att_hm_index.append(att_hm_ind) ori_hm_ind = ori_hm_index_re[hm_ind].item() ori_hm_ind = ori_hm_ind + (n_i - 1) * W + (n_j - 1) ori_hm_ind = max(0, min(H * W - 1, ori_hm_ind)) n_ori_hm_index_re.append(ori_hm_ind) # print(n_att_hm_index, n_ori_hm_index_re) loss += ((1 - outputs['hm'].view(-1).sigmoid()[n_att_hm_index]) ** 2 * torch.log(outputs['hm'].view(-1).sigmoid()[n_att_hm_index])).mean() loss += ((outputs['hm'].view(-1).sigmoid()[n_ori_hm_index_re]) ** 2 * torch.log(1 - outputs['hm'].view(-1).sigmoid()[n_ori_hm_index_re])).mean() loss -= smoothL1(outputs['wh'].view(2, -1)[:, n_att_hm_index].T, wh_ori.view(2, -1)[:, n_ori_hm_index_re].T) loss -= smoothL1(outputs['reg'].view(2, -1)[:, n_att_hm_index].T, reg_ori.view(2, -1)[:, n_ori_hm_index_re].T) loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data id_features_, outputs_, ae_attack_id, ae_target_id, hm_index_ = self.forwardFeatureSg( im_blob, img0, dets, inds, remain_inds, attack_id, attack_ind, target_id, target_ind, last_info ) if id_features_ is not None: id_features = id_features_ if outputs_ is not None: outputs = outputs_ # if hm_index_ is not None: # hm_index = hm_index_ if ae_attack_id != attack_id and ae_attack_id is not None: break if i > 60: if noise_0 is not None: return noise_0, i_0, suc elif noise_1 is not None: return noise_1, i_1, suc if self.opt.no_f_noise: return None, i, False else: suc = False break return noise, i, suc def attack_sg_random( self, im_blob, img0, id_features, dets, inds, remain_inds, last_info, outputs_ori, attack_id, attack_ind, target_id, target_ind ): im_blob_ori = im_blob.clone().data suc = False noise = torch.rand(im_blob_ori.size()).to(im_blob_ori.device) noise /= (noise**2).sum().sqrt() noise *= random.uniform(2, 8) im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data id_features_, outputs_, ae_attack_id, ae_target_id, hm_index_ = self.forwardFeatureSg( im_blob, img0, dets, inds, remain_inds, attack_id, attack_ind, target_id, target_ind, last_info, grad=False ) if ae_attack_id != attack_id and ae_attack_id is not None: suc = True return noise, 1, suc def attack_mt_random( self, im_blob, img0, id_features, dets, inds, remain_inds, last_info, outputs_ori, attack_ids, attack_inds, target_ids, target_inds ): im_blob_ori = im_blob.clone().data suc = False noise = torch.rand(im_blob_ori.size()).to(im_blob_ori.device) noise /= (noise ** 2).sum().sqrt() noise *= random.uniform(2, 8) im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data id_features, outputs, fail_ids = self.forwardFeatureMt( im_blob, img0, dets, inds, remain_inds, attack_ids, attack_inds, target_ids, target_inds, last_info, grad=False ) if fail_ids == 0: suc = True return noise, 1, suc def attack_sg( self, im_blob, img0, id_features, dets, inds, remain_inds, last_info, outputs_ori, attack_id, attack_ind, target_id, target_ind ): img0_h, img0_w = img0.shape[:2] H, W = outputs_ori['hm'].size()[2:] r_w, r_h = img0_w / W, img0_h / H r_max = max(r_w, r_h) noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori wh_ori = outputs['wh'].clone().data reg_ori = outputs['reg'].clone().data last_ad_id_features = [None for _ in range(len(id_features[0]))] strack_pool = copy.deepcopy(last_info['last_strack_pool']) last_attack_det = None last_target_det = None STrack.multi_predict(strack_pool) for strack in strack_pool: if strack.track_id == attack_id: last_ad_id_features[attack_ind] = strack.smooth_feat last_attack_det = torch.from_numpy(strack.tlbr).cuda().float() last_attack_det[[0, 2]] = (last_attack_det[[0, 2]] - 0.5 * W * (r_w - r_max)) / r_max last_attack_det[[1, 3]] = (last_attack_det[[1, 3]] - 0.5 * H * (r_h - r_max)) / r_max elif strack.track_id == target_id: last_ad_id_features[target_ind] = strack.smooth_feat last_target_det = torch.from_numpy(strack.tlbr).cuda().float() last_target_det[[0, 2]] = (last_target_det[[0, 2]] - 0.5 * W * (r_w - r_max)) / r_max last_target_det[[1, 3]] = (last_target_det[[1, 3]] - 0.5 * H * (r_h - r_max)) / r_max last_attack_det_center = torch.round( (last_attack_det[:2] + last_attack_det[2:]) / 2) if last_attack_det is not None else None last_target_det_center = torch.round( (last_target_det[:2] + last_target_det[2:]) / 2) if last_target_det is not None else None hm_index = inds[0][remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][[attack_ind, target_ind]] i = 0 j = -1 suc = True ori_hm_index = hm_index[[attack_ind, target_ind]].clone() ori_hm_index_re = hm_index[[target_ind, attack_ind]].clone() att_hm_index = None noise_0 = None i_0 = None noise_1 = None i_1 = None while True: i += 1 loss = 0 loss_feat = 0 for id_i, id_feature in enumerate(id_features): if last_ad_id_features[attack_ind] is not None: last_ad_id_feature = torch.from_numpy(last_ad_id_features[attack_ind]).unsqueeze(0).cuda() sim_1 = torch.mm(id_feature[0:0 + 1], last_ad_id_feature.T).squeeze() sim_2 = torch.mm(id_feature[1:1 + 1], last_ad_id_feature.T).squeeze() loss_feat += sim_2 - sim_1 if last_ad_id_features[target_ind] is not None: last_ad_id_feature = torch.from_numpy(last_ad_id_features[target_ind]).unsqueeze(0).cuda() sim_1 = torch.mm(id_feature[1:1 + 1], last_ad_id_feature.T).squeeze() sim_2 = torch.mm(id_feature[0:0 + 1], last_ad_id_feature.T).squeeze() loss_feat += sim_2 - sim_1 if last_ad_id_features[attack_ind] is None and last_ad_id_features[target_ind] is None: loss_feat += torch.mm(id_feature[0:0 + 1], id_feature[1:1 + 1].T).squeeze() loss += loss_feat / len(id_features) if i in [10, 20, 30, 35, 40, 45, 50, 55]: attack_det_center = torch.stack([hm_index[attack_ind] % W, hm_index[attack_ind] // W]).float() target_det_center = torch.stack([hm_index[target_ind] % W, hm_index[target_ind] // W]).float() if last_target_det_center is not None: attack_center_delta = attack_det_center - last_target_det_center if torch.max(torch.abs(attack_center_delta)) > 1: attack_center_delta /= torch.max(torch.abs(attack_center_delta)) attack_det_center = torch.round(attack_det_center - attack_center_delta).int() hm_index[attack_ind] = attack_det_center[0] + attack_det_center[1] * W if last_attack_det_center is not None: target_center_delta = target_det_center - last_attack_det_center if torch.max(torch.abs(target_center_delta)) > 1: target_center_delta /= torch.max(torch.abs(target_center_delta)) target_det_center = torch.round(target_det_center - target_center_delta).int() hm_index[target_ind] = target_det_center[0] + target_det_center[1] * W att_hm_index = hm_index[[attack_ind, target_ind]].clone() if att_hm_index is not None: n_att_hm_index = [] n_ori_hm_index_re = [] for hm_ind in range(len(att_hm_index)): for n_i in range(3): for n_j in range(3): att_hm_ind = att_hm_index[hm_ind].item() att_hm_ind = att_hm_ind + (n_i - 1) * W + (n_j - 1) att_hm_ind = max(0, min(H*W-1, att_hm_ind)) n_att_hm_index.append(att_hm_ind) ori_hm_ind = ori_hm_index_re[hm_ind].item() ori_hm_ind = ori_hm_ind + (n_i - 1) * W + (n_j - 1) ori_hm_ind = max(0, min(H * W - 1, ori_hm_ind)) n_ori_hm_index_re.append(ori_hm_ind) # print(n_att_hm_index, n_ori_hm_index_re) loss += ((1 - outputs['hm'].view(-1).sigmoid()[n_att_hm_index]) ** 2 * torch.log(outputs['hm'].view(-1).sigmoid()[n_att_hm_index])).mean() loss += ((outputs['hm'].view(-1).sigmoid()[n_ori_hm_index_re]) ** 2 * torch.log(1 - outputs['hm'].view(-1).sigmoid()[n_ori_hm_index_re])).mean() loss -= smoothL1(outputs['wh'].view(2, -1)[:, n_att_hm_index].T, wh_ori.view(2, -1)[:, n_ori_hm_index_re].T) loss -= smoothL1(outputs['reg'].view(2, -1)[:, n_att_hm_index].T, reg_ori.view(2, -1)[:, n_ori_hm_index_re].T) loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data id_features_, outputs_, ae_attack_id, ae_target_id, hm_index_ = self.forwardFeatureSg( im_blob, img0, dets, inds, remain_inds, attack_id, attack_ind, target_id, target_ind, last_info ) if id_features_ is not None: id_features = id_features_ if outputs_ is not None: outputs = outputs_ # if hm_index_ is not None: # hm_index = hm_index_ if ae_attack_id != attack_id and ae_attack_id is not None: break if i > 60: if noise_0 is not None: return noise_0, i_0, suc elif noise_1 is not None: return noise_1, i_1, suc if self.opt.no_f_noise: return None, i, False else: suc = False break return noise, i, suc def attack_mt( self, im_blob, img0, id_features, dets, inds, remain_inds, last_info, outputs_ori, attack_ids, attack_inds, target_ids, target_inds ): img0_h, img0_w = img0.shape[:2] H, W = outputs_ori['hm'].size()[2:] r_w, r_h = img0_w / W, img0_h / H r_max = max(r_w, r_h) noise = torch.zeros_like(im_blob) im_blob_ori = im_blob.clone().data outputs = outputs_ori wh_ori = outputs['wh'].clone().data reg_ori = outputs['reg'].clone().data i = 0 j = -1 last_ad_id_features = [None for _ in range(len(id_features[0]))] strack_pool = copy.deepcopy(last_info['last_strack_pool']) ad_attack_ids = [self.multiple_ori2att[attack_id] for attack_id in attack_ids] ad_target_ids = [self.multiple_ori2att[target_id] for target_id in target_ids] last_attack_dets = [None] * len(ad_attack_ids) last_target_dets = [None] * len(ad_target_ids) STrack.multi_predict(strack_pool) for strack in strack_pool: if strack.track_id in ad_attack_ids: index = ad_attack_ids.index(strack.track_id) last_ad_id_features[attack_inds[index]] = strack.smooth_feat last_attack_dets[index] = torch.from_numpy(strack.tlbr).cuda().float() last_attack_dets[index][[0, 2]] = (last_attack_dets[index][[0, 2]] - 0.5 * W * (r_w - r_max)) / r_max last_attack_dets[index][[1, 3]] = (last_attack_dets[index][[1, 3]] - 0.5 * H * (r_h - r_max)) / r_max if strack.track_id in ad_target_ids: index = ad_target_ids.index(strack.track_id) last_ad_id_features[target_inds[index]] = strack.smooth_feat last_target_dets[index] = torch.from_numpy(strack.tlbr).cuda().float() last_target_dets[index][[0, 2]] = (last_target_dets[index][[0, 2]] - 0.5 * W * (r_w - r_max)) / r_max last_target_dets[index][[1, 3]] = (last_target_dets[index][[1, 3]] - 0.5 * H * (r_h - r_max)) / r_max last_attack_dets_center = [] for det in last_attack_dets: if det is None: last_attack_dets_center.append(None) else: last_attack_dets_center.append((det[:2] + det[2:]) / 2) last_target_dets_center = [] for det in last_target_dets: if det is None: last_target_dets_center.append(None) else: last_target_dets_center.append((det[:2] + det[2:]) / 2) hm_index = inds[0][remain_inds] ori_hm_index_re_lst = [] for ind in range(len(attack_ids)): attack_ind = attack_inds[ind] target_ind = target_inds[ind] ori_hm_index_re_lst.append(hm_index[[target_ind, attack_ind]].clone()) att_hm_index_lst = [] best_i = None best_noise = None best_fail = np.inf while True: i += 1 loss = 0 loss_feat = 0 for index, attack_id in enumerate(attack_ids): target_id = target_ids[index] attack_ind = attack_inds[index] target_ind = target_inds[index] for id_i, id_feature in enumerate(id_features): if last_ad_id_features[attack_ind] is not None: last_ad_id_feature = torch.from_numpy(last_ad_id_features[attack_ind]).unsqueeze(0).cuda() sim_1 = torch.mm(id_feature[attack_ind:attack_ind + 1], last_ad_id_feature.T).squeeze() sim_2 = torch.mm(id_feature[target_ind:target_ind + 1], last_ad_id_feature.T).squeeze() if self.opt.hard_sample > 0: loss_feat += torch.clamp(sim_2 - sim_1, max=self.opt.hard_sample) else: loss_feat += sim_2 - sim_1 if last_ad_id_features[target_ind] is not None: last_ad_id_feature = torch.from_numpy(last_ad_id_features[target_ind]).unsqueeze(0).cuda() sim_1 = torch.mm(id_feature[target_ind:target_ind + 1], last_ad_id_feature.T).squeeze() sim_2 = torch.mm(id_feature[attack_ind:attack_ind + 1], last_ad_id_feature.T).squeeze() if self.opt.hard_sample > 0: loss_feat += torch.clamp(sim_2 - sim_1, max=self.opt.hard_sample) else: loss_feat += sim_2 - sim_1 if last_ad_id_features[attack_ind] is None and last_ad_id_features[target_ind] is None: loss_feat += torch.mm(id_feature[attack_ind:attack_ind + 1], id_feature[target_ind:target_ind + 1].T).squeeze() if i in [10, 20, 30, 35, 40, 45, 50, 55]: attack_det_center = torch.stack([hm_index[attack_ind] % W, hm_index[attack_ind] // W]).float() target_det_center = torch.stack([hm_index[target_ind] % W, hm_index[target_ind] // W]).float() if last_target_dets_center[index] is not None: attack_center_delta = attack_det_center - last_target_dets_center[index] if torch.max(torch.abs(attack_center_delta)) > 1: attack_center_delta /= torch.max(torch.abs(attack_center_delta)) attack_det_center = torch.round(attack_det_center - attack_center_delta).int() hm_index[attack_ind] = attack_det_center[0] + attack_det_center[1] * W if last_attack_dets_center[index] is not None: target_center_delta = target_det_center - last_attack_dets_center[index] if torch.max(torch.abs(target_center_delta)) > 1: target_center_delta /= torch.max(torch.abs(target_center_delta)) target_det_center = torch.round(target_det_center - target_center_delta).int() hm_index[target_ind] = target_det_center[0] + target_det_center[1] * W if index == 0: att_hm_index_lst = [] att_hm_index_lst.append(hm_index[[attack_ind, target_ind]].clone()) loss += loss_feat / len(id_features) if len(att_hm_index_lst): assert len(att_hm_index_lst) == len(ori_hm_index_re_lst) n_att_hm_index_lst = [] n_ori_hm_index_re_lst = [] for lst_ind in range(len(att_hm_index_lst)): for hm_ind in range(len(att_hm_index_lst[lst_ind])): for n_i in range(3): for n_j in range(3): att_hm_ind = att_hm_index_lst[lst_ind][hm_ind].item() att_hm_ind = att_hm_ind + (n_i - 1) * W + (n_j - 1) att_hm_ind = max(0, min(H*W-1, att_hm_ind)) n_att_hm_index_lst.append(att_hm_ind) ori_hm_ind = ori_hm_index_re_lst[lst_ind][hm_ind].item() ori_hm_ind = ori_hm_ind + (n_i - 1) * W + (n_j - 1) ori_hm_ind = max(0, min(H * W - 1, ori_hm_ind)) n_ori_hm_index_re_lst.append(ori_hm_ind) # print(n_att_hm_index, n_ori_hm_index_re) loss += ((1 - outputs['hm'].view(-1).sigmoid()[n_att_hm_index_lst]) ** 2 * torch.log(outputs['hm'].view(-1).sigmoid()[n_att_hm_index_lst])).mean() loss += ((outputs['hm'].view(-1).sigmoid()[n_ori_hm_index_re_lst]) ** 2 * torch.log(1 - outputs['hm'].view(-1).sigmoid()[n_ori_hm_index_re_lst])).mean() loss -= smoothL1(outputs['wh'].view(2, -1)[:, n_att_hm_index_lst].T, wh_ori.view(2, -1)[:, n_ori_hm_index_re_lst].T) loss -= smoothL1(outputs['reg'].view(2, -1)[:, n_att_hm_index_lst].T, reg_ori.view(2, -1)[:, n_ori_hm_index_re_lst].T) loss.backward() grad = im_blob.grad grad /= (grad ** 2).sum().sqrt() + 1e-8 noise += grad im_blob = torch.clip(im_blob_ori + noise, min=0, max=1).data id_features, outputs, fail_ids = self.forwardFeatureMt( im_blob, img0, dets, inds, remain_inds, attack_ids, attack_inds, target_ids, target_inds, last_info ) if fail_ids is not None: if fail_ids == 0: break elif fail_ids <= best_fail: best_fail = fail_ids best_i = i best_noise = noise.clone() if i > 60: if self.opt.no_f_noise: return None, i, False else: if best_i is not None: noise = best_noise i = best_i return noise, i, False return noise, i, True def forwardFeatureDet(self, im_blob, img0, dets_, attack_inds, thr=0, vs=[]): width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] ious = bbox_ious(np.ascontiguousarray(dets_[:, :4], dtype=np.float), np.ascontiguousarray(dets[:, :4], dtype=np.float)) row_inds, col_inds = linear_sum_assignment(-ious) if not isinstance(thr, list): thr = [thr for _ in range(len(attack_inds))] fail_n = 0 for i in range(len(row_inds)): if row_inds[i] in attack_inds: if ious[row_inds[i], col_inds[i]] > thr[attack_inds.index(row_inds[i])]: fail_n += 1 elif len(vs): d_o = dets_[row_inds[i], :4] d_a = dets[col_inds[i], :4] c_o = (d_o[[0, 1]] + d_o[[2, 3]]) / 2 c_a = (d_a[[0, 1]] + d_a[[2, 3]]) / 2 c_d = ((c_a - c_o) / 4).astype(np.int) * vs[0] if c_d[0] >= 0 or c_d[1] >= 0: fail_n += 1 return output, fail_n == 0, fail_n def forwardFeatureSg(self, im_blob, img0, dets_, inds_, remain_inds_, attack_id, attack_ind, target_id, target_ind, last_info, grad=True): width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} im_blob.requires_grad = True self.model.zero_grad() if grad: output = self.model(im_blob)[-1] else: with torch.no_grad(): output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] if target_ind is None: ious = bbox_ious(np.ascontiguousarray(dets_[[attack_ind], :4], dtype=np.float), np.ascontiguousarray(dets[:, :4], dtype=np.float)) else: ious = bbox_ious(np.ascontiguousarray(dets_[[attack_ind, target_ind], :4], dtype=np.float), np.ascontiguousarray(dets[:, :4], dtype=np.float)) # det_ind = np.argmax(ious, axis=1) row_inds, col_inds = linear_sum_assignment(-ious) match = True if target_ind is None: if ious[row_inds[0], col_inds[0]] < 0.8: dets = dets_ inds = inds_ remain_inds = remain_inds_ match = False else: if len(col_inds) < 2 or ious[row_inds[0], col_inds[0]] < 0.6 or ious[row_inds[1], col_inds[1]] < 0.6: dets = dets_ inds = inds_ remain_inds = remain_inds_ match = False # assert match id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] ae_attack_id = None ae_target_id = None if not match: for i in range(len(id_features)): if target_ind is not None: id_features[i] = id_features[i][[attack_ind, target_ind]] else: id_features[i] = id_features[i][[attack_ind]] return id_features, output, ae_attack_id, ae_target_id, None if row_inds[0] == 0: ae_attack_ind = col_inds[0] ae_target_ind = col_inds[1] if target_ind is not None else None else: ae_attack_ind = col_inds[1] ae_target_ind = col_inds[0] if target_ind is not None else None # ae_attack_ind = det_ind[0] # ae_target_ind = det_ind[1] if target_ind is not None else None hm_index = None # if target_ind is not None: # hm_index[[attack_ind, target_ind]] = hm_index[[ae_attack_ind, ae_target_ind]] id_features_ = [None for _ in range(len(id_features))] for i in range(len(id_features)): if target_ind is None: id_features_[i] = id_features[i][[ae_attack_ind]] else: try: id_features_[i] = id_features[i][[ae_attack_ind, ae_target_ind]] except: import pdb; pdb.set_trace() id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) id_feature = id_feature[remain_inds] id_feature = id_feature.detach().cpu().numpy() if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] unconfirmed = copy.deepcopy(last_info['last_unconfirmed']) strack_pool = copy.deepcopy(last_info['last_strack_pool']) kalman_filter = copy.deepcopy(last_info['kalman_filter']) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(kalman_filter, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] if idet == ae_attack_ind: ae_attack_id = track.track_id elif idet == ae_target_ind: ae_target_id = track.track_id # if ae_attack_id is not None and ae_target_id is not None: # return id_features_, output, ae_attack_id, ae_target_id ''' Step 3: Second association, with IOU''' for i, idet in enumerate(u_detection): if idet == ae_attack_ind: ae_attack_ind = i elif idet == ae_target_ind: ae_target_ind = i detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] if idet == ae_attack_ind: ae_attack_id = track.track_id elif idet == ae_target_ind: ae_target_id = track.track_id # if ae_attack_id is not None and ae_target_id is not None: # return id_features_, output, ae_attack_id, ae_target_id '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' for i, idet in enumerate(u_detection): if idet == ae_attack_ind: ae_attack_ind = i elif idet == ae_target_ind: ae_target_ind = i detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = unconfirmed[itracked] if idet == ae_attack_ind: ae_attack_id = track.track_id elif idet == ae_target_ind: ae_target_id = track.track_id return id_features_, output, ae_attack_id, ae_target_id, hm_index def forwardFeatureMt(self, im_blob, img0, dets_, inds_, remain_inds_, attack_ids, attack_inds, target_ids, target_inds, last_info, grad=True): width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} im_blob.requires_grad = True self.model.zero_grad() if grad: output = self.model(im_blob)[-1] else: with torch.no_grad(): output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] dets_index = [i for i in range(len(dets))] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] ious = bbox_ious(np.ascontiguousarray(dets_[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) row_inds, col_inds = linear_sum_assignment(-ious) match = True if target_inds is not None: for index, attack_ind in enumerate(attack_inds): target_ind = target_inds[index] if attack_ind not in row_inds or target_ind not in row_inds: match = False break att_index = row_inds.tolist().index(attack_ind) tar_index = row_inds.tolist().index(target_ind) if ious[attack_ind, col_inds[att_index]] < 0.6 or ious[target_ind, col_inds[tar_index]] < 0.6: match = False break else: for index, attack_ind in enumerate(attack_inds): if attack_ind not in row_inds: match = False break att_index = row_inds.tolist().index(attack_ind) if ious[attack_ind, col_inds[att_index]] < 0.8: match = False break if not match: dets = dets_ inds = inds_ remain_inds = remain_inds_ # assert match id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] fail_ids = 0 if not match: return id_features, output, None ae_attack_inds = [] ae_attack_ids = [] for i in range(len(row_inds)): if ious[row_inds[i], col_inds[i]] > 0.6: if row_inds[i] in attack_inds: ae_attack_inds.append(col_inds[i]) index = attack_inds.tolist().index(row_inds[i]) ae_attack_ids.append(self.multiple_ori2att[attack_ids[index]]) # ae_attack_inds = [col_inds[row_inds == attack_ind] for attack_ind in attack_inds] # ae_attack_inds = np.concatenate(ae_attack_inds) id_features_ = [torch.zeros([len(dets_), id_features[0].size(1)]).to(id_features[0].device) for _ in range(len(id_features))] for i in range(9): id_features_[i][row_inds] = id_features[i][col_inds] id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) id_feature = id_feature[remain_inds] id_feature = id_feature.detach().cpu().numpy() if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] unconfirmed = copy.deepcopy(last_info['last_unconfirmed']) strack_pool = copy.deepcopy(last_info['last_strack_pool']) kalman_filter = copy.deepcopy(last_info['kalman_filter']) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(kalman_filter, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] if dets_index[idet] in ae_attack_inds: index = ae_attack_inds.index(dets_index[idet]) if track.track_id == ae_attack_ids[index]: fail_ids += 1 ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] if dets_index[idet] in ae_attack_inds: index = ae_attack_inds.index(dets_index[idet]) if track.track_id == ae_attack_ids[index]: fail_ids += 1 '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = unconfirmed[itracked] if dets_index[idet] in ae_attack_inds: index = ae_attack_inds.index(dets_index[idet]) if track.track_id == ae_attack_ids[index]: fail_ids += 1 return id_features_, output, fail_ids def CheckFit(self, dets, id_feature, attack_ids, attack_inds): ad_attack_ids_ = [self.multiple_ori2att[attack_id] for attack_id in attack_ids] \ if self.opt.attack == 'multiple' else attack_ids attack_dets = dets[attack_inds, :4] ad_attack_dets = [] ad_attack_ids = [] if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] unconfirmed = copy.deepcopy(self.ad_last_info['last_unconfirmed']) strack_pool = copy.deepcopy(self.ad_last_info['last_strack_pool']) kalman_filter = copy.deepcopy(self.ad_last_info['kalman_filter']) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(kalman_filter, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] if track.track_id in ad_attack_ids_: ad_attack_dets.append(det.tlbr) ad_attack_ids.append(track.track_id) ''' Step 3: Second association, with IOU''' detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] if track.track_id in ad_attack_ids_: ad_attack_dets.append(det.tlbr) ad_attack_ids.append(track.track_id) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = unconfirmed[itracked] det = detections[idet] if track.track_id in ad_attack_ids_: ad_attack_dets.append(det.tlbr) ad_attack_ids.append(track.track_id) if len(ad_attack_dets) == 0: return [] ori_dets = np.array(attack_dets) ad_dets = np.array(ad_attack_dets) ious = bbox_ious(ori_dets.astype(np.float64), ad_dets.astype(np.float64)) row_ind, col_ind = linear_sum_assignment(-ious) attack_index = [] for i in range(len(row_ind)): if self.opt.attack == 'multiple': if ious[row_ind[i], col_ind[i]] > 0.9 and self.multiple_ori2att[attack_ids[row_ind[i]]] == ad_attack_ids[col_ind[i]]: attack_index.append(row_ind[i]) else: if ious[row_ind[i], col_ind[i]] > 0.9: attack_index.append(row_ind[i]) return attack_index def update_attack_sg(self, im_blob, img0, **kwargs): self.frame_id_ += 1 attack_id = kwargs['attack_id'] self_track_id_ori = kwargs.get('track_id', {}).get('origin', None) self_track_id_att = kwargs.get('track_id', {}).get('attack', None) activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) # dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_, track_id=self_track_id_ori) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) noise = None suc = 0 for attack_ind, track_id in enumerate(dets_ids): if track_id == attack_id: if self.opt.attack_id > 0: if not hasattr(self, f'frames_{attack_id}'): setattr(self, f'frames_{attack_id}', 0) if getattr(self, f'frames_{attack_id}') < self.FRAME_THR: setattr(self, f'frames_{attack_id}', getattr(self, f'frames_{attack_id}') + 1) break fit = self.CheckFit(dets, id_feature, [attack_id], [attack_ind]) ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious[range(len(dets)), range(len(dets))] = 0 dis = bbox_dis(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) dis[range(len(dets)), range(len(dets))] = np.inf target_ind = np.argmax(ious[attack_ind]) if ious[attack_ind][target_ind] >= self.attack_iou_thr: if ious[attack_ind][target_ind] == 0: target_ind = np.argmin(dis[attack_ind]) target_id = dets_ids[target_ind] if fit: if self.opt.rand: noise, attack_iter, suc = self.attack_sg_random( im_blob, img0, id_features, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_id=attack_id, attack_ind=attack_ind, target_id=target_id, target_ind=target_ind ) else: noise, attack_iter, suc = self.attack_sg( im_blob, img0, id_features, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_id=attack_id, attack_ind=attack_ind, target_id=target_id, target_ind=target_ind ) self.attack_iou_thr = 0 if suc: suc = 1 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 2 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item() if noise is not None else None}\titeration: {attack_iter}') else: suc = 3 if ious[attack_ind][target_ind] == 0: self.temp_i += 1 if self.temp_i >= 10: self.attack_iou_thr = self.ATTACK_IOU_THR else: self.temp_i = 0 else: self.attack_iou_thr = self.ATTACK_IOU_THR if fit: suc = 2 if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) # adImg = np.clip(img0 + noise, a_min=0, a_max=255) # noise = adImg - img0 noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0, track_id=self_track_id_att) adImg = self.recoverNoise(adImg.detach(), img0) return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis, suc def update_attack_mt(self, im_blob, img0, **kwargs): self.frame_id_ += 1 activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] id_set = set([track.track_id for track in output_stracks_ori]) for i in range(len(dets_ids)): if dets_ids[i] is not None and dets_ids[i] not in id_set: dets_ids[i] = None output_stracks_ori_ind = [] for ind, track in enumerate(output_stracks_ori): if track.track_id not in self.multiple_ori_ids: self.multiple_ori_ids[track.track_id] = 0 self.multiple_ori_ids[track.track_id] += 1 if self.multiple_ori_ids[track.track_id] <= self.FRAME_THR: output_stracks_ori_ind.append(ind) logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) attack_ids = [] target_ids = [] attack_inds = [] target_inds = [] noise = None if len(dets) > 0: ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious[range(len(dets)), range(len(dets))] = 0 ious_inds = np.argmax(ious, axis=1) dis = bbox_dis(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) dis[range(len(dets)), range(len(dets))] = np.inf dis_inds = np.argmin(dis, axis=1) for attack_ind, track_id in enumerate(dets_ids): if track_id is None or self.multiple_ori_ids[track_id] <= self.FRAME_THR \ or dets_ids[ious_inds[attack_ind]] not in self.multiple_ori2att \ or track_id not in self.multiple_ori2att: continue if ious[attack_ind, ious_inds[attack_ind]] > self.ATTACK_IOU_THR or ( track_id in self.low_iou_ids and ious[attack_ind, ious_inds[attack_ind]] > 0 ): attack_ids.append(track_id) target_ids.append(dets_ids[ious_inds[attack_ind]]) attack_inds.append(attack_ind) target_inds.append(ious_inds[attack_ind]) if hasattr(self, f'temp_i_{track_id}'): self.__setattr__(f'temp_i_{track_id}', 0) elif ious[attack_ind, ious_inds[attack_ind]] == 0 and track_id in self.low_iou_ids: if hasattr(self, f'temp_i_{track_id}'): self.__setattr__(f'temp_i_{track_id}', self.__getattribute__(f'temp_i_{track_id}') + 1) else: self.__setattr__(f'temp_i_{track_id}', 1) if self.__getattribute__(f'temp_i_{track_id}') > 10: self.low_iou_ids.remove(track_id) elif dets_ids[dis_inds[attack_ind]] in self.multiple_ori2att: attack_ids.append(track_id) target_ids.append(dets_ids[dis_inds[attack_ind]]) attack_inds.append(attack_ind) target_inds.append(dis_inds[attack_ind]) fit_index = self.CheckFit(dets, id_feature, attack_ids, attack_inds) if len(attack_ids) else [] if fit_index: attack_ids = np.array(attack_ids)[fit_index] target_ids = np.array(target_ids)[fit_index] attack_inds = np.array(attack_inds)[fit_index] target_inds = np.array(target_inds)[fit_index] if self.opt.rand: noise, attack_iter, suc = self.attack_mt_random( im_blob, img0, id_features, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_ids=attack_ids, attack_inds=attack_inds, target_ids=target_ids, target_inds=target_inds ) else: noise, attack_iter, suc = self.attack_mt( im_blob, img0, id_features, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_ids=attack_ids, attack_inds=attack_inds, target_ids=target_ids, target_inds=target_inds ) self.low_iou_ids.update(set(attack_ids)) if suc: self.attacked_ids.update(set(attack_ids)) print( f'attack ids: {attack_ids}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: print(f'attack ids: {attack_ids}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item() if noise is not None else None}\titeration: {attack_iter}') if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0) adImg = self.recoverNoise(adImg.detach(), img0) output_stracks_att_ind = [] for ind, track in enumerate(output_stracks_att): if track.track_id not in self.multiple_att_ids: self.multiple_att_ids[track.track_id] = 0 self.multiple_att_ids[track.track_id] += 1 if self.multiple_att_ids[track.track_id] <= self.FRAME_THR: output_stracks_att_ind.append(ind) if len(output_stracks_ori_ind) and len(output_stracks_att_ind): ori_dets = [track.curr_tlbr for i, track in enumerate(output_stracks_ori) if i in output_stracks_ori_ind] att_dets = [track.curr_tlbr for i, track in enumerate(output_stracks_att) if i in output_stracks_att_ind] ori_dets = np.stack(ori_dets).astype(np.float64) att_dets = np.stack(att_dets).astype(np.float64) ious = bbox_ious(ori_dets, att_dets) row_ind, col_ind = linear_sum_assignment(-ious) for i in range(len(row_ind)): if ious[row_ind[i], col_ind[i]] > 0.9: ori_id = output_stracks_ori[output_stracks_ori_ind[row_ind[i]]].track_id att_id = output_stracks_att[output_stracks_att_ind[col_ind[i]]].track_id self.multiple_ori2att[ori_id] = att_id return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis def update_attack_sg_feat(self, im_blob, img0, **kwargs): self.frame_id_ += 1 attack_id = kwargs['attack_id'] self_track_id_ori = kwargs.get('track_id', {}).get('origin', None) self_track_id_att = kwargs.get('track_id', {}).get('attack', None) activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) # dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_, track_id=self_track_id_ori) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) noise = None suc = 0 for attack_ind, track_id in enumerate(dets_ids): if track_id == attack_id: if self.opt.attack_id > 0: if not hasattr(self, f'frames_{attack_id}'): setattr(self, f'frames_{attack_id}', 0) if getattr(self, f'frames_{attack_id}') < self.FRAME_THR: setattr(self, f'frames_{attack_id}', getattr(self, f'frames_{attack_id}') + 1) break fit = self.CheckFit(dets, id_feature, [attack_id], [attack_ind]) ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious[range(len(dets)), range(len(dets))] = 0 dis = bbox_dis(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) dis[range(len(dets)), range(len(dets))] = np.inf target_ind = np.argmax(ious[attack_ind]) if ious[attack_ind][target_ind] >= self.attack_iou_thr: if ious[attack_ind][target_ind] == 0: target_ind = np.argmin(dis[attack_ind]) target_id = dets_ids[target_ind] if fit: noise, attack_iter, suc = self.attack_sg_feat( im_blob, img0, id_features, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_id=attack_id, attack_ind=attack_ind, target_id=target_id, target_ind=target_ind ) self.attack_iou_thr = 0 if suc: suc = 1 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 2 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 3 if ious[attack_ind][target_ind] == 0: self.temp_i += 1 if self.temp_i >= 10: self.attack_iou_thr = self.ATTACK_IOU_THR else: self.temp_i = 0 else: self.attack_iou_thr = self.ATTACK_IOU_THR if fit: suc = 2 if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0, track_id=self_track_id_att) adImg = self.recoverNoise(adImg.detach(), img0) return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis, suc def update_attack_sg_cl(self, im_blob, img0, **kwargs): self.frame_id_ += 1 attack_id = kwargs['attack_id'] self_track_id_ori = kwargs.get('track_id', {}).get('origin', None) self_track_id_att = kwargs.get('track_id', {}).get('attack', None) activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) # dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_, track_id=self_track_id_ori) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) noise = None suc = 0 for attack_ind, track_id in enumerate(dets_ids): if track_id == attack_id: if self.opt.attack_id > 0: if not hasattr(self, f'frames_{attack_id}'): setattr(self, f'frames_{attack_id}', 0) if getattr(self, f'frames_{attack_id}') < self.FRAME_THR: setattr(self, f'frames_{attack_id}', getattr(self, f'frames_{attack_id}') + 1) break fit = self.CheckFit(dets, id_feature, [attack_id], [attack_ind]) ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious[range(len(dets)), range(len(dets))] = 0 dis = bbox_dis(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) dis[range(len(dets)), range(len(dets))] = np.inf target_ind = np.argmax(ious[attack_ind]) if ious[attack_ind][target_ind] >= self.attack_iou_thr: if ious[attack_ind][target_ind] == 0: target_ind = np.argmin(dis[attack_ind]) target_id = dets_ids[target_ind] if fit: noise, attack_iter, suc = self.attack_sg_cl( im_blob, img0, id_features, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_id=attack_id, attack_ind=attack_ind, target_id=target_id, target_ind=target_ind ) self.attack_iou_thr = 0 if suc: suc = 1 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 2 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item() if noise is not None else None}\titeration: {attack_iter}') else: suc = 3 if ious[attack_ind][target_ind] == 0: self.temp_i += 1 if self.temp_i >= 10: self.attack_iou_thr = self.ATTACK_IOU_THR else: self.temp_i = 0 else: self.attack_iou_thr = self.ATTACK_IOU_THR if fit: suc = 2 if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) # adImg = np.clip(img0 + noise, a_min=0, a_max=255) # noise = adImg - img0 noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0, track_id=self_track_id_att) adImg = self.recoverNoise(adImg.detach(), img0) return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis, suc def update_attack_sg_det(self, im_blob, img0, **kwargs): self.frame_id_ += 1 attack_id = kwargs['attack_id'] self_track_id_ori = kwargs.get('track_id', {}).get('origin', None) self_track_id_att = kwargs.get('track_id', {}).get('attack', None) activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) # dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_, track_id=self_track_id_ori) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) noise = None suc = 0 for attack_ind, track_id in enumerate(dets_ids): if track_id == attack_id: if self.opt.attack_id > 0: if not hasattr(self, f'frames_{attack_id}'): setattr(self, f'frames_{attack_id}', 0) if getattr(self, f'frames_{attack_id}') < self.FRAME_THR: setattr(self, f'frames_{attack_id}', getattr(self, f'frames_{attack_id}') + 1) break ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious = self.processIoUs(ious) ious = ious + ious.T target_ind = np.argmax(ious[attack_ind]) if ious[attack_ind][target_ind] >= self.attack_iou_thr: fit = self.CheckFit(dets, id_feature, [attack_id], [attack_ind]) if fit: noise, attack_iter, suc = self.attack_sg_det( im_blob, img0, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_id=attack_id, attack_ind=attack_ind ) self.attack_iou_thr = 0 if suc: suc = 1 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 2 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 3 if ious[attack_ind][target_ind] == 0: self.temp_i += 1 if self.temp_i >= 10: self.attack_iou_thr = self.ATTACK_IOU_THR else: self.temp_i = 0 else: self.attack_iou_thr = self.ATTACK_IOU_THR break if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0, track_id=self_track_id_att) adImg = self.recoverNoise(adImg.detach(), img0) return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis, suc def update_attack_sg_hj(self, im_blob, img0, **kwargs): self.frame_id_ += 1 attack_id = kwargs['attack_id'] self_track_id_ori = kwargs.get('track_id', {}).get('origin', None) self_track_id_att = kwargs.get('track_id', {}).get('attack', None) activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) # dists = matching.gate_cost_matrix(self.kalman_filter, dists, strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_, track_id=self_track_id_ori) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) noise = None suc = 0 att_tracker = None if self.ad_bbox: for t in output_stracks_ori: if t.track_id == attack_id: att_tracker = t for attack_ind, track_id in enumerate(dets_ids): if track_id == attack_id: if self.opt.attack_id > 0: if not hasattr(self, f'frames_{attack_id}'): setattr(self, f'frames_{attack_id}', 0) if getattr(self, f'frames_{attack_id}') < self.FRAME_THR: setattr(self, f'frames_{attack_id}', getattr(self, f'frames_{attack_id}') + 1) break ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious = self.processIoUs(ious) ious = ious + ious.T target_ind = np.argmax(ious[attack_ind]) if ious[attack_ind][target_ind] >= self.attack_iou_thr: fit = self.CheckFit(dets, id_feature, [attack_id], [attack_ind]) if fit: noise, attack_iter, suc = self.attack_sg_hj( im_blob, img0, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_id=attack_id, attack_ind=attack_ind, ad_bbox=self.ad_bbox, track_v=att_tracker.get_v() if att_tracker is not None else None ) self.attack_iou_thr = 0 if suc: suc = 1 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 2 print( f'attack id: {attack_id}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: suc = 3 if ious[attack_ind][target_ind] == 0: self.temp_i += 1 if self.temp_i >= 10: self.attack_iou_thr = self.ATTACK_IOU_THR else: self.temp_i = 0 else: self.attack_iou_thr = self.ATTACK_IOU_THR break if noise is not None: self.ad_bbox = False l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0, track_id=self_track_id_att) adImg = self.recoverNoise(adImg.detach(), img0) return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis, suc def update_attack_mt_det(self, im_blob, img0, **kwargs): self.frame_id_ += 1 activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] id_set = set([track.track_id for track in output_stracks_ori]) for i in range(len(dets_ids)): if dets_ids[i] is not None and dets_ids[i] not in id_set: dets_ids[i] = None output_stracks_ori_ind = [] for ind, track in enumerate(output_stracks_ori): if track.track_id not in self.multiple_ori_ids: self.multiple_ori_ids[track.track_id] = 0 self.multiple_ori_ids[track.track_id] += 1 if self.multiple_ori_ids[track.track_id] <= self.FRAME_THR: output_stracks_ori_ind.append(ind) logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) attack_ids = [] target_ids = [] attack_inds = [] target_inds = [] noise = None if len(dets) > 0: ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious[range(len(dets)), range(len(dets))] = 0 ious_inds = np.argmax(ious, axis=1) dis = bbox_dis(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) dis[range(len(dets)), range(len(dets))] = np.inf dis_inds = np.argmin(dis, axis=1) for attack_ind, track_id in enumerate(dets_ids): if track_id is None or self.multiple_ori_ids[track_id] <= self.FRAME_THR \ or dets_ids[ious_inds[attack_ind]] not in self.multiple_ori2att \ or track_id not in self.multiple_ori2att: continue if ious[attack_ind, ious_inds[attack_ind]] > self.ATTACK_IOU_THR or ( track_id in self.low_iou_ids and ious[attack_ind, ious_inds[attack_ind]] > 0 ): attack_ids.append(track_id) target_ids.append(dets_ids[ious_inds[attack_ind]]) attack_inds.append(attack_ind) target_inds.append(ious_inds[attack_ind]) if hasattr(self, f'temp_i_{track_id}'): self.__setattr__(f'temp_i_{track_id}', 0) elif ious[attack_ind, ious_inds[attack_ind]] == 0 and track_id in self.low_iou_ids: if hasattr(self, f'temp_i_{track_id}'): self.__setattr__(f'temp_i_{track_id}', self.__getattribute__(f'temp_i_{track_id}') + 1) else: self.__setattr__(f'temp_i_{track_id}', 1) if self.__getattribute__(f'temp_i_{track_id}') > 10: self.low_iou_ids.remove(track_id) elif dets_ids[dis_inds[attack_ind]] in self.multiple_ori2att: attack_ids.append(track_id) target_ids.append(dets_ids[dis_inds[attack_ind]]) attack_inds.append(attack_ind) target_inds.append(dis_inds[attack_ind]) fit_index = self.CheckFit(dets, id_feature, attack_ids, attack_inds) if len(attack_ids) else [] if fit_index: attack_ids = np.array(attack_ids)[fit_index] target_ids = np.array(target_ids)[fit_index] attack_inds = np.array(attack_inds)[fit_index] target_inds = np.array(target_inds)[fit_index] noise, attack_iter, suc = self.attack_mt_det( im_blob, img0, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_ids=attack_ids, attack_inds=attack_inds ) self.low_iou_ids.update(set(attack_ids)) if suc: self.attacked_ids.update(set(attack_ids)) print( f'attack ids: {attack_ids}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: print(f'attack ids: {attack_ids}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item() if noise is not None else None}\titeration: {attack_iter}') if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0) adImg = self.recoverNoise(adImg.detach(), img0) output_stracks_att_ind = [] for ind, track in enumerate(output_stracks_att): if track.track_id not in self.multiple_att_ids: self.multiple_att_ids[track.track_id] = 0 self.multiple_att_ids[track.track_id] += 1 if self.multiple_att_ids[track.track_id] <= self.FRAME_THR: output_stracks_att_ind.append(ind) if len(output_stracks_ori_ind) and len(output_stracks_att_ind): ori_dets = [track.curr_tlbr for i, track in enumerate(output_stracks_ori) if i in output_stracks_ori_ind] att_dets = [track.curr_tlbr for i, track in enumerate(output_stracks_att) if i in output_stracks_att_ind] ori_dets = np.stack(ori_dets).astype(np.float64) att_dets = np.stack(att_dets).astype(np.float64) ious = bbox_ious(ori_dets, att_dets) row_ind, col_ind = linear_sum_assignment(-ious) for i in range(len(row_ind)): if ious[row_ind[i], col_ind[i]] > 0.9: ori_id = output_stracks_ori[output_stracks_ori_ind[row_ind[i]]].track_id att_id = output_stracks_att[output_stracks_att_ind[col_ind[i]]].track_id self.multiple_ori2att[ori_id] = att_id return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis def update_attack_mt_hj(self, im_blob, img0, **kwargs): self.frame_id_ += 1 activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' # with torch.no_grad(): im_blob.requires_grad = True self.model.zero_grad() output = self.model(im_blob)[-1] hm = output['hm'].sigmoid() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets_raw, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_features = [] for i in range(3): for j in range(3): id_feature_exp = _tranpose_and_gather_feat_expand(id_feature, inds, bias=(i - 1, j - 1)).squeeze(0) id_features.append(id_feature_exp) id_feature = _tranpose_and_gather_feat_expand(id_feature, inds) id_feature = id_feature.squeeze(0) dets = self.post_process(dets_raw.clone(), meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] for i in range(len(id_features)): id_features[i] = id_features[i][remain_inds] id_feature = id_feature.detach().cpu().numpy() last_id_features = [None for _ in range(len(dets))] last_ad_id_features = [None for _ in range(len(dets))] dets_index = [i for i in range(len(dets))] dets_ids = [None for _ in range(len(dets))] tracks_ad = [] # import pdb; pdb.set_trace() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks_: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks_) STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter_, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) # import pdb; pdb.set_trace() for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = track.smooth_feat last_ad_id_features[dets_index[idet]] = track.smooth_feat_ad tracks_ad.append((track, dets_index[idet])) if track.state == TrackState.Tracked: track.update(det, self.frame_id_) activated_starcks.append(track) else: track.re_activate_(det, self.frame_id_, new_id=False) refind_stracks.append(track) dets_ids[dets_index[idet]] = track.track_id for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: assert last_id_features[dets_index[idet]] is None assert last_ad_id_features[dets_index[idet]] is None last_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat last_ad_id_features[dets_index[idet]] = unconfirmed[itracked].smooth_feat_ad tracks_ad.append((unconfirmed[itracked], dets_index[idet])) unconfirmed[itracked].update(detections[idet], self.frame_id_) activated_starcks.append(unconfirmed[itracked]) dets_ids[dets_index[idet]] = unconfirmed[itracked].track_id for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate_(self.kalman_filter_, self.frame_id_) activated_starcks.append(track) dets_ids[dets_index[inew]] = track.track_id """ Step 5: Update state""" for track in self.lost_stracks_: if self.frame_id_ - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks_ = [t for t in self.tracked_stracks_ if t.state == TrackState.Tracked] self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, activated_starcks) self.tracked_stracks_ = joint_stracks(self.tracked_stracks_, refind_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.tracked_stracks_) self.lost_stracks_.extend(lost_stracks) self.lost_stracks_ = sub_stracks(self.lost_stracks_, self.removed_stracks_) self.removed_stracks_.extend(removed_stracks) self.tracked_stracks_, self.lost_stracks_ = remove_duplicate_stracks(self.tracked_stracks_, self.lost_stracks_) # get scores of lost tracks output_stracks_ori = [track for track in self.tracked_stracks_ if track.is_activated] id_set = set([track.track_id for track in output_stracks_ori]) for i in range(len(dets_ids)): if dets_ids[i] is not None and dets_ids[i] not in id_set: dets_ids[i] = None output_stracks_ori_ind = [] for ind, track in enumerate(output_stracks_ori): if track.track_id not in self.multiple_ori_ids: self.multiple_ori_ids[track.track_id] = 0 self.multiple_ori_ids[track.track_id] += 1 if self.multiple_ori_ids[track.track_id] <= self.FRAME_THR: output_stracks_ori_ind.append(ind) logger.debug('===========Frame {}=========='.format(self.frame_id_)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) attack_ids = [] target_ids = [] attack_inds = [] target_inds = [] noise = None if len(dets) > 0: ious = bbox_ious(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) ious[range(len(dets)), range(len(dets))] = 0 ious_inds = np.argmax(ious, axis=1) dis = bbox_dis(np.ascontiguousarray(dets[:, :4], dtype=np.float64), np.ascontiguousarray(dets[:, :4], dtype=np.float64)) dis[range(len(dets)), range(len(dets))] = np.inf dis_inds = np.argmin(dis, axis=1) for attack_ind, track_id in enumerate(dets_ids): if track_id is None or self.multiple_ori_ids[track_id] <= self.FRAME_THR \ or dets_ids[ious_inds[attack_ind]] not in self.multiple_ori2att \ or track_id not in self.multiple_ori2att: continue if ious[attack_ind, ious_inds[attack_ind]] > self.ATTACK_IOU_THR or ( track_id in self.low_iou_ids and ious[attack_ind, ious_inds[attack_ind]] > 0 ): attack_ids.append(track_id) target_ids.append(dets_ids[ious_inds[attack_ind]]) attack_inds.append(attack_ind) target_inds.append(ious_inds[attack_ind]) if hasattr(self, f'temp_i_{track_id}'): self.__setattr__(f'temp_i_{track_id}', 0) elif ious[attack_ind, ious_inds[attack_ind]] == 0 and track_id in self.low_iou_ids: if hasattr(self, f'temp_i_{track_id}'): self.__setattr__(f'temp_i_{track_id}', self.__getattribute__(f'temp_i_{track_id}') + 1) else: self.__setattr__(f'temp_i_{track_id}', 1) if self.__getattribute__(f'temp_i_{track_id}') > 10: self.low_iou_ids.remove(track_id) elif dets_ids[dis_inds[attack_ind]] in self.multiple_ori2att: attack_ids.append(track_id) target_ids.append(dets_ids[dis_inds[attack_ind]]) attack_inds.append(attack_ind) target_inds.append(dis_inds[attack_ind]) fit_index = self.CheckFit(dets, id_feature, attack_ids, attack_inds) if len(attack_ids) else [] if fit_index: attack_ids = np.array(attack_ids)[fit_index] target_ids = np.array(target_ids)[fit_index] attack_inds = np.array(attack_inds)[fit_index] target_inds = np.array(target_inds)[fit_index] att_trackers = [] for attack_id in attack_ids: if attack_id not in self.ad_ids: for t in output_stracks_ori: if t.track_id == attack_id: att_trackers.append(t) noise, attack_iter, suc = self.attack_mt_hj( im_blob, img0, dets, inds, remain_inds, last_info=self.ad_last_info, outputs_ori=output, attack_ids=attack_ids, attack_inds=attack_inds, ad_ids=self.ad_ids, track_vs=[t.get_v() for t in att_trackers] ) self.ad_ids.update(attack_ids) self.low_iou_ids.update(set(attack_ids)) if suc: self.attacked_ids.update(set(attack_ids)) print( f'attack ids: {attack_ids}\tattack frame {self.frame_id_}: SUCCESS\tl2 distance: {(noise ** 2).sum().sqrt().item()}\titeration: {attack_iter}') else: print(f'attack ids: {attack_ids}\tattack frame {self.frame_id_}: FAIL\tl2 distance: {(noise ** 2).sum().sqrt().item() if noise is not None else None}\titeration: {attack_iter}') if noise is not None: l2_dis = (noise ** 2).sum().sqrt().item() adImg = torch.clip(im_blob + noise, min=0, max=1) noise = self.recoverNoise(noise, img0) noise = (noise - np.min(noise)) / (np.max(noise) - np.min(noise)) noise = (noise * 255).astype(np.uint8) else: l2_dis = None adImg = im_blob output_stracks_att = self.update(adImg, img0) adImg = self.recoverNoise(adImg.detach(), img0) output_stracks_att_ind = [] for ind, track in enumerate(output_stracks_att): if track.track_id not in self.multiple_att_ids: self.multiple_att_ids[track.track_id] = 0 self.multiple_att_ids[track.track_id] += 1 if self.multiple_att_ids[track.track_id] <= self.FRAME_THR: output_stracks_att_ind.append(ind) if len(output_stracks_ori_ind) and len(output_stracks_att_ind): ori_dets = [track.curr_tlbr for i, track in enumerate(output_stracks_ori) if i in output_stracks_ori_ind] att_dets = [track.curr_tlbr for i, track in enumerate(output_stracks_att) if i in output_stracks_att_ind] ori_dets = np.stack(ori_dets).astype(np.float64) att_dets = np.stack(att_dets).astype(np.float64) ious = bbox_ious(ori_dets, att_dets) row_ind, col_ind = linear_sum_assignment(-ious) for i in range(len(row_ind)): if ious[row_ind[i], col_ind[i]] > 0.9: ori_id = output_stracks_ori[output_stracks_ori_ind[row_ind[i]]].track_id att_id = output_stracks_att[output_stracks_att_ind[col_ind[i]]].track_id self.multiple_ori2att[ori_id] = att_id return output_stracks_ori, output_stracks_att, adImg, noise, l2_dis def update(self, im_blob, img0, **kwargs): self.frame_id += 1 self_track_id = kwargs.get('track_id', None) activated_starcks = [] refind_stracks = [] lost_stracks = [] removed_stracks = [] width = img0.shape[1] height = img0.shape[0] inp_height = im_blob.shape[2] inp_width = im_blob.shape[3] c = np.array([width / 2., height / 2.], dtype=np.float32) s = max(float(inp_width) / float(inp_height) * height, width) * 1.0 meta = {'c': c, 's': s, 'out_height': inp_height // self.opt.down_ratio, 'out_width': inp_width // self.opt.down_ratio} ''' Step 1: Network forward, get detections & embeddings''' with torch.no_grad(): output = self.model(im_blob)[-1] hm = output['hm'].sigmoid_() wh = output['wh'] id_feature = output['id'] id_feature = F.normalize(id_feature, dim=1) reg = output['reg'] if self.opt.reg_offset else None dets, inds = mot_decode(hm, wh, reg=reg, cat_spec_wh=self.opt.cat_spec_wh, K=self.opt.K) id_feature_ = id_feature.permute(0, 2, 3, 1).view(-1, 512) id_feature = _tranpose_and_gather_feat(id_feature, inds) id_feature = id_feature.squeeze(0) id_feature = id_feature.detach().cpu().numpy() dets = self.post_process(dets, meta) dets = self.merge_outputs([dets])[1] remain_inds = dets[:, 4] > self.opt.conf_thres dets = dets[remain_inds] id_feature = id_feature[remain_inds] # import pdb; pdb.set_trace() dets_index = inds[0][remain_inds].tolist() # vis ''' for i in range(0, dets.shape[0]): bbox = dets[i][0:4] cv2.rectangle(img0, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2) cv2.imshow('dets', img0) cv2.waitKey(0) id0 = id0-1 ''' if len(dets) > 0: '''Detections''' detections = [STrack(STrack.tlbr_to_tlwh(tlbrs[:4]), tlbrs[4], f, 30) for (tlbrs, f) in zip(dets[:, :5], id_feature)] else: detections = [] ''' Add newly detected tracklets to tracked_stracks''' unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks) # Predict the current location with KF # for strack in strack_pool: # strack.predict() STrack.multi_predict(strack_pool) dists = matching.embedding_distance(strack_pool, detections) dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: track = strack_pool[itracked] det = detections[idet] if track.state == TrackState.Tracked: track.update(detections[idet], self.frame_id) activated_starcks.append(track) else: track.re_activate(det, self.frame_id, new_id=False) refind_stracks.append(track) ''' Step 3: Second association, with IOU''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked] dists = matching.iou_distance(r_tracked_stracks, detections) matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.5) for itracked, idet in matches: track = r_tracked_stracks[itracked] det = detections[idet] if track.state == TrackState.Tracked: track.update(det, self.frame_id) activated_starcks.append(track) else: track.re_activate(det, self.frame_id, new_id=False) refind_stracks.append(track) for it in u_track: track = r_tracked_stracks[it] if not track.state == TrackState.Lost: track.mark_lost() lost_stracks.append(track) '''Deal with unconfirmed tracks, usually tracks with only one beginning frame''' dets_index = [dets_index[i] for i in u_detection] detections = [detections[i] for i in u_detection] dists = matching.iou_distance(unconfirmed, detections) matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7) for itracked, idet in matches: unconfirmed[itracked].update(detections[idet], self.frame_id) activated_starcks.append(unconfirmed[itracked]) for it in u_unconfirmed: track = unconfirmed[it] track.mark_removed() removed_stracks.append(track) """ Step 4: Init new stracks""" for inew in u_detection: track = detections[inew] if track.score < self.det_thresh: continue track.activate(self.kalman_filter, self.frame_id, track_id=self_track_id) activated_starcks.append(track) """ Step 5: Update state""" for track in self.lost_stracks: if self.frame_id - track.end_frame > self.max_time_lost: track.mark_removed() removed_stracks.append(track) # print('Ramained match {} s'.format(t4-t3)) self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked] self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks) self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks) self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks) self.lost_stracks.extend(lost_stracks) self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks) self.removed_stracks.extend(removed_stracks) self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks) # get scores of lost tracks output_stracks = [track for track in self.tracked_stracks if track.is_activated] logger.debug('===========Frame {}=========='.format(self.frame_id)) logger.debug('Activated: {}'.format([track.track_id for track in activated_starcks])) logger.debug('Refind: {}'.format([track.track_id for track in refind_stracks])) logger.debug('Lost: {}'.format([track.track_id for track in lost_stracks])) logger.debug('Removed: {}'.format([track.track_id for track in removed_stracks])) unconfirmed = [] tracked_stracks = [] # type: list[STrack] for track in self.tracked_stracks: if not track.is_activated: unconfirmed.append(track) else: tracked_stracks.append(track) ''' Step 2: First association, with embedding''' strack_pool = joint_stracks(tracked_stracks, self.lost_stracks) self.ad_last_info = { 'last_strack_pool': copy.deepcopy(strack_pool), 'last_unconfirmed': copy.deepcopy(unconfirmed), 'kalman_filter': copy.deepcopy(self.kalman_filter_) } return output_stracks def _nms(self, heat, kernel=3): pad = (kernel - 1) // 2 hmax = nn.functional.max_pool2d( heat, (kernel, kernel), stride=1, padding=pad) keep = (hmax == heat).float return keep def computer_targets(self, boxes, gt_box): an_ws = boxes[:, 2] an_hs = boxes[:, 3] ctr_x = boxes[:, 0] ctr_y = boxes[:, 1] gt_ws = gt_box[:, 2] gt_hs = gt_box[:, 3] gt_ctr_x = gt_box[:, 0] gt_ctr_y = gt_box[:, 1] targets_dx = (gt_ctr_x - ctr_x) / an_ws targets_dy = (gt_ctr_y - ctr_y) / an_hs targets_dw = np.log(gt_ws / an_ws) targets_dh = np.log(gt_hs / an_hs) targets = np.vstack((targets_dx, targets_dy, targets_dw, targets_dh)).T return targets def joint_stracks(tlista, tlistb): exists = {} res = [] for t in tlista: exists[t.track_id] = 1 res.append(t) for t in tlistb: tid = t.track_id if not exists.get(tid, 0): exists[tid] = 1 res.append(t) return res def sub_stracks(tlista, tlistb): stracks = {} for t in tlista: stracks[t.track_id] = t for t in tlistb: tid = t.track_id if stracks.get(tid, 0): del stracks[tid] return list(stracks.values()) def remove_duplicate_stracks(stracksa, stracksb): pdist = matching.iou_distance(stracksa, stracksb) pairs = np.where(pdist < 0.15) dupa, dupb = list(), list() for p, q in zip(*pairs): timep = stracksa[p].frame_id - stracksa[p].start_frame timeq = stracksb[q].frame_id - stracksb[q].start_frame if timep > timeq: dupb.append(q) else: dupa.append(p) resa = [t for i, t in enumerate(stracksa) if not i in dupa] resb = [t for i, t in enumerate(stracksb) if not i in dupb] return resa, resb def save(obj, name): with open(f'/home/derry/Desktop/{name}.pth', 'wb') as f: pickle.dump(obj, f) def load(name): with open(f'/home/derry/Desktop/{name}.pth', 'rb') as f: obj = pickle.load(f) return obj

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7

c1219d3a603a918463a09f2d7d78debd9182f482

192

py

Python

eula-scan/app/counts.py

davidbstein/ml-law

2db439a9b618384c57acb51ddc0d55cf864ed8be

[ "MIT" ]

null

eula-scan/app/counts.py

davidbstein/ml-law

2db439a9b618384c57acb51ddc0d55cf864ed8be

[ "MIT" ]

null

eula-scan/app/counts.py

davidbstein/ml-law

2db439a9b618384c57acb51ddc0d55cf864ed8be

[ "MIT" ]

null

import model print(dict(model._ex("select count(*) policies from tos_text").fetchone())) print(dict(model._ex("select count(*) companies from company where last_error is null").fetchone()))

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c1724e3fcc8f514b866413548912dc98400bc49f

2,790

py

Python

entrepreneurial_property/migrations/0015_auto_20180905_0646.py

CzechInvest/ciis

c6102598f564a717472e5e31e7eb894bba2c8104

[ "MIT" ]

1

2019-05-26T22:24:01.000Z

entrepreneurial_property/migrations/0015_auto_20180905_0646.py

CzechInvest/ciis

c6102598f564a717472e5e31e7eb894bba2c8104

[ "MIT" ]

6

2019-01-22T14:53:43.000Z

2020-09-22T16:20:28.000Z

entrepreneurial_property/migrations/0015_auto_20180905_0646.py

CzechInvest/ciis

c6102598f564a717472e5e31e7eb894bba2c8104

[ "MIT" ]

null

# Generated by Django 2.0.5 on 2018-09-05 06:46 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('entrepreneurial_property', '0014_auto_20180905_0629'), ] operations = [ migrations.AlterModelOptions( name='brownfieldwastewater', options={'verbose_name': 'Odpadní voda', 'verbose_name_plural': 'Odpadní vody'}, ), migrations.AlterModelOptions( name='developmentparkwastewater', options={'verbose_name': 'Odpadní voda', 'verbose_name_plural': 'Odpadní vody'}, ), migrations.AlterModelOptions( name='greenfieldwastewater', options={'verbose_name': 'Odpadní voda', 'verbose_name_plural': 'Odpadní vody'}, ), migrations.AlterModelOptions( name='industrialarealwastewater', options={'verbose_name': 'Odpadní voda', 'verbose_name_plural': 'Odpadní vody'}, ), migrations.AlterModelOptions( name='officewastewater', options={'verbose_name': 'Odpadní voda', 'verbose_name_plural': 'Odpadní vody'}, ), migrations.AlterModelOptions( name='scientificparkwastewater', options={'verbose_name': 'Odpadní voda', 'verbose_name_plural': 'Odpadní vody'}, ), migrations.AddField( model_name='brownfield', name='uuid', field=models.CharField(default='6cfe1b7c-0699-466a-99c0-04523b11b3ea', max_length=36), preserve_default=False, ), migrations.AddField( model_name='developmentpark', name='uuid', field=models.CharField(default='6cfe1b7c-0699-466a-99c0-04523b11b3ea', max_length=36), preserve_default=False, ), migrations.AddField( model_name='greenfield', name='uuid', field=models.CharField(default='6cfe1b7c-0699-466a-99c0-04523b11b3ea', max_length=36), preserve_default=False, ), migrations.AddField( model_name='industrialareal', name='uuid', field=models.CharField(default='6cfe1b7c-0699-466a-99c0-04523b11b3ea', max_length=36), preserve_default=False, ), migrations.AddField( model_name='office', name='uuid', field=models.CharField(default='6cfe1b7c-0699-466a-99c0-04523b11b3ea', max_length=36), preserve_default=False, ), migrations.AddField( model_name='scientificpark', name='uuid', field=models.CharField(default='6cfe1b7c-0699-466a-99c0-04523b11b3ea', max_length=36), preserve_default=False, ), ]

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7

c17b28f08989d2fa2b75ad5040f70d15e7571b14

2,842

py

Python

OpenThermML/backup.py

wwlorey/open-thermostat-software

a0521b0d3b65fe9f2bd23f5059971d3a8d773e54

[ "MIT" ]

1

2020-12-14T02:44:10.000Z

OpenThermML/backup.py

wwlorey/open-thermostat-software

a0521b0d3b65fe9f2bd23f5059971d3a8d773e54

[ "MIT" ]

null

OpenThermML/backup.py

wwlorey/open-thermostat-software

a0521b0d3b65fe9f2bd23f5059971d3a8d773e54

[ "MIT" ]

1

2020-12-12T20:24:43.000Z

predictions = [ [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, 0.0, 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, 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, 0.0, 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, 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, 0.0, 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, 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, ], ] prediction_counts = [ [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, 7, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, ], ]

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14

c183f4d6f31778737b79a4e9462050d2a9596ead

872

py

Python

carla_env/__init__.py

janwithb/Carla-Gym-Wrapper

f1ea9fe89427c5a654f5561f214a5fba139b2568

[ "Apache-2.0" ]

6

2021-04-15T09:22:44.000Z

2022-02-15T01:07:23.000Z

carla_env/__init__.py

janwithb/Carla-Gym-Wrapper

f1ea9fe89427c5a654f5561f214a5fba139b2568

[ "Apache-2.0" ]

2

2021-08-23T02:47:40.000Z

2022-01-17T02:20:47.000Z

carla_env/__init__.py

janwithb/Carla-Gym-Wrapper

f1ea9fe89427c5a654f5561f214a5fba139b2568

[ "Apache-2.0" ]

2

2021-07-12T06:32:37.000Z

2021-11-24T14:43:13.000Z

from gym.envs.registration import register register( id='CarlaEnv-state-v1', entry_point='carla_env.carla_env:CarlaEnv', max_episode_steps=500, kwargs={ 'render': True, 'carla_port': 2000, 'changing_weather_speed': 0.1, 'frame_skip': 1, 'observations_type': 'state', 'traffic': True, 'vehicle_name': 'tesla.cybertruck', 'map_name': 'Town05', 'autopilot': True } ) register( id='CarlaEnv-pixel-v1', entry_point='carla_env.carla_env:CarlaEnv', max_episode_steps=500, kwargs={ 'render': True, 'carla_port': 2000, 'changing_weather_speed': 0.1, 'frame_skip': 1, 'observations_type': 'pixel', 'traffic': True, 'vehicle_name': 'tesla.cybertruck', 'map_name': 'Town05', 'autopilot': True } )

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0.066528

0.074844

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0.806653

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24.222222

0.721078

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0.396789

0.114679

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0

0.030303

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0

1

0

1

0

null

0

1

0

8

a9b2db3f369fcfd5b77d040b3bc04025ef934688

7,215

py

Python

experiment-2/02_gm_correlations_across_masks.py

NBCLab/power-replication

7a938cac6fd132f8cbd76535255680aeb2e550cb

[ "Apache-2.0" ]

1

2021-12-20T13:30:23.000Z

experiment-2/02_gm_correlations_across_masks.py

NBCLab/power-replication

7a938cac6fd132f8cbd76535255680aeb2e550cb

[ "Apache-2.0" ]

14

2020-12-21T15:58:45.000Z

2022-03-16T22:20:25.000Z

experiment-2/02_gm_correlations_across_masks.py

NBCLab/power-replication

7a938cac6fd132f8cbd76535255680aeb2e550cb

[ "Apache-2.0" ]

null

"""Experiment 2, Analysis Group 2. Comparing measures of global signal. Mean cortical signal of MEDN correlated with signal of all gray matter - Distribution of Pearson correlation coefficients - Page 2, right column, first paragraph Mean cortical signal of MEDN correlated with signal of whole brain - Distribution of Pearson correlation coefficients - Page 2, right column, first paragraph """ import os.path as op import sys import numpy as np from nilearn import image, masking from scipy.stats import ttest_1samp sys.path.append("..") from utils import get_prefixes # noqa: E402 def correlate_cort_with_gm(project_dir, participants_df): """Correlate mean cortical signal from MEDN files with signal from all gray matter. - Distribution of Pearson correlation coefficients - Page 2, right column, first paragraph """ ALPHA = 0.05 corrs = [] for i_run, participant_row in participants_df.iterrows(): if participant_row["exclude"] == 1: print(f"Skipping {participant_row['participant_id']}.") continue subj_id = participant_row["participant_id"] dset = participant_row["dataset"] dset_prefix = get_prefixes()[dset] subj_prefix = dset_prefix.format(participant_id=subj_id) cort_mask = op.join( project_dir, dset, "derivatives", "power", subj_id, "anat", f"{subj_id}_space-scanner_res-bold_label-CGM_mask.nii.gz", ) dseg_file = op.join( project_dir, dset, "derivatives", "power", subj_id, "anat", f"{subj_id}_space-scanner_res-bold_desc-totalMaskWithCSF_dseg.nii.gz", ) # Values 1-3 are cortical ribbon, subcortical structures, and cerebellum, respectively. gm_mask = image.math_img( "np.logical_and(img > 0, img <= 3).astype(int)", img=dseg_file ) medn_file = op.join( project_dir, dset, "derivatives", "tedana", subj_id, "func", f"{subj_prefix}_desc-optcomDenoised_bold.nii.gz", ) cort_data = masking.apply_mask(medn_file, cort_mask) gm_data = masking.apply_mask(medn_file, gm_mask) # Average across voxels cort_data = np.mean(cort_data, axis=1) # TODO: CHECK AXIS ORDER gm_data = np.mean(gm_data, axis=1) corr = np.corrcoef((cort_data, gm_data)) assert corr.shape == (2, 2), corr.shape corr = corr[1, 0] corrs.append(corr) corrs = np.array(corrs) # Convert r values to normally distributed z values with Fisher's # transformation (not test statistics though) z_values = np.arctanh(corrs) mean_z = np.mean(z_values) sd_z = np.std(z_values) # And now a significance test!! # TODO: Should we compute confidence intervals from z-values then # convert back to r-values? I think so, but there's so little in the # literature about dealing with *distributions* of correlation # coefficients. t, p = ttest_1samp(z_values, popmean=0, alternative="greater") if p <= ALPHA: print( "ANALYSIS 1: Correlations between the mean multi-echo denoised signal extracted from " "the cortical ribbon and that extracted from all gray matter " f"(M[Z] = {mean_z}, SD[Z] = {sd_z}) were significantly higher than zero, " f"t({participants_df.shape[0] - 1}) = {t:.03f}, p = {p:.03f}." ) else: print( "ANALYSIS 1: Correlations between the mean multi-echo denoised signal extracted from " "the cortical ribbon and that extracted from all gray matter " f"(M[Z] = {mean_z}, SD[Z] = {sd_z}) were not significantly higher than zero, " f"t({participants_df.shape[0] - 1}) = {t:.03f}, p = {p:.03f}." ) def correlate_cort_with_wb(project_dir, participants_df): """Correlate mean cortical signal from MEDN files with signal from whole brain. - Distribution of Pearson correlation coefficients - Page 2, right column, first paragraph """ ALPHA = 0.05 corrs = [] for i_run, participant_row in participants_df.iterrows(): if participant_row["exclude"] == 1: print(f"Skipping {participant_row['participant_id']}.") continue subj_id = participant_row["participant_id"] dset = participant_row["dataset"] dset_prefix = get_prefixes()[dset] subj_prefix = dset_prefix.format(participant_id=subj_id) cort_mask = op.join( project_dir, dset, "derivatives", "power", subj_id, "anat", f"{subj_id}_space-scanner_res-bold_label-CGM_mask.nii.gz", ) dseg_file = op.join( project_dir, dset, "derivatives", "power", subj_id, "anat", f"{subj_id}_space-scanner_res-bold_desc-totalMaskWithCSF_dseg.nii.gz", ) # Values 1+ are brain. wb_mask = image.math_img("img > 0", img=dseg_file) medn_file = op.join( project_dir, dset, "derivatives", "tedana", subj_id, "func", f"{subj_prefix}_desc-optcomDenoised_bold.nii.gz", ) cort_data = masking.apply_mask(medn_file, cort_mask) wb_data = masking.apply_mask(medn_file, wb_mask) # Average across voxels cort_data = np.mean(cort_data, axis=1) # TODO: CHECK AXIS ORDER wb_data = np.mean(wb_data, axis=1) corr = np.corrcoef((cort_data, wb_data)) assert corr.shape == (2, 2), corr.shape corr = corr[1, 0] corrs.append(corr) # Convert r values to normally distributed z values with Fisher's # transformation (not test statistics though) z_values = np.arctanh(corrs) mean_z = np.mean(z_values) sd_z = np.std(z_values) # And now a significance test!! # TODO: Should we compute confidence intervals from z-values then # convert back to r-values? I think so, but there's so little in the # literature about dealing with *distributions* of correlation # coefficients. t, p = ttest_1samp(z_values, popmean=0, alternative="greater") if p <= ALPHA: print( "ANALYSIS 2: Correlations between the mean multi-echo denoised signal extracted from " "the cortical ribbon and that extracted from the whole brain " f"(M[Z] = {mean_z}, SD[Z] = {sd_z}) were significantly higher than zero, " f"t({participants_df.shape[0] - 1}) = {t:.03f}, p = {p:.03f}." ) else: print( "ANALYSIS 2: Correlations between the mean multi-echo denoised signal extracted from " "the cortical ribbon and that extracted from the whole brain " f"(M[Z] = {mean_z}, SD[Z] = {sd_z}) were not significantly higher than zero, " f"t({participants_df.shape[0] - 1}) = {t:.03f}, p = {p:.03f}." )

35.195122

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926

7,215

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7,215

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null

0

1

0

1

0

null

0

7

e7ab93c674e1c7b0c7b235ef1ec15c5f79897159

184

py

Python

ptb/ledger/views.py

vkpdeveloper/ShaktiDeep-Traders-Bill-Management-Project

566a64268fabf256e80bee680d1fbde2c6c0787d

[ "MIT" ]

2

2019-11-26T11:57:56.000Z

2020-06-17T05:16:47.000Z

ptb/ledger/views.py

vkpdeveloper/ShaktiDeep-Traders-Bill-Management-Project

566a64268fabf256e80bee680d1fbde2c6c0787d

[ "MIT" ]

null

ptb/ledger/views.py

vkpdeveloper/ShaktiDeep-Traders-Bill-Management-Project

566a64268fabf256e80bee680d1fbde2c6c0787d

[ "MIT" ]

null

from django.shortcuts import render from django.shortcuts import render from django.http import HttpResponse def index(request): return render(request, 'ledger/index.html')

26.285714

47

0.777174

24

184

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0.541667

0.20979

0.265734

0.34965

0.503497

0

0.152174

184

7

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26.285714

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false

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1

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1

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null

0

1

0

8

e7ce0818904d46d11eb74c9bc1c1d5e0067a9008

68,039

py

Python

GraphTrace.py

a-dera/Graphe

70886565cc1dbda9f343dc11edcc480e2372934f

[ "MIT" ]

null

GraphTrace.py

a-dera/Graphe

70886565cc1dbda9f343dc11edcc480e2372934f

[ "MIT" ]

null

GraphTrace.py

a-dera/Graphe

70886565cc1dbda9f343dc11edcc480e2372934f

[ "MIT" ]

null

################################################## # Importation des Bibliotheques et fonctions: from tkinter import * from PIL import ImageGrab from tkinter import PhotoImage import tkinter as tk from tkinter import ttk from collections import defaultdict class Euler: def __init__(self,vertices): self.V= vertices #No. of vertices self.graph = defaultdict(list) # default dictionary to store graph # function to add an edge to graph def addEdge(self,u,v): # alternatives self.graph[u].append(v) self.graph[v].append(u) #A function used by isConnected def DFSUtil(self,v,visited): # Mark the current node as visited visited[v]= True #Recur for all the vertices adjacent to this vertex for i in self.graph[v]: if visited[i]==False: self.DFSUtil(i,visited) '''Method to check if all non-zero degree vertices are connected. It mainly does DFS traversal starting from node with non-zero degree''' def isConnected(self): # Mark all the vertices as not visited visited =[False]*(self.V) # Find a vertex with non-zero degree for i in range(self.V): if len(self.graph[i]) > 1: break # If there are no edges in the graph, return true if i == self.V-1: return True # Start DFS traversal from a vertex with non-zero degree self.DFSUtil(i,visited) # Check if all non-zero degree vertices are visited for i in range(self.V): if visited[i]==False and len(self.graph[i]) > 0: return False return True '''The function returns one of the following values 0 --> If grpah is not Eulerian 1 --> If graph has an Euler path (Semi-Eulerian) 2 --> If graph has an Euler Circuit (Eulerian) ''' def isEulerian(self): # Check if all non-zero degree vertices are connected if self.isConnected() == False: return 0 else: #Count vertices with odd degree odd = 0 for i in range(self.V): if len(self.graph[i]) % 2 !=0: odd +=1 '''If odd count is 2, then semi-eulerian. If odd count is 0, then eulerian If count is more than 2, then graph is not Eulerian Note that odd count can never be 1 for undirected graph''' if odd == 0: return 2 elif odd == 2: return 1 elif odd > 2: return 0 # Function to run test cases def test(self): res = self.isEulerian() if res == 0: #print ("Le graphe n'est pas eulerien") resultat="Le graphe n'est pas eulerien" return resultat elif res ==1 : #print ("Le graphe comporte un chemain eulerien") resultat="Le graphe comporte un chemain eulerien" return resultat else: #print ("Le graphe comporte un cycle eulerien") resultat="Le graphe comporte un cycle eulerien" return resultat class Hamilton(): def __init__(self, vertices): self.graph = [[0 for column in range(vertices)] for row in range(vertices)] self.V = vertices ''' Check if this vertex is an adjacent vertex of the previously added vertex and is not included in the path earlier ''' def isSafe(self, v, pos, path): # Check if current vertex and last vertex # in path are adjacent if self.graph[ path[pos-1] ][v] == 0: return False # Check if current vertex not already in path for vertex in path: if vertex == v: return False return True ############################################################# # A recursive utility function to solve # hamiltonian cycle problem def hamCycleUtil(self, path, pos): # base case: if all vertices are # included in the path if pos == self.V: # Last vertex must be adjacent to the # first vertex in path to make a cyle if self.graph[ path[pos-1] ][ path[0] ] == 1: return True else: return False # Try different vertices as a next candidate # in Hamiltonian Cycle. We don't try for 0 as # we included 0 as starting point in in hamCycle() for v in range(1,self.V): if self.isSafe(v, pos, path) == True: path[pos] = v if self.hamCycleUtil(path, pos+1) == True: return True # Remove current vertex if it doesn't # lead to a solution path[pos] = -1 return False def hamCycle(self): path = [-1] * self.V ''' Let us put vertex 0 as the first vertex in the path. If there is a Hamiltonian Cycle, then the path can be started from any point of the cycle as the graph is undirected ''' path[0] = 0 if self.hamCycleUtil(path,1) == False: #print ("Solution does not exist\n") resultat="Le graphe n'est pas hamiltonien" return resultat return self.printSolution(path) def printSolution(self, path): #print ("Solution Exists: Following is one Hamiltonian Cycle") resultat="Le graphe est hamiltonien: " for vertex in path: #print (vertex) resultat+=str(vertex)+" " #print (path[0], "\n") resultat+=str(path[0]) return resultat ########################################## class Max_flow: def __init__(self,graph): self.graph = graph # residual graph self. ROW = len(graph) #self.COL = len(gr[0]) '''Returns true if there is a path from source 's' to sink 't' in residual graph. Also fills parent[] to store the path ''' def BFS(self,s, t, parent): # Mark all the vertices as not visited visited =[False]*(self.ROW) # Create a queue for BFS queue=[] # Mark the source node as visited and enqueue it queue.append(s) visited[s] = True # Standard BFS Loop while queue: #Dequeue a vertex from queue and print it u = queue.pop(0) # Get all adjacent vertices of the dequeued vertex u # If a adjacent has not been visited, then mark it # visited and enqueue it for ind, val in enumerate(self.graph[u]): if visited[ind] == False and val > 0 : queue.append(ind) visited[ind] = True parent[ind] = u # If we reached sink in BFS starting from source, then return # true, else false return True if visited[t] else False # Returns tne maximum flow from s to t in the given graph def FordFulkerson(self, source, sink): # This array is filled by BFS and to store path parent = [-1]*(self.ROW) max_flow = 0 # There is no flow initially # Augment the flow while there is path from source to sink while self.BFS(source, sink, parent) : # Find minimum residual capacity of the edges along the # path filled by BFS. Or we can say find the maximum flow # through the path found. path_flow = float("Inf") s = sink while(s != source): path_flow = min (path_flow, self.graph[parent[s]][s]) s = parent[s] # Add path flow to overall flow max_flow += path_flow # update residual capacities of the edges and reverse edges # along the path v = sink while(v != source): u = parent[v] self.graph[u][v] -= path_flow self.graph[v][u] += path_flow v = parent[v] return max_flow ################################################################# # - Fenetre Graphe Orienter - # # /////////////////////////////////////////////// # # Description: Programme traitant sur les graphes # # Orienter # # /////////////////////////////////////////////// # class GrapheOriente(Tk): def __init__(self): Tk.__init__(self) # constructeur de la classe parente #recupere la taille de l'ecrant de l'ordinateur width=self.winfo_screenwidth() height=self.winfo_screenheight() self.largeure=900 self.hauteure=500 self.x=(width/2)-(self.largeure/2) self.y=(height/2)-(self.hauteure/2) #initialisation du canvas self.graphe =Canvas(self, width =self.largeure, height =self.hauteure, bg ="white") self.geometry('{}x{}+{}+{}'.format(self.largeure,self.hauteure,int(self.x),int(self.y))) self.resizable(False,False) self.wm_title('Graphe Oriente') self.graphe.pack(side =TOP, padx =5, pady =5) #evenement declancher par les clic de la sourie self.bind("<Double-Button-1>", self.sommet) self.bind("<Button-3>", self.arc) #menu de la fenetre menubar = Menu(self) filemenu = Menu(menubar, tearoff = 0) filemenu.add_separator() filemenu.add_command(label = "Quitter ?", command = self.destroy) filemenu.add_command(label = "Sauvegarder", command = self.save) menubar.add_cascade(label = "Fichier", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu.add_separator() filemenu.add_command(label = "Ordre du graphe", command=self.ordre_graphe) filemenu.add_command(label = "Degre du sommet", command=self.degres_sommet) filemenu.add_command(label = "Matrice d'adjacence", command=self.matriceAdj) filemenu.add_command(label = "Successeur du sommet", command=self.successeur) filemenu.add_command(label = "Predecesseur du sommet", command=self.predeccesseur) filemenu.add_command(label = "Demi degre supperieur du sommet", command=self.demi_deg_sup) filemenu.add_command(label = "Demi degre inferieur du sommet", command=self.demi_deg_inf) filemenu.add_command(label = "Graphe Hamiltonien ?", command=self.hamilton) filemenu.add_command(label = "Graphe Eulerien ?", command=self.euler) filemenu.add_command(label = "Flow maximal", command=self.maxflow) menubar.add_cascade(label = "Traitement", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu.add_separator() filemenu.add_command(label = "Tout effacer ?", command =self.delete) menubar.add_cascade(label = "Effacer", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu = Menu(menubar, tearoff = 0) filemenu.add_command(label = "Aide", command =self.aide) menubar.add_cascade(label = "Aide", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) self.config(menu = menubar) #variable globale self.i=int(0) self.compt=int() self.temp=list() self.connect=list() self.point=list() self.sommets=list() self.couple=list() self.matrice=list() self.var=StringVar() self.entier=int() def delete(self): for element in self.graphe.find_all(): self.graphe.delete(element) self.i=int(0) self.compt=int() self.temp=list() self.connect=list() self.point=list() self.sommets=list() self.couple=list() self.matrice=list() self.var=StringVar() self.entier=int() pass # fonction permettant de fermer la fenetre fille def Close_Toplevel (self): self.compt=int() self.temp=list() self.wm_attributes("-disable",False) self.toplevel_dialog.destroy() self.deiconify() #fenetre permettant de fermet la fenetre fille de sauvegarde def Close_Save (self,event=None): if len(self.var.get())>0: x=self.graphe.winfo_rootx() y=self.graphe.winfo_rooty() w=self.graphe.winfo_width() h=self.graphe.winfo_height() image=ImageGrab.grab((x+2,y+2,x+w-2,y+h-2)) image.save("save/{}.png".format(self.var.get())) else: x=self.graphe.winfo_rootx() y=self.graphe.winfo_rooty() w=self.graphe.winfo_width() h=self.graphe.winfo_height() image=ImageGrab.grab((x+2,y+2,x+w-2,y+h-2)) image.save("save/Graphe.png") self.wm_attributes("-disable",False) self.toplevel_dialog.destroy() self.deiconify() def aide(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,150) self.toplevel_dialog.wm_title("Aide") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=150 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) self.toplevel_dialog.focus() aide=""" Tracer un sommet: Double clic Tracer un arc: clic gauche sur chaque sommet """ self.label=tk.Label(self.toplevel_dialog, text=aide,justify='left',font='Century 13 bold') self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction de sauvegarde du graphe dessiner def save(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Sauvegarder") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Save) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer le nom de limage: ') self.label.pack(side='left') self.var=tk.Entry(self.toplevel_dialog) self.var.pack(side='left') self.var.bind("<Return>", self.Close_Save) self.var.bind("<Escape>", self.Close_Toplevel) self.var.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_Save) self.yes_button.pack(side='right',fill='x',expand=True) # fonction permettant de detecter si le graphe est eulerien def euler(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Graphe eulerien") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) l=len(self.couple) lg=len(self.sommets) if lg>=2: g1 = Euler(lg) for i in range(l): g1.addEdge(self.couple[i][0],self.couple[i][1]) self.var=g1.test() self.label=tk.Label(self.toplevel_dialog, text=self.var) self.label.pack(side='top') else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction permettant de detecter si le graphe est hamiltonien def hamilton(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Graphe hamiltonien") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) lg=len(self.couple) if lg>1: l=len(self.sommets) self.matrice=list() for i in range(l): self.matrice.append([]) for j in range(l): k=int(0) temp=list() temp.append(self.sommets[i]) temp.append(self.sommets[j]) for element in self.couple: if temp[0]==element[0] and temp[1]==element[1]: self.matrice[i].append(1) k+=1 if k==0: self.matrice[i].append(0) g1 = Hamilton(l) g1.graph = self.matrice self.var=g1.hamCycle() self.label=tk.Label(self.toplevel_dialog, text=self.var) self.label.pack(side='top') pass else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction permettant de connetre le flow maximal def maxflow(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,200) self.toplevel_dialog.wm_title("Flow maximal") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=200 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet source: ') self.label.grid(row=1) self.valeur1=tk.Entry(self.toplevel_dialog) self.valeur1.grid(row=1,column=1) self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet destination: ') self.label.grid(row=2) self.valeur2=tk.Entry(self.toplevel_dialog) self.valeur2.grid(row=2,column=1) self.label=tk.Label(self.toplevel_dialog, text='\n\n') self.label.grid(row=3) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_maxflow) self.yes_button.grid(row=4,column=1) self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=4,column=3) pass def Close_maxflow (self): lg=len(self.couple) if self.valeur1.get() in str(self.sommets) and self.valeur2.get() in str(self.sommets) and lg>0 and self.valeur1.get()!=self.valeur2.get() : l=len(self.sommets) self.matrice=list() for i in range(l): self.matrice.append([]) for j in range(l): k=int(0) temp=list() temp.append(self.sommets[i]) temp.append(self.sommets[j]) for element in self.couple: if temp[0]==element[0] and temp[1]==element[1]: self.matrice[i].append(element[2]) k+=1 if k==0: self.matrice[i].append(0) g = Max_flow(self.matrice) src=int(self.valeur1.get()) des=int(self.valeur2.get()) self.label=tk.Label(self.toplevel_dialog, text="Le flow maximal est %d " % g.FordFulkerson(src, des)) self.label.grid(row=6) else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.grid(row=6) pass def matriceAdj(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(300,300) self.toplevel_dialog.wm_title("Matrice D'adjacence") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=300 hauteure=300 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) lg=len(self.couple) if lg>0: l=len(self.sommets) self.matrice=list() for i in range(l): resultat="" self.matrice.append([]) for j in range(l): k=int(0) temp=list() temp.append(self.sommets[i]) temp.append(self.sommets[j]) for element in self.couple: if temp[0]==element[0] and temp[1]==element[1]: self.matrice[i].append(1) resultat+="1 " k+=1 if k==0: self.matrice[i].append(0) resultat+="0 " self.label=tk.Label(self.toplevel_dialog, text=resultat) self.label.pack(side='top') pass else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction permettant de donner le successeur d'un sommet def successeur(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(650,100) self.toplevel_dialog.wm_title("Successeur d'un sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=650 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.focus() self.toplevel_dialog.bind("<Return>", self.Close_suc) self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_suc) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=6) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_suc) self.yes_button.grid(row=1,column=4) pass def Close_suc(self): if self.valeur.get() in str(self.sommets): resultat="" for element in self.couple: if self.valeur.get() == str(element[0]): resultat+=str(element[1])+" " self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le(s) successeur du sommet {} est: {}'.format(self.valeur.get(),resultat)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def predeccesseur(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(650,100) self.toplevel_dialog.wm_title("Predecesseur d'un sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=650 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_pred) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_pred) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=6) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_pred) self.yes_button.grid(row=1,column=4) def Close_pred(self): if self.valeur.get() in str(self.sommets): resultat="" for element in self.couple: if self.valeur.get() == str(element[1]): resultat+=str(element[0])+" " self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le(s) predecesseur du sommet {} est: {}'.format(self.valeur.get(),resultat)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def demi_deg_sup(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(700,100) self.toplevel_dialog.wm_title("Demi degre supperieur d'un sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=700 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_degre_sup) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_degre_sup) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=6) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_degre_sup) self.yes_button.grid(row=1,column=4) def Close_degre_sup(self): if self.valeur.get() in str(self.sommets): k=int(0) for element in self.couple: if self.valeur.get() == str(element[0]): k+=1 self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le demi degre supperieur du sommet {} est: {}'.format(self.valeur.get(),k)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def demi_deg_inf(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(700,100) self.toplevel_dialog.wm_title("Demi degre inferieur d'un sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=700 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_degre_inf) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_degre_inf) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=6) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_degre_inf) self.yes_button.grid(row=1,column=4) def Close_degre_inf(self): if self.valeur.get() in str(self.sommets): k=int(0) for element in self.couple: if self.valeur.get() == str(element[1]): k+=1 self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le demi degre inferieur du sommet {} est: {}'.format(self.valeur.get(),k)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def degres_sommet(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Degre du sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_degre) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_degre) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=5) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_degre) self.yes_button.grid(row=1,column=3) def Close_degre(self): if self.valeur.get() in str(self.sommets): k=int(0) for element in self.couple: if self.valeur.get() in str(element): k+=1 self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le degre du sommet {} est: {}'.format(self.valeur.get(),k)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def ordre_graphe(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(502,50) self.toplevel_dialog.wm_title("Ordre du graphe") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=502 hauteure=50 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) n=len(self.sommets) self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='L ordre du graphe est: {}'.format(n)) self.toplevel_dialog_label.pack(side='top') self.toplevel_dialog_yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.toplevel_dialog_yes_button.pack(side='right',fill='x',expand=True) for i in range(3): self.toplevel_dialog_label3=tk.Label(self.toplevel_dialog, text='\n') self.toplevel_dialog_label3.pack() pass def sommet(self, event): x,y=event.x,event.y if self.point==[]: self.sommet=self.graphe.create_oval(x-10,y-10,x+10,y+10, fill="cyan") self.numero=self.graphe.create_text(x,y,text="{}".format(self.i)) self.point.append([event.x,event.y,self.sommet,self.numero,self.i]) self.sommets.append(self.i) self.i+=1 else: controle=0 for element in self.point: if element[0]-25 < event.x < element[0]+25 and element[1]-25 < event.y < element[1]+25: controle=1 if controle==0: self.sommet=self.graphe.create_oval(x-10,y-10,x+10,y+10, fill="cyan") self.numero=self.graphe.create_text(x,y,text="{}".format(self.i)) self.point.append([event.x,event.y,self.sommet,self.numero,self.i]) self.sommets.append(self.i) self.i+=1 #procedure permettant de dessiner un arc entre deux sommets def arc(self, event): for element in self.point: if element[0]-10 < event.x < element[0]+10 and element[1]-10 < event.y < element[1]+10: self.temp.append(element) self.compt+=1 if self.compt==2: self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(502,100) self.toplevel_dialog.wm_title("Arc") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=502 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_arc) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer la distance entre le sommet {} et le sommet {}: '.format(self.temp[0][4],self.temp[1][4])) self.label.pack(side='top') self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.pack(side='top') self.valeur.bind("<Return>", self.Close_arc) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_arc) self.yes_button.pack(side='right',fill='x',expand=True) def Close_arc (self,event=None): if self.temp[0][0] < self.temp[1][0]: a=[self.temp[0][0]+10,self.temp[0][1]] b=[self.temp[1][0]-10,self.temp[1][1]] self.graphe.create_line(a,b,arrow="last") try: self.entier=int(self.valeur.get()) except ValueError: pass if self.entier>0 or self.entier<0 : pass else: self.entier=int(1) self.couple.append([self.temp[0][4],self.temp[1][4],self.entier]) elif self.temp[0][0]==self.temp[1][0]: self.graphe.delete(self.temp[0][2]) self.graphe.delete(self.temp[0][3]) self.graphe.create_oval(self.temp[0][0]-10,self.temp[0][1]-25,self.temp[0][0]+1,self.temp[0][1]) self.graphe.create_oval(self.temp[0][0]-10,self.temp[0][1]-10,self.temp[0][0]+10,self.temp[0][1]+10,fill="cyan") self.graphe.create_text(self.temp[0][0],self.temp[0][1],text="{}".format(self.temp[0][4])) a=(self.temp[0][0],self.temp[0][1]-10.5) b=(self.temp[0][0],self.temp[0][1]-10) self.graphe.create_line(a,b,arrow="last") try: self.entier=int(self.valeur.get()) except ValueError: pass if self.entier>0 or self.entier<0 : pass else: self.entier=int(1) self.couple.append([self.temp[0][4],self.temp[1][4],self.entier]) else: a=[self.temp[0][0]-10,self.temp[0][1]] b=[self.temp[1][0]+10,self.temp[1][1]] self.graphe.create_line(a,b,arrow="last") try: self.entier=int(self.valeur.get()) except ValueError: pass if self.entier>0 or self.entier<0 : pass else: self.entier=int(1) self.couple.append([self.temp[0][4],self.temp[1][4],self.entier]) self.compt=int() self.temp=list() self.wm_attributes("-disable",False) self.toplevel_dialog.destroy() self.deiconify() ########################################### # - Fenetre Graphe Orienter - # # /////////////////////////////////////////////// # # Description: Programme traitant sur les graphes # # Orienter # # /////////////////////////////////////////////// # class Graphe_Non_Oriente(Tk): def __init__(self): Tk.__init__(self) # constructeur de la classe parente #recupere la taille de l'ecrant de l'ordinateur width=self.winfo_screenwidth() height=self.winfo_screenheight() self.largeure=900 self.hauteure=500 self.x=(width/2)-(self.largeure/2) self.y=(height/2)-(self.hauteure/2) #initialisation du canvas self.graphe =Canvas(self, width =self.largeure, height =self.hauteure, bg ="white") self.geometry('{}x{}+{}+{}'.format(self.largeure,self.hauteure,int(self.x),int(self.y))) self.resizable(False,False) self.wm_title('Graphe Non Oriente') self.graphe.pack(side =TOP, padx =5, pady =5) #evenement declancher par les clic de la sourie self.bind("<Double-Button-1>", self.sommet) self.bind("<Button-3>", self.arc) #menu de la fenetre menubar = Menu(self) filemenu = Menu(menubar, tearoff = 0) filemenu.add_separator() filemenu.add_command(label = "Quitter ?", command = self.destroy) filemenu.add_command(label = "Sauvegarder", command = self.save) menubar.add_cascade(label = "Fichier", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu.add_separator() filemenu.add_command(label = "Ordre du graphe", command=self.ordre_graphe) filemenu.add_command(label = "Degre du sommet", command=self.degres_sommet) filemenu.add_command(label = "Matrice d'adjacence", command=self.matriceAdj) filemenu.add_command(label = "Successeur du sommet", command=self.successeur) filemenu.add_command(label = "Predecesseur du sommet", command=self.predeccesseur) filemenu.add_command(label = "Graphe Hamiltonien ?", command=self.hamilton) filemenu.add_command(label = "Graphe Eulerien ?", command=self.euler) filemenu.add_command(label = "Flow maximal", command=self.maxflow) menubar.add_cascade(label = "Traitement", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu.add_command(label = "Tout effacer ?", command =self.delete) menubar.add_cascade(label = "Effacer", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu.add_command(label = "Aide", command =self.aide) menubar.add_cascade(label = "Aide", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) self.config(menu = menubar) #variable globale self.i=int(0) self.compt=int() self.temp=list() self.connect=list() self.point=list() self.sommets=list() self.couple=list() self.matrice=list() self.var=StringVar() self.entier=int() def delete(self): for element in self.graphe.find_all(): self.graphe.delete(element) self.i=int(0) self.compt=int() self.temp=list() self.connect=list() self.point=list() self.sommets=list() self.couple=list() self.matrice=list() self.var=StringVar() self.entier=int() pass # fonction permettant de fermer la fenetre fille def Close_Toplevel (self): self.compt=int() self.temp=list() self.wm_attributes("-disable",False) self.toplevel_dialog.destroy() self.deiconify() def aide(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,150) self.toplevel_dialog.wm_title("Aide") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=150 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) self.toplevel_dialog.focus() aide=""" Tracer un sommet: Double clic Tracer un arc: clic gauche sur chaque sommet """ self.label=tk.Label(self.toplevel_dialog, text=aide,justify='left',font='Century 13 bold') self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fenetre permettant de fermet la fenetre fille de sauvegarde def Close_Save (self,event=None): if len(self.var.get())>0: x=self.graphe.winfo_rootx() y=self.graphe.winfo_rooty() w=self.graphe.winfo_width() h=self.graphe.winfo_height() image=ImageGrab.grab((x+2,y+2,x+w-2,y+h-2)) image.save("save/{}.png".format(self.var.get())) else: x=self.graphe.winfo_rootx() y=self.graphe.winfo_rooty() w=self.graphe.winfo_width() h=self.graphe.winfo_height() image=ImageGrab.grab((x+2,y+2,x+w-2,y+h-2)) image.save("save/Graphe.png") self.wm_attributes("-disable",False) self.toplevel_dialog.destroy() self.deiconify() #fonction de sauvegarde du graphe dessiner def save(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Sauvegarder") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Save) self.label=tk.Label(self.toplevel_dialog, text='Entrer le nom de limage: ') self.label.pack(side='left') self.var=tk.Entry(self.toplevel_dialog) self.var.pack(side='left') self.var.bind("<Return>", self.Close_Save) self.var.bind("<Escape>", self.Close_Toplevel) self.var.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_Save) self.yes_button.pack(side='right',fill='x',expand=True) # fonction permettant de detecter si le graphe est eulerien def euler(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Graphe eulerien") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) self.toplevel_dialog.focus() l=len(self.couple) lg=len(self.sommets) if lg>=2: g1 = Euler(lg) for i in range(l): g1.addEdge(self.couple[i][0],self.couple[i][1]) self.var=g1.test() self.label=tk.Label(self.toplevel_dialog, text=self.var) self.label.pack(side='top') else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction permettant de detecter si le graphe est hamiltonien def hamilton(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Graphe hamiltonien") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) self.toplevel_dialog.focu() lg=len(self.couple) if lg>1: l=len(self.sommets) self.matrice=list() for i in range(l): self.matrice.append([]) for j in range(l): k=int(0) temp=list() temp.append(self.sommets[i]) temp.append(self.sommets[j]) for element in self.couple: if temp[0]==element[0] and temp[1]==element[1]: self.matrice[i].append(1) k+=1 if k==0: self.matrice[i].append(0) g1 = Hamilton(l) g1.graph = self.matrice self.var=g1.hamCycle() self.label=tk.Label(self.toplevel_dialog, text=self.var) self.label.pack(side='top') pass else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction permettant de connetre le flow maximal def maxflow(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,200) self.toplevel_dialog.wm_title("Flow maximal") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=200 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet source: ') self.label.grid(row=1) self.valeur1=tk.Entry(self.toplevel_dialog) self.valeur1.grid(row=1,column=1) self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet destination: ') self.label.grid(row=2) self.valeur2=tk.Entry(self.toplevel_dialog) self.valeur2.grid(row=2,column=1) self.label=tk.Label(self.toplevel_dialog, text='\n\n') self.label.grid(row=3) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_maxflow) self.yes_button.grid(row=4,column=1) self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=4,column=3) pass def Close_maxflow (self): lg=len(self.couple) if self.valeur1.get() in str(self.sommets) and self.valeur2.get() in str(self.sommets) and lg>0 and self.valeur1.get()!=self.valeur2.get() : l=len(self.sommets) self.matrice=list() for i in range(l): self.matrice.append([]) for j in range(l): k=int(0) temp=list() temp.append(self.sommets[i]) temp.append(self.sommets[j]) for element in self.couple: if temp[0]==element[0] and temp[1]==element[1]: self.matrice[i].append(element[2]) k+=1 if k==0: self.matrice[i].append(0) g = Max_flow(self.matrice) src=int(self.valeur1.get()) des=int(self.valeur2.get()) self.label=tk.Label(self.toplevel_dialog, text="Le flow maximal est %d " % g.FordFulkerson(src, des)) self.label.grid(row=6) else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.grid(row=6) pass def matriceAdj(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(300,300) self.toplevel_dialog.wm_title("Matrice D'adjacence") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=300 hauteure=300 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) self.toplevel_dialog.focus() lg=len(self.couple) if lg>0: l=len(self.sommets) self.matrice=list() for i in range(l): resultat="" resultat+=str(self.sommets[i])+"| " self.matrice.append([]) for j in range(l): k=int(0) temp=list() temp.append(self.sommets[i]) temp.append(self.sommets[j]) for element in self.couple: if temp[0]==element[0] and temp[1]==element[1]: self.matrice[i].append(1) resultat+="1 " k+=1 if k==0: self.matrice[i].append(0) resultat+="0 " self.label=tk.Label(self.toplevel_dialog, text=resultat) self.label.pack(side='top') pass else: self.label=tk.Label(self.toplevel_dialog, text="Votre requette ne peut etre traiter") self.label.pack(side='top') self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) #fonction permettant de donner le successeur d'un sommet def successeur(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(650,100) self.toplevel_dialog.wm_title("Successeur d'un sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=650 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_suc) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_suc) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=6) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_suc) self.yes_button.grid(row=1,column=4) pass def Close_suc(self): if self.valeur.get() in str(self.sommets): resultat="" for element in self.couple: if self.valeur.get() == str(element[0]): resultat+=str(element[1])+" " self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le(s) successeur du sommet {} est: {}'.format(self.valeur.get(),resultat)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def predeccesseur(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(650,100) self.toplevel_dialog.wm_title("Predecesseur d'un sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=650 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_pred) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_pred) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=6) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_pred) self.yes_button.grid(row=1,column=4) def Close_pred(self): if self.valeur.get() in str(self.sommets): resultat="" for element in self.couple: if self.valeur.get() == str(element[1]): resultat+=str(element[0])+" " self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le(s) predecesseur du sommet {} est: {}'.format(self.valeur.get(),resultat)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def degres_sommet(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(600,100) self.toplevel_dialog.wm_title("Degre du sommet") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=600 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_degre) self.toplevel_dialog.focus() self.label=tk.Label(self.toplevel_dialog, text='Entrer sommet: ') self.label.grid(row=1) self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.grid(row=1,column=1) self.valeur.bind("<Return>", self.Close_degre) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.grid(row=1,column=5) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_degre) self.yes_button.grid(row=1,column=3) def Close_degre(self): if self.valeur.get() in str(self.sommets): k=int(0) for element in self.couple: if self.valeur.get() == str(element[1]): k+=1 self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Le degre du sommet {} est: {}'.format(self.valeur.get(),k)) self.toplevel_dialog_label.grid(row=2) else: self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='Valeur entrer incorrecte') self.toplevel_dialog_label.grid(row=2) def ordre_graphe(self): self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(502,50) self.toplevel_dialog.wm_title("Ordre du graphe") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=502 hauteure=50 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_Toplevel) self.toplevel_dialog.fovus() n=len(self.sommets) self.toplevel_dialog_label=tk.Label(self.toplevel_dialog, text='L ordre du graphe est: {}'.format(n)) self.toplevel_dialog_label.pack(side='top') self.toplevel_dialog_yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=82,command=self.Close_Toplevel) self.toplevel_dialog_yes_button.pack(side='right',fill='x',expand=True) for i in range(3): self.toplevel_dialog_label3=tk.Label(self.toplevel_dialog, text='\n') self.toplevel_dialog_label3.pack() pass def sommet(self, event): x,y=event.x,event.y if self.point==[]: self.sommet=self.graphe.create_oval(x-10,y-10,x+10,y+10, fill="cyan") self.numero=self.graphe.create_text(x,y,text="{}".format(self.i)) self.point.append([event.x,event.y,self.sommet,self.numero,self.i]) self.sommets.append(self.i) self.i+=1 else: controle=0 for element in self.point: if element[0]-25 < event.x < element[0]+25 and element[1]-25 < event.y < element[1]+25: controle=1 if controle==0: self.sommet=self.graphe.create_oval(x-10,y-10,x+10,y+10, fill="cyan") self.numero=self.graphe.create_text(x,y,text="{}".format(self.i)) self.point.append([event.x,event.y,self.sommet,self.numero,self.i]) self.sommets.append(self.i) self.i+=1 #procedure permettant de dessiner un arc entre deux sommets def arc(self, event): for element in self.point: if element[0]-10 < event.x < element[0]+10 and element[1]-10 < event.y < element[1]+10: self.temp.append(element) self.compt+=1 if self.compt==2: self.wm_attributes("-disable",True) self.toplevel_dialog=tk.Toplevel(self) self.toplevel_dialog.minsize(502,100) self.toplevel_dialog.wm_title("Arc") width=self.toplevel_dialog.winfo_screenwidth() height=self.toplevel_dialog.winfo_screenheight() largeure=502 hauteure=100 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) self.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) self.toplevel_dialog.transient(self) self.toplevel_dialog.protocol("WM_DELETE_WINDOW", self.Close_Toplevel) self.toplevel_dialog.bind("<Return>", self.Close_arc) self.toplevel_dialog.focus self.label=tk.Label(self.toplevel_dialog, text='Entrer la distance entre le sommet {} et le sommet {}: '.format(self.temp[0][4],self.temp[1][4])) self.label.pack(side='top') self.valeur=tk.Entry(self.toplevel_dialog) self.valeur.pack(side='top') self.valeur.bind("<Return>", self.Close_arc) self.valeur.bind("<Escape>", self.Close_Toplevel) self.valeur.focus_set() self.yes_button=ttk.Button(self.toplevel_dialog,text='Retour',width=25,command=self.Close_Toplevel) self.yes_button.pack(side='right',fill='x',expand=True) self.yes_button=ttk.Button(self.toplevel_dialog,text='Valider',width=25,command=self.Close_arc) self.yes_button.pack(side='right',fill='x',expand=True) def Close_arc (self,event=None): if self.temp[0][0] < self.temp[1][0]: a=[self.temp[0][0]+10,self.temp[0][1]] b=[self.temp[1][0]-10,self.temp[1][1]] self.graphe.create_line(a,b) try: self.entier=int(self.valeur.get()) except ValueError: pass if self.entier!=0 : pass else: self.entier=int(1) self.couple.append([self.temp[0][4],self.temp[1][4],self.entier]) self.couple.append([self.temp[1][4],self.temp[0][4],self.entier]) elif self.temp[0][0]==self.temp[1][0]: self.graphe.delete(self.temp[0][2]) self.graphe.delete(self.temp[0][3]) self.graphe.create_oval(self.temp[0][0]-10,self.temp[0][1]-25,self.temp[0][0]+1,self.temp[0][1]) self.graphe.create_oval(self.temp[0][0]-10,self.temp[0][1]-10,self.temp[0][0]+10,self.temp[0][1]+10,fill="cyan") self.graphe.create_text(self.temp[0][0],self.temp[0][1],text="{}".format(self.temp[0][4])) a=(self.temp[0][0],self.temp[0][1]-10.5) b=(self.temp[0][0],self.temp[0][1]-10) self.graphe.create_line(a,b) try: self.entier=int(self.valeur.get()) except ValueError: pass if self.entier>0 or self.entier<0 : pass else: self.entier=int(1) self.couple.append([self.temp[0][4],self.temp[1][4],self.entier]) self.couple.append([self.temp[1][4],self.temp[0][4],self.entier]) else: a=[self.temp[0][0]-10,self.temp[0][1]] b=[self.temp[1][0]+10,self.temp[1][1]] self.graphe.create_line(a,b) try: self.entier=int(self.valeur.get()) except ValueError: pass if self.entier>0 or self.entier<0 : pass else: self.entier=int(1) self.couple.append([self.temp[0][4],self.temp[1][4],self.entier]) self.couple.append([self.temp[1][4],self.temp[0][4],self.entier]) self.compt=int() self.temp=list() self.wm_attributes("-disable",False) self.toplevel_dialog.destroy() self.deiconify() ###################################################### # - Programme Principale - # # /////////////////////////////////////////////// # # Description: Fenetre Principale du Programme # # /////////////////////////////////////////////// # if __name__ == '__main__': #initialisation du canvas fen =Tk() width=fen.winfo_screenwidth() height=fen.winfo_screenheight() largeure=900 hauteure=500 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) graphe =Canvas(fen, width =largeure, height =hauteure ,bg="light yellow") fen.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) fen.wm_title("Graphe Trace") graphe.pack(side =TOP, padx =5, pady =5) fen.resizable(False,False) icon=PhotoImage(file='img/img.png') fen.tk.call('wm','iconphoto',fen._w,icon) photo = PhotoImage(file="img/img.png",width=largeure,height=hauteure) graphe.create_image(300, 90, anchor=NW, image=photo) def menu(): menubar = Menu(fen) filemenu = Menu(menubar, tearoff = 0) filemenu.add_command(label="Graphe Oriente", command = graphe_oriente) filemenu.add_command(label="Graphe Non Oriente", command = graphe_non_oriente) filemenu.add_separator() filemenu.add_command(label = "Quitter", command = fen.destroy) menubar.add_cascade(label = "Graphe", menu = filemenu) filemenu = Menu(menubar, tearoff = 0) filemenu.add_command(label = "Auteur", command = Auteur) filemenu.add_command(label="Description", command = Description) filemenu.add_command(label="Version", command = Version) menubar.add_cascade(label = "A Propos", menu = filemenu) fen.config(menu = menubar) fen.mainloop() pass def donothing(): #filewin = Toplevel(root) #button = Button(filewin, text="Do nothing button") #button.pack() pass def graphe_oriente(): # mise en place du canevas app = GrapheOriente() app.mainloop() fen.mainloop() def graphe_non_oriente(): # mise en place du canevas app = Graphe_Non_Oriente() app.mainloop() fen.mainloop() def Auteur(): a_propos=""" Ce logiciel a ete creer par des etudiants en deuxiemme annnee Miage. Notamment par: Sawadogo R.R Sylvain Sawadogo Sidbewende Omar Yameogo Pingdwinde Boris """ fen.wm_attributes("-disable",True) fen.toplevel_dialog=tk.Toplevel(fen) fen.toplevel_dialog.minsize(502,210) fen.toplevel_dialog.wm_title("Auteur") width=fen.toplevel_dialog.winfo_screenwidth() height=fen.toplevel_dialog.winfo_screenheight() largeure=502 hauteure=210 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) fen.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) fen.toplevel_dialog.transient(fen) fen.toplevel_dialog.protocol("WM_DELETE_WINDOW", Close_Toplevel) fen.label=tk.Label(fen.toplevel_dialog, text=a_propos,justify='left',font='Century 13 bold') fen.label.grid(row=1,padx =5, pady =5) fen.yes_button=ttk.Button(fen.toplevel_dialog,text='Ok',width=82,command=Close_Toplevel) fen.yes_button.grid(row=2) def Description(): a_propos=""" Ce logiciel a ete creer dans le cadre de traitement de graphe. """ fen.wm_attributes("-disable",True) fen.toplevel_dialog=tk.Toplevel(fen) fen.toplevel_dialog.minsize(502,126) fen.toplevel_dialog.wm_title("Description") width=fen.toplevel_dialog.winfo_screenwidth() height=fen.toplevel_dialog.winfo_screenheight() largeure=502 hauteure=126 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) fen.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) fen.toplevel_dialog.transient(fen) fen.toplevel_dialog.protocol("WM_DELETE_WINDOW", Close_Toplevel) fen.label=tk.Label(fen.toplevel_dialog, text=a_propos,justify='left',font='Century 13 bold') fen.label.grid(row=1,padx =5, pady =5) fen.yes_button=ttk.Button(fen.toplevel_dialog,text='Ok',width=82,command=Close_Toplevel) fen.yes_button.grid(row=2) def Version(): a_propos="""Version 1.0.0""" fen.wm_attributes("-disable",True) fen.toplevel_dialog=tk.Toplevel(fen) fen.toplevel_dialog.minsize(300,64) fen.toplevel_dialog.wm_title("Version") width=fen.toplevel_dialog.winfo_screenwidth() height=fen.toplevel_dialog.winfo_screenheight() largeure=300 hauteure=64 x=(width/2)-(largeure/2) y=(height/2)-(hauteure/2) fen.toplevel_dialog.geometry('{}x{}+{}+{}'.format(largeure,hauteure,int(x),int(y))) fen.toplevel_dialog.transient(fen) fen.toplevel_dialog.protocol("WM_DELETE_WINDOW", Close_Toplevel) fen.label=tk.Label(fen.toplevel_dialog, text=a_propos,justify='left',font='Century 13 bold') fen.label.grid(row=1,padx =5, pady =5) fen.yes_button=ttk.Button(fen.toplevel_dialog,text='Ok',width=48,command=Close_Toplevel) fen.yes_button.grid(row=4) def Close_Toplevel (): fen.wm_attributes("-disable",False) fen.toplevel_dialog.destroy() fen.deiconify() menu() fen.mainloop()

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null

0

7

99dffa459bc7dfffb6f4a430b3ffac5f8cc05734

194

py

Python

smores/__init__.py

codylandry/Smores

cc0717b5edd0c09982820cc8705f73119641d0a2

[ "MIT" ]

7

2017-09-18T13:04:30.000Z

2021-06-03T06:48:26.000Z

smores/__init__.py

codylandry/Smores

cc0717b5edd0c09982820cc8705f73119641d0a2

[ "MIT" ]

1

2017-11-22T20:45:27.000Z

smores/__init__.py

codylandry/Smores

cc0717b5edd0c09982820cc8705f73119641d0a2

[ "MIT" ]

null

from .smores import Smores, AutocompleteResponse, TemplateString, TemplateFile, Schema, Nested __all__ = ['Smores', 'AutocompleteResponse', 'TemplateString', 'TemplateFile', 'Schema', 'Nested']

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8

412e848b7e0488d32c3276f0473edaef8c8bebb6

4,834

py

Python

scripts/figures/gene_abundance.py

vic-cheung/vectorseq

6f1aaeb3035c3c939b442e30076504ff84e43aa5

[ "MIT" ]

1

2022-03-30T19:56:43.000Z

scripts/figures/gene_abundance.py

vic-cheung/vectorseq

6f1aaeb3035c3c939b442e30076504ff84e43aa5

[ "MIT" ]

null

scripts/figures/gene_abundance.py

vic-cheung/vectorseq

6f1aaeb3035c3c939b442e30076504ff84e43aa5

[ "MIT" ]

null

#%% import scanpy as sc import pandas as pd from pathlib import Path from vectorseq.utils import check_gene_abundance, create_dir from vectorseq.marker_constants import BrainGenes data_dir = Path("/spare_volume/vectorseq-data") figure_save_dir = create_dir(data_dir / "gene_abundance") #%% [markdown] # ## Gene Abundance Table for Experiment: 3250, Brain Region: v1 #%% experiment_id = "3250" brain_region = "v1" run_dir = data_dir / experiment_id / brain_region all_cells_output_dir = create_dir(run_dir / "all_cells") adata = sc.read_h5ad(all_cells_output_dir / "filter" / "adata.h5ad") filtered_tg_list = [ gene for gene in BrainGenes.TG_MARKERS if gene.upper() in adata.obs.columns ] endogenous_genes_list = [ "Snap25", "Rbfox3", "Slc17a6", "Camk2a", "Gad1", "Gad2", "Mog", "Flt1", ] gene_list = filtered_tg_list + endogenous_genes_list count_fractions_df = pd.DataFrame() for gene in gene_list: temp = check_gene_abundance(adata, gene_of_interest=gene) if not temp.empty: count_fractions_df = count_fractions_df.append( pd.DataFrame.from_dict( { "gene": gene, "number_of_expressing_cells": temp.shape[0], "number_of_reads": temp.goi_counts.sum(), "abundance_in_expressing_cells": f"{round(temp.percent_count_goi.mean(),2)} +/- {round(temp.percent_count_goi.std(),2)}", }, orient="index", ).T ) print(f"{gene} detected.") else: print(f"{gene} not detected.") count_fractions_df.set_index(keys="gene", drop=True, inplace=True) count_fractions_df.to_csv( figure_save_dir / f"{experiment_id}_{brain_region}_all_cells_gene_abundance.csv" ) # %% #%% [markdown] # ## Gene Abundance Table for Experiment: 3382, Brain Region: snr #%% experiment_id = "3382" brain_region = "snr" run_dir = data_dir / experiment_id / brain_region all_cells_output_dir = create_dir(run_dir / "all_cells") adata = sc.read_h5ad(all_cells_output_dir / "filter" / "adata.h5ad") filtered_tg_list = [ gene for gene in BrainGenes.TG_MARKERS if gene.upper() in adata.obs.columns ] endogenous_genes_list = [ "Snap25", "Rbfox3", "Slc17a6", "Camk2a", "Gad1", "Gad2", "Mog", "Flt1", ] gene_list = filtered_tg_list + endogenous_genes_list count_fractions_df = pd.DataFrame() for gene in gene_list: temp = check_gene_abundance(adata, gene_of_interest=gene) if not temp.empty: count_fractions_df = count_fractions_df.append( pd.DataFrame.from_dict( { "gene": gene, "number_of_expressing_cells": temp.shape[0], "number_of_reads": temp.goi_counts.sum(), "abundance_in_expressing_cells": f"{round(temp.percent_count_goi.mean(),2)} +/- {round(temp.percent_count_goi.std(),2)}", }, orient="index", ).T ) print(f"{gene} detected.") else: print(f"{gene} not detected.") count_fractions_df.set_index(keys="gene", drop=True, inplace=True) count_fractions_df.to_csv( figure_save_dir / f"{experiment_id}_{brain_region}_all_cells_gene_abundance.csv" ) # %% #%% [markdown] # ## Gene Abundance Table for Experiment: 3454, Brain Region: sc #%% data_dir = Path("/spare_volume/vectorseq-data") experiment_id = "3454" brain_region = "sc" run_dir = data_dir / experiment_id / brain_region all_cells_output_dir = create_dir(run_dir / "all_cells") adata = sc.read_h5ad(all_cells_output_dir / "filter" / "adata.h5ad") filtered_tg_list = [ gene for gene in BrainGenes.TG_MARKERS if gene.upper() in adata.obs.columns ] endogenous_genes_list = [ "Snap25", "Rbfox3", "Slc17a6", "Camk2a", "Gad1", "Gad2", "Mog", "Flt1", ] gene_list = filtered_tg_list + endogenous_genes_list count_fractions_df = pd.DataFrame() for gene in gene_list: temp = check_gene_abundance(adata, gene_of_interest=gene) if not temp.empty: count_fractions_df = count_fractions_df.append( pd.DataFrame.from_dict( { "gene": gene, "number_of_expressing_cells": temp.shape[0], "number_of_reads": temp.goi_counts.sum(), "abundance_in_expressing_cells": f"{round(temp.percent_count_goi.mean(),2)} +/- {round(temp.percent_count_goi.std(),2)}", }, orient="index", ).T ) print(f"{gene} detected.") else: print(f"{gene} not detected.") count_fractions_df.set_index(keys="gene", drop=True, inplace=True) count_fractions_df.to_csv( figure_save_dir / f"{experiment_id}_{brain_region}_all_cells_gene_abundance.csv" ) #%%

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418053ee5e7fc1cc778869d9ebfee56bc7e30f8e

190

py

Python

built-in/TensorFlow/Official/nlp/Transformer_for_TensorFlow/noahnmt/attentions/__init__.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

null

built-in/TensorFlow/Official/nlp/Transformer_for_TensorFlow/noahnmt/attentions/__init__.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

3

2021-03-31T20:15:40.000Z

2022-02-09T23:50:46.000Z

built-in/TensorFlow/Official/nlp/Transformer_for_TensorFlow/noahnmt/attentions/__init__.py

Huawei-Ascend/modelzoo

df51ed9c1d6dbde1deef63f2a037a369f8554406

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright Huawei Noah's Ark Lab. from noahnmt.attentions import dot_attention from noahnmt.attentions import dot_prod_attention from noahnmt.attentions import sum_attention

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8

68efa2f47f268526a9d0f5230984fd8a4c74294f

5,184

py

Python

tests/test_plasmid_extractor.py

lowandrew/Plasmid_Assembler

7366e5e5a88e3a87d164934de0c6a3ee51f241b3

[ "MIT" ]

5

2018-01-16T04:55:10.000Z

2020-10-23T08:59:52.000Z

tests/test_plasmid_extractor.py

lowandrew/Plasmid_Assembler

7366e5e5a88e3a87d164934de0c6a3ee51f241b3

[ "MIT" ]

null

tests/test_plasmid_extractor.py

lowandrew/Plasmid_Assembler

7366e5e5a88e3a87d164934de0c6a3ee51f241b3

[ "MIT" ]

3

2018-02-16T18:49:07.000Z

2021-06-20T06:45:02.000Z

import os import shutil """ Remaining things to test: find_plasmid_kmer_scores find_score filter_similar_plasmids generate_consensus """ parentdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) os.sys.path.insert(0, parentdir) from plasmidextractor.PlasmidExtractor import * def test_mash_paired_gzipped(): mash_for_potential_plasmids(forward_reads='tests/test_fastqs/paired_R1.fastq.gz', reverse_reads='tests/test_fastqs/paired_R2.fastq.gz', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/mash', identity_cutoff=-1) assert os.path.isfile('tests/mash/screen_results.tsv') shutil.rmtree('tests/mash') def test_mash_unpaired_gzipped(): mash_for_potential_plasmids(forward_reads='tests/test_fastqs/paired_R1.fastq.gz', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/mash', identity_cutoff=-1) assert os.path.isfile('tests/mash/screen_results.tsv') shutil.rmtree('tests/mash') def test_mash_paired_uncompressed(): mash_for_potential_plasmids(forward_reads='tests/test_fastqs/paired_R1.fastq', reverse_reads='tests/test_fastqs/paired_R2.fastq', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/mash', identity_cutoff=-1) assert os.path.isfile('tests/mash/screen_results.tsv') shutil.rmtree('tests/mash') def test_mash_unpaired_uncompressed(): mash_for_potential_plasmids(forward_reads='tests/test_fastqs/paired_R1.fastq', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/mash', identity_cutoff=-1) assert os.path.isfile('tests/mash/screen_results.tsv') shutil.rmtree('tests/mash') def test_bait_and_trim_paired_gzipped(): bait_and_trim(forward_reads='tests/test_fastqs/paired_R1.fastq.gz', reverse_reads='tests/test_fastqs/paired_R2.fastq.gz', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/out') assert os.path.isfile('tests/out/plasmid_reads_R1.fastq.gz') and os.path.isfile('tests/out/plasmid_reads_R2.fastq.gz') shutil.rmtree('tests/out') def test_bait_and_trim_paired_uncompressed(): bait_and_trim(forward_reads='tests/test_fastqs/paired_R1.fastq', reverse_reads='tests/test_fastqs/paired_R2.fastq', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/out') assert os.path.isfile('tests/out/plasmid_reads_R1.fastq.gz') and os.path.isfile('tests/out/plasmid_reads_R2.fastq.gz') shutil.rmtree('tests/out') def test_bait_and_trim_unpaired_gzipped(): bait_and_trim(forward_reads='tests/test_fastqs/paired_R1.fastq.gz', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/out') assert os.path.isfile('tests/out/plasmid_reads_R1.fastq.gz') shutil.rmtree('tests/out') def test_bait_and_trim_unpaired_uncompressed(): bait_and_trim(forward_reads='tests/test_fastqs/paired_R1.fastq', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/out') assert os.path.isfile('tests/out/plasmid_reads_R1.fastq.gz') shutil.rmtree('tests/out') def test_bait_and_trim_paired_gzipped_lowmem(): bait_and_trim(forward_reads='tests/test_fastqs/paired_R1.fastq.gz', reverse_reads='tests/test_fastqs/paired_R2.fastq.gz', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/out', low_memory=True) assert os.path.isfile('tests/out/plasmid_reads_R1.fastq.gz') and os.path.isfile('tests/out/plasmid_reads_R2.fastq.gz') shutil.rmtree('tests/out') def test_bait_and_trim_unpaired_gzipped_lowmem(): bait_and_trim(forward_reads='tests/test_fastqs/paired_R1.fastq.gz', plasmid_db='tests/test_fasta/dummy_db.fasta', output_dir='tests/out', low_memory=True) assert os.path.isfile('tests/out/plasmid_reads_R1.fastq.gz') shutil.rmtree('tests/out') def test_fasta_write(): create_individual_fastas(plasmid_db='tests/test_fasta/dummy_db.fasta', potential_plasmid_list=['seq1'], output_dir='tests/fasta/') assert os.path.isfile('tests/fasta/seq1') and not os.path.isfile('tests/fasta/seq2') shutil.rmtree('tests/fasta') def test_fasta_kmerization(): kmerize_individual_fastas(potential_plasmid_list=['dummy_db.fasta'], fasta_dir='tests/test_fasta', output_dir='tests/kmerization') assert os.path.isfile('tests/kmerization/dummy_db.fasta.kmc_pre') and os.path.isfile('tests/kmerization/dummy_db.fasta.kmc_suf') shutil.rmtree('tests/kmerization')

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0

null

0

1

0

1

0

null

0

7

ec0bbf6fa340a30833399fb89ab800ad2004fd7e

8,517

py

Python

src/rest-api/tests/routes/v1/test_dex.py

geometry-labs/craft-multi-token-api

e533fd02c928c4857076ee11e14d8c0608bf367d

[ "Apache-2.0" ]

null

src/rest-api/tests/routes/v1/test_dex.py

geometry-labs/craft-multi-token-api

e533fd02c928c4857076ee11e14d8c0608bf367d

[ "Apache-2.0" ]

null

src/rest-api/tests/routes/v1/test_dex.py

geometry-labs/craft-multi-token-api

e533fd02c928c4857076ee11e14d8c0608bf367d

[ "Apache-2.0" ]

null

from fastapi.testclient import TestClient from app.core.config import settings def test_get_transactions(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/transactions") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/transactions?limit=6") response = r.json() assert r.status_code == 200 assert response assert len(response) == 6 def test_get_transactions_by_method(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/transactions/add") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/transactions/add?limit=4") response = r.json() assert r.status_code == 200 assert response assert len(response) == 4 r = client.get(f"{settings.PREFIX}/dex/transactions/remove") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/transactions/remove?limit=2") response = r.json() assert r.status_code == 200 assert response assert len(response) == 2 def test_get_logs(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/logs") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/logs?limit=3") response = r.json() assert r.status_code == 200 assert response assert len(response) == 3 def test_get_logs_by_method(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/logs/TransferSingle") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/logs/TransferSingle?limit=1") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/logs/Swap") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 r = client.get(f"{settings.PREFIX}/dex/logs/Swap?limit=1") response = r.json() assert r.status_code == 200 assert response assert len(response) == 1 def test_get_stats(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/stats/1") response = r.json() assert r.status_code == 200 assert response def test_get_stats_invalid_market_id(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/stats/bad-market-id") response = r.json() assert r.status_code == 400 # Bad request assert response def test_get_balance_of(prep_fixtures, client: TestClient) -> None: # Fine to leave request as constant since the blockchain is immutable r = client.get(f"{settings.PREFIX}/dex/balance-of/hxe7af5fcfd8dfc67530a01a0e403882687528dfcb/2") response = r.json() assert r.status_code == 200 assert response def test_get_balance_of_invalid_address(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/balance-of/0xbadaddress/2") response = r.json() assert r.status_code == 400 # Bad request assert response def test_get_swap_chart_5m(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/swap-chart/0/5m/0/1000000000") response = r.json() assert r.status_code == 200 assert len(response) == 4 assert response[0][0] == 0 # Timestamp assert response[0][1] == 1 # Open assert response[0][2] == 3 # Close assert response[0][3] == 3 # High assert response[0][4] == 1 # Low assert response[0][5] == 3 # Volume assert response[1][0] == 300000000 assert response[1][1] == 3 assert response[1][2] == 6 assert response[1][3] == 6 assert response[1][4] == 3 assert response[1][5] == 3 assert response[2][0] == 600000000 assert response[2][1] == 6 assert response[2][2] == 6 assert response[2][3] == 6 assert response[2][4] == 6 assert response[2][5] == 0 assert response[3][0] == 900000000 assert response[3][1] == 6 assert response[3][2] == 9 assert response[3][3] == 9 assert response[3][4] == 6 assert response[3][5] == 3 def test_get_swap_chart_15m(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/swap-chart/1/15m/0/3000000000") response = r.json() assert r.status_code == 200 assert len(response) == 4 assert response[0][0] == 0 assert response[0][1] == 1 assert response[0][2] == 3 assert response[0][3] == 3 assert response[0][4] == 1 assert response[0][5] == 3 assert response[1][0] == 900000000 assert response[1][1] == 3 assert response[1][2] == 6 assert response[1][3] == 6 assert response[1][4] == 3 assert response[1][5] == 3 assert response[2][0] == 1800000000 assert response[2][1] == 6 assert response[2][2] == 6 assert response[2][3] == 6 assert response[2][4] == 6 assert response[2][5] == 0 assert response[3][0] == 2700000000 assert response[3][1] == 6 assert response[3][2] == 9 assert response[3][3] == 9 assert response[3][4] == 6 assert response[3][5] == 3 def test_get_swap_chart_1h(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/swap-chart/2/1h/0/20000000000") response = r.json() assert r.status_code == 200 assert len(response) == 6 assert response[0][0] == 0 assert response[0][1] == 1 assert response[0][2] == 3 assert response[0][3] == 3 assert response[0][4] == 1 assert response[0][5] == 3 assert response[1][0] == 3600000000 assert response[1][1] == 3 assert response[1][2] == 6 assert response[1][3] == 6 assert response[1][4] == 3 assert response[1][5] == 3 assert response[2][0] == 7200000000 assert response[2][1] == 6 assert response[2][2] == 6 assert response[2][3] == 6 assert response[2][4] == 6 assert response[2][5] == 0 assert response[3][0] == 10800000000 assert response[3][1] == 6 assert response[3][2] == 9 assert response[3][3] == 9 assert response[3][4] == 6 assert response[3][5] == 3 def test_get_swap_chart_4h(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/swap-chart/3/4h/0/50000000000") response = r.json() assert r.status_code == 200 assert len(response) == 4 assert response[0][0] == 0 assert response[0][1] == 1 assert response[0][2] == 3 assert response[0][3] == 3 assert response[0][4] == 1 assert response[0][5] == 3 assert response[1][0] == 14400000000 assert response[1][1] == 3 assert response[1][2] == 6 assert response[1][3] == 6 assert response[1][4] == 3 assert response[1][5] == 3 assert response[2][0] == 28800000000 assert response[2][1] == 6 assert response[2][2] == 6 assert response[2][3] == 6 assert response[2][4] == 6 assert response[2][5] == 0 assert response[3][0] == 43200000000 assert response[3][1] == 6 assert response[3][2] == 9 assert response[3][3] == 9 assert response[3][4] == 6 assert response[3][5] == 3 def test_get_swap_chart_1d(prep_fixtures, client: TestClient) -> None: r = client.get(f"{settings.PREFIX}/dex/swap-chart/4/1d/0/300000000000") response = r.json() assert r.status_code == 200 assert len(response) == 4 assert response[0][0] == 0 assert response[0][1] == 1 assert response[0][2] == 3 assert response[0][3] == 3 assert response[0][4] == 1 assert response[0][5] == 3 assert response[1][0] == 86400000000 assert response[1][1] == 3 assert response[1][2] == 6 assert response[1][3] == 6 assert response[1][4] == 3 assert response[1][5] == 3 assert response[2][0] == 172800000000 assert response[2][1] == 6 assert response[2][2] == 6 assert response[2][3] == 6 assert response[2][4] == 6 assert response[2][5] == 0 assert response[3][0] == 259200000000 assert response[3][1] == 6 assert response[3][2] == 9 assert response[3][3] == 9 assert response[3][4] == 6 assert response[3][5] == 3

29.470588

100

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4.183981

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0.360879

0.116566

0.043783

0.88116

0.857278

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0

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8,517

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101

29.572917

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0

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0

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0

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1

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false

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null

1

0

1

0

1

0

null

0

1

0

9

d411989115cbd46c3d67f874ee5a2a13e7088d38

1,991

py

Python

ARC_B/ARC005_B.py

ryosuke0825/atcoder_python

185cdbe7db44ecca1aaf357858d16d31ce515ddb

[ "MIT" ]

null

ARC_B/ARC005_B.py

ryosuke0825/atcoder_python

185cdbe7db44ecca1aaf357858d16d31ce515ddb

[ "MIT" ]

null

ARC_B/ARC005_B.py

ryosuke0825/atcoder_python

185cdbe7db44ecca1aaf357858d16d31ce515ddb

[ "MIT" ]

null

xyw = input().split() X = int(xyw[0])-1 Y = int(xyw[1])-1 C = [] for _ in range(9): li = list(input()) C.append(li) ans = '' flg = True if xyw[2] == 'R': x_add = 1 for i in range(4): ans += C[Y][X] if X == 8: x_add = -1 X += x_add elif xyw[2] == 'L': x_add = -1 for i in range(4): ans += C[Y][X] if X == 0: x_add = 1 X += x_add elif xyw[2] == 'U': y_add = -1 for i in range(4): ans += C[Y][X] if Y == 0: y_add = 1 Y += y_add elif xyw[2] == 'D': y_add = 1 for i in range(4): ans += C[Y][X] if Y == 8: y_add = -1 Y += y_add elif xyw[2] == 'RU': x_add = 1 y_add = -1 for i in range(4): ans += C[Y][X] if X == 8 and Y == 0: x_add = -1 y_add = 1 elif X == 8 and Y != 0: x_add = -1 elif X != 8 and Y == 0: y_add = 1 X += x_add Y += y_add elif xyw[2] == 'RD': x_add = 1 y_add = 1 for i in range(4): ans += C[Y][X] if X == 8 and Y == 8: x_add = -1 y_add = -1 elif X == 8 and Y != 8: x_add = -1 elif X != 8 and Y == 8: y_add = -1 X += x_add Y += y_add elif xyw[2] == 'LU': x_add = -1 y_add = -1 for i in range(4): ans += C[Y][X] if X == 0 and Y == 0: x_add = 1 y_add = 1 elif X == 0 and Y != 0: x_add = 1 elif X != 0 and Y == 0: y_add = 1 X += x_add Y += y_add elif xyw[2] == 'LD': x_add = -1 y_add = 1 for i in range(4): ans += C[Y][X] if X == 0 and Y == 8: x_add = 1 y_add = -1 elif X == 0 and Y != 8: x_add = 1 elif X != 0 and Y == 8: y_add = -1 X += x_add Y += y_add print(ans)

20.525773

31

0.353591

349

1,991

1.888252

0.103152

0.194234

0.121396

0.097117

0.849772

0.846737

0.7739

0.664643

0

0.079882

0.490708

1,991

96

32

20.739583

0.57002

0

0.638298

0

0.006027

0

1

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false

0

0.010638

0

1

null

0

1

0

null

0

7

d413f6505308e4d39c96e7ca124b0fa0f103d091

8,548

py

Python

t2dm/manager/intelligence/interactions/inpatient1.py

nhsx-mirror/SynPath_Diabetes

1d9bd1c83f20820a35125c94e8b058bdd1a6ac3c

[ "MIT" ]

null

t2dm/manager/intelligence/interactions/inpatient1.py

nhsx-mirror/SynPath_Diabetes

1d9bd1c83f20820a35125c94e8b058bdd1a6ac3c

[ "MIT" ]

null

t2dm/manager/intelligence/interactions/inpatient1.py

nhsx-mirror/SynPath_Diabetes

1d9bd1c83f20820a35125c94e8b058bdd1a6ac3c

[ "MIT" ]

1

2021-09-29T10:00:23.000Z

import datetime # Interactions for inpatient care # "review_and_consultation", # "bd_hypoglycaemic_ep", # "bd_hyperglycaemic_ep", # "bd_lower_limb_compl", # "enhanced_independence", # "retinal_procedure", # "amputation" # Inpatient interaction 1: Inpatient review and consultation (might take out if not in spell) def review_and_consultation(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "review and consultation", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "review and consultation", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, PSSRU 2018-19 } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, ) # Inpatient interaction 2: Hypoglycaemic episode bed day def bd_hypoglycaemic_ep(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "hypoglycaemic ep bd", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "hypoglycaemic ep bd", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, PSSRU 2018-19 } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, ) # Inpatient interaction 3: Hyperglycaemic episode bed day def bd_hyperglycaemic_ep(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "hyperglycaemic ep bd", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "hyperglycaemic ep bd", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, PSSRU 2018-19 } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, ) # Inpatient interaction 4: Lower limb complications bed day def bd_lower_limb_ep(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "lower limb ep bd", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "lower limb ep bd", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, to be updated } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, ) # Inpatient interaction 5: Enhanced independence def enhanced_indep(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "enhanced independence", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "enhanced independence", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, to be updated } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, ) # Inpatient interaction 6: Retinal procedure def retinal_procedure(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "retinal procedure", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "retinal procedure", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, to be updated } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, ) # Inpatient interaction 7: Amputation def amputation(patient, environment, patient_time): encounter = { "resource_type": "Encounter", "name" : "amputation", "start": patient_time, } entry = { "resource_type" : "Observation", "name": "amputation", "start": encounter["start"] + datetime.timedelta(minutes=15), "cost": 3053, # NEL long stay from PSSRU 2018-19 - to be updated "glucose": -1, # dummy glucose impact, to be updated "carbon": 5032, # carbon impact, to be updated } new_patient_record_entries = [encounter, entry] next_environment_id_to_prob = {2: 0.5, 30: 0.3, 40: 0.2} next_environment_id_to_time = { 2: datetime.timedelta(days=10), # TODO: from initial patient_time (not last) 30: datetime.timedelta(days=20), 40: datetime.timedelta(days=20), } update_data = {"new_patient_record_entries": new_patient_record_entries} return ( patient, environment, update_data, next_environment_id_to_prob, next_environment_id_to_time, )

31.776952

94

0.62576

996

8,548

5.123494

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0.093278

0.104252

0.892808

0.866941

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0.825789

0.776406

0

0.04752

0.266378

8,548

269

95

31.776952

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0

null

0

1

0

1

0

null

0

7

d44a39453789db4c72fb031457a84e78d700988f

355

py

Python

auth/auth.py

helthazar/contestparser

43843de5fd3cb7af7f24c8cbbd5ea068abb7f469

[ "MIT" ]

null

auth/auth.py

helthazar/contestparser

43843de5fd3cb7af7f24c8cbbd5ea068abb7f469

[ "MIT" ]

null

auth/auth.py

helthazar/contestparser

43843de5fd3cb7af7f24c8cbbd5ea068abb7f469

[ "MIT" ]

null

class Auth: @staticmethod def opencup(): return {'login': '', 'password' : ''} @staticmethod def yandexcontest(): return {'login': '', 'password' : ''} @staticmethod def atcoder(): return {'login': '', 'password' : ''} @staticmethod def hackerrank(): return {'login': '', 'password' : ''}

22.1875

45

0.512676

26

355

7

0.423077

0.32967

0.417582

0.510989

0.56044

0

0.292958

355

16

46

22.1875

0.7251

0

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true

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null

0

1

0

1

0

9

d458328c51a408b45453324ab4274a2b24ca3ca7

4,133

py

Python

test_requests.py

charlax/vcrpy

1d3fe5c33ecf06b494fa6cbea4acd62585820687

[ "MIT" ]

null

test_requests.py

charlax/vcrpy

1d3fe5c33ecf06b494fa6cbea4acd62585820687

[ "MIT" ]

null

test_requests.py

charlax/vcrpy

1d3fe5c33ecf06b494fa6cbea4acd62585820687

[ "MIT" ]

null

# coding=utf-8 import os import unittest import vcr import requests TEST_CASSETTE_FILE = 'cassettes/test_req.yaml' class TestRequestsGet(unittest.TestCase): def setUp(self): self.unmolested_response = requests.get('http://httpbin.org/') with vcr.use_cassette(TEST_CASSETTE_FILE): self.initial_response = requests.get('http://httpbin.org/') self.cached_response = requests.get('http://httpbin.org/') def tearDown(self): try: os.remove(TEST_CASSETTE_FILE) except OSError: pass def test_initial_response_code(self): self.assertEqual(self.unmolested_response.status_code, self.initial_response.status_code) def test_cached_response_code(self): self.assertEqual(self.unmolested_response.status_code, self.cached_response.status_code) def test_initial_response_headers(self): self.assertEqual(self.unmolested_response.headers['content-type'], self.initial_response.headers['content-type']) def test_cached_response_headers(self): self.assertEqual(self.unmolested_response.headers['content-type'], self.cached_response.headers['content-type']) def test_initial_response_text(self): self.assertEqual(self.unmolested_response.text, self.initial_response.text) def test_cached_response_text(self): self.assertEqual(self.unmolested_response.text, self.cached_response.text) class TestRequestsAuth(unittest.TestCase): def setUp(self): self.unmolested_response = requests.get('https://httpbin.org/basic-auth/user/passwd', auth=('user', 'passwd')) with vcr.use_cassette(TEST_CASSETTE_FILE): self.initial_response = requests.get('https://httpbin.org/basic-auth/user/passwd', auth=('user', 'passwd')) self.cached_response = requests.get('https://httpbin.org/basic-auth/user/passwd', auth=('user', 'passwd')) def tearDown(self): try: os.remove(TEST_CASSETTE_FILE) except OSError: pass def test_initial_response_code(self): self.assertEqual(self.unmolested_response.status_code, self.initial_response.status_code) def test_cached_response_code(self): self.assertEqual(self.unmolested_response.status_code, self.cached_response.status_code) def test_cached_response_auth_can_fail(self): auth_fail_cached = requests.get('https://httpbin.org/basic-auth/user/passwd', auth=('user', 'passwdzzz')) self.assertNotEqual(self.unmolested_response.status_code, auth_fail_cached.status_code) class TestRequestsPost(unittest.TestCase): def setUp(self): payload = {'key1': 'value1', 'key2': 'value2'} self.unmolested_response = requests.post('http://httpbin.org/post', payload) with vcr.use_cassette(TEST_CASSETTE_FILE): self.initial_response = requests.post('http://httpbin.org/post', payload) self.cached_response = requests.post('http://httpbin.org/post', payload) def tearDown(self): try: os.remove(TEST_CASSETTE_FILE) except OSError: pass def test_initial_post_response_text(self): self.assertEqual(self.unmolested_response.text, self.initial_response.text) def test_cached_post_response_text(self): self.assertEqual(self.unmolested_response.text, self.cached_response.text) class TestRequestsHTTPS(unittest.TestCase): maxDiff = None def setUp(self): self.unmolested_response = requests.get('https://httpbin.org/get') with vcr.use_cassette(TEST_CASSETTE_FILE): self.initial_response = requests.get('https://httpbin.org/get') self.cached_response = requests.get('https://httpbin.org/get') def tearDown(self): try: os.remove(TEST_CASSETTE_FILE) except OSError: pass def test_initial_https_response_text(self): self.assertEqual(self.unmolested_response.text, self.initial_response.text) def test_cached_https_response_text(self): self.assertEqual(self.unmolested_response.text, self.cached_response.text)

37.572727

121

0.709412

502

4,133

5.603586

0.125498

0.076786

0.132954

0.098116

0.873445

0.84856

0.803413

0.795236

0.733736

0

0.001471

0.177595

4,133

109

122

37.917431

0.826125

0.002903

0

0.538462

0

0.12066

0.005584

0

0.166667

1

0.269231

false

0.102564

0.051282

0

0.384615

0

null

0

1

0

null

0

1

0

1

0

8

2e4ce02ba83abe7b1d661489dcf0a33228a2cf30

8,373

py

Python

game_jam/Source/main.py

AustinLittle2020/CS-Hangout-Game_Jam

44216ad0166023e1b45b4b00c83098fce1cb8264

[ "MIT" ]

null

game_jam/Source/main.py

AustinLittle2020/CS-Hangout-Game_Jam

44216ad0166023e1b45b4b00c83098fce1cb8264

[ "MIT" ]

1

2021-12-20T07:19:45.000Z

game_jam/Source/main.py

AustinLittle2020/CS-Hangout-Game_Jam

44216ad0166023e1b45b4b00c83098fce1cb8264

[ "MIT" ]

1

2021-12-20T07:15:15.000Z

import pygame import os import time from spaceship import * from pygame.locals import * # constants WIDTH, HEIGHT = 700, 800 WINDOW = pygame.display.set_mode((WIDTH, HEIGHT)) pygame.display.set_caption("Failure is Inevitable") BLACK = (0, 0, 0) FPS = 60 VEL = 4 p_x = 330 p_y = 650 width = 50 height = 50 movement = False # player perameters class Player(pygame.sprite.Sprite): def __init__(self): super().__init__() self.sprite = [pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship3.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship4.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship5.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship6.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship7.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship8.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship9.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship10.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship1.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship2.png'))] self.current_sprite = 0 self.image = self.sprite[self.current_sprite] self.rect = self.image.get_rect(center=(WIDTH/2, HEIGHT-100)) def update(self): self.current_sprite += 0.3 if self.current_sprite >= len(self.sprite): self.current_sprite = 0 self.image = self.sprite[int(self.current_sprite)] self.rect.center = pygame.mouse.get_pos() def create_bullet(self): return Bullet(pygame.mouse.get_pos()[0], pygame.mouse.get_pos()[1]) # bullet perameters class Bullet(pygame.sprite.Sprite): def __init__(self, pos_x, pos_y): super().__init__() self.image = pygame.image.load( os.path.join('game_jam', 'Assets', 'Laser Animations', 'laser1.png')) self.rect = self.image.get_rect(center=(pos_x, pos_y)) def update(self): self.rect.y -= 5 if self.rect.y <= 0: self.kill() # player and bullet groups player = Player() player_group = pygame.sprite.Group() player_group.add(player) pygame.mouse.set_visible(False) bullet_group = pygame.sprite.Group() # main function def main(): # background image BACKGROUND = pygame.transform.scale(pygame.image.load(os.path.join( 'game_jam', 'Assets', 'Background', 'Galaxy_bg', 'Purple_Nebula', 'PN1.png')), (WIDTH, HEIGHT)).convert() # variables clock = pygame.time.Clock() run = True y = 0 # constants WIDTH, HEIGHT = 700, 800 WINDOW = pygame.display.set_mode((WIDTH, HEIGHT)) pygame.display.set_caption("Failure is Inevitable") BLACK = (0, 0, 0) FPS = 60 VEL = 4 p_x = 330 p_y = 650 width = 50 height = 50 movement = False # player perameters class Player(pygame.sprite.Sprite): def __init__(self): super().__init__() self.sprite = [pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship3.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship4.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship5.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship6.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship7.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship8.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship9.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship10.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship1.png')), pygame.image.load( os.path.join("game_jam", "Assets", 'Spaceship', 'ship', 'ship2.png'))] self.current_sprite = 0 self.image = self.sprite[self.current_sprite] self.rect = self.image.get_rect(center=(WIDTH/2, HEIGHT-100)) def update(self): self.current_sprite += 0.3 if self.current_sprite >= len(self.sprite): self.current_sprite = 0 self.image = self.sprite[int(self.current_sprite)] self.rect.center = pygame.mouse.get_pos() def create_bullet(self): return Bullet(pygame.mouse.get_pos()[0], pygame.mouse.get_pos()[1]) # bullet perameters class Bullet(pygame.sprite.Sprite): def __init__(self, pos_x, pos_y): super().__init__() self.image = pygame.image.load( os.path.join('game_jam', 'Assets', 'Laser Animations', 'laser1.png')) self.rect = self.image.get_rect(center=(pos_x, pos_y)) def update(self): self.rect.y -= 5 if self.rect.y <= 0: self.kill() # player and bullet groups player = Player() player_group = pygame.sprite.Group() player_group.add(player) pygame.mouse.set_visible(False) bullet_group = pygame.sprite.Group() # main function def main(): # background image BACKGROUND = pygame.transform.scale(pygame.image.load(os.path.join( 'game_jam', 'Assets', 'Background', 'Galaxy_bg', 'Purple_Nebula', 'PN1.png')), (WIDTH, HEIGHT)).convert() # variables # Initializes mixer pygame.mixer.init() # Grabs sound file pygame.mixer.music.load(os.path.join( 'game_jam', 'Assets', 'Sounds', 'spaceship_music', 'Far-Out_OST', 'OST', 'Far-Out-Hurry_Up.wav')) # Plays music indefinitely pygame.mixer.music.play(-1) # Sets music volume pygame.mixer.music.set_volume(0.3) clock = pygame.time.Clock() run = True y = 0 # music pygame.mixer.init() pygame.mixer.music.load(os.path.join( 'game_jam', 'Assets', 'Sounds', 'Far Out Hurry Up.wav')) pygame.mixer.music.play(-1) pygame.mixer.music.set_volume(0.3) # run while run: clock.tick(FPS) for event in pygame.event.get(): if event.type == pygame.QUIT: run = False if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE: run = False if event.type == pygame.MOUSEBUTTONDOWN: bullet_group.add(player.create_bullet()) if event.type == pygame.KEYDOWN: player.animate() if event.type == pygame.KEYDOWN and event.key == pygame.K_ESCAPE: run = False # updates backgroud for scrolling effect WINDOW.fill(BLACK) rel_y = y % BACKGROUND.get_rect().height WINDOW.blit(BACKGROUND, (0, rel_y - BACKGROUND.get_rect().height)) if rel_y < HEIGHT: WINDOW.blit(BACKGROUND, (0, rel_y)) y += 1 # update screen bullet_group.draw(WINDOW) bullet_group.update() player_group.draw(WINDOW) player_group.update() pygame.display.update() # starts main function if __name__ == "__main__": main() main.py

30.227437

105

0.54413

969

8,373

4.560372

0.150671

0.035302

0.058837

0.082372

0.879384

0.847929

0.842272

0.816022

0.802897

0

0.018274

0.313747

8,373

276

106

30.336957

0.750783

0.045981

0

0.853535

0

0.133978

0

1

0.060606

false

0

0.025253

0.010101

0.116162

0

null

0

1

0

1

0

null

0

7

2e84df5100eb34e590dde3692727724451d660e6

186

py

Python

congregation/codegen/python/libs/external/__init__.py

CCD-HRI/congregation

a552856b03a64a4295792184107c4e529ca3f4ae

[ "MIT" ]

3

2020-10-05T16:30:15.000Z

2021-01-22T13:38:02.000Z

congregation/codegen/python/libs/external/__init__.py

multiparty/congregation

a552856b03a64a4295792184107c4e529ca3f4ae

[ "MIT" ]

null

congregation/codegen/python/libs/external/__init__.py

multiparty/congregation

a552856b03a64a4295792184107c4e529ca3f4ae

[ "MIT" ]

1

2021-08-13T07:28:30.000Z

from congregation.codegen.python.libs.external.unary import * from congregation.codegen.python.libs.external.binary import * from congregation.codegen.python.libs.external.nary import *

46.5

62

0.83871

24

186

6.5

0.416667

0.307692

0.442308

0.557692

0.865385

0.602564

0

0.064516

186

3

63

62

0.896552

0

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true

0

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

9

5cfb44436bfafdbb15a5fdebd26adcefad3628c4

1,809

py

Python

Ago-Dic-2021/valera-rangel-pablo/Practica 3/test_calculator_pytest.py

AnhellO/DAS_Sistemas

07b4eca78357d02d225d570033d05748d91383e3

[ "MIT" ]

41

2017-09-26T09:36:32.000Z

2022-03-19T18:05:25.000Z

Ago-Dic-2021/valera-rangel-pablo/Practica 3/test_calculator_pytest.py

AnhellO/DAS_Sistemas

07b4eca78357d02d225d570033d05748d91383e3

[ "MIT" ]

67

2017-09-11T05:06:12.000Z

2022-02-14T04:44:04.000Z

Ago-Dic-2021/valera-rangel-pablo/Practica 3/test_calculator_pytest.py

AnhellO/DAS_Sistemas

07b4eca78357d02d225d570033d05748d91383e3

[ "MIT" ]

210

2017-09-01T00:10:08.000Z

2022-03-19T18:05:12.000Z

import pytest from calculator import * @pytest.mark.parametrize("input_a, input_b, expected_result", [ (-5, 2, 'Imposible Raiz de un Negativo') ]) def testRaizDeNegativo(input_a, input_b, expected_result): assert Calculator(input_a, input_b).raiz() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (5, 0, 'ZeroDivisionError: division by zero') ]) def testSobreCero(input_a, input_b, expected_result): assert Calculator(input_a, input_b).division() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (100, 54, 154) ]) def testSumaDosNumeros(input_a, input_b, expected_result): assert Calculator(input_a, input_b).suma() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (150, 75, 75) ]) def testRestaDosNumeros(input_a, input_b, expected_result): assert Calculator(input_a, input_b).resta() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (5, 2, 25) ]) def testPotencia(input_a, input_b, expected_result): assert Calculator(input_a, input_b).potencia() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (100, 0, 'Sin Definir') ]) def testRaizCero(input_a, input_b, expected_result): assert Calculator(input_a, input_b).raiz() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (10, 0, 1) ]) def testPotenciaALaCero(input_a, input_b, expected_result): assert Calculator(input_a, input_b).potencia() == expected_result @pytest.mark.parametrize("input_a, input_b, expected_result", [ (100, 23, 2300) ]) def testMultiplicacion(input_a, input_b, expected_result): assert Calculator(input_a, input_b).multiplicacion() == expected_result

30.661017

75

0.739635

245

1,809

5.167347

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cf372286c3b00f6d57b36a97cb015d54cb8dfc38

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py

Python

IndoorPositionEstimator/cflib/drone_quaternion.py

capriele/Crazyflie-Indoor-Position-Logger-Controller

6f7a44984553d85a66a29c169a2f7c758a2aaac7

[ "Apache-2.0" ]

6

2017-04-23T15:47:57.000Z

2020-03-15T17:52:15.000Z

IndoorPositionEstimator/cflib/drone_quaternion.py

capriele/Crazyflie-Indoor-Position-Logger-Controller

6f7a44984553d85a66a29c169a2f7c758a2aaac7

[ "Apache-2.0" ]

null

IndoorPositionEstimator/cflib/drone_quaternion.py

capriele/Crazyflie-Indoor-Position-Logger-Controller

6f7a44984553d85a66a29c169a2f7c758a2aaac7

[ "Apache-2.0" ]

null

""" Quadcopter Model + LQR Control + BackStepping Control """ # # Author: Alberto Petrucci (petrucci.alberto@gmail.com) 2017 # #__author__ = "Alberto Petrucci" #__copyright__ = "Copyright 2017, Alberto Petrucci" #__credits__ = ["Alberto Petrucci"] #__license__ = "Apache" #__version__ = "1.0.0" #__maintainer__ = "Alberto Petrucci" #__email__ = "petrucci.alberto@gmail.com" #__status__ = "Production" from __future__ import division from numpy import * from math import * from control import * class Quadcopter: def __init__(self, dt): ## Parametri ambiente self.g = 9.81 self.airFriction = 0 self.dt = dt self.t = 0 ## Parametri drone self.m = 27/1000 # massa del drone in g self.d = (65.0538/1000)*sin(pi/4) # distanza dal centro ai motori self.c = 0.1 # inerzia delle eliche self.alpha = 1 self.Ix = self.m * self.d * self.d self.Iy = self.m * self.d * self.d self.Iz = 2 * self.m * self.d * self.d # Cambiando tali parametri diamo priorita maggiori o minori self.beta1 = 0.3 self.beta2 = 0.3 self.beta3x = 0.2#1.0 self.beta3y = 0.2#1.0 self.beta3z = 0.2#0.5 self.beta3x = 5.0#5.0 self.beta3y = 5.0#5.0 self.beta3z = 1.0#1.0 self.beta4 = 0.2 self.beta = 500 #self.beta = 3000 self.thrustGain = 1 #self.thrustGain = 1.34 #self.thrustGain = 1.37 self.Tf = dt self.Mat_J = matrix([ [self.m*self.d*self.d, 0, 0], [0, self.m*self.d*self.d, 0], [0, 0, 2*self.m*self.d*self.d] ]) self.Mat_Jinv = self.Mat_J.I self.Mat_T = matrix([ [1, 1, 1, 1], [-self.d, -self.d, self.d, self.d], [self.d, -self.d, -self.d, self.d], [self.c, -self.c, self.c, -self.c] ]) self.Mat_Tinv = self.Mat_T.I ## Modello linearizzato self.A = matrix([ [0, 0, 0, 0, 0, 0, -0.5*sqrt(1-self.alpha*self.alpha), 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0.5*self.alpha, -0.5*sqrt(1-self.alpha*self.alpha), 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0.5*sqrt(1-self.alpha*self.alpha), 0.5*self.alpha, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0.5*self.alpha, 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, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 2*self.g*sqrt(1-self.alpha*self.alpha), 2*self.g*self.alpha, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, -2*self.g*self.alpha, 2*self.g*sqrt(1-self.alpha*self.alpha), 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ]) self.B = matrix([ [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], [1/self.m, 0, 0, 0], ]) self.C = eye(13) self.D = zeros((13, 4)) ## SATURAZIONE MOTORI self.fmotmax = 0.5886/4 # max forza generata dai motori self.q_bar = matrix([ [self.alpha], [0], [0], [sqrt(1 - self.alpha*self.alpha)] ]) self.omega_bar = zeros((3, 1)) self.p_bar = matrix([ [0], [0], [1] ]) self.v_bar = zeros((3, 1)) self.ftot_bar = self.m * self.g self.tau_bar = matrix([ [0], [0], [0] ]) self.x_bar = vstack((self.q_bar, self.omega_bar, self.p_bar, self.v_bar)) self.u_bar = vstack((self.ftot_bar, self.tau_bar)) self.u = matrix([ [0], [0], [0], [0] ]) self.Qm = matrix([ [self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, self.beta2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, self.beta2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, self.beta2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, self.beta3x, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, self.beta3y, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta3z, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta4], ]) self.R = self.beta * eye(4) ## LQR self.Amm = matrix([ [0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0.5, 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, 1.0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0], [0, 19.62, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [-19.62, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] ]) self.Bmm = matrix([ [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 8.7393e+03, 0, 0], [0, 0, 8.7393e+03, 0], [0, 0, 0, 4.3696e+03], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [37.0370, 0, 0, 0] ]) self.Cmm = matrix([ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] ]) self.Qmm = matrix([ [self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, self.beta1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, self.beta2, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, self.beta2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, self.beta2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, self.beta3x, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, self.beta3y, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, self.beta3z, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta4, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta4, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, self.beta4] ]) self.Ut = matrix([ [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] ]) [self.Km, self.Pm, self.em] = lqr(self.Amm, self.Bmm, self.Qmm, self.R) self.K_LQR = self.Km*self.Ut ''' # stampo guadagni lqr per c for k in range(0, 4): string = "" for i in range(0, 13): string += str(self.K_LQR.item((k, i)))+", " # rimuovo gli ultimi due caratteri string = string[:-2] print "{"+string+"}," ''' # Stato self.q = matrix([ [self.alpha], [0], [0], [sqrt(1-self.alpha*self.alpha)] ]) self.omega = matrix([ [0], [0], [0] ]) self.p = matrix([ [0], [0], [0] ]) self.v = matrix([ [0], [0], [0] ]) self.x = vstack(( self.q, self.omega, self.p, self.v )) self.setPoint = self.x # Variabili per l'osservatore (ricostruzione stato) self.x_hat = self.x # variabili misurate (quaternioni + posizioni) self.y = matrix([ [0], [0], [0], [0], [0], [0], [0] ]) # Variabili per BackStepping controller self.backsteppingSetPoint = matrix([ # Roll [0, 0, 0], # Pitch [0, 0, 0], # Yaw [0, 0, 0], # X [0, 0, 0], # Y [0, 0, 0], # Z [0, 0, 0], ]) def setSetPoint(self, q0, q1, q2, q3, omegax, omegay, omegaz, px, py, pz, vx, vy, vz): self.setPoint = matrix([ [q0], [q1], [q2], [q3], [omegax], [omegay], [omegaz], [px], [py], [pz], [vx], [vy], [vz], ]) def setBacksteppingSetPoint(self, xd): self.backsteppingSetPoint = xd def setState(self, q0, q1, q2, q3, omegax, omegay, omegaz, px, py, pz, vx, vy, vz): self.q = matrix([ [q0], [q1], [q2], [q3] ]) self.q = self.q/linalg.norm(self.q) deg2rad = pi/180.0 self.omega = matrix([ [omegax*deg2rad], [omegay*deg2rad], [omegaz*deg2rad] ]) self.p = matrix([ [px], [py], [pz] ]) self.v = matrix([ [vx], [vy], [vz] ]) ''' # Aggiorno variabili misurate self.y = matrix([ [q0], [q1], [q2], [q3], [px], [py], [pz] ]) # Aggiorno l'osservatore self.update_observer() # Aggiorno lo stato (misurato + stimato) self.x = vstack(( self.q, [self.x_hat[4, 0]*deg2rad], [self.x_hat[5, 0]*deg2rad], [self.x_hat[6, 0]*deg2rad], self.p, [self.x_hat[10, 0]], [self.x_hat[11, 0]], [self.x_hat[12, 0]] )) ''' # Nel caso in cui misuro tutto (e' lento => stimo) self.x = vstack(( self.q, self.omega, self.p, self.v )) def update(self): self.u = self.u_bar - self.K_LQR * (self.x - self.setPoint) # Calcolo le forze f1 f2 f3 f4 f = self.Mat_Tinv*self.u # Applico la saturazione for i in range(0, 4): if f[i, 0] > self.fmotmax: f[i, 0] = self.fmotmax if f[i, 0] < 0: f[i, 0] = 0 # Calcolo l'ingresso saturato self.u = self.Mat_T*f #self.predict(self.u) def backstepping2(self): # Current State x1 = self.q[0, 0] # wq3 x2 = self.q[1, 0] # wq3 x3 = self.q[2, 0] # wq3 x4 = self.q[3, 0] # wq3 # Angular Speeds x5 = self.omega[0, 0] # wx x6 = self.omega[1, 0] # wy x7 = self.omega[2, 0] # wz # Positions x8 = self.p[0, 0] # x x9 = self.p[1, 0] # y x10 = self.p[2, 0] # z # Speeds x11 = self.v[0, 0] # vx x12 = self.v[1, 0] # vy x13 = self.v[2, 0] # vz # contiene il riferimento + la sua derivata 1a e 2a xd = self.backsteppingSetPoint print matrix([ [xd[3, 0], xd[4, 0], xd[5, 0]], [x8, x9, x10], ]) # Z c10 = 8 c13 = 3 e10 = xd[5, 0] - x10 e13 = x13 - xd[5, 1] - c10 * e10 u1 = self.m * (self.g + e10 + xd[5, 2] - c13 * e13 + c10 * (xd[5, 1] - x13)) / (x1*x1 - x2*x2 - x3*x3 + x4*x4) if u1 != 0: # X c8 = 8#8 c11 = 4#4 e8 = xd[3, 0] - x8 e11 = x11 - xd[3, 1] - c8 * e8 Ux = self.m * (e8 + xd[3, 2] - c11 * e11 + c8 * (xd[3, 1] - x11)) / (2*u1) # Y c9 = 8#8 c12 = 4#4 e9 = xd[4, 0] - x9 e12 = x12 - xd[4, 1] - c9 * e9 Uy = self.m * (e9 + xd[4, 2] - c12 * e12 + c9 * (xd[4, 1] - x12)) / (2*u1) else: Ux = 0 Uy = 0 # Desired Quaternion qd = matrix([ [1], [-(Uy-x3*x4)/x1], [(Ux-x2*x4)/x1], [xd[2, 0]], ]) qd = qd / linalg.norm(qd) # Compute quaternion error q = matrix([ [x1], [-x2], [-x3], [-x4] ]) qe = self.quaternionProduct(q, qd) w = matrix([ [0], [-x5], [-x6], [-x7] ]) norm_w = linalg.norm(w) if norm_w != 0: w = w / norm_w we = self.quaternionProduct(w, qe) c4 = 20 c44 = 10 e4 = qe[3, 0] e44 = 0.5 * (-x3 * x5 + x2 * x6 + x1 * x7) - c4 * e4 xd4d = we[3, 0] c3 = 60 c33 = 60 e3 = qe[2, 0] e33 = 0.5 * (x4 * x5 + x1 * x6 - x2 * x7) - c3 * e3 xd3d = we[2, 0] c2 = 60 c22 = 60 e2 = qe[1, 0] e22 = 0.5 * (x1 * x5 - x4 * x6 + x3 * x7) - c2 * e2 xd2d = we[1, 0] x1_2 = x1 * x1 x2_2 = x2 * x2 x3_2 = x3 * x3 x4_2 = x4 * x4 x5_2 = x5 * x5 x6_2 = x6 * x6 x7_2 = x7 * x7 x1_3 = x1_2 * x1 x2_3 = x2_2 * x2 x3_3 = x3_2 * x3 x4_3 = x4_2 * x4 div = x1 * (x1_2 + x2_2 + x3_2 + x4_2) mult = self.s * self.d * self.m u2 = 0 u3 = 0 u4 = 0 if div != 0: u4 = (mult * (x4_3 * x6_2 - x4_3 * x5_2 + x4_3 * x7_2 + 4 * e4 * x1_2 + 4 * e4 * x4_2 - 2 * c4 * x1_3 * x7 + 4 * c4 * x1_2 * xd4d + 4 * c4 * x4_2 * xd4d - 2 * x1_3 * x5 * x6 - x1_2 * x4 * x5_2 + x1_2 * x4 * x6_2 + x2_2 * x4 * x5_2 + x1_2 * x4 * x7_2 + x2_2 * x4 * x6_2 + x3_2 * x4 * x5_2 + x2_2 * x4 * x7_2 + x3_2 * x4 * x6_2 + x3_2 * x4 * x7_2 + 4 * e2 * x1 * x3 - 4 * e3 * x1 * x2 + 4 * e2 * x2 * x4 + 4 * e3 * x3 * x4 - 4 * c44 * e44 * x1_2 - 4 * c44 * e44 * x4_2 - 4 * c22 * e22 * x1 * x3 - 4 * c22 * e22 * x2 * x4 + 4 * c33 * e33 * x1 * x2 - 4 * c33 * e33 * x3 * x4 + 4 * c2 * x1 * x3 * xd2d - 4 * c3 * x1 * x2 * xd3d + 4 * c2 * x2 * x4 * xd2d + 4 * c3 * x3 * x4 * xd3d - 2 * c2 * x1_2 * x3 * x5 + 2 * c3 * x1_2 * x2 * x6 - 2 * c2 * x1 * x3_2 * x7 - 2 * c3 * x1 * x2_2 * x7 - 2 * c4 * x1_2 * x2 * x6 + 2 * c4 * x1_2 * x3 * x5 + 2 * c2 * x2 * x4_2 * x6 - 2 * c3 * x3 * x4_2 * x5 - 2 * c4 * x1 * x4_2 * x7 - 2 * c4 * x2 * x4_2 * x6 + 2 * c4 * x3 * x4_2 * x5 + 2 * x1_2 * x2 * x5 * x7 - 2 * x1 * x4_2 * x5 * x6 + 2 * x2 * x4_2 * x5 * x7 - 2 * c2 * x1 * x2 * x4 * x5 + 2 * c3 * x1 * x2 * x4 * x5 + 2 * c2 * x1 * x3 * x4 * x6 - 2 * c3 * x1 * x3 * x4 * x6 - 2 * c2 * x2 * x3 * x4 * x7 + 2 * c3 * x2 * x3 * x4 * x7)) / div u3 = (mult * (x3_3 * x5_2 + x3_3 * x6_2 + x3_3 * x7_2 + 4 * e3 * x1_2 + 4 * e3 * x3_2 - 2 * c3 * x1_3 * x6 + 4 * c3 * x1_2 * xd3d + 4 * c3 * x3_2 * xd3d - 2 * x1_3 * x5 * x7 + x1_2 * x3 * x5_2 + x1_2 * x3 * x6_2 + x2_2 * x3 * x5_2 + x1_2 * x3 * x7_2 + x2_2 * x3 * x6_2 - x3 * x4_2 * x5_2 + x2_2 * x3 * x7_2 + x3 * x4_2 * x6_2 + x3 * x4_2 * x7_2 - 4 * e2 * x1 * x4 + 4 * e2 * x2 * x3 + 4 * e4 * x1 * x2 + 4 * e4 * x3 * x4 - 4 * c33 * e33 * x1_2 - 4 * c33 * e33 * x3_2 + 4 * c22 * e22 * x1 * x4 - 4 * c22 * e22 * x2 * x3 - 4 * c44 * e44 * x1 * x2 - 4 * c44 * e44 * x3 * x4 - 4 * c2 * x1 * x4 * xd2d + 4 * c2 * x2 * x3 * xd2d + 4 * c4 * x1 * x2 * xd4d + 4 * c4 * x3 * x4 * xd4d + 2 * c2 * x1_2 * x4 * x5 - 2 * c2 * x1 * x4_2 * x6 - 2 * c3 * x1 * x3_2 * x6 + 2 * c3 * x1_2 * x2 * x7 - 2 * c3 * x1_2 * x4 * x5 - 2 * c4 * x1 * x2_2 * x6 - 2 * c2 * x2 * x3_2 * x7 - 2 * c4 * x1_2 * x2 * x7 + 2 * c3 * x2 * x3_2 * x7 - 2 * c3 * x3_2 * x4 * x5 + 2 * c4 * x3_2 * x4 * x5 - 2 * x1 * x2 * x4 * x5_2 - 2 * x1_2 * x2 * x5 * x6 - 2 * x1 * x3_2 * x5 * x7 - 2 * x1 * x4_2 * x5 * x7 - 2 * c2 * x1 * x2 * x3 * x5 + 2 * c4 * x1 * x2 * x3 * x5 + 2 * c2 * x1 * x3 * x4 * x7 + 2 * c2 * x2 * x3 * x4 * x6 - 2 * c4 * x1 * x3 * x4 * x7 - 2 * c4 * x2 * x3 * x4 * x6 - 2 * x1 * x3 * x4 * x5 * x6 + 2 * x2 * x3 * x4 * x5 * x7)) / (2 * div) u2 = (mult * (x2_3 * x5_2 + x2_3 * x6_2 + x2_3 * x7_2 + 4 * e2 * x1_2 + 4 * e2 * x2_2 - 2 * c2 * x1_3 * x5 + 4 * c2 * x1_2 * xd2d + 4 * c2 * x2_2 * xd2d + 2 * x1_3 * x6 * x7 + x1_2 * x2 * x5_2 + x1_2 * x2 * x6_2 + x2 * x3_2 * x5_2 + x1_2 * x2 * x7_2 + x2 * x3_2 * x6_2 - x2 * x4_2 * x5_2 + x2 * x3_2 * x7_2 + x2 * x4_2 * x6_2 + x2 * x4_2 * x7_2 + 4 * e3 * x1 * x4 + 4 * e3 * x2 * x3 - 4 * e4 * x1 * x3 + 4 * e4 * x2 * x4 - 4 * c22 * e22 * x1_2 - 4 * c22 * e22 * x2_2 - 4 * c33 * e33 * x1 * x4 - 4 * c33 * e33 * x2 * x3 + 4 * c44 * e44 * x1 * x3 - 4 * c44 * e44 * x2 * x4 + 4 * c3 * x1 * x4 * xd3d + 4 * c3 * x2 * x3 * xd3d - 4 * c4 * x1 * x3 * xd4d + 4 * c4 * x2 * x4 * xd4d - 2 * c2 * x1 * x2_2 * x5 - 2 * c2 * x1_2 * x3 * x7 + 2 * c2 * x1_2 * x4 * x6 - 2 * c3 * x1 * x4_2 * x5 - 2 * c4 * x1 * x3_2 * x5 - 2 * c2 * x2_2 * x3 * x7 + 2 * c2 * x2_2 * x4 * x6 - 2 * c3 * x1_2 * x4 * x6 + 2 * c3 * x2_2 * x3 * x7 + 2 * c4 * x1_2 * x3 * x7 - 2 * c4 * x2_2 * x4 * x6 + 2 * x1 * x3 * x4 * x5_2 + 2 * x1_2 * x3 * x5 * x6 + 2 * x1 * x2_2 * x6 * x7 + 2 * x1 * x3_2 * x6 * x7 + 2 * x1 * x4_2 * x6 * x7 + 2 * x2_2 * x4 * x5 * x7 - 2 * c3 * x1 * x2 * x3 * x6 + 2 * c4 * x1 * x2 * x3 * x6 + 2 * c3 * x1 * x2 * x4 * x7 - 2 * c3 * x2 * x3 * x4 * x5 - 2 * c4 * x1 * x2 * x4 * x7 + 2 * c4 * x2 * x3 * x4 * x5 - 2 * x1 * x2 * x3 * x5 * x7 - 2 * x1 * x2 * x4 * x5 * x6)) / (2 * div) self.u = matrix([ [abs(u1)], [u2], [u3], [u4] ]) def update_observer(self): x_hat_dot = self.observer_function(self.x_hat) # Eulero # self.x_hat = self.x_hat + x_hat_dot*self.dt # Runge Kutta 4 m1 = x_hat_dot k1 = self.x_hat + m1 * self.dt m2 = self.observer_function(k1) k2 = self.x_hat + (m1 + m2) * self.dt / 4 m3 = self.observer_function(k2) self.x_hat = self.x_hat + (m1 + m2 + 4 * m3) * (self.dt / 6) def observer_function(self, x_hat): x1 = x_hat[0, 0] x2 = x_hat[1, 0] x3 = x_hat[2, 0] x4 = x_hat[3, 0] x5 = x_hat[4, 0] x6 = x_hat[5, 0] x7 = x_hat[6, 0] x8 = x_hat[7, 0] x9 = x_hat[8, 0] x10 = x_hat[9, 0] x11 = x_hat[10, 0] x12 = x_hat[11, 0] x13 = x_hat[12, 0] # Funzione stato F = matrix([ [-(x2 * x5 + x3 * x6 + x4 * x7) / 2], [(x1 * x5 - x4 * x6 + x3 * x7) / 2], [(x4 * x5 + x1 * x6 - x2 * x7) / 2], [(-x3 * x5 + x2 * x6 + x1 * x7) / 2], [-x6 * x7], [x5 * x7], [0], [x11], [x12], [x13], [0], [0], [-self.g] ]) # Funzione ingressi G = matrix([ [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 1/(self.m*self.d*self.d), 0, 0], [0, 0, 1/(self.m*self.d*self.d), 0], [0, 0, 0, 1/(2*self.m*self.d*self.d)], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [(2*x2*x4+2*x1*x3)/self.m, 0, 0, 0], [(2*x3*x4-2*x1*x2)/self.m, 0, 0, 0], [(x1*x1-x2*x2-x3*x3+x4*x4)/self.m, 0, 0, 0], ]) # Funzione misure H = matrix([ [x1], [x2], [x3], [x4], [x8], [x9], [x10] ]) # Inversa di Q Qinv = matrix([ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0,1,0,0,0,0,0, 0, 0, 0, 0, 0, 0], [0,0,0,1,0,0,0, 0, 0, 0, 0, 0, 0], [0,0,0,0,0,1,0, 0, 0, 0, 0, 0, 0], [-(x1*x1*x5 + x2*x2*x5 - x1*x3*x7 + x1*x4*x6 + x2*x3*x6 + x2*x4*x7)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (x1*x3*x6 + x1*x4*x7 + x2*x3*x7 - x2*x4*x6)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (2*(x1*x1 + x2*x2))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(x7*x1*x1 + x3*x5*x1 + x7*x2*x2 - x4*x5*x2)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (2*(x1*x4 + x2*x3))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (18*x1*x3 - 18*x2*x4 + x1*x1*x6 + x2*x2*x6 - x1*x4*x5 - x2*x3*x5)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(2*(x1*x3 - x2*x4))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), 0, 0, 0, 0, 0, 0], [-(x1*x1*x6 + x3*x3*x6 + x1*x2*x7 - x1*x4*x5 + x2*x3*x5 + x3*x4*x7)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (x7*x1*x1 - x2*x6*x1 + x7*x3*x3 - x4*x6*x3)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(2*(x1*x4 - x2*x3))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (x1*x2*x5 + x1*x4*x7 - x2*x3*x7 + x3*x4*x5)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (2*(x1*x1 + x3*x3))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(18*x1*x2 + 18*x3*x4 + x1*x1*x5 + x3*x3*x5 + x1*x4*x6 - x2*x3*x6)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (2*(x1*x2 + x3*x4))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), 0, 0, 0, 0, 0, 0], [-(x1*x1*x7 + x4*x4*x7 - x1*x2*x6 + x1*x3*x5 + x2*x4*x5 + x3*x4*x6)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(x6*x1*x1 + x2*x7*x1 + x6*x4*x4 - x3*x7*x4)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (2*(x1*x3 + x2*x4))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (x5*x1*x1 - x3*x7*x1 + x5*x4*x4 - x2*x7*x4)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(2*(x1*x2 - x3*x4))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), -(18*x1*x1 + 18*x4*x4 - x1*x2*x5 - x1*x3*x6 - x2*x4*x6 + x3*x4*x5)/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), (2*(x1*x1 + x4*x4))/(x1*x1*x1 + x1*x2*x2 + x1*x3*x3 + x1*x4*x4), 0, 0, 0, 0, 0, 0], [0,0,0,0,0,0,0, 1, 0, 0, 0, 0, 0], [0,0,0,0,0,0,0, 0, 0, 1, 0, 0, 0], [0,0,0,0,0,0,0, 0, 0, 0, 0, 1, 0], [0,0,0,0,0,0,0, 0, 1, 0, 0, 0, 0], [0,0,0,0,0,0,0, 0, 0, 0, 1, 0, 0], [0,0,0,0,0,0,0, 0, 0, 0, 0, 0, 1] ]) # Guadagni per la convergenza K = matrix([ [100, 0, 0, 0, 0, 0, 0], [0, 100, 0, 0, 0, 0, 0], [0, 500, 0, 0, 0, 0, 0], [0, 0, 100, 0, 0, 0, 0], [0, 0, 500, 0, 0, 0, 0], [0, 0, 0, 100, 0, 0, 0], [0, 0, 0, 500, 0, 0, 0], [0, 0, 0, 0, 100, 0, 0], [0, 0, 0, 0, 10000, 0, 0], [0, 0, 0, 0, 0, 100, 0], [0, 0, 0, 0, 0, 10000, 0], [0, 0, 0, 0, 0, 0, 100], [0, 0, 0, 0, 0, 0, 10000] ]) # Aggiorno lo stato predetto x_hat_dot = F + G*self.u + Qinv*K*(self.y - H) return x_hat_dot def predict(self, u): # Faccio evolvere il sistema F_b = matrix([ [0], [0], [u[0, 0]] ]) Mw = 0*matrix([ [0.1], [-0.1], [0.2] ]) Fv = 0*matrix([ [1], [1], [1] ]) Q = matrix([ [-self.q[1, 0], -self.q[2, 0], -self.q[3, 0]], [self.q[0, 0], -self.q[3, 0], self.q[2, 0]], [self.q[3, 0], self.q[0, 0], -self.q[1, 0]], [-self.q[2, 0], self.q[1, 0], self.q[0, 0]] ]) # Aggiorno lo stato q_dot = 0.5 * Q * self.omega self.q = self.q + q_dot * self.dt self.q = self.q/linalg.norm(self.q) U = matrix([ [u[1, 0]], [u[2, 0]], [u[3, 0]] ]) omega_dot = self.Mat_Jinv * (U - self.VectorialProduct(self.omega) * self.Mat_J * self.omega) + self.Mat_Jinv * Mw self.omega = self.omega + omega_dot * self.dt p_dot = self.v self.p = self.p + p_dot * self.dt R = self.quaternion2RotationMatrix() G = matrix([ [0], [0], [self.g] ]) v_dot = (1 / self.m) * (R * F_b + Fv) - G - self.airFriction * linalg.norm(self.v) * self.v self.v = self.v + v_dot * self.dt self.x = vstack(( self.q, self.omega, self.p, self.v )) def getMotorInput(self): scaleFactor = self.thrustGain * 65535.0 / (self.fmotmax * 4) u = self.u u[0, 0] = u[0, 0]*scaleFactor u[1, 0] = (u[1, 0]/2.0)/self.d u[2, 0] = (u[2, 0]/2.0)/self.d u[3, 0] = 0/self.c percentual = 1 if u[1, 0] < -65536 * percentual: u[1, 0] = -65536 * percentual elif u[1, 0] > 65536 * percentual: u[1, 0] = 65536 * percentual if u[2, 0] < -65536 * percentual: u[2, 0] = -65536 * percentual elif u[2, 0] > 65536 * percentual: u[2, 0] = 65536 * percentual if u[3, 0] < -65536 * percentual: u[3, 0] = -65536 * percentual elif u[3, 0] > 65536 * percentual: u[3, 0] = 65536 * percentual m1 = u[0, 0] - u[1, 0] + u[2, 0] + u[3, 0] m2 = u[0, 0] - u[1, 0] - u[2, 0] - u[3, 0] m3 = u[0, 0] + u[1, 0] - u[2, 0] + u[3, 0] m4 = u[0, 0] + u[1, 0] + u[2, 0] - u[3, 0] return m1, m2, m3, m4 def quaternionProduct(self, q, p): """ Compute the quaternion product q*p :param self: :param q: :param p: :return: """ Qq = matrix([ [q[0, 0], -q[1, 0], -q[2, 0], -q[3, 0]], [q[1, 0], q[0, 0], -q[3, 0], q[2, 0]], [q[2, 0], q[3, 0], q[0, 0], -q[1, 0]], [q[3, 0], -q[2, 0], q[1, 0], q[0, 0]] ]) return Qq*p def quaternion2RotationMatrix(self): """ Genera la matrice di rotazione partendo dai quaternioni dello stato :return: """ q0 = self.q[0, 0] q1 = self.q[1, 0] q2 = self.q[2, 0] q3 = self.q[3, 0] R = matrix([ [1-2*(q2*q2+q3*q3), 2*(q1*q2-q0*q3), 2*(q0*q2+q1*q3)], [2*(q1*q2+q0*q3), 1-2*(q1*q1+q3*q3), 2*(q2*q3-q0*q1)], [2*(q1*q3-q0*q2), 2*(q0*q1+q2*q3), 1-2*(q1*q1+q2*q2)] ]) return R def VectorialProduct(self, v): """ Questa funzione prende in ingresso un vettore di tre elementi e ne genera la matrice che effettua il prodotto vettoriale :param v: :return: M """ M = matrix([ [0, -v[2,0], v[1,0]], [v[2,0], 0, -v[0,0]], [-v[1,0], v[0,0], 0] ]) return M def quaternion2RPY(self): q = self.q g = 2 * (q[0, 0]*q[2, 0] - q[1, 0]*q[3, 0]) if g > 1: g = 1 elif g < -1: g = -1 yaw = atan2(2*(q[1, 0]*q[2, 0] + q[0, 0]*q[3, 0]), q[0, 0] * q[0, 0] + q[1, 0] * q[1, 0] - q[2, 0] * q[2, 0] - q[3, 0] * q[3, 0]) pitch = asin(g) roll = atan2(2*(q[2, 0]*q[3, 0] + q[0, 0]*q[1, 0]), q[0, 0] * q[0, 0] - q[1, 0] * q[1, 0] - q[2, 0] * q[2, 0] + q[3, 0] * q[3, 0]) rad2deg = 180/pi #euler = matrix([ # [roll * rad2deg], [pitch * rad2deg], [yaw * rad2deg] #]) #return euler[0, 0], euler[1, 0], euler[2, 0] return roll, pitch, yaw

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null

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7

cf47aca5fbdc5c963454eb2445883327bc3c473e

267

py

Python

libp2p/protocol_muxer/exceptions.py

lithp/py-libp2p

f38899e26edabe59b291e466143d1c696c44de8d

[ "Apache-2.0", "MIT" ]

null

libp2p/protocol_muxer/exceptions.py

lithp/py-libp2p

f38899e26edabe59b291e466143d1c696c44de8d

[ "Apache-2.0", "MIT" ]

null

libp2p/protocol_muxer/exceptions.py

lithp/py-libp2p

f38899e26edabe59b291e466143d1c696c44de8d

[ "Apache-2.0", "MIT" ]

null

from libp2p.exceptions import BaseLibp2pError class MultiselectError(BaseLibp2pError): """Raised when an error occurs in multiselect process""" class MultiselectClientError(BaseLibp2pError): """Raised when an error occurs in protocol selection process"""

26.7

67

0.790262

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267

7.535714

0.642857

0.199052

0.236967

0.255924

0.379147

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0.138577

267

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null

0

1

0

1

0

1

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7

cf5b37ee1fc82e3da020ac4e175a1718c4b48d19

115

py

Python

env.py

olukotun-sandbox/name-button

8205dc783dd72765d44378b0b6ca354352d21ad5

[ "MIT" ]

null

env.py

olukotun-sandbox/name-button

8205dc783dd72765d44378b0b6ca354352d21ad5

[ "MIT" ]

null

env.py

olukotun-sandbox/name-button

8205dc783dd72765d44378b0b6ca354352d21ad5

[ "MIT" ]

null

import os print('this is home:', os.environ['HOME']) print('this is circle branch:', os.environ['CIRCLE_BRANCH'])

23

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0.704348

18

115

4.444444

0.5

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7

cf74741b8ea29334e97b4fd26bf8a8d8ea156e23

18,806

py

Python

tests/data/ec2_offer.py

andrewmcgilvray/awspricing

fd37598dbdb08545db03c99492ce01f7290ab6f5

[ "Apache-2.0" ]

null

tests/data/ec2_offer.py

andrewmcgilvray/awspricing

fd37598dbdb08545db03c99492ce01f7290ab6f5

[ "Apache-2.0" ]

null

tests/data/ec2_offer.py

andrewmcgilvray/awspricing

fd37598dbdb08545db03c99492ce01f7290ab6f5

[ "Apache-2.0" ]

null

BASIC_EC2_OFFER_SKU = '4C7N4APU9GEUZ6H6' BASIC_EC2_OFFER_MODIFIED_FORMAT = { 'offerCode': 'AmazonEC2', 'version': '20161213014831', 'products': { '4C7N4APU9GEUZ6H6' : { 'sku' : '4C7N4APU9GEUZ6H6', 'productFamily' : 'Compute Instance', 'attributes' : { 'servicecode' : 'AmazonEC2', 'location' : 'US East (N. Virginia)', 'locationType' : 'AWS Region', 'instanceType' : 'c4.large', 'currentGeneration' : 'Yes', 'instanceFamily' : 'Compute optimized', 'vcpu' : '2', 'physicalProcessor' : 'Intel Xeon E5-2666 v3 (Haswell)', 'clockSpeed' : '2.9 GHz', 'memory' : '3.75 GiB', 'storage' : 'EBS only', 'networkPerformance' : 'Moderate', 'processorArchitecture' : '64-bit', 'tenancy' : 'Shared', 'operatingSystem' : 'Linux', 'licenseModel' : 'No License required', 'usagetype' : 'BoxUsage:c4.large', 'operation' : 'RunInstances', 'dedicatedEbsThroughput' : '500 Mbps', 'enhancedNetworkingSupported' : 'Yes', 'preInstalledSw' : 'NA', 'processorFeatures' : 'Intel AVX; Intel AVX2; Intel Turbo' } }, 'BNSJSY9CBT29VNPD':{ 'sku': 'BNSJSY9CBT29VNPD', 'attributes': { 'servicecode': 'AWSDataTransfer', 'transferType': 'Inter Region Peering Data Transfer Inbound', 'fromLocation': 'External', 'fromLocationType': 'AWS Region', 'toLocation': 'US East (Ohio)', 'toLocationType': 'AWS Region', 'usagetype': 'USE2-AWS-In-Bytes', 'operation': '', 'servicename': 'AWS Data Transfer' } }, }, 'terms': { 'OnDemand': { '4C7N4APU9GEUZ6H6' : { '4C7N4APU9GEUZ6H6.JRTCKXETXF' : { 'offerTermCode' : 'JRTCKXETXF', 'sku' : '4C7N4APU9GEUZ6H6', 'effectiveDate' : '2016-12-01T00:00:00Z', 'priceDimensions' : { '4C7N4APU9GEUZ6H6.JRTCKXETXF.6YS6EN2CT7' : { 'rateCode' : '4C7N4APU9GEUZ6H6.JRTCKXETXF.6YS6EN2CT7', 'description' : '$0.1 per On Demand Linux c4.large Instance Hour', 'beginRange' : '0', 'endRange' : 'Inf', 'unit' : 'Hrs', 'pricePerUnit' : { 'USD' : '0.1000000000' }, 'appliesTo' : [ ] } }, 'termAttributes' : { } } }, }, 'Reserved': { "4C7N4APU9GEUZ6H6" : { "4C7N4APU9GEUZ6H6.HU7G6KETJZ" : { "offerTermCode" : "HU7G6KETJZ", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2016-11-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.HU7G6KETJZ.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.HU7G6KETJZ.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large instance-hours used this month", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0300000000" }, "appliesTo" : [ ] }, "4C7N4APU9GEUZ6H6.HU7G6KETJZ.2TG2D8R56U" : { "rateCode" : "4C7N4APU9GEUZ6H6.HU7G6KETJZ.2TG2D8R56U", "description" : "Upfront Fee", "unit" : "Quantity", "pricePerUnit" : { "USD" : "263" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "1yr", "OfferingClass" : "standard", "PurchaseOption" : "Partial Upfront" } }, "4C7N4APU9GEUZ6H6.38NPMPTW36" : { "offerTermCode" : "38NPMPTW36", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2016-11-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.38NPMPTW36.2TG2D8R56U" : { "rateCode" : "4C7N4APU9GEUZ6H6.38NPMPTW36.2TG2D8R56U", "description" : "Upfront Fee", "unit" : "Quantity", "pricePerUnit" : { "USD" : "539" }, "appliesTo" : [ ] }, "4C7N4APU9GEUZ6H6.38NPMPTW36.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.38NPMPTW36.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large instance-hours used this month", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0210000000" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "3yr", "OfferingClass" : "standard", "PurchaseOption" : "Partial Upfront" } }, "4C7N4APU9GEUZ6H6.R5XV2EPZQZ" : { "offerTermCode" : "R5XV2EPZQZ", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2016-11-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.2TG2D8R56U" : { "rateCode" : "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.2TG2D8R56U", "description" : "Upfront Fee", "unit" : "Quantity", "pricePerUnit" : { "USD" : "710" }, "appliesTo" : [ ] }, "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large instance-hours used this month", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0270000000" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "3yr", "OfferingClass" : "convertible", "PurchaseOption" : "Partial Upfront" } }, "4C7N4APU9GEUZ6H6.4NA7Y494T4" : { "offerTermCode" : "4NA7Y494T4", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2017-04-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.4NA7Y494T4.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.4NA7Y494T4.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large reserved instance applied", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0630000000" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "1yr", "OfferingClass" : "standard", "PurchaseOption" : "No Upfront" } }, }, } } } # Includes one variation of the c4.xlarge product and just Partial Upfront RIs. BASIC_EC2_OFFER_DATA = { 'offerCode': 'AmazonEC2', 'version': '20161213014831', 'products': { '4C7N4APU9GEUZ6H6' : { 'sku' : '4C7N4APU9GEUZ6H6', 'productFamily' : 'Compute Instance', 'attributes' : { 'servicecode' : 'AmazonEC2', 'location' : 'US East (N. Virginia)', 'locationType' : 'AWS Region', 'instanceType' : 'c4.large', 'currentGeneration' : 'Yes', 'instanceFamily' : 'Compute optimized', 'vcpu' : '2', 'physicalProcessor' : 'Intel Xeon E5-2666 v3 (Haswell)', 'clockSpeed' : '2.9 GHz', 'memory' : '3.75 GiB', 'storage' : 'EBS only', 'networkPerformance' : 'Moderate', 'processorArchitecture' : '64-bit', 'tenancy' : 'Shared', 'operatingSystem' : 'Linux', 'licenseModel' : 'No License required', 'usagetype' : 'BoxUsage:c4.large', 'operation' : 'RunInstances', 'dedicatedEbsThroughput' : '500 Mbps', 'enhancedNetworkingSupported' : 'Yes', 'preInstalledSw' : 'NA', 'processorFeatures' : 'Intel AVX; Intel AVX2; Intel Turbo' } }, }, 'terms': { 'OnDemand': { '4C7N4APU9GEUZ6H6' : { '4C7N4APU9GEUZ6H6.JRTCKXETXF' : { 'offerTermCode' : 'JRTCKXETXF', 'sku' : '4C7N4APU9GEUZ6H6', 'effectiveDate' : '2016-12-01T00:00:00Z', 'priceDimensions' : { '4C7N4APU9GEUZ6H6.JRTCKXETXF.6YS6EN2CT7' : { 'rateCode' : '4C7N4APU9GEUZ6H6.JRTCKXETXF.6YS6EN2CT7', 'description' : '$0.1 per On Demand Linux c4.large Instance Hour', 'beginRange' : '0', 'endRange' : 'Inf', 'unit' : 'Hrs', 'pricePerUnit' : { 'USD' : '0.1000000000' }, 'appliesTo' : [ ] } }, 'termAttributes' : { } } }, }, 'Reserved': { "4C7N4APU9GEUZ6H6" : { "4C7N4APU9GEUZ6H6.HU7G6KETJZ" : { "offerTermCode" : "HU7G6KETJZ", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2016-11-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.HU7G6KETJZ.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.HU7G6KETJZ.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large instance-hours used this month", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0300000000" }, "appliesTo" : [ ] }, "4C7N4APU9GEUZ6H6.HU7G6KETJZ.2TG2D8R56U" : { "rateCode" : "4C7N4APU9GEUZ6H6.HU7G6KETJZ.2TG2D8R56U", "description" : "Upfront Fee", "unit" : "Quantity", "pricePerUnit" : { "USD" : "263" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "1yr", "OfferingClass" : "standard", "PurchaseOption" : "Partial Upfront" } }, "4C7N4APU9GEUZ6H6.38NPMPTW36" : { "offerTermCode" : "38NPMPTW36", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2016-11-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.38NPMPTW36.2TG2D8R56U" : { "rateCode" : "4C7N4APU9GEUZ6H6.38NPMPTW36.2TG2D8R56U", "description" : "Upfront Fee", "unit" : "Quantity", "pricePerUnit" : { "USD" : "539" }, "appliesTo" : [ ] }, "4C7N4APU9GEUZ6H6.38NPMPTW36.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.38NPMPTW36.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large instance-hours used this month", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0210000000" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "3yr", "OfferingClass" : "standard", "PurchaseOption" : "Partial Upfront" } }, "4C7N4APU9GEUZ6H6.R5XV2EPZQZ" : { "offerTermCode" : "R5XV2EPZQZ", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2016-11-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.2TG2D8R56U" : { "rateCode" : "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.2TG2D8R56U", "description" : "Upfront Fee", "unit" : "Quantity", "pricePerUnit" : { "USD" : "710" }, "appliesTo" : [ ] }, "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.R5XV2EPZQZ.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large instance-hours used this month", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0270000000" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "3yr", "OfferingClass" : "convertible", "PurchaseOption" : "Partial Upfront" } }, "4C7N4APU9GEUZ6H6.4NA7Y494T4" : { "offerTermCode" : "4NA7Y494T4", "sku" : "4C7N4APU9GEUZ6H6", "effectiveDate" : "2017-04-30T23:59:59Z", "priceDimensions" : { "4C7N4APU9GEUZ6H6.4NA7Y494T4.6YS6EN2CT7" : { "rateCode" : "4C7N4APU9GEUZ6H6.4NA7Y494T4.6YS6EN2CT7", "description" : "Linux/UNIX (Amazon VPC), c4.large reserved instance applied", "beginRange" : "0", "endRange" : "Inf", "unit" : "Hrs", "pricePerUnit" : { "USD" : "0.0630000000" }, "appliesTo" : [ ] } }, "termAttributes" : { "LeaseContractLength" : "1yr", "OfferingClass" : "standard", "PurchaseOption" : "No Upfront" } }, }, } } } BARE_METAL_EC2_SKU = 'SBVNSX4BKU246KVM' BARE_METAL_EC2_OFFER = { 'offerCode': 'AmazonEC2', 'version': '20161213014831', 'products': { "SBVNSX4BKU246KVM": { "productFamily": "Compute Instance (bare metal)", "sku": "SBVNSX4BKU246KVM", "attributes": { "servicename": "Amazon Elastic Compute Cloud", "preInstalledSw": "SQL Ent", "normalizationSizeFactor": "128", "ecu": "208", "capacitystatus": "Used", "operation": "RunInstances:0102", "physicalProcessor": "Intel Xeon E5-2686 v4 (Broadwell)", "vcpu": "72", "instanceFamily": "Storage optimized", "currentGeneration": "Yes", "instanceType": "i3.metal", "locationType": "AWS Region", "location": "EU (Ireland)", "servicecode": "AmazonEC2", "memory": "512 GiB", "storage": "8 x 1900 NVMe SSD", "networkPerformance": "25 Gigabit", "processorArchitecture": "64-bit", "tenancy": "Shared", "operatingSystem": "Windows", "licenseModel": "No License required", "usagetype": "EU-BoxUsage:i3.metal" }, } } }

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1

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7

cf89cd77b7a7a86eb1c509ae0d28c2801e9db09a

9,359

py

Python

util/dynamic_signal_lights.py

ashwxn/Intelligent-Traffic-Management-System-Using-ML-YOLO

cc111d9895efc19f052656f7d140c6895458a819

[ "CC0-1.0" ]

1

2021-03-11T06:58:31.000Z

util/dynamic_signal_lights.py

ashwxn/Intelligent-Traffic-Management-System-Using-ML-YOLO

cc111d9895efc19f052656f7d140c6895458a819

[ "CC0-1.0" ]

null

util/dynamic_signal_lights.py

ashwxn/Intelligent-Traffic-Management-System-Using-ML-YOLO

cc111d9895efc19f052656f7d140c6895458a819

[ "CC0-1.0" ]

null

import time import emoji def switch_signal(denser_lane,seconds): print('\033[1m' + '\n\033[99m' + "OPENING LANE-{}: ".format(str(denser_lane))+ '\033[0m' ) print("----------------------------------------------------------------------------------") if denser_lane==1: print( "Lane 1 Lane 2 Lane 3 Lane 4" ) time.sleep(1) print( " "+ emoji.emojize(":white_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":green_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") print('\033[0m' + '\n\033[99m' + "LANE-{} is now OPEN and will CLOSE after {} seconds ".format(str(denser_lane),str(seconds))+ '\033[0m' ,end="") while seconds: mins, secs = divmod(seconds, 60) print('\033[99m'+".", end="") time.sleep(1) seconds -= 1 print() print('\033[1m' + '\n\033[99m' + "CLOSING LANE-{}: ".format(str(denser_lane))+ '\033[0m' ) print("----------------------------------------------------------------------------------") time.sleep(1) print() print( "Lane 1 Lane 2 Lane 3 Lane 4" ) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") elif denser_lane==2: print( "Lane 1 Lane 2 Lane 3 Lane 4" ) time.sleep(1) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":green_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") print('\033[0m' + '\n\033[99m' + "LANE-{} is now OPEN and will CLOSE after {} seconds ".format(str(denser_lane),str(seconds))+ '\033[0m' ,end="") while seconds: mins, secs = divmod(seconds, 60) print('\033[99m'+".", end="") time.sleep(1) seconds -= 1 print() print('\033[1m' + '\n\033[99m' + "CLOSING LANE-{}: ".format(str(denser_lane))+ '\033[0m' ) print("----------------------------------------------------------------------------------") time.sleep(1) print() print( "Lane 1 Lane 2 Lane 3 Lane 4" ) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") elif denser_lane==3: print( "Lane 1 Lane 2 Lane 3 Lane 4" ) time.sleep(1) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":green_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") print('\033[0m' + '\n\033[99m' + "LANE-{} is now OPEN and will CLOSE after {} seconds ".format(str(denser_lane),str(seconds))+ '\033[0m' ,end="") while seconds: mins, secs = divmod(seconds, 60) print('\033[99m'+".", end="") time.sleep(1) seconds -= 1 print() print('\033[1m' + '\n\033[99m' + "CLOSING LANE-{}: ".format(str(denser_lane))+ '\033[0m' ) print("----------------------------------------------------------------------------------") time.sleep(1) print() print( "Lane 1 Lane 2 Lane 3 Lane 4" ) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") elif denser_lane==4: print( "Lane 1 Lane 2 Lane 3 Lane 4" ) time.sleep(1) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":green_circle:") + "\n") print('\033[0m' + '\n\033[99m' + "LANE-{} is now OPEN and will CLOSE after {} seconds ".format(str(denser_lane),str(seconds))+ '\033[0m' ,end="") while seconds: mins, secs = divmod(seconds, 60) print('\033[99m'+".", end="") time.sleep(1) seconds -= 1 print() print('\033[1m' + '\n\033[99m' + "CLOSING LANE-{}: ".format(str(denser_lane))+ '\033[0m' ) print("----------------------------------------------------------------------------------") time.sleep(1) print() print( "Lane 1 Lane 2 Lane 3 Lane 4" ) print( " "+ emoji.emojize(":red_circle:") + " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ " "+emoji.emojize(":red_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ "\n " + emoji.emojize(":white_circle:") + " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:")+ " "+emoji.emojize(":white_circle:") + "\n") print('\033[0m' + '\n\033[99m' + "LANE-{} is now CLOSED ".format(str(denser_lane)+ '\033[0m' ))

69.843284

221

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4.542033

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134

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69.843284

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false

0

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0.325758

0

null

1

0

1

0

null

0

10

d8617eb30998d8220d39ad8ca6c7311751fdbf18

16,601

py

Python

tests/tests.py

ipashchenko/uvmod

5f81f9f621ccd2f83e99f22eb0c302ae8d8a218d

[ "MIT" ]

null

tests/tests.py

ipashchenko/uvmod

5f81f9f621ccd2f83e99f22eb0c302ae8d8a218d

[ "MIT" ]

5

2015-01-28T07:53:30.000Z

2015-04-16T11:21:58.000Z

tests/tests.py

ipashchenko/uvmod

5f81f9f621ccd2f83e99f22eb0c302ae8d8a218d

[ "MIT" ]

null

#!/usr/bin python # -*- coding: utf-8 -*- from __future__ import print_function from unittest import (TestCase, skip, skipIf) from uvmod.stats import LnLike, LS_estimates, LnPrior, LnPost, hdi_of_mcmc from uvmod.models import Model_1d, Model_2d_isotropic, Model_2d_anisotropic # TODO: Use ``np.random.uniform`` instead try: from scipy.stats import uniform is_scipy = True except ImportError: is_scipy = False try: import emcee is_emcee = True except ImportError: is_emcee = False import numpy as np import math # TODO: Add tests for data wo uncertainties # TODO: Add tests for not installed packages # TODO: Fix random state to guarantee passing class Test_1D(TestCase): def setUp(self): self.p = [2, 0.3] self.x = np.array([0., 0.1, 0.2, 0.4, 0.6]) self.model_1d = Model_1d self.model_1d_detections = Model_1d(self.x) self.y = self.model_1d_detections(self.p) + np.random.normal(0, 0.1, size=5) self.sy = np.random.normal(0.15, 0.025, size=5) self.xl = np.array([0.5, 0.7]) self.yl = np.array([0.6, 0.2]) self.syl = np.random.normal(0.1, 0.03, size=2) self.p1 = np.asarray(self.p) + np.array([1., 0.]) self.p2 = np.asarray(self.p) + np.array([-1., 0.]) self.p3 = np.asarray(self.p) + np.array([0., 0.2]) self.p4 = np.asarray(self.p) + np.array([0., -0.2]) self.p0_range = [0., 10.] self.p1_range = [0., 2.] @skipIf(not is_scipy, "``scipy`` is not installed") def test_LnLike(self): lnlike = LnLike(self.x, self.y, self.model_1d, sy=self.sy, x_limits=self.xl, y_limits=self.yl, sy_limits=self.syl, jitter=False, outliers=False) lnlik0 = lnlike._lnprob[0].__call__(self.p) lnlik1 = lnlike._lnprob[1].__call__(self.p) self.assertEqual(lnlike(self.p), lnlik0 + lnlik1) self.assertGreater(lnlike(self.p), lnlike(self.p1)) self.assertGreater(lnlike(self.p), lnlike(self.p2)) self.assertGreater(lnlike(self.p), lnlike(self.p3)) self.assertGreater(lnlike(self.p), lnlike(self.p4)) @skipIf(not is_scipy, "``scipy`` is not installed") def test_LS_estimates(self): lsq = LS_estimates(self.x, self.y, self.model_1d, sy=self.sy) p, pcov = lsq.fit([1., 1.]) delta0 = 3. * np.sqrt(pcov[0, 0]) delta1 = 5. * np.sqrt(pcov[1, 1]) self.assertAlmostEqual(self.p[0], p[0], delta=delta0) self.assertAlmostEqual(self.p[1], abs(p[1]), delta=delta1) @skipIf(not is_scipy, "``scipy`` is not installed") def test_LnPrior(self): lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),),) lnpr = LnPrior(lnprs) self.assertTrue(np.isinf(lnpr([-1., 1.]))) self.assertTrue(np.isinf(lnpr([1., -1.]))) self.assertTrue(np.isinf(lnpr([15., 1.]))) self.assertTrue(np.isinf(lnpr([1., 5.]))) @skipIf(not is_scipy, "``scipy`` is not installed") def test_LnPost(self): lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),),) lnpr = LnPrior(lnprs) lnlike = LnLike(self.x, self.y, self.model_1d, sy=self.sy, x_limits=self.xl, y_limits=self.yl, sy_limits=self.syl, jitter=False, outliers=False) lnpost = LnPost(self.x, self.y, self.model_1d, sy=self.sy, x_limits=self.xl, y_limits=self.yl, sy_limits=self.syl, lnpr=lnpr, jitter=False, outliers=False) self.assertEqual(lnpost._lnpr(self.p), lnpr(self.p)) self.assertEqual(lnpost._lnlike(self.p), lnlike(self.p)) self.assertGreater(lnpost(self.p), lnpost(self.p1)) self.assertGreater(lnpost(self.p), lnpost(self.p2)) self.assertGreater(lnpost(self.p), lnpost(self.p3)) self.assertGreater(lnpost(self.p), lnpost(self.p4)) @skipIf((not is_emcee) or (not is_scipy), "``emcee`` and/or ``scipy`` not" " installed") def test_MCMC(self): nwalkers = 250 ndim = 2 p0 = np.random.uniform(low=self.p1_range[0], high=self.p1_range[1], size=(nwalkers, ndim)) lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),),) lnpr = LnPrior(lnprs) lnpost = LnPost(self.x, self.y, self.model_1d, sy=self.sy, x_limits=self.xl, y_limits=self.yl, sy_limits=self.syl, lnpr=lnpr, jitter=False, outliers=False) sampler = emcee.EnsembleSampler(nwalkers, ndim, lnpost) pos, prob, state = sampler.run_mcmc(p0, 250) sampler.reset() sampler.run_mcmc(pos, 500) sample_vec0 = sampler.flatchain[::10, 0] sample_vec1 = sampler.flatchain[::10, 1] p0_hdi_min, p0_hdi_max = hdi_of_mcmc(sample_vec0) p1_hdi_min, p1_hdi_max = hdi_of_mcmc(sample_vec1) self.assertTrue((p0_hdi_min < self.p[0] < p0_hdi_max)) self.assertTrue((p1_hdi_min < self.p[1] < p1_hdi_max)) class Test_2D_isoptopic(TestCase): def setUp(self): np.random.seed(1) self.p = [2, 0.3] self.x1 = np.random.uniform(low=-1, high=1, size=10) self.x2 = np.random.uniform(low=-1, high=1, size=10) self.xx = np.column_stack((self.x1, self.x2)) self.model_2d = Model_2d_isotropic self.model_2d_detections = Model_2d_isotropic(self.xx) self.y = self.model_2d_detections(self.p) + np.random.normal(0, 0.1, size=10) self.sy = np.random.normal(0.15, 0.025, size=10) self.x1l = np.hstack((np.random.uniform(low=-1, high=-0.5, size=2), np.random.uniform(low=0.5, high=1, size=2),)) self.x2l = np.hstack((np.random.uniform(low=-1, high=-0.5, size=2), np.random.uniform(low=0.5, high=1, size=2),)) self.xxl = np.column_stack((self.x1l, self.x2l)) self.model_2d_limits = Model_2d_isotropic(self.xxl) self.yl = self.model_2d_limits(self.p) + abs(np.random.normal(0, 0.1, size=4)) self.syl = np.random.normal(0.1, 0.03, size=4) self.p1 = np.asarray(self.p) + np.array([1., 0.]) self.p2 = np.asarray(self.p) + np.array([-1., 0.]) self.p3 = np.asarray(self.p) + np.array([0., 0.2]) self.p4 = np.asarray(self.p) + np.array([0., -0.2]) self.p0_range = [0., 10.] self.p1_range = [0., 2.] @skipIf(not is_scipy, "``scipy`` is not installed") def test_LnLike(self): lnlike = LnLike(self.xx, self.y, self.model_2d, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, jitter=False, outliers=False) lnlik0 = lnlike._lnprob[0].__call__(self.p) lnlik1 = lnlike._lnprob[1].__call__(self.p) self.assertEqual(lnlike(self.p), lnlik0 + lnlik1) self.assertGreater(lnlike(self.p), lnlike(self.p1)) self.assertGreater(lnlike(self.p), lnlike(self.p2)) self.assertGreater(lnlike(self.p), lnlike(self.p3)) self.assertGreater(lnlike(self.p), lnlike(self.p4)) @skipIf(not is_scipy, "``scipy`` is not installed") def test_LS_estimates(self): lsq = LS_estimates(self.xx, self.y, self.model_2d, sy=self.sy) p, pcov = lsq.fit([1., 1.]) delta0 = 3. * np.sqrt(pcov[0, 0]) delta1 = 5. * np.sqrt(pcov[1, 1]) self.assertAlmostEqual(self.p[0], p[0], delta=delta0) # FIXME: use variance as parameter so p[1] > 0 self.assertAlmostEqual(self.p[1], abs(p[1]), delta=delta1) @skipIf(not is_scipy, "``scipy`` is not installed") def test_LnPost(self): lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),),) lnpr = LnPrior(lnprs) lnlike = LnLike(self.xx, self.y, self.model_2d, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, jitter=False, outliers=False) lnpost = LnPost(self.xx, self.y, self.model_2d, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, lnpr=lnpr, jitter=False, outliers=False) self.assertEqual(lnpost._lnpr(self.p), lnpr(self.p)) self.assertEqual(lnpost._lnlike(self.p), lnlike(self.p)) self.assertGreater(lnpost(self.p), lnpost(self.p1)) self.assertGreater(lnpost(self.p), lnpost(self.p2)) self.assertGreater(lnpost(self.p), lnpost(self.p3)) self.assertGreater(lnpost(self.p), lnpost(self.p4)) @skipIf((not is_emcee) or (not is_scipy), "``emcee`` and/or ``scipy`` not" " installed") def test_MCMC(self): nwalkers = 250 ndim = 2 p0 = np.random.uniform(low=self.p1_range[0], high=self.p1_range[1], size=(nwalkers, ndim)) lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),),) lnpr = LnPrior(lnprs) lnpost = LnPost(self.xx, self.y, self.model_2d, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, lnpr=lnpr, jitter=False, outliers=False) sampler = emcee.EnsembleSampler(nwalkers, ndim, lnpost) pos, prob, state = sampler.run_mcmc(p0, 250) sampler.reset() sampler.run_mcmc(pos, 500) sample_vec0 = sampler.flatchain[::10, 0] sample_vec1 = sampler.flatchain[::10, 1] p0_hdi_min, p0_hdi_max = hdi_of_mcmc(sample_vec0) p1_hdi_min, p1_hdi_max = hdi_of_mcmc(sample_vec1) self.assertTrue((p0_hdi_min < self.p[0] < p0_hdi_max)) self.assertTrue((p1_hdi_min < self.p[1] < p1_hdi_max)) class Test_2D_anisoptopic(TestCase): def setUp(self): self.p = [2, 0.7, 0.3, 1.] self.x1 = np.random.uniform(low=-1, high=1, size=10) self.x2 = np.random.uniform(low=-1, high=1, size=10) self.xx = np.column_stack((self.x1, self.x2)) self.model_2d_anisotropic = Model_2d_anisotropic self.model_2d_detections = Model_2d_anisotropic(self.xx) self.y = self.model_2d_detections(self.p) + np.random.normal(0, 0.05, size=10) self.sy = np.random.normal(0.15, 0.025, size=10) self.x1l = np.hstack((np.random.uniform(low=-1, high=-0.5, size=2), np.random.uniform(low=0.5, high=1, size=2),)) self.x2l = np.hstack((np.random.uniform(low=-1, high=-0.5, size=2), np.random.uniform(low=0.5, high=1, size=2),)) self.xxl = np.column_stack((self.x1l, self.x2l)) self.model_2d_limits = Model_2d_anisotropic(self.xxl) self.yl = self.model_2d_limits(self.p) + abs(np.random.normal(0, 0.05, size=4)) self.syl = np.random.normal(0.1, 0.03, size=4) self.p1 = np.asarray(self.p) + np.array([1., 0., 0., 0.]) self.p2 = np.asarray(self.p) + np.array([-1., 0., 0., 0.]) self.p3 = np.asarray(self.p) + np.array([0., 0.2, 0., 0.]) self.p4 = np.asarray(self.p) + np.array([0., -0.2, 0., 0.]) self.p5 = np.asarray(self.p) + np.array([0., 0., 0.4, 0.]) self.p6 = np.asarray(self.p) + np.array([0., 0., -0.4, 0.]) self.p7 = np.asarray(self.p) + np.array([0., 0., 0., math.pi / 2.]) self.p8 = np.asarray(self.p) + np.array([0., 0., 0., -math.pi / 2.]) self.p0_range = [0., 10.] self.p1_range = [0., 2.] self.p2_range = [0., 1.] self.p3_range = [0., math.pi] @skipIf(not is_scipy, "``scipy`` is not installed") def test_LS_estimates(self): lsq = LS_estimates(self.xx, self.y, self.model_2d_anisotropic, sy=self.sy) p, pcov = lsq.fit([1., 0.5, 0.5, 1.]) delta0 = 3. * np.sqrt(pcov[0, 0]) delta1 = 5. * np.sqrt(pcov[1, 1]) delta2 = 5. * np.sqrt(pcov[2, 2]) delta3 = 5. * np.sqrt(pcov[3, 3]) self.assertAlmostEqual(self.p[0], p[0], delta=delta0) # FIXME: use variance as parameter so p[1] > 0 self.assertAlmostEqual(self.p[1], abs(p[1]), delta=delta1) self.assertAlmostEqual(self.p[2], p[2], delta=delta2) self.assertAlmostEqual(self.p[3], p[3], delta=delta3) def test_LnLike(self): lnlike = LnLike(self.xx, self.y, self.model_2d_anisotropic, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, jitter=False, outliers=False) lnlik0 = lnlike._lnprob[0].__call__(self.p) lnlik1 = lnlike._lnprob[1].__call__(self.p) self.assertEqual(lnlike(self.p), lnlik0 + lnlik1) self.assertGreater(lnlike(self.p), lnlike(self.p1)) self.assertGreater(lnlike(self.p), lnlike(self.p2)) self.assertGreater(lnlike(self.p), lnlike(self.p3)) self.assertGreater(lnlike(self.p), lnlike(self.p4)) self.assertGreater(lnlike(self.p), lnlike(self.p5)) self.assertGreater(lnlike(self.p), lnlike(self.p6)) self.assertGreater(lnlike(self.p), lnlike(self.p7)) self.assertGreater(lnlike(self.p), lnlike(self.p8)) @skipIf(not is_scipy, "``scipy`` is not installed") def test_LnPost(self): lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),), (uniform.logpdf, self.p2_range, dict(),), (uniform.logpdf, self.p3_range, dict(),),) lnpr = LnPrior(lnprs) lnlike = LnLike(self.xx, self.y, self.model_2d_anisotropic, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, jitter=False, outliers=False) lnpost = LnPost(self.xx, self.y, self.model_2d_anisotropic, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, lnpr=lnpr, jitter=False, outliers=False) self.assertEqual(lnpost._lnpr(self.p), lnpr(self.p)) self.assertEqual(lnpost._lnlike(self.p), lnlike(self.p)) self.assertGreater(lnpost(self.p), lnpost(self.p1)) self.assertGreater(lnpost(self.p), lnpost(self.p2)) self.assertGreater(lnpost(self.p), lnpost(self.p3)) self.assertGreater(lnpost(self.p), lnpost(self.p4)) @skipIf((not is_emcee) or (not is_scipy), "``emcee`` and/or ``scipy`` not" " installed") def test_MCMC(self): nwalkers = 250 ndim = 4 p0 = np.random.uniform(low=self.p1_range[0], high=self.p1_range[1], size=(nwalkers, ndim)) lnprs = ((uniform.logpdf, self.p0_range, dict(),), (uniform.logpdf, self.p1_range, dict(),), (uniform.logpdf, self.p2_range, dict(),), (uniform.logpdf, self.p3_range, dict(),),) lnpr = LnPrior(lnprs) lnpost = LnPost(self.xx, self.y, self.model_2d_anisotropic, sy=self.sy, x_limits=self.xxl, y_limits=self.yl, sy_limits=self.syl, lnpr=lnpr, jitter=False, outliers=False) sampler = emcee.EnsembleSampler(nwalkers, ndim, lnpost) pos, prob, state = sampler.run_mcmc(p0, 250) sampler.reset() sampler.run_mcmc(pos, 500) sample_vec0 = sampler.flatchain[::10, 0] sample_vec1 = sampler.flatchain[::10, 1] sample_vec2 = sampler.flatchain[::10, 2] sample_vec3 = sampler.flatchain[::10, 3] p0_hdi_min, p0_hdi_max = hdi_of_mcmc(sample_vec0) p1_hdi_min, p1_hdi_max = hdi_of_mcmc(sample_vec1) p2_hdi_min, p2_hdi_max = hdi_of_mcmc(sample_vec2) p3_hdi_min, p3_hdi_max = hdi_of_mcmc(sample_vec3) self.assertTrue((p0_hdi_min < self.p[0] < p0_hdi_max)) self.assertTrue((p1_hdi_min < self.p[1] < p1_hdi_max)) self.assertTrue((p2_hdi_min < self.p[2] < p2_hdi_max)) self.assertTrue((p3_hdi_min < self.p[3] < p3_hdi_max))

49.555224

80

0.575267

2,375

16,601

3.884632

0.070316

0.048233

0.029807

0.03501

0.882723

0.870692

0.848472

0.841318

0

0.047336

0.269562

16,601

334

81

49.703593

0.713508

0.01789

0

0.763333

0

0.021907

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0.002994

0.19

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0.053333

false

0

0.033333

0

0.096667

0.003333

0

null

0

1

0

1

0

null

0

7

d873c9d5b73ad6048ae3ed992ae54074f4373aad

44

py

Python

snowav/database/__init__.py

robertson-mark/SNOWAV

ef7a470dd45a342ee454d74b6476da5807f14301

[ "CC0-1.0" ]

1

2018-09-11T17:14:01.000Z

snowav/database/__init__.py

robertson-mark/SNOWAV

ef7a470dd45a342ee454d74b6476da5807f14301

[ "CC0-1.0" ]

15

2018-10-24T21:59:57.000Z

2021-07-01T20:37:05.000Z

snowav/database/__init__.py

USDA-ARS-NWRC/snowav

ef7a470dd45a342ee454d74b6476da5807f14301

[ "CC0-1.0" ]

null

from . import tables from . import database

14.666667

22

0.772727

6

44

5.666667

0.666667

0.588235

0

0.181818

44

2

23

22

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1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

d87785dc3dfe82dc39a25b2fc439096204f29f6f

158

py

Python

distributions/admin.py

lueho/BRIT

1eae630c4da6f072aa4e2139bc406db4f4756391

[ "MIT" ]

null

distributions/admin.py

lueho/BRIT

1eae630c4da6f072aa4e2139bc406db4f4756391

[ "MIT" ]

4

2022-03-29T20:52:31.000Z

distributions/admin.py

lueho/BRIT

1eae630c4da6f072aa4e2139bc406db4f4756391

[ "MIT" ]

null

from django.contrib import admin from .models import Timestep, TemporalDistribution admin.site.register(TemporalDistribution) admin.site.register(Timestep)

22.571429

50

0.848101

18

158

7.444444

0.555556

0.373134

0.432836

0.552239

0

0.082278

158

6

51

26.333333

0.924138

0

1

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true

0

0.5

0

0.5

0

1

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null

1

0

1

0

null

0

1

0

1

0

7

2b028209a00a7c4331c52f1ca61c13b4b8eaf902

107

py

Python

parquet_metadata/test_parquet_metadata.py

dzamo/parquet-metadata

221bff0253bcaefc7c95e6e16ae376e3bba6ee9f

[ "Apache-2.0" ]

11

2018-09-11T02:56:32.000Z

2022-02-16T18:49:39.000Z

parquet_metadata/test_parquet_metadata.py

dzamo/parquet-metadata

221bff0253bcaefc7c95e6e16ae376e3bba6ee9f

[ "Apache-2.0" ]

null

parquet_metadata/test_parquet_metadata.py

dzamo/parquet-metadata

221bff0253bcaefc7c95e6e16ae376e3bba6ee9f

[ "Apache-2.0" ]

4

2019-05-30T22:44:33.000Z

2022-02-16T18:49:40.000Z

from . import parquet_metadata def test_smoke_test(): parquet_metadata.dump('parquets/types.parquet')

21.4

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107

5.714286

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52

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null

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0

1

0

7

2b31dd8ff5a66ac8bf0442f51e45b1fcb61fee3b

23,469

py

Python

src/test/test_data.py

opploans/cbc-syslog

72a203b1dbe6ddd97f02dc87f36631d758564022

[ "MIT" ]

14

2020-04-28T12:52:50.000Z

2021-08-25T00:36:51.000Z

src/test/test_data.py

opploans/cbc-syslog

72a203b1dbe6ddd97f02dc87f36631d758564022

[ "MIT" ]

21

2016-10-24T20:16:39.000Z

2020-02-11T21:30:50.000Z

src/test/test_data.py

opploans/cbc-syslog

72a203b1dbe6ddd97f02dc87f36631d758564022

[ "MIT" ]

15

2016-12-19T20:39:24.000Z

2020-01-02T16:26:34.000Z

# -*- coding: utf-8 -*- null = "" true = "true" false = "false" raw_notifications = { "notifications": [{ "threatInfo": { "incidentId": "Z7NG6", "score": 7, "summary": "A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.", "indicators": [{ "indicatorName": "PACKED_CALL", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "TARGET_MALWARE_APP", "applicationName": "explorer.exe", "sha256Hash": "1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455" }, { "indicatorName": "HAS_PACKED_CODE", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "KNOWN_DOWNLOADER", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "ENUMERATE_PROCESSES", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "SET_SYSTEM_SECURITY", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "MODIFY_MEMORY_PROTECTION", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "KNOWN_PASSWORD_STEALER", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "RUN_MALWARE_APP", "applicationName": "explorer.exe", "sha256Hash": "1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455" }, { "indicatorName": "MODIFY_PROCESS", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" }, { "indicatorName": "MALWARE_APP", "applicationName": "ShippingInvoice.pdf.exe", "sha256Hash": "cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc" } ], "time": 1460703240678 }, "url": "https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6", "eventTime": 1460703240678, "eventId": "f279d0e6035211e6be8701df2c083974", "eventDescription": "[syslog alert] [Cb Defense has detected a threat against your company.] [https://testserver.company.net/ui#device/2004118/incident/Z7NG6] [A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.] [Incident id: Z7NG6] [Threat score: 7] [Group: default] [Email: FirstName.LastName@company.net.demo] [Name: Demo_CaretoPC] [Type and OS: WINDOWS XP x86 SP: 0]\n", "deviceInfo": { "email": "COMPANY\\FirstName.LastName", "groupName": "default", "internalIpAddress": null, "externalIpAddress": null, "deviceType": "WINDOWS", "deviceVersion": "XP x86 SP: 0", "targetPriorityType": "MEDIUM", "deviceId": 2004118, "deviceName": "COMPANY\\Demo_CaretoPC", "deviceHostName": null, "targetPriorityCode": 0 }, "ruleName": "syslog alert", "type": "THREAT" }, { "policyAction": { "sha256Hash": "2552332222112552332222112552332222112552332222112552332222112552", "action": "TERMINATE", "reputation": "KNOWN_MALWARE", "applicationName": "firefox.exe" }, "type": "POLICY_ACTION", "eventTime": 1423163263482, "eventId": "EV1", "url": "http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action", "deviceInfo": { "deviceType": "WINDOWS", "email": "tester@carbonblack.com", "deviceId": 100, "deviceName": "testers-pc", "deviceHostName": null, "deviceVersion": "7 SP1", "targetPriorityType": "HIGH", "targetPriorityCode": 0, "internalIpAddress": "55.33.22.11", "groupName": "Executives", "externalIpAddress": "255.233.222.211" }, "eventDescription": "Policy action 1", "ruleName": "Alert Rule 1" }, { "threatHunterInfo": { "incidentId": "WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660", "score": 1, "summary": "PowerShell - File and Directory Discovery Enumeration", "time": 1554652050250, "indicators": [ { "applicationName": "powershell.exe", "sha256Hash": "ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436", "indicatorName": "565660-0" } ], "watchLists": [ { "id": "a3xW2ZiaRyAqRtuVES8Q", "name": "ATT&CK Framework", "alert": true } ], "iocId": "565660-0", "count": 0, "dismissed": false, "documentGuid": "7a9fQEsTRfuFmXcogI8CMQ", "firstActivityTime": 1554651811577, "md5": "097ce5761c89434367598b34fe32893b", "policyId": 9815, "processGuid": "WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf", "processPath": "c:\\windows\\system32\\windowspowershell\\v1.0\\powershell.exe", "reportName": "PowerShell - File and Directory Discovery Enumeration", "reportId": "j0MkcneCQXy1fIbhber6rw-565660", "reputation": "TRUSTED_WHITE_LIST", "responseAlarmId": "WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660", "responseSeverity": 1, "runState": "RAN", "sha256": "ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436", "status": "UNRESOLVED", "tags": null, "targetPriority": "MEDIUM", "threatCause": { "reputation": "TRUSTED_WHITE_LIST", "actor": "ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436", "actorName": "powershell.exe", "reason": "Process powershell.exe was detected by the report \"PowerShell - File and Directory Discovery Enumeration\" in watchlist \"ATT&CK Framework\"", "actorType": null, "threatCategory": "RESPONSE_WATCHLIST", "actorProcessPPid": null, "causeEventId": null, "originSourceType": "UNKNOWN" }, "threatId": "a2b724aa094af97c06c758d325240460", "lastUpdatedTime": 0, "orgId": 428 }, "eventDescription": "[sm-sentinel-notification] [Carbon Black has detected a threat against your company.] [https://defense-eap01.conferdeploy.net#device/18900/incident/WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [PowerShell - File and Directory Discovery Enumeration] [Incident id: WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [Threat score: 1] [Group: sm-detection] [Email: smultani@carbonblack.com] [Name: win-559j1nqvfgj] [Type and OS: WINDOWS pscr-sensor] [Severity: 1]\n", "eventTime": 1554651811577, "deviceInfo": { "deviceId": 18900, "targetPriorityCode": 0, "groupName": "sm-detection", "deviceName": "win-559j1nqvfgj", "deviceType": "WINDOWS", "email": "smultani@carbonblack.com", "deviceHostName": null, "deviceVersion": "pscr-sensor", "targetPriorityType": "MEDIUM", "uemId": null, "internalIpAddress": "192.168.81.148", "externalIpAddress": "73.69.152.214" }, "url": "https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660", "ruleName": "sm-sentinel-notification", "type": "THREAT_HUNTER" }], "success": true, "message": "Success" } cef_notifications = ['test CEF:0|CarbonBlack|CbDefense_Syslog_Connector|2.0|Active_Threat|A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.|7|rt="Apr 15 2016 06:54:00" sntdom=COMPANY dvchost=Demo_CaretoPC duser=FirstName.LastName dvc= cs3Label="Link" cs3="https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6" cs4Label="Threat_ID" cs4="Z7NG6" act=Alert', 'test CEF:0|CarbonBlack|CbDefense_Syslog_Connector|2.0|Policy_Action|Confer Sensor Policy Action|1|rt="Feb 05 2015 19:07:43" dvchost=testers-pc duser=tester@carbonblack.com dvc=55.33.22.11 cs3Label="Link" cs3="http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action" act=TERMINATE hash=2552332222112552332222112552332222112552332222112552332222112552 deviceprocessname=firefox.exe', 'test CEF:0|CarbonBlack|CbDefense_Syslog_Connector|2.0|Threat_Hunter|PowerShell - File and Directory Discovery Enumeration|1|rt="Apr 07 2019 15:43:31" dvchost=win-559j1nqvfgj duser=smultani@carbonblack.com dvc=192.168.81.148 cs3Label="Link" cs3="https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660" cs4Label="Threat_ID" cs4="WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660" hash=ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436'] leef_notifications = ['LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=PACKED_CALL\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=explorer.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=TARGET_MALWARE_APP\tsev=Z7NG6\tsha256Hash=1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=HAS_PACKED_CODE\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=KNOWN_DOWNLOADER\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=ENUMERATE_PROCESSES\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=SET_SYSTEM_SECURITY\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=MODIFY_MEMORY_PROTECTION\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=KNOWN_PASSWORD_STEALER\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=explorer.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=RUN_MALWARE_APP\tsev=Z7NG6\tsha256Hash=1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=MODIFY_PROCESS\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=ShippingInvoice.pdf.exe\tcat=INDICATOR\tincidentId=Z7NG6\tindicatorName=MALWARE_APP\tsev=Z7NG6\tsha256Hash=cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc', 'LEEF:2.0|CarbonBlack|Cloud|1.0|THREAT|x09|cat=THREAT\tdevTime=Apr-15-2016 06:54:00 GMT\tdevTimeFormat=MMM dd yyyy HH:mm:ss z\tdeviceId=2004118\tdeviceType=WINDOWS\teventId=f279d0e6035211e6be8701df2c083974\tidentHostName=\tidentSrc=\tincidentId=Z7NG6\trealm=default\tresource=COMPANY\\Demo_CaretoPC\truleName=syslog alert\tsev=7\tsummary=A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.\ttargetPriorityType=MEDIUM\turl=https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6', 'LEEF:2.0|CarbonBlack|Cloud|1.0|POLICY_ACTION|x09|action=TERMINATE\tapplicationName=firefox.exe\tcat=POLICY_ACTION\tdevTime=Feb-05-2015 19:07:43 GMT\tdevTimeFormat=MMM dd yyyy HH:mm:ss z\tdeviceId=100\tdeviceType=WINDOWS\teventId=EV1\tidentHostName=\tidentSrc=55.33.22.11\trealm=Executives\treputation=KNOWN_MALWARE\tresource=testers-pc\truleName=Alert Rule 1\tsev=1\tsha256=2552332222112552332222112552332222112552332222112552332222112552\tsummary=\ttargetPriorityType=HIGH\turl=http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action', 'LEEF:2.0|CarbonBlack|Cloud|1.0|INDICATOR|x09|applicationName=powershell.exe\tcat=INDICATOR\tincidentId=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\tindicatorName=565660-0\tsev=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\tsha256Hash=ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'LEEF:2.0|CarbonBlack|Cloud|1.0|THREAT_HUNTER|x09|cat=THREAT_HUNTER\tdevTime=Apr-07-2019 15:43:31 GMT\tdevTimeFormat=MMM dd yyyy HH:mm:ss z\tdeviceId=18900\tdeviceType=WINDOWS\teventId=None\tidentHostName=\tidentSrc=192.168.81.148\tincidentId=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\tprocessGuid=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf\tprocessPath=c:\\windows\\system32\\windowspowershell\\v1.0\\powershell.exe\trealm=sm-detection\treportName=PowerShell - File and Directory Discovery Enumeration\treputation=TRUSTED_WHITE_LIST\tresource=win-559j1nqvfgj\truleName=sm-sentinel-notification\trunState=RAN\tsev=1\tsha256=ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436\tsummary=PowerShell - File and Directory Discovery Enumeration\ttargetPriorityType=MEDIUM\turl=https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660\twatchlists=ATT&CK Framework'] json_notifications = [{'threatInfo': {'incidentId': 'Z7NG6', 'score': 7, 'summary': 'A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.', 'indicators': [{'indicatorName': 'PACKED_CALL', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'TARGET_MALWARE_APP', 'applicationName': 'explorer.exe', 'sha256Hash': '1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455'}, {'indicatorName': 'HAS_PACKED_CODE', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'KNOWN_DOWNLOADER', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'ENUMERATE_PROCESSES', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'SET_SYSTEM_SECURITY', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'MODIFY_MEMORY_PROTECTION', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'KNOWN_PASSWORD_STEALER', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'RUN_MALWARE_APP', 'applicationName': 'explorer.exe', 'sha256Hash': '1e675cb7df214172f7eb0497f7275556038a0d09c6e5a3e6862c5e26885ef455'}, {'indicatorName': 'MODIFY_PROCESS', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}, {'indicatorName': 'MALWARE_APP', 'applicationName': 'ShippingInvoice.pdf.exe', 'sha256Hash': 'cfe0ae57f314a9f747a7cec605907cdaf1984b3cdea74ee8d5893d00ae0886cc'}], 'time': 1460703240678}, 'url': 'https://testserver.company.net/ui#investigate/events/device/2004118/incident/Z7NG6', 'eventTime': 1460703240678, 'eventId': 'f279d0e6035211e6be8701df2c083974', 'eventDescription': '[syslog alert] [Cb Defense has detected a threat against your company.] [https://testserver.company.net/ui#device/2004118/incident/Z7NG6] [A known virus (Sality: Keylogger, Password or Data stealer, Backdoor) was detected running.] [Incident id: Z7NG6] [Threat score: 7] [Group: default] [Email: FirstName.LastName@company.net.demo] [Name: Demo_CaretoPC] [Type and OS: WINDOWS XP x86 SP: 0]\n', 'deviceInfo': {'email': 'COMPANY\\FirstName.LastName', 'groupName': 'default', 'internalIpAddress': '', 'externalIpAddress': '', 'deviceType': 'WINDOWS', 'deviceVersion': 'XP x86 SP: 0', 'targetPriorityType': 'MEDIUM', 'deviceId': 2004118, 'deviceName': 'COMPANY\\Demo_CaretoPC', 'deviceHostName': '', 'targetPriorityCode': 0}, 'ruleName': 'syslog alert', 'type': 'THREAT', 'source': 'test'}, {'policyAction': {'sha256Hash': '2552332222112552332222112552332222112552332222112552332222112552', 'action': 'TERMINATE', 'reputation': 'KNOWN_MALWARE', 'applicationName': 'firefox.exe'}, 'type': 'POLICY_ACTION', 'eventTime': 1423163263482, 'eventId': 'EV1', 'url': 'http://carbonblack.com/ui#device/100/hash/2552332222112552332222112552332222112552332222112552332222112552/app/firefox.exe/keyword/terminate policy action', 'deviceInfo': {'deviceType': 'WINDOWS', 'email': 'tester@carbonblack.com', 'deviceId': 100, 'deviceName': 'testers-pc', 'deviceHostName': '', 'deviceVersion': '7 SP1', 'targetPriorityType': 'HIGH', 'targetPriorityCode': 0, 'internalIpAddress': '55.33.22.11', 'groupName': 'Executives', 'externalIpAddress': '255.233.222.211'}, 'eventDescription': 'Policy action 1', 'ruleName': 'Alert Rule 1', 'source': 'test'}, {'threatHunterInfo': {'incidentId': 'WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660', 'score': 1, 'summary': 'PowerShell - File and Directory Discovery Enumeration', 'time': 1554652050250, 'indicators': [{'applicationName': 'powershell.exe', 'sha256Hash': 'ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'indicatorName': '565660-0'}], 'watchLists': [{'id': 'a3xW2ZiaRyAqRtuVES8Q', 'name': 'ATT&CK Framework', 'alert': 'true'}], 'iocId': '565660-0', 'count': 0, 'dismissed': 'false', 'documentGuid': '7a9fQEsTRfuFmXcogI8CMQ', 'firstActivityTime': 1554651811577, 'md5': '097ce5761c89434367598b34fe32893b', 'policyId': 9815, 'processGuid': 'WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf', 'processPath': 'c:\\windows\\system32\\windowspowershell\\v1.0\\powershell.exe', 'reportName': 'PowerShell - File and Directory Discovery Enumeration', 'reportId': 'j0MkcneCQXy1fIbhber6rw-565660', 'reputation': 'TRUSTED_WHITE_LIST', 'responseAlarmId': 'WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660', 'responseSeverity': 1, 'runState': 'RAN', 'sha256': 'ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'status': 'UNRESOLVED', 'tags': '', 'targetPriority': 'MEDIUM', 'threatCause': {'reputation': 'TRUSTED_WHITE_LIST', 'actor': 'ba4038fd20e474c047be8aad5bfacdb1bfc1ddbe12f803f473b7918d8d819436', 'actorName': 'powershell.exe', 'reason': 'Process powershell.exe was detected by the report "PowerShell - File and Directory Discovery Enumeration" in watchlist "ATT&CK Framework"', 'actorType': '', 'threatCategory': 'RESPONSE_WATCHLIST', 'actorProcessPPid': '', 'causeEventId': '', 'originSourceType': 'UNKNOWN'}, 'threatId': 'a2b724aa094af97c06c758d325240460', 'lastUpdatedTime': 0, 'orgId': 428}, 'eventDescription': '[sm-sentinel-notification] [Carbon Black has detected a threat against your company.] [https://defense-eap01.conferdeploy.net#device/18900/incident/WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [PowerShell - File and Directory Discovery Enumeration] [Incident id: WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660] [Threat score: 1] [Group: sm-detection] [Email: smultani@carbonblack.com] [Name: win-559j1nqvfgj] [Type and OS: WINDOWS pscr-sensor] [Severity: 1]\n', 'eventTime': 1554651811577, 'deviceInfo': {'deviceId': 18900, 'targetPriorityCode': 0, 'groupName': 'sm-detection', 'deviceName': 'win-559j1nqvfgj', 'deviceType': 'WINDOWS', 'email': 'smultani@carbonblack.com', 'deviceHostName': '', 'deviceVersion': 'pscr-sensor', 'targetPriorityType': 'MEDIUM', 'uemId': '', 'internalIpAddress': '192.168.81.148', 'externalIpAddress': '73.69.152.214'}, 'url': 'https://defense-eap01.conferdeploy.net/investigate?s[searchWindow]=ALL&s[c][DEVICE_ID][0]=18900&s[c][INCIDENT_ID][0]=WNEXFKQ7-000049d4-00001ef0-00000000-1d4ed58a5f07dbf-j0MkcneCQXy1fIbhber6rw-565660', 'ruleName': 'sm-sentinel-notification', 'type': 'THREAT_HUNTER', 'source': 'test'}]

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117.345

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2b3e16bac852a65f995976462d16cdfaa6ccae29

36,434

py

Python

networks_utils.py

jviquerat/U-net_laminar_flow

6a6029f8bd4d036f8675df8dd74e2d13476aa069

[ "MIT" ]

null

networks_utils.py

jviquerat/U-net_laminar_flow

6a6029f8bd4d036f8675df8dd74e2d13476aa069

[ "MIT" ]

1

2021-11-18T09:29:19.000Z

2021-12-14T08:33:44.000Z

networks_utils.py

jviquerat/u-net_laminar_flow

6a6029f8bd4d036f8675df8dd74e2d13476aa069

[ "MIT" ]

1

2021-07-20T08:23:27.000Z

# Import stuff import sys import math import keras # Additional imports from keras from keras import regularizers from keras import optimizers from keras.models import Model from keras.layers import Input from keras.layers import concatenate from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import AveragePooling2D from keras.layers import Flatten from keras.layers import Dense from keras.layers import Activation from keras.layers import Dropout from keras.layers import Conv2DTranspose from keras.layers import Lambda from keras.layers import BatchNormalization from keras.layers.convolutional import ZeroPadding2D # Custom imports from datasets_utils import * ### ************************************************ ### I/O convolutional layer def io_conv_2D(x, filters = 8, kernel_size = 3, strides = 1, padding = 'same', activation = 'relu'): x = Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding, activation = activation)(x) return x ### ************************************************ ### I/O max-pooling layer def io_maxp_2D(x, pool_size = 2, strides = 2): x = MaxPooling2D(pool_size = pool_size, strides = strides)(x) return x ### ************************************************ ### I/O avg-pooling layer def io_avgp_2D(x, pool_size = 2, strides = 2): x = AveragePooling2D(pool_size = pool_size, strides = strides)(x) return x ### ************************************************ ### I/O convolutional transposed layer def io_conv_2D_transp(in_layer, n_filters, kernel_size, stride_size): out_layer = Conv2DTranspose(filters=n_filters, kernel_size=kernel_size, strides=stride_size, padding='same')(in_layer) return out_layer ### ************************************************ ### I/O concatenate + zero-pad def io_concat_pad(in_layer_1, in_layer_2, axis): # Compute padding sizes shape1_x = np.asarray(keras.backend.int_shape(in_layer_1)[1]) shape1_y = np.asarray(keras.backend.int_shape(in_layer_1)[2]) shape2_x = np.asarray(keras.backend.int_shape(in_layer_2)[1]) shape2_y = np.asarray(keras.backend.int_shape(in_layer_2)[2]) dx = shape2_x - shape1_x dy = shape2_y - shape1_y # Pad and concat pad_layer = ZeroPadding2D(((dx,0),(dy,0)))(in_layer_1) out_layer = concatenate([pad_layer, in_layer_2], axis=axis) return out_layer ### ************************************************ ### Classic U-net for field prediction def U_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv10]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_absolute_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Stacked U-nets def StackedU_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_size_2, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) # 2 convolutions + maxPool conv21 = io_conv_2D(conv10, n_filters_initial*(2**0), kernel_size_2) conv21 = io_conv_2D(conv21, n_filters_initial*(2**0), kernel_size_2) pool21 = io_maxp_2D(conv21, pool_size) # 2 convolutions + maxPool conv22 = io_conv_2D(pool21, n_filters_initial*(2**1), kernel_size_2) conv22 = io_conv_2D(conv22, n_filters_initial*(2**1), kernel_size_2) pool22 = io_maxp_2D(conv22, pool_size) # 2 convolutions + maxPool conv23 = io_conv_2D(pool22, n_filters_initial*(2**2), kernel_size_2) conv23 = io_conv_2D(conv23, n_filters_initial*(2**2), kernel_size_2) pool23 = io_maxp_2D(conv23, pool_size) # 2 convolutions + maxPool conv24 = io_conv_2D(pool23, n_filters_initial*(2**3), kernel_size_2) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size_2) pool24 = io_maxp_2D(conv24, pool_size) # 2 convolutions conv25 = io_conv_2D(pool24, n_filters_initial*(2**4), kernel_size_2) conv25 = io_conv_2D(conv25, n_filters_initial*(2**4), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre26 = io_conv_2D_transp(conv25, n_filters_initial*(2**3), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv24, 3) conv26 = io_conv_2D(up26, n_filters_initial*(2**3), kernel_size_2) conv26 = io_conv_2D(conv26, n_filters_initial*(2**3), kernel_size_2) pre27 = io_conv_2D_transp(conv26, n_filters_initial*(2**2), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv23, 3) conv27 = io_conv_2D(up27, n_filters_initial*(2**2), kernel_size_2) conv27 = io_conv_2D(conv27, n_filters_initial*(2**2), kernel_size_2) pre28 = io_conv_2D_transp(conv27, n_filters_initial*(2**1), (2,2), (2,2)) up28 = io_concat_pad(pre28, conv22, 3) conv28 = io_conv_2D(up28, n_filters_initial*(2**1), kernel_size_2) conv28 = io_conv_2D(conv28, n_filters_initial*(2**1), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre29 = io_conv_2D_transp(conv28, n_filters_initial*(2**0), (2,2), (2,2)) up29 = io_concat_pad(pre29, conv21, 3) conv29 = io_conv_2D(up29, n_filters_initial*(2**0), kernel_size_2) conv29 = io_conv_2D(conv29, n_filters_initial*(2**0), kernel_size_2) # final 1x1 convolution conv20 = io_conv_2D(conv29, 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv20]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_absolute_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Coupled U-nets def CpU_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) ##### the output of 1-st U-net # 2 convolutions + maxPool conv21 = io_conv_2D(concatenate([conv0, conv10], axis=3), n_filters_initial*(2**0), kernel_size) conv21 = io_conv_2D(conv21, n_filters_initial*(2**0), kernel_size) pool21 = io_maxp_2D(conv21, pool_size) # 2 convolutions + maxPool conv22 = io_conv_2D(concatenate([pool1, pool21], axis=3), n_filters_initial*(2**1), kernel_size) conv22 = io_conv_2D(conv22, n_filters_initial*(2**1), kernel_size) pool22 = io_maxp_2D(conv22, pool_size) # 2 convolutions + maxPool conv23 = io_conv_2D(concatenate([pool2, pool22], axis=3), n_filters_initial*(2**2), kernel_size) conv23 = io_conv_2D(conv23, n_filters_initial*(2**2), kernel_size) pool23 = io_maxp_2D(conv23, pool_size) # 2 convolutions + maxPool conv24 = io_conv_2D(concatenate([pool3, pool23], axis=3), n_filters_initial*(2**3), kernel_size) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size) pool24 = io_maxp_2D(conv24, pool_size) # 2 convolutions conv25 = io_conv_2D(concatenate([pool4, pool24], axis=3), n_filters_initial*(2**4), kernel_size) conv25 = io_conv_2D(conv25, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre26 = io_conv_2D_transp(concatenate([conv5, conv25], axis=3), n_filters_initial*(2**3), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv24, 3) conv26 = io_conv_2D(up26, n_filters_initial*(2**3), kernel_size) conv26 = io_conv_2D(conv26, n_filters_initial*(2**3), kernel_size) pre27 = io_conv_2D_transp(concatenate([conv6, conv26], axis=3), n_filters_initial*(2**2), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv23, 3) conv27 = io_conv_2D(up27, n_filters_initial*(2**2), kernel_size) conv27 = io_conv_2D(conv27, n_filters_initial*(2**2), kernel_size) pre28 = io_conv_2D_transp(concatenate([conv7, conv27], axis=3), n_filters_initial*(2**1), (2,2), (2,2)) up28 = io_concat_pad(pre28, conv22, 3) conv28 = io_conv_2D(up28, n_filters_initial*(2**1), kernel_size) conv28 = io_conv_2D(conv28, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre29 = io_conv_2D_transp(concatenate([conv8, conv28], axis=3), n_filters_initial*(2**0), (2,2), (2,2)) up29 = io_concat_pad(pre29, conv21, 3) conv29 = io_conv_2D(up29, n_filters_initial*(2**0), kernel_size) conv29 = io_conv_2D(conv29, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv20 = io_conv_2D(concatenate([conv9, conv29], axis=3), 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv20]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_absolute_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Multilevel U-nets def Multi_level_U_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) ######################################################################################################################## ##Here is the bottle of mini U-net pre4 = io_conv_2D_transp(conv3, n_filters_initial*(2**1), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up4 = io_concat_pad(pre4, conv2, 3) conv4 = io_conv_2D(up4, n_filters_initial*(2**1), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre5 = io_conv_2D_transp(conv4, n_filters_initial*(2**0), (2,2), (2,2)) up5 = io_concat_pad(pre5, conv1, 3) conv5 = io_conv_2D(up5, n_filters_initial*(2**0), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**0), kernel_size) # output of mini U-net conv6 = io_conv_2D(conv5, 3, 1) ######################################################################################################################## pool3 = io_maxp_2D(conv3, pool_size) conv24 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size) # Here is the bottleneck of small U-net pre25 = io_conv_2D_transp(conv24, n_filters_initial*(2**2), (2,2), (2,2)) up25 = io_concat_pad(pre25, conv3, 3) conv25 = io_conv_2D(up25, n_filters_initial*(2**2), kernel_size) conv25 = io_conv_2D(conv25, n_filters_initial*(2**2), kernel_size) pre26 = io_conv_2D_transp(conv25, n_filters_initial*(2**1), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv2, 3)#an alternate is to concatenate pre26 with conv2 conv26 = io_conv_2D(up26, n_filters_initial*(2**1), kernel_size) conv26 = io_conv_2D(conv26, n_filters_initial*(2**1), kernel_size) pre27 = io_conv_2D_transp(conv26, n_filters_initial*(2**0), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv1, 3)#an alternate is to concatenate pre26 with conv1 conv27 = io_conv_2D(up27, n_filters_initial*(2**0), kernel_size) conv27 = io_conv_2D(conv27, n_filters_initial*(2**0), kernel_size) # output of small U-net conv28 = io_conv_2D(conv27, 3, 1) ######################################################################################################################## pool24 = io_maxp_2D(conv24, pool_size) conv35 = io_conv_2D(pool24, n_filters_initial*(2**4), kernel_size) conv35 = io_conv_2D(conv35, n_filters_initial*(2**4), kernel_size) # Here is the bottleneck of U-net pre36 = io_conv_2D_transp(conv35, n_filters_initial*(2**3), (2,2), (2,2)) up36 = io_concat_pad(pre36, conv24, 3) conv36 = io_conv_2D(up36, n_filters_initial*(2**3), kernel_size) conv36 = io_conv_2D(conv36, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre37 = io_conv_2D_transp(conv36, n_filters_initial*(2**2), (2,2), (2,2))## conv36, conv26? up37 = io_concat_pad(pre37, conv3, 3)#an alternate is to concatenate pre37 with conv3 conv37 = io_conv_2D(up37, n_filters_initial*(2**2), kernel_size) conv37 = io_conv_2D(conv37, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre38 = io_conv_2D_transp(conv37, n_filters_initial*(2**1), (2,2), (2,2)) up38 = io_concat_pad(pre38, conv2, 3)#two alternates are to concatenate pre38 with conv2 or conv4 conv38 = io_conv_2D(up38, n_filters_initial*(2**1), kernel_size) conv38 = io_conv_2D(conv38, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre39 = io_conv_2D_transp(conv38, n_filters_initial*(2**0), (2,2), (2,2)) up39 = io_concat_pad(pre39, conv1, 3)#two alternates are concatenate pre39 with conv1 or conv5 or conv27 conv39 = io_conv_2D(up39, n_filters_initial*(2**0), kernel_size) conv39 = io_conv_2D(conv39, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv30 = io_conv_2D(conv39, 3, 1) ######################################################################################################################## # average the output of three U-nets conv10 = keras.layers.Average()([conv6, conv28, conv30]) #concatenate the output of three U-nets #conv10 = io_conv_2D(concatenate([conv6, conv28, conv30], axis=3), 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv10]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_squared_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Inverse multilevel U-net def InvMU_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) conv24 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size) pool24 = io_maxp_2D(conv24, pool_size) conv35 = io_conv_2D(pool24, n_filters_initial * (2 ** 4), kernel_size) conv35 = io_conv_2D(conv35, n_filters_initial * (2 ** 4), kernel_size) # Here is the bottleneck of U-net pre36 = io_conv_2D_transp(conv35, n_filters_initial * (2 ** 3), (2, 2), (2, 2)) up36 = io_concat_pad(pre36, conv24, 3) conv36 = io_conv_2D(up36, n_filters_initial * (2 ** 3), kernel_size) conv36 = io_conv_2D(conv36, n_filters_initial * (2 ** 3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre37 = io_conv_2D_transp(conv36, n_filters_initial * (2 ** 2), (2, 2), (2, 2)) ## conv36, conv26? up37 = io_concat_pad(pre37, conv3, 3) # an alternate is to concatenate pre37 with conv3 conv37 = io_conv_2D(up37, n_filters_initial * (2 ** 2), kernel_size) conv37 = io_conv_2D(conv37, n_filters_initial * (2 ** 2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre38 = io_conv_2D_transp(conv37, n_filters_initial * (2 ** 1), (2, 2), (2, 2)) up38 = io_concat_pad(pre38, conv2, 3) # two alternates are to concatenate pre38 with conv2 or conv4 conv38 = io_conv_2D(up38, n_filters_initial * (2 ** 1), kernel_size) conv38 = io_conv_2D(conv38, n_filters_initial * (2 ** 1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre39 = io_conv_2D_transp(conv38, n_filters_initial * (2 ** 0), (2, 2), (2, 2)) up39 = io_concat_pad(pre39, conv1, 3) # two alternates are concatenate pre39 with conv1 or conv5 or conv27 conv39 = io_conv_2D(up39, n_filters_initial * (2 ** 0), kernel_size) conv39 = io_conv_2D(conv39, n_filters_initial * (2 ** 0), kernel_size) # final 1x1 convolution conv30 = io_conv_2D(conv39, 3, 1) conv30 = Lambda(lambda x: x * 1.5)(conv30) ######################################################################################################################## pre25 = io_conv_2D_transp(conv24, n_filters_initial * (2 ** 2), (2, 2), (2, 2)) up25 = io_concat_pad(pre25, conv37, 3) conv25 = io_conv_2D(up25, n_filters_initial * (2 ** 2), kernel_size) conv25 = io_conv_2D(conv25, n_filters_initial * (2 ** 2), kernel_size) pre26 = io_conv_2D_transp(conv25, n_filters_initial * (2 ** 1), (2, 2), (2, 2)) up26 = io_concat_pad(pre26, conv38, 3) conv26 = io_conv_2D(up26, n_filters_initial * (2 ** 1), kernel_size) conv26 = io_conv_2D(conv26, n_filters_initial * (2 ** 1), kernel_size) pre27 = io_conv_2D_transp(conv26, n_filters_initial * (2 ** 0), (2, 2), (2, 2)) up27 = io_concat_pad(pre27, conv39, 3) # an alternate is to concatenate pre26 with conv1 conv27 = io_conv_2D(up27, n_filters_initial * (2 ** 0), kernel_size) conv27 = io_conv_2D(conv27, n_filters_initial * (2 ** 0), kernel_size) # output of small U-net conv28 = io_conv_2D(conv27, 3, 1) ######################################################################################################################## pre4 = io_conv_2D_transp(conv3, n_filters_initial*(2**1), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up4 = io_concat_pad(pre4, conv38, 3) conv4 = io_conv_2D(up4, n_filters_initial*(2**1), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre5 = io_conv_2D_transp(conv4, n_filters_initial*(2**0), (2,2), (2,2)) up5 = io_concat_pad(pre5, conv39, 3) conv5 = io_conv_2D(up5, n_filters_initial*(2**0), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**0), kernel_size) # output of mini U-net conv6 = io_conv_2D(conv5, 3, 1) conv6 = Lambda(lambda x: x * 0.5)(conv6) ######################################################################################################################## # average the output of three U-nets conv10 = keras.layers.Average()([conv6, conv28, conv30]) # construct model model = Model(inputs=[conv0], outputs=[conv10]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_squared_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Parallel U-nets def Parallel_U_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_size_2, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) #conv10 = keras.layers.Add()([conv0, conv10]) ##### the output of 1-st U-net # 2 convolutions + maxPool conv21 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size_2) conv21 = io_conv_2D(conv21, n_filters_initial*(2**0), kernel_size_2) pool21 = io_maxp_2D(conv21, pool_size) # 2 convolutions + maxPool conv22 = io_conv_2D(pool21, n_filters_initial*(2**1), kernel_size_2) conv22 = io_conv_2D(conv22, n_filters_initial*(2**1), kernel_size_2) pool22 = io_maxp_2D(conv22, pool_size) # 2 convolutions + maxPool conv23 = io_conv_2D(pool22, n_filters_initial*(2**2), kernel_size_2) conv23 = io_conv_2D(conv23, n_filters_initial*(2**2), kernel_size_2) pool23 = io_maxp_2D(conv23, pool_size) # 2 convolutions + maxPool conv24 = io_conv_2D(pool23, n_filters_initial*(2**3), kernel_size_2) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size_2) pool24 = io_maxp_2D(conv24, pool_size) # 2 convolutions conv25 = io_conv_2D(pool24, n_filters_initial*(2**4), kernel_size_2) conv25 = io_conv_2D(conv25, n_filters_initial*(2**4), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre26 = io_conv_2D_transp(conv25, n_filters_initial*(2**3), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv24, 3) conv26 = io_conv_2D(up26, n_filters_initial*(2**3), kernel_size_2) conv26 = io_conv_2D(conv26, n_filters_initial*(2**3), kernel_size_2) pre27 = io_conv_2D_transp(conv26, n_filters_initial*(2**2), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv23, 3) conv27 = io_conv_2D(up27, n_filters_initial*(2**2), kernel_size_2) conv27 = io_conv_2D(conv27, n_filters_initial*(2**2), kernel_size_2) pre28 = io_conv_2D_transp(conv27, n_filters_initial*(2**1), (2,2), (2,2)) up28 = io_concat_pad(pre28, conv22, 3) conv28 = io_conv_2D(up28, n_filters_initial*(2**1), kernel_size_2) conv28 = io_conv_2D(conv28, n_filters_initial*(2**1), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre29 = io_conv_2D_transp(conv28, n_filters_initial*(2**0), (2,2), (2,2)) up29 = io_concat_pad(pre29, conv21, 3) conv29 = io_conv_2D(up29, n_filters_initial*(2**0), kernel_size_2) conv29 = io_conv_2D(conv29, n_filters_initial*(2**0), kernel_size_2) # final 1x1 convolution conv20 = io_conv_2D(conv29, 3, 1) conv30 = keras.layers.Average()([conv10, conv20]) # construct model model = Model(inputs=[conv0], outputs=[conv30]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_squared_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model)

41.261608

124

0.625542

5,183

36,434

4.074474

0.044183

0.069325

0.092433

0.172744

0.916375

0.910976

0.90752

0.893551

0.889857

0.882659

0

0.083696

0.224104

36,434

882

125

41.30839

0.663341

0.14481

0

0.827586

0

0.007675

0

1

0.019964

false

0

0.036298

0

0.065336

0

null

0

1

0

1

0

null

0

8

2b42e860d0ae1b98081e8bfa9a5444fe0146b12b

943

py

Python

hangman/hangman_art.py

EdwinZurawik/hangman

6b865e40bab2a35b71731b3f9bee254ac1d992be

[ "MIT" ]

null

hangman/hangman_art.py

EdwinZurawik/hangman

6b865e40bab2a35b71731b3f9bee254ac1d992be

[ "MIT" ]

null

hangman/hangman_art.py

EdwinZurawik/hangman

6b865e40bab2a35b71731b3f9bee254ac1d992be

[ "MIT" ]

null

hangman_title = r""" ____ ____ ____ ____ ____ ____ ____ ||H ||||a ||||n ||||g ||||m ||||a ||||n || ||__||||__||||__||||__||||__||||__||||__|| |/__\||/__\||/__\||/__\||/__\||/__\||/__\| """ hangman_stages = [r""" ____ |/ | | | | | ===== """, r""" ____ |/ | | O | | | ===== """, r""" ____ |/ | | O | | | | ===== """, r""" ____ |/ | | O | /| | | ===== """, r""" ____ |/ | | O | /|\ | | ===== """, r""" ____ |/ | | O | /|\ | / | ===== """, r""" ____ |/ | | O | /|\ | / \ | ===== """]

16.839286

52

0.139979

25

943

1.84

0.4

0.26087

0.326087

0.434783

0.26087

0

0.604454

943

55

53

17.145455

0.123324

0

0.545455

0

0.888653

0.089077

0

1

0

false

0

1

0

1

null

1

0

1

0

1

0

1

0

null

0

8

2b5f838d508a37329b5f07cb4bf0d798c35b1875

125

py

Python

colornamer/__init__.py

stitchfix/colornamer

1bbaf061feee42322f2428fbf7d9e9a20be255aa

[ "Apache-2.0" ]

55

2020-09-02T20:10:30.000Z

2022-03-16T01:25:15.000Z

colornamer/__init__.py

stitchfix/colornamer

1bbaf061feee42322f2428fbf7d9e9a20be255aa

[ "Apache-2.0" ]

1

2021-01-24T12:21:27.000Z

2021-12-07T02:10:14.000Z

colornamer/__init__.py

stitchfix/colornamer

1bbaf061feee42322f2428fbf7d9e9a20be255aa

[ "Apache-2.0" ]

5

2020-09-13T13:48:28.000Z

2021-11-18T09:45:02.000Z

from .colornamer import get_color_from_rgb from .colornamer import get_color_from_lab from .colornamer import get_color_json

31.25

42

0.88

20

125

5.1

0.4

0.411765

0.588235

0.676471

0.901961

0.627451

0

0.096

125

3

43

41.666667

0.902655

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

9

99481855b36edd94fabfd379b091a1551b3a37b4

2,838

py

Python

test_q2.py

karolineos/Desafio-de-Programa-o-Capgemini-

cb0aeceeb2d6ba393fc208c3c557c2980469e179

[ "MIT" ]

null

test_q2.py

karolineos/Desafio-de-Programa-o-Capgemini-

cb0aeceeb2d6ba393fc208c3c557c2980469e179

[ "MIT" ]

null

test_q2.py

karolineos/Desafio-de-Programa-o-Capgemini-

cb0aeceeb2d6ba393fc208c3c557c2980469e179

[ "MIT" ]

null

from unittest import TestCase from q2 import * class TestQuestao2Busca(TestCase): """ Nomeclatura: test_quando_..._deve_retornar_... def test_quando_..._deve_retornar_...(self): Metodos herdados de TestCase: |nome: | Ação: | setUp | Antes de cada teste | tearDown | Depois de cada teste | setUpClass | Antes de todos os testes | tearDownClass | Depois de todos os testes """ # def teste_quando_array_crescente_deve_retornar_mediana(self): # self.assertEqual(hello_world(), 'hello world') # def teste_quando_array_decrescente_deve_retornar_mediana(self): # def setUp(self): print("\nTestando Questao2 Busca") def Teste(self, lista, deslocamento,saida_esperada): print(f"Busca d. - teste: lista: {lista}, deslocamento: {deslocamento}; saida esperada: {saida_esperada}; ", end="") saida = buscar_deslocamentos(lista, deslocamento) #saida, pares = buscar_deslocamentos(lista, deslocamento) print(f"saida:{saida}") #print(f"saida:{saida}, pares: {pares}") self.assertEqual(saida, saida_esperada) def teste_Busca_quando_parametros_sao_do_exemplo1_deve_retornar_3(self): n = [1, 5, 3, 4, 2] x = 2 saida_esperada = 3 self.Teste(n,x,saida_esperada) class TestQuestao2BuscaMelhorada(TestCase): """ Nomeclatura: test_quando_..._deve_retornar_... def test_quando_..._deve_retornar_...(self): Metodos herdados de TestCase: |nome: | Ação: | setUp | Antes de cada teste | tearDown | Depois de cada teste | setUpClass | Antes de todos os testes | tearDownClass | Depois de todos os testes """ # def teste_quando_array_crescente_deve_retornar_mediana(self): # self.assertEqual(hello_world(), 'hello world') # def teste_quando_array_decrescente_deve_retornar_mediana(self): # def setUp(self): print("\nTestando Questao2 Busca Melhorada") def Teste(self, lista, deslocamento,saida_esperada): print(f"Busca d. Melhor - teste: lista: {lista}, deslocamento: {deslocamento}; saida esperada: {saida_esperada}; ", end="") saida, pares = buscar_deslocamentos_melhorado(lista, deslocamento) print(f"saida:{saida}, pares: {pares}") self.assertEqual(saida, saida_esperada) def teste_Busca_quando_parametros_sao_do_exemplo1_deve_retornar_3(self): n = [1, 5, 3, 4, 2] x = 2 saida_esperada = 3 self.Teste(n,x,saida_esperada)

37.342105

132

0.597604

299

2,838

5.411371

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0.096415

0.034611

0.054388

0.873918

0.843016

0

0.011675

0.305849

2,838

75

133

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0.809645

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0.08

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null

0

1

0

null

0

1

0

1

0

8

9964cf19b644bec6e8da1584aa1fc11f4f7d82fe

245

py

Python

6_json_to_csv.py

pLINaROF/income_of_russian_deputies

6c27fe968825aa2131b613da7265364a03e47397

[ "MIT" ]

null

6_json_to_csv.py

pLINaROF/income_of_russian_deputies

6c27fe968825aa2131b613da7265364a03e47397

[ "MIT" ]

null

6_json_to_csv.py

pLINaROF/income_of_russian_deputies

6c27fe968825aa2131b613da7265364a03e47397

[ "MIT" ]

null

import pandas df = pandas.read_json('data_with_income_rub.json') df.to_csv('data_with_income_rub.csv', index=False) df = pandas.read_json('data_with_income_rub_from_csv.json') df.to_csv('data_with_income_rub_from_csv.csv', index=False)

30.625

60

0.791837

44

245

3.954545

0.318182

0.183908

0.321839

0.390805

0.781609

0.701149

0

0.085714

245

7

61

35

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0

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null

0

1

0

1

0

1

0

1

null

0

7

999ac636b504000a178a19bf4c2635260e522f9b

1,920

py

Python

tempset/package_data.py

IMMM-SFA/tempset

86da8415cda47a2158cc1a481b0fa8ceaf5f2e1e

[ "BSD-2-Clause" ]

null

tempset/package_data.py

IMMM-SFA/tempset

86da8415cda47a2158cc1a481b0fa8ceaf5f2e1e

[ "BSD-2-Clause" ]

null

tempset/package_data.py

IMMM-SFA/tempset

86da8415cda47a2158cc1a481b0fa8ceaf5f2e1e

[ "BSD-2-Clause" ]

null

import pkg_resources def get_example_eplus_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/json/eplus_params.json') def get_example_batch_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/json/batch_params.json') def get_example_htgsetp_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/json/htgsetp_params.json') def get_example_htgsetp_params_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/electric/htgsetp_params_electric.csv') def get_example_clgsetp_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/json/clgsetp_params.json') def get_example_summary_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/electric/summary.zip') def get_example_idd_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/eplus/Energy+.idd') def get_example_electric_idf_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/idf/electric.idf') def get_example_gas_idf_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/idf/gas.idf') def get_example_main_idf_file(): """Convenience wrapper to retrieve file path from package data.""" return pkg_resources.resource_filename('tempset', 'data/idf/main.idf')

30.967742

98

0.758854

253

1,920

5.517787

0.134387

0.094556

0.093123

0.17192

0.82808

0.795129

0.752149

0

0.13125

1,920

61

99

31.47541

0.83693

0.317188

0

0.257143

0.175397

0

1

0.47619

true

0

0.047619

0

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null

0

1

0

null

0

1

0

9

999d5936d86373d84e9ba7d3368a0e7424c747d6

14,919

py

Python

gdmtl/datasets/mtl_dataset.py

binshengliu/gdmtl

fb8bfe0e87bbd6d8535cc8449012fb4119430d4c

[ "MIT" ]

null

gdmtl/datasets/mtl_dataset.py

binshengliu/gdmtl

fb8bfe0e87bbd6d8535cc8449012fb4119430d4c

[ "MIT" ]

null

gdmtl/datasets/mtl_dataset.py

binshengliu/gdmtl

fb8bfe0e87bbd6d8535cc8449012fb4119430d4c

[ "MIT" ]

1

2022-02-26T00:49:03.000Z

from __future__ import annotations import logging from typing import Any, Dict, Mapping, Optional, Union import numpy as np import torch from transformers import PreTrainedTokenizer from .assembler import Assembler from .qa_dataset import QADataset from .rank_dataset import RankGroupDataset from .tsv_dataset import TsvCollection from .utils import ( make_targets_mlm_inputs, make_targets_ntp_inputs, mask_difference, mask_whole_word, ) log = logging.getLogger(__name__) class MtlSepDataset(RankGroupDataset): def __init__( self, array: Mapping[str, np.ndarray], tokenizer: PreTrainedTokenizer, query_col: TsvCollection, doc_col: TsvCollection, num_dup: int, num_neg: int, decoder_start_token_id: int, src_max_length: int, tgt_max_length: int, sample: Optional[Union[float, int]] = None, sort: Optional[str] = None, max_length: Optional[int] = None, summarizer_prefix_token_ids: Optional[str] = None, rank_prefix_token_ids: Optional[str] = None, pad_to_max_length: bool = True, **kwargs: Any, ): if kwargs: log.warning(f"Unused parameters: {kwargs}") super().__init__( array, tokenizer, query_col, doc_col, num_dup, num_neg, sample, sort, max_length, summarizer_prefix_token_ids, pad_to_max_length, ) self._pas_pad = tokenizer.pad_token_id self._sum_assembler = Assembler( tokenizer=tokenizer, max_length=src_max_length, prefix_token_ids=summarizer_prefix_token_ids, pad_to_max_length=pad_to_max_length, ) self._rank_assembler = Assembler( tokenizer=tokenizer, max_length=src_max_length, prefix_token_ids=rank_prefix_token_ids, pad_to_max_length=pad_to_max_length, ) decoder_start_token = tokenizer.decode(decoder_start_token_id) self._decoder_assembler = Assembler( tokenizer=tokenizer, max_length=tgt_max_length, prefix_token_ids=decoder_start_token, pad_to_max_length=False, add_special_tokens=False, return_token_type_ids=None, ) self._label_assembler = Assembler( tokenizer=tokenizer, max_length=tgt_max_length, suffix_token_ids=tokenizer.eos_token, pad_to_max_length=False, add_special_tokens=False, return_token_type_ids=None, ) def __getitem__(self, index: int) -> Dict[str, torch.Tensor]: qid = self._array["qid"][index] did = self._array["did"][index] label = self._array["label"][index] assert qid.shape == (self._num_neg + 1,) assert did.shape == (self._num_neg + 1,) assert label.shape == (self._num_neg + 1,) queries = [self._query_col[x] for x in qid] passages = [self._doc_col[x] for x in did] sum_inputs = self._sum_assembler.batch_assemble(passages) sum_decoder_inputs = self._decoder_assembler.batch_assemble(queries) lm_labels = self._label_assembler.batch_assemble(queries) lm_labels["input_ids"].masked_fill_(~lm_labels["attention_mask"].bool(), -100) rank_inputs = self._rank_assembler.batch_assemble(passages) rank_decoder_inputs = self._decoder_assembler.batch_assemble(queries) item: Dict[str, Any] = { "qids": torch.tensor([int(x) for x in qid]), "dnos": torch.tensor([int(x) for x in did]), "sum_input_ids": sum_inputs["input_ids"], "sum_attention_mask": sum_inputs["attention_mask"], "sum_decoder_input_ids": sum_decoder_inputs["input_ids"], "sum_decoder_attention_mask": sum_decoder_inputs["attention_mask"], "rank_input_ids": rank_inputs["input_ids"], "rank_attention_mask": rank_inputs["attention_mask"], "rank_decoder_input_ids": rank_decoder_inputs["input_ids"], "rank_decoder_attention_mask": rank_decoder_inputs["attention_mask"], "lm_labels": lm_labels["input_ids"], } assert item["sum_input_ids"].dim() == 2 assert item["sum_attention_mask"].dim() == 2 assert item["sum_decoder_input_ids"].dim() == 2 assert item["sum_decoder_attention_mask"].dim() == 2 assert item["rank_input_ids"].dim() == 2 assert item["rank_attention_mask"].dim() == 2 assert item["rank_decoder_input_ids"].dim() == 2 assert item["rank_decoder_attention_mask"].dim() == 2 assert item["lm_labels"].dim() == 2 return item class MtlMixedDataset(RankGroupDataset): def __init__( self, array: Mapping[str, np.ndarray], tokenizer: PreTrainedTokenizer, query_col: TsvCollection, doc_col: TsvCollection, num_dup: int, num_neg: int, src_max_length: int, sample: Optional[Union[float, int]] = None, sort: Optional[str] = None, max_length: Optional[int] = None, summarizer_prefix_token_ids: Optional[str] = None, rank_prefix_token_ids: Optional[str] = None, pad_to_max_length: bool = True, qa_data: Optional[Union[QADataset, str]] = None, qa_prefix: str = "", mask_whole_word_prob: float = 0.0, mask_qgen_query: bool = False, mask_query_from_passage: float = 0.0, min_rel_for_qgen: int = 1, **kwargs: Any, ): if kwargs: log.warning(f"Unused params {kwargs}") super(MtlMixedDataset, self).__init__( array, tokenizer, query_col, doc_col, num_dup, num_neg, sample, sort, max_length, summarizer_prefix_token_ids, pad_to_max_length, ) self._pas_pad = tokenizer.pad_token_id self._tokenizer = tokenizer self._mask_whole_word_prob = mask_whole_word_prob self._mask_query_from_passage = mask_query_from_passage self._mask_qgen_query = mask_qgen_query self._min_rel_for_qgen = min_rel_for_qgen self._sum_assembler = Assembler( tokenizer=tokenizer, max_length=src_max_length, prefix_token_ids=summarizer_prefix_token_ids, pad_to_max_length=pad_to_max_length, ) self._rank_assembler = Assembler( tokenizer=tokenizer, max_length=src_max_length, prefix_token_ids=rank_prefix_token_ids, pad_to_max_length=pad_to_max_length, ) if qa_data is not None: if isinstance(qa_data, str): self._qa = QADataset( path=qa_data, tokenizer=tokenizer, max_length=max_length, prefix=qa_prefix, ) else: self._qa = qa_data def __getitem__(self, index: int) -> Dict[str, Any]: qid = self._array["qid"][index] did = self._array["did"][index] label = self._array["label"][index] assert qid.shape == (self._num_neg + 1,) assert did.shape == (self._num_neg + 1,) assert label.shape == (self._num_neg + 1,) if label[0] < self._min_rel_for_qgen: idx = (self._array["label"][:, 0] >= self._min_rel_for_qgen).nonzero()[0] sample = np.random.choice(idx) qgen_queries = [self._query_col[x] for x in self._array["qid"][sample]] passages = [self._doc_col[x] for x in self._array["did"][sample]] qgen_passages = [self._doc_col[x] for x in self._array["did"][sample]] sum_input_weights = torch.tensor( self._array["label"][sample][:1], dtype=torch.float ) else: qgen_queries = [self._query_col[x] for x in qid] passages = [self._doc_col[x] for x in did] qgen_passages = [self._doc_col[x] for x in did] sum_input_weights = torch.tensor(label[:1], dtype=torch.float) if self._mask_query_from_passage > 0.0: qgen_passages = [ mask_difference(self._tokenizer, x, y, self._mask_query_from_passage) for x, y in zip(qgen_passages, qgen_queries) ] if self._mask_whole_word_prob > 0: qgen_passages = [ mask_whole_word(self._tokenizer, x, self._mask_whole_word_prob) for x in qgen_passages ] if self._mask_qgen_query: sum_inputs = make_targets_mlm_inputs( self._assembler, self._tokenizer, passages[:1], qgen_queries[:1], qgen_passages[:1], ) else: sum_inputs = make_targets_ntp_inputs( self._assembler, self._tokenizer, passages[:1], qgen_queries[:1], qgen_passages[:1], ) rank_queries = [self._query_col[x] for x in qid] rank_passages = [self._doc_col[x] for x in did] rank_inputs = self._rank_assembler.batch_assemble(rank_passages, rank_queries) item: Dict[str, Any] = { "qids": torch.tensor([int(x) for x in qid]), "dnos": torch.tensor([int(x) for x in did]), "sum_input_ids": sum_inputs["input_ids"], "sum_token_type_ids": sum_inputs["token_type_ids"], "sum_attention_mask": sum_inputs["attention_mask"], "sum_input_weights": sum_input_weights, "rank_input_ids": rank_inputs["input_ids"], "rank_token_type_ids": rank_inputs["token_type_ids"], "rank_attention_mask": rank_inputs["attention_mask"], "lm_labels": sum_inputs["lm_labels"], } assert item["sum_input_ids"].dim() == 2 if self._mask_qgen_query: assert item["sum_attention_mask"].dim() == 2 else: assert item["sum_attention_mask"].dim() == 3 assert item["sum_token_type_ids"].dim() == 2 assert item["sum_input_weights"].dim() == 1 assert item["rank_input_ids"].dim() == 2 assert item["rank_token_type_ids"].dim() == 2 assert item["rank_attention_mask"].dim() == 2 assert item["lm_labels"].dim() == 2 if hasattr(self, "_qa"): pos_qid = qid[0] qa_inputs = {f"qa_{k}": v for k, v in self._qa.by_qid(pos_qid).items()} item.update(qa_inputs) return item class MtlCatDataset(RankGroupDataset): def __init__( self, array: Mapping[str, np.ndarray], tokenizer: PreTrainedTokenizer, query_col: TsvCollection, doc_col: TsvCollection, num_dup: int, num_neg: int, src_max_length: int, tgt_max_length: int, decoder_start_token_id: int, sample: Optional[Union[float, int]] = None, sort: Optional[str] = None, max_length: Optional[int] = None, summarizer_prefix_token_ids: Optional[str] = None, rank_prefix_token_ids: Optional[str] = None, pad_to_max_length: bool = True, **kwargs: Any, ): if kwargs: log.warning(f"Unused params {kwargs}") super().__init__( array, tokenizer, query_col, doc_col, num_dup, num_neg, sample, sort, max_length, summarizer_prefix_token_ids, pad_to_max_length, ) self._pas_pad = tokenizer.pad_token_id self._tokenizer = tokenizer self._sum_assembler = Assembler( tokenizer=tokenizer, max_length=src_max_length, prefix_token_ids=summarizer_prefix_token_ids, pad_to_max_length=pad_to_max_length, ) self._rank_assembler = Assembler( tokenizer=tokenizer, max_length=src_max_length, prefix_token_ids=rank_prefix_token_ids, pad_to_max_length=pad_to_max_length, ) decoder_start_token = tokenizer.decode(decoder_start_token_id) self._decoder_assembler = Assembler( tokenizer=tokenizer, max_length=tgt_max_length, prefix_token_ids=decoder_start_token, pad_to_max_length=False, add_special_tokens=False, return_token_type_ids=None, ) self._label_assembler = Assembler( tokenizer=tokenizer, max_length=tgt_max_length, suffix_token_ids=tokenizer.eos_token, pad_to_max_length=False, add_special_tokens=False, return_token_type_ids=None, ) def __getitem__(self, index: int) -> Dict[str, Any]: qid = self._array["qid"][index] did = self._array["did"][index] label = self._array["label"][index] assert qid.shape == (self._num_neg + 1,) assert did.shape == (self._num_neg + 1,) assert label.shape == (self._num_neg + 1,) queries = [self._query_col[x] for x in qid] passages = [self._doc_col[x] for x in did] sum_inputs = self._sum_assembler.batch_assemble(passages) sum_decoder_inputs = self._decoder_assembler.batch_assemble(queries) lm_labels = self._label_assembler.batch_assemble(queries) lm_labels["input_ids"].masked_fill_(~lm_labels["attention_mask"].bool(), -100) rank_passages = [self._doc_col[x] for x in did] rank_inputs = self._rank_assembler.batch_assemble(rank_passages, queries) item: Dict[str, Any] = { "qids": torch.tensor([int(x) for x in qid]), "dnos": torch.tensor([int(x) for x in did]), "sum_input_ids": sum_inputs["input_ids"], "sum_attention_mask": sum_inputs["attention_mask"], "sum_decoder_input_ids": sum_decoder_inputs["input_ids"], "sum_decoder_attention_mask": sum_decoder_inputs["attention_mask"], "rank_input_ids": rank_inputs["input_ids"], "rank_attention_mask": rank_inputs["attention_mask"], "lm_labels": lm_labels["input_ids"], } assert item["sum_input_ids"].dim() == 2 assert item["sum_attention_mask"].dim() == 2 assert item["sum_decoder_input_ids"].dim() == 2 assert item["sum_decoder_attention_mask"].dim() == 2 assert item["rank_input_ids"].dim() == 2 assert item["rank_attention_mask"].dim() == 2 assert item["lm_labels"].dim() == 2 return item

36.65602

86

0.597359

1,793

14,919

4.559955

0.083659

0.060543

0.039384

0.037671

0.827299

0.793542

0.78498

0.756849

0.747065

0.728596

0

0.005754

0.301093

14,919

406

87

36.746305

0.778364

0

0.720548

0

0.088076

0.01917

0

0.093151

1

0.016438

false

0.068493

0.030137

0

0.063014

0

null

0

1

0

1

0

null

0

1

0

8

99a1c1776fe99e2a906834405a696a3f2033b0ed

572

py

Python

e_learning/api/models.py

Aaditya1978/Accessible-E-Learning-Platform

bf846b6f7e3aaca3d7f7ecd0a83a5c4dfc595f6d

[ "MIT" ]

3

2021-07-15T06:09:08.000Z

2022-02-01T13:47:03.000Z

e_learning/api/models.py

Aaditya1978/Accessible-E-Learning-Platform

bf846b6f7e3aaca3d7f7ecd0a83a5c4dfc595f6d

[ "MIT" ]

null

e_learning/api/models.py

Aaditya1978/Accessible-E-Learning-Platform

bf846b6f7e3aaca3d7f7ecd0a83a5c4dfc595f6d

[ "MIT" ]

null

from django.db import models # Create your models here. class Teacher(models.Model): first_name = models.CharField(max_length=255) last_name = models.CharField(max_length=255) email = models.EmailField() org_name = models.CharField(max_length=255) password = models.CharField(max_length=255) class Student(models.Model): first_name = models.CharField(max_length=255) last_name = models.CharField(max_length=255) email = models.EmailField() class_code = models.CharField(max_length=255) password = models.CharField(max_length=255)

33.647059

49

0.748252

77

572

5.376623

0.311688

0.289855

0.347826

0.463768

0.806763

0.797101

0

0.049281

0.148601

572

16

50

35.75

0.800821

0.041958

0

0.615385

0

1

0

false

0.153846

0.076923

0

1

0

null

1

0

null

0

1

0

10

41efa0b1f23a58e01d79993abae419de0e29ae35

5,994

py

Python

fonts/diamonstealth64_8x14.py

ccccmagicboy/st7735_mpy

b15f1bde69fbe6e0eb4931c57e71c136d8e7f024

[ "MIT" ]

6

2020-07-11T16:59:19.000Z

2021-07-16T19:32:49.000Z

ports/esp32/user_modules/st7735_mpy/fonts/diamonstealth64_8x14.py

d4niele/micropython

a1f7b37d392bf46b28045ce215ae899fda8d8c38

[ "MIT" ]

1

2020-04-14T03:14:45.000Z

fonts/diamonstealth64_8x14.py

ccccmagicboy/st7735_mpy

b15f1bde69fbe6e0eb4931c57e71c136d8e7f024

[ "MIT" ]

null

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510ef8a7510ae1de9af9073513790cd07023ac0b

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py

Python

wikidata_research/dictionary/dictionary_evaluation.py

sjuenger/WikiMETA

13ed293b4bda8ff0fc10b532907ca35c24a12616

[ "MIT" ]

null

wikidata_research/dictionary/dictionary_evaluation.py

sjuenger/WikiMETA

13ed293b4bda8ff0fc10b532907ca35c24a12616

[ "MIT" ]

null

wikidata_research/dictionary/dictionary_evaluation.py

sjuenger/WikiMETA

13ed293b4bda8ff0fc10b532907ca35c24a12616

[ "MIT" ]

null

# module to evaluate the property dictionary import json # global variable for the path to the dictionary path_to_json_dictionary = "data/property_dictionary.json" # overload method # # recommended == true # query only those references or qualifiers, that are intended by Wikidata # .. for References: these are properties, which are a facet of "Wikipedia:Citing sources" # .. for Qualifiers: these are properties, which are a facet of "restrictive qualifier" # ,"non-restrictive qualifier" # ,"Wikidata property used as \"depicts\" (P180) qualifier on Commons" # ,"Wikidata qualifier" # # recommended == false # query only those references or qualifiers, that are NOT intended by Wikidata # i.e., who do not fulfil the above mentioned requirements # BUT are min. 1x times used as a reference / qualifier in Wikidata # # recommended == None # query every property available to the mode def get_top_x_metadata(x, mode, recommended = None): with open(path_to_json_dictionary, "r") as dict_data: property_dictionary = json.load(dict_data) result_dictionary = {} result_dictionary["properties"] = {} result_dictionary["total_usages_of_" + mode] = 0 result_dictionary["total_unique_properties"] = 0 for PID in property_dictionary: # check, if the property is /is not a recommended reference/qualifier by Wikidata recommended_bool = False if recommended == True: if mode == "reference": recommended_bool = bool(property_dictionary[PID]["is_reference"]) elif mode == "qualifier": recommended_bool = property_dictionary[PID]["qualifier_class"] != [] else: recommended_bool = False elif recommended == False: # --> but they are min. 1x times used as a reference/qualifier, but not recommended if mode == "reference" and int(property_dictionary[PID][mode + "_no"]) > 0\ and not bool(property_dictionary[PID]["is_reference"]): recommended_bool = True # --> but they are min. 1x times used as a reference/qualifier, but not recommended elif mode == "qualifier" and int(property_dictionary[PID][mode + "_no"]) > 0\ and property_dictionary[PID]["qualifier_class"] == []: recommended_bool = True else: recommended_bool = False elif recommended is None: # just exclude those, who either aren't a recommended qualifier/reference property # .. and are never used as a reference / qualifier if (mode == "reference" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or bool(property_dictionary[PID]["is_reference"]))): recommended_bool = True elif (mode == "qualifier" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or property_dictionary[PID]["qualifier_class"] != [])): recommended_bool = True else: recommended_bool = False if recommended_bool: result_dictionary["total_usages_of_" + mode] += \ int(property_dictionary[PID][mode + "_no"]) result_dictionary["total_unique_properties"] += 1 # check, if the current property is smaller than any property in the result dictionary and swap them # or, if the result dictionary has not yet got 'X' entries, just add the property if len(result_dictionary["properties"]) < x: result_dictionary["properties"][PID] = property_dictionary[PID] else: # no need to check for (non-) recommended properties here (only (non-) recommended properties # can be added to this dictionary) for result_PID in result_dictionary["properties"]: if PID != result_PID \ and (int(property_dictionary[PID][mode + "_no"]) > int(result_dictionary["properties"][result_PID][mode + "_no"])): # swap with the smallest in the result property smallest_PID = "" for test_PID in result_dictionary["properties"]: if smallest_PID == "" or \ int(result_dictionary["properties"][test_PID][mode + "_no"]) \ < int(result_dictionary["properties"][smallest_PID][mode + "_no"]): smallest_PID = test_PID result_dictionary["properties"].pop(smallest_PID) result_dictionary["properties"][PID] = property_dictionary[PID] break # once all the top x entries are created, store them in a .json file if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/properties/top_" + str(x) + ".json", "w") \ as result_json: json.dump(result_dictionary, result_json) # query the top facets (properties have) from qualifier / reference # .. of recommended / non-recommended / overall properties # # if a property has no facet -> count it "as" itself # def get_top_x_facets_by_metadata(x, mode, recommended = None): with open(path_to_json_dictionary, "r") as dict_data: property_dictionary = json.load(dict_data) facets_dictionary = {} facets_dictionary["facets"] = {} # add a counter for the total amount of facets and properties facets_dictionary["total_facets"] = 0 facets_dictionary["total_properties_without_facets"] = 0 facets_dictionary["total_properties"] = 0 for PID in property_dictionary: # check, if the property is /is not a recommended reference/qualifier by Wikidata recommended_bool = True if recommended == True: if mode == "reference": recommended_bool = bool(property_dictionary[PID]["is_reference"]) elif mode == "qualifier": recommended_bool = property_dictionary[PID]["qualifier_class"] != [] else: recommended_bool = False elif recommended == False: # --> but they are min. 1x times used as a reference/qualifier, but not recommended if mode == "reference" and int(property_dictionary[PID][mode + "_no"]) > 0\ and not bool(property_dictionary[PID]["is_reference"]): recommended_bool = True # --> but they are min. 1x times used as a reference/qualifier, but not recommended elif mode == "qualifier" and int(property_dictionary[PID][mode + "_no"]) > 0\ and property_dictionary[PID]["qualifier_class"] == []: recommended_bool = True else: recommended_bool = False elif recommended is None: # just exclude those, who either aren't a recommended qualifier/reference property # .. and are never used as a reference / qualifier if mode == "reference" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or bool(property_dictionary[PID]["is_reference"])): recommended_bool = True elif mode == "qualifier" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or property_dictionary[PID]["qualifier_class"] != []): recommended_bool = True else: recommended_bool = False if recommended_bool: facets_dictionary["total_properties"] += 1 current_facet_list = property_dictionary[PID]["facet_of"] # if no facet can be found for the property # -> count the property with its ID if len(current_facet_list) == 0: facets_dictionary["total_properties_without_facets"] += 1 facets_dictionary["facets"][PID] = 1 for facet in current_facet_list: facets_dictionary["total_facets"] += 1 # add the facet as keys to a dictionary, if it wasn't added before if facet not in facets_dictionary["facets"]: facets_dictionary["facets"][facet] = 1 else: facets_dictionary["facets"][facet] += 1 # store the facet dictionary if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/facets/facets.json", "w") \ as result_json: json.dump(facets_dictionary, result_json) # extract the top x facets by usages result_facets_dictionary = {"facets" : {}} result_facets_dictionary["total_facets"] = facets_dictionary["total_facets"] result_facets_dictionary["total_properties"] = facets_dictionary["total_properties"] result_facets_dictionary["total_properties_without_facet"] =\ facets_dictionary["total_properties_without_facets"] result_facets_dictionary["total_unique_facets"] = len(facets_dictionary["facets"]) for facet in facets_dictionary["facets"]: if len(result_facets_dictionary["facets"]) < x: result_facets_dictionary["facets"][facet] = facets_dictionary["facets"][facet] else: # swap with the smallest in the result list -> it is greater than that smallest_ID = "" for facet_ID in result_facets_dictionary["facets"]: if smallest_ID == "" or \ int(result_facets_dictionary["facets"][facet_ID]) \ < int(result_facets_dictionary["facets"][smallest_ID]): smallest_ID = facet_ID if facets_dictionary["facets"][facet] > facets_dictionary["facets"][smallest_ID]: result_facets_dictionary["facets"].pop(smallest_ID) result_facets_dictionary["facets"][facet] = facets_dictionary["facets"][facet] # once all the top x entries are creaed, store them in a .json file if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/facets/top_" + str(x) + ".json", "w") \ as result_json: json.dump(result_facets_dictionary, result_json) # get the used datatypes for every metadata # -> a datatype can e.g. be String, WikibaseItem, etc. # def get_datatypes_by_metadata(mode, recommended = None): with open(path_to_json_dictionary, "r") as dict_data: property_dictionary = json.load(dict_data) datatypes_dictionary = {} datatypes_dictionary["datatypes"] = {} # add a counter for the total amount of datatypes and properties datatypes_dictionary["total_properties"] = 0 for PID in property_dictionary: # check, if the property is /is not a recommended reference/qualifier by Wikidata recommended_bool = True if recommended == True: if mode == "reference": recommended_bool = bool(property_dictionary[PID]["is_reference"]) elif mode == "qualifier": recommended_bool = property_dictionary[PID]["qualifier_class"] != [] else: recommended_bool = False elif recommended == False: # --> but they are min. 1x times used as a reference/qualifier, but not recommended if mode == "reference" and int(property_dictionary[PID][mode + "_no"]) > 0\ and not bool(property_dictionary[PID]["is_reference"]): recommended_bool = True # --> but they are min. 1x times used as a reference/qualifier, but not recommended elif mode == "qualifier" and int(property_dictionary[PID][mode + "_no"]) > 0\ and property_dictionary[PID]["qualifier_class"] == []: recommended_bool = True else: recommended_bool = False elif recommended is None: # just exclude those, who either aren't a recommended qualifier/reference property # .. and are never used as a reference / qualifier if mode == "reference" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or bool(property_dictionary[PID]["is_reference"])): recommended_bool = True elif mode == "qualifier" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or property_dictionary[PID]["qualifier_class"] != []): recommended_bool = True else: recommended_bool = False if recommended_bool: datatypes_dictionary["total_properties"] += 1 current_datatype = property_dictionary[PID]["datatype"] # add the datatype as a key to the dictionary, if it wasn't added before if current_datatype not in datatypes_dictionary["datatypes"]: datatypes_dictionary["datatypes"][current_datatype] = 1 else: datatypes_dictionary["datatypes"][current_datatype] += 1 datatypes_dictionary["total_unique_datatypes"] = len(datatypes_dictionary["datatypes"]) # once all the top x entries are creaed, store them in a .json file if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/datatypes/datatypes.json", "w") \ as result_json: json.dump(datatypes_dictionary, result_json) # get the accumulated facets by occurences of a (recommended) property in Wikidata # so, e.g. if "Series Ordinal" occures as a reference 5Miox times in Wikidata, count all of his facets 5Miox times # # if a property has no facet -> count it "as" itself # def get_top_x_facets_by_accumulated_properties(x, mode, recommended = None): with open(path_to_json_dictionary, "r") as dict_data: property_dictionary = json.load(dict_data) facets_dictionary = {} facets_dictionary["facets"] = {} # add a counter for the total amount of facets and properties facets_dictionary["total_accumulated_facets"] = 0 facets_dictionary["total_accumulated_properties_without_facets"] = 0 facets_dictionary["total_accumulated_properties"] = 0 for PID in property_dictionary: # check, if the property is /is not a recommended reference/qualifier by Wikidata recommended_bool = True if recommended == True: if mode == "reference": recommended_bool = bool(property_dictionary[PID]["is_reference"]) elif mode == "qualifier": recommended_bool = property_dictionary[PID]["qualifier_class"] != [] else: recommended_bool = False elif recommended == False: # --> but they are min. 1x times used as a reference/qualifier, but not recommended if mode == "reference" and int(property_dictionary[PID][mode + "_no"]) > 0\ and not bool(property_dictionary[PID]["is_reference"]): recommended_bool = True # --> but they are min. 1x times used as a reference/qualifier, but not recommended elif mode == "qualifier" and int(property_dictionary[PID][mode + "_no"]) > 0\ and property_dictionary[PID]["qualifier_class"] == []: recommended_bool = True else: recommended_bool = False elif recommended is None: # just exclude those, who either aren't a recommended qualifier/reference property # .. and are never used as a reference / qualifier if mode == "reference" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or bool(property_dictionary[PID]["is_reference"])): recommended_bool = True elif mode == "qualifier" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or property_dictionary[PID]["qualifier_class"] != []): recommended_bool = True else: recommended_bool = False if recommended_bool: facets_dictionary["total_accumulated_properties"] += int(property_dictionary[PID][mode + "_no"]) current_facet_list = property_dictionary[PID]["facet_of"] facets_dictionary["total_accumulated_facets"] += \ len(current_facet_list) * int(property_dictionary[PID][mode + "_no"]) # if no facet can be found for the property # -> count the property with its ID if len(current_facet_list) == 0: facets_dictionary["total_accumulated_properties_without_facets"] += \ int(property_dictionary[PID][mode + "_no"]) facets_dictionary["facets"][PID] = int(property_dictionary[PID][mode + "_no"]) for facet in current_facet_list: # add the facet as keys to a dictionary, if it wasn't added before if facet not in facets_dictionary["facets"]: facets_dictionary["facets"][facet] = int(property_dictionary[PID][mode + "_no"]) else: facets_dictionary["facets"][facet] += int(property_dictionary[PID][mode + "_no"]) # extract the top x facets by usages result_facets_dictionary = {"facets": {}} result_facets_dictionary["total_accumulated_facets"] = facets_dictionary["total_accumulated_facets"] result_facets_dictionary["total_accumulated_properties"] = facets_dictionary["total_accumulated_properties"] result_facets_dictionary["total_accumulated_properties_without_facets"] =\ facets_dictionary["total_accumulated_properties_without_facets"] # store the dictionar< of accumulated facets if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/accumulated_facets/accumulated_facets.json", "w") \ as result_json: json.dump(facets_dictionary, result_json) for facet in facets_dictionary["facets"]: if len(result_facets_dictionary["facets"]) < x: result_facets_dictionary["facets"][facet] = facets_dictionary["facets"][facet] else: # swap with the smallest in the result list -> it is greater than that smallest_ID = "" for facet_ID in result_facets_dictionary["facets"]: if smallest_ID == "" or \ int(result_facets_dictionary["facets"][facet_ID]) \ < int(result_facets_dictionary["facets"][smallest_ID]): smallest_ID = facet_ID if facets_dictionary["facets"][facet] > facets_dictionary["facets"][smallest_ID]: result_facets_dictionary["facets"].pop(smallest_ID) result_facets_dictionary["facets"][facet] = facets_dictionary["facets"][facet] # once all the top x entries are creaed, store them in a .json file if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/accumulated_facets/top_" + str(x) + ".json", "w") \ as result_json: json.dump(result_facets_dictionary, result_json) # get the accumulated datatypes by occurences of a (recommended) property in Wikidata # so, e.g. if "Series Ordinal" occures as a reference 5Miox times in Wikidata, count his datatype 5Miox times def get_datatypes_by_accumulated_properties(mode, recommended = None): with open(path_to_json_dictionary, "r") as dict_data: property_dictionary = json.load(dict_data) datatypes_dictionary = {} datatypes_dictionary["datatypes"] = {} # add a counter for the total amount of datatypes and properties datatypes_dictionary["total_properties"] = 0 datatypes_dictionary["total_accumulated_datatypes"] = 0 for PID in property_dictionary: # check, if the property is /is not a recommended reference/qualifier by Wikidata recommended_bool = True if recommended == True: if mode == "reference": recommended_bool = bool(property_dictionary[PID]["is_reference"]) elif mode == "qualifier": recommended_bool = property_dictionary[PID]["qualifier_class"] != [] else: recommended_bool = False elif recommended == False: # --> but they are min. 1x times used as a reference/qualifier, but not recommended if mode == "reference" and int(property_dictionary[PID][mode + "_no"]) > 0\ and not bool(property_dictionary[PID]["is_reference"]): recommended_bool = True # --> but they are min. 1x times used as a reference/qualifier, but not recommended elif mode == "qualifier" and int(property_dictionary[PID][mode + "_no"]) > 0\ and property_dictionary[PID]["qualifier_class"] == []: recommended_bool = True else: recommended_bool = False elif recommended is None: # just exclude those, who either aren't a recommended qualifier/reference property # .. and are never used as a reference / qualifier if mode == "reference" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or bool(property_dictionary[PID]["is_reference"])): recommended_bool = True elif mode == "qualifier" and (int(property_dictionary[PID][mode + "_no"]) > 0\ or property_dictionary[PID]["qualifier_class"] != []): recommended_bool = True else: recommended_bool = False if recommended_bool: datatypes_dictionary["total_properties"] += 1 datatypes_dictionary["total_accumulated_datatypes"] += int(property_dictionary[PID][mode + "_no"]) current_datatype = property_dictionary[PID]["datatype"] # add the datatype as a key to the dictionary, if it wasn't added before if current_datatype not in datatypes_dictionary["datatypes"]: datatypes_dictionary["datatypes"][current_datatype] = int(property_dictionary[PID][mode + "_no"]) else: datatypes_dictionary["datatypes"][current_datatype] += int(property_dictionary[PID][mode + "_no"]) datatypes_dictionary["total_unique_datatypes"] = len(datatypes_dictionary["datatypes"]) # once all the top x entries are creaed, store them in a .json file if recommended: tmp_string = "recommended" elif recommended is not None: tmp_string = "non_recommended" else: tmp_string = "all" with open("data/statistical_information/wikidata_research/" + mode + "/" + tmp_string + "/accumulated_datatypes/accumulated_datatypes.json", "w") \ as result_json: json.dump(datatypes_dictionary, result_json) # get the acummulated facets by occurences of a (recommended) property in Wikidata def get_top_x_metadata_recommended_by_facet(x, mode): return # get all datatypes, that are available inside the property dictionary def get_all_datatypes_from_property_dictionary(): with open(path_to_json_dictionary, "r") as dict_data: property_dictionary = json.load(dict_data) result_dict = {} i = 0 for PID in property_dictionary: tmp_datatype = property_dictionary[PID]["datatype"] if tmp_datatype not in result_dict: result_dict[tmp_datatype] = 0 dict_data.close() return result_dict

48.83859

128

0.584334

2,761

26,324

5.352409

0.063021

0.102314

0.09663

0.050345

0.875694

0.839153

0.803762

0.766816

0.74435

0.737989

0

0.003615

0.327534

26,324

538

129

48.929368

0.831206

0.201793

0

0.769663

0

0.137989

0.054775

0

1

0.019663

false

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0.002809

0.02809

0

null

0

1

0

1

0

null

0

7

510fcdad15a6c8447153e32692aae7694f685210

2,456

py

Python

oxe-api/test/resource/user/test_update_user.py

CybersecurityLuxembourg/openxeco

8d4e5578bde6a07f5d6d569b16b4de224abf7bf0

[ "BSD-2-Clause" ]

null

oxe-api/test/resource/user/test_update_user.py

CybersecurityLuxembourg/openxeco

8d4e5578bde6a07f5d6d569b16b4de224abf7bf0

[ "BSD-2-Clause" ]

null

oxe-api/test/resource/user/test_update_user.py

CybersecurityLuxembourg/openxeco

8d4e5578bde6a07f5d6d569b16b4de224abf7bf0

[ "BSD-2-Clause" ]

null

from test.BaseCase import BaseCase class TestUpdateUser(BaseCase): @BaseCase.login @BaseCase.grant_access("/user/update_user") def test_ok(self, token): self.db.insert({ "id": 14, "email": "myemail@test.lu", "password": "MySecret2!", "is_admin": 0, }, self.db.tables["User"]) payload = { "id": 14, "is_admin": True } response = self.application.post('/user/update_user', headers=self.get_standard_post_header(token), json=payload) users = self.db.get(self.db.tables["User"], {"id": 14}) self.assertEqual(200, response.status_code) self.assertEqual(len(users), 1) self.assertEqual(users[0].is_admin, 1) @BaseCase.login @BaseCase.grant_access("/user/update_user") def test_ko_password_param(self, token): self.db.insert({ "id": 2, "email": "myemail@test.lu", "password": "MySecret2!", "is_admin": 0, }, self.db.tables["User"]) payload = { "id": 2, "is_admin": True, "password": "new pass" } response = self.application.post('/user/update_user', headers=self.get_standard_post_header(token), json=payload) users = self.db.get(self.db.tables["User"], {"id": 2}) self.assertEqual("422 UNPROCESSABLE ENTITY", response.status) self.assertEqual(users[0].is_admin, 0) @BaseCase.login @BaseCase.grant_access("/user/update_user") def test_ko_email_param(self, token): self.db.insert({ "id": 2, "email": "myemail@test.lu", "password": "MySecret2!", "is_admin": 0, }, self.db.tables["User"]) payload = { "id": 2, "is_admin": True, "email": "myemail@test.lu" } response = self.application.post('/user/update_user', headers=self.get_standard_post_header(token), json=payload) users = self.db.get(self.db.tables["User"], {"id": 2}) self.assertEqual("422 UNPROCESSABLE ENTITY", response.status) self.assertEqual(users[0].is_admin, 0)

30.7

86

0.506107

254

2,456

4.755906

0.208661

0.059603

0.069536

0.07947

0.84851

0.806291

0

0.021438

0.354235

2,456

79

87

31.088608

0.740227

0

0.758065

0

0.158795

0

0.112903

1

0.048387

false

0.080645

0.016129

0

0.080645

0

null

0

1

0

1

0

null

0

1

0

8

515fe6ec967d9adf1eabbb5dad94f9dd79c8c9e6

90

py

Python

tests/test_wb2k.py

reillysiemens/wb2k

54edaa1afe4904a78746356555468d7c04685b28

[ "ISC" ]

6

2016-06-09T04:06:29.000Z

2019-12-22T15:29:54.000Z

tests/test_wb2k.py

reillysiemens/wb2k

54edaa1afe4904a78746356555468d7c04685b28

[ "ISC" ]

19

2016-06-03T22:00:13.000Z

2019-09-25T09:03:16.000Z

tests/test_wb2k.py

reillysiemens/wb2k

54edaa1afe4904a78746356555468d7c04685b28

[ "ISC" ]

4

2016-10-06T20:45:44.000Z

2017-10-28T22:01:20.000Z

# TODO: Write actual tests. This just makes pytest-cov pick up on the module. import wb2k

30

77

0.766667

16

90

4.3125

1

0

0.013514

0.177778

90

2

78

45

0.918919

0.833333

0

0.5

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

1

0

7

51666c4b9d618cca914f038ac201e94c03f87f6e

34,907

py

Python

post_optimization_studies/mad_analyses/four_cuts_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_4.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

post_optimization_studies/mad_analyses/four_cuts_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_4.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

post_optimization_studies/mad_analyses/four_cuts_eff_flow_chart/Output/Histos/MadAnalysis5job_0/selection_4.py

sheride/axion_pheno

7d3fc08f5ae5b17a3500eba19a2e43f87f076ce5

[ "MIT" ]

null

def selection_4(): # Library import import numpy import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec # Library version matplotlib_version = matplotlib.__version__ numpy_version = numpy.__version__ # Histo binning xBinning = numpy.linspace(-8.0,8.0,161,endpoint=True) # Creating data sequence: middle of each bin xData = numpy.array([-7.95,-7.85,-7.75,-7.65,-7.55,-7.45,-7.35,-7.25,-7.15,-7.05,-6.95,-6.85,-6.75,-6.65,-6.55,-6.45,-6.35,-6.25,-6.15,-6.05,-5.95,-5.85,-5.75,-5.65,-5.55,-5.45,-5.35,-5.25,-5.15,-5.05,-4.95,-4.85,-4.75,-4.65,-4.55,-4.45,-4.35,-4.25,-4.15,-4.05,-3.95,-3.85,-3.75,-3.65,-3.55,-3.45,-3.35,-3.25,-3.15,-3.05,-2.95,-2.85,-2.75,-2.65,-2.55,-2.45,-2.35,-2.25,-2.15,-2.05,-1.95,-1.85,-1.75,-1.65,-1.55,-1.45,-1.35,-1.25,-1.15,-1.05,-0.95,-0.85,-0.75,-0.65,-0.55,-0.45,-0.35,-0.25,-0.15,-0.05,0.05,0.15,0.25,0.35,0.45,0.55,0.65,0.75,0.85,0.95,1.05,1.15,1.25,1.35,1.45,1.55,1.65,1.75,1.85,1.95,2.05,2.15,2.25,2.35,2.45,2.55,2.65,2.75,2.85,2.95,3.05,3.15,3.25,3.35,3.45,3.55,3.65,3.75,3.85,3.95,4.05,4.15,4.25,4.35,4.45,4.55,4.65,4.75,4.85,4.95,5.05,5.15,5.25,5.35,5.45,5.55,5.65,5.75,5.85,5.95,6.05,6.15,6.25,6.35,6.45,6.55,6.65,6.75,6.85,6.95,7.05,7.15,7.25,7.35,7.45,7.55,7.65,7.75,7.85,7.95]) # Creating weights for histo: y5_ETA_0 y5_ETA_0_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.93650155414,2.16577055928,2.86585876426,3.2752672163,3.82387435003,4.39295226636,5.03572372006,5.70305915288,6.52597045348,7.36934973668,8.43790882849,9.65385179504,10.7346868764,11.6640460865,13.5022885242,14.5462796369,14.8819953516,16.2903621546,16.7284257823,17.4162331977,17.174681403,17.7683248984,17.7069129506,17.3384452638,17.0764254865,15.6557786939,15.2299910558,13.7847802841,12.7407891714,11.2996703962,9.60881583332,8.6794566232,7.11142195589,5.67030718072,4.68772801583,3.77883958831,2.8331059921,2.13711178364,1.64991579771,1.08902627442,0.888416044921,0.65505344326,0.405314455517,0.302962222508,0.159669264295,0.131010688652,0.0614112678056,0.0163763340815,0.0286585876426,0.00409408452037,0.00409408452037,0.0122822535611,0.065505344326,0.0736934973668,0.10644618953,0.180139686896,0.282491799906,0.454443213762,0.749217363229,0.822910900595,1.07674428486,1.54346948818,2.12892379059,2.70618969997,3.63964050661,4.68772801583,5.76856309721,7.32431777495,8.39696486329,9.67022778112,11.5371301944,12.6097772828,13.8789442041,14.9556872889,16.2330422033,16.6301698658,17.5267731037,17.891148794,17.9320887592,17.8543008254,17.6250330202,16.585133904,15.979210419,15.3159669827,14.2105639222,12.8513290775,12.1389576829,10.7060309008,9.67431977765,8.58938869975,7.6559374931,6.73476627602,5.75628310765,4.98659576181,4.66725603323,3.78702758135,3.32849037106,2.87814115382,2.10845340799,2.04294786367,0.00409408452037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_1 y5_ETA_1_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121240822392,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121753353338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0121313846429,0.012170493784,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_2 y5_ETA_2_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0200482816269,0.010032919325,0.0200940397991,0.0100262832744,0.010040728874,0.0100696696577,0.0100271592661,0.0301165257319,0.0401877986198,0.0,0.0502017229729,0.010040728874,0.0,0.0301145712787,0.0402058597513,0.0,0.0100568562125,0.0401512714171,0.0,0.0301337191358,0.0100702894631,0.0,0.050140155629,0.0300994521571,0.0100355638284,0.0100184158769,0.0401671012489,0.0100262832744,0.0301196784758,0.0,0.0,0.0100367001384,0.0100153623019,0.0,0.0,0.0,0.0100369728528,0.0100609841169,0.0,0.0,0.0100568562125,0.0100602899348,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100340928234,0.0100568562125,0.0,0.0,0.0100532489446,0.0200646444914,0.0301498340782,0.0200868128673,0.0301214387233,0.0301310828965,0.010045943504,0.0200609297906,0.0200572522781,0.0,0.0301088194839,0.010045943504,0.0,0.0301025759767,0.0200638924608,0.0201178403294,0.0300965762597,0.0100299566548,0.0200777058588,0.0100187051194,0.0301077410223,0.0301692711779,0.0100355638284,0.0100324441408,0.0200798255935,0.0,0.0100369728528,0.0200832055994,0.0,0.0,0.0100696696577,0.0,0.0100187051194,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_3 y5_ETA_3_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0275282721771,0.0274997813219,0.00549710596009,0.0164555265735,0.0110053636951,0.0770293110214,0.0440010037659,0.0550185105048,0.0330098306536,0.0549834910877,0.0880414145324,0.121011383177,0.0934765336867,0.0880213454001,0.0990525836506,0.148391326591,0.170522298147,0.115509109618,0.0880932530198,0.142979039121,0.143099738296,0.137527304026,0.142956573069,0.132021455399,0.131943372662,0.0934995278747,0.0934989997397,0.0935291846896,0.104510168858,0.0825596976154,0.071452814174,0.0495039679626,0.0549952725621,0.0274769699499,0.0275034010784,0.0110008136084,0.0220214688448,0.0275283574913,0.0,0.0110360564673,0.021979583672,0.0219691225352,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0110095197118,0.0,0.00550243199904,0.00551802823363,0.00547487959922,0.0220146884032,0.0275094786942,0.0385276029448,0.0384989861497,0.0220041419523,0.0495313903599,0.0165169689939,0.0550053883798,0.0825226875355,0.104575210722,0.126506425347,0.066013876197,0.104442608196,0.126527185118,0.0990133797788,0.170543342297,0.0825431629255,0.181466759744,0.1538988815,0.20895098952,0.132032830616,0.115526172443,0.0824931525979,0.126454668112,0.104502043703,0.08245756442,0.0825350783965,0.0990397052803,0.0770264265915,0.0385428294849,0.0935220345535,0.0329914596786,0.043969193785,0.0494868645118,0.0274664559996,0.0164761360286,0.0165097701068,0.0110073624832,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_4 y5_ETA_4_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00295952755959,0.00197733276196,0.00197153597409,0.00295929588047,0.00592188256639,0.00888223262692,0.00986622754723,0.0207193880908,0.0266393306452,0.0276408337728,0.028623718397,0.0355146850478,0.0365133262569,0.0562514891552,0.0592211905545,0.0720236262363,0.0769833630669,0.0878251039945,0.100658684083,0.116441122468,0.105610244004,0.12136374254,0.104612220072,0.122368861145,0.107579035503,0.124337372063,0.114461228008,0.0977014885466,0.0799371916409,0.0838926916906,0.0818885871641,0.0473736095304,0.0503348935223,0.0305824370646,0.0296045868517,0.0236809646878,0.025663292179,0.01382357141,0.0108591409111,0.00690936069003,0.0049344204759,0.00296236222172,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00197140370055,0.00197179531041,0.000988009622793,0.00394563925743,0.00493570713674,0.00987045228407,0.00888712273977,0.020722578689,0.0157902465318,0.0246790126701,0.033550310684,0.0444057519442,0.0463809967883,0.0631546850964,0.0799404383551,0.08090190669,0.08585110172,0.103606500224,0.0976781202204,0.0977050960069,0.115473882196,0.132224963753,0.1322358663,0.117436981924,0.104620837893,0.107570056935,0.0986854033011,0.0976784008007,0.0799142241437,0.075986220958,0.0641623289231,0.0661257893969,0.0592049169003,0.0414675156152,0.0375125005685,0.0266518204744,0.0197401189434,0.0167707462239,0.0128311630906,0.0118483786737,0.00395132701983,0.00987041220117,0.00296029153951,0.00395140718562,0.000988172359335,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_5 y5_ETA_5_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000504690405707,0.000503681322579,0.00176459470884,0.000504669999982,0.000755187890129,0.00201616849525,0.00327598677425,0.00529427346382,0.0068047412699,0.00630216825829,0.00806693941664,0.010840665667,0.01310830592,0.0178963013326,0.0183999386429,0.0226806437203,0.025963360783,0.0310093045706,0.035792138535,0.0400765646566,0.0519272497141,0.0415845077543,0.0476444880596,0.0441119769099,0.050664615445,0.0526808211507,0.0473877760315,0.0398268505919,0.0317628720061,0.0352897295695,0.0277249790567,0.0264671781437,0.01915871554,0.0146224307521,0.00958156839025,0.0110892754217,0.00579553410721,0.00529300110682,0.0035290593812,0.00201740124113,0.00075590049007,0.000252358446569,0.0,0.000252130822703,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000251770961733,0.000252017871011,0.0,0.0012607777482,0.00100783957898,0.00226974524365,0.0032772871391,0.00453781321669,0.011097473722,0.0110881991197,0.0126025360114,0.0186502288691,0.0219320576825,0.0284887093436,0.0340287397618,0.0400856872162,0.0451133778823,0.0463772524995,0.0509192869005,0.0516829011549,0.0499042020887,0.0499201265568,0.0456294026682,0.0410958106359,0.0451322231699,0.039827538785,0.0239494277149,0.0272195212356,0.0229396403894,0.0196642173734,0.0146193658922,0.012354134315,0.00907918743253,0.00730944287875,0.00453884950745,0.00478865559789,0.00403269148997,0.00352844480876,0.00252244454586,0.00252372690566,0.00100823288933,0.00126131029763,0.000251614677883,0.000756485454199,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_6 y5_ETA_6_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000283957625274,0.000287128707592,0.000572547709956,0.000861351732727,0.000858235034671,0.00142977823532,0.000286450303915,0.00143123581351,0.000859256738999,0.00314945657622,0.00143529663835,0.00429543394091,0.00544119536808,0.00543670366794,0.00687030779608,0.00572291084221,0.00886473662672,0.0131701286858,0.00973534068191,0.0134554561147,0.0177613000431,0.0174690846075,0.0151850655816,0.0186052458129,0.0208887349921,0.0174626364735,0.0197556129072,0.0174692045728,0.0154570468727,0.0123066885575,0.010887799171,0.00944630632579,0.00601141435195,0.00457853701348,0.00315233774244,0.00228783792074,0.0017190425249,0.000574099460894,0.00085594249811,0.000286764712928,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00085828172116,0.00257774402458,0.00229116695735,0.00515367657338,0.00314700028705,0.00745015599335,0.0103183139748,0.0122993706752,0.0143236348717,0.0151823563656,0.0186132135071,0.0191729715183,0.0237481674986,0.0226402381232,0.0217478463732,0.0211806505145,0.0189065386215,0.0180207549592,0.0117413421635,0.0131746173868,0.0146080025783,0.00887664717991,0.00715153841053,0.00629180320999,0.00514740738762,0.00373118123045,0.0042993318129,0.00286459701196,0.00228898858775,0.00114435383452,0.00257892468291,0.00114002408751,0.00200925553992,0.0,0.000568809991618,0.00057289490948,0.000571732745799,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_7 y5_ETA_7_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.1617996646e-05,8.63041639912e-05,0.000129543836942,0.000129532437545,0.0,0.00021618093038,8.64047469058e-05,0.000237509621227,0.000129647940258,0.000322270298485,0.000410490734636,0.000430353219956,0.000626435839991,0.000475241698333,0.000691605605799,0.000819277594701,0.000970180465002,0.00112314528505,0.00105669727804,0.00114471529107,0.00118825302473,0.0014903026789,0.0017929520587,0.00114463650112,0.00161964099142,0.00144665053218,0.00170707143259,0.00164166529694,0.00123133310612,0.00123126898451,0.000928988408734,0.000496921088276,0.000366997383194,0.000345373062397,0.000172778396927,2.15827549073e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.15983662139e-05,0.0,0.0,6.48950068125e-05,0.000107900405391,0.000280954818599,0.0004105766073,0.000496826372698,0.00094881791124,0.00116613190815,0.00172641059339,0.00185748940943,0.00190084693203,0.00185734859335,0.00172633389892,0.002138216743,0.00196600957321,0.00140282697541,0.00108005933632,0.000926656561499,0.00125301165787,0.000777599387679,0.00108001868406,0.00099358569012,0.000907322010752,0.000712771184686,0.000691494964593,0.000496688909381,0.00010813861926,0.000280705372971,0.000302205809145,0.000365607872139,0.000194619348176,0.000172678819842,0.000151298580251,0.000108110372225,0.000108019428507,8.64090216796e-05,4.32315844076e-05,4.32170417946e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_8 y5_ETA_8_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.84292642893e-05,0.0,2.83973993922e-05,2.83973993922e-05,5.66030748554e-05,2.84292642893e-05,0.000141723376559,0.000113644061894,0.000112773137535,0.000198503757442,0.000141120452911,8.50868331672e-05,0.000142062857986,0.000225435534901,0.000111915784712,0.000255442735234,0.000113583286953,5.67183438192e-05,0.000198632048547,0.000170429981271,0.000198798351831,0.000198741185077,0.000142022856106,0.000113357649033,0.000113596531822,0.0,2.83973993922e-05,2.83973993922e-05,2.83498693196e-05,5.64019963668e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.67658887402e-05,2.83684893481e-05,0.000142025796111,5.68978177294e-05,8.45111565312e-05,0.000141783943621,0.000113497477428,0.000198740145682,0.000141909131387,0.000198819139742,0.000142041461286,0.000113259010398,0.000113446992503,2.84292642893e-05,5.51480250594e-05,5.65914781711e-05,0.000113545022349,0.000113683677712,8.51264489853e-05,0.000170163302076,2.84080903176e-05,2.83973993922e-05,0.000113642859165,8.52862783201e-05,5.6878173154e-05,2.84489088647e-05,0.0,8.51961775765e-05,0.0,0.0,2.83684893481e-05,2.84489088647e-05,0.0,2.83684893481e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_9 y5_ETA_9_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_10 y5_ETA_10_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0521138287,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0529581672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.05462838872,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_11 y5_ETA_11_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.230673161153,0.0,0.0,0.0,0.0,0.691026068854,0.230619670779,0.690578010125,0.460783443723,0.229982512821,0.230364746113,0.0,0.229982512821,0.921962685466,0.461195765349,0.691490266921,0.0,0.460080998305,0.461188848491,0.690561870788,0.230752243903,0.461033219172,0.230597152562,0.230428265931,0.229982512821,0.459723627277,0.230360173301,0.230020171273,0.0,0.0,0.0,0.0,0.0,0.0,0.229952462913,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.230020171273,0.0,0.0,0.0,0.690728643934,0.0,0.229982512821,0.230465309552,0.0,0.0,0.0,0.0,0.921987663011,0.691353466828,0.690780136104,0.0,0.691670105244,0.0,0.0,1.15198896663,0.461138893401,0.230020171273,0.0,0.460124420806,0.0,0.230551270733,0.230752243903,0.229932019753,0.230752243903,0.230587737949,0.460570173916,0.0,0.459889631883,0.0,0.690224097526,0.0,0.230619670779,0.230360173301,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_12 y5_ETA_12_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0554467039881,0.0831726910094,0.0276908199867,0.0277244637063,0.0277261678187,0.138459100715,0.110763733864,0.110696415651,0.138481027219,0.0553453804604,0.110839668808,0.0276896813472,0.249341468504,0.304489895107,0.193882570303,0.165968553895,0.415543020352,0.221455956552,0.110817588434,0.41551493904,0.24918529025,0.138688482718,0.415323370639,0.332468383129,0.304440925915,0.30428051623,0.193931154819,0.193743817848,0.138552153719,0.33202835282,0.110822973891,0.110800931985,0.221591477732,0.0553593057136,0.0830496717832,0.027763192836,0.0276953706979,0.0,0.0830522875767,0.0276873271331,0.0,0.0,0.0,0.0,0.0276896813472,0.0276896813472,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.027763192836,0.0,0.0554193535598,0.027603817928,0.0554837482391,0.0277263947773,0.0276586841951,0.0831603813932,0.0553866946095,0.110896946991,0.138389897592,0.0830404011036,0.276814223641,0.221656410957,0.276896121056,0.193704811752,0.193765744352,0.193745241148,0.304459505742,0.304710891184,0.221625021436,0.193771283679,0.166210553255,0.193715351861,0.221506118237,0.332359866169,0.193744356394,0.249216833641,0.19377143755,0.221591593134,0.1384076696,0.110700454743,0.166063030199,0.166173124328,0.0831490719334,0.166071069917,0.0554810939782,0.0830438247156,0.0276586841951,0.0276929395487,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_13 y5_ETA_13_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100450209578,0.0,0.0200719202967,0.0302570227044,0.0201730639343,0.0807196141628,0.040348427967,0.0302623268889,0.0604818076191,0.0504090944485,0.0403768120299,0.0504226704906,0.0504235808197,0.0403129979564,0.0706525268263,0.0706297079089,0.120951914474,0.0705543933437,0.0604464807792,0.0605190340126,0.0907542330928,0.0302540671691,0.13107884078,0.0302726621592,0.0604138485135,0.0604985091246,0.050332353701,0.0604686624662,0.0301965950553,0.0,0.0302875915573,0.0302954446635,0.0605230030477,0.0100953560911,0.040347511569,0.0,0.0100921881456,0.0100996953267,0.0201665945285,0.0100953560911,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0201987048721,0.0201875442367,0.0201245615971,0.0201424829437,0.0101000473206,0.0504036264047,0.0302668906724,0.050308903622,0.0503705389745,0.0706056145306,0.0504038145394,0.110960869966,0.100781812144,0.0806490333092,0.0806486084889,0.0806237261587,0.0706145357563,0.0402893536739,0.0605118484812,0.0806438140887,0.0806781031534,0.100820045968,0.050385189205,0.0403465891021,0.0605020047886,0.0705439549028,0.0201625041162,0.0705455328066,0.0201750545207,0.0403691045764,0.0201483758077,0.050360561767,0.0100592767124,0.0604937875508,0.0403312045394,0.0100853121261,0.0100921881456,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_14 y5_ETA_14_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00566304279343,0.0113243080673,0.00565465151583,0.0113270474481,0.0226121500506,0.0141291878309,0.00565787567476,0.0141412072492,0.00849151124252,0.0198041884835,0.0282808755203,0.0339622436944,0.0254604059094,0.0226399170613,0.0113074601055,0.0396079460531,0.0396194499137,0.0396201424538,0.0339383202812,0.0481071444908,0.00849126115859,0.0254815707045,0.0339557068853,0.0113223958871,0.0169703182216,0.0283032714978,0.0141288069338,0.0226442338946,0.0197992483641,0.0282745349309,0.0141461896905,0.0113219688207,0.0141390603748,0.0056663977655,0.0113208722989,0.00566218481319,0.00282800521671,0.00283012131147,0.00282190547736,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00282347946712,0.00565759865872,0.0,0.00283355053922,0.00849388896355,0.00566924872226,0.0113134197978,0.0141524456361,0.0113166093298,0.0113165477706,0.0254648920303,0.0169795905641,0.0198015222041,0.0282696871502,0.0226120269323,0.0339307869839,0.0395990199806,0.0282947532545,0.0226330955413,0.0254486750495,0.039614178914,0.0226347076208,0.0424486301401,0.0395931718642,0.0339560146809,0.0395998279441,0.0198092017043,0.016970391323,0.028310854812,0.0198078820307,0.0339708581238,0.0198045462959,0.0169803023414,0.0113155205028,0.00282142762469,0.0113133543913,0.00849043780536,0.0,0.00282930950057,0.00283041371729,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_15 y5_ETA_15_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00152260673975,0.00153821483072,0.00151881876977,0.0,0.00304355646424,0.0,0.00456097823541,0.0015356572123,0.00456914512233,0.00152449658811,0.00305783256077,0.00303609280421,0.00304190063088,0.0,0.00304325980887,0.00151265401114,0.00152644434928,0.0,0.00150849610837,0.00154541020084,0.00152162931349,0.00152305585944,0.0030361719911,0.00152449658811,0.00153629543501,0.0,0.0,0.00152495989053,0.00153629543501,0.00153333597266,0.00152162931349,0.00152094972449,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00154541020084,0.00153219780883,0.00151265401114,0.00305061000709,0.00150849610837,0.00152162931349,0.00151727403443,0.0,0.0,0.0,0.00306015380041,0.00306014316337,0.00305496528615,0.00607878412211,0.00301699221674,0.00306438379871,0.00152192833265,0.00151115655156,0.00305428569715,0.00150849610837,0.00304681258196,0.0,0.00303898371671,0.0,0.0,0.0,0.00151265401114,0.0,0.00152094972449,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating weights for histo: y5_ETA_16 y5_ETA_16_weights = numpy.array([0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180755028423,0.0,0.0,0.0,0.0,0.000180686039232,0.0,0.000542211148965,0.000541323760103,0.0,0.0,0.0,0.0,0.000361533925874,0.000180154568376,0.000722930104357,0.000360550714414,0.000360973850682,0.000180970234659,0.000180626135672,0.000541377657908,0.00018065712691,0.0,0.000180533816432,0.0,0.0,0.0,0.000180553027149,0.000180626135672,0.000180003616023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.000180553027149,0.0,0.0,0.000180626135672,0.00018065712691,0.000361237718936,0.000180626135672,0.0,0.0,0.0,0.000179998688224,0.0,0.000180766962937,0.0,0.000360616354241,0.000902862921764,0.0,0.0,0.0,0.00018065712691,0.000542553400027,0.0,0.000361314061327,0.0,0.000180402036298,0.0,0.0,0.000360206615427,0.0,0.000180755028423,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) # Creating a new Canvas fig = plt.figure(figsize=(12,6),dpi=80) frame = gridspec.GridSpec(1,1,right=0.7) pad = fig.add_subplot(frame[0]) # Creating a new Stack pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights+y5_ETA_13_weights+y5_ETA_14_weights+y5_ETA_15_weights+y5_ETA_16_weights,\ label="$bg\_dip\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#e5e5e5", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights+y5_ETA_13_weights+y5_ETA_14_weights+y5_ETA_15_weights,\ label="$bg\_dip\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#f2f2f2", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights+y5_ETA_13_weights+y5_ETA_14_weights,\ label="$bg\_dip\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights+y5_ETA_13_weights,\ label="$bg\_dip\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ccc6aa", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights,\ label="$bg\_dip\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#c1bfa8", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights,\ label="$bg\_dip\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#bab5a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights,\ label="$bg\_dip\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b2a596", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights,\ label="$bg\_dip\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#b7a39b", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights,\ label="$bg\_vbf\_1600\_inf$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#ad998c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights,\ label="$bg\_vbf\_1200\_1600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#9b8e82", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights,\ label="$bg\_vbf\_800\_1200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#876656", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights,\ label="$bg\_vbf\_600\_800$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#afcec6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights,\ label="$bg\_vbf\_400\_600$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#84c1a3", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights,\ label="$bg\_vbf\_200\_400$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#89a8a0", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights,\ label="$bg\_vbf\_100\_200$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#829e8c", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights+y5_ETA_1_weights,\ label="$bg\_vbf\_0\_100$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#adbcc6", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") pad.hist(x=xData, bins=xBinning, weights=y5_ETA_0_weights,\ label="$signal$", histtype="step", rwidth=1.0,\ color=None, edgecolor="#7a8e99", linewidth=1, linestyle="solid",\ bottom=None, cumulative=False, normed=False, align="mid", orientation="vertical") # Axis plt.rc('text',usetex=False) plt.xlabel(r"\eta [ j_{2} ] ",\ fontsize=16,color="black") plt.ylabel(r"$\mathrm{Events}$ $(\mathcal{L}_{\mathrm{int}} = 40.0\ \mathrm{fb}^{-1})$ ",\ fontsize=16,color="black") # Boundary of y-axis ymax=(y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights+y5_ETA_13_weights+y5_ETA_14_weights+y5_ETA_15_weights+y5_ETA_16_weights).max()*1.1 ymin=0 # linear scale #ymin=min([x for x in (y5_ETA_0_weights+y5_ETA_1_weights+y5_ETA_2_weights+y5_ETA_3_weights+y5_ETA_4_weights+y5_ETA_5_weights+y5_ETA_6_weights+y5_ETA_7_weights+y5_ETA_8_weights+y5_ETA_9_weights+y5_ETA_10_weights+y5_ETA_11_weights+y5_ETA_12_weights+y5_ETA_13_weights+y5_ETA_14_weights+y5_ETA_15_weights+y5_ETA_16_weights) if x])/100. # log scale plt.gca().set_ylim(ymin,ymax) # Log/Linear scale for X-axis plt.gca().set_xscale("linear") #plt.gca().set_xscale("log",nonposx="clip") # Log/Linear scale for Y-axis plt.gca().set_yscale("linear") #plt.gca().set_yscale("log",nonposy="clip") # Legend plt.legend(bbox_to_anchor=(1.05,1), loc=2, borderaxespad=0.) # Saving the image plt.savefig('../../HTML/MadAnalysis5job_0/selection_4.png') plt.savefig('../../PDF/MadAnalysis5job_0/selection_4.png') plt.savefig('../../DVI/MadAnalysis5job_0/selection_4.eps') # Running! if __name__ == '__main__': selection_4()

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5a98fddec1d3cbb33837e1f1d6a6a5b060873682

12,100

py

Python

python/tinkoff/cloud/longrunning/v1/longrunning_pb2_grpc.py

qwertBR/voicekit-examples

273a63e4cf11841339108cdcdf8b485b7c96298a

[ "Apache-2.0" ]

3

2022-02-11T04:34:18.000Z

2022-03-29T19:35:57.000Z

python/tinkoff/cloud/longrunning/v1/longrunning_pb2_grpc.py

qwertBR/voicekit-examples

273a63e4cf11841339108cdcdf8b485b7c96298a

[ "Apache-2.0" ]

3

2022-01-27T15:40:38.000Z

2022-03-31T10:03:35.000Z

python/tinkoff/cloud/longrunning/v1/longrunning_pb2_grpc.py

qwertBR/voicekit-examples

273a63e4cf11841339108cdcdf8b485b7c96298a

[ "Apache-2.0" ]

5

2022-01-27T15:15:06.000Z

2022-03-24T22:06:18.000Z

# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! """Client and server classes corresponding to protobuf-defined services.""" import grpc from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 from tinkoff.cloud.longrunning.v1 import longrunning_pb2 as tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2 class OperationsStub(object): """Missing associated documentation comment in .proto file.""" def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.GetOperation = channel.unary_unary( '/tinkoff.cloud.longrunning.v1.Operations/GetOperation', request_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.GetOperationRequest.SerializeToString, response_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.Operation.FromString, ) self.WaitOperation = channel.unary_unary( '/tinkoff.cloud.longrunning.v1.Operations/WaitOperation', request_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WaitOperationRequest.SerializeToString, response_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.Operation.FromString, ) self.ListOperations = channel.unary_unary( '/tinkoff.cloud.longrunning.v1.Operations/ListOperations', request_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.ListOperationsRequest.SerializeToString, response_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.ListOperationsResponse.FromString, ) self.WatchOperations = channel.unary_stream( '/tinkoff.cloud.longrunning.v1.Operations/WatchOperations', request_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WatchOperationsRequest.SerializeToString, response_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WatchOperationsResponse.FromString, ) self.DeleteOperation = channel.unary_unary( '/tinkoff.cloud.longrunning.v1.Operations/DeleteOperation', request_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.DeleteOperationRequest.SerializeToString, response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString, ) self.CancelOperation = channel.unary_unary( '/tinkoff.cloud.longrunning.v1.Operations/CancelOperation', request_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.CancelOperationRequest.SerializeToString, response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString, ) class OperationsServicer(object): """Missing associated documentation comment in .proto file.""" def GetOperation(self, request, context): """Starts polling for operation statuses Returns operation status """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def WaitOperation(self, request, context): """Wait for operation update """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def ListOperations(self, request, context): """List operations """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def WatchOperations(self, request, context): """Watch operations """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def DeleteOperation(self, request, context): """Deletes specified operations """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def CancelOperation(self, request, context): """Cancels specified operations """ context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_OperationsServicer_to_server(servicer, server): rpc_method_handlers = { 'GetOperation': grpc.unary_unary_rpc_method_handler( servicer.GetOperation, request_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.GetOperationRequest.FromString, response_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.Operation.SerializeToString, ), 'WaitOperation': grpc.unary_unary_rpc_method_handler( servicer.WaitOperation, request_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WaitOperationRequest.FromString, response_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.Operation.SerializeToString, ), 'ListOperations': grpc.unary_unary_rpc_method_handler( servicer.ListOperations, request_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.ListOperationsRequest.FromString, response_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.ListOperationsResponse.SerializeToString, ), 'WatchOperations': grpc.unary_stream_rpc_method_handler( servicer.WatchOperations, request_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WatchOperationsRequest.FromString, response_serializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WatchOperationsResponse.SerializeToString, ), 'DeleteOperation': grpc.unary_unary_rpc_method_handler( servicer.DeleteOperation, request_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.DeleteOperationRequest.FromString, response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString, ), 'CancelOperation': grpc.unary_unary_rpc_method_handler( servicer.CancelOperation, request_deserializer=tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.CancelOperationRequest.FromString, response_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'tinkoff.cloud.longrunning.v1.Operations', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) # This class is part of an EXPERIMENTAL API. class Operations(object): """Missing associated documentation comment in .proto file.""" @staticmethod def GetOperation(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, '/tinkoff.cloud.longrunning.v1.Operations/GetOperation', tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.GetOperationRequest.SerializeToString, tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.Operation.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def WaitOperation(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, '/tinkoff.cloud.longrunning.v1.Operations/WaitOperation', tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WaitOperationRequest.SerializeToString, tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.Operation.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def ListOperations(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, '/tinkoff.cloud.longrunning.v1.Operations/ListOperations', tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.ListOperationsRequest.SerializeToString, tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.ListOperationsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def WatchOperations(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_stream(request, target, '/tinkoff.cloud.longrunning.v1.Operations/WatchOperations', tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WatchOperationsRequest.SerializeToString, tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.WatchOperationsResponse.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def DeleteOperation(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, '/tinkoff.cloud.longrunning.v1.Operations/DeleteOperation', tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.DeleteOperationRequest.SerializeToString, google_dot_protobuf_dot_empty__pb2.Empty.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata) @staticmethod def CancelOperation(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, '/tinkoff.cloud.longrunning.v1.Operations/CancelOperation', tinkoff_dot_cloud_dot_longrunning_dot_v1_dot_longrunning__pb2.CancelOperationRequest.SerializeToString, google_dot_protobuf_dot_empty__pb2.Empty.FromString, options, channel_credentials, insecure, call_credentials, compression, wait_for_ready, timeout, metadata)

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0

null

0

1

0

1

0

null

0

7

5aeccbb2cf767b475bd8dc50612ec4c125139aee

20,131

py

Python

tests/test_attack_log.py

Thorsten-Sick/PurpleDome

297d746ef2e17a4207f8274b7fccbe2ce43c4a5f

[ "MIT" ]

7

2021-11-30T19:54:29.000Z

2022-03-05T23:15:23.000Z

tests/test_attack_log.py

Thorsten-Sick/PurpleDome

297d746ef2e17a4207f8274b7fccbe2ce43c4a5f

[ "MIT" ]

null

tests/test_attack_log.py

Thorsten-Sick/PurpleDome

297d746ef2e17a4207f8274b7fccbe2ce43c4a5f

[ "MIT" ]

2

2021-11-30T11:16:27.000Z

2022-02-02T13:36:01.000Z

#!/usr/bin/env python3 # Testing the attack log class import unittest from app.attack_log import AttackLog import app.attack_log # from unittest.mock import patch, call # from app.exceptions import ConfigurationError # https://docs.python.org/3/library/unittest.html class TestMachineConfig(unittest.TestCase): """ Test machine specific config """ def test_init(self): """ The init is empty """ al = AttackLog() self.assertIsNotNone(al) default = {"boilerplate": {'log_format_major_version': 1, 'log_format_minor_version': 1}, "system_overview": [], "attack_log": []} self.assertEqual(al.get_dict(), default) def test_caldera_attack_start(self): """ Starting a caldera attack """ al = AttackLog() source = "asource" paw = "apaw" group = "agroup" ability_id = "aability_id" ttp = "1234" name = "aname" description = "adescription" al.start_caldera_attack(source=source, paw=paw, group=group, ability_id=ability_id, ttp=ttp, name=name, description=description ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "caldera") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target_paw"], paw) self.assertEqual(data["attack_log"][0]["target_group"], group) self.assertEqual(data["attack_log"][0]["ability_id"], ability_id) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) self.assertEqual(data["attack_log"][0]["name"], name) self.assertEqual(data["attack_log"][0]["description"], description) def test_caldera_attack_stop(self): """ Stopping a caldera attack """ al = AttackLog() source = "asource" paw = "apaw" group = "agroup" ability_id = "aability_id" ttp = "1234" name = "aname" description = "adescription" al.stop_caldera_attack(source=source, paw=paw, group=group, ability_id=ability_id, ttp=ttp, name=name, description=description ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "caldera") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target_paw"], paw) self.assertEqual(data["attack_log"][0]["target_group"], group) self.assertEqual(data["attack_log"][0]["ability_id"], ability_id) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) self.assertEqual(data["attack_log"][0]["name"], name) self.assertEqual(data["attack_log"][0]["description"], description) def test_kali_attack_start(self): """ Starting a kali attack """ al = AttackLog() source = "asource" target = "a target" ttp = "1234" attack_name = "a name" al.start_kali_attack(source=source, target=target, attack_name=attack_name, ttp=ttp, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "kali") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["kali_name"], attack_name) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) def test_kali_attack_stop(self): """ Stopping a kali attack """ al = AttackLog() source = "asource" target = "a target" ttp = "1234" attack_name = "a name" al.stop_kali_attack(source=source, target=target, attack_name=attack_name, ttp=ttp, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "kali") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["kali_name"], attack_name) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) def test_narration_start(self): """ Starting a narration """ al = AttackLog() text = "texttextext" al.start_narration(text ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "narration") self.assertEqual(data["attack_log"][0]["sub_type"], "user defined narration") self.assertEqual(data["attack_log"][0]["text"], text) def test_build_start(self): """ Starting a build """ al = AttackLog() dl_uri = "asource" dl_uris = "a target" payload = "1234" platform = "a name" architecture = "arch" lhost = "lhost" lport = 8080 filename = "afilename" encoding = "encoded" encoded_filename = "ef" sRDI_conversion = True for_step = 4 comment = "this is a comment" al.start_build(dl_uri=dl_uri, dl_uris=dl_uris, payload=payload, platform=platform, architecture=architecture, lhost=lhost, lport=lport, filename=filename, encoding=encoding, encoded_filename=encoded_filename, sRDI_conversion=sRDI_conversion, for_step=for_step, comment=comment ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "build") self.assertEqual(data["attack_log"][0]["dl_uri"], dl_uri) self.assertEqual(data["attack_log"][0]["dl_uris"], dl_uris) self.assertEqual(data["attack_log"][0]["payload"], payload) self.assertEqual(data["attack_log"][0]["platform"], platform) self.assertEqual(data["attack_log"][0]["architecture"], architecture) self.assertEqual(data["attack_log"][0]["lhost"], lhost) self.assertEqual(data["attack_log"][0]["lport"], lport) self.assertEqual(data["attack_log"][0]["filename"], filename) self.assertEqual(data["attack_log"][0]["encoding"], encoding) self.assertEqual(data["attack_log"][0]["encoded_filename"], encoded_filename) self.assertEqual(data["attack_log"][0]["sRDI_conversion"], sRDI_conversion) self.assertEqual(data["attack_log"][0]["for_step"], for_step) self.assertEqual(data["attack_log"][0]["comment"], comment) def test_build_start_default(self): """ Starting a build default values""" al = AttackLog() al.start_build() data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "build") self.assertEqual(data["attack_log"][0]["dl_uri"], None) self.assertEqual(data["attack_log"][0]["dl_uris"], None) self.assertEqual(data["attack_log"][0]["payload"], None) self.assertEqual(data["attack_log"][0]["platform"], None) self.assertEqual(data["attack_log"][0]["architecture"], None) self.assertEqual(data["attack_log"][0]["lhost"], None) self.assertEqual(data["attack_log"][0]["lport"], None) self.assertEqual(data["attack_log"][0]["filename"], None) self.assertEqual(data["attack_log"][0]["encoding"], None) self.assertEqual(data["attack_log"][0]["encoded_filename"], None) self.assertEqual(data["attack_log"][0]["sRDI_conversion"], False) self.assertEqual(data["attack_log"][0]["for_step"], None) self.assertEqual(data["attack_log"][0]["comment"], None) def test_build_stop(self): """ Stopping a build """ al = AttackLog() logid = "lid" al.stop_build(logid=logid) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "build") self.assertEqual(data["attack_log"][0]["logid"], logid) def test_metasploit_attack_start(self): """ Starting a metasploit attack """ al = AttackLog() source = "asource" target = "a target" ttp = "1234" attack_name = "a name" al.start_metasploit_attack(source=source, target=target, metasploit_command=attack_name, ttp=ttp, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "metasploit") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["metasploit_command"], attack_name) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) def test_metasploit_attack_stop(self): """ Stopping a metasploit attack """ al = AttackLog() source = "asource" target = "a target" ttp = "1234" attack_name = "a name" al.stop_metasploit_attack(source=source, target=target, metasploit_command=attack_name, ttp=ttp, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "metasploit") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["metasploit_command"], attack_name) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) def test_attack_plugin_start(self): """ Starting a attack plugin """ al = AttackLog() source = "asource" target = "a target" ttp = "1234" attack_name = "a name" al.start_attack_plugin(source=source, target=target, plugin_name=attack_name, ttp=ttp, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "attack_plugin") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["plugin_name"], attack_name) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) def test_attack_plugin_stop(self): """ Stopping a attack plugin""" al = AttackLog() source = "asource" target = "a target" ttp = "1234" attack_name = "a name" al.stop_attack_plugin(source=source, target=target, plugin_name=attack_name, ttp=ttp, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "attack") self.assertEqual(data["attack_log"][0]["sub_type"], "attack_plugin") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["plugin_name"], attack_name) self.assertEqual(data["attack_log"][0]["hunting_tag"], "MITRE_" + ttp) def test_file_write_start(self): """ Starting a file write """ al = AttackLog() source = "asource" target = "a target" file_name = "a generic filename" al.start_file_write(source=source, target=target, file_name=file_name, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "dropping_file") self.assertEqual(data["attack_log"][0]["sub_type"], "by PurpleDome") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["file_name"], file_name) def test_file_write_stop(self): """ Stopping a file write """ al = AttackLog() source = "asource" target = "a target" file_name = "a generic filename" al.stop_file_write(source=source, target=target, file_name=file_name, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "dropping_file") self.assertEqual(data["attack_log"][0]["sub_type"], "by PurpleDome") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["file_name"], file_name) def test_execute_payload_start(self): """ Starting a execute payload """ al = AttackLog() source = "asource" target = "a target" command = "a generic command" al.start_execute_payload(source=source, target=target, command=command, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "start") self.assertEqual(data["attack_log"][0]["type"], "execute_payload") self.assertEqual(data["attack_log"][0]["sub_type"], "by PurpleDome") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["command"], command) def test_execute_payload_stop(self): """ Stopping a execute payload """ al = AttackLog() source = "asource" target = "a target" command = "a generic command" al.stop_execute_payload(source=source, target=target, command=command, ) data = al.get_dict() self.assertEqual(data["attack_log"][0]["event"], "stop") self.assertEqual(data["attack_log"][0]["type"], "execute_payload") self.assertEqual(data["attack_log"][0]["sub_type"], "by PurpleDome") self.assertEqual(data["attack_log"][0]["source"], source) self.assertEqual(data["attack_log"][0]["target"], target) self.assertEqual(data["attack_log"][0]["command"], command) def test_mitre_fix_ttp_is_none(self): """ Testing the mitre ttp fix for ttp being none """ self.assertEqual(app.attack_log.__mitre_fix_ttp__(None), "") def test_mitre_fix_ttp_is_MITRE_SOMETHING(self): """ Testing the mitre ttp fix for ttp being MITRE_ """ self.assertEqual(app.attack_log.__mitre_fix_ttp__("MITRE_FOO"), "MITRE_FOO") # tests for a bunch of default data covering caldera attacks. That way we will have some fallback if no data is submitted: def test_get_caldera_default_name_missing(self): """ Testing getting the caldera default name """ al = AttackLog() self.assertEqual(al.get_caldera_default_name("missing"), None) def test_get_caldera_default_name(self): """ Testing getting the caldera default name """ al = AttackLog() self.assertEqual(al.get_caldera_default_name("bd527b63-9f9e-46e0-9816-b8434d2b8989"), "whoami") def test_get_caldera_default_description_missing(self): """ Testing getting the caldera default description """ al = AttackLog() self.assertEqual(al.get_caldera_default_description("missing"), None) def test_get_caldera_default_description(self): """ Testing getting the caldera default description """ al = AttackLog() self.assertEqual(al.get_caldera_default_description("bd527b63-9f9e-46e0-9816-b8434d2b8989"), "Obtain user from current session") def test_get_caldera_default_tactics_missing(self): """ Testing getting the caldera default tactics """ al = AttackLog() self.assertEqual(al.get_caldera_default_tactics("missing", None), None) def test_get_caldera_default_tactics(self): """ Testing getting the caldera default tactics """ al = AttackLog() self.assertEqual(al.get_caldera_default_tactics("bd527b63-9f9e-46e0-9816-b8434d2b8989", None), "System Owner/User Discovery") def test_get_caldera_default_tactics_id_missing(self): """ Testing getting the caldera default tactics_id """ al = AttackLog() self.assertEqual(al.get_caldera_default_tactics_id("missing", None), None) def test_get_caldera_default_tactics_id(self): """ Testing getting the caldera default tactics_id """ al = AttackLog() self.assertEqual(al.get_caldera_default_tactics_id("bd527b63-9f9e-46e0-9816-b8434d2b8989", None), "T1033") def test_get_caldera_default_situation_description_missing(self): """ Testing getting the caldera default situation_description """ al = AttackLog() self.assertEqual(al.get_caldera_default_situation_description("missing"), None) def test_get_caldera_default_situation_description(self): """ Testing getting the caldera default situation_description """ al = AttackLog() self.assertEqual(al.get_caldera_default_situation_description("bd527b63-9f9e-46e0-9816-b8434d2b8989"), None) def test_get_caldera_default_countermeasure_missing(self): """ Testing getting the caldera default countermeasure """ al = AttackLog() self.assertEqual(al.get_caldera_default_countermeasure("missing"), None) def test_get_caldera_default_countermeasure(self): """ Testing getting the caldera default countermeasure """ al = AttackLog() self.assertEqual(al.get_caldera_default_countermeasure("bd527b63-9f9e-46e0-9816-b8434d2b8989"), None)

45.035794

136

0.581442

2,212

20,131

5.076854

0.073237

0.184328

0.208103

0.27382

0.848976

0.834105

0.808103

0.728673

0.675868

0.661264

0

0.021274

0.276141

20,131

446

137

45.136771

0.749382

0.071581

0

0.615804

0

0.184674

0.014276

0

0.378747

1

0.084469

false

0

0.008174

0

0.095368

0

null

0

1

0

1

0

null

0

7

85226a980a1a03102f137cc51fddc714bf4ccf38

78

py

Python

vk_advanced_api/__init__.py

Ar4ikov/vk_advanced_api

73bd2770f987441fa6b0db8c87e108cbc2b226e8

[ "MIT" ]

11

2018-03-14T07:51:41.000Z

2022-02-16T08:20:23.000Z

vk_advanced_api/__init__.py

Ar4ikov/vk_advanced_api

73bd2770f987441fa6b0db8c87e108cbc2b226e8

[ "MIT" ]

1

2018-04-10T21:12:07.000Z

2018-04-17T07:36:41.000Z

vk_advanced_api/__init__.py

Ar4ikov/vk_advanced_api

73bd2770f987441fa6b0db8c87e108cbc2b226e8

[ "MIT" ]

1

2018-03-30T07:28:45.000Z

from vk_advanced_api.vkapi import VKAPI from vk_advanced_api.Auth import Auth

39

40

0.871795

14

78

4.571429

0.5

0.1875

0.4375

0.53125

0

0.102564

78

2

41

39

0.914286

0

1

0

true

0

1

0

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0

1

0

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0

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1

0

null

0

1

0

1

0

1

0

7

cfbcbb60ae3fc053905862a67c48f5b31ef61170

106

py

Python

autonmt/search/__init__.py

salvacarrion/autonlp

5cc462901e451b9259219f44225034fc8eedf6d3

[ "MIT" ]

5

2022-01-10T07:59:16.000Z

2022-01-14T01:02:52.000Z

autonmt/search/__init__.py

salvacarrion/autonlp

5cc462901e451b9259219f44225034fc8eedf6d3

[ "MIT" ]

2

2022-01-01T06:10:27.000Z

2022-01-14T01:10:48.000Z

autonmt/search/__init__.py

salvacarrion/autonlp

5cc462901e451b9259219f44225034fc8eedf6d3

[ "MIT" ]

2

2022-01-10T08:20:02.000Z

2022-02-22T08:10:16.000Z

from autonmt.search.beam_search import beam_search from autonmt.search.greedy_search import greedy_search

35.333333

54

0.886792

16

106

5.625

0.375

0.244444

0.377778

0

0.075472

106

2

55

53

0.918367

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

cfd6ee08050d84e7c5fe8334aa7dac72cbb2dd09

125

py

Python

data/module/util/got/manager/__init__.py

williamducfer/eras

d85b8b53431f4416152e3f536bc8c99481a63dd7

[ "MIT" ]

1

2020-09-09T19:51:44.000Z

data/module/util/got/manager/__init__.py

williamducfer/eras

d85b8b53431f4416152e3f536bc8c99481a63dd7

[ "MIT" ]

null

data/module/util/got/manager/__init__.py

williamducfer/eras

d85b8b53431f4416152e3f536bc8c99481a63dd7

[ "MIT" ]

2

2021-09-11T07:31:19.000Z

2022-03-17T16:27:10.000Z

from module.util.got.manager.TweetCriteria import TweetCriteria from module.util.got.manager.TweetManager import TweetManager

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7

cfed38f69dc3fe98cfb032a42e95725930455fd7

87

py

Python

lectures/code/numpy_scalar_ops.py

naskoch/python_course

84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3

[ "MIT" ]

4

2015-08-10T17:46:55.000Z

2020-04-18T21:09:03.000Z

lectures/code/numpy_scalar_ops.py

naskoch/python_course

84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3

[ "MIT" ]

null

lectures/code/numpy_scalar_ops.py

naskoch/python_course

84adfd3f8d48ca3ad5837f7acc59d2fa051e95d3

[ "MIT" ]

2

2019-04-24T03:31:02.000Z

2019-05-13T07:36:06.000Z

A = np.ones((3,3)) print 3 * A - 1 # [[ 2. 2. 2.] # [ 2. 2. 2.] # [ 2. 2. 2.]]

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0

null

0

1

0

1

0

10

7a9dbb50b13d01bd1c12413cedbd4dd221c63516

106

py

Python

tests/__init__.py

pythoncatcoder/go.py

2fe83bbeac4190770678e3cf9df0a908b61be08d

[ "MIT" ]

34

2015-05-25T05:24:17.000Z

2022-01-18T08:49:46.000Z

tests/__init__.py

pythoncatcoder/go.py

2fe83bbeac4190770678e3cf9df0a908b61be08d

[ "MIT" ]

1

2019-12-14T20:31:20.000Z

2019-12-17T02:30:53.000Z

tests/__init__.py

pythoncatcoder/go.py

2fe83bbeac4190770678e3cf9df0a908b61be08d

[ "MIT" ]

18

2015-01-15T19:14:32.000Z

2021-05-17T23:09:54.000Z

from .location_test import * from .array_test import * from .view_test import * from .board_test import *

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7

8f8a00c79c4bc75d5fe1451c7619bf46e7178afd

54,850

py

Python

tests/sql/build_test.py

DataJunction/datajunction

d2293255bb7df0e5144c7e448a0ca2b590b6c20f

[ "MIT" ]

null

tests/sql/build_test.py

DataJunction/datajunction

d2293255bb7df0e5144c7e448a0ca2b590b6c20f

[ "MIT" ]

null

tests/sql/build_test.py

DataJunction/datajunction

d2293255bb7df0e5144c7e448a0ca2b590b6c20f

[ "MIT" ]

null

""" Tests for ``datajunction.sql.build``. """ # pylint: disable=invalid-name, too-many-lines, line-too-long import datetime import pytest from pytest_mock import MockerFixture from sqlalchemy.engine import create_engine from sqlmodel import Session from datajunction.models.column import Column from datajunction.models.database import Database from datajunction.models.node import Node, NodeType from datajunction.models.table import Table from datajunction.sql.build import ( find_on_clause, get_dimensions_from_filters, get_filter, get_join_columns, get_query_for_node, get_query_for_sql, ) from datajunction.typing import ColumnType def test_get_query_for_node(mocker: MockerFixture) -> None: """ Test ``get_query_for_node``. """ database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) parent = Node(name="A") child = Node( name="B", tables=[ Table( database=database, table="B", columns=[Column(name="cnt", type=ColumnType.INT)], ), ], type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[parent], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE B (cnt INTEGER)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session = mocker.MagicMock() create_query = get_query_for_node(session, child, [], []) assert create_query.database_id == 1 assert create_query.submitted_query == 'SELECT "B".cnt \nFROM "B"' def test_get_query_for_node_with_groupbys(mocker: MockerFixture) -> None: """ Test ``get_query_for_node`` with group bys. """ database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) parent = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="user_id", type=ColumnType.INT), Column(name="comment", type=ColumnType.STR), ], ), ], columns=[ Column(name="user_id", type=ColumnType.INT), Column(name="comment", type=ColumnType.STR), ], ) child = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[parent], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (user_id INTEGER, comment TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session = mocker.MagicMock() create_query = get_query_for_node(session, child, ["A.user_id"], []) space = " " assert create_query.database_id == 1 assert ( create_query.submitted_query == f"""SELECT count('*') AS cnt, "A".user_id{space} FROM (SELECT "A".user_id AS user_id, "A".comment AS comment{space} FROM "A") AS "A" GROUP BY "A".user_id""" ) def test_get_query_for_node_specify_database(mocker: MockerFixture) -> None: """ Test ``get_query_for_node`` when a database is specified. """ database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) parent = Node(name="A") child = Node( name="B", tables=[ Table( database=database, table="B", columns=[Column(name="cnt", type=ColumnType.INT)], ), ], type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[parent], columns=[Column(name="cnt", type=ColumnType.INT)], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE B (cnt INTEGER)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session = mocker.MagicMock() session.exec().one.return_value = database create_query = get_query_for_node(session, child, [], [], 1) assert create_query.database_id == 1 assert create_query.submitted_query == 'SELECT "B".cnt \nFROM "B"' with pytest.raises(Exception) as excinfo: get_query_for_node(session, child, [], [], 2) assert str(excinfo.value) == "Database ID 2 is not valid" def test_get_query_for_node_no_databases(mocker: MockerFixture) -> None: """ Test ``get_query_for_node``. """ database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) parent = Node(name="A") child = Node( name="B", tables=[ Table( database=database, table="B", columns=[Column(name="one", type=ColumnType.STR)], ), ], type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[parent], columns=[Column(name="one", type=ColumnType.STR)], ) mocker.patch("datajunction.sql.dag.get_computable_databases", return_value=set()) session = mocker.MagicMock() with pytest.raises(Exception) as excinfo: get_query_for_node(session, child, [], []) assert str(excinfo.value) == "No valid database was found" def test_get_query_for_node_with_dimensions(mocker: MockerFixture) -> None: """ Test ``get_query_for_node`` when filtering/grouping by a dimension. """ database = Database(id=1, name="one", URI="sqlite://") dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) parent = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ) child = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[parent], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE dim_users (id INTEGER, age INTEGER, gender TEXT)") connection.execute("CREATE TABLE comments (ds TEXT, user_id INTEGER, text TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session = mocker.MagicMock() session.exec().one.return_value = dimension create_query = get_query_for_node( session, child, ["core.users.gender"], ["core.users.age>25"], ) space = " " assert create_query.database_id == 1 assert ( create_query.submitted_query == f"""SELECT count('*') AS count_1, "core.users".gender{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id{space} WHERE "core.users".age > 25 GROUP BY "core.users".gender""" ) with pytest.raises(Exception) as excinfo: get_query_for_node(session, child, ["aaaa"], []) assert str(excinfo.value) == "Invalid dimension: aaaa" with pytest.raises(Exception) as excinfo: get_query_for_node(session, child, ["aaaa", "bbbb"], []) assert str(excinfo.value) == "Invalid dimensions: aaaa, bbbb" def test_get_query_for_node_with_multiple_dimensions(mocker: MockerFixture) -> None: """ Test ``get_query_for_node`` when filtering/grouping by a dimension. """ database = Database(id=1, name="one", URI="sqlite://") dimension_1 = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) dimension_2 = Node( name="core.bands", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_bands", columns=[ Column(name="uuid", type=ColumnType.INT), Column(name="name", type=ColumnType.STR), Column(name="genre", type=ColumnType.STR), ], ), ], columns=[ Column(name="uuid", type=ColumnType.INT), Column(name="name", type=ColumnType.STR), Column(name="genre", type=ColumnType.STR), ], ) parent = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="band_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension_1), Column( name="band_id", type=ColumnType.INT, dimension=dimension_2, dimension_column="uuid", ), Column(name="text", type=ColumnType.STR), ], ) child = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[parent], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE dim_users (id INTEGER, age INTEGER, gender TEXT)") connection.execute("CREATE TABLE dim_bands (uuid INTEGER, name TEXT, genre TEXT)") connection.execute( "CREATE TABLE comments (ds TEXT, user_id INTEGER, band_id INTEGER, text TEXT)", ) mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session = mocker.MagicMock() session.exec().one.side_effect = [dimension_1, dimension_2] create_query = get_query_for_node( session, child, ["core.users.gender"], ["core.bands.genre='rock'"], ) space = " " assert create_query.database_id == 1 assert ( create_query.submitted_query == f"""SELECT count('*') AS count_1, "core.users".gender{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.band_id AS band_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id, (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.band_id AS band_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_bands.uuid AS uuid, dim_bands.name AS name, dim_bands.genre AS genre{space} FROM dim_bands) AS "core.bands" ON "core.comments".band_id = "core.bands".uuid{space} WHERE "core.bands".genre = 'rock' GROUP BY "core.users".gender""" ) def test_get_filter(mocker: MockerFixture) -> None: """ Test ``get_filter``. """ greater_than = mocker.MagicMock() less_than = mocker.MagicMock() equals = mocker.MagicMock() mocker.patch( "datajunction.sql.build.COMPARISONS", new={ ">": greater_than, "<": less_than, "=": equals, }, ) column_a = mocker.MagicMock() column_date = mocker.MagicMock() column_date.type.python_type = datetime.date column_dt = mocker.MagicMock() column_dt.type.python_type = datetime.datetime columns = {"a": column_a, "day": column_date, "dt": column_dt} # basic get_filter(columns, "a>0") greater_than.assert_called_with(column_a, 0) # date get_filter(columns, "day=2020-01-01") equals.assert_called_with(column_date, "2020-01-01 00:00:00") get_filter(columns, "day<20200202") less_than.assert_called_with(column_date, "2020-02-02 00:00:00") get_filter(columns, "day=3/3/2020") equals.assert_called_with(column_date, "2020-03-03 00:00:00") # datetime get_filter(columns, "dt=2012-01-19 17:21:00") equals.assert_called_with(column_dt, "2012-01-19 17:21:00") with pytest.raises(Exception) as excinfo: get_filter(columns, "dt>foo/bar-baz") assert str(excinfo.value) == "Invalid date or datetime value: foo/bar-baz" # exceptions with pytest.raises(Exception) as excinfo: get_filter(columns, "invalid") assert ( str(excinfo.value) == """The filter "invalid" is invalid The following error happened: - The filter "invalid" is not a valid filter. Filters should consist of a dimension name, follow by a valid operator (<=|<|>=|>|!=|=), followed by a value. If the value is a string or date/time it should be enclosed in single quotes. (error code: 100)""" ) with pytest.raises(Exception) as excinfo: get_filter(columns, "b>0") assert str(excinfo.value) == "Invalid column name: b" with pytest.raises(Exception) as excinfo: get_filter(columns, "a>open('/etc/passwd').read()") assert str(excinfo.value) == "Invalid value: open('/etc/passwd').read()" def test_get_query_for_sql(mocker: MockerFixture, session: Session) -> None: """ Test ``get_query_for_sql``. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) session.commit() sql = "SELECT B FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f'''SELECT count('*') AS "B"{space} FROM (SELECT "A".one AS one, "A".two AS two{space} FROM "A") AS "A"''' ) def test_get_query_for_sql_no_metrics(mocker: MockerFixture, session: Session) -> None: """ Test ``get_query_for_sql`` when no metrics are requested. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="db", URI="sqlite://") dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE dim_users (id INTEGER, age INTEGER, gender TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session.add(dimension) session.commit() sql = 'SELECT "core.users.gender", "core.users.age" FROM metrics' create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f'''SELECT "core.users".gender, "core.users".age{space} FROM (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users"''' ) other_dimension = Node( name="core.other_dim", type=NodeType.DIMENSION, columns=[ Column(name="full_name", type=ColumnType.STR), ], ) session.add(other_dimension) session.commit() sql = 'SELECT "core.users.gender", "core.other_dim.full_name" FROM metrics' with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert ( str(excinfo.value) == "Cannot query from multiple dimensions when no metric is specified" ) def test_get_query_for_sql_no_tables(mocker: MockerFixture, session: Session) -> None: """ Test ``get_query_for_sql`` when no tables are involved. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="memory", URI="sqlite://") session.add(database) session.commit() sql = "SELECT 1" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 assert create_query.submitted_query == "SELECT 1" def test_get_query_for_sql_having(mocker: MockerFixture, session: Session) -> None: """ Test ``get_query_for_sql``. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) session.commit() sql = "SELECT B FROM metrics HAVING B > 10" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f"""SELECT count('*') AS "B"{space} FROM (SELECT "A".one AS one, "A".two AS two{space} FROM "A") AS "A"{space} HAVING count('*') > 10""" ) sql = "SELECT B FROM metrics HAVING C > 10" with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "Invalid dimension: C" def test_get_query_for_sql_with_dimensions( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with dimensions in the query. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) parent = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ) child = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[parent], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE dim_users (id INTEGER, age INTEGER, gender TEXT)") connection.execute("CREATE TABLE comments (ds TEXT, user_id INTEGER, text TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session.add(child) session.add(dimension) session.commit() sql = """ SELECT "core.users.gender", "core.num_comments" FROM metrics WHERE "core.users.age" > 25 GROUP BY "core.users.gender" """ create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f"""SELECT "core.users".gender, count('*') AS "core.num_comments"{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id{space} WHERE "core.users".age > 25 GROUP BY "core.users".gender""" ) sql = """ SELECT "core.users.invalid", "core.num_comments" FROM metrics WHERE "core.users.age" > 25 GROUP BY "core.users.invalid" """ with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "Invalid dimension: core.users.invalid" def test_get_query_for_sql_with_dimensions_order_by( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with dimensions in the query and ``ORDER BY``. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) parent = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ) child = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[parent], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE dim_users (id INTEGER, age INTEGER, gender TEXT)") connection.execute("CREATE TABLE comments (ds TEXT, user_id INTEGER, text TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) session.add(child) session.add(dimension) session.commit() sql = """ SELECT "core.users.gender" AS "core.users.gender", "core.num_comments" AS "core.num_comments" FROM main.metrics GROUP BY "core.users.gender" ORDER BY "core.num_comments" DESC LIMIT 100; """ create_query = get_query_for_sql(sql) space = " " assert create_query.database_id == 1 assert ( create_query.submitted_query == f"""SELECT "core.users".gender AS "core.users.gender", count('*') AS "core.num_comments"{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id GROUP BY "core.users".gender ORDER BY count('*') DESC LIMIT 100 OFFSET 0""" ) sql = """ SELECT "core.users.gender" AS "core.users.gender", "core.num_comments" AS "core.num_comments" FROM main.metrics GROUP BY "core.users.gender" ORDER BY "core.num_comments" ASC LIMIT 100; """ create_query = get_query_for_sql(sql) assert ( create_query.submitted_query == f"""SELECT "core.users".gender AS "core.users.gender", count('*') AS "core.num_comments"{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id GROUP BY "core.users".gender ORDER BY count('*') LIMIT 100 OFFSET 0""" ) sql = """ SELECT "core.users.gender" AS "core.users.gender", "core.num_comments" AS "core.num_comments" FROM main.metrics GROUP BY "core.users.gender" ORDER BY "core.num_comments" ASC LIMIT 100; """ create_query = get_query_for_sql(sql) assert ( create_query.submitted_query == f"""SELECT "core.users".gender AS "core.users.gender", count('*') AS "core.num_comments"{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id GROUP BY "core.users".gender ORDER BY count('*') LIMIT 100 OFFSET 0""" ) sql = """ SELECT "core.users.gender" AS "core.users.gender", "core.num_comments" AS "core.num_comments" FROM main.metrics GROUP BY "core.users.gender" ORDER BY "core.users.gender" ASC LIMIT 100; """ create_query = get_query_for_sql(sql) assert ( create_query.submitted_query == f"""SELECT "core.users".gender AS "core.users.gender", count('*') AS "core.num_comments"{space} FROM (SELECT comments.ds AS ds, comments.user_id AS user_id, comments.text AS text{space} FROM comments) AS "core.comments" JOIN (SELECT dim_users.id AS id, dim_users.age AS age, dim_users.gender AS gender{space} FROM dim_users) AS "core.users" ON "core.comments".user_id = "core.users".id GROUP BY "core.users".gender ORDER BY "core.users".gender LIMIT 100 OFFSET 0""" ) sql = """ SELECT "core.users.gender" AS "core.users.gender", "core.num_comments" AS "core.num_comments" FROM main.metrics GROUP BY "core.users.gender" ORDER BY invalid ASC LIMIT 100; """ with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "Invalid identifier: invalid" def test_get_query_for_sql_compound_names( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with nodes with compound names. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="core.A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="core.B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM core.A", parents=[A], ) session.add(B) session.commit() sql = "SELECT core.B FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f'''SELECT count('*') AS "core.B"{space} FROM (SELECT "A".one AS one, "A".two AS two{space} FROM "A") AS "core.A"''' ) def test_get_query_for_sql_multiple_databases( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` when the parents are in multiple databases. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database_1 = Database(id=1, name="slow", URI="sqlite://", cost=10.0) database_2 = Database(id=2, name="fast", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database_1, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), Table( database=database_2, table="A", columns=[ Column(name="one", type=ColumnType.STR), ], ), ], columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ) engine = create_engine(database_1.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) session.commit() sql = "SELECT B FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 2 # fast B.expression = "SELECT COUNT(two) AS cnt FROM A" session.add(B) session.commit() sql = "SELECT B FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 # slow def test_get_query_for_sql_multiple_metrics( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with multiple metrics. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) C = Node( name="C", type=NodeType.METRIC, expression="SELECT MAX(one) AS max_one FROM A", parents=[A], ) session.add(C) session.commit() sql = "SELECT B, C FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f'''SELECT count('*') AS "B", max("A".one) AS "C"{space} FROM (SELECT "A".one AS one, "A".two AS two{space} FROM "A") AS "A"''' ) def test_get_query_for_sql_non_identifiers( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with metrics and non-identifiers in the ``SELECT``. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) C = Node( name="C", type=NodeType.METRIC, expression="SELECT MAX(one) AS max_one FROM A", parents=[A], ) session.add(C) session.commit() sql = "SELECT B, C, 'test' FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f'''SELECT count('*') AS "B", max("A".one) AS "C", test{space} FROM (SELECT "A".one AS one, "A".two AS two{space} FROM "A") AS "A"''' ) def test_get_query_for_sql_different_parents( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with metrics with different parents. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) B = Node( name="B", tables=[ Table( database=database, table="B", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) C = Node( name="C", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(C) D = Node( name="D", type=NodeType.METRIC, expression="SELECT MAX(one) AS max_one FROM A", parents=[B], ) session.add(D) session.commit() sql = "SELECT C, D FROM metrics" with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "Metrics C and D have non-shared parents" def test_get_query_for_sql_not_metric(mocker: MockerFixture, session: Session) -> None: """ Test ``get_query_for_sql`` when the projection is not a metric node. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) B = Node( name="B", expression="SELECT one FROM A", parents=[A], ) session.add(B) session.commit() sql = "SELECT B FROM metrics" with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "Invalid dimension: B" def test_get_query_for_sql_no_databases( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` when no common databases are found. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session A = Node( name="A", tables=[], ) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) session.commit() sql = "SELECT B FROM metrics" with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "No valid database was found" def test_get_query_for_sql_alias(mocker: MockerFixture, session: Session) -> None: """ Test ``get_query_for_sql`` with aliases. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) A = Node( name="A", tables=[ Table( database=database, table="A", columns=[ Column(name="one", type=ColumnType.STR), Column(name="two", type=ColumnType.STR), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE A (one TEXT, two TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) B = Node( name="B", type=NodeType.METRIC, expression="SELECT COUNT(*) AS cnt FROM A", parents=[A], ) session.add(B) session.commit() sql = "SELECT B AS my_metric FROM metrics" create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f'''SELECT count('*') AS my_metric{space} FROM (SELECT "A".one AS one, "A".two AS two{space} FROM "A") AS "A"''' ) def test_get_query_for_sql_where_groupby( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with a where and a group by. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) comments = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="user_id", type=ColumnType.INT), Column(name="comment", type=ColumnType.STR), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE comments (user_id INT, comment TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) num_comments = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[comments], ) session.add(num_comments) session.commit() sql = """ SELECT "core.num_comments", "core.comments.user_id" FROM metrics WHERE "core.comments.user_id" > 1 GROUP BY "core.comments.user_id" """ create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f"""SELECT count('*') AS "core.num_comments", "core.comments".user_id{space} FROM (SELECT comments.user_id AS user_id, comments.comment AS comment{space} FROM comments) AS "core.comments"{space} WHERE "core.comments".user_id > 1 GROUP BY "core.comments".user_id""" ) def test_get_query_for_sql_date_trunc( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with a call to ``DATE_TRUNC``. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="db", URI="sqlite://") comments = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="user_id", type=ColumnType.INT), Column(name="timestamp", type=ColumnType.DATETIME), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE comments (user_id INT, timestamp DATETIME)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) num_comments = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[comments], ) session.add(num_comments) session.commit() sql = """ SELECT DATE_TRUNC('day', "core.comments.timestamp") AS "__timestamp", "core.num_comments" FROM metrics GROUP BY DATE_TRUNC('day', "core.comments.timestamp") """ create_query = get_query_for_sql(sql) assert create_query.database_id == 1 space = " " assert ( create_query.submitted_query == f"""SELECT datetime("core.comments".timestamp, 'start of day') AS __timestamp, count('*') AS "core.num_comments"{space} FROM (SELECT comments.user_id AS user_id, comments.timestamp AS timestamp{space} FROM comments) AS "core.comments" GROUP BY datetime("core.comments".timestamp, 'start of day')""" ) def test_get_query_for_sql_invalid_column( mocker: MockerFixture, session: Session, ) -> None: """ Test ``get_query_for_sql`` with an invalid column. """ get_session = mocker.patch("datajunction.sql.build.get_session") get_session().__next__.return_value = session database = Database(id=1, name="slow", URI="sqlite://", cost=1.0) comments = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="user_id", type=ColumnType.INT), Column(name="comment", type=ColumnType.STR), ], ), ], ) engine = create_engine(database.URI) connection = engine.connect() connection.execute("CREATE TABLE comments (user_id INT, comment TEXT)") mocker.patch("datajunction.sql.transpile.create_engine", return_value=engine) num_comments = Node( name="core.num_comments", type=NodeType.METRIC, expression="SELECT COUNT(*) FROM core.comments", parents=[comments], ) session.add(num_comments) session.commit() sql = """ SELECT "core.num_comments" FROM metrics WHERE "core.some_other_parent.user_id" > 1 """ with pytest.raises(Exception) as excinfo: get_query_for_sql(sql) assert str(excinfo.value) == "Invalid dimension: core.some_other_parent.user_id" def test_get_dimensions_from_filters() -> None: """ Test ``get_dimensions_from_filters``. """ assert get_dimensions_from_filters(["a>1", "b=10"]) == {"a", "b"} with pytest.raises(Exception) as excinfo: get_dimensions_from_filters(["aaaa"]) assert ( str(excinfo.value) == """The filter "aaaa" is invalid The following error happened: - The filter "aaaa" is not a valid filter. Filters should consist of a dimension name, follow by a valid operator (<=|<|>=|>|!=|=), followed by a value. If the value is a string or date/time it should be enclosed in single quotes. (error code: 100)""" ) def test_find_on_clause(mocker: MockerFixture) -> None: """ Test ``find_on_clause``. """ database = Database(id=1, name="one", URI="sqlite://") dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) parent = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ) child = Node(name="core.num_comments", parents=[parent]) node_select = mocker.MagicMock() subquery = mocker.MagicMock() find_on_clause(child, node_select, dimension, subquery) assert node_select.columns.__getitem__.called_with("user_id") assert subquery.columns.__getitem__.called_with("id") def test_find_on_clause_parent_no_columns(mocker: MockerFixture) -> None: """ Test ``find_on_clause`` when a parent has no columns. I think we expect all nodes to have at least one column, so this test is just for completeness. """ database = Database(id=1, name="one", URI="sqlite://") dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) parent_1 = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ) parent_2 = Node( name="a_weird_node", tables=[ Table( database=database, table="empty", columns=[], ), ], columns=[], ) child = Node(name="core.num_comments", parents=[parent_2, parent_1]) node_select = mocker.MagicMock() subquery = mocker.MagicMock() find_on_clause(child, node_select, dimension, subquery) assert node_select.columns.__getitem__.called_with("user_id") def test_find_on_clause_parent_invalid_reference(mocker: MockerFixture) -> None: """ Test ``find_on_clause`` when a parent has no columns. The compiler should check that the dimension is valid, but the table could change. """ database = Database(id=1, name="one", URI="sqlite://") dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) parent = Node( name="core.comments", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ) child = Node(name="core.num_comments", parents=[parent]) node_select = mocker.MagicMock() subquery = mocker.MagicMock() with pytest.raises(Exception) as excinfo: find_on_clause(child, node_select, dimension, subquery) assert ( str(excinfo.value) == "Node core.num_comments has no columns with dimension core.users" ) def test_get_join_columns() -> None: """ Test ``get_join_columns``. """ database = Database(id=1, name="one", URI="sqlite://") dimension = Node( name="core.users", type=NodeType.DIMENSION, tables=[ Table( database=database, table="dim_users", columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ), ], columns=[ Column(name="id", type=ColumnType.INT), Column(name="age", type=ColumnType.INT), Column(name="gender", type=ColumnType.STR), ], ) orphan = Node(name="orphan") with pytest.raises(Exception) as excinfo: get_join_columns(orphan, dimension) assert str(excinfo.value) == "Node orphan has no columns with dimension core.users" parent_without_columns = Node(name="parent_without_columns") broken = Node(name="broken", parents=[parent_without_columns]) with pytest.raises(Exception) as excinfo: get_join_columns(broken, dimension) assert str(excinfo.value) == "Node broken has no columns with dimension core.users" parent = Node( name="parent", tables=[ Table( database=database, table="comments", columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT), Column(name="text", type=ColumnType.STR), ], ), ], columns=[ Column(name="ds", type=ColumnType.STR), Column(name="user_id", type=ColumnType.INT, dimension=dimension), Column(name="text", type=ColumnType.STR), ], ) child = Node(name="child", parents=[parent_without_columns, parent]) parent_name, column_name, dimension_column = get_join_columns(child, dimension) assert parent_name == "parent" assert column_name == "user_id" assert dimension_column == "id"

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0.832067

0.816684

0.797712

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0.784823

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null

0

1

0

1

0

null

0

7

8fd73c44229c291bcccd7266f8ab8045ad9ba324

28,491

py

Python

html/backend/variables.py

programmersidharth/web

e7df13308e301afc604ade82992ff4b4162d4810

[ "MIT" ]

1

2022-03-14T05:37:15.000Z

html/backend/variables.py

programmersidharth/web

e7df13308e301afc604ade82992ff4b4162d4810

[ "MIT" ]

null

html/backend/variables.py

programmersidharth/web

e7df13308e301afc604ade82992ff4b4162d4810

[ "MIT" ]

null

# icons used for filtering ICONS = ['10k', '10mp', '11mp', '12mp', '13mp', '14mp', '15mp', '16mp', '17mp', '18mp', '19mp', '1k', '1k_plus', '1x_mobiledata', '20mp', '21mp', '22mp', '23mp', '24mp', '2k', '2k_plus', '2mp', '30fps', '30fps_select', '360', '3d_rotation', '3g_mobiledata', '3k', '3k_plus', '3mp', '3p', '4g_mobiledata', '4g_plus_mobiledata', '4k', '4k_plus', '4mp', '5g', '5k', '5k_plus', '5mp', '6_ft_apart', '60fps', '60fps_select', '6k', '6k_plus', '6mp', '7k', '7k_plus', '7mp', '8k', '8k_plus', '8mp', '9k', '9k_plus', '9mp', 'ac_unit', 'access_alarm', 'access_alarms', 'access_time', 'access_time_filled', 'accessibility', 'accessibility_new', 'accessible', 'accessible_forward', 'account_balance', 'account_balance_wallet', 'account_box', 'account_circle', 'account_tree', 'ad_units', 'adb', 'add', 'add_a_photo', 'add_alarm', 'add_alert', 'add_box', 'add_business', 'add_chart', 'add_circle', 'add_circle_outline', 'add_comment', 'add_ic_call', 'add_link', 'add_location', 'add_location_alt', 'add_moderator', 'add_photo_alternate', 'add_reaction', 'add_road', 'add_shopping_cart', 'add_task', 'add_to_drive', 'add_to_home_screen', 'add_to_photos', 'add_to_queue', 'addchart', 'adjust', 'admin_panel_settings', 'agriculture', 'air', 'airline_seat_flat', 'airline_seat_flat_angled', 'airline_seat_individual_suite', 'airline_seat_legroom_extra', 'airline_seat_legroom_normal', 'airline_seat_legroom_reduced', 'airline_seat_recline_extra', 'airline_seat_recline_normal', 'airplane_ticket', 'airplanemode_active', 'airplanemode_inactive', 'airplay', 'airport_shuttle', 'alarm', 'alarm_add', 'alarm_off', 'alarm_on', 'album', 'align_horizontal_center', 'align_horizontal_left', 'align_horizontal_right', 'align_vertical_bottom', 'align_vertical_center', 'align_vertical_top', 'all_inbox', 'all_inclusive', 'all_out', 'alt_route', 'alternate_email', 'amp_stories', 'analytics', 'anchor', 'android', 'animation', 'announcement', 'aod', 'apartment', 'api', 'app_blocking', 'app_registration', 'app_settings_alt', 'approval', 'apps', 'architecture', 'archive', 'arrow_back', 'arrow_back_ios', 'arrow_back_ios_new', 'arrow_circle_down', 'arrow_circle_up', 'arrow_downward', 'arrow_drop_down', 'arrow_drop_down_circle', 'arrow_drop_up', 'arrow_forward', 'arrow_forward_ios', 'arrow_left', 'arrow_right', 'arrow_right_alt', 'arrow_upward', 'art_track', 'article', 'aspect_ratio', 'assessment', 'assignment', 'assignment_ind', 'assignment_late', 'assignment_return', 'assignment_returned', 'assignment_turned_in', 'assistant', 'assistant_direction', 'assistant_photo', 'atm', 'attach_email', 'attach_file', 'attach_money', 'attachment', 'attractions', 'attribution', 'audiotrack', 'auto_awesome', 'auto_awesome_mosaic', 'auto_awesome_motion', 'auto_delete', 'auto_fix_high', 'auto_fix_normal', 'auto_fix_off', 'auto_graph', 'auto_stories', 'autofps_select', 'autorenew', 'av_timer', 'baby_changing_station', 'backpack', 'backspace', 'backup', 'backup_table', 'badge', 'bakery_dining', 'balcony', 'ballot', 'bar_chart', 'barcode', 'batch_prediction', 'bathroom', 'bathtub', 'battery_20', 'battery_30', 'battery_50', 'battery_60', 'battery_80', 'battery_90', 'battery_alert', 'battery_charging_20', 'battery_charging_30', 'battery_charging_50', 'battery_charging_60', 'battery_charging_80', 'battery_charging_90', 'battery_charging_full', 'battery_full', 'battery_saver', 'battery_std', 'battery_unknown', 'beach_access', 'bed', 'bedroom_baby', 'bedroom_child', 'bedroom_parent', 'bedtime', 'beenhere', 'bento', 'bike_scooter', 'biotech', 'blender', 'block', 'bloodtype', 'bluetooth', 'bluetooth_audio', 'bluetooth_connected', 'bluetooth_disabled', 'bluetooth_drive', 'bluetooth_searching', 'blur_circular', 'blur_linear', 'blur_off', 'blur_on', 'bolt', 'book', 'book_online', 'bookmark', 'bookmark_add', 'bookmark_added', 'bookmark_border', 'bookmark_remove', 'bookmarks', 'border_all', 'border_bottom', 'border_clear', 'border_color', 'border_horizontal', 'border_inner', 'border_left', 'border_outer', 'border_right', 'border_style', 'border_top', 'border_vertical', 'branding_watermark', 'breakfast_dining', 'brightness_1', 'brightness_2', 'brightness_3', 'brightness_4', 'brightness_5', 'brightness_6', 'brightness_7', 'brightness_auto', 'brightness_high', 'brightness_low', 'brightness_medium', 'broken_image', 'browser_not_supported', 'brunch_dining', 'brush', 'bubble_chart', 'bug_report', 'build', 'build_circle', 'bungalow', 'burst_mode', 'bus_alert', 'business', 'business_center', 'cabin', 'cable', 'cached', 'cake', 'calculate', 'calendar_today', 'calendar_view_day', 'calendar_view_month', 'calendar_view_week', 'call', 'call_end', 'call_made', 'call_merge', 'call_missed', 'call_missed_outgoing', 'call_received', 'call_split', 'call_to_action', 'camera', 'camera_alt', 'camera_enhance', 'camera_front', 'camera_indoor', 'camera_outdoor', 'camera_rear', 'camera_roll', 'cameraswitch', 'campaign', 'cancel', 'cancel_presentation', 'cancel_schedule_send', 'car_rental', 'car_repair', 'card_giftcard', 'card_membership', 'card_travel', 'carpenter', 'cases', 'casino', 'cast', 'cast_connected', 'cast_for_education', 'catching_pokemon', 'category', 'celebration', 'cell_wifi', 'center_focus_strong', 'center_focus_weak', 'chair', 'chair_alt', 'chalet', 'change_circle', 'change_history', 'charging_station', 'chat', 'chat_bubble', 'chat_bubble_outline', 'check', 'check_box', 'check_box_outline_blank', 'check_circle', 'check_circle_outline', 'checkroom', 'chevron_left', 'chevron_right', 'child_care', 'child_friendly', 'chrome_reader_mode', 'circle', 'circle_notifications', 'class', 'clean_hands', 'cleaning_services', 'clear', 'clear_all', 'close', 'close_fullscreen', 'closed_caption', 'closed_caption_disabled', 'closed_caption_off', 'cloud', 'cloud_circle', 'cloud_done', 'cloud_download', 'cloud_off', 'cloud_queue', 'cloud_upload', 'code', 'code_off', 'coffee', 'coffee_maker', 'collections', 'collections_bookmark', 'color_lens', 'colorize', 'comment', 'comment_bank', 'commute', 'compare', 'compare_arrows', 'compass_calibration', 'compress', 'computer', 'confirmation_number', 'connect_without_contact', 'connected_tv', 'construction', 'contact_mail', 'contact_page', 'contact_phone', 'contact_support', 'contactless', 'contacts', 'content_copy', 'content_cut', 'content_paste', 'content_paste_off', 'control_camera', 'control_point', 'control_point_duplicate', 'copy_all', 'copyright', 'coronavirus', 'corporate_fare', 'cottage', 'countertops', 'create', 'create_new_folder', 'credit_card', 'credit_card_off', 'credit_score', 'crib', 'crop', 'crop_16_9', 'crop_3_2', 'crop_5_4', 'crop_7_5', 'crop_din', 'crop_free', 'crop_landscape', 'crop_original', 'crop_portrait', 'crop_rotate', 'crop_square', 'dangerous', 'dark_mode', 'dashboard', 'dashboard_customize', 'data_saver_off', 'data_saver_on', 'data_usage', 'date_range', 'deck', 'dehaze', 'delete', 'delete_forever', 'delete_outline', 'delete_sweep', 'delivery_dining', 'departure_board', 'description', 'design_services', 'desktop_access_disabled', 'desktop_mac', 'desktop_windows', 'details', 'developer_board', 'developer_board_off', 'developer_mode', 'device_hub', 'device_thermostat', 'device_unknown', 'devices', 'devices_other', 'dialer_sip', 'dialpad', 'dining', 'dinner_dining', 'directions', 'directions_bike', 'directions_boat', 'directions_boat_filled', 'directions_bus', 'directions_bus_filled', 'directions_car', 'directions_car_filled', 'directions_off', 'directions_railway', 'directions_railway_filled', 'directions_run', 'directions_subway', 'directions_subway_filled', 'directions_transit', 'directions_transit_filled', 'directions_walk', 'dirty_lens', 'disabled_by_default', 'disc_full', 'divide', 'dns', 'do_disturb', 'do_disturb_alt', 'do_disturb_off', 'do_disturb_on', 'do_not_disturb', 'do_not_disturb_alt', 'do_not_disturb_off', 'do_not_disturb_on', 'do_not_disturb_on_total_silence', 'do_not_step', 'do_not_touch', 'dock', 'domain', 'domain_disabled', 'domain_verification', 'done', 'done_all', 'done_outline', 'donut_large', 'donut_small', 'door_back', 'door_front', 'door_sliding', 'doorbell', 'double_arrow', 'downhill_skiing', 'download', 'download_done', 'download_for_offline', 'downloading', 'drafts', 'drag_handle', 'drag_indicator', 'drive_eta', 'drive_file_move', 'drive_file_rename_outline', 'drive_folder_upload', 'dry', 'dry_cleaning', 'duo', 'dvr', 'dynamic_feed', 'dynamic_form', 'e_mobiledata', 'earbuds', 'earbuds_battery', 'east', 'eco', 'edgesensor_high', 'edgesensor_low', 'edit', 'edit_attributes', 'edit_location', 'edit_location_alt', 'edit_notifications', 'edit_off', 'edit_road', 'eject', 'elderly', 'electric_bike', 'electric_car', 'electric_moped', 'electric_rickshaw', 'electric_scooter', 'electrical_services', 'elevator', 'email', 'emoji_emotions', 'emoji_events', 'emoji_flags', 'emoji_food_beverage', 'emoji_nature', 'emoji_objects', 'emoji_people', 'emoji_symbols', 'emoji_transportation', 'engineering', 'enhanced_encryption', 'equalizer', 'equals', 'error', 'error_outline', 'escalator', 'escalator_warning', 'euro', 'euro_symbol', 'ev_station', 'event', 'event_available', 'event_busy', 'event_note', 'event_seat', 'exit_to_app', 'expand', 'expand_less', 'expand_more', 'explicit', 'explore', 'explore_off', 'exposure', 'exposure_neg_1', 'exposure_neg_2', 'exposure_plus_1', 'exposure_plus_2', 'exposure_zero', 'extension', 'extension_off', 'face', 'face_retouching_natural', 'face_retouching_off', 'facebook', 'fact_check', 'family_restroom', 'fast_forward', 'fast_rewind', 'fastfood', 'favorite', 'favorite_border', 'featured_play_list', 'featured_video', 'feed', 'feedback', 'female', 'fence', 'festival', 'fiber_dvr', 'fiber_manual_record', 'fiber_new', 'fiber_pin', 'fiber_smart_record', 'file_copy', 'file_download', 'file_download_done', 'file_download_off', 'file_present', 'file_upload', 'filter', 'filter_1', 'filter_2', 'filter_3', 'filter_4', 'filter_5', 'filter_6', 'filter_7', 'filter_8', 'filter_9', 'filter_9_plus', 'filter_alt', 'filter_b_and_w', 'filter_center_focus', 'filter_drama', 'filter_frames', 'filter_hdr', 'filter_list', 'filter_none', 'filter_tilt_shift', 'filter_vintage', 'find_in_page', 'find_replace', 'fingerprint', 'fire_extinguisher', 'fireplace', 'first_page', 'fit_screen', 'fitness_center', 'flag', 'flaky', 'flare', 'flash_auto', 'flash_off', 'flash_on', 'flashlight_off', 'flashlight_on', 'flatware', 'flight', 'flight_land', 'flight_takeoff', 'flip', 'flip_camera_android', 'flip_camera_ios', 'flip_to_back', 'flip_to_front', 'flourescent', 'flutter_dash', 'fmd_bad', 'fmd_good', 'folder', 'folder_open', 'folder_shared', 'folder_special', 'follow_the_signs', 'font_download', 'font_download_off', 'food_bank', 'format_align_center', 'format_align_justify', 'format_align_left', 'format_align_right', 'format_bold', 'format_clear', 'format_color_fill', 'format_color_reset', 'format_color_text', 'format_indent_decrease', 'format_indent_increase', 'format_italic', 'format_line_spacing', 'format_list_bulleted', 'format_list_numbered', 'format_list_numbered_rtl', 'format_paint', 'format_quote', 'format_shapes', 'format_size', 'format_strikethrough', 'format_textdirection_l_to_r', 'format_textdirection_r_to_l', 'format_underlined', 'forum', 'forward', 'forward_10', 'forward_30', 'forward_5', 'forward_to_inbox', 'foundation', 'free_breakfast', 'fullscreen', 'fullscreen_exit', 'functions', 'g_mobiledata', 'g_translate', 'gamepad', 'games', 'garage', 'gavel', 'gesture', 'get_app', 'gif', 'gite', 'golf_course', 'gpp_bad', 'gpp_good', 'gpp_maybe', 'gps_fixed', 'gps_not_fixed', 'gps_off', 'grade', 'gradient', 'grading', 'grain', 'graphic_eq', 'grass', 'greater_than', 'greater_than_equal', 'grid_3x3', 'grid_4x4', 'grid_goldenratio', 'grid_off', 'grid_on', 'grid_view', 'group', 'group_add', 'group_work', 'groups', 'h_mobiledata', 'h_plus_mobiledata', 'hail', 'handyman', 'hardware', 'hd', 'hdr_auto', 'hdr_auto_select', 'hdr_enhanced_select', 'hdr_off', 'hdr_off_select', 'hdr_on', 'hdr_on_select', 'hdr_plus', 'hdr_strong', 'hdr_weak', 'headphones', 'headphones_battery', 'headset', 'headset_mic', 'headset_off', 'healing', 'health_and_safety', 'hearing', 'hearing_disabled', 'height', 'help', 'help_center', 'help_outline', 'hevc', 'hide_image', 'hide_source', 'high_quality', 'highlight', 'highlight_alt', 'highlight_off', 'hiking', 'history', 'history_edu', 'history_toggle_off', 'holiday_village', 'home', 'home_max', 'home_mini', 'home_repair_service', 'home_work', 'horizontal_distribute', 'horizontal_rule', 'horizontal_split', 'hot_tub', 'hotel', 'hourglass_bottom', 'hourglass_disabled', 'hourglass_empty', 'hourglass_full', 'hourglass_top', 'house', 'house_siding', 'houseboat', 'how_to_reg', 'how_to_vote', 'http', 'https', 'hvac', 'ice_skating', 'icecream', 'image', 'image_aspect_ratio', 'image_not_supported', 'image_search', 'imagesearch_roller', 'import_contacts', 'import_export', 'important_devices', 'inbox', 'indeterminate_check_box', 'info', 'input', 'insert_chart', 'insert_chart_outlined', 'insert_comment', 'insert_drive_file', 'insert_emoticon', 'insert_invitation', 'insert_link', 'insert_photo', 'insights', 'integration_instructions', 'inventory', 'inventory_2', 'invert_colors', 'invert_colors_off', 'ios_share', 'iron', 'iso', 'kayaking', 'keyboard', 'keyboard_alt', 'keyboard_arrow_down', 'keyboard_arrow_left', 'keyboard_arrow_right', 'keyboard_arrow_up', 'keyboard_backspace', 'keyboard_capslock', 'keyboard_hide', 'keyboard_return', 'keyboard_tab', 'keyboard_voice', 'king_bed', 'kitchen', 'kitesurfing', 'label', 'label_important', 'label_off', 'landscape', 'language', 'laptop', 'laptop_chromebook', 'laptop_mac', 'laptop_windows', 'last_page', 'launch', 'layers', 'layers_clear', 'leaderboard', 'leak_add', 'leak_remove', 'leave_bags_at_home', 'legend_toggle', 'lens', 'lens_blur', 'less_than', 'less_than_equal', 'library_add', 'library_add_check', 'library_books', 'library_music', 'light', 'light_mode', 'lightbulb', 'line_style', 'line_weight', 'linear_scale', 'link', 'link_off', 'linked_camera', 'liquor', 'list', 'list_alt', 'live_help', 'live_tv', 'living', 'local_activity', 'local_airport', 'local_atm', 'local_bar', 'local_cafe', 'local_car_wash', 'local_convenience_store', 'local_dining', 'local_drink', 'local_fire_department', 'local_florist', 'local_gas_station', 'local_grocery_store', 'local_hospital', 'local_hotel', 'local_laundry_service', 'local_library', 'local_mall', 'local_movies', 'local_offer', 'local_parking', 'local_pharmacy', 'local_phone', 'local_pizza', 'local_play', 'local_police', 'local_post_office', 'local_printshop', 'local_see', 'local_shipping', 'local_taxi', 'location_city', 'location_disabled', 'location_off', 'location_on', 'location_searching', 'lock', 'lock_clock', 'lock_open', 'log_in', 'log_out', 'login', 'logout', 'looks', 'looks_3', 'looks_4', 'looks_5', 'looks_6', 'looks_one', 'looks_two', 'loop', 'loupe', 'low_priority', 'loyalty', 'lte_mobiledata', 'lte_plus_mobiledata', 'luggage', 'lunch_dining', 'mail', 'mail_outline', 'male', 'manage_accounts', 'manage_search', 'map', 'maps_home_work', 'maps_ugc', 'margin', 'mark_as_unread', 'mark_chat_read', 'mark_chat_unread', 'mark_email_read', 'mark_email_unread', 'markunread', 'markunread_mailbox', 'masks', 'maximize', 'media_bluetooth_off', 'media_bluetooth_on', 'mediation', 'medical_services', 'medication', 'meeting_room', 'memory', 'menu', 'menu_book', 'menu_open', 'merge_type', 'message', 'mic', 'mic_external_off', 'mic_external_on', 'mic_none', 'mic_off', 'microwave', 'military_tech', 'minimize', 'minus', 'miscellaneous_services', 'missed_video_call', 'mms', 'mobile_friendly', 'mobile_off', 'mobile_screen_share', 'mobiledata_off', 'mode', 'mode_comment', 'mode_edit', 'mode_edit_outline', 'mode_night', 'mode_standby', 'model_training', 'monetization_on', 'money', 'money_off', 'money_off_csred', 'monitor', 'monitor_weight', 'monochrome_photos', 'mood', 'mood_bad', 'moped', 'more', 'more_horiz', 'more_time', 'more_vert', 'motion_photos_auto', 'motion_photos_off', 'motion_photos_on', 'motion_photos_pause', 'motion_photos_paused', 'motorcycle', 'mouse', 'move_to_inbox', 'movie', 'movie_creation', 'movie_filter', 'moving', 'mp', 'multiline_chart', 'multiple_stop', 'museum', 'music_note', 'music_off', 'music_video', 'my_location', 'nat', 'nature', 'nature_people', 'navigate_before', 'navigate_next', 'navigation', 'near_me', 'near_me_disabled', 'nearby_error', 'nearby_off', 'network_cell', 'network_check', 'network_locked', 'network_wifi', 'new_releases', 'next_plan', 'next_week', 'nfc', 'night_shelter', 'nightlife', 'nightlight', 'nightlight_round', 'nights_stay', 'no_accounts', 'no_backpack', 'no_cell', 'no_drinks', 'no_encryption', 'no_encryption_gmailerrorred', 'no_flash', 'no_food', 'no_luggage', 'no_meals', 'no_meeting_room', 'no_photography', 'no_sim', 'no_stroller', 'no_transfer', 'nordic_walking', 'north', 'north_east', 'north_west', 'not_accessible', 'not_equal', 'not_interested', 'not_listed_location', 'not_started', 'note', 'note_add', 'note_alt', 'notes', 'notification_add', 'notification_important', 'notifications', 'notifications_active', 'notifications_none', 'notifications_off', 'notifications_paused', 'offline_bolt', 'offline_pin', 'offline_share', 'ondemand_video', 'online_prediction', 'opacity', 'open_in_browser', 'open_in_full', 'open_in_new', 'open_in_new_off', 'open_with', 'other_houses', 'outbond', 'outbound', 'outbox', 'outdoor_grill', 'outlet', 'outlined_flag', 'padding', 'pages', 'pageview', 'paid', 'palette', 'pan_tool', 'panorama', 'panorama_fish_eye', 'panorama_horizontal', 'panorama_horizontal_select', 'panorama_photosphere', 'panorama_photosphere_select', 'panorama_vertical', 'panorama_vertical_select', 'panorama_wide_angle', 'panorama_wide_angle_select', 'paragliding', 'park', 'party_mode', 'password', 'pattern', 'pause', 'pause_circle', 'pause_circle_filled', 'pause_circle_outline', 'pause_presentation', 'payment', 'payments', 'pedal_bike', 'pending', 'pending_actions', 'people', 'people_alt', 'people_outline', 'percentage', 'perm_camera_mic', 'perm_contact_calendar', 'perm_data_setting', 'perm_device_information', 'perm_identity', 'perm_media', 'perm_phone_msg', 'perm_scan_wifi', 'person', 'person_add', 'person_add_alt', 'person_add_alt_1', 'person_add_disabled', 'person_off', 'person_outline', 'person_pin', 'person_pin_circle', 'person_remove', 'person_remove_alt_1', 'person_search', 'personal_video', 'pest_control', 'pest_control_rodent', 'pets', 'phone', 'phone_android', 'phone_bluetooth_speaker', 'phone_callback', 'phone_disabled', 'phone_enabled', 'phone_forwarded', 'phone_in_talk', 'phone_iphone', 'phone_locked', 'phone_missed', 'phone_paused', 'phonelink', 'phonelink_erase', 'phonelink_lock', 'phonelink_off', 'phonelink_ring', 'phonelink_setup', 'photo', 'photo_album', 'photo_camera', 'photo_camera_back', 'photo_camera_front', 'photo_filter', 'photo_library', 'photo_size_select_actual', 'photo_size_select_large', 'photo_size_select_small', 'piano', 'piano_off', 'picture_as_pdf', 'picture_in_picture', 'picture_in_picture_alt', 'pie_chart', 'pie_chart_outline', 'pin', 'pin_drop', 'pin_off', 'pivot_table_chart', 'place', 'plagiarism', 'play_arrow', 'play_circle', 'play_circle_filled', 'play_circle_filled_white', 'play_circle_outline', 'play_disabled', 'play_for_work', 'play_lesson', 'playlist_add', 'playlist_add_check', 'playlist_play', 'plumbing', 'plus', 'plus_minus', 'plus_minus_alt', 'plus_one', 'podcasts', 'point_of_sale', 'policy', 'poll', 'polymer', 'pool', 'portable_wifi_off', 'portrait', 'post_add', 'power', 'power_input', 'power_off', 'power_settings_new', 'precision_manufacturing', 'pregnant_woman', 'present_to_all', 'preview', 'price_change', 'price_check', 'print', 'print_disabled', 'priority_high', 'privacy_tip', 'production_quantity_limits', 'psychology', 'public', 'public_off', 'publish', 'published_with_changes', 'push_pin', 'qr_code', 'qr_code_2', 'qr_code_scanner', 'qrcode', 'query_builder', 'query_stats', 'question_answer', 'queue', 'queue_music', 'queue_play_next', 'quickreply', 'quiz', 'r_mobiledata', 'radar', 'radio', 'radio_button_checked', 'radio_button_unchecked', 'railway_alert', 'ramen_dining', 'rate_review', 'raw_off', 'raw_on', 'read_more', 'receipt', 'receipt_long', 'recent_actors', 'recommend', 'record_voice_over', 'redeem', 'redo', 'reduce_capacity', 'refresh', 'remember_me', 'remove', 'remove_circle', 'remove_circle_outline', 'remove_done', 'remove_from_queue', 'remove_moderator', 'remove_red_eye', 'remove_shopping_cart', 'reorder', 'repeat', 'repeat_on', 'repeat_one', 'repeat_one_on', 'replay', 'replay_10', 'replay_30', 'replay_5', 'replay_circle_filled', 'reply', 'reply_all', 'report', 'report_gmailerrorred', 'report_off', 'report_problem', 'request_page', 'request_quote', 'reset_tv', 'restart_alt', 'restaurant', 'restaurant_menu', 'restore', 'restore_from_trash', 'restore_page', 'reviews', 'rice_bowl', 'ring_volume', 'rocket', 'roofing', 'room', 'room_preferences', 'room_service', 'rotate_90_degrees_ccw', 'rotate_left', 'rotate_right', 'rounded_corner', 'router', 'rowing', 'rss_feed', 'rsvp', 'rtt', 'rule', 'rule_folder', 'run_circle', 'running_with_errors', 'rv_hookup', 'safety_divider', 'sailing', 'sanitizer', 'satellite', 'save', 'save_alt', 'saved_search', 'savings', 'scanner', 'scatter_plot', 'schedule', 'schedule_send', 'schema', 'school', 'science', 'score', 'screen_lock_landscape', 'screen_lock_portrait', 'screen_lock_rotation', 'screen_rotation', 'screen_search_desktop', 'screen_share', 'screenshot', 'sd', 'sd_card', 'sd_card_alert', 'sd_storage', 'search', 'search_off', 'security', 'security_update', 'security_update_good', 'security_update_warning', 'segment', 'select_all', 'self_improvement', 'sell', 'send', 'send_and_archive', 'send_to_mobile', 'sensor_door', 'sensor_window', 'sensors', 'sensors_off', 'sentiment_dissatisfied', 'sentiment_neutral', 'sentiment_satisfied', 'sentiment_satisfied_alt', 'sentiment_slightly_dissatisfied', 'sentiment_very_dissatisfied', 'sentiment_very_satisfied', 'set_meal', 'settings', 'settings_accessibility', 'settings_applications', 'settings_backup_restore', 'settings_bluetooth', 'settings_brightness', 'settings_cell', 'settings_ethernet', 'settings_input_antenna', 'settings_input_component', 'settings_input_composite', 'settings_input_hdmi', 'settings_input_svideo', 'settings_overscan', 'settings_phone', 'settings_power', 'settings_remote', 'settings_suggest', 'settings_system_daydream', 'settings_voice', 'share', 'share_arrival_time', 'share_location', 'shield', 'shop', 'shop_2', 'shop_two', 'shopping_bag', 'shopping_basket', 'shopping_cart', 'short_text', 'shortcut', 'show_chart', 'shower', 'shuffle', 'shuffle_on', 'shutter_speed', 'sick', 'signal_cellular_0_bar', 'signal_cellular_1_bar', 'signal_cellular_2_bar', 'signal_cellular_3_bar', 'signal_cellular_4_bar', 'signal_cellular_alt', 'signal_cellular_connected_no_internet_0_bar', 'signal_cellular_connected_no_internet_1_bar', 'signal_cellular_connected_no_internet_2_bar', 'signal_cellular_connected_no_internet_3_bar', 'signal_cellular_connected_no_internet_4_bar', 'signal_cellular_no_sim', 'signal_cellular_nodata', 'signal_cellular_null', 'signal_cellular_off', 'signal_wifi_0_bar', 'signal_wifi_1_bar', 'signal_wifi_1_bar_lock', 'signal_wifi_2_bar', 'signal_wifi_2_bar_lock', 'signal_wifi_3_bar', 'signal_wifi_3_bar_lock', 'signal_wifi_4_bar', 'signal_wifi_4_bar_lock', 'signal_wifi_bad', 'signal_wifi_connected_no_internet_4', 'signal_wifi_off', 'signal_wifi_statusbar_4_bar', 'signal_wifi_statusbar_connected_no_internet_4', 'signal_wifi_statusbar_null', 'sim_card', 'sim_card_alert', 'sim_card_download', 'single_bed', 'sip', 'skateboarding', 'skip_next', 'skip_previous', 'sledding', 'slideshow', 'slow_motion_video', 'smart_button', 'smart_display', 'smart_screen', 'smart_toy', 'smartphone', 'smoke_free', 'smoking_rooms', 'sms', 'sms_failed', 'snippet_folder', 'snooze', 'snowboarding', 'snowmobile', 'snowshoeing', 'soap', 'social_distance', 'sort', 'sort_by_alpha', 'source', 'south', 'south_east', 'south_west', 'spa', 'space_bar', 'speaker', 'speaker_group', 'speaker_notes', 'speaker_notes_off', 'speaker_phone', 'speed', 'spellcheck', 'splitscreen', 'sports', 'sports_bar', 'sports_baseball', 'sports_basketball', 'sports_cricket', 'sports_esports', 'sports_football', 'sports_golf', 'sports_handball', 'sports_hockey', 'sports_kabaddi', 'sports_mma', 'sports_motorsports', 'sports_rugby', 'sports_score', 'sports_soccer', 'sports_tennis', 'sports_volleyball', 'square_foot', 'stacked_bar_chart', 'stacked_line_chart', 'stairs', 'star', 'star_border', 'star_border_purple500', 'star_half', 'star_outline', 'star_purple500', 'star_rate', 'stars', 'stay_current_landscape', 'stay_current_portrait', 'stay_primary_landscape', 'stay_primary_portrait', 'sticky_note_2', 'stop', 'stop_circle', 'stop_screen_share', 'storage', 'store', 'store_mall_directory', 'storefront', 'storm', 'straighten', 'stream', 'streetview', 'strikethrough_s', 'stroller', 'style', 'subdirectory_arrow_left', 'subdirectory_arrow_right', 'subject', 'subscript', 'subscriptions', 'subtitles', 'subtitles_off', 'subway', 'summarize', 'superscript', 'supervised_user_circle', 'supervisor_account', 'support', 'support_agent', 'surfing', 'surround_sound', 'swap_calls', 'swap_horiz', 'swap_horizontal_circle', 'swap_vert', 'swap_vertical_circle', 'swipe', 'switch_account', 'switch_camera', 'switch_left', 'switch_right', 'switch_video', 'sync', 'sync_alt', 'sync_disabled', 'sync_problem', 'system_security_update', 'system_security_update_good', 'system_security_update_warning', 'system_update', 'system_update_alt', 'tab', 'tab_unselected', 'table_chart', 'table_rows', 'table_view', 'tablet', 'tablet_android', 'tablet_mac', 'tag', 'tag_faces', 'takeout_dining', 'tap_and_play', 'tapas', 'task', 'task_alt', 'taxi_alert', 'terrain', 'text_fields', 'text_format', 'text_rotate_up', 'text_rotate_vertical', 'text_rotation_angledown', 'text_rotation_angleup', 'text_rotation_down', 'text_rotation_none', 'text_snippet', 'textsms', 'texture', 'theater_comedy', 'theaters', 'thermostat', 'thermostat_auto', 'thumb_down', 'thumb_down_alt', 'thumb_down_off_alt', 'thumb_up', 'thumb_up_alt', 'thumb_up_off_alt', 'thumbs_up_down', 'time_to_leave', 'timelapse', 'timeline', 'timer', 'timer_10', 'timer_10_select', 'timer_3', 'timer_3_select', 'timer_off', 'title', 'toc', 'today', 'toggle_off', 'toggle_on', 'toll', 'tonality', 'topic', 'touch_app', 'tour', 'toys', 'track_changes', 'traffic', 'train', 'tram', 'transfer_within_a_station', 'transform', 'transgender', 'transit_enterexit', 'translate', 'travel_explore', 'trending_down', 'trending_flat', 'trending_up', 'trip_origin', 'try', 'tty', 'tune', 'tungsten', 'turned_in', 'turned_in_not', 'tv', 'tv_off', 'two_wheeler', 'umbrella', 'unarchive', 'undo', 'unfold_less', 'unfold_more', 'unpublished', 'unsubscribe', 'upcoming', 'update', 'update_disabled', 'upgrade', 'upload', 'upload_file', 'usb', 'usb_off', 'verified', 'verified_user', 'vertical_align_bottom', 'vertical_align_center', 'vertical_align_top', 'vertical_distribute', 'vertical_split', 'vibration', 'video_call', 'video_camera_back', 'video_camera_front', 'video_label', 'video_library', 'video_settings', 'video_stable', 'videocam', 'videocam_off', 'videogame_asset', 'videogame_asset_off', 'view_agenda', 'view_array', 'view_carousel', 'view_column', 'view_comfy', 'view_compact', 'view_day', 'view_headline', 'view_in_ar', 'view_list', 'view_module', 'view_quilt', 'view_sidebar', 'view_stream', 'view_week', 'vignette', 'villa', 'visibility', 'visibility_off', 'voice_chat', 'voice_over_off', 'voicemail', 'volume_down', 'volume_mute', 'volume_off', 'volume_up', 'volunteer_activism', 'vpn_key', 'vpn_lock', 'vrpano', 'wallpaper', 'warning', 'warning_amber', 'wash', 'watch', 'watch_later', 'water', 'water_damage', 'waterfall_chart', 'waves', 'wb_auto', 'wb_cloudy', 'wb_incandescent', 'wb_iridescent', 'wb_shade', 'wb_sunny', 'wb_twilight', 'wc', 'web', 'web_asset', 'web_asset_off', 'weekend', 'west', 'whatshot', 'wheelchair_pickup', 'where_to_vote', 'widgets', 'wifi', 'wifi_calling', 'wifi_calling_3', 'wifi_lock', 'wifi_off', 'wifi_protected_setup', 'wifi_tethering', 'wifi_tethering_error_rounded', 'wifi_tethering_off', 'window', 'wine_bar', 'work', 'work_off', 'work_outline', 'workspaces', 'wrap_text', 'wrong_location', 'wysiwyg', 'yard', 'youtube_searched_for', 'zoom_in', 'zoom_out', 'zoom_out_map']

5,698.2

14,241

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28,491

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14,242

7,122.75

0.72371

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0.744976

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false

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null

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8

8ff65882f9235b10adf0a5603fc7d6647c9a1d7c

32,488

py

Python

ambari-server/src/test/python/TestSensitiveDataEncryption.py

samyzh/ambari

ff73620da41697ed2ca9ece676f71ec9ba28a7d5

[ "Apache-2.0" ]

1,664

2015-01-03T09:35:21.000Z

2022-03-31T04:55:24.000Z

ambari-server/src/test/python/TestSensitiveDataEncryption.py

samyzh/ambari

ff73620da41697ed2ca9ece676f71ec9ba28a7d5

[ "Apache-2.0" ]

3,018

2015-02-19T20:16:10.000Z

2021-11-13T20:47:48.000Z

ambari-server/src/test/python/TestSensitiveDataEncryption.py

samyzh/ambari

ff73620da41697ed2ca9ece676f71ec9ba28a7d5

[ "Apache-2.0" ]

1,673

2015-01-06T14:14:42.000Z

2022-03-31T07:22:30.000Z

''' Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import os import sys from ambari_commons.exceptions import FatalException from mock.mock import patch, MagicMock, call with patch.object(os, "geteuid", new=MagicMock(return_value=0)): from resource_management.core import sudo reload(sudo) import operator import platform import StringIO from unittest import TestCase os.environ["ROOT"] = "" from only_for_platform import get_platform, os_distro_value, PLATFORM_WINDOWS from ambari_commons import os_utils if get_platform() != PLATFORM_WINDOWS: pass import shutil project_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)),os.path.normpath("../../../../")) shutil.copyfile(project_dir+"/ambari-server/conf/unix/ambari.properties", "/tmp/ambari.properties") # We have to use this import HACK because the filename contains a dash _search_file = os_utils.search_file def search_file_proxy(filename, searchpatch, pathsep=os.pathsep): global _search_file if "ambari.properties" in filename: return "/tmp/ambari.properties" return _search_file(filename, searchpatch, pathsep) os_utils.search_file = search_file_proxy with patch.object(platform, "linux_distribution", return_value = MagicMock(return_value=('Redhat', '6.4', 'Final'))): with patch("os.path.isdir", return_value = MagicMock(return_value=True)): with patch("os.access", return_value = MagicMock(return_value=True)): with patch.object(os_utils, "parse_log4j_file", return_value={'ambari.log.dir': '/var/log/ambari-server'}): with patch("platform.linux_distribution", return_value = os_distro_value): with patch("os.symlink"): with patch("glob.glob", return_value = ['/etc/init.d/postgresql-9.3']): _ambari_server_ = __import__('ambari-server') with patch("__builtin__.open"): from ambari_server.properties import Properties from ambari_server.serverConfiguration import configDefaults, JDBC_RCA_PASSWORD_FILE_PROPERTY, JDBC_PASSWORD_PROPERTY, \ JDBC_RCA_PASSWORD_ALIAS, SSL_TRUSTSTORE_PASSWORD_PROPERTY, SECURITY_IS_ENCRYPTION_ENABLED, \ SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED, SSL_TRUSTSTORE_PASSWORD_ALIAS, SECURITY_KEY_ENV_VAR_NAME from ambari_server.setupSecurity import get_alias_string, setup_sensitive_data_encryption, sensitive_data_encryption from ambari_server.serverClassPath import ServerClassPath @patch.object(platform, "linux_distribution", new = MagicMock(return_value=('Redhat', '6.4', 'Final'))) @patch("ambari_server.dbConfiguration_linux.get_postgre_hba_dir", new = MagicMock(return_value = "/var/lib/pgsql/data")) @patch("ambari_server.dbConfiguration_linux.get_postgre_running_status", new = MagicMock(return_value = "running")) class TestSensitiveDataEncryption(TestCase): def setUp(self): out = StringIO.StringIO() sys.stdout = out def tearDown(self): sys.stdout = sys.__stdout__ @patch("os.path.isdir", new = MagicMock(return_value=True)) @patch("os.access", new = MagicMock(return_value=True)) @patch.object(ServerClassPath, "get_full_ambari_classpath_escaped_for_shell", new = MagicMock(return_value = 'test' + os.pathsep + 'path12')) @patch("ambari_server.setupSecurity.find_jdk") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.run_os_command") def test_sensitive_data_encryption(self, run_os_command_mock, get_ambari_properties_method, find_jdk_mock): find_jdk_mock.return_value = "/" environ = os.environ.copy() run_os_command_mock.return_value = 0,"","" properties = Properties() get_ambari_properties_method.return_value = properties options = self._create_empty_options_mock() sensitive_data_encryption(options, "encription") run_os_command_mock.assert_called_with('None -cp test:path12 org.apache.ambari.server.security.encryption.SensitiveDataEncryption encription > /var/log/ambari-server/ambari-server.out 2>&1', environ) pass @patch("ambari_server.setupSecurity.print_error_msg") @patch("ambari_server.setupSecurity.find_jdk") def test_sensitive_data_encryption_nojdk(self, find_jdk_mock, print_mock): find_jdk_mock.return_value = None options = self._create_empty_options_mock() code = sensitive_data_encryption(options, "encription") self.assertEquals(code, 1) print_mock.assert_called_with("No JDK found, please run the \"setup\" " "command to install a JDK automatically or install any " "JDK manually to " + configDefaults.JDK_INSTALL_DIR) pass @patch("os.path.isdir", new = MagicMock(return_value=True)) @patch("os.access", new = MagicMock(return_value=True)) @patch.object(ServerClassPath, "get_full_ambari_classpath_escaped_for_shell", new = MagicMock(return_value = 'test' + os.pathsep + 'path12')) @patch("ambari_server.setupSecurity.find_jdk") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.run_os_command") def test_sensitive_data_decryption_not_persisted(self, run_os_command_mock, get_ambari_properties_method, find_jdk_mock): find_jdk_mock.return_value = "/" environ = os.environ.copy() master = "master" environ[SECURITY_KEY_ENV_VAR_NAME] = master run_os_command_mock.return_value = 0,"","" properties = Properties() get_ambari_properties_method.return_value = properties options = self._create_empty_options_mock() sensitive_data_encryption(options, "decryption", master) run_os_command_mock.assert_called_with('None -cp test:path12 org.apache.ambari.server.security.encryption.SensitiveDataEncryption decryption > /var/log/ambari-server/ambari-server.out 2>&1', environ) pass @patch("ambari_server.setupSecurity.get_is_persisted") @patch("ambari_server.setupSecurity.get_is_secure") @patch("os.path.exists") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("ambari_server.setupSecurity.read_passwd_for_alias") @patch("ambari_server.setupSecurity.update_properties_2") @patch("ambari_server.setupSecurity.save_master_key") @patch("ambari_server.setupSecurity.get_validated_string_input") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.setupSecurity.search_file") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.sensitive_data_encryption") @patch("ambari_server.setupSecurity.get_original_master_key") def test_reset_master_key_not_persisted(self, get_original_master_key_mock, sensitive_data_encryption_metod, is_root_method, get_ambari_properties_method, search_file_message, get_YN_input_method, get_validated_string_input_method, save_master_key_method, update_properties_method, read_passwd_for_alias_method, save_passwd_for_alias_method, read_ambari_user_method, exists_mock, get_is_secure_method, get_is_persisted_method): is_root_method.return_value = True search_file_message.return_value = False read_ambari_user_method.return_value = None p = Properties() FAKE_PWD_STRING = '${alias=fakealias}' p.process_pair(JDBC_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(SSL_TRUSTSTORE_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(JDBC_RCA_PASSWORD_FILE_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p master_key = "aaa" get_YN_input_method.side_effect = [False, True, False] get_validated_string_input_method.return_value = master_key get_original_master_key_mock.return_value = master_key read_passwd_for_alias_method.return_value = "fakepassword" save_passwd_for_alias_method.return_value = 0 exists_mock.return_value = False get_is_secure_method.return_value = True get_is_persisted_method.return_value = (False, "") options = self._create_empty_options_mock() setup_sensitive_data_encryption(options) calls = [call(options, "decryption", master_key), call(options, "encryption", master_key)] sensitive_data_encryption_metod.assert_has_calls(calls) self.assertFalse(save_master_key_method.called) self.assertTrue(get_original_master_key_mock.called) self.assertTrue(get_YN_input_method.called) self.assertTrue(get_validated_string_input_method.called) self.assertTrue(update_properties_method.called) self.assertTrue(read_passwd_for_alias_method.called) self.assertTrue(2, read_passwd_for_alias_method.call_count) self.assertTrue(2, save_passwd_for_alias_method.call_count) self.assertFalse(save_master_key_method.called) result_expected = {JDBC_PASSWORD_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), JDBC_RCA_PASSWORD_FILE_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), SSL_TRUSTSTORE_PASSWORD_PROPERTY: get_alias_string(SSL_TRUSTSTORE_PASSWORD_ALIAS), SECURITY_IS_ENCRYPTION_ENABLED: 'true', SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED: 'true'} sorted_x = sorted(result_expected.iteritems(), key=operator.itemgetter(0)) sorted_y = sorted(update_properties_method.call_args[0][1].iteritems(), key=operator.itemgetter(0)) self.assertEquals(sorted_x, sorted_y) pass @patch("ambari_server.setupSecurity.get_is_persisted") @patch("ambari_server.setupSecurity.get_is_secure") @patch("os.path.exists") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("ambari_server.setupSecurity.read_passwd_for_alias") @patch("ambari_server.setupSecurity.update_properties_2") @patch("ambari_server.setupSecurity.save_master_key") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.setupSecurity.search_file") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.sensitive_data_encryption") @patch("ambari_server.setupSecurity.get_original_master_key") def test_encrypt_part_not_persisted(self, get_original_master_key_mock, sensitive_data_encryption_metod, is_root_method, get_ambari_properties_method, search_file_message, get_YN_input_method, save_master_key_method, update_properties_method, read_passwd_for_alias_method, save_passwd_for_alias_method, read_ambari_user_method, exists_mock, get_is_secure_method, get_is_persisted_method): is_root_method.return_value = True search_file_message.return_value = False read_ambari_user_method.return_value = None p = Properties() FAKE_PWD_STRING = '${alias=fakealias}' p.process_pair(JDBC_PASSWORD_PROPERTY, get_alias_string(JDBC_RCA_PASSWORD_ALIAS)) p.process_pair(SSL_TRUSTSTORE_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(JDBC_RCA_PASSWORD_FILE_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p master_key = "aaa" get_YN_input_method.side_effect = [False, False, False] get_original_master_key_mock.return_value = master_key read_passwd_for_alias_method.return_value = "fakepassword" save_passwd_for_alias_method.return_value = 0 exists_mock.return_value = False get_is_secure_method.return_value = True get_is_persisted_method.return_value = (False, "filePath") options = self._create_empty_options_mock() setup_sensitive_data_encryption(options) calls = [call(options, "encryption", master_key)] sensitive_data_encryption_metod.assert_has_calls(calls) self.assertFalse(save_master_key_method.called) self.assertTrue(get_YN_input_method.called) self.assertTrue(get_original_master_key_mock.called) self.assertTrue(update_properties_method.called) self.assertTrue(read_passwd_for_alias_method.called) self.assertTrue(2, read_passwd_for_alias_method.call_count) self.assertTrue(2, save_passwd_for_alias_method.call_count) self.assertFalse(save_master_key_method.called) result_expected = {JDBC_PASSWORD_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), JDBC_RCA_PASSWORD_FILE_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), SSL_TRUSTSTORE_PASSWORD_PROPERTY: get_alias_string(SSL_TRUSTSTORE_PASSWORD_ALIAS), SECURITY_IS_ENCRYPTION_ENABLED: 'true', SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED: 'true'} sorted_x = sorted(result_expected.iteritems(), key=operator.itemgetter(0)) sorted_y = sorted(update_properties_method.call_args[0][1].iteritems(), key=operator.itemgetter(0)) self.assertEquals(sorted_x, sorted_y) pass @patch("ambari_server.setupSecurity.get_is_persisted") @patch("ambari_server.setupSecurity.get_is_secure") @patch("os.path.exists") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("ambari_server.setupSecurity.read_passwd_for_alias") @patch("ambari_server.setupSecurity.save_master_key") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.setupSecurity.search_file") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.get_original_master_key") def test_decrypt_missed_masterkey_not_persisted(self, get_original_master_key_mock, is_root_method, get_ambari_properties_method, search_file_message, get_YN_input_method, save_master_key_method, read_passwd_for_alias_method, save_passwd_for_alias_method, read_ambari_user_method, exists_mock, get_is_secure_method, get_is_persisted_method): is_root_method.return_value = True search_file_message.return_value = False read_ambari_user_method.return_value = None p = Properties() FAKE_PWD_STRING = '${alias=fakealias}' p.process_pair(JDBC_PASSWORD_PROPERTY, get_alias_string(JDBC_RCA_PASSWORD_ALIAS)) p.process_pair(SSL_TRUSTSTORE_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(JDBC_RCA_PASSWORD_FILE_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p get_YN_input_method.side_effect = [True, False] get_original_master_key_mock.return_value = None read_passwd_for_alias_method.return_value = "fakepassword" save_passwd_for_alias_method.return_value = 0 exists_mock.return_value = False get_is_secure_method.return_value = True get_is_persisted_method.return_value = (False, "filePath") options = self._create_empty_options_mock() self.assertTrue(setup_sensitive_data_encryption(options) == 1) self.assertFalse(save_master_key_method.called) self.assertTrue(get_YN_input_method.called) pass @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") def test_setup_sensitive_data_encryption_no_ambari_prop_not_root(self, is_root_method, get_ambari_properties_method): is_root_method.return_value = False get_ambari_properties_method.return_value = -1 options = self._create_empty_options_mock() try: setup_sensitive_data_encryption(options) self.fail("Should throw exception") except FatalException as fe: self.assertTrue('Failed to read properties file.' == fe.reason) pass pass @patch("os.path.exists") @patch("ambari_server.setupSecurity.get_is_secure") @patch("ambari_server.setupSecurity.get_is_persisted") @patch("ambari_server.setupSecurity.remove_password_file") @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("ambari_server.setupSecurity.read_master_key") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.update_properties_2") @patch("ambari_server.setupSecurity.save_master_key") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.sensitive_data_encryption") @patch("ambari_server.setupSecurity.adjust_directory_permissions") def test_setup_sensitive_data_encryption_not_persist(self, adjust_directory_permissions_mock, sensitive_data_encryption_metod, is_root_method, get_ambari_properties_method, get_YN_input_method, save_master_key_method, update_properties_method, read_ambari_user_method, read_master_key_method, save_passwd_for_alias_method, remove_password_file_method, get_is_persisted_method, get_is_secure_method, exists_mock): is_root_method.return_value = True p = Properties() FAKE_PWD_STRING = "fakepasswd" p.process_pair(JDBC_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(SSL_TRUSTSTORE_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(JDBC_RCA_PASSWORD_FILE_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p master_key = "aaa" read_master_key_method.return_value = master_key get_YN_input_method.return_value = False read_ambari_user_method.return_value = "asd" save_passwd_for_alias_method.return_value = 0 get_is_persisted_method.return_value = (True, "filepath") get_is_secure_method.return_value = False exists_mock.return_value = False options = self._create_empty_options_mock() setup_sensitive_data_encryption(options) self.assertTrue(get_YN_input_method.called) self.assertTrue(read_master_key_method.called) self.assertTrue(read_ambari_user_method.called) self.assertTrue(update_properties_method.called) self.assertFalse(save_master_key_method.called) self.assertTrue(save_passwd_for_alias_method.called) self.assertEquals(2, save_passwd_for_alias_method.call_count) self.assertTrue(remove_password_file_method.called) self.assertTrue(adjust_directory_permissions_mock.called) sensitive_data_encryption_metod.assert_called_with(options, "encryption", master_key) result_expected = {JDBC_PASSWORD_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), JDBC_RCA_PASSWORD_FILE_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), SSL_TRUSTSTORE_PASSWORD_PROPERTY: get_alias_string(SSL_TRUSTSTORE_PASSWORD_ALIAS), SECURITY_IS_ENCRYPTION_ENABLED: 'true', SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED: 'true'} sorted_x = sorted(result_expected.iteritems(), key=operator.itemgetter(0)) sorted_y = sorted(update_properties_method.call_args[0][1].iteritems(), key=operator.itemgetter(0)) self.assertEquals(sorted_x, sorted_y) pass @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("os.path.exists") @patch("ambari_server.setupSecurity.get_is_secure") @patch("ambari_server.setupSecurity.get_is_persisted") @patch("ambari_server.setupSecurity.read_master_key") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.update_properties_2") @patch("ambari_server.setupSecurity.save_master_key") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.serverConfiguration.search_file") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.sensitive_data_encryption") def test_setup_sensitive_data_encryption_persist(self, sensitive_data_encryption_metod, is_root_method, get_ambari_properties_method, search_file_message, get_YN_input_method, save_master_key_method, update_properties_method, read_ambari_user_method, read_master_key_method, get_is_persisted_method, get_is_secure_method, exists_mock, save_passwd_for_alias_method): is_root_method.return_value = True p = Properties() FAKE_PWD_STRING = "fakepasswd" p.process_pair(JDBC_PASSWORD_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p search_file_message.return_value = "propertiesfile" master_key = "aaa" read_master_key_method.return_value = master_key get_YN_input_method.return_value = True read_ambari_user_method.return_value = None get_is_persisted_method.return_value = (True, "filepath") get_is_secure_method.return_value = False exists_mock.return_value = False save_passwd_for_alias_method.return_value = 0 options = self._create_empty_options_mock() setup_sensitive_data_encryption(options) self.assertTrue(get_YN_input_method.called) self.assertTrue(read_master_key_method.called) self.assertTrue(read_ambari_user_method.called) self.assertTrue(update_properties_method.called) self.assertTrue(save_master_key_method.called) sensitive_data_encryption_metod.assert_called_with(options, "encryption") result_expected = {JDBC_PASSWORD_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), SECURITY_IS_ENCRYPTION_ENABLED: 'true', SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED: 'true'} sorted_x = sorted(result_expected.iteritems(), key=operator.itemgetter(0)) sorted_y = sorted(update_properties_method.call_args[0][1].iteritems(), key=operator.itemgetter(0)) self.assertEquals(sorted_x, sorted_y) pass @patch("ambari_server.setupSecurity.read_master_key") @patch("os.path.exists") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("ambari_server.setupSecurity.read_passwd_for_alias") @patch("ambari_server.setupSecurity.update_properties_2") @patch("ambari_server.setupSecurity.save_master_key") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.setupSecurity.search_file") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.sensitive_data_encryption") @patch("ambari_server.setupSecurity.get_is_secure") @patch("ambari_server.setupSecurity.get_is_persisted") def test_reset_master_key_persisted(self, get_is_persisted_method, get_is_secure_method, sensitive_data_encryption_metod, is_root_method, get_ambari_properties_method, search_file_message, get_YN_input_method, save_master_key_method, update_properties_method, read_passwd_for_alias_method, save_passwd_for_alias_method, read_ambari_user_method, exists_mock, read_master_key_method): # Testing call under root is_root_method.return_value = True search_file_message.return_value = "filepath" read_ambari_user_method.return_value = None p = Properties() FAKE_PWD_STRING = '${alias=fakealias}' p.process_pair(JDBC_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(SSL_TRUSTSTORE_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(JDBC_RCA_PASSWORD_FILE_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p master_key = "aaa" get_is_persisted_method.return_value = (True, "filepath") get_is_secure_method.return_value = True get_YN_input_method.side_effect = [False, True, True] read_master_key_method.return_value = master_key read_passwd_for_alias_method.return_value = "fakepassword" save_passwd_for_alias_method.return_value = 0 exists_mock.return_value = False options = self._create_empty_options_mock() setup_sensitive_data_encryption(options) calls = [call(options, "decryption"), call(options, "encryption")] sensitive_data_encryption_metod.assert_has_calls(calls) self.assertTrue(save_master_key_method.called) self.assertTrue(get_YN_input_method.called) self.assertTrue(read_master_key_method.called) self.assertTrue(update_properties_method.called) self.assertTrue(read_passwd_for_alias_method.called) self.assertTrue(2, read_passwd_for_alias_method.call_count) self.assertTrue(2, save_passwd_for_alias_method.call_count) result_expected = {JDBC_PASSWORD_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), JDBC_RCA_PASSWORD_FILE_PROPERTY: get_alias_string(JDBC_RCA_PASSWORD_ALIAS), SSL_TRUSTSTORE_PASSWORD_PROPERTY: get_alias_string(SSL_TRUSTSTORE_PASSWORD_ALIAS), SECURITY_IS_ENCRYPTION_ENABLED: 'true', SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED: 'true'} sorted_x = sorted(result_expected.iteritems(), key=operator.itemgetter(0)) sorted_y = sorted(update_properties_method.call_args[0][1].iteritems(), key=operator.itemgetter(0)) self.assertEquals(sorted_x, sorted_y) pass @patch("os.path.exists") @patch("ambari_server.setupSecurity.read_ambari_user") @patch("ambari_server.setupSecurity.save_passwd_for_alias") @patch("ambari_server.setupSecurity.read_passwd_for_alias") @patch("ambari_server.setupSecurity.update_properties_2") @patch("ambari_server.setupSecurity.get_YN_input") @patch("ambari_server.setupSecurity.search_file") @patch("ambari_server.setupSecurity.get_ambari_properties") @patch("ambari_server.setupSecurity.is_root") @patch("ambari_server.setupSecurity.sensitive_data_encryption") @patch("ambari_server.setupSecurity.get_is_secure") @patch("ambari_server.setupSecurity.get_is_persisted") def test_decrypt_sensitive_data_persister(self, get_is_persisted_method, get_is_secure_method, sensitive_data_encryption_metod, is_root_method, get_ambari_properties_method, search_file_message, get_YN_input_method, update_properties_method, read_passwd_for_alias_method, save_passwd_for_alias_method, read_ambari_user_method, exists_mock): # Testing call under root is_root_method.return_value = True search_file_message.return_value = "filepath" read_ambari_user_method.return_value = None p = Properties() FAKE_PWD_STRING = '${alias=fakealias}' p.process_pair(JDBC_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(SSL_TRUSTSTORE_PASSWORD_PROPERTY, FAKE_PWD_STRING) p.process_pair(JDBC_RCA_PASSWORD_FILE_PROPERTY, FAKE_PWD_STRING) get_ambari_properties_method.return_value = p get_is_persisted_method.return_value = (True, "filepath") get_is_secure_method.return_value = True get_YN_input_method.side_effect = [True, False] read_passwd_for_alias_method.return_value = "fakepassword" save_passwd_for_alias_method.return_value = 0 exists_mock.return_value = False options = self._create_empty_options_mock() setup_sensitive_data_encryption(options) calls = [call(options, "decryption")] sensitive_data_encryption_metod.assert_has_calls(calls) self.assertTrue(get_YN_input_method.called) self.assertTrue(update_properties_method.called) self.assertTrue(read_passwd_for_alias_method.called) self.assertTrue(2, read_passwd_for_alias_method.call_count) self.assertTrue(2, save_passwd_for_alias_method.call_count) result_expected = {JDBC_PASSWORD_PROPERTY: "fakepassword", JDBC_RCA_PASSWORD_FILE_PROPERTY: "fakepassword", SSL_TRUSTSTORE_PASSWORD_PROPERTY: "fakepassword", SECURITY_IS_ENCRYPTION_ENABLED: 'false', SECURITY_SENSITIVE_DATA_ENCRYPTON_ENABLED: 'false'} sorted_x = sorted(result_expected.iteritems(), key=operator.itemgetter(0)) sorted_y = sorted(update_properties_method.call_args[0][1].iteritems(), key=operator.itemgetter(0)) self.assertEquals(sorted_x, sorted_y) pass def _create_empty_options_mock(self): options = MagicMock() options.ldap_enabled = None options.ldap_enabled_ambari = None options.ldap_manage_services = None options.ldap_enabled_services = None options.ldap_url = None options.ldap_primary_host = None options.ldap_primary_port = None options.ldap_secondary_url = None options.ldap_secondary_host = None options.ldap_secondary_port = None options.ldap_ssl = None options.ldap_user_class = None options.ldap_user_attr = None options.ldap_user_group_member_attr = None options.ldap_group_class = None options.ldap_group_attr = None options.ldap_member_attr = None options.ldap_dn = None options.ldap_base_dn = None options.ldap_manager_dn = None options.ldap_manager_password = None options.ldap_save_settings = None options.ldap_referral = None options.ldap_bind_anonym = None options.ldap_force_setup = None options.ambari_admin_username = None options.ambari_admin_password = None options.ldap_sync_admin_name = None options.ldap_sync_username_collisions_behavior = None options.ldap_sync_disable_endpoint_identification = None options.ldap_force_lowercase_usernames = None options.ldap_pagination_enabled = None options.ldap_sync_admin_password = None options.custom_trust_store = None options.trust_store_type = None options.trust_store_path = None options.trust_store_password = None options.security_option = None options.api_ssl = None options.api_ssl_port = None options.import_cert_path = None options.import_cert_alias = None options.pem_password = None options.import_key_path = None options.master_key = None options.master_key_persist = None options.jaas_principal = None options.jaas_keytab = None return options

48.780781

203

0.731409

3,961

32,488

5.540773

0.083817

0.061785

0.076685

0.131225

0.806033

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0.751948

0.740238

0.722696

0.715815

0

0.002848

0.189516

32,488

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null

0

1

0

7

64f2e2b530af5be77d35403bbf827b176b5a071a

2,297

py

Python

Chinese_zodiac.py

2019-fall-csc-226/a01-breaking-bad-iransi_a01

720a648e9068af9e1202893942e311163bd0e1c1

[ "MIT" ]

null

Chinese_zodiac.py

2019-fall-csc-226/a01-breaking-bad-iransi_a01

720a648e9068af9e1202893942e311163bd0e1c1

[ "MIT" ]

null

Chinese_zodiac.py

2019-fall-csc-226/a01-breaking-bad-iransi_a01

720a648e9068af9e1202893942e311163bd0e1c1

[ "MIT" ]

null

birthyear = int(input("what year were you born? {2000-2011}")) if birthyear == 2000 : print("you're a fire breathing dragon") elif birthyear == 2001: print("sssss you're a snake ") if birthyear == 2002: print("Haaayyy. Get it? cause your a horse :)") if birthyear == 2003 : print("you are the GOAT") elif birthyear == 2004 : print("what did one monkey say to another? 'I was born in 2004'") if birthyear == 2005 : print("why did the turkey cross the road? " "To prove he wasn't chicken and " "neither are you because you're a rooster!") elif birthyear == 2006 : print("woof woof you're a dog") if birthyear == 2007: print("oink oink you're a pig") elif birthyear == 2008 : print("pitty pat pat you are a rat") if birthyear == 2009 : print("you are an ox") elif birthyear == 2010 : print("you're a tiger grrr") if birthyear == 2011 : print(" is your name Thumper? Because you're a rabbit!") elif birthyear < 2000 : print("you're too old get out out here !") elif birthyear > 2011 : print(" umm I said a year between 2000 and 2011. TRy Again") birthyear = int(input("now put in a friend's birth year {2000 and up}")) if birthyear == 2000 : print("you're a fire breathing dragon!!") elif birthyear == 2001: print("sssss you're a snake ") if birthyear == 2002: print("Haaayyy. Get it? cause you're a horse :)") if birthyear == 2003 : print("you are the GOAT") elif birthyear == 2004 : print("what did one monkey say to another?") print( '"I was born in 2004"') if birthyear == 2005 : print("why did the turkey cross the road? to prove he wasn't chicken and neither are you you're a rooster") elif birthyear == 2006 : print("woof woof you're a dog") if birthyear == 2007: print("oink oink you're a pig") elif birthyear == 2008 : print("pitty pat pat you are a rat!") if birthyear == 2009 : print("you are an ox") elif birthyear == 2010 : print("a tiger grrr") if birthyear == 2011 : print(" is your name Thumper? Because you're a rabbit!") elif birthyear < 2000 : print("you're too old get out of here !") elif birthyear > 2011 : print(" too young ! try again with someone who was born between 2000 and 2011")

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2,297

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null

0

1

0

7

64f5cef49fb1fe4a41de9751140db69b63b91e0f

212

py

Python

scripts/library/__init__.py

geozeke/ubuntu

49b7649b4306e6f3eb39c5dd9419cddc5c10d077

[ "MIT" ]

null

scripts/library/__init__.py

geozeke/ubuntu

49b7649b4306e6f3eb39c5dd9419cddc5c10d077

[ "MIT" ]

77

2020-07-08T18:52:48.000Z

2022-01-21T20:13:31.000Z

scripts/library/__init__.py

geozeke/ubuntu

49b7649b4306e6f3eb39c5dd9419cddc5c10d077

[ "MIT" ]

null

from .classes import Environment from .utilities import clear from .utilities import runOneCommand from .utilities import runManyArguments from .utilities import minPythonVersion from .utilities import copyFiles

30.285714

39

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24

212

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212

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0.968085

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

64f6cfdf3012940080f0da657680c3dfea60fa6b

17,679

py

Python

AutomatedTesting/Gem/PythonTests/largeworlds/dyn_veg/TestSuite_Periodic.py

LB-JakubSkorupka/o3de

e224fc2ee5ec2a12e75a10acae268b7b38ae3a32

[ "Apache-2.0", "MIT" ]

11

2021-07-08T09:58:26.000Z

2022-03-17T17:59:26.000Z

AutomatedTesting/Gem/PythonTests/largeworlds/dyn_veg/TestSuite_Periodic.py

LB-JakubSkorupka/o3de

e224fc2ee5ec2a12e75a10acae268b7b38ae3a32

[ "Apache-2.0", "MIT" ]

29

2021-07-06T19:33:52.000Z

2022-03-22T10:27:49.000Z

AutomatedTesting/Gem/PythonTests/largeworlds/dyn_veg/TestSuite_Periodic.py

LB-JakubSkorupka/o3de

e224fc2ee5ec2a12e75a10acae268b7b38ae3a32

[ "Apache-2.0", "MIT" ]

4

2021-07-06T19:24:43.000Z

2022-03-31T12:42:27.000Z

""" Copyright (c) Contributors to the Open 3D Engine Project. For complete copyright and license terms please see the LICENSE at the root of this distribution. SPDX-License-Identifier: Apache-2.0 OR MIT """ import os import pytest import sys import ly_test_tools.environment.waiter as waiter import ly_test_tools.environment.file_system as file_system import editor_python_test_tools.hydra_test_utils as hydra from ly_remote_console.remote_console_commands import RemoteConsole as RemoteConsole sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/../../automatedtesting_shared') from base import TestAutomationBase @pytest.fixture def remove_test_slice(request, workspace, project): file_system.delete([os.path.join(workspace.paths.engine_root(), project, "slices", "TestSlice_1.slice")], True, True) file_system.delete([os.path.join(workspace.paths.engine_root(), project, "slices", "TestSlice_2.slice")], True, True) def teardown(): file_system.delete([os.path.join(workspace.paths.engine_root(), project, "slices", "TestSlice_1.slice")], True, True) file_system.delete([os.path.join(workspace.paths.engine_root(), project, "slices", "TestSlice_2.slice")], True, True) request.addfinalizer(teardown) @pytest.fixture def remote_console_instance(request): console = RemoteConsole() def teardown(): if console.connected: console.stop() request.addfinalizer(teardown) return console @pytest.mark.SUITE_periodic @pytest.mark.parametrize("launcher_platform", ['windows_editor']) @pytest.mark.parametrize("project", ["AutomatedTesting"]) class TestAutomation(TestAutomationBase): def test_AltitudeFilter_ComponentAndOverrides_InstancesPlantAtSpecifiedAltitude(self, request, workspace, editor, launcher_platform): from .EditorScripts import AltitudeFilter_ComponentAndOverrides_InstancesPlantAtSpecifiedAltitude as test_module self._run_test(request, workspace, editor, test_module) def test_AltitudeFilter_ShapeSample_InstancesPlantAtSpecifiedAltitude(self, request, workspace, editor, launcher_platform): from .EditorScripts import AltitudeFilter_ShapeSample_InstancesPlantAtSpecifiedAltitude as test_module self._run_test(request, workspace, editor, test_module) def test_AltitudeFilter_FilterStageToggle(self, request, workspace, editor, launcher_platform): from .EditorScripts import AltitudeFilter_FilterStageToggle as test_module self._run_test(request, workspace, editor, test_module) def test_SpawnerSlices_SliceCreationAndVisibilityToggleWorks(self, request, workspace, editor, remove_test_slice, launcher_platform): from .EditorScripts import SpawnerSlices_SliceCreationAndVisibilityToggleWorks as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_AssetListCombiner_CombinedDescriptorsExpressInConfiguredArea(self, request, workspace, editor, launcher_platform): from .EditorScripts import AssetListCombiner_CombinedDescriptorsExpressInConfiguredArea as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_AssetWeightSelector_InstancesExpressBasedOnWeight(self, request, workspace, editor, launcher_platform): from .EditorScripts import AssetWeightSelector_InstancesExpressBasedOnWeight as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.xfail(reason="https://github.com/o3de/o3de/issues/4155") def test_DistanceBetweenFilter_InstancesPlantAtSpecifiedRadius(self, request, workspace, editor, launcher_platform): from .EditorScripts import DistanceBetweenFilter_InstancesPlantAtSpecifiedRadius as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.xfail(reason="https://github.com/o3de/o3de/issues/4155") def test_DistanceBetweenFilterOverrides_InstancesPlantAtSpecifiedRadius(self, request, workspace, editor, launcher_platform): from .EditorScripts import DistanceBetweenFilterOverrides_InstancesPlantAtSpecifiedRadius as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SurfaceDataRefreshes_RemainsStable(self, request, workspace, editor, launcher_platform): from .EditorScripts import SurfaceDataRefreshes_RemainsStable as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_VegetationInstances_DespawnWhenOutOfRange(self, request, workspace, editor, launcher_platform): from .EditorScripts import VegetationInstances_DespawnWhenOutOfRange as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_InstanceSpawnerPriority_LayerAndSubPriority_HigherValuesPlantOverLower(self, request, workspace, editor, launcher_platform): from .EditorScripts import InstanceSpawnerPriority_LayerAndSubPriority as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_LayerBlocker_InstancesBlockedInConfiguredArea(self, request, workspace, editor, launcher_platform): from .EditorScripts import LayerBlocker_InstancesBlockedInConfiguredArea as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_LayerSpawner_InheritBehaviorFlag(self, request, workspace, editor, launcher_platform): from .EditorScripts import LayerSpawner_InheritBehaviorFlag as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_LayerSpawner_InstancesPlantInAllSupportedShapes(self, request, workspace, editor, launcher_platform): from .EditorScripts import LayerSpawner_InstancesPlantInAllSupportedShapes as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_LayerSpawner_FilterStageToggle(self, request, workspace, editor, launcher_platform): from .EditorScripts import LayerSpawner_FilterStageToggle as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.xfail(reason="https://github.com/o3de/o3de/issues/2038") def test_LayerSpawner_InstancesRefreshUsingCorrectViewportCamera(self, request, workspace, editor, launcher_platform): from .EditorScripts import LayerSpawner_InstancesRefreshUsingCorrectViewportCamera as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_MeshBlocker_InstancesBlockedByMesh(self, request, workspace, editor, launcher_platform): from .EditorScripts import MeshBlocker_InstancesBlockedByMesh as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_MeshBlocker_InstancesBlockedByMeshHeightTuning(self, request, workspace, editor, launcher_platform): from .EditorScripts import MeshBlocker_InstancesBlockedByMeshHeightTuning as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_MeshSurfaceTagEmitter_DependentOnMeshComponent(self, request, workspace, editor, launcher_platform): from .EditorScripts import MeshSurfaceTagEmitter_DependentOnMeshComponent as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_MeshSurfaceTagEmitter_SurfaceTagsAddRemoveSuccessfully(self, request, workspace, editor, launcher_platform): from .EditorScripts import MeshSurfaceTagEmitter_SurfaceTagsAddRemoveSuccessfully as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_PhysXColliderSurfaceTagEmitter_E2E_Editor(self, request, workspace, editor, launcher_platform): from .EditorScripts import PhysXColliderSurfaceTagEmitter_E2E_Editor as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_PositionModifier_ComponentAndOverrides_InstancesPlantAtSpecifiedOffsets(self, request, workspace, editor, launcher_platform): from .EditorScripts import PositionModifier_ComponentAndOverrides_InstancesPlantAtSpecifiedOffsets as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_PositionModifier_AutoSnapToSurfaceWorks(self, request, workspace, editor, launcher_platform): from .EditorScripts import PositionModifier_AutoSnapToSurfaceWorks as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_RotationModifier_InstancesRotateWithinRange(self, request, workspace, editor, launcher_platform): from .EditorScripts import RotationModifier_InstancesRotateWithinRange as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_RotationModifierOverrides_InstancesRotateWithinRange(self, request, workspace, editor, launcher_platform): from .EditorScripts import RotationModifierOverrides_InstancesRotateWithinRange as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_ScaleModifier_InstancesProperlyScale(self, request, workspace, editor, launcher_platform): from .EditorScripts import ScaleModifier_InstancesProperlyScale as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_ScaleModifierOverrides_InstancesProperlyScale(self, request, workspace, editor, launcher_platform): from .EditorScripts import ScaleModifierOverrides_InstancesProperlyScale as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_ShapeIntersectionFilter_InstancesPlantInAssignedShape(self, request, workspace, editor, launcher_platform): from .EditorScripts import ShapeIntersectionFilter_InstancesPlantInAssignedShape as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_ShapeIntersectionFilter_FilterStageToggle(self, request, workspace, editor, launcher_platform): from .EditorScripts import ShapeIntersectionFilter_FilterStageToggle as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SlopeAlignmentModifier_InstanceSurfaceAlignment(self, request, workspace, editor, launcher_platform): from .EditorScripts import SlopeAlignmentModifier_InstanceSurfaceAlignment as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SlopeAlignmentModifierOverrides_InstanceSurfaceAlignment(self, request, workspace, editor, launcher_platform): from .EditorScripts import SlopeAlignmentModifierOverrides_InstanceSurfaceAlignment as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SurfaceMaskFilter_BasicSurfaceTagCreation(self, request, workspace, editor, launcher_platform): from .EditorScripts import SurfaceMaskFilter_BasicSurfaceTagCreation as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SurfaceMaskFilter_ExclusiveSurfaceTags_Function(self, request, workspace, editor, launcher_platform): from .EditorScripts import SurfaceMaskFilter_ExclusionList as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SurfaceMaskFilter_InclusiveSurfaceTags_Function(self, request, workspace, editor, launcher_platform): from .EditorScripts import SurfaceMaskFilter_InclusionList as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SurfaceMaskFilterOverrides_MultipleDescriptorOverridesPlantAsExpected(self, request, workspace, editor, launcher_platform): from .EditorScripts import SurfaceMaskFilterOverrides_MultipleDescriptorOverridesPlantAsExpected as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SystemSettings_SectorPointDensity(self, request, workspace, editor, launcher_platform): from .EditorScripts import SystemSettings_SectorPointDensity as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SystemSettings_SectorSize(self, request, workspace, editor, launcher_platform): from .EditorScripts import SystemSettings_SectorSize as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) def test_SlopeFilter_ComponentAndOverrides_InstancesPlantOnValidSlopes(self, request, workspace, editor, launcher_platform): from .EditorScripts import SlopeFilter_ComponentAndOverrides_InstancesPlantOnValidSlope as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.SUITE_periodic @pytest.mark.parametrize("project", ["AutomatedTesting"]) @pytest.mark.parametrize("level", ["tmp_level"]) class TestAutomationE2E(TestAutomationBase): # The following tests must run in order, please do not move tests out of order @pytest.mark.parametrize("launcher_platform", ['windows_editor']) def test_DynamicSliceInstanceSpawner_Embedded_E2E_Editor(self, request, workspace, project, level, editor, launcher_platform): # Ensure our test level does not already exist file_system.delete([os.path.join(workspace.paths.engine_root(), project, "Levels", level)], True, True) from .EditorScripts import DynamicSliceInstanceSpawner_Embedded_E2E as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.parametrize("launcher_platform", ['windows']) def test_DynamicSliceInstanceSpawner_Embedded_E2E_Launcher(self, workspace, launcher, level, remote_console_instance, project, launcher_platform): expected_lines = [ "Instances found in area = 400" ] hydra.launch_and_validate_results_launcher(launcher, level, remote_console_instance, expected_lines, launch_ap=False) # Cleanup our temp level file_system.delete([os.path.join(workspace.paths.engine_root(), project, "Levels", level)], True, True) @pytest.mark.parametrize("launcher_platform", ['windows_editor']) def test_DynamicSliceInstanceSpawner_External_E2E_Editor(self, request, workspace, project, level, editor, launcher_platform): # Ensure our test level does not already exist file_system.delete([os.path.join(workspace.paths.engine_root(), project, "Levels", level)], True, True) from .EditorScripts import DynamicSliceInstanceSpawner_External_E2E as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.parametrize("launcher_platform", ['windows']) def test_DynamicSliceInstanceSpawner_External_E2E_Launcher(self, workspace, launcher, level, remote_console_instance, project, launcher_platform): expected_lines = [ "Instances found in area = 400" ] hydra.launch_and_validate_results_launcher(launcher, level, remote_console_instance, expected_lines, launch_ap=False) # Cleanup our temp level file_system.delete([os.path.join(workspace.paths.engine_root(), project, "Levels", level)], True, True) @pytest.mark.parametrize("launcher_platform", ['windows_editor']) def test_LayerBlender_E2E_Editor(self, request, workspace, project, level, editor, launcher_platform): # Ensure our test level does not already exist file_system.delete([os.path.join(workspace.paths.engine_root(), project, "Levels", level)], True, True) from .EditorScripts import LayerBlender_E2E_Editor as test_module self._run_test(request, workspace, editor, test_module, enable_prefab_system=False) @pytest.mark.parametrize("launcher_platform", ['windows']) @pytest.mark.xfail(reason="https://github.com/o3de/o3de/issues/4170") def test_LayerBlender_E2E_Launcher(self, workspace, launcher, level, remote_console_instance, project, launcher_platform): launcher.args.extend(["-rhi=Null"]) launcher.start(launch_ap=False) assert launcher.is_alive(), "Launcher failed to start" # Wait for test script to quit the launcher. If wait_for returns exc, test was not successful waiter.wait_for(lambda: not launcher.is_alive(), timeout=300) # Verify launcher quit successfully and did not crash ret_code = launcher.get_returncode() assert ret_code == 0, "Test failed. See Game.log for details" # Cleanup our temp level file_system.delete([os.path.join(workspace.paths.engine_root(), project, "Levels", level)], True, True)

62.25

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0.783755

1,868

17,679

7.131156

0.123662

0.099692

0.130471

0.049246

0.736131

0.720892

0.712859

0.668944

0

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0.14571

17,679

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62.469965

0.878501

0.035353

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0

null

0

1

0

1

0

7

8f1dda3eb63e47622769b82f7f990a3303f3a4ae

2,514

py

Python

ui/Pytest/test_LineEditMinimumMaximumController.py

MoisesHenr/OCEAN

e99c853893adc89652794ace62fcc8ffa78aa7ac

[ "MIT" ]

15

2021-06-15T13:48:03.000Z

2022-01-26T13:51:46.000Z

ui/Pytest/test_LineEditMinimumMaximumController.py

MoisesHenr/OCEAN

e99c853893adc89652794ace62fcc8ffa78aa7ac

[ "MIT" ]

1

2021-07-04T02:58:29.000Z

ui/Pytest/test_LineEditMinimumMaximumController.py

MoisesHenr/OCEAN

e99c853893adc89652794ace62fcc8ffa78aa7ac

[ "MIT" ]

2

2021-06-21T20:44:01.000Z

2021-06-23T11:10:56.000Z

# Author: Moises Henrique Pereira # this class handle the functions tests of controller of the component of the numerical features import pytest import sys from PyQt5 import QtWidgets from ui.mainTest import StaticObjects @pytest.mark.parametrize('featureName', [1, 2.9, False, ('t1', 't2'), None]) def test_CILEMMC_initializeView_wrong_type_featureName_parameter(featureName): with pytest.raises(AssertionError): app = QtWidgets.QApplication(sys.argv) counterfactualInterfaceLineEditMinimumMaximumController = StaticObjects.staticCounterfactualInterfaceLineEditMinimumMaximumController() counterfactualInterfaceLineEditMinimumMaximumController.initializeView(featureName, 0, 1) def test_CILEMMC_initializeView_none_min_parameter(): with pytest.raises(AssertionError): app = QtWidgets.QApplication(sys.argv) counterfactualInterfaceLineEditMinimumMaximumController = StaticObjects.staticCounterfactualInterfaceLineEditMinimumMaximumController() counterfactualInterfaceLineEditMinimumMaximumController.initializeView('featureName', None, 1) def test_CILEMMC_initializeView_none_max_parameter(): with pytest.raises(AssertionError): app = QtWidgets.QApplication(sys.argv) counterfactualInterfaceLineEditMinimumMaximumController = StaticObjects.staticCounterfactualInterfaceLineEditMinimumMaximumController() counterfactualInterfaceLineEditMinimumMaximumController.initializeView('featureName', 0, None) def test_CILEMMC_initializeView_right_parameters(): app = QtWidgets.QApplication(sys.argv) counterfactualInterfaceLineEditMinimumMaximumController = StaticObjects.staticCounterfactualInterfaceLineEditMinimumMaximumController() counterfactualInterfaceLineEditMinimumMaximumController.initializeView('featureName', 0, 1) def test_CILEMMC_setSelectedValue_none_parameter(): with pytest.raises(AssertionError): app = QtWidgets.QApplication(sys.argv) counterfactualInterfaceLineEditMinimumMaximumController = StaticObjects.staticCounterfactualInterfaceLineEditMinimumMaximumController() counterfactualInterfaceLineEditMinimumMaximumController.setSelectedValue(None) def test_CILEMMC_setSelectedValue_right_parameter(): app = QtWidgets.QApplication(sys.argv) counterfactualInterfaceLineEditMinimumMaximumController = StaticObjects.staticCounterfactualInterfaceLineEditMinimumMaximumController() counterfactualInterfaceLineEditMinimumMaximumController.setSelectedValue(0.5)

55.866667

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0.838902

175

2,514

11.891429

0.32

0.020183

0.040365

0.077847

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0

0.006206

0.102625

2,514

45

144

55.866667

0.916223

0.050119

0

0.484848

0

0.020134

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1

0.181818

false

0

0.121212

0

0.30303

0

1

null

0

1

0

null

0

7

8f31782a6012b73d5ada6e6de802c8e5d232912b

33,930

py

Python

mapmint-services/datastores/postgis/pgConnection.py

fenilgmehta/mapmint

7c28c42dbe9b17b11f5f6f080fd2c397f4f6937f

[ "MIT" ]

null

mapmint-services/datastores/postgis/pgConnection.py

fenilgmehta/mapmint

7c28c42dbe9b17b11f5f6f080fd2c397f4f6937f

[ "MIT" ]

2

2019-03-31T01:11:06.000Z

2020-03-15T13:43:16.000Z

mapmint-services/datastores/postgis/pgConnection.py

fenilgmehta/mapmint

7c28c42dbe9b17b11f5f6f080fd2c397f4f6937f

[ "MIT" ]

null

import psycopg2 import lxml # import libxslt from lxml import etree import osgeo.ogr import sys import zoo import json try: from manage_users.manage_users import mm_md5 except: from manage_users import mm_md5 class pgConnection: def __init__(self, conf, dbfile): self.dbfile = dbfile self.conf = conf def parseConf(self): #libxml2.initParser() #doc = libxml2.parseFile(self.conf["main"]["dataPath"] + "/PostGIS/" + self.dbfile + ".xml") doc = etree.parse(self.conf["main"]["dataPath"] + "/PostGIS/" + self.dbfile + ".xml") #styledoc = libxml2.parseFile(self.conf["main"]["dataPath"] + "/PostGIS/conn.xsl") #style = etree.XSLT(styledoc) styledoc = etree.parse(self.conf["main"]["dataPath"] + "/PostGIS/conn.xsl") style = etree.XSLT(styledoc) res = style(doc) self.db_string = str(res).replace("PG: ", "") def connect(self): try: self.conn = psycopg2.connect(self.db_string) self.cur = self.conn.cursor() return True except Exception as e: self.conf["lenv"]["message"] = "Unable to connect: " + str(e) return False def execute(self, req): try: self.ex = self.cur.execute(req) if req.count("SELECT") > 0 or req.count("select") > 0: return self.cur.fetchall() else: return True except Exception as e: self.conf["lenv"]["message"] = "Unable to execute " + req.encode('utf-8') + " due to: " + str(e) # print("Unable to execute "+req+str(e), file=sys.stderr) return False def listSchemas(conf, inputs, outputs): print(inputs["dataStore"]["value"], file=sys.stderr) db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() if db.connect(): res = db.execute( "select nspname as schema from pg_namespace WHERE nspname NOT LIKE 'information_schema' AND nspname NOT LIKE 'pg_%' ORDER BY nspname") if res: outputs["Result"]["value"] = json.dumps(res) return zoo.SERVICE_SUCCEEDED else: print("Unable to connect", file=sys.stderr) return zoo.SERVICE_FAILED def listTables(conf, inputs, outputs): import authenticate.service as auth if not (auth.is_ftable(inputs["schema"]["value"])): conf["lenv"]["message"] = zoo._("Unable to identify your parameter as table or field name") return zoo.SERVICE_FAILED db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() if db.connect(): req = "select schemaname||'.'||tablename as tablename, tablename as display from pg_tables WHERE schemaname NOT LIKE 'information_schema' AND schemaname NOT LIKE 'pg_%' AND tablename NOT LIKE 'spatial_ref_sys' AND tablename NOT LIKE 'geometry_columns' " if "schema" in inputs: req += "AND schemaname='" + inputs["schema"]["value"] + "'" req += " ORDER BY schemaname||'.'||tablename" res = db.execute(req) outputs["Result"]["value"] = json.dumps(res) return zoo.SERVICE_SUCCEEDED # return zoo.SERVICE_SUCCEEDED else: print("Unable to connect", file=sys.stderr) return zoo.SERVICE_FAILED def listTablesAndViews(conf, inputs, outputs): db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() if db.connect(): req = "select schemaname||'.'||tablename as tablename, tablename as display from pg_tables WHERE schemaname NOT LIKE 'information_schema' AND schemaname NOT LIKE 'pg_%' AND tablename NOT LIKE 'tmp%' AND tablename NOT LIKE 'spatial_ref_sys' AND tablename NOT LIKE 'geometry_columns' " req1 = "select schemaname||'.'||viewname as tablename, viewname as display from pg_views WHERE schemaname NOT LIKE 'information_schema' AND schemaname NOT LIKE 'pg_%' " if "schema" in inputs: req += " AND schemaname='" + inputs["schema"]["value"] + "'" req1 += " AND schemaname='" + inputs["schema"]["value"] + "'" res = db.execute("SELECT * from (" + req + ") as foo UNION (" + req1 + ") ORDER BY display") if res: outputs["Result"]["value"] = json.dumps(res) return zoo.SERVICE_SUCCEEDED else: print("Unable to connect", file=sys.stderr) return zoo.SERVICE_FAILED def getDesc(cur, table): tmp = table.split('.') if len(tmp) == 1: tmp1 = tmp[0] tmp = ["public", tmp1]; req = "SELECT b.relname as t FROM pg_inherits, pg_class a, pg_class b WHERE inhrelid=a.oid AND inhparent=b.oid AND a.relname = '" + \ tmp[1] + "' AND a.relnamespace=(select oid from pg_namespace where nspname='" + tmp[0] + "')" res0 = cur.execute(req) res = cur.fetchall() if res != False and len(res) > 0: return "SELECT * FROM (SELECT DISTINCT ON (\"Pos\",\"Field\") * FROM ((SELECT DISTINCT on (\"Pos\") \"Pos\"-1 as \"Pos\",\"Field\",\"Type\",\"Key\", \"Ref\", \"RefCol\", \"RefCols\",array_upper(\"RefCols\",1) from (SELECT attnum AS \"Pos\", attname AS \"Field\",CASE WHEN atttypmod >0 THEN b.typname || '(' || atttypmod-4 || ')' ELSE b.typname END AS \"Type\" FROM pg_catalog.pg_attribute a, pg_catalog.pg_type b WHERE a.atttypid=b.oid AND a.attrelid = (SELECT pg_class.oid FROM pg_class, pg_namespace WHERE relname='" + \ res[0][0] + "' AND pg_namespace.oid=relnamespace AND nspname='" + tmp[ 0] + "') AND a.attnum > 0 AND NOT a.attisdropped ORDER BY attnum) a LEFT JOIN (SELECT conkey,c.conname AS constraint_name, CASE c.contype WHEN 'c' THEN 'CHECK' WHEN 'f' THEN 'FOR' WHEN 'p' THEN 'PRI' WHEN 'u' THEN 'UNIQUE' END AS \"Key\", t3.nspname||'.'||t2.relname AS \"Ref\", (SELECT attname from pg_catalog.pg_attribute WHERE attrelid=c.confrelid AND confkey[1] = attnum) AS \"RefCol\" FROM pg_constraint c LEFT JOIN pg_class t ON c.conrelid = t.oid LEFT JOIN pg_class t2 ON c.confrelid = t2.oid LEFT JOIN pg_namespace t3 ON t2.relnamespace=t3.oid WHERE t.relname = '" + \ res[0][ 0] + "') b ON get_nb_of(conkey,\"Pos\")>0 LEFT JOIN (SELECT DISTINCT ON (at2.attnum) c.*, at2.attnum AS \"myid\", ARRAY(SELECT attname AS \"RefCol\" FROM pg_constraint AS c, pg_catalog.pg_attribute, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '" + \ res[0][ 0] + "' AND attrelid=confrelid AND get_nb_of(confkey,attnum) > 0) AS \"RefCols\", at2.attnum, at2.attname AS atn, get_index_of(conkey,at2.attnum) AS \"RealOrigColNum\", at1.attnum, at1.attname, get_index_of(confkey,at1.attnum) AS \"RealRefColNum\", t.relname as orig, t2.relname as ref FROM pg_constraint AS c, pg_catalog.pg_attribute AS at1, pg_catalog.pg_attribute AS at2, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '" + \ res[0][ 0] + "' AND at1.attrelid=confrelid AND get_nb_of(conkey,at2.attnum) > 0 AND get_nb_of(confkey,at1.attnum) > 0 AND t.relname='" + \ res[0][ 0] + "' AND at2.attrelid=t.oid) AS foreigns ON foreigns.myid=a.\"Pos\") UNION (SELECT DISTINCT on (\"Pos\") \"Pos\"-1 as \"Pos\",\"Field\",\"Type\",\"Key\", \"Ref\", \"RefCol\", \"RefCols\",array_upper(\"RefCols\",1) from (SELECT attnum AS \"Pos\", attname AS \"Field\",CASE WHEN atttypmod >0 THEN b.typname || '(' || atttypmod-4 || ')' ELSE b.typname END AS \"Type\" FROM pg_catalog.pg_attribute a, pg_catalog.pg_type b WHERE a.atttypid=b.oid AND a.attrelid = (SELECT pg_class.oid FROM pg_class, pg_namespace WHERE relname='" + \ tmp[1] + "' AND pg_namespace.oid=relnamespace AND nspname='" + tmp[ 0] + "') AND a.attnum > 0 AND NOT a.attisdropped ORDER BY attnum) a LEFT JOIN (SELECT conkey,c.conname AS constraint_name, CASE c.contype WHEN 'c' THEN 'CHECK' WHEN 'f' THEN 'FOR' WHEN 'p' THEN 'PRI' WHEN 'u' THEN 'UNIQUE' END AS \"Key\", t3.nspname||'.'||t2.relname AS \"Ref\", (SELECT attname from pg_catalog.pg_attribute WHERE attrelid=c.confrelid AND confkey[1] = attnum) AS \"RefCol\" FROM pg_constraint c LEFT JOIN pg_class t ON c.conrelid = t.oid LEFT JOIN pg_class t2 ON c.confrelid = t2.oid LEFT JOIN pg_namespace t3 ON t2.relnamespace=t3.oid WHERE t.relname = '" + \ tmp[1] + "' and t.relnamespace=(select oid from pg_namespace where nspname='" + tmp[ 0] + "') ) b ON get_nb_of(conkey,\"Pos\")>0 LEFT JOIN (SELECT DISTINCT ON (at2.attnum) c.*, at2.attnum AS \"myid\", ARRAY(SELECT attname AS \"RefCol\" FROM pg_constraint AS c, pg_catalog.pg_attribute, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '" + \ tmp[ 1] + "' AND attrelid=confrelid AND get_nb_of(confkey,attnum) > 0) AS \"RefCols\", at2.attnum, at2.attname AS atn, get_index_of(conkey,at2.attnum) AS \"RealOrigColNum\", at1.attnum, at1.attname, get_index_of(confkey,at1.attnum) AS \"RealRefColNum\", t.relname as orig, t2.relname as ref FROM pg_constraint AS c, pg_catalog.pg_attribute AS at1, pg_catalog.pg_attribute AS at2, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '" + \ tmp[ 1] + "' AND at1.attrelid=confrelid AND get_nb_of(conkey,at2.attnum) > 0 AND get_nb_of(confkey,at1.attnum) > 0 AND t.relname='" + \ tmp[1] + "' and t.relnamespace=(select oid from pg_namespace where nspname='" + tmp[ 0] + "') AND at2.attrelid=t.oid) AS foreigns ON foreigns.myid=a.\"Pos\")) As foo) as foo1 ORDER BY \"Pos\",\"Key\"" else: # print("SELECT DISTINCT on (\"Pos\") \"Pos\"-1 as \"Pos\",\"Field\",\"Type\",\"Key\", \"Ref\", \"RefCol\", \"RefCols\",array_upper(\"RefCols\",1) from (SELECT attnum AS \"Pos\", attname AS \"Field\",CASE WHEN atttypmod >0 THEN b.typname || '(' || atttypmod-4 || ')' ELSE b.typname END AS \"Type\" FROM pg_catalog.pg_attribute a, pg_catalog.pg_type b WHERE a.atttypid=b.oid AND a.attrelid = (SELECT pg_class.oid FROM pg_class, pg_namespace WHERE relname='"+tmp[1]+"' AND pg_namespace.oid=relnamespace AND nspname='"+tmp[0]+"') AND a.attnum > 0 AND NOT a.attisdropped ORDER BY attnum) a LEFT JOIN (SELECT conkey,c.conname AS constraint_name, CASE c.contype WHEN 'c' THEN 'CHECK' WHEN 'f' THEN 'FOR' WHEN 'p' THEN 'PRI' WHEN 'u' THEN 'UNIQUE' END AS \"Key\", t3.nspname||'.'||t2.relname AS \"Ref\", (SELECT attname from pg_catalog.pg_attribute WHERE attrelid=c.confrelid AND confkey[1] = attnum) AS \"RefCol\" FROM pg_constraint c LEFT JOIN pg_class t ON c.conrelid = t.oid LEFT JOIN pg_class t2 ON c.confrelid = t2.oid LEFT JOIN pg_namespace t3 ON t2.relnamespace=t3.oid WHERE t.relname = '"+tmp[1]+"' and t.relnamespace=(select oid from pg_namespace where nspname='"+tmp[0]+"')) b ON get_nb_of(conkey,\"Pos\")>0 LEFT JOIN (SELECT DISTINCT ON (at2.attnum) c.*, at2.attnum AS \"myid\", ARRAY(SELECT attname AS \"RefCol\" FROM pg_constraint AS c, pg_catalog.pg_attribute, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '"+tmp[1]+"' AND attrelid=confrelid AND get_nb_of(confkey,attnum) > 0 and t.relnamespace=(select oid from pg_namespace where nspname='"+tmp[0]+"')) AS \"RefCols\", at2.attnum, at2.attname AS atn, get_index_of(conkey,at2.attnum) AS \"RealOrigColNum\", at1.attnum, at1.attname, get_index_of(confkey,at1.attnum) AS \"RealRefColNum\", t.relname as orig, t2.relname as ref FROM pg_constraint AS c, pg_catalog.pg_attribute AS at1, pg_catalog.pg_attribute AS at2, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '"+tmp[1]+"' AND at1.attrelid=confrelid AND get_nb_of(conkey,at2.attnum) > 0 AND get_nb_of(confkey,at1.attnum) > 0 AND t.relname='"+tmp[1]+"' AND at2.attrelid=t.oid and t.relnamespace=(select oid from pg_namespace where nspname='"+tmp[0]+"')) AS foreigns ON foreigns.myid=a.\"Pos\"", file=sys.stderr) return "SELECT DISTINCT on (\"Pos\") \"Pos\"-1 as \"Pos\",\"Field\",\"Type\",\"Key\", \"Ref\", \"RefCol\", \"RefCols\",array_upper(\"RefCols\",1) from (SELECT * FROM (SELECT attnum AS \"Pos\", attname AS \"Field\",CASE WHEN atttypmod >0 THEN b.typname || '(' || atttypmod-4 || ')' ELSE b.typname END AS \"Type\" FROM pg_catalog.pg_attribute a, pg_catalog.pg_type b WHERE a.atttypid=b.oid AND a.attrelid = (SELECT pg_class.oid FROM pg_class, pg_namespace WHERE relname='" + \ tmp[1] + "' AND pg_namespace.oid=relnamespace AND nspname='" + tmp[ 0] + "') AND a.attnum > 0 AND NOT a.attisdropped ORDER BY attnum) a LEFT JOIN (SELECT conkey,c.conname AS constraint_name, CASE c.contype WHEN 'c' THEN 'CHECK' WHEN 'f' THEN 'FOR' WHEN 'p' THEN 'PRI' WHEN 'u' THEN 'UNIQUE' END AS \"Key\", t3.nspname||'.'||t2.relname AS \"Ref\", (SELECT attname from pg_catalog.pg_attribute WHERE attrelid=c.confrelid AND confkey[1] = attnum) AS \"RefCol\" FROM pg_constraint c LEFT JOIN pg_class t ON c.conrelid = t.oid LEFT JOIN pg_class t2 ON c.confrelid = t2.oid LEFT JOIN pg_namespace t3 ON t2.relnamespace=t3.oid WHERE t.relname = '" + \ tmp[1] + "' and t.relnamespace=(select oid from pg_namespace where nspname='" + tmp[ 0] + "')) b ON get_nb_of(conkey,\"Pos\")>0 LEFT JOIN (SELECT DISTINCT ON (at2.attnum) c.*, at2.attnum AS \"myid\", ARRAY(SELECT attname AS \"RefCol\" FROM pg_constraint AS c, pg_catalog.pg_attribute, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '" + \ tmp[ 1] + "' AND attrelid=confrelid AND get_nb_of(confkey,attnum) > 0 and t.relnamespace=(select oid from pg_namespace where nspname='" + \ tmp[ 0] + "')) AS \"RefCols\", at2.attnum, at2.attname AS atn, get_index_of(conkey,at2.attnum) AS \"RealOrigColNum\", at1.attnum, at1.attname, get_index_of(confkey,at1.attnum) AS \"RealRefColNum\", t.relname as orig, t2.relname as ref FROM pg_constraint AS c, pg_catalog.pg_attribute AS at1, pg_catalog.pg_attribute AS at2, pg_class t, pg_class t2 WHERE c.conrelid = t.oid AND c.confrelid = t2.oid AND t.relname = '" + \ tmp[ 1] + "' AND at1.attrelid=confrelid AND get_nb_of(conkey,at2.attnum) > 0 AND get_nb_of(confkey,at1.attnum) > 0 AND t.relname='" + \ tmp[1] + "' AND at2.attrelid=t.oid and t.relnamespace=(select oid from pg_namespace where nspname='" + \ tmp[0] + "')) AS foreigns ON foreigns.myid=a.\"Pos\" order by \"Key\"='PRI' or \"Key\"='FOR' desc) As f" def getTableDescription(conf, inputs, outputs): import authenticate.service as auth # if not(auth.is_ftable(inputs["table"]["value"])): # conf["lenv"]["message"]=zoo._("Unable to identify your parameter as table or field name") # return zoo.SERVICE_FAILED db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() if db.connect(): tmp = inputs["table"]["value"].split('.') req = getDesc(db.cur, inputs["table"]["value"]) # print(req, file=sys.stderr) res = db.execute(req) if res != False and len(res) > 0: outputs["Result"]["value"] = json.dumps(res) return zoo.SERVICE_SUCCEEDED else: print("unable to run request " + req, file=sys.stderr) return zoo.SERVICE_FAILED else: print("Unable to connect", file=sys.stderr) return zoo.SERVICE_FAILED def getTableContent(conf, inputs, outputs): import authenticate.service as auth # if not(auth.is_ftable(inputs["table"]["value"])): # conf["lenv"]["message"]=zoo._("Unable to identify your parameter as table or field name") # return zoo.SERVICE_FAILED db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() getTableDescription(conf, inputs, outputs) tmp = eval(outputs["Result"]["value"].replace("null", "None")) pkey = 0 geom = [] files = [] fields = "" for i in range(0, len(tmp)): if tmp[i][3] == "PRI": pkey = tmp[i][0] if tmp[i][2] == "geometry": geom += [i] if tmp[i][2] == "bytea": files += [i] if tmp[i][3] == "FOR" and not ("force" in inputs): input1 = inputs otbl = inputs["table"]["value"] inputs["table"]["value"] = tmp[i][4] getTableDescription(conf, inputs, outputs) tmp2 = eval(outputs["Result"]["value"].replace("null", "None")) pkey1 = 0 for j in range(0, len(tmp2)): if tmp2[j][3] == "PRI": pkey1 = j break hasV = False for j in range(0, len(tmp2)): if not (hasV) and (tmp2[j][2].count("char") > 0 or tmp2[j][2].count("text") > 0): if fields != "": fields += "," hasV = True fields += "(SELECT " + tmp2[j][1] + " FROM " + tmp[i][4] + " as a WHERE a." + tmp2[pkey][ 1] + "=" + otbl + "." + tmp[i][1] + ")" if not (hasV): if fields != "": fields += "," fields += "(SELECT " + tmp2[0][1] + " FROM " + tmp[i][4] + " as a WHERE a." + tmp2[pkey][ 1] + "=" + otbl + "." + tmp[i][1] + ")" inputs["table"]["value"] = otbl else: if fields != "": fields += "," fields += tmp[i][1] if db.connect(): tmp1 = inputs["table"]["value"].split(".") tmp1[0] = '"' + tmp1[0] + '"' tmp1[1] = '"' + tmp1[1] + '"' inputs["table"]["value"] = (".").join(tmp1) req = "select count(*) from " + inputs["table"]["value"] if "clause" in inputs and inputs["clause"]["value"] != "NULL": req += " WHERE " + inputs["clause"]["value"] if "search" in inputs and inputs["search"]["value"] != "NULL" and inputs["search"]["value"] != "asc": req += " WHERE " print(req, file=sys.stderr) cnt = 0 print(req, file=sys.stderr) for i in range(0, len(tmp)): if cnt > 0: req += " OR " req += tmp[i][1] + "::varchar like '%" + inputs["search"]["value"] + "%'" cnt += 1 res = db.execute(req) if res != False: total = res[0][0] req = "select " if "cols" in inputs and inputs["cols"]["value"] != "NULL": req += inputs["cols"]["value"] else: req += fields req += " from " + inputs["table"]["value"] if "clause" in inputs and inputs["clause"]["value"] != "NULL": req += " WHERE " + inputs["clause"]["value"] if "search" in inputs and inputs["search"]["value"] != "NULL" and inputs["search"]["value"] != "asc": req += " WHERE " print(req, file=sys.stderr) cnt = 0 print(req, file=sys.stderr) for i in range(0, len(tmp)): if cnt > 0: req += " OR " req += tmp[i][1] + "::varchar like '%" + inputs["search"]["value"] + "%'" cnt += 1 if "sortname" in inputs and inputs["sortname"]["value"] != "NULL": req += " ORDER BY " + inputs["sortname"]["value"] + " " + inputs["sortorder"]["value"] if "limit" in inputs and inputs["limit"]["value"] != "NULL": if "page" in inputs and inputs["page"]["value"] != "": req += " OFFSET " + str((int(inputs["page"]["value"]) - 1) * int(inputs["limit"]["value"])) page = inputs["page"]["value"] req += " LIMIT " + inputs["limit"]["value"] else: page = 1 req += " LIMIT 10" print(req, file=sys.stderr) res = db.execute(req) if res != False: rows = [] for i in range(0, len(res)): res0 = [] for k in range(0, len(res[i])): try: tmp = str(res[i][k].decode('utf-8')) print(dir(tmp), file=sys.stderr) except Exception as e: print(e, file=sys.stderr) tmp = str(res[i][k]) res0 += [str(tmp)] if len(geom) > 0: for j in range(0, len(geom)): res0[geom[j]] = "GEOMETRY" if len(files) > 0: for j in range(0, len(files)): res0[files[j]] = "BINARY FILE" rows += [{"id": res[i][pkey], "cell": res0}] outputs["Result"]["value"] = json.dumps({"page": page, "total": total, "rows": rows}, ensure_ascii=False) return zoo.SERVICE_SUCCEEDED else: print("unable to run request", file=sys.stderr) return zoo.SERVICE_FAILED else: print("Unable to connect", file=sys.stderr) return zoo.SERVICE_FAILED def getTableContent1(conf, inputs, outputs): import authenticate.service as auth # if not(auth.is_ftable(inputs["table"]["value"])): # conf["lenv"]["message"]=zoo._("Unable to identify your parameter as table or field name") # return zoo.SERVICE_FAILED db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() getTableDescription(conf, inputs, outputs) tmp = eval(outputs["Result"]["value"].replace("null", "None")) pkey = 0 geom = [] files = [] fields = "" for i in range(0, len(tmp)): if tmp[i][3] == "PRI": pkey = tmp[i][0] if tmp[i][2] == "geometry": geom += [i] if tmp[i][2] == "bytea": files += [i] if tmp[i][3] == "FOR" and not ("force" in inputs): input1 = inputs otbl = inputs["table"]["value"] inputs["table"]["value"] = tmp[i][4] getTableDescription(conf, inputs, outputs) tmp2 = eval(outputs["Result"]["value"].replace("null", "None")) pkey1 = 0 for j in range(0, len(tmp2)): if tmp2[j][3] == "PRI": pkey1 = j break hasV = False for j in range(0, len(tmp2)): if not (hasV) and (tmp2[j][2].count("char") > 0 or tmp2[j][2].count("text") > 0): if fields != "": fields += "," hasV = True fields += "(SELECT " + tmp2[j][1] + " FROM " + tmp[i][4] + " as a WHERE a." + tmp2[pkey][ 1] + "=" + otbl + "." + tmp[i][1] + ")" if not (hasV): if fields != "": fields += "," fields += "(SELECT " + tmp2[0][1] + " FROM " + tmp[i][4] + " as a WHERE a." + tmp2[pkey][ 1] + "=" + otbl + "." + tmp[i][1] + ")" inputs["table"]["value"] = otbl else: if fields != "": fields += "," fields += tmp[i][1] if db.connect(): tmp1 = inputs["table"]["value"].split(".") tmp1[0] = '"' + tmp1[0] + '"' tmp1[1] = '"' + tmp1[1] + '"' inputs["table"]["value"] = (".").join(tmp1) req = "select count(*) from " + inputs["table"]["value"] if "clause" in inputs and inputs["clause"]["value"] != "NULL": req += " WHERE " + inputs["clause"]["value"] if "search" in inputs and inputs["search"]["value"] != "NULL" and inputs["search"]["value"] != "asc": req += " WHERE " print(req, file=sys.stderr) cnt = 0 print(req, file=sys.stderr) for i in range(0, len(tmp)): if cnt > 0: req += " OR " req += tmp[i][1] + "::varchar like '%" + inputs["search"]["value"] + "%'" cnt += 1 print(req, file=sys.stderr) res = db.execute(req) if res != False: total = res[0][0] req = "select " if "cols" in inputs and inputs["cols"]["value"] != "NULL": req += inputs["cols"]["value"] else: req += fields req += " from " + inputs["table"]["value"] if "clause" in inputs and inputs["clause"]["value"] != "NULL": req += " WHERE " + inputs["clause"]["value"] if "search" in inputs and inputs["search"]["value"] != "NULL" and inputs["search"]["value"] != "asc": req += " WHERE " print(req, file=sys.stderr) cnt = 0 print(req, file=sys.stderr) for i in range(0, len(tmp)): if cnt > 0: req += " OR " req += tmp[i][1] + "::varchar like '%" + inputs["search"]["value"] + "%'" cnt += 1 print(req, file=sys.stderr) if "sortname" in inputs and inputs["sortname"]["value"] != "NULL": req += " ORDER BY " + inputs["sortname"]["value"] + " " + inputs["sortorder"]["value"] if "limit" in inputs and inputs["limit"]["value"] != "NULL": if "page" in inputs and inputs["page"]["value"] != "": req += " OFFSET " + str((int(inputs["page"]["value"]) - 1) * int(inputs["limit"]["value"])) page = inputs["page"]["value"] req += " LIMIT " + inputs["limit"]["value"] else: page = 1 req += " LIMIT 10" print(req, file=sys.stderr) res = db.execute(req) if res != False: rows = [] for i in range(0, len(res)): res0 = [] for k in range(0, len(res[i])): try: tmp = str(res[i][k].decode('utf-8')) # print(dir(tmp), file=sys.stderr) except Exception as e: # print(e, file=sys.stderr) tmp = str(res[i][k]) res0 += [str(tmp)] if len(geom) > 0: for j in range(0, len(geom)): res0[geom[j]] = "GEOMETRY" if len(files) > 0: for j in range(0, len(files)): res0[files[j]] = "BINARY FILE" rows += [{"id": res[i][pkey], "cell": res0}] outputs["Result"]["value"] = json.dumps({"page": page, "total": total, "rows": rows}, ensure_ascii=False) return zoo.SERVICE_SUCCEEDED else: print("unable to run request", file=sys.stderr) return zoo.SERVICE_FAILED else: print("Unable to connect", file=sys.stderr) return zoo.SERVICE_FAILED def deleteTuple(conf, inputs, outputs): db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() if db.connect(): res = db.execute("DELETE FROM " + inputs["table"]["value"] + " WHERE " + inputs["clause"]["value"]) if res == False: conf = db.conf return zoo.SERVICE_FAILED else: db.conn.commit() outputs["Result"]["value"] = "Tuple deleted" return zoo.SERVICE_SUCCEEDED else: conf = db.conf return zoo.SERVICE_FAILED import psycopg2, json from psycopg2.extensions import * def editTuple(conf, inputs, outputs): # TODO: confirm assumption: inputs is a Python 3 dictionary object getTableDescription(conf, inputs, outputs) desc = eval(outputs["Result"]["value"].replace("null", "None")) tmp = json.loads(inputs["obj"]["value"]) if "clause" in inputs and inputs["clause"]["value"] != "NULL": req = "UPDATE " + inputs["table"]["value"] + " set " fields = "" tkeys = list(tmp.keys()) for i in tkeys: fd = None for k in desc: if k[1] == i: fd = k[2] if fd is not None: print(tmp, file=sys.stderr) print(fd, file=sys.stderr) td = testDesc(tmp[i], fd) if td is not None: if fields != "": fields += ", " fields += '"' + i + '"=' + td if "content" in inputs: if fields != "": fields += "," print(inputs["content"]["value"], file=sys.stderr) tmp1 = inputs["content"]["value"] fields += '"content"=%s' % adapt( inputs["content"]["value"].replace('<?xml version="1.0" encoding="utf-8"?>\n', '')) req += fields + " WHERE " + inputs["clause"]["value"] outputs["Result"]["value"] = "Tuple updated" else: req = "INSERT INTO " + inputs["table"]["value"] + " " fields = "(" values = "(" cnt = 0 for i in tmp: fd = None for k in desc: if k[1] == i: fd = k[2] td = testDesc(tmp[i], fd) if td is not None: if fields != "(": fields += "," if values != "(": values += "," fields += i values += td cnt += 1 if list(inputs.keys()).count("content") > 0: if fields != "(": fields += "," if values != "(": values += "," fields += "content" values += '%s' % adapt(inputs["content"]["value"].replace('<?xml version="1.0" encoding="utf-8"?>\n', '')) fields += ")" values += ")" req += fields + " VALUES " + values outputs["Result"]["value"] = "Tuple inserted" print(req.encode("utf-8"), file=sys.stderr) db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() if db.connect(): try: res = db.execute(req) if res == False: conf["lenv"]["message"] = db.conf["lenv"]["message"] return zoo.SERVICE_FAILED db.conn.commit() # print(res, file=sys.stderr) return zoo.SERVICE_SUCCEEDED except Exception as e: conf["lenv"]["message"] = "Unable to run the request " + str(e) return zoo.SERVICE_FAILED def testDesc(val, desc): if desc == "bool": if val == "t" or val: return "true" else: return "false" if desc.count("char") > 0 or desc.count("text") > 0: if desc.count("varchar(40)"): if val != 'NULL': return "'" + mm_md5(val) + "'" else: return None else: if val != 'NULL': tmp = adapt(val)#.encode('utf-8').decode('utf-8')) tmp.encoding = "utf-8" return str(tmp)#.decode('utf-8') else: return "NULL" else: if desc.count("date") > 0: tmp = val.split("/") return "'" + tmp[2] + "-" + tmp[1] + "-" + tmp[0] + "'" else: if desc.count("geometry") > 0: if val != 'NULL': return "'" + val + "'" else: return val else: return val def fetchType(conf, ftype): db = pgConnection(conf, conf["main"]["dbuserName"]) db.parseConf() if db.connect(): res = db.execute("SELECT code from mm_tables.ftypes where id=" + ftype) if res: return str(res[0][0]) return None def addColumn(conf, inputs, outputs): print(inputs["dataStore"]["value"], file=sys.stderr) db = pgConnection(conf, inputs["dataStore"]["value"]) db.parseConf() req = [] if db.connect(): if inputs["field_type"]["value"] != "18": req += ["ALTER TABLE quote_ident(" + inputs["table"]["value"] + ") ADD COLUMN " + inputs["field_name"][ "value"] + " " + fetchType(conf, inputs["field_type"]["value"])] outputs["Result"]["value"] = zoo._("Column added") else: tblInfo = inputs["table"]["value"].split(".") if len(tblInfo) == 1: tmp = tblInfo[0] tblInfo[0] = "public" tblInfo[1] = tmpl req += ["SELECT AddGeometryColumn('" + tblInfo[0] + "','" + tblInfo[ 1] + "','wkb_geometry',(select srid from spatial_ref_sys where auth_name||':'||auth_srid = '" + inputs["proj"]["value"] + "'),'" + inputs["geo_type"]["value"] + "',2)"] outputs["Result"]["value"] = zoo._("Geometry column added.") if list(inputs.keys()).count("geo_x") > 0 and list(inputs.keys()).count("geo_y") > 0: req += ["CREATE TRIGGER mm_tables_" + inputs["table"]["value"].replace(".", "_") + "_update_geom BEFORE UPDATE OR INSERT ON " + inputs["table"][ "value"] + " FOR EACH ROW EXECUTE PROCEDURE automatically_update_geom_property('" + inputs["geo_x"]["value"] + "','" + inputs["geo_y"]["value"] + "','" + inputs["proj"][ "value"] + "')"] outputs["Result"]["value"] += " " + zoo._("Trigger in place") print(req, file=sys.stderr) for i in range(0, len(req)): if not (db.execute(req[i])): return zoo.SERVICE_FAILED db.conn.commit() return zoo.SERVICE_SUCCEEDED else: conf["lenv"]["message"] = zoo._("Unable to connect") return zoo.SERVICE_FAILED

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py

Python

Zebrafish spinal locomotor circuit/Version 2/Beat_and_glide_with_sigmas.py

Bui-lab/Code

6ce5972a4bd0c059ab167522ab1d945f3b0f5707

[ "MIT" ]

null

Zebrafish spinal locomotor circuit/Version 2/Beat_and_glide_with_sigmas.py

Bui-lab/Code

6ce5972a4bd0c059ab167522ab1d945f3b0f5707

[ "MIT" ]

null

Zebrafish spinal locomotor circuit/Version 2/Beat_and_glide_with_sigmas.py

Bui-lab/Code

6ce5972a4bd0c059ab167522ab1d945f3b0f5707

[ "MIT" ]

2

2021-08-25T08:14:52.000Z

2021-11-29T12:56:17.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jul 3 15:47:19 2018 @author: Yann Roussel and Tuan Bui Editted by: Emine Topcu on Oct 2021 """ from random import gauss from Beat_and_glide import Beat_and_glide_base from Izhikevich_class import Izhikevich_9P, Leaky_Integrator class Beat_and_glide_with_sigmas(Beat_and_glide_base): sigmaD = 0 sigmaL = 0 sigmaP = 0 sigmaW = 0 def __init__ (self, stim0 = 2.89, sigma = 0, sigma_LR = 0.1, sigmaD = 0, sigmaL = 0, sigmaP = 0, sigmaW = 0, E_glu = 0, E_gly = -70, cv = 0.80, nMN = 15, ndI6 = 15, nV0v = 15, nV2a = 15, nV1 = 15, nMuscle = 15, R_str = 1.0): super().__init__(stim0, sigma, sigma_LR, E_glu, E_gly, cv, nMN, ndI6, nV0v, nV2a, nV1, nMuscle, R_str) self.sigmaD = sigmaD self.sigmaL = sigmaL self.sigmaP = sigmaP self.sigmaW = sigmaW def initNeurons(self): ## Declare Neuron Types self.L_MN = [ Izhikevich_9P(a = 0.5*gauss(1, self.sigmaP), b = 0.01*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 100*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -65*gauss(1, self.sigmaP), vt = -58*gauss(1, self.sigmaP), k = 0.5*gauss(1, self.sigmaP), Cm = 20*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.0+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nMN)] self.R_MN = [ Izhikevich_9P(a = 0.5*gauss(1, self.sigmaP), b = 0.01*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 100*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -65*gauss(1, self.sigmaP), vt = -58*gauss(1, self.sigmaP), k = 0.5*gauss(1, self.sigmaP), Cm = 20*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.0+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nMN)] self.L_dI6 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.ndI6)] self.R_dI6 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.ndI6)] self.L_V0v = [ Izhikevich_9P(a = 0.01*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 2*gauss(1, self.sigmaP), vmax = 8*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nV0v)] self.R_V0v = [ Izhikevich_9P(a = 0.01*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 2*gauss(1, self.sigmaP), vmax = 8*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nV0v)] self.L_V2a = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nV2a)] self.R_V2a = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 5.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nV2a)] self.L_V1 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 7.1+1.6*i*gauss(1, self.sigma), y = -1) for i in range(self.nV1)] self.R_V1 = [ Izhikevich_9P(a = 0.1*gauss(1, self.sigmaP), b = 0.002*gauss(1, self.sigmaP), c = -55*gauss(1, self.sigmaP), d = 4*gauss(1, self.sigmaP), vmax = 10*gauss(1, self.sigmaP), vr = -60*gauss(1, self.sigmaP), vt = -54*gauss(1, self.sigmaP), k = 0.3*gauss(1, self.sigmaP), Cm = 10*gauss(1, self.sigmaP), dt = self.getdt(), x = 7.1+1.6*i*gauss(1, self.sigma), y = 1) for i in range(self.nV1)] self.L_Muscle = [ Leaky_Integrator(1.0, 3.0, self.getdt(), 5.0+1.6*i,-1) for i in range(self.nMuscle)] self.R_Muscle = [ Leaky_Integrator(1.0, 3.0, self.getdt(), 5.0+1.6*i, 1) for i in range(self.nMuscle)] def getStimulus(self, t): if t > 2000: # Let the initial conditions dissipate for the first 200 ms return self.stim0 * gauss(1, self.sigmaD) return 0 def rangeNoiseMultiplier(self): return gauss(1, self.sigmaL) def weightNoiseMultiplier(self): return gauss(1, self.sigmaW)

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