xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

a18cf5bd148f07ee046a651b4ef4dbaa5dac3751

381

py

Python

app/main/errors.py

fossabot/upland

6f56347a145d090a770c149462cd7b7215101b30

[ "MIT" ]

null

app/main/errors.py

fossabot/upland

6f56347a145d090a770c149462cd7b7215101b30

[ "MIT" ]

null

app/main/errors.py

fossabot/upland

6f56347a145d090a770c149462cd7b7215101b30

[ "MIT" ]

null

from flask import render_template from . import main @main.app_errorhandler(403) def forbidden(e): return render_template('error/403.html'), 403 @main.app_errorhandler(404) def page_not_found(e): return render_template('error/404.html'), 404 @main.app_errorhandler(500) def internal_server_error(e): return render_template('error/500.html'), 500

21.166667

50

0.727034

54

381

4.925926

0.407407

0.210526

0.214286

0.236842

0.293233

0

0.084375

0.160105

381

17

51

22.411765

0.746875

0

0.115385

0

1

0.272727

false

0

0.181818

0.272727

0.727273

0

null

1

0

null

0

1

0

1

0

6

a19e8ed9a02e5f9ce66545580ec5dc2566c29d47

47,963

py

Python

Packs/Sixgill-Darkfeed/Integrations/Sixgill_Darkfeed/Sixgill_Darkfeed_test.py

cbrake1/content

5b031129f98935c492056675eeee0fefcacbd87b

[ "MIT" ]

2

2020-07-27T10:35:41.000Z

2020-12-14T15:44:18.000Z

Packs/Sixgill-Darkfeed/Integrations/Sixgill_Darkfeed/Sixgill_Darkfeed_test.py

cbrake1/content

5b031129f98935c492056675eeee0fefcacbd87b

[ "MIT" ]

48

2022-03-08T13:45:00.000Z

2022-03-31T14:32:05.000Z

Packs/Sixgill-Darkfeed/Integrations/Sixgill_Darkfeed/Sixgill_Darkfeed_test.py

cbrake1/content

5b031129f98935c492056675eeee0fefcacbd87b

[ "MIT" ]

2

2020-12-10T12:02:45.000Z

2020-12-15T09:20:01.000Z

import requests import pytest import json import demistomock as demisto bundle_index = 0 submitted_indicators = 0 mocked_get_token_response = '''{"access_token": "fababfafbh"}''' iocs_bundle = [{"id": "bundle--716fd67b-ba74-44db-8d4c-2efde05ddbaa", "objects": [ {"created": "2017-01-20T00:00:00.000Z", "definition": {"tlp": "amber"}, "definition_type": "tlp", "id": "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82", "type": "marking-definition"}, {"created": "2019-12-26T00:00:00Z", "definition": {"statement": "Copyright Sixgill 2020. All rights reserved."}, "definition_type": "statement", "id": "marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "type": "marking-definition"}, {"created": "2020-01-09T07:31:16.708Z", "description": "Shell access to this domain is being sold on dark web markets", "id": "indicator--7fc19d6d-2d58-45d6-a410-85554b12aea9", "kill_chain_phases": [ {"kill_chain_name": "lockheed-martin-cyber-kill-chain", "phase_name": "weaponization"}], "labels": ["compromised"], "lang": "en", "modified": "2020-01-09T07:31:16.708Z", "object_marking_refs": ["marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82"], "pattern": "[file:hashes.MD5 = '8f8ff6b696859c3afe7936c345b098bd' OR " "file:hashes.'SHA-1' = '9bb88f703e234a89ff523514a5c676ac12ae6225' OR " "file:hashes.'SHA-256' = " "'9cd46027d63c36e53f4347d43554336c2ea050d38be3ff9a608cb94cca6ab74b']", "sixgill_actor": "some_actor", "sixgill_confidence": 90, "sixgill_feedid": "darkfeed_002", "sixgill_feedname": "compromised_sites", "sixgill_postid": "6e407c41fe6591d591cd8bbf0d105f7c15ed8991", "sixgill_posttitle": "Credit Card Debt Help, somewebsite.com", "sixgill_severity": 70, "sixgill_source": "market_magbo", "spec_version": "2.0", "type": "indicator", "valid_from": "2019-12-07T00:57:04Z"}, {"created": "2020-01-09T07:31:16.824Z", "description": "Shell access to this domain is being sold on dark web markets", "id": "indicator--67b2378f-cbdd-4263-b1c4-668014d376f2", "kill_chain_phases": [ {"kill_chain_name": "lockheed-martin-cyber-kill-chain", "phase_name": "weaponization"}], "labels": ["compromised"], "lang": "ru", "modified": "2020-01-09T07:31:16.824Z", "object_marking_refs": ["marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82"], "pattern": "[ipv4-addr:value = '121.165.45.1']", "sixgill_actor": "some_actor", "sixgill_confidence": 90, "sixgill_feedid": "darkfeed_004", "sixgill_feedname": "compromised_sites", "sixgill_postid": "59f08fbf692f84f15353a5e946d2a1cebab92418", "sixgill_posttitle": "somewebsite.com", "sixgill_severity": 70, "sixgill_source": "market_magbo", "spec_version": "2.0", "type": "indicator", "valid_from": "2019-12-06T17:10:04Z"}, {"created": "2020-01-09T07:31:16.757Z", "description": "Shell access to this domain is being sold on dark web markets", "id": "indicator--6e8b5f57-3ee2-4c4a-9283-8547754dfa09", "kill_chain_phases": [ {"kill_chain_name": "lockheed-martin-cyber-kill-chain", "phase_name": "weaponization"}], "labels": ["url"], "lang": "en", "modified": "2020-01-09T07:31:16.757Z", "object_marking_refs": ["marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82"], "pattern": "[url:value = 'http://somewebsite.rar[.]html']", "sixgill_actor": "some_actor", "sixgill_confidence": 90, "sixgill_feedid": "darkfeed_010", "sixgill_feedname": "compromised_sites", "sixgill_postid": "f46cdfc3332d9a04aa63078d82c1e453fd76ba50", "sixgill_posttitle": "somewebsite.com", "sixgill_severity": 70, "sixgill_source": "market_magbo", "spec_version": "2.0", "type": "indicator", "valid_from": "2019-12-06T23:24:51Z"}, {"created": "2020-01-09T07:31:16.834Z", "description": "Shell access to this domain is being sold on dark web markets", "id": "indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d", "kill_chain_phases": [ {"kill_chain_name": "lockheed-martin-cyber-kill-chain", "phase_name": "weaponization"}], "labels": ["ip"], "lang": "en", "modified": "2020-01-09T07:31:16.834Z", "object_marking_refs": ["marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82"], "pattern": "[ipv4-addr:value = '31.31.77.83']", "sixgill_actor": "some_actor", "sixgill_confidence": 60, "sixgill_feedid": "darkfeed_005", "sixgill_feedname": "compromised_sites", "sixgill_postid": "c3f266e67f163e1a6181c0789e225baba89212a2", "sixgill_posttitle": "somewebsite.com", "sixgill_severity": 70, "sixgill_source": "market_magbo", "spec_version": "2.0", "type": "indicator", "valid_from": "2019-12-06T14:37:16Z"}, {"created": "2020-01-09T07:31:16.834Z", "description": "Shell access to this domain is being sold on dark web markets", "id": "indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d", "kill_chain_phases": [ {"kill_chain_name": "lockheed-martin-cyber-kill-chain", "phase_name": "weaponization"}], "labels": ["malware hash"], "lang": "en", "modified": "2020-01-09T07:31:16.834Z", "object_marking_refs": ["marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82"], "pattern": "[file:hashes.MD5 = '2f4e41ea7006099f365942349b05a269' OR " "file:hashes.'SHA-1' = '835e4574e01c12552c2a3b62b942d177c4d7aaca' OR " "file:hashes.'SHA-256' = 'a925164d6c0c479967b3d9870267a03adf65e8145']", "sixgill_actor": "some_actor", "sixgill_confidence": 80, "sixgill_feedid": "darkfeed_002", "sixgill_feedname": "compromised_sites", "sixgill_postid": "c3f266e67f163e1a6181c0789e225baba89212a2", "sixgill_posttitle": "somewebsite.com", "sixgill_severity": 70, "sixgill_source": "market_magbo", "spec_version": "2.0", "type": "indicator", "valid_from": "2019-12-06T14:37:16Z"}, {"created": "2020-02-09T06:41:41.266Z", "description": "IP address was listed as a proxy", "external_reference": [ { "description": "Mitre attack tactics and technique reference", "mitre_attack_tactic": "Adversary OPSEC", "mitre_attack_tactic_id": "TA0021", "mitre_attack_tactic_url": "https://attack.mitre.org/tactics/TA0021/", "mitre_attack_technique": "Proxy/protocol relays", "mitre_attack_technique_id": "T1304", "mitre_attack_technique_url": "https://attack.mitre.org/techniques/T1304/", "source_name": "mitre-attack" } ], "id": "indicator--2ed98497-cef4-468c-9cee-4f05292b5142", "labels": [ "anonymization", ], "lang": "en", "modified": "2020-02-09T06:41:41.266Z", "object_marking_refs": [ "marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82" ], "pattern": "[ipv4-addr:value = '182.253.121.14']", "sixgill_actor": "LunarEclipsed", "sixgill_confidence": 70, "sixgill_feedid": "darkfeed_009", "sixgill_feedname": "proxy_ips", "sixgill_postid": "00f74eea142e746415457d0dd4a4fc747add3a1b", "sixgill_posttitle": "✅ 9.7K HTTP/S PROXY LIST (FRESH) ✅", "sixgill_severity": 40, "sixgill_source": "forum_nulled", "spec_version": "2.0", "type": "indicator", "valid_from": "2020-01-25T21:08:25Z" } ], "spec_version": "2.0", "type": "bundle"}, {"id": "bundle--716fd67b-ba74-44db-8d4c-2efde05ddbaa", "objects": [ {"created": "2017-01-20T00:00:00.000Z", "definition": {"tlp": "amber"}, "definition_type": "tlp", "id": "marking-definition--f88d31f6-486f-44da-b317-01333bde0b82", "type": "marking-definition"}, {"created": "2019-12-26T00:00:00Z", "definition": {"statement": "Copyright Sixgill 2020. All rights reserved."}, "definition_type": "statement", "id": "marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4", "type": "marking-definition"} ], "spec_version": "2.0", "type": "bundle"} ] expected_ioc_output = [{'value': '9cd46027d63c36e53f4347d43554336c2ea050d38be3ff9a608cb94cca6ab74b', 'type': 'File', 'rawJSON': {'created': '2020-01-09T07:31:16.708Z', 'description': 'Shell access to this domain is being sold on dark web markets', 'id': 'indicator--7fc19d6d-2d58-45d6-a410-85554b12aea9', 'kill_chain_phases': [ {'kill_chain_name': 'lockheed-martin-cyber-kill-chain', 'phase_name': 'weaponization'}], 'labels': ['compromised'], 'lang': 'en', 'modified': '2020-01-09T07:31:16.708Z', 'object_marking_refs': ['marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4', 'marking-definition--f88d31f6-486f-44da-b317-01333bde0b82'], 'pattern': "[file:hashes.MD5 = '8f8ff6b696859c3afe7936c345b098bd' OR " "file:hashes.'SHA-1' = '9bb88f703e234a89ff523514a5c676ac12ae6225' OR " "file:hashes.'SHA-256' = " "'9cd46027d63c36e53f4347d43554336c2ea050d38be3ff9a608cb94cca6ab74b']", 'sixgill_actor': 'some_actor', 'sixgill_confidence': 90, 'sixgill_feedid': 'darkfeed_002', 'sixgill_feedname': 'compromised_sites', 'sixgill_postid': '6e407c41fe6591d591cd8bbf0d105f7c15ed8991', 'sixgill_posttitle': 'Credit Card Debt Help, somewebsite.com', 'sixgill_severity': 70, 'sixgill_source': 'market_magbo', 'spec_version': '2.0', 'type': 'indicator', 'valid_from': '2019-12-07T00:57:04Z'}, 'fields': {'name': 'compromised_sites', 'actor': 'some_actor', 'tags': ['compromised'], 'firstseenbysource': '2020-01-09T07:31:16.708Z', 'description': 'Description: Shell access to this domain is being sold on dark web ' 'markets\nCreated On: 2020-01-09T07:31:16.708Z\nPost ' 'Title: Credit Card Debt Help, somewebsite.com\nThreat ' 'Actor Name: some_actor\nSource: market_magbo\nSixgill ' 'Feed ID: darkfeed_002\nSixgill Feed Name: compromised_sites\n' 'Sixgill Post ID: 6e407c41fe6591d591cd8bbf0d105f7c15ed8991\n' 'Language: en\n' 'Indicator ID: indicator--7fc19d6d-2d58-45d6-a410-85554b12aea9\n' 'External references (e.g. MITRE ATT&CK): None\n', 'sixgillactor': 'some_actor', 'sixgillfeedname': 'compromised_sites', 'sixgillsource': 'market_magbo', 'sixgilllanguage': 'en', 'sixgillposttitle': 'Credit Card Debt Help, somewebsite.com', 'sixgillfeedid': 'darkfeed_002', 'sixgillpostreference': 'https://portal.cybersixgill.com/#/search?q=' '_id:6e407c41fe6591d591cd8bbf0d105f7c15ed8991', 'sixgillindicatorid': 'indicator--7fc19d6d-2d58-45d6-a410-85554b12aea9', 'sixgilldescription': 'Shell access to this domain is being sold on ' 'dark web markets', 'sixgillvirustotaldetectionrate': None, 'sixgillvirustotalurl': None, 'sixgillmitreattcktactic': None, 'sixgillmitreattcktechnique': None, 'md5': '8f8ff6b696859c3afe7936c345b098bd', 'sha1': '9bb88f703e234a89ff523514a5c676ac12ae6225', 'sha256': '9cd46027d63c36e53f4347d43554336c2ea050d38be3ff9a608cb94cca6ab74b'}, 'score': 3}, {'value': '121.165.45.1', 'type': 'IP', 'rawJSON': {'created': '2020-01-09T07:31:16.824Z', 'description': 'Shell access to this domain is being sold on ' 'dark web markets', 'id': 'indicator--67b2378f-cbdd-4263-b1c4-668014d376f2', 'kill_chain_phases': [ {'kill_chain_name': 'lockheed-martin-cyber-kill-chain', 'phase_name': 'weaponization'}], 'labels': ['compromised'], 'lang': 'ru', 'modified': '2020-01-09T07:31:16.824Z', 'object_marking_refs': [ 'marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4', 'marking-definition--f88d31f6-486f-44da-b317-01333bde0b82'], 'pattern': "[ipv4-addr:value = '121.165.45.1']", 'sixgill_actor': 'some_actor', 'sixgill_confidence': 90, 'sixgill_feedid': 'darkfeed_004', 'sixgill_feedname': 'compromised_sites', 'sixgill_postid': '59f08fbf692f84f15353a5e946d2a1cebab92418', 'sixgill_posttitle': 'somewebsite.com', 'sixgill_severity': 70, 'sixgill_source': 'market_magbo', 'spec_version': '2.0', 'type': 'indicator', 'valid_from': '2019-12-06T17:10:04Z'}, 'fields': {'name': 'compromised_sites', 'actor': 'some_actor', 'tags': ['compromised'], 'firstseenbysource': '2020-01-09T07:31:16.824Z', 'description': 'Description: Shell access to this domain is being ' 'sold on dark web markets\n' 'Created On: 2020-01-09T07:31:16.824Z\n' 'Post Title: somewebsite.com\n' 'Threat Actor Name: some_actor\n' 'Source: market_magbo\nSixgill Feed ID: darkfeed_004\n' 'Sixgill Feed Name: compromised_sites\n' 'Sixgill Post ID: ' '59f08fbf692f84f15353a5e946d2a1cebab92418\n' 'Language: ru\n' 'Indicator ID: ' 'indicator--67b2378f-cbdd-4263-b1c4-668014d376f2\n' 'External references (e.g. MITRE ATT&CK): None\n', 'sixgillactor': 'some_actor', 'sixgillfeedname': 'compromised_sites', 'sixgillsource': 'market_magbo', 'sixgilllanguage': 'ru', 'sixgillposttitle': 'somewebsite.com', 'sixgillfeedid': 'darkfeed_004', 'sixgillpostreference': 'https://portal.cybersixgill.com/#/search?q=' '_id:59f08fbf692f84f15353a5e946d2a1cebab92418', 'sixgillindicatorid': 'indicator--67b2378f-cbdd-4263-b1c4-668014d376f2', 'sixgilldescription': 'Shell access to this domain is being sold ' 'on dark web markets', 'sixgillvirustotaldetectionrate': None, 'sixgillvirustotalurl': None, 'sixgillmitreattcktactic': None, 'sixgillmitreattcktechnique': None}, 'score': 3}, {'value': 'http://somewebsite.rar.html', 'type': 'URL', 'rawJSON': {'created': '2020-01-09T07:31:16.757Z', 'description': 'Shell access to this domain is ' 'being sold on dark web markets', 'id': 'indicator--6e8b5f57-3ee2-4c4a-9283-8547754dfa09', 'kill_chain_phases': [{ 'kill_chain_name': 'lockheed-martin-cyber-kill-chain', 'phase_name': 'weaponization'}], 'labels': ['url'], 'lang': 'en', 'modified': '2020-01-09T07:31:16.757Z', 'object_marking_refs': [ 'marking-definition--' '41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4', 'marking-definition--' 'f88d31f6-486f-44da-b317-01333bde0b82'], 'pattern': "[url:value = " "'http://somewebsite.rar[.]html']", 'sixgill_actor': 'some_actor', 'sixgill_confidence': 90, 'sixgill_feedid': 'darkfeed_010', 'sixgill_feedname': 'compromised_sites', 'sixgill_postid': 'f46cdfc3332d9a04aa63078d82c1e453fd76ba50', 'sixgill_posttitle': 'somewebsite.com', 'sixgill_severity': 70, 'sixgill_source': 'market_magbo', 'spec_version': '2.0', 'type': 'indicator', 'valid_from': '2019-12-06T23:24:51Z'}, 'fields': {'name': 'compromised_sites', 'actor': 'some_actor', 'tags': ['url'], 'firstseenbysource': '2020-01-09T07:31:16.757Z', 'description': 'Description: Shell access to this ' 'domain is being sold on dark ' 'web markets\n' 'Created On: 2020-01-09T07:31:16.757Z\n' 'Post Title: somewebsite.com\n' 'Threat Actor Name: some_actor\n' 'Source: market_magbo\n' 'Sixgill Feed ID: darkfeed_010\n' 'Sixgill Feed Name: ' 'compromised_sites\n' 'Sixgill Post ID: ' 'f46cdfc3332d9a04aa63078d82c1e453fd76ba50' '\nLanguage: en\n' 'Indicator ID: indicator--' '6e8b5f57-3ee2-4c4a-9283-8547754dfa09\n' 'External references ' '(e.g. MITRE ATT&CK): None\n', 'sixgillactor': 'some_actor', 'sixgillfeedname': 'compromised_sites', 'sixgillsource': 'market_magbo', 'sixgilllanguage': 'en', 'sixgillposttitle': 'somewebsite.com', 'sixgillfeedid': 'darkfeed_010', 'sixgillpostreference': 'https://portal.cybersixgill.com/#/search?q=' '_id:f46cdfc3332d9a04aa63078d82c1e453fd76ba50', 'sixgillindicatorid': 'indicator--6e8b5f57-3ee2-4c4a-9283-8547754dfa09', 'sixgilldescription': 'Shell access to this domain is ' 'being sold on dark web markets', 'sixgillvirustotaldetectionrate': None, 'sixgillvirustotalurl': None, 'sixgillmitreattcktactic': None, 'sixgillmitreattcktechnique': None}, 'score': 3}, {'value': '31.31.77.83', 'type': 'IP', 'rawJSON': {'created': '2020-01-09T07:31:16.834Z', 'description': 'Shell access to this domain ' 'is being sold on ' 'dark web markets', 'id': 'indicator--85d3d87b-76ed-' '4cab-b709-a43dfbdc5d8d', 'kill_chain_phases': [{'kill_chain_name': 'lockheed-martin-cyber-kill-chain', 'phase_name': 'weaponization'}], 'labels': ['ip'], 'lang': 'en', 'modified': '2020-01-09T07:31:16.834Z', 'object_marking_refs': [ 'marking-definition--' '41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4', 'marking-definition--' 'f88d31f6-486f-44da-b317-01333bde0b82'], 'pattern': "[ipv4-addr:value = " "'31.31.77.83']", 'sixgill_actor': 'some_actor', 'sixgill_confidence': 60, 'sixgill_feedid': 'darkfeed_005', 'sixgill_feedname': 'compromised_sites', 'sixgill_postid': 'c3f266e67f163e1a6' '181c0789e225baba89212a2', 'sixgill_posttitle': 'somewebsite.com', 'sixgill_severity': 70, 'sixgill_source': 'market_magbo', 'spec_version': '2.0', 'type': 'indicator', 'valid_from': '2019-12-06T14:37:16Z'}, 'fields': {'name': 'compromised_sites', 'actor': 'some_actor', 'tags': ['ip'], 'firstseenbysource': '2020-01-09T07:31:16.834Z', 'description': 'Description: Shell access to this domain is being sold on ' 'dark web markets\nCreated On: 2020-01-09T07:31:16.834Z\n' 'Post Title: somewebsite.com\nThreat Actor Name: some_actor\n' 'Source: market_magbo\nSixgill Feed ID: darkfeed_005\n' 'Sixgill Feed Name: compromised_sites\n' 'Sixgill Post ID: c3f266e67f163e1a6181c0789e225baba89212a2\n' 'Language: en\nIndicator ID: ' 'indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d\n' 'External references (e.g. MITRE ATT&CK): None\n', 'sixgillactor': 'some_actor', 'sixgillfeedname': 'compromised_sites', 'sixgillsource': 'market_magbo', 'sixgilllanguage': 'en', 'sixgillposttitle': 'somewebsite.com', 'sixgillfeedid': 'darkfeed_005', 'sixgillpostreference': 'https://portal.cybersixgill.com/#/search?q=' '_id:c3f266e67f163e1a6181c0789e225baba89212a2', 'sixgillindicatorid': 'indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d', 'sixgilldescription': 'Shell access to this domain is being sold on ' 'dark web markets', 'sixgillvirustotaldetectionrate': None, 'sixgillvirustotalurl': None, 'sixgillmitreattcktactic': None, 'sixgillmitreattcktechnique': None}, 'score': 3}, {'value': 'a925164d6c0c479967b3d9870267a03adf65e8145', 'type': 'File', 'rawJSON': {'created': '2020-01-09T07:31:16.834Z', 'description': 'Shell access to this domain is being sold on dark web markets', 'id': 'indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d', 'kill_chain_phases': [{ 'kill_chain_name': 'lockheed-martin-cyber-kill-chain', 'phase_name': 'weaponization'}], 'labels': ['malware hash'], 'lang': 'en', 'modified': '2020-01-09T07:31:16.834Z', 'object_marking_refs': ['marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4', 'marking-definition--f88d31f6-486f-44da-b317-01333bde0b82'], 'pattern': "[file:hashes.MD5 = '2f4e41ea7006099f365942349b05a269' OR " "file:hashes.'SHA-1' = '835e4574e01c12552c2a3b62b942d177c4d7aaca' OR " "file:hashes.'SHA-256' = 'a925164d6c0c479967b3d9870267a03adf65e8145']", 'sixgill_actor': 'some_actor', 'sixgill_confidence': 80, 'sixgill_feedid': 'darkfeed_002', 'sixgill_feedname': 'compromised_sites', 'sixgill_postid': 'c3f266e67f163e1a6181c0789e225baba89212a2', 'sixgill_posttitle': 'somewebsite.com', 'sixgill_severity': 70, 'sixgill_source': 'market_magbo', 'spec_version': '2.0', 'type': 'indicator', 'valid_from': '2019-12-06T14:37:16Z'}, 'fields': {'name': 'compromised_sites', 'actor': 'some_actor', 'tags': ['malware hash'], 'firstseenbysource': '2020-01-09T07:31:16.834Z', 'description': 'Description: Shell access to this domain is being sold on dark ' 'web markets\nCreated On: 2020-01-09T07:31:16.834Z\n' 'Post Title: somewebsite.com\nThreat Actor Name: some_actor\n' 'Source: market_magbo\nSixgill Feed ID: darkfeed_002\n' 'Sixgill Feed Name: compromised_sites\n' 'Sixgill Post ID: c3f266e67f163e1a6181c0789e225baba89212a2\n' 'Language: en\nIndicator ID: ' 'indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d\n' 'External references (e.g. MITRE ATT&CK): None\n', 'sixgillactor': 'some_actor', 'sixgillfeedname': 'compromised_sites', 'sixgillsource': 'market_magbo', 'sixgilllanguage': 'en', 'sixgillposttitle': 'somewebsite.com', 'sixgillfeedid': 'darkfeed_002', 'sixgillpostreference': 'https://portal.cybersixgill.com/#/search?q=' '_id:c3f266e67f163e1a6181c0789e225baba89212a2', 'sixgillindicatorid': 'indicator--85d3d87b-76ed-4cab-b709-a43dfbdc5d8d', 'sixgilldescription': 'Shell access to this domain is being sold on dark' ' web markets', 'sixgillvirustotaldetectionrate': None, 'sixgillvirustotalurl': None, 'sixgillmitreattcktactic': None, 'sixgillmitreattcktechnique': None, 'md5': '2f4e41ea7006099f365942349b05a269', 'sha1': '835e4574e01c12552c2a3b62b942d177c4d7aaca', 'sha256': 'a925164d6c0c479967b3d9870267a03adf65e8145'}, 'score': 3}, {'value': '182.253.121.14', 'type': 'IP', 'rawJSON': {'created': '2020-02-09T06:41:41.266Z', 'description': 'IP address was listed ' 'as a proxy', 'external_reference': [{'description': 'Mitre attack tactics and technique reference', 'mitre_attack_tactic': 'Adversary OPSEC', 'mitre_attack_tactic_id': 'TA0021', 'mitre_attack_tactic_url': 'https://attack.mitre.org/tactics/TA0021/', 'mitre_attack_technique': 'Proxy/protocol relays', 'mitre_attack_technique_id': 'T1304', 'mitre_attack_technique_url': 'https://attack.mitre.org/techniques/T1304/', 'source_name': 'mitre-attack'}], 'id': 'indicator--2ed98497-cef4' '-468c-9cee-4f05292b5142', 'labels': ['anonymization'], 'lang': 'en', 'modified': '2020-02-09T06:41:41.266Z', 'object_marking_refs': [ 'marking-definition--41eaaf7c-0bc0-4c56-abdf-d89a7f096ac4', 'marking-definition--f88d31f6-486f-44da-b317-01333bde0b82'], 'pattern': "[ipv4-addr:value = '182.253.121.14']", 'sixgill_actor': 'LunarEclipsed', 'sixgill_confidence': 70, 'sixgill_feedid': 'darkfeed_009', 'sixgill_feedname': 'proxy_ips', 'sixgill_postid': '00f74eea142e746415457d0dd4a4fc747add3a1b', 'sixgill_posttitle': '✅ 9.7K HTTP/S PROXY LIST (FRESH) ✅', 'sixgill_severity': 40, 'sixgill_source': 'forum_nulled', 'spec_version': '2.0', 'type': 'indicator', 'valid_from': '2020-01-25T21:08:25Z'}, 'fields': {'name': 'proxy_ips', 'actor': 'LunarEclipsed', 'tags': ['anonymization'], 'firstseenbysource': '2020-02-09T06:41:41.266Z', 'description': "Description: IP address was listed as a proxy\n" "Created On: 2020-02-09T06:41:41.266Z\n" "Post Title: ✅ 9.7K HTTP/S PROXY LIST (FRESH) ✅\n" "Threat Actor Name: LunarEclipsed\nSource: forum_nulled\n" "Sixgill Feed ID: darkfeed_009\nSixgill Feed Name: proxy_ips\n" "Sixgill Post ID: 00f74eea142e746415457d0dd4a4fc747add3a1b\n" "Language: en\nIndicator ID: " "indicator--2ed98497-cef4-468c-9cee-4f05292b5142\n" "External references (e.g. MITRE ATT&CK): " "[{'description': 'Mitre attack tactics and technique reference', " "'mitre_attack_tactic': 'Adversary OPSEC', " "'mitre_attack_tactic_id': 'TA0021', 'mitre_attack_tactic_url': " "'https://attack.mitre.org/tactics/TA0021/', " "'mitre_attack_technique': 'Proxy/protocol relays', " "'mitre_attack_technique_id': 'T1304', " "'mitre_attack_technique_url': " "'https://attack.mitre.org/techniques/T1304/', " "'source_name': 'mitre-attack'}]\n", 'sixgillactor': 'LunarEclipsed', 'sixgillfeedname': 'proxy_ips', 'sixgillsource': 'forum_nulled', 'sixgilllanguage': 'en', 'sixgillposttitle': '✅ 9.7K HTTP/S PROXY LIST (FRESH) ✅', 'sixgillfeedid': 'darkfeed_009', 'sixgillpostreference': 'https://portal.cybersixgill.com/#/search?q=' '_id:00f74eea142e746415457d0dd4a4fc747add3a1b', 'sixgillindicatorid': 'indicator--2ed98497-cef4-468c-9cee-4f05292b5142', 'sixgilldescription': 'IP address was listed as a proxy', 'sixgillvirustotaldetectionrate': None, 'sixgillvirustotalurl': None, 'sixgillmitreattcktactic': 'Adversary OPSEC', 'sixgillmitreattcktechnique': 'Proxy/protocol relays', 'feedrelatedindicators': [{'type': 'MITRE ATT&CK', 'value': 'TA0021', 'description': 'https://attack.mitre.org/tactics/TA0021/'}]}, 'score': 3}] class MockedResponse(object): def __init__(self, status_code, text, reason=None, url=None, method=None): self.status_code = status_code self.text = text self.reason = reason self.url = url self.request = requests.Request('GET') self.ok = True if self.status_code == 200 else False def json(self): return json.loads(self.text) def init_params(): return { 'client_id': 'WRONG_CLIENT_ID_TEST', 'client_secret': 'CLIENT_SECRET_TEST', } def mocked_request(*args, **kwargs): global bundle_index global submitted_indicators request = kwargs.get("request", {}) end_point = request.path_url method = request.method response_dict = { 'POST': { '/auth/token': MockedResponse(200, mocked_get_token_response), '/darkfeed/ioc/ack': MockedResponse(200, str(submitted_indicators)) }, 'GET': { '/darkfeed/ioc?limit=1000': MockedResponse(200, json.dumps(iocs_bundle[bundle_index])), }, } response_dict = response_dict.get(method) response = response_dict.get(end_point) if method == 'GET' and end_point == '/darkfeed/ioc?limit=1000': submitted_indicators = len(iocs_bundle[bundle_index].get("objects")) - 2 bundle_index += 1 return response def test_test_module_command_raise_exception(mocker): mocker.patch.object(demisto, 'params', return_value=init_params()) mocker.patch('requests.sessions.Session.send', return_value=MockedResponse(400, "error")) from Sixgill_Darkfeed import test_module_command with pytest.raises(Exception): test_module_command() def test_test_module_command(mocker): mocker.patch.object(demisto, 'params', return_value=init_params()) mocker.patch('requests.sessions.Session.send', return_value=MockedResponse(200, "ok")) from Sixgill_Darkfeed import test_module_command test_module_command() def test_fetch_indicators_command(mocker): global bundle_index global submitted_indicators mocker.patch.object(demisto, 'params', return_value=init_params()) mocker.patch('requests.sessions.Session.send', new=mocked_request) from Sixgill_Darkfeed import fetch_indicators_command from sixgill.sixgill_feed_client import SixgillFeedClient from sixgill.sixgill_constants import FeedStream client = SixgillFeedClient("client_id", "client_secret", "some_channel", FeedStream.DARKFEED, demisto, 1000) output = fetch_indicators_command(client) bundle_index = 0 submitted_indicators = 0 assert output == expected_ioc_output def test_get_indicators_command(mocker): global bundle_index global submitted_indicators mocker.patch.object(demisto, 'params', return_value=init_params()) mocker.patch('requests.sessions.Session.send', new=mocked_request) from Sixgill_Darkfeed import get_indicators_command from sixgill.sixgill_feed_client import SixgillFeedClient from sixgill.sixgill_constants import FeedStream client = SixgillFeedClient("client_id", "client_secret", "some_channel", FeedStream.DARKFEED, demisto, 1000) output = get_indicators_command(client, {"limit": 10}) bundle_index = 0 submitted_indicators = 0 assert output[2] == expected_ioc_output @pytest.mark.parametrize('tlp_color', ['', None, 'AMBER']) def test_feed_tags_and_tlp_color(mocker, tlp_color): """ Given: - feedTags parameter When: - Executing fetch command on feed Then: - Validate the tags supplied are added to the tags list in addition to the tags that were there before """ global bundle_index global submitted_indicators mocker.patch.object(demisto, 'params', return_value=init_params()) mocker.patch('requests.sessions.Session.send', new=mocked_request) from Sixgill_Darkfeed import fetch_indicators_command from sixgill.sixgill_feed_client import SixgillFeedClient from sixgill.sixgill_constants import FeedStream client = SixgillFeedClient("client_id", "client_secret", "some_channel", FeedStream.DARKFEED, demisto, 1000) output = fetch_indicators_command(client, tags=['tag1', 'tag2'], tlp_color=tlp_color) assert all(item in output[0]['fields']['tags'] for item in ['tag1', 'tag2']) assert any(item in output[0]['fields']['tags'] for item in ['compromised', 'ip', 'url']) if tlp_color: assert output[0]['fields']['trafficlightprotocol'] == tlp_color else: assert not output[0]['fields'].get('trafficlightprotocol') bundle_index -= 1

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0.425265

3,280

47,963

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0.019599

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null

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6

a1b065365f8e41ac132be0b4be0fdcca6efdab17

176

py

Python

argparse_to_json/__init__.py

childsish/argparse-to-json

5a75c859a6df05b444ec5491a07a4f51b1d97baa

[ "MIT" ]

1

2022-01-20T19:50:49.000Z

argparse_to_json/__init__.py

childsish/argparse-to-json

5a75c859a6df05b444ec5491a07a4f51b1d97baa

[ "MIT" ]

null

argparse_to_json/__init__.py

childsish/argparse-to-json

5a75c859a6df05b444ec5491a07a4f51b1d97baa

[ "MIT" ]

null

import argparse from argparse_to_json.converter import Converter def convert_parser_to_json(parser: argparse.ArgumentParser) -> dict: return Converter().convert(parser)

22

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null

0

1

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1

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6

a1fde297016f925528ee7641ba827cb64f582a0c

284

py

Python

cupy/io/__init__.py

fukuta0614/Chainer

337fe78e1c27924c1195b8b677a9b2cd3ea68828

[ "MIT" ]

null

cupy/io/__init__.py

fukuta0614/Chainer

337fe78e1c27924c1195b8b677a9b2cd3ea68828

[ "MIT" ]

1

2016-11-09T06:32:32.000Z

2016-11-09T10:20:04.000Z

cupy/io/__init__.py

fukuta0614/Chainer

337fe78e1c27924c1195b8b677a9b2cd3ea68828

[ "MIT" ]

1

2018-11-18T00:36:51.000Z

# Functions from the following NumPy document # http://docs.scipy.org/doc/numpy/reference/routines.io.html # "NOQA" to suppress flake8 warning from cupy.io import formatting # NOQA from cupy.io import npz # NOQA from cupy.io import rawfile # NOQA from cupy.io import text # NOQA

31.555556

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284

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null

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1

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1

0

6

1a0e95a3d4781bc417bd77fc89619755b53a1b9d

8,090

py

Python

cmif/extract.py

herreio/cmif

10c5cde63fffe6cbb45670c1ead0f8cc198b0787

[ "MIT" ]

null

cmif/extract.py

herreio/cmif

10c5cde63fffe6cbb45670c1ead0f8cc198b0787

[ "MIT" ]

1

2022-02-02T14:04:05.000Z

cmif/extract.py

herreio/cmif

10c5cde63fffe6cbb45670c1ead0f8cc198b0787

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ extract XML data in CMI format """ import re from .build import ns_cs, ns_xml def title(data): """ extract text of TEI element <title> """ try: return data.find(".//title", namespaces=data.nsmap).text except AttributeError: pass return None def editor(data, multi=False): """ | extract TEI element <editor> | set multi to True if multiple editors exist """ return data.find(".//editor", namespaces=data.nsmap) if not multi else \ data.findall(".//editor", namespaces=data.nsmap) def editor_name(data, multi=False): """ | extract text of TEI element <editor> | set multi to True if multiple editors exist """ try: return editor(data, multi=multi).text.strip() if not multi else \ [e.text.strip() for e in editor(data, multi=multi)] except AttributeError: pass return None def editor_email(data, multi=False): """ | extract text of TEI element <email> from parent <editor> | set multi to True if multiple editors exist """ try: return editor(data, multi=multi).find(".//email", namespaces=data.nsmap).text if not multi else \ [e.find(".//email", namespaces=data.nsmap).text for e in editor(data, multi=multi)] except AttributeError: pass return None def publisher(data): """ extract text from child <ref> of TEI element <publisher> """ try: return data.find(".//publisher/ref", namespaces=data.nsmap).text except AttributeError: pass return None def publisher_target(data): """ extract @target from child <ref> of TEI element <publisher> """ try: return data.find(".//publisher/ref", namespaces=data.nsmap).attrib["target"] except (AttributeError, KeyError): pass return None def idno(data): """ extract text from TEI element <idno> """ try: return data.find(".//idno", namespaces=data.nsmap).text except AttributeError: pass return None def date_attrib(data): """ extract @ from TEI element <date> """ try: return data.find(".//date", namespaces=data.nsmap).attrib except AttributeError: pass return None def date_when(data): """ extract @when from TEI element <date> """ try: return data.find(".//date", namespaces=data.nsmap).attrib["when"] except (AttributeError, KeyError): pass return None def date_from(data): """ extract @when from TEI element <date> """ try: return data.find(".//date", namespaces=data.nsmap).attrib["from"] except (AttributeError, KeyError): pass return None def date_to(data): """ extract @when from TEI element <date> """ try: return data.find(".//date", namespaces=data.nsmap).attrib["to"] except (AttributeError, KeyError): pass return None def date_not_before(data): """ extract @when from TEI element <date> """ try: return data.find(".//date", namespaces=data.nsmap).attrib["notBefore"] except (AttributeError, KeyError): pass return None def date_not_after(data): """ extract @when from TEI element <date> """ try: return data.find(".//date", namespaces=data.nsmap).attrib["notAfter"] except (AttributeError, KeyError): pass return None def license(data): """ extract text of TEI element <licence> """ try: return data.find(".//licence", namespaces=data.nsmap).text except AttributeError: pass return None def license_target(data): """ extract @target from TEI element <licence> """ try: return data.find(".//licence", namespaces=data.nsmap).attrib["target"] except (AttributeError, KeyError): pass return None def bibl(data, multi=False): """ | extract TEI element <bibl> | set multi to True if multiple references exist """ return data.find(".//bibl", namespaces=data.nsmap) if not multi else \ data.findall(".//bibl", namespaces=data.nsmap) def bibl_id(data, multi=False): """ | extract @xml:id from TEI element <bibl> | set multi to True if multiple references exist """ bibl_data = bibl(data, multi=multi) try: return bibl_data.attrib[ns_xml("id")] if not multi else \ [b.attrib[ns_xml("id")] for b in bibl_data] except (AttributeError, KeyError): pass return None def bibl_type(data, multi=False): """ | extract @type from TEI element <bibl> | set multi to True if multiple references exist """ bibl_data = bibl(data, multi=multi) try: return bibl_data.attrib["type"] if not multi else \ [b.attrib["type"] for b in bibl_data] except (AttributeError, KeyError): pass return None def bibl_text(data, multi=False): """ | extract text of TEI element <bibl> | set multi to True if multiple references exist """ bibl_data = bibl(data, multi=multi) try: return re.sub("[ \r\n]+", " ", "".join([l for l in list(bibl_data.itertext())]).strip()) if not multi else \ [re.sub("[ \r\n]+", " ", "".join([l for l in list(b.itertext())]).strip()) for b in bibl_data] except AttributeError: pass return None def correspdesc(data): """ extract TEI elements <correspDesc> """ return data.findall(".//correspDesc", namespaces=data.nsmap) def correspdesc_source(data): """ extract @source from TEI elements <correspDesc> """ correspdesc_data = correspdesc(data) try: return [cd.attrib["source"].replace("#", "") for cd in correspdesc_data] except KeyError: pass try: return [cd.attrib[ns_cs("source")].replace("#", "") for cd in correspdesc_data] except KeyError: pass return [] def correspdesc_key(data): """ extract @source from TEI elements <correspDesc> """ correspdesc_data = correspdesc(data) try: return [cd.attrib["key"].replace("#", "") for cd in correspdesc_data] except KeyError: pass return [] def correspaction(data): """ extract TEI elements <correspAction> """ return data.findall(".//correspAction", namespaces=data.nsmap) def correspaction_type(data): """ extract @type from TEI elements <correspAction> """ correspaction_data = correspaction(data) try: return [ca.attrib["type"] for ca in correspaction_data] except (AttributeError, KeyError): pass return None def org_name(data): """ extract text from TEI element <orgName> """ try: return data.find(".//orgName", namespaces=data.nsmap).text except AttributeError: pass return None def org_name_ref(data): """ extract @ref from TEI element <orgName> """ try: return data.find(".//orgName", namespaces=data.nsmap).attrib["ref"] except (AttributeError, KeyError): pass return None def pers_name(data): """ extract text from TEI element <persName> """ try: return data.find(".//persName", namespaces=data.nsmap).text except AttributeError: pass return None def pers_name_ref(data): """ extract @ref from TEI element <persName> """ try: return data.find(".//persName", namespaces=data.nsmap).attrib["ref"] except (AttributeError, KeyError): pass return None def place_name(data): """ extract text from TEI element <placeName> """ try: return data.find(".//placeName", namespaces=data.nsmap).text except AttributeError: pass return None def place_name_ref(data): """ extract @ref from TEI element <placeName> """ try: return data.find(".//placeName", namespaces=data.nsmap).attrib["ref"] except (AttributeError, KeyError): pass return None

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0.079959

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0.702658

0.640286

0.601022

0

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false

0.147541

0.010929

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0

null

0

1

0

1

0

null

0

1

0

6

c52120589d13816742363fd0034c96086a816942

6,657

py

Python

moesifdjango/update_companies.py

Moesif/moesifdjango

67529381b7ffc234263e6989ae16cf8ef1ca62a6

[ "Apache-2.0" ]

13

2016-11-02T18:53:03.000Z

2022-01-25T21:47:24.000Z

moesifdjango/update_companies.py

Moesif/moesifdjango

67529381b7ffc234263e6989ae16cf8ef1ca62a6

[ "Apache-2.0" ]

10

2017-12-13T11:56:48.000Z

2021-07-16T12:34:14.000Z

moesifdjango/update_companies.py

Moesif/moesifdjango

67529381b7ffc234263e6989ae16cf8ef1ca62a6

[ "Apache-2.0" ]

5

2018-02-02T13:51:49.000Z

2021-12-17T00:46:24.000Z

from moesifapi.models import * from moesifapi.exceptions.api_exception import * from moesifapi.api_helper import * class Company: def __init__(self): pass @classmethod def update_company(cls, company_profile, api_client, DEBUG): if not company_profile: print('Expecting the input to be either of the type - CompanyModel, dict or json while updating user') else: if isinstance(company_profile, dict): if 'company_id' in company_profile: try: api_client.update_company(CompanyModel.from_dictionary(company_profile)) if DEBUG: print('Company Profile updated successfully') except APIException as inst: if 401 <= inst.response_code <= 403: print("Unauthorized access sending event to Moesif. Please check your Appplication Id.") if DEBUG: print("Error while updating company, with status code:") print(inst.response_code) else: print('To update a company, a company_id field is required') elif isinstance(company_profile, CompanyModel): if company_profile.company_id is not None: try: api_client.update_company(company_profile) if DEBUG: print('Company Profile updated successfully') except APIException as inst: if 401 <= inst.response_code <= 403: print("Unauthorized access sending event to Moesif. Please check your Appplication Id.") if DEBUG: print("Error while updating company, with status code:") print(inst.response_code) else: print('To update a company, a company_id field is required') else: try: company_profile_json = APIHelper.json_deserialize(company_profile) if 'company_id' in company_profile_json: try: api_client.update_company(CompanyModel.from_dictionary(company_profile_json)) if DEBUG: print('Company Profile updated successfully') except APIException as inst: if 401 <= inst.response_code <= 403: print("Unauthorized access sending event to Moesif. Please check your Appplication Id.") if DEBUG: print("Error while updating company, with status code:") print(inst.response_code) else: print('To update a company, a company_id field is required') except: print('Error while deserializing the json, please make sure the json is valid') @classmethod def update_companies_batch(cls, companies_profiles, api_client, DEBUG): if not companies_profiles: print('Expecting the input to be either of the type - List of CompanyModel, dict or json while updating users') else: if all(isinstance(company, dict) for company in companies_profiles): if all('company_id' in company for company in companies_profiles): try: batch_profiles = [CompanyModel.from_dictionary(d) for d in companies_profiles] api_client.update_companies_batch(batch_profiles) if DEBUG: print('Companies Profile updated successfully') except APIException as inst: if 401 <= inst.response_code <= 403: print("Unauthorized access sending event to Moesif. Please check your Appplication Id.") if DEBUG: print("Error while updating companies, with status code:") print(inst.response_code) else: print('To update companies, an company_id field is required') elif all(isinstance(company, CompanyModel) for company in companies_profiles): if all(company.company_id is not None for company in companies_profiles): try: api_client.update_companies_batch(companies_profiles) if DEBUG: print('Companies Profile updated successfully') except APIException as inst: if 401 <= inst.response_code <= 403: print("Unauthorized access sending event to Moesif. Please check your Appplication Id.") if DEBUG: print("Error while updating companues, with status code:") print(inst.response_code) else: print('To update companies, a company_id field is required') else: try: company_profiles_json = [APIHelper.json_deserialize(d) for d in companies_profiles] if all(isinstance(company, dict) for company in company_profiles_json) and all( 'company_id' in company for company in company_profiles_json): try: batch_profiles = [CompanyModel.from_dictionary(d) for d in company_profiles_json] api_client.update_companies_batch(batch_profiles) if DEBUG: print('Companies Profile updated successfully') except APIException as inst: if 401 <= inst.response_code <= 403: print("Unauthorized access sending event to Moesif. Please check your Appplication Id.") if DEBUG: print("Error while updating companies, with status code:") print(inst.response_code) else: print('To update companies, an company_id field is required') except: print('Error while deserializing the json, please make sure the json is valid')

55.941176

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0.527865

640

6,657

5.346875

0.139063

0.061368

0.042081

0.056108

0.848042

0.810637

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0.675628

0.66014

0

0.009293

0.418056

6,657

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56.415254

0.874032

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0.702703

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0.250413

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0

null

0

1

0

1

0

1

0

null

0

6

c535fd75123f8d37fba1084a35cb8615db426175

31

py

Python

nni/algorithms/compression/tensorflow/pruning/__init__.py

dutxubo/nni

c16f4e1c89b54b8b80661ef0072433d255ad2d24

[ "MIT" ]

9,680

2019-05-07T01:42:30.000Z

2022-03-31T16:48:33.000Z

nni/algorithms/compression/tensorflow/pruning/__init__.py

dutxubo/nni

c16f4e1c89b54b8b80661ef0072433d255ad2d24

[ "MIT" ]

1,957

2019-05-06T21:44:21.000Z

2022-03-31T09:21:53.000Z

nni/algorithms/compression/tensorflow/pruning/__init__.py

dutxubo/nni

c16f4e1c89b54b8b80661ef0072433d255ad2d24

[ "MIT" ]

1,571

2019-05-07T06:42:55.000Z

2022-03-31T03:19:24.000Z

from .one_shot_pruner import *

15.5

30

0.806452

5

31

4.6

1

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0.129032

31

1

31

0.851852

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null

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1

0

1

0

1

0

6

c53d0a6ab8cd95e818082ab9ddd5984a95a0cc1f

60

py

Python

python_stylesheets_color_changer/__init__.py

yjg30737/python-stylesheets-color-changer

1851ef3256d61c73c5c884a7617fccfe30b771de

[ "MIT" ]

null

python_stylesheets_color_changer/__init__.py

yjg30737/python-stylesheets-color-changer

1851ef3256d61c73c5c884a7617fccfe30b771de

[ "MIT" ]

null

python_stylesheets_color_changer/__init__.py

yjg30737/python-stylesheets-color-changer

1851ef3256d61c73c5c884a7617fccfe30b771de

[ "MIT" ]

null

from .styleSheetsColorChanger import StyleSheetsColorChanger

60

0.933333

4

60

14

0.75

0

0.05

60

1

60

0.982456

0

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true

0

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1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

3d6a770ee5ce7c67f4245e7416e8875a63c8bb65

3,234

py

Python

tests/ci/jenkins_test.py

VJftw/invoke-tools

9584a1f8a402118310b6f2a495062f388fc8dc3a

[ "MIT" ]

2

2017-07-02T02:46:58.000Z

2018-07-24T03:36:30.000Z

tests/ci/jenkins_test.py

VJftw/invoke-tools

9584a1f8a402118310b6f2a495062f388fc8dc3a

[ "MIT" ]

null

tests/ci/jenkins_test.py

VJftw/invoke-tools

9584a1f8a402118310b6f2a495062f388fc8dc3a

[ "MIT" ]

1

2019-11-27T14:43:03.000Z

""" tests.invoke_tools.ci.jenkins_test """ import unittest import mock import json from invoke_tools import ci class JenkinsTests(unittest.TestCase): """ Tests for Jenkins """ def test_init(self): """ invoke_tools.ci.jenkins.init: Should initialise the Jenkins object """ jenkins = ci.Jenkins("https://jenkins.example.org", "job-name") self.assertIsInstance(jenkins, ci.Jenkins) git = mock.Mock() jenkins = ci.Jenkins("https://jenkins.example.org", "job-name", git) self.assertIsInstance(jenkins, ci.Jenkins) def test_get_last_successful_build_for_multi_branch(self): """ invoke_tools.ci.jenkins.get_last_successful_build_sha: Should return the last successful build for a multi branch project """ git = mock.Mock() git.get_branch = mock.Mock(return_value="develop") jenkins = ci.Jenkins("https://jenkins.example.org", "job-name", git) def requests_get(url): if url == "https://jenkins.example.org/job/job-name/job/develop/api/json?tree=lastSuccessfulBuild[number,url,timestamp]": json_file = "tests/json/ci-jenkins-multi-branch.json" elif url == "https://jenkins.example.org/job/job-name/job/develop/18/api/json?tree=actions[*[revision[SHA1]]]": json_file = "tests/json/ci-jenkins-multi-branch-build.json" else: raise ValueError("Invalid url: {0}".format(url)) json_mock = mock.Mock() with open(json_file) as file: file_dict = json.loads(file.read()) json_mock.json = mock.Mock(return_value=file_dict) return json_mock with mock.patch("invoke_tools.ci.jenkins.requests.get", side_effect=requests_get): self.assertEqual( jenkins.get_last_successful_build_sha(), "fd48c805a7684a5d268d0df4849c4cce3be6ce2f" ) def test_get_last_successful_build_for_single_branch(self): """ invoke_tools.ci.jenkins.get_last_successful_build_sha: Should return the last successful build for a single branch project """ jenkins = ci.Jenkins("https://jenkins.example.org", "job-name") def requests_get(url): if url == "https://jenkins.example.org/job/job-name/api/json?tree=lastSuccessfulBuild[number,url,timestamp]": json_file = "tests/json/ci-jenkins-single-branch.json" elif url == "https://jenkins.example.org/job/job-name/62/api/json?tree=actions[*[revision[SHA1]]]": json_file = "tests/json/ci-jenkins-single-branch-build.json" else: raise ValueError("Invalid url: {0}".format(url)) json_mock = mock.Mock() with open(json_file) as file: file_dict = json.loads(file.read()) json_mock.json = mock.Mock(return_value=file_dict) return json_mock with mock.patch("invoke_tools.ci.jenkins.requests.get", side_effect=requests_get): self.assertEqual( jenkins.get_last_successful_build_sha(), "1b5cdf46844d011596b9b6a34c105b9a26c26a19" )

39.439024

133

0.631416

390

3,234

5.069231

0.192308

0.072838

0.076884

0.089024

0.81133

0.762772

0.7304

0.719272

0.673748

0

0.023438

0.24799

3,234

81

134

39.925926

0.789474

0.112554

0

0.588235

0

0.078431

0.318689

0.115952

0

0.078431

1

0.098039

false

0

0.078431

0

0.235294

0

null

0

1

0

null

0

6

3da313bc449c9f1323849ff499bfc88935992dea

34

py

Python

algolia_analytics/__init__.py

dsfcode/algolia-analytics

06c5bc2c44b8a99368d0ca175028dba22680bbc8

[ "MIT" ]

1

2022-01-04T16:32:39.000Z

algolia_analytics/__init__.py

dan-sf/algolia-analytics

06c5bc2c44b8a99368d0ca175028dba22680bbc8

[ "MIT" ]

null

algolia_analytics/__init__.py

dan-sf/algolia-analytics

06c5bc2c44b8a99368d0ca175028dba22680bbc8

[ "MIT" ]

null

from .api import AlgoliaAnalytics

17

33

0.852941

4

34

7.25

1

0

0.117647

34

1

34

0.966667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

3dd6c8f9eb5cd56813fdf220f26253d072ed7098

365

py

Python

benedict/core/items_sorted.py

next-franciscoalgaba/python-benedict

81ff459304868327238c322a0a8a203d9d5d4314

[ "MIT" ]

365

2019-05-21T05:50:30.000Z

2022-03-29T11:35:35.000Z

benedict/core/items_sorted.py

next-franciscoalgaba/python-benedict

81ff459304868327238c322a0a8a203d9d5d4314

[ "MIT" ]

78

2019-11-16T12:22:54.000Z

2022-03-14T12:21:30.000Z

benedict/core/items_sorted.py

next-franciscoalgaba/python-benedict

81ff459304868327238c322a0a8a203d9d5d4314

[ "MIT" ]

26

2019-12-16T06:34:12.000Z

2022-02-28T07:16:41.000Z

# -*- coding: utf-8 -*- def _items_sorted_by_item_at_index(d, index, reverse): return sorted(d.items(), key=lambda item: item[index], reverse=reverse) def items_sorted_by_keys(d, reverse=False): return _items_sorted_by_item_at_index(d, 0, reverse) def items_sorted_by_values(d, reverse=False): return _items_sorted_by_item_at_index(d, 1, reverse)

26.071429

75

0.750685

60

365

4.166667

0.333333

0.22

0.26

0.192

0.636

0.452

0.352

0

0.009434

0.128767

365

13

76

28.076923

0.77673

0.057534

0

1

0.5

false

0

0.5

1

0

null

1

0

null

0

1

0

1

0

6

3de0d776faa235c99406886dd71181a76acc7454

59

py

Python

testchild.py

JRogersESQ/animated-garbanzo

da6c4f109b2506b8ceb1c0622e1e1756724bc65a

[ "Apache-2.0" ]

null

testchild.py

JRogersESQ/animated-garbanzo

da6c4f109b2506b8ceb1c0622e1e1756724bc65a

[ "Apache-2.0" ]

null

testchild.py

JRogersESQ/animated-garbanzo

da6c4f109b2506b8ceb1c0622e1e1756724bc65a

[ "Apache-2.0" ]

null

### Add file to child branch print ("inside child branch")

19.666667

29

0.711864

9

59

4.666667

0.777778

0.52381

0

0.169492

59

2

30

29.5

0.857143

0.40678

0

0.612903

0

1

0

true

0

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

9aeadc93ceef4048834a1b1cd863f8e5f2874176

26

py

Python

easycrypto/__init__.py

emartech/python-easy-crypto

ef09b42e43fb6649498bfb7b5ffbbf490a94d85d

[ "MIT" ]

3

2019-11-03T18:26:35.000Z

2021-03-07T02:37:52.000Z

easycrypto/__init__.py

emartech/python-easy-crypto

ef09b42e43fb6649498bfb7b5ffbbf490a94d85d

[ "MIT" ]

4

2019-06-05T01:48:19.000Z

2019-07-19T11:53:51.000Z

easycrypto/__init__.py

emartech/python-easy-crypto

ef09b42e43fb6649498bfb7b5ffbbf490a94d85d

[ "MIT" ]

2

2019-07-11T08:59:03.000Z

2022-02-17T19:41:21.000Z

from .crypto import Crypto

26

0.846154

4

26

5.5

0.75

0

0.115385

26

1

26

0.956522

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

9aff9fa5e502c32e6b4ace7b1c8296b3bdf1aee9

39

py

Python

pyLineSPM/__init__.py

rbeucher/pyLineSPM

07ab561f638cae0caccd4f27c74b03f1f1364202

[ "MIT" ]

null

pyLineSPM/__init__.py

rbeucher/pyLineSPM

07ab561f638cae0caccd4f27c74b03f1f1364202

[ "MIT" ]

null

pyLineSPM/__init__.py

rbeucher/pyLineSPM

07ab561f638cae0caccd4f27c74b03f1f1364202

[ "MIT" ]

null

from .river import * from .spm import *

19.5

20

0.717949

6

39

4.666667

0.666667

0

0.179487

39

2

21

19.5

0.875

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b10b2fcb25dfdb6124a8bcbd3a9be22797d13ea0

157

py

Python

gmso/external/__init__.py

rsdefever/gmso

3ff3829cb4bc492b41e5e520d26d35c09c5338a4

[ "MIT" ]

null

gmso/external/__init__.py

rsdefever/gmso

3ff3829cb4bc492b41e5e520d26d35c09c5338a4

[ "MIT" ]

null

gmso/external/__init__.py

rsdefever/gmso

3ff3829cb4bc492b41e5e520d26d35c09c5338a4

[ "MIT" ]

null

from .convert_mbuild import from_mbuild, to_mbuild, from_mbuild_box from .convert_parmed import from_parmed, to_parmed from .convert_openmm import to_openmm

39.25

67

0.866242

25

157

5.04

0.32

0.261905

0

0.095541

157

3

68

52.333333

0.887324

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

b18da9e260418c267c73a7d3d56acadbdc3b3636

96

py

Python

albumentations/albumentations/augmentations/geometric/__init__.py

hfzx01/Substation

760e2f1a5d21102a6a05973cc31bc8252659757c

[ "Apache-2.0" ]

6,316

2019-11-18T14:19:17.000Z

2022-03-31T22:25:23.000Z

albumentations/albumentations/augmentations/geometric/__init__.py

hfzx01/Substation

760e2f1a5d21102a6a05973cc31bc8252659757c

[ "Apache-2.0" ]

558

2019-11-19T00:36:01.000Z

2022-03-30T22:04:15.000Z

albumentations/albumentations/augmentations/geometric/__init__.py

hfzx01/Substation

760e2f1a5d21102a6a05973cc31bc8252659757c

[ "Apache-2.0" ]

889

2019-11-18T16:49:44.000Z

2022-03-28T11:00:14.000Z

from .functional import * from .resize import * from .rotate import * from .transforms import *

19.2

25

0.75

12

96

6

0.5

0.416667

0

0.166667

96

4

26

24

0.9

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

495f8ac698df39c7dcc0678cab08f18a42d8cb80

9,262

py

Python

TestFileSize_img_fig.py

ytyaru/Python.FileSize.201702071138

569c45d5e9b91befbaece50520eb69955e148c65

[ "CC0-1.0" ]

null

TestFileSize_img_fig.py

ytyaru/Python.FileSize.201702071138

569c45d5e9b91befbaece50520eb69955e148c65

[ "CC0-1.0" ]

6

2017-02-09T00:54:50.000Z

2017-02-09T10:56:13.000Z

TestFileSize_img_fig.py

ytyaru/Python.FileSize.201702071138

569c45d5e9b91befbaece50520eb69955e148c65

[ "CC0-1.0" ]

null

import unittest import FileSize from decimal import Decimal class TestFileSize_img_fig(unittest.TestCase): def test_int_fig_2(self): with self.assertRaises(Exception) as e: int_fig=2; self.__target = FileSize.FileSize(integral_figure_num=int_fig) self.assertEqual('桁上がりするまでの桁数は3または4のみ有効です。無効値: {0}'.format(int_fig), e.exception.args[0]) def test_int_fig_5(self): with self.assertRaises(Exception) as e: int_fig=5; self.__target = FileSize.FileSize(integral_figure_num=int_fig) self.assertEqual('桁上がりするまでの桁数は3または4のみ有効です。無効値: {0}'.format(int_fig), e.exception.args[0]) def test_img_fig_nega(self): with self.assertRaises(Exception) as e: img_fig=-1; self.__target = FileSize.FileSize(imaginary_figure_num=img_fig) self.assertEqual('虚数部の桁数は0〜{0}までの整数値のみ有効です。無効値: {1}'.format(3, img_fig), e.exception.args[0]) def test_img_fig_5(self): with self.assertRaises(Exception) as e: img_fig=4; self.__target = FileSize.FileSize(imaginary_figure_num=img_fig) self.assertEqual('虚数部の桁数は0〜{0}までの整数値のみ有効です。無効値: {1}'.format(3, img_fig), e.exception.args[0]) def test_9999_999KiB_4_3(self): unit=1024; int_fig=4; img_fig=3; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "9999.999 KiB") def test_9999_99KiB_4_2(self): unit=1024; int_fig=4; img_fig=2; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "9999.99 KiB") def test_9999_9KiB_4_1(self): unit=1024; int_fig=4; img_fig=1; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "9999.9 KiB") def test_9999KiB_4_0(self): unit=1024; int_fig=4; img_fig=0; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "9999 KiB") def test_9999_999KiB_3_3(self): unit=1024; int_fig=3; img_fig=3; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "999.999 KiB") def test_9999_99KiB_3_2(self): unit=1024; int_fig=3; img_fig=2; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "999.99 KiB") def test_9999_9KiB_3_1(self): unit=1024; int_fig=3; img_fig=1; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "999.9 KiB") def test_9999KiB_3_0(self): unit=1024; int_fig=3; img_fig=0; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig) actual = ((unit ** 1) * (10 ** int_fig)) - 1 self.assertEqual(self.__target.Get(actual), "999 KiB") def test_10_000KiB_4_3_zero(self): unit=1024; int_fig=4; img_fig=3; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10.000 KiB") def test_10_00KiB_4_2_zero(self): unit=1024; int_fig=4; img_fig=2; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10.00 KiB") def test_10_0KiB_4_1_zero(self): unit=1024; int_fig=4; img_fig=1; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10.0 KiB") def test_10KiB_4_0_zero(self): unit=1024; int_fig=4; img_fig=0; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10 KiB") def test_1_000KiB_3_3_zero(self): unit=1024; int_fig=3; img_fig=3; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1.000 KiB") def test_1_00KiB_3_2_zero(self): unit=1024; int_fig=3; img_fig=2; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1.00 KiB") def test_1_0KiB_3_1_zero(self): unit=1024; int_fig=3; img_fig=1; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1.0 KiB") def test_1KiB_3_0_zero(self): unit=1024; int_fig=3; img_fig=0; zero=False; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1 KiB") def test_10KiB_4_3(self): unit=1024; int_fig=4; img_fig=3; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10 KiB") def test_10KiB_4_2(self): unit=1024; int_fig=4; img_fig=2; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10 KiB") def test_10KiB_4_1(self): unit=1024; int_fig=4; img_fig=1; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10 KiB") def test_10KiB_4_0(self): unit=1024; int_fig=4; img_fig=0; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = ((unit ** 1) * (10)) self.assertEqual(self.__target.Get(actual), "10 KiB") def test_1KiB_3_3(self): unit=1024; int_fig=3; img_fig=3; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1 KiB") def test_1KiB_3_2(self): unit=1024; int_fig=3; img_fig=2; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1 KiB") def test_1KiB_3_1(self): unit=1024; int_fig=3; img_fig=1; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1 KiB") def test_1KiB_3_0(self): unit=1024; int_fig=3; img_fig=0; zero=True; self.__target = FileSize.FileSize(byte_size_of_unit=unit, integral_figure_num=int_fig, imaginary_figure_num=img_fig, is_hidden_imaginary_all_zero=zero) actual = (unit ** 1) self.assertEqual(self.__target.Get(actual), "1 KiB")

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0.698337

1,422

9,262

4.151899

0.048523

0.065041

0.085366

0.123306

0.957486

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null

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1

0

null

0

6

498710ab6a4a6e43d65eb5915475d9de76164b9e

90

py

Python

libdlt/__init__.py

datalogistics/libdlt

f3d8afb06a237fe6e4114c1a55e6f407ba9cc7b0

[ "BSD-3-Clause" ]

null

libdlt/__init__.py

datalogistics/libdlt

f3d8afb06a237fe6e4114c1a55e6f407ba9cc7b0

[ "BSD-3-Clause" ]

2

2018-05-20T21:33:03.000Z

2019-02-15T16:48:37.000Z

libdlt/__init__.py

datalogistics/libdlt

f3d8afb06a237fe6e4114c1a55e6f407ba9cc7b0

[ "BSD-3-Clause" ]

null

from libdlt.util import util from libdlt.api import * from libdlt.sessions import Session

22.5

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null

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6

498a12c07aece3061953a278e9e6bc94a0d23623

159

py

Python

vedastr_cstr/vedastr/models/bodies/feature_extractors/encoders/__init__.py

bsm8734/formula-image-latex-recognition

86d5070e8f907571a47967d64facaee246d92a35

[ "MIT" ]

13

2021-06-20T18:11:23.000Z

2021-12-07T18:06:42.000Z

vedastr_cstr/vedastr/models/bodies/feature_extractors/encoders/__init__.py

bsm8734/formula-image-latex-recognition

86d5070e8f907571a47967d64facaee246d92a35

[ "MIT" ]

9

2021-06-16T14:55:07.000Z

2021-06-23T14:45:36.000Z

vedastr_cstr/vedastr/models/bodies/feature_extractors/encoders/__init__.py

bsm8734/formula-image-latex-recognition

86d5070e8f907571a47967d64facaee246d92a35

[ "MIT" ]

6

2021-06-17T15:16:50.000Z

2021-07-05T20:41:26.000Z

from .backbones import build_backbone # noqa 401 from .builder import build_encoder # noqa 401 from .enhance_modules import build_enhance_module # noqa 401

39.75

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159

5.391304

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null

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1

0

1

0

1

0

6

b8fd3350022f9e526ac1df7865de8ffaec66ea50

7,897

py

Python

baseline_mixed_model.py

Vignesh-Sairaj/cm-229

63ae1b8f3529e602352bc3eb92066c9520dfa805

[ "MIT" ]

null

baseline_mixed_model.py

Vignesh-Sairaj/cm-229

63ae1b8f3529e602352bc3eb92066c9520dfa805

[ "MIT" ]

null

baseline_mixed_model.py

Vignesh-Sairaj/cm-229

63ae1b8f3529e602352bc3eb92066c9520dfa805

[ "MIT" ]

null

import os import sys import pandas as pd import numpy as np from data_import import * import statsmodels.api as sm from phenotype_correlation import * from sklearn.linear_model import Ridge """ baseline mixed model Y = ZA + XB + E we calculate the ZA using OLS first and perform ridge on the residuals (Y - ZA ~ N(XB, sigma) """ def baseline_mixed_model_analysis(geno_df, pheno_df, phenotype_1, phenotype_2, missing_rate = 0.1, sample_list = list(), verbose = False): corr_mat = calculate_highly_correlated_phenotypes(pheno_df) print("The correlation between %s and %s is %f" % (phenotype_1, phenotype_2, corr_mat[phenotype_1][phenotype_2])) # bind phenotype into list to extract phenotype_list = [phenotype_1, phenotype_2] # extract the phenotypes geno_select, pheno_select = select_phenotype_multiple_phenotypes(geno_df, pheno_df, phenotype_list = phenotype_list, verbose = verbose) # separate training and test dataset geno_tr, pheno_tr, geno_test, pheno_test, test_sample_list = separate_training_test(geno_select, pheno_select, missing_rate = missing_rate, sample_list_select = sample_list) # perform OLS lm = sm.OLS(endog = pheno_tr[phenotype_2], exog = pheno_tr[phenotype_1]).fit() if verbose: print("The linear model summary for predicting phenotype %a based on phenotype %a" % (phenotype_2, phenotype_1)) print(lm.summary()) print(lm.params) # prediction for fixed effect predictions_fe = lm.predict(pheno_test[phenotype_1]) # perform ridge regression on the residual (random effect part) residuals = pheno_tr[phenotype_2] - lm.predict(pheno_tr[phenotype_1]) lm_re = sm.OLS(endog = residuals, exog = geno_tr.transpose()).fit_regularized(L1_wt = 1.0) if verbose: print(lm_re.params) predictions_re = lm_re.predict(geno_test.transpose()) # combine the result from both total_prediction = predictions_fe + predictions_re mse = calculate_MSE(total_prediction, pheno_test[phenotype_2]) return(mse, test_sample_list) def top_N_snp_mixed_model_analysis(geno_df, pheno_df, phenotype_1, phenotype_2, top_N = 100, missing_rate = 0.1, sample_list = list(), verbose = False): corr_mat = calculate_highly_correlated_phenotypes(pheno_df) print("The correlation between %s and %s is %f" % (phenotype_1, phenotype_2, corr_mat[phenotype_1][phenotype_2])) # bind phenotype into list to extract phenotype_list = [phenotype_1, phenotype_2] # extract the phenotypes geno_select, pheno_select = select_phenotype_multiple_phenotypes(geno_df, pheno_df, phenotype_list = phenotype_list, verbose = verbose) # separate training and test dataset geno_tr, pheno_tr, geno_test, pheno_test, test_sample_list = separate_training_test(geno_select, pheno_select, missing_rate = missing_rate, sample_list_select = sample_list) # remove duplciates geno_test_new = geno_test.loc[:,~geno_test.columns.duplicated()] geno_test = geno_test_new[pheno_test[phenotype_2].index] # saving below # # perform simple ridge to identify the top SNPs # lm_ridge = sm.OLS(endog = pheno_tr[phenotype_2], exog = geno_tr.transpose()).fit_regularized(L1_wt = 1.0) # if verbose: # print(lm_ridge.params) # # select top SNPs with highest effect size for select run # top_N_idx = np.argsort(abs(lm_ridge.params))[-top_N:] # if verbose: # top_N_values = [lm_re.params[i] for i in top_N_idx] # print(top_N_values) # top_N_snps = geno_tr.iloc[top_N_idx].index # sklearn test # clf = Ridge(alpha = 1.0) # a = clf.fit(y = pheno_tr[phenotype_2], X = geno_tr.transpose()) # # select top N # top_N = 10 # top_N_idx = np.argsort(abs(a.coef_))[-top_N:] # print (top_N_idx) # top_N_values = [a.coef_[i] for i in top_N_idx] # print (top_N_values) # top_N_snps = geno_tr.iloc[top_N_idx].index # print(top_N_snps) # perform OLS lm = sm.OLS(endog = pheno_tr[phenotype_2], exog = pheno_tr[phenotype_1]).fit() if verbose: print("The linear model summary for predicting phenotype %a based on phenotype %a" % (phenotype_2, phenotype_1)) print(lm.summary()) print(lm.params) # prediction for fixed effect predictions_fe = lm.predict(pheno_test[phenotype_1]) # perform ridge regression on the residual (random effect part) residuals = pheno_tr[phenotype_2] - lm.predict(pheno_tr[phenotype_1]) # check marginal num_SNPs = geno_tr.shape[0] beta_list = [] for snp_idx in range(num_SNPs): lm_snp = sm.OLS(endog = residuals, exog = geno_tr.iloc[snp_idx].transpose()).fit_regularized(L1_wt = 1.0, alpha = 1.0) # clf = Ridge(alpha = 1.0) # a = clf.fit(y = residuals, X = geno_tr.iloc[snp_idx].transpose()) beta_list.append(lm_snp.params) if snp_idx % 1000 == 0: print(snp_idx) beta = pd.concat(beta_list) top_N_idx = np.argsort(abs(beta))[-top_N:] top_N_values = [beta[i] for i in top_N_idx] top_N_snps = geno_tr.iloc[top_N_idx].index lm_re = sm.OLS(endog = residuals, exog = geno_tr.loc[top_N_snps].transpose()).fit_regularized(L1_wt = 1.0, alpha = 1.0) if verbose: print(lm_re.params) predictions_re = lm_re.predict(geno_test.loc[top_N_snps].transpose()) # combine the result from both total_prediction = predictions_fe + predictions_re print (predictions_re, predictions_fe) mse = calculate_MSE(total_prediction, pheno_test[phenotype_2]) return(mse, test_sample_list) def top_N_snp_mixed_model_analysis_p(geno_df, pheno_df, phenotype_1, phenotype_2, top_N = 100, missing_rate = 0.1, sample_list = list(), verbose = False): corr_mat = calculate_highly_correlated_phenotypes(pheno_df) print("The correlation between %s and %s is %f" % (phenotype_1, phenotype_2, corr_mat[phenotype_1][phenotype_2])) # bind phenotype into list to extract phenotype_list = [phenotype_1, phenotype_2] # extract the phenotypes geno_select, pheno_select = select_phenotype_multiple_phenotypes(geno_df, pheno_df, phenotype_list = phenotype_list, verbose = verbose) # separate training and test dataset geno_tr, pheno_tr, geno_test, pheno_test, test_sample_list = separate_training_test(geno_select, pheno_select, missing_rate = missing_rate, sample_list_select = sample_list) # remove duplciates geno_test_new = geno_test.loc[:,~geno_test.columns.duplicated()] geno_test = geno_test_new[pheno_test[phenotype_2].index] # perform OLS lm = sm.OLS(endog = pheno_tr[phenotype_2], exog = pheno_tr[phenotype_1]).fit() if verbose: print("The linear model summary for predicting phenotype %a based on phenotype %a" % (phenotype_2, phenotype_1)) print(lm.summary()) print(lm.params) # prediction for fixed effect predictions_fe = lm.predict(pheno_test[phenotype_1]) # perform ridge regression on the residual (random effect part) residuals = pheno_tr[phenotype_2] - lm.predict(pheno_tr[phenotype_1]) # check marginal num_SNPs = geno_tr.shape[0] beta_list = [] p_beta_list = [] for snp_idx in range(num_SNPs): lm_snp = sm.OLS(endog = residuals, exog = geno_tr.iloc[snp_idx].transpose()).fit() p_val = lm_snp.pvalues[0] beta = lm_snp.params[0] if p_val < 0.05: beta_list.append(beta) p_beta_list.append(pd.Series([beta, p_val], name = geno_tr.iloc[snp_idx].name)) if snp_idx % 1000 == 0: print(snp_idx) p_beta_df = pd.concat(p_beta_list, axis = 1).transpose() p_beta_df.columns = ["beta", "pval"] p_beta_df.sort_values(by = ['pval'], inplace = True) top_N = min(top_N, p_beta_df.shape[0]) top_N_snps = p_beta_df.iloc[range(top_N)].index lm_re = sm.OLS(endog = residuals, exog = geno_tr.loc[top_N_snps].transpose()).fit_regularized(L1_wt = 1.0, alpha = 1.0) if verbose: print(lm_re.params) predictions_re = lm_re.predict(geno_test.loc[top_N_snps].transpose()) # combine the result from both total_prediction = predictions_fe + predictions_re print (predictions_re, predictions_fe) mse = calculate_MSE(total_prediction, pheno_test[phenotype_2]) return(mse, test_sample_list, top_N)

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0.186275

0

null

0

1

0

null

0

6

772b71d48e3505086046ce9e1f6d36f41148da8b

4,172

py

Python

tests/test_engine/test_queries/test_queryop_comparsion_eq.py

gitter-badger/MontyDB

849d03dc2cfed35739481e9acb1ff0bd8095c91b

[ "BSD-3-Clause" ]

null

tests/test_engine/test_queries/test_queryop_comparsion_eq.py

gitter-badger/MontyDB

849d03dc2cfed35739481e9acb1ff0bd8095c91b

[ "BSD-3-Clause" ]

null

tests/test_engine/test_queries/test_queryop_comparsion_eq.py

gitter-badger/MontyDB

849d03dc2cfed35739481e9acb1ff0bd8095c91b

[ "BSD-3-Clause" ]

null

from bson.binary import Binary from bson.code import Code from bson.int64 import Int64 from bson.decimal128 import Decimal128 from bson.py3compat import PY3 def test_qop_eq_1(monty_find, mongo_find): docs = [ {"a": 1}, {"a": 0} ] spec = {"a": 1} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 1 assert monty_c.count() == mongo_c.count() assert next(mongo_c) == next(monty_c) def test_qop_eq_2(monty_find, mongo_find): docs = [ {"a": 1}, {"a": 0} ] spec = {"a": {"$eq": 1}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 1 assert monty_c.count() == mongo_c.count() assert next(mongo_c) == next(monty_c) def test_qop_eq_3(monty_find, mongo_find): docs = [ {"a": [1]}, {"a": 1} ] spec = {"a": {"$eq": 1}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 2 assert monty_c.count() == mongo_c.count() for i in range(2): assert next(mongo_c) == next(monty_c) def test_qop_eq_4(monty_find, mongo_find): docs = [ {"a": [1]}, {"a": [[1], 2]} ] spec = {"a": {"$eq": [1]}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 2 assert monty_c.count() == mongo_c.count() for i in range(2): assert next(mongo_c) == next(monty_c) def test_qop_eq_5(monty_find, mongo_find): docs = [ {"a": [2, 1]}, {"a": [1, 2]}, {"a": [[2, 1], 3]}, {"a": [[1, 2], 3]}, ] spec = {"a": {"$eq": [2, 1]}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 2 assert monty_c.count() == mongo_c.count() for i in range(2): assert next(mongo_c) == next(monty_c) def test_qop_eq_6(monty_find, mongo_find): docs = [ {"a": [{"b": Binary(b"00")}]}, {"a": [{"b": Binary(b"01")}]}, ] spec = {"a.b": {"$eq": b"01"}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) count = 1 if PY3 else 0 assert mongo_c.count() == count assert monty_c.count() == mongo_c.count() if PY3: assert next(mongo_c) == next(monty_c) mongo_c.rewind() assert next(mongo_c)["_id"] == 1 def test_qop_eq_7(monty_find, mongo_find): docs = [ {"a": [{"b": Code("a")}]}, ] spec = {"a.b": {"$eq": "a"}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 0 assert monty_c.count() == mongo_c.count() def test_qop_eq_8(monty_find, mongo_find): docs = [ {"a": [{"b": "a"}]}, ] spec = {"a.b": {"$eq": Code("a")}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 0 assert monty_c.count() == mongo_c.count() def test_qop_eq_9(monty_find, mongo_find): docs = [ {"a": 1}, ] spec = {"a": {"$eq": Int64(1)}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 1 assert monty_c.count() == mongo_c.count() def test_qop_eq_10(monty_find, mongo_find): docs = [ {"a": 1}, {"a": 1.0}, ] spec = {"a": {"$eq": Decimal128("1")}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 2 assert monty_c.count() == mongo_c.count() def test_qop_eq_11(monty_find, mongo_find): docs = [ {"a": 1}, {"a": 1.0}, ] spec = {"a": {"$eq": Decimal128("1.0")}} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 2 assert monty_c.count() == mongo_c.count() def test_qop_eq_12(monty_find, mongo_find): docs = [ {"tags": [["ssl", "security"], "warning"]} ] spec = {"tags.0": "security"} monty_c = monty_find(docs, spec) mongo_c = mongo_find(docs, spec) assert mongo_c.count() == 0 assert monty_c.count() == mongo_c.count()

22.430108

50

0.551774

628

4,172

3.41242

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0.123192

0.14559

0.067196

0.836211

0.814279

0.803546

0.733551

0.709286

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0.031565

0.263423

4,172

185

51

22.551351

0.665799

0

0.595588

0

0.029969

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null

0

6

7753eb5e6420ca68d8ffef287ff38769166ae880

18,133

py

Python

lino_xl/lib/sepa/fixtures/sample_ibans.py

khchine5/xl

b1634937a9ce87af1e948eb712b934b11f221d9d

[ "BSD-2-Clause" ]

1

2018-01-12T14:09:48.000Z

lino_xl/lib/sepa/fixtures/sample_ibans.py

khchine5/xl

b1634937a9ce87af1e948eb712b934b11f221d9d

[ "BSD-2-Clause" ]

1

2019-09-10T05:03:47.000Z

lino_xl/lib/sepa/fixtures/sample_ibans.py

khchine5/xl

b1634937a9ce87af1e948eb712b934b11f221d9d

[ "BSD-2-Clause" ]

null

# -*- coding: UTF-8 -*- # Copyright 2015-2018 Rumma & Ko Ltd # License: BSD (see file COPYING for details) """Contains a random list of example IBANs to be used for generating demo data. Thanks to `www.mobilefish.com <http://www.mobilefish.com/services/random_iban_generator/random_iban_generator.php>`_. This is being tested in :doc:`/specs/iban`. """ IBANS = """\ AL32293653370340154130927280 AL68897217980092911449710922 AL87834345355057404249973940 AL73552891583236787384690218 AL52238964890057847269894484 AL56320936759864956119671013 AL20697342929432609350613358 AL70060940492321966744594065 AL66383778998922195726400092 AL95065292037102725798481031 AD5257784281812851432256 AD8878195033144625652270 AD2045350237593959919746 AD6523598450190238508072 AD3217704582405114672833 AD0925341412842777761708 AD7106167154302413624767 AD3275655950079340831543 AD5158473228250917101533 AD0203514247355959437856 AT689713187429950994 AT455998560003573572 AT397563080929053546 AT718255015615458671 AT778047296008343102 AT233377816198914246 AT316966754324807854 AT866472820010769219 AT583809717053621489 AT420427319442037888 AZ26CSHL28857497955447355397 AZ78MMVD81193974286497530319 AZ12GMWX67840235162066893769 AZ23OWYX21759158476039403442 AZ57XKMM26906912594709450600 AZ14GUHJ07000171328006790958 AZ20LSOS77176596643066514721 AZ32BUGZ39044794148736641526 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654df0e800d89cf2735a297e4c338a796e8ea104

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py

Python

light_cnn/__init__.py

NateThom/similarity_classifiers

5b320e150181232a00813482d8361590ff1fd47e

[ "MIT" ]

null

light_cnn/__init__.py

NateThom/similarity_classifiers

5b320e150181232a00813482d8361590ff1fd47e

[ "MIT" ]

null

light_cnn/__init__.py

NateThom/similarity_classifiers

5b320e150181232a00813482d8361590ff1fd47e

[ "MIT" ]

null

from .light_cnn import LightCnn

31

0.870968

5

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6

6560bd4f514ac3ea3a02b05113e17ef901543f0a

9,895

py

Python

usaspending_api/disaster/tests/integration/test_recipient_spending.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

null

usaspending_api/disaster/tests/integration/test_recipient_spending.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

null

usaspending_api/disaster/tests/integration/test_recipient_spending.py

g4brielvs/usaspending-api

bae7da2c204937ec1cdf75c052405b13145728d5

[ "CC0-1.0" ]

null

import pytest from rest_framework import status from usaspending_api.search.tests.data.utilities import setup_elasticsearch_test url = "/api/v2/disaster/recipient/spending/" @pytest.mark.django_db def test_correct_response_defc_no_results( client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["N"]) expected_results = [] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results @pytest.mark.django_db def test_correct_response_single_defc(client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L"]) expected_results = [ { "code": "987654321", "award_count": 2, "description": "RECIPIENT, 3", "id": ["d2894d22-67fc-f9cb-4005-33fa6a29ef86-C", "d2894d22-67fc-f9cb-4005-33fa6a29ef86-R"], "obligation": 2200.0, "outlay": 1100.0, }, { "code": "456789123", "award_count": 1, "description": "RECIPIENT 2", "id": ["3c92491a-f2cd-ec7d-294b-7daf91511866-R"], "obligation": 20.0, "outlay": 10.0, }, { "code": "DUNS Number not provided", "award_count": 1, "description": "RECIPIENT 1", "id": ["5f572ec9-8b49-e5eb-22c7-f6ef316f7689-R"], "obligation": 2.0, "outlay": 1.0, }, ] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results @pytest.mark.django_db def test_correct_response_multiple_defc( client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"]) expected_results = [ { "code": "987654321", "award_count": 3, "description": "RECIPIENT, 3", "id": ["d2894d22-67fc-f9cb-4005-33fa6a29ef86-C", "d2894d22-67fc-f9cb-4005-33fa6a29ef86-R"], "obligation": 202200.0, "outlay": 101100.0, }, { "code": "456789123", "award_count": 1, "description": "RECIPIENT 2", "id": ["3c92491a-f2cd-ec7d-294b-7daf91511866-R"], "obligation": 20.0, "outlay": 10.0, }, { "code": "DUNS Number not provided", "award_count": 1, "description": "RECIPIENT 1", "id": ["5f572ec9-8b49-e5eb-22c7-f6ef316f7689-R"], "obligation": 2.0, "outlay": 1.0, }, { "code": "096354360", "award_count": 1, "description": "MULTIPLE RECIPIENTS", "id": None, "obligation": 20000.0, "outlay": 10000.0, }, { "code": "DUNS Number not provided", "award_count": 1, "description": "MULTIPLE RECIPIENTS", "id": None, "obligation": 2000000.0, "outlay": 1000000.0, }, ] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results @pytest.mark.django_db def test_correct_response_with_query(client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], query="GIBBERISH") expected_results = [] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], query="3") expected_results = [ { "code": "987654321", "award_count": 3, "description": "RECIPIENT, 3", "id": ["d2894d22-67fc-f9cb-4005-33fa6a29ef86-C", "d2894d22-67fc-f9cb-4005-33fa6a29ef86-R"], "obligation": 202200.0, "outlay": 101100.0, } ] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], query="ENT, 3") expected_results = [ { "code": "987654321", "award_count": 3, "description": "RECIPIENT, 3", "id": ["d2894d22-67fc-f9cb-4005-33fa6a29ef86-C", "d2894d22-67fc-f9cb-4005-33fa6a29ef86-R"], "obligation": 202200.0, "outlay": 101100.0, } ] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], query="ReCiPiEnT,") expected_results = [ { "code": "987654321", "award_count": 3, "description": "RECIPIENT, 3", "id": ["d2894d22-67fc-f9cb-4005-33fa6a29ef86-C", "d2894d22-67fc-f9cb-4005-33fa6a29ef86-R"], "obligation": 202200.0, "outlay": 101100.0, } ] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results @pytest.mark.django_db def test_correct_response_with_award_type_codes( client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions ): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], award_type_codes=["IDV_A"]) expected_results = [] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], award_type_codes=["07", "A", "B"]) expected_results = [ { "code": "987654321", "award_count": 1, "description": "RECIPIENT, 3", "id": ["d2894d22-67fc-f9cb-4005-33fa6a29ef86-C", "d2894d22-67fc-f9cb-4005-33fa6a29ef86-R"], "obligation": 2000.0, "outlay": 1000.0, }, { "code": "456789123", "award_count": 1, "description": "RECIPIENT 2", "id": ["3c92491a-f2cd-ec7d-294b-7daf91511866-R"], "obligation": 20.0, "outlay": 10.0, }, { "code": "DUNS Number not provided", "award_count": 1, "description": "RECIPIENT 1", "id": ["5f572ec9-8b49-e5eb-22c7-f6ef316f7689-R"], "obligation": 2.0, "outlay": 1.0, }, { "code": "096354360", "award_count": 1, "description": "MULTIPLE RECIPIENTS", "id": None, "obligation": 20000.0, "outlay": 10000.0, }, ] assert resp.status_code == status.HTTP_200_OK assert resp.json()["results"] == expected_results @pytest.mark.django_db def test_invalid_defc(client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["ZZ"]) assert resp.status_code == status.HTTP_400_BAD_REQUEST assert resp.data["detail"] == "Field 'filter|def_codes' is outside valid values ['L', 'M', 'N']" @pytest.mark.django_db def test_invalid_defc_type(client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes="100") assert resp.status_code == status.HTTP_400_BAD_REQUEST assert resp.data["detail"] == "Invalid value in 'filter|def_codes'. '100' is not a valid type (array)" @pytest.mark.django_db def test_missing_defc(client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url) assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY assert resp.data["detail"] == "Missing value: 'filter|def_codes' is a required field" @pytest.mark.django_db def test_pagination_page_and_limit(client, monkeypatch, helpers, elasticsearch_award_index, awards_and_transactions): setup_elasticsearch_test(monkeypatch, elasticsearch_award_index) resp = helpers.post_for_spending_endpoint(client, url, def_codes=["L", "M"], page=2, limit=1) expected_results = { "totals": {"award_count": 7, "obligation": 2222222.0, "outlay": 1111111.0}, "results": [ { "code": "456789123", "award_count": 1, "description": "RECIPIENT 2", "id": ["3c92491a-f2cd-ec7d-294b-7daf91511866-R"], "obligation": 20.0, "outlay": 10.0, } ], "page_metadata": { "page": 2, "total": 5, "limit": 1, "next": 3, "previous": 1, "hasNext": True, "hasPrevious": True, }, "messages": [ "Notice! API Request to sort on 'id' field isn't fully " "implemented. Results were actually sorted using 'description' " "field." ], } assert resp.status_code == status.HTTP_200_OK assert resp.json() == expected_results

36.245421

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9,895

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0.045328

0.072176

0.040795

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0.867678

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0.826011

0.820084

0

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0.260536

9,895

272

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36.378676

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0.037815

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0

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0

0.05042

0

null

0

1

0

null

0

6

659fea67015d91605eb401fdac04d9b519e90de3

2,816

py

Python

cogs/srtr.py

tasuren/tensei_disko

7ec1d88e3e80f13cc2a17700aae672f5bf9a876d

[ "MIT" ]

null

cogs/srtr.py

tasuren/tensei_disko

7ec1d88e3e80f13cc2a17700aae672f5bf9a876d

[ "MIT" ]

null

cogs/srtr.py

tasuren/tensei_disko

7ec1d88e3e80f13cc2a17700aae672f5bf9a876d

[ "MIT" ]

null

from discord.ext import commands import discord import pickle import n_fc class srtr(commands.Cog): def __init__(self, bot: commands.Bot): self.bot = bot @commands.command() async def srtr(self, ctx: commands.Context): if ctx.message.content == "n#srtr": embed = discord.Embed(title="しりとり", description=f"`n!srtr start`でそのチャンネルでしりとり（風対話）を実行し、`n!srtr stop`でしりとりを停止します。", color=0x00ff00) await ctx.message.reply(embed=embed) return if ctx.message.content == "n#srtr start": try: if ctx.message.guild.id in n_fc.srtr_bool_list: if ctx.message.channel.id in n_fc.srtr_bool_list: embed = discord.Embed(title="しりとり", description=f"{ctx.message.channel.name}でしりとりは既にに実行されています。", color=0x00ff00) await ctx.message.reply(embed=embed) return else: n_fc.srtr_bool_list[ctx.message.guild.id] = {ctx.message.channel.id:1} if ctx.message.guild.id not in n_fc.srtr_bool_list: n_fc.srtr_bool_list[ctx.message.guild.id] = {ctx.message.channel.id:1} with open('srtr_bool_list.nira', 'wb') as f: pickle.dump(n_fc.srtr_bool_list, f) except BaseException as err: await ctx.message.reply("err") return embed = discord.Embed(title="しりとり", description=f"{ctx.message.channel.name}でしりとりを始めます。", color=0x00ff00) await ctx.message.reply(embed=embed) return if ctx.message.content == "n#srtr stop": try: if ctx.message.guild.id not in n_fc.srtr_bool_list: embed = discord.Embed(title="しりとり", description=f"{ctx.message.guild.name}でしりとりは実行されていません。", color=0x00ff00) await ctx.message.reply(embed=embed) return if ctx.message.channel.id not in n_fc.srtr_bool_list[ctx.message.guild.id]: embed = discord.Embed(title="しりとり", description=f"{ctx.message.channel.name}でしりとりは実行されていません。", color=0x00ff00) await ctx.message.reply(embed=embed) return del n_fc.srtr_bool_list[ctx.message.guild.id][ctx.message.channel.id] with open('srtr_bool_list.nira', 'wb') as f: pickle.dump(n_fc.srtr_bool_list, f) except BaseException as err: await ctx.message.reply("err") embed = discord.Embed(title="しりとり", description=f"{ctx.message.channel.name}でのしりとりを終了します。", color=0x00ff00) await ctx.message.reply(embed=embed) return def setup(bot): bot.add_cog(srtr(bot))

51.2

142

0.584517

353

2,816

4.549575

0.184136

0.174346

0.089664

0.068493

0.807597

0.791407

0.762142

0.725405

0.721046

0.6401

0

0.016318

0.303622

2,816

55

143

51.2

0.802652

0

0.519231

0

0.12957

0.084487

0

0.017039

0

1

0.038462

false

0

0.076923

0

0.269231

0

null

0

1

0

null

0

6

65acdeafe551513b7c2679223546249143fbfdfd

3,043

py

Python

tests/pytests/functional/utils/user/test_chugid_and_umask.py

ifraixedes/saltstack-salt

b54becb8b43cc9b7c00b2c0bc637ac534dc62896

[ "Apache-2.0" ]

3

2015-08-30T04:23:47.000Z

2018-07-15T00:35:23.000Z

tests/pytests/functional/utils/user/test_chugid_and_umask.py

ifraixedes/saltstack-salt

b54becb8b43cc9b7c00b2c0bc637ac534dc62896

[ "Apache-2.0" ]

4

2016-05-10T22:05:34.000Z

2016-05-20T18:10:13.000Z

tests/pytests/functional/utils/user/test_chugid_and_umask.py

ifraixedes/saltstack-salt

b54becb8b43cc9b7c00b2c0bc637ac534dc62896

[ "Apache-2.0" ]

1

2022-02-22T10:43:09.000Z

import functools import os import subprocess import pytest import salt.utils.user pytestmark = [ pytest.mark.destructive_test, pytest.mark.skip_if_not_root, pytest.mark.skip_on_windows, ] @pytest.fixture(scope="module") def account_1(): with pytest.helpers.create_account(create_group=True) as _account: yield _account @pytest.fixture(scope="module") def account_2(account_1): with pytest.helpers.create_account(group_name=account_1.group.name) as _account: yield _account def test_chugid(account_1, tmp_path): # Since we're changing accounts to touch the file, the parent directory must be user and group writable tmp_path.chmod(0o770) testfile = tmp_path / "testfile" # We should fail because the parent directory group owner is not the account running the test ret = subprocess.run( ["touch", str(testfile)], preexec_fn=functools.partial( salt.utils.user.chugid_and_umask, runas=account_1.username, umask=None, group=None, ), check=False, ) assert ret.returncode != 0 # However if we change the group ownership to one of the account's groups, it should succeed os.chown(str(tmp_path), 0, account_1.group.info.gid) ret = subprocess.run( ["touch", str(testfile)], preexec_fn=functools.partial( salt.utils.user.chugid_and_umask, runas=account_1.username, umask=None, group=None, ), check=False, ) assert ret.returncode == 0 assert testfile.exists() testfile_stat = testfile.stat() assert testfile_stat.st_uid == account_1.info.uid assert testfile_stat.st_gid == account_1.info.gid def test_chugid_and_group(account_1, account_2, tmp_path): # Since we're changing accounts to touch the file, the parent directory must be world-writable tmp_path.chmod(0o770) testfile = tmp_path / "testfile" # We should fail because the parent directory group owner is not the account running the test ret = subprocess.run( ["touch", str(testfile)], preexec_fn=functools.partial( salt.utils.user.chugid_and_umask, runas=account_2.username, umask=None, group=account_1.group.name, ), check=False, ) assert ret.returncode != 0 # However if we change the group ownership to one of the account's groups, it should succeed os.chown(str(tmp_path), 0, account_1.group.info.gid) ret = subprocess.run( ["touch", str(testfile)], preexec_fn=functools.partial( salt.utils.user.chugid_and_umask, runas=account_2.username, umask=None, group=account_1.group.name, ), check=False, ) assert ret.returncode == 0 assert testfile.exists() testfile_stat = testfile.stat() assert testfile_stat.st_uid == account_2.info.uid assert testfile_stat.st_gid == account_1.group.info.gid

28.980952

107

0.659547

404

3,043

4.80198

0.227723

0.057732

0.040206

0.043299

0.835567

0.829381

0.797938

0.758763

0.719588

0

0.014448

0.249425

3,043

104

108

29.259615

0.834939

0.184029

0

0.692308

0

0.019386

0

0.128205

1

0.051282

false

0

0.064103

0

0.115385

0

null

0

1

0

null

0

6

65d2f4a74b021735c340a1be2b2510e8335d2269

117

py

Python

URI/2747.py

namelew/PythonExercices

e6701dddf163b616987fc9edd8b9ef8e9a207e84

[ "MIT" ]

null

URI/2747.py

namelew/PythonExercices

e6701dddf163b616987fc9edd8b9ef8e9a207e84

[ "MIT" ]

1

2020-11-09T17:20:58.000Z

2020-11-09T17:21:10.000Z

URI/2747.py

namelew/PythonExercices

e6701dddf163b616987fc9edd8b9ef8e9a207e84

[ "MIT" ]

null

x = 1 print("-"*39) while x <= 5: print("|",end="") print(end=" "*37) print("|") x += 1 print("-"*39)

14.625

21

0.410256

17

117

2.823529

0.470588

0.083333

0.291667

0.375

0

0.104651

0.264957

117

8

22

14.625

0.453488

0

0.25

0

0.042373

0

1

0

false

0

0.625

1

0

null

0

1

0

1

0

1

0

null

0

1

0

6

029e613fe9ea82daf80c7507f2ee11a12e283277

90

py

Python

10/03/2/package1/package11/module1.py

pylangstudy/201706

f1cc6af6b18e5bd393cda27f5166067c4645d4d3

[ "CC0-1.0" ]

null

10/03/2/package1/package11/module1.py

pylangstudy/201706

f1cc6af6b18e5bd393cda27f5166067c4645d4d3

[ "CC0-1.0" ]

70

2017-06-01T11:02:51.000Z

2017-06-30T00:35:32.000Z

10/03/3/package1/package11/module1.py

pylangstudy/201706

f1cc6af6b18e5bd393cda27f5166067c4645d4d3

[ "CC0-1.0" ]

null

print('0/package/module1.py Run!!') def some_method(): print('module1.some_method()')

22.5

35

0.688889

13

90

4.615385

0.692308

0.333333

0

0.037037

0.1

90

3

36

30

0.703704

0

0.522222

0.233333

0

1

0.333333

true

0

0.333333

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

6

02a715c6564fe587885db6c48b02edac2967b559

86

py

Python

Programa/menu/__init__.py

NicolasGandolfi/Exercicios-Python

935fe3577c149192f9e29568e9798e970a620131

[ "MIT" ]

null

Programa/menu/__init__.py

NicolasGandolfi/Exercicios-Python

935fe3577c149192f9e29568e9798e970a620131

[ "MIT" ]

null

Programa/menu/__init__.py

NicolasGandolfi/Exercicios-Python

935fe3577c149192f9e29568e9798e970a620131

[ "MIT" ]

null

def linha(txt): print('\033[0;35m—'*50) print(f' {txt}') print('—'*50)

12.285714

27

0.476744

15

86

2.866667

0.666667

0.372093

0

0.153846

0.244186

86

6

28

14.333333

0.476923

0

0.22619

0

1

0.25

false

0

0.25

0.75

1

0

null

1

0

1

0

1

0

null

0

1

0

1

0

6

02f47dadea79576a9f157ac8f16ff4cc069abf71

106

py

Python

verilogparser/__init__.py

sepandhaghighi/verilogparser

8983b8d74fa28605b6a6772c6a02eafa6e6ba213

[ "MIT" ]

13

2017-10-29T15:52:19.000Z

2022-02-06T18:32:20.000Z

verilogparser/__init__.py

sepandhaghighi/verilogparser

8983b8d74fa28605b6a6772c6a02eafa6e6ba213

[ "MIT" ]

null

verilogparser/__init__.py

sepandhaghighi/verilogparser

8983b8d74fa28605b6a6772c6a02eafa6e6ba213

[ "MIT" ]

4

2020-01-20T07:13:26.000Z

2022-02-06T18:32:59.000Z

# -*- coding: utf-8 -*- from .verilogparser import * from .logics import * from .deductivelogic import *

17.666667

29

0.688679

12

106

6.083333

0.666667

0.273973

0

0.011364

0.169811

106

6

29

17.666667

0.818182

0.198113

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

b82486939a3556979f307de4e951e7d15a60a4d1

38

py

Python

tests/test_video.py

hixan/av_slice

0fbb4f45281f701c8e9eb9c764f380a719b5d1c0

[ "MIT" ]

null

tests/test_video.py

hixan/av_slice

0fbb4f45281f701c8e9eb9c764f380a719b5d1c0

[ "MIT" ]

2

2020-05-05T07:55:38.000Z

2021-11-15T17:48:40.000Z

tests/test_video.py

hixan/av_slice

0fbb4f45281f701c8e9eb9c764f380a719b5d1c0

[ "MIT" ]

null

def test_remove_sections(): pass

9.5

27

0.710526

5

38

5

1

0

0.210526

38

3

28

12.666667

0.833333

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

b846a8c31d3bdf2cd5d292aca7bed13053efd337

95

py

Python

job/src/slipstream/job/__init__.py

slipstream/SlipStreamJobEngine

9860283b66ee053022c8261517d85b2a1088610c

[ "Apache-2.0" ]

3

2019-04-27T10:36:21.000Z

2019-04-29T12:41:57.000Z

code/src/nuvla/job/__init__.py

nuvla/job-engine

58d42bd24d8dd2c6e28541c08df1455c9ac909f6

[ "Apache-2.0" ]

131

2019-02-13T06:00:49.000Z

2022-03-29T15:06:03.000Z

job/src/slipstream/job/__init__.py

slipstream/SlipStreamJobEngine

9860283b66ee053022c8261517d85b2a1088610c

[ "Apache-2.0" ]

1

2020-12-03T11:35:21.000Z

# -*- coding: utf-8 -*- from .distributor import * from .executor import * from .job import *

15.833333

26

0.652632

12

95

5.166667

0.666667

0.322581

0

0.012987

0.189474

95

5

27

19

0.792208

0.221053

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

b8574142b53ed1a09720632bf05e3d45b60cc5c3

32

py

Python

Modules/TrainUS/TrainUSLib/__init__.py

EBATINCA/TrainUS

f24c894d23f4f608ccef77914215eba8c5559101

[ "Apache-2.0" ]

2

2022-01-18T22:39:03.000Z

2022-01-20T10:28:21.000Z

Modules/TrainUS/TrainUSLib/__init__.py

EBATINCA/TrainUS

f24c894d23f4f608ccef77914215eba8c5559101

[ "Apache-2.0" ]

2

2022-01-28T13:11:57.000Z

2022-03-29T11:22:23.000Z

Modules/TrainUS/TrainUSLib/__init__.py

EBATINCA/TrainUS

f24c894d23f4f608ccef77914215eba8c5559101

[ "Apache-2.0" ]

null

from .TrainUSParameters import *

32

0.84375

3

32

9

1

0

0.09375

32

1

32

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

b868ad5d7e98a14225c6fd781e4afd72adc90c51

265

py

Python

lib/machine_learning/context_classification/context_classifiers/__init__.py

thesmarthomeninja/Video_Gaming_ML

e9c147f33a790a9cd3e4ee631ddbf6bbf91c3921

[ "MIT" ]

null

lib/machine_learning/context_classification/context_classifiers/__init__.py

thesmarthomeninja/Video_Gaming_ML

e9c147f33a790a9cd3e4ee631ddbf6bbf91c3921

[ "MIT" ]

4

2020-09-25T22:39:46.000Z

2022-02-09T23:39:43.000Z

lib/machine_learning/context_classification/context_classifiers/__init__.py

AsimKhan2019/Serpent-AI

e9c147f33a790a9cd3e4ee631ddbf6bbf91c3921

[ "MIT" ]

null

from lib.machine_learning.context_classification.context_classifiers.svm_context_classifier import SVMContextClassifier from lib.machine_learning.context_classification.context_classifiers.cnn_inception_v3_context_classifier import CNNInceptionV3ContextClassifier

66.25

143

0.935849

28

265

8.428571

0.535714

0.059322

0.118644

0.186441

0.516949

0

0.007813

0.033962

265

3

144

88.333333

0.914063

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

6

b886a4fbaf44bb591caaabce7624bbaaf45ab8b8

130

py

Python

privx_api/utils.py

hokenssh/privx-sdk-for-python

24627d25c0343f350c9b2396677344b771f8aec6

[ "Apache-2.0" ]

4

2020-06-15T17:14:18.000Z

2021-12-20T12:12:56.000Z

privx_api/utils.py

hokenssh/privx-sdk-for-python

24627d25c0343f350c9b2396677344b771f8aec6

[ "Apache-2.0" ]

5

2019-11-25T07:04:07.000Z

2021-05-19T08:09:53.000Z

privx_api/utils.py

hokenssh/privx-sdk-for-python

24627d25c0343f350c9b2396677344b771f8aec6

[ "Apache-2.0" ]

23

2019-11-22T08:17:58.000Z

2022-02-21T15:50:36.000Z

from typing import Any def get_value(obj: Any, default_value: Any) -> Any: return obj if obj is not None else default_value

21.666667

52

0.738462

23

130

4.043478

0.652174

0.258065

0

0.2

130

5

53

26

0.894231

0

1

0.333333

false

0

0.333333

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

b88e654e466ad3288c50eb35d774882f8b85c7e5

169

py

Python

Databaselayer/IPostStatus.py

rohitgs28/FindMyEmployer

d4b369eb488f44e40ef371ac09847f8ccc39994c

[ "MIT" ]

null

Databaselayer/IPostStatus.py

rohitgs28/FindMyEmployer

d4b369eb488f44e40ef371ac09847f8ccc39994c

[ "MIT" ]

null

Databaselayer/IPostStatus.py

rohitgs28/FindMyEmployer

d4b369eb488f44e40ef371ac09847f8ccc39994c

[ "MIT" ]

null

import hashlib, os import logging class IPostStatus: def insertUserStatus(self): raise NotImplementedError def getUserStatuses(self): raise NotImplementedError

24.142857

57

0.810651

17

169

8.058824

0.705882

0.131387

0.408759

0

0.142012

169

6

58

28.166667

0.944828

0

1

0.4

false

0

0.4

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

b22bd61bfb3ebf0345e574b99e45efbbff54a732

40,169

py

Python

cameo/localdb.py

muchu1983/104_cameo

8c7f78de198a5bd8d870589402e3b7e8b59f520a

[ "BSD-3-Clause" ]

null

cameo/localdb.py

muchu1983/104_cameo

8c7f78de198a5bd8d870589402e3b7e8b59f520a

[ "BSD-3-Clause" ]

null

cameo/localdb.py

muchu1983/104_cameo

8c7f78de198a5bd8d870589402e3b7e8b59f520a

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Copyright (C) 2015, MuChu Hsu Contributed by Muchu Hsu (muchu1983@gmail.com) This file is part of BSD license <https://opensource.org/licenses/BSD-3-Clause> """ from bennu.localdb import SQLite3Db from bennu.localdb import MongoDb import random """ 本地端資料庫存取 """ #匯入json class LocalDbForJsonImporter: #建構子 def __init__(self): self.mongodb = MongoDb().getClient().localdb #匯率API class LocalDbForCurrencyApi: #建構子 def __init__(self): self.mongodb = MongoDb().getClient().localdb #crunchbase class LocalDbForCRUNCHBASE: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS crunchbase_account(" "id INTEGER PRIMARY KEY," "strEmail TEXT NOT NULL," "strPassword TEXT NOT NULL," "strStatus TEXT NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS crunchbase_organization(" "id INTEGER PRIMARY KEY," "strOrganizationUrl TEXT NOT NULL," "isGot BOOLEAN NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存 account def insertAccountIfNotExists(self, strEmail=None, strPassword=None): strSQL = "SELECT * FROM crunchbase_account WHERE strEmail='%s'"%strEmail lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO crunchbase_account VALUES(NULL, '%s', '%s', 'ready')"%(strEmail, strPassword) self.db.commitSQL(strSQL=strSQL) #隨機取得可用的 account def fetchRandomReadyAccount(self): strSQL = "SELECT * FROM crunchbase_account WHERE strStatus='ready'" lstRowData = self.db.fetchallSQL(strSQL=strSQL) rowDataAccount = lstRowData[random.randint(0, len(lstRowData)-1)] return (rowDataAccount["strEmail"], rowDataAccount["strPassword"]) #若無重覆儲存 organization URL def insertOrganizationUrlIfNotExists(self, strOrganizationUrl=None): strSQL = "SELECT * FROM crunchbase_organization WHERE strOrganizationUrl='%s'"%strOrganizationUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO crunchbase_organization VALUES(NULL, '%s', 0)"%strOrganizationUrl self.db.commitSQL(strSQL=strSQL) #取得所有尚未完成下載的 organization url def fetchallNotObtainedOrganizationUrl(self): strSQL = "SELECT strOrganizationUrl FROM crunchbase_organization WHERE isGot=0" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrCompanyUrl = [] for rowData in lstRowData: lstStrCompanyUrl.append(rowData["strOrganizationUrl"]) return lstStrCompanyUrl #檢查 organization 是否已下載 def checkOrganizationIsGot(self, strOrganizationUrl=None): isGot = True strSQL = "SELECT * FROM crunchbase_organization WHERE strOrganizationUrl='%s'"%strOrganizationUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 organization 為已完成下載狀態 def updateOrganizationStatusIsGot(self, strOrganizationUrl=None): strSQL = "UPDATE crunchbase_organization SET isGot=1 WHERE strOrganizationUrl='%s'"%strOrganizationUrl self.db.commitSQL(strSQL=strSQL) #取得所有已完成下載的 organization url def fetchallCompletedObtainedOrganizationUrl(self): strSQL = "SELECT strOrganizationUrl FROM crunchbase_organization WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrCompanyUrl = [] for rowData in lstRowData: lstStrCompanyUrl.append(rowData["strOrganizationUrl"]) return lstStrCompanyUrl #更新 organization 尚未開始下載狀態 def updateOrganizationStatusIsNotGot(self, strOrganizationUrl=None): strSQL = "UPDATE crunchbase_organization SET isGot=0 WHERE strOrganizationUrl='%s'"%strOrganizationUrl self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM crunchbase_organization" self.db.commitSQL(strSQL=strSQL) #crowdcube class LocalDbForCROWDCUBE: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS crowdcube_account(" "id INTEGER PRIMARY KEY," "strEmail TEXT NOT NULL," "strPassword TEXT NOT NULL," "strStatus TEXT NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS crowdcube_company(" "id INTEGER PRIMARY KEY," "strCompanyUrl TEXT NOT NULL," "isGot BOOLEAN NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存 account def insertAccountIfNotExists(self, strEmail=None, strPassword=None): strSQL = "SELECT * FROM crowdcube_account WHERE strEmail='%s'"%strEmail lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO crowdcube_account VALUES(NULL, '%s', '%s', 'ready')"%(strEmail, strPassword) self.db.commitSQL(strSQL=strSQL) #隨機取得可用的 account def fetchRandomReadyAccount(self): strSQL = "SELECT * FROM crowdcube_account WHERE strStatus='ready'" lstRowData = self.db.fetchallSQL(strSQL=strSQL) rowDataAccount = lstRowData[random.randint(0, len(lstRowData)-1)] return (rowDataAccount["strEmail"], rowDataAccount["strPassword"]) #若無重覆儲存 company URL def insertCompanyUrlIfNotExists(self, strCompanyUrl=None): strSQL = "SELECT * FROM crowdcube_company WHERE strCompanyUrl='%s'"%strCompanyUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO crowdcube_company VALUES(NULL, '%s', 0)"%strCompanyUrl self.db.commitSQL(strSQL=strSQL) #取得所有尚未完成下載的 company url def fetchallNotObtainedCompanyUrl(self): strSQL = "SELECT strCompanyUrl FROM crowdcube_company WHERE isGot=0" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrCompanyUrl = [] for rowData in lstRowData: lstStrCompanyUrl.append(rowData["strCompanyUrl"]) return lstStrCompanyUrl #檢查 company 是否已下載 def checkCompanyIsGot(self, strCompanyUrl=None): isGot = True strSQL = "SELECT * FROM crowdcube_company WHERE strCompanyUrl='%s'"%strCompanyUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 company 為已完成下載狀態 def updateCompanyStatusIsGot(self, strCompanyUrl=None): strSQL = "UPDATE crowdcube_company SET isGot=1 WHERE strCompanyUrl='%s'"%strCompanyUrl self.db.commitSQL(strSQL=strSQL) #取得所有已完成下載的 company url def fetchallCompletedObtainedCompanyUrl(self): strSQL = "SELECT strCompanyUrl FROM crowdcube_company WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrCompanyUrl = [] for rowData in lstRowData: lstStrCompanyUrl.append(rowData["strCompanyUrl"]) return lstStrCompanyUrl #更新 company 尚未開始下載狀態 def updateCompanyStatusIsNotGot(self, strCompanyUrl=None): strSQL = "UPDATE crowdcube_company SET isGot=0 WHERE strCompanyUrl='%s'"%strCompanyUrl self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM crowdcube_company" self.db.commitSQL(strSQL=strSQL) #京東眾籌 class LocalDbForJD: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS jd_category(" "id INTEGER PRIMARY KEY," "strCategoryPage1Url TEXT NOT NULL," "strCategoryName TEXT NOT NULL," "isGot BOOLEAN NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS jd_project(" "id INTEGER PRIMARY KEY," "strProjectUrl TEXT NOT NULL," "intCategoryId INTEGER NOT NULL," "isGot BOOLEAN NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ( "CREATE TABLE IF NOT EXISTS jd_funder(" "id INTEGER PRIMARY KEY," "strFunderUrl TEXT NOT NULL," "intCategoryId INTEGER NOT NULL," "isGot BOOLEAN NOT NULL)" ) self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存 category def insertCategoryIfNotExists(self, strCategoryPage1Url=None, strCategoryName=None): strSQL = "SELECT * FROM jd_category WHERE strCategoryPage1Url='%s'"%strCategoryPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO jd_category VALUES(NULL, '%s', '%s', 0)"%(strCategoryPage1Url, strCategoryName) self.db.commitSQL(strSQL=strSQL) #取得 category 名稱 def fetchCategoryNameByUrl(self, strCategoryPage1Url=None): strSQL = "SELECT * FROM jd_category WHERE strCategoryPage1Url='%s'"%strCategoryPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) return lstRowData[0]["strCategoryName"] #取得所有 category 第一頁 url (指定 isGot 狀態) def fetchallCategoryUrl(self, isGot=False): dicIsGotCode = {True:"1", False:"0"} strSQL = "SELECT strCategoryPage1Url FROM jd_category WHERE isGot=%s"%dicIsGotCode[isGot] lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrCategoryPage1Url = [] for rowData in lstRowData: lstStrCategoryPage1Url.append(rowData["strCategoryPage1Url"]) return lstStrCategoryPage1Url #取得所有未完成下載的 category 第一頁 url def fetchallNotObtainedCategoryUrl(self): return self.fetchallCategoryUrl(isGot=False) #取得所有已完成下載的 category 第一頁 url def fetchallCompletedObtainedCategoryUrl(self): return self.fetchallCategoryUrl(isGot=True) #更新 category 為已完成下載狀態 def updateCategoryStatusIsGot(self, strCategoryPage1Url=None): strSQL = "UPDATE jd_category SET isGot=1 WHERE strCategoryPage1Url='%s'"%strCategoryPage1Url self.db.commitSQL(strSQL=strSQL) #取得 category id def fetchCategoryIdByUrl(self, strCategoryPage1Url=None): strSQL = "SELECT * FROM jd_category WHERE strCategoryPage1Url='%s'"%strCategoryPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) return lstRowData[0]["id"] #若無重覆儲存 project URL def insertProjectUrlIfNotExists(self, strProjectUrl=None, strCategoryPage1Url=None): intCategoryId = self.fetchCategoryIdByUrl(strCategoryPage1Url=strCategoryPage1Url) #insert project url if not exists strSQL = "SELECT * FROM jd_project WHERE strProjectUrl='%s'"%strProjectUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO jd_project VALUES(NULL, '%s', %d,0)"%(strProjectUrl, intCategoryId) self.db.commitSQL(strSQL=strSQL) #若無重覆儲存 funder URL def insertFunderUrlIfNotExists(self, strFunderUrl=None, strCategoryPage1Url=None): intCategoryId = self.fetchCategoryIdByUrl(strCategoryPage1Url=strCategoryPage1Url) #insert funder url if not exists strSQL = "SELECT * FROM jd_funder WHERE strFunderUrl='%s'"%strFunderUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO jd_funder VALUES(NULL, '%s', %d,0)"%(strFunderUrl, intCategoryId) self.db.commitSQL(strSQL=strSQL) #取得指定 category 的 project url def fetchallProjectUrlByCategoryUrl(self, strCategoryPage1Url=None): intCategoryId = self.fetchCategoryIdByUrl(strCategoryPage1Url=strCategoryPage1Url) strSQL = "SELECT * FROM jd_project WHERE intCategoryId=%d"%intCategoryId lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrProjectUrl = [] for rowData in lstRowData: lstStrProjectUrl.append(rowData["strProjectUrl"]) return lstStrProjectUrl #取得指定 category 的 funder url def fetchallFunderUrlByCategoryUrl(self, strCategoryPage1Url=None): intCategoryId = self.fetchCategoryIdByUrl(strCategoryPage1Url=strCategoryPage1Url) strSQL = "SELECT * FROM jd_funder WHERE intCategoryId=%d"%intCategoryId lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrFunderUrl = [] for rowData in lstRowData: lstStrFunderUrl.append(rowData["strFunderUrl"]) return lstStrFunderUrl #檢查 project 是否已下載 def checkProjectIsGot(self, strProjectUrl=None): isGot = True strSQL = "SELECT * FROM jd_project WHERE strProjectUrl='%s'"%strProjectUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #檢查 funder 是否已下載 def checkFunderIsGot(self, strFunderUrl=None): isGot = True strSQL = "SELECT * FROM jd_funder WHERE strFunderUrl='%s'"%strFunderUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 project 為已完成下載狀態 def updateProjectStatusIsGot(self, strProjectUrl=None): strSQL = "UPDATE jd_project SET isGot=1 WHERE strProjectUrl='%s'"%strProjectUrl self.db.commitSQL(strSQL=strSQL) #更新 funder 為已完成下載狀態 def updateFunderStatusIsGot(self, strFunderUrl=None): strSQL = "UPDATE jd_funder SET isGot=1 WHERE strFunderUrl='%s'"%strFunderUrl self.db.commitSQL(strSQL=strSQL) #取得所有已完成下載的 project url def fetchallCompletedObtainedProjectUrl(self): strSQL = "SELECT strProjectUrl FROM jd_project WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrProjectUrl = [] for rowData in lstRowData: lstStrProjectUrl.append(rowData["strProjectUrl"]) return lstStrProjectUrl #取得所有已完成下載的 funder url def fetchallCompletedObtainedFunderUrl(self): strSQL = "SELECT strFunderUrl FROM jd_funder WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrFunderUrl = [] for rowData in lstRowData: lstStrFunderUrl.append(rowData["strFunderUrl"]) return lstStrFunderUrl #更新 project 尚未開始下載狀態 def updateProjectStatusIsNotGot(self, strProjectUrl=None): strSQL = "UPDATE jd_project SET isGot=0 WHERE strProjectUrl='%s'"%strProjectUrl self.db.commitSQL(strSQL=strSQL) #更新 funder 尚未開始下載狀態 def updateFunderStatusIsNotGot(self, strFunderUrl=None): strSQL = "UPDATE jd_funder SET isGot=0 WHERE strFunderUrl='%s'"%strFunderUrl self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM jd_category" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM jd_project" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM jd_funder" self.db.commitSQL(strSQL=strSQL) #TECHCRUNCH class LocalDbForTECHCRUNCH: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS techcrunch_news(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "intTopicId INTEGER NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS techcrunch_topic(" "id INTEGER PRIMARY KEY," "strTopicPage1Url TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存 topic def insertTopicIfNotExists(self, strTopicPage1Url=None): strSQL = "SELECT * FROM techcrunch_topic WHERE strTopicPage1Url='%s'"%strTopicPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO techcrunch_topic VALUES(NULL, '%s', 0)"%strTopicPage1Url self.db.commitSQL(strSQL=strSQL) #取得所有 topic 第一頁 url (指定 isGot 狀態) def fetchallTopicUrl(self, isGot=False): dicIsGotCode = {True:"1", False:"0"} strSQL = "SELECT strTopicPage1Url FROM techcrunch_topic WHERE isGot=%s"%dicIsGotCode[isGot] lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTopicPage1Url = [] for rowData in lstRowData: lstStrTopicPage1Url.append(rowData["strTopicPage1Url"]) return lstStrTopicPage1Url #取得所有未完成下載的 topic 第一頁 url def fetchallNotObtainedTopicUrl(self): return self.fetchallTopicUrl(isGot=False) #取得所有已完成下載的 topic 第一頁 url def fetchallCompletedObtainedTopicUrl(self): return self.fetchallTopicUrl(isGot=True) #更新 topic 為已完成下載狀態 def updateTopicStatusIsGot(self, strTopicPage1Url=None): strSQL = "UPDATE techcrunch_topic SET isGot=1 WHERE strTopicPage1Url='%s'"%strTopicPage1Url self.db.commitSQL(strSQL=strSQL) #取得 topic id def fetchTopicIdByUrl(self, strTopicPage1Url=None): strSQL = "SELECT * FROM techcrunch_topic WHERE strTopicPage1Url='%s'"%strTopicPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) return lstRowData[0]["id"] #若無重覆儲存 news URL def insertNewsUrlIfNotExists(self, strNewsUrl=None, strTopicPage1Url=None): intTopicId = self.fetchTopicIdByUrl(strTopicPage1Url=strTopicPage1Url) #insert news url if not exists strSQL = "SELECT * FROM techcrunch_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO techcrunch_news VALUES(NULL, '%s', %d,0)"%(strNewsUrl, intTopicId) self.db.commitSQL(strSQL=strSQL) #取得指定 topic 的 news url def fetchallNewsUrlByTopicUrl(self, strTopicPage1Url=None): intTopicId = self.fetchTopicIdByUrl(strTopicPage1Url=strTopicPage1Url) strSQL = "SELECT * FROM techcrunch_news WHERE intTopicId=%d"%intTopicId lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #檢查 news 是否已下載 def checkNewsIsGot(self, strNewsUrl=None): isGot = True strSQL = "SELECT * FROM techcrunch_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 news 為已完成下載狀態 def updateNewsStatusIsGot(self, strNewsUrl=None): strSQL = "UPDATE techcrunch_news SET isGot=1 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #取得所有已完成下載的 news url def fetchallCompletedObtainedNewsUrl(self): strSQL = "SELECT strNewsUrl FROM techcrunch_news WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #更新 news 尚未開始下載狀態 def updateNewsStatusIsNotGot(self, strNewsUrl=None): strSQL = "UPDATE techcrunch_news SET isGot=0 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM techcrunch_news" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM techcrunch_topic" self.db.commitSQL(strSQL=strSQL) #硬塞的 class LocalDbForINSIDE: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS inside_news(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS inside_tag(" "id INTEGER PRIMARY KEY," "strTagPage1Url TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS inside_newstag(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "strTagPage1Url TEXT NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存Tag def insertTagIfNotExists(self, strTagPage1Url=None): strSQL = "SELECT * FROM inside_tag WHERE strTagPage1Url='%s'"%strTagPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO inside_tag VALUES(NULL, '%s', 0)"%strTagPage1Url self.db.commitSQL(strSQL=strSQL) #取得所有未完成下載的 Tag 第一頁 url def fetchallNotObtainedTagPage1Url(self): strSQL = "SELECT strTagPage1Url FROM inside_tag WHERE isGot=0" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTagPage1Url = [] for rowData in lstRowData: lstStrTagPage1Url.append(rowData["strTagPage1Url"]) return lstStrTagPage1Url #取得所有已完成下載的 Tag 第一頁 url def fetchallCompletedObtainedTagPage1Url(self): strSQL = "SELECT strTagPage1Url FROM inside_tag WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTagPage1Url = [] for rowData in lstRowData: lstStrTagPage1Url.append(rowData["strTagPage1Url"]) return lstStrTagPage1Url #更新 Tag 為已完成下載狀態 def updateTagStatusIsGot(self, strTagPage1Url=None): strSQL = "UPDATE inside_tag SET isGot=1 WHERE strTagPage1Url='%s'"%strTagPage1Url self.db.commitSQL(strSQL=strSQL) #儲存 news URL 以及 URL 所對應的 tag def insertNewsUrlAndNewsTagMappingIfNotExists(self, strNewsUrl=None, strTagPage1Url=None): #insert news url if not exists strSQL = "SELECT * FROM inside_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO inside_news VALUES(NULL, '%s', 0)"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #insert news tag mapping if not exists strSQL = "SELECT * FROM inside_newstag WHERE strNewsUrl='%s' AND strTagPage1Url='%s'"%(strNewsUrl, strTagPage1Url) lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO inside_newstag VALUES(NULL, '%s', '%s')"%(strNewsUrl, strTagPage1Url) self.db.commitSQL(strSQL=strSQL) #取得指定 tag 的 news url def fetchallNewsUrlByTagPage1Url(self, strTagPage1Url=None): strSQL = "SELECT * FROM inside_newstag WHERE strTagPage1Url='%s'"%strTagPage1Url lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #檢查 news 是否已下載 def checkNewsIsGot(self, strNewsUrl=None): isGot = True strSQL = "SELECT * FROM inside_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 news 為已完成下載狀態 def updateNewsStatusIsGot(self, strNewsUrl=None): strSQL = "UPDATE inside_news SET isGot=1 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #更新 news 為尚未開始下載狀態 def updateNewsStatusIsNotGot(self, strNewsUrlPart=None): strSQL = "UPDATE inside_news SET isGot=0 WHERE strNewsUrl LIKE'%" + strNewsUrlPart + "%'" self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM inside_news" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM inside_tag" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM inside_newstag" self.db.commitSQL(strSQL=strSQL) #投資界 class LocalDbForPEDAILY: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS pedaily_news(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "intCategoryId INTEGER NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS pedaily_category(" "id INTEGER PRIMARY KEY," "strCategoryName TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存 category def insertCategoryIfNotExists(self, strCategoryName=None): strSQL = "SELECT * FROM pedaily_category WHERE strCategoryName='%s'"%strCategoryName lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO pedaily_category VALUES(NULL, '%s', 0)"%strCategoryName self.db.commitSQL(strSQL=strSQL) #取得所有 category 名稱 def fetchallCategoryName(self, isGot=False): dicIsGotCode = {True:"1", False:"0"} strSQL = "SELECT strCategoryName FROM pedaily_category WHERE isGot=%s"%dicIsGotCode[isGot] lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrCategoryName = [] for rowData in lstRowData: lstStrCategoryName.append(rowData["strCategoryName"]) return lstStrCategoryName #取得所有未完成下載的 category 名稱 def fetchallNotObtainedCategoryName(self): return self.fetchallCategoryName(isGot=False) #取得所有已完成下載的 category 名稱 def fetchallCompletedObtainedCategoryName(self): return self.fetchallCategoryName(isGot=True) #更新 category 為已完成下載狀態 def updateCategoryStatusIsGot(self, strCategoryName=None): strSQL = "UPDATE pedaily_category SET isGot=1 WHERE strCategoryName='%s'"%strCategoryName self.db.commitSQL(strSQL=strSQL) #取得 category id def fetchCategoryIdByName(self, strCategoryName=None): strSQL = "SELECT * FROM pedaily_category WHERE strCategoryName='%s'"%strCategoryName lstRowData = self.db.fetchallSQL(strSQL=strSQL) return lstRowData[0]["id"] #若無重覆儲存 news URL def insertNewsUrlIfNotExists(self, strNewsUrl=None, strCategoryName=None): intCategoryId = self.fetchCategoryIdByName(strCategoryName=strCategoryName) #insert news url if not exists strSQL = "SELECT * FROM pedaily_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO pedaily_news VALUES(NULL, '%s', %d,0)"%(strNewsUrl, intCategoryId) self.db.commitSQL(strSQL=strSQL) #取得指定 category 的 news url def fetchallNewsUrlByCategoryName(self, strCategoryName=None): intCategoryId = self.fetchCategoryIdByName(strCategoryName=strCategoryName) strSQL = "SELECT * FROM pedaily_news WHERE intCategoryId=%d"%intCategoryId lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #檢查 news 是否已下載 def checkNewsIsGot(self, strNewsUrl=None): isGot = True strSQL = "SELECT * FROM pedaily_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 news 為已完成下載狀態 def updateNewsStatusIsGot(self, strNewsUrl=None): strSQL = "UPDATE pedaily_news SET isGot=1 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #取得所有已完成下載的 news url def fetchallCompletedObtainedNewsUrl(self): strSQL = "SELECT strNewsUrl FROM pedaily_news WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #更新 news 尚未開始下載狀態 def updateNewsStatusIsNotGot(self, strNewsUrl=None): strSQL = "UPDATE pedaily_news SET isGot=0 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM pedaily_news" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM pedaily_category" self.db.commitSQL(strSQL=strSQL) #數位時代 class LocalDbForBNEXT: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS bnext_news(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS bnext_tag(" "id INTEGER PRIMARY KEY," "strTagName TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS bnext_newstag(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "strTagName TEXT NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存Tag def insertTagIfNotExists(self, strTagName=None): strSQL = "SELECT * FROM bnext_tag WHERE strTagName='%s'"%strTagName lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO bnext_tag VALUES(NULL, '%s', 0)"%strTagName self.db.commitSQL(strSQL=strSQL) #取得所有未完成下載的 Tag 名稱 def fetchallNotObtainedTagName(self): strSQL = "SELECT strTagName FROM bnext_tag WHERE isGot=0" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTagName = [] for rowData in lstRowData: lstStrTagName.append(rowData["strTagName"]) return lstStrTagName #取得所有已完成下載的 Tag 名稱 def fetchallCompletedObtainedTagName(self): strSQL = "SELECT strTagName FROM bnext_tag WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTagName = [] for rowData in lstRowData: lstStrTagName.append(rowData["strTagName"]) return lstStrTagName #更新 Tag 為已完成下載狀態 def updateTagStatusIsGot(self, strTagName=None): strSQL = "UPDATE bnext_tag SET isGot=1 WHERE strTagName='%s'"%strTagName self.db.commitSQL(strSQL=strSQL) #儲存 news URL 以及 URL 所對應的 tag def insertNewsUrlAndNewsTagMappingIfNotExists(self, strNewsUrl=None, strTagName=None): #insert news url if not exists strSQL = "SELECT * FROM bnext_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO bnext_news VALUES(NULL, '%s', 0)"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #insert news tag mapping if not exists strSQL = "SELECT * FROM bnext_newstag WHERE strNewsUrl='%s' AND strTagName='%s'"%(strNewsUrl, strTagName) lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO bnext_newstag VALUES(NULL, '%s', '%s')"%(strNewsUrl, strTagName) self.db.commitSQL(strSQL=strSQL) #取得指定 tag 的 news url def fetchallNewsUrlByTagName(self, strTagName=None): strSQL = "SELECT * FROM bnext_newstag WHERE strTagName='%s'"%strTagName lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #檢查 news 是否已下載 def checkNewsIsGot(self, strNewsUrl=None): isGot = True strSQL = "SELECT * FROM bnext_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 news 為已完成下載狀態 def updateNewsStatusIsGot(self, strNewsUrl=None): strSQL = "UPDATE bnext_news SET isGot=1 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM bnext_news" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM bnext_tag" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM bnext_newstag" self.db.commitSQL(strSQL=strSQL) #科技報橘 class LocalDbForTECHORANGE: #建構子 def __init__(self): self.db = SQLite3Db(strResFolderPath="cameo_res") self.initialDb() #初取化資料庫 def initialDb(self): strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS techorange_news(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS techorange_tag(" "id INTEGER PRIMARY KEY," "strTagName TEXT NOT NULL," "isGot BOOLEAN NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) strSQLCreateTable = ("CREATE TABLE IF NOT EXISTS techorange_newstag(" "id INTEGER PRIMARY KEY," "strNewsUrl TEXT NOT NULL," "strTagName TEXT NOT NULL)") self.db.commitSQL(strSQL=strSQLCreateTable) #若無重覆，儲存Tag def insertTagIfNotExists(self, strTagName=None): strSQL = "SELECT * FROM techorange_tag WHERE strTagName='%s'"%strTagName lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO techorange_tag VALUES(NULL, '%s', 0)"%strTagName self.db.commitSQL(strSQL=strSQL) #取得所有未完成下載的 Tag 名稱 def fetchallNotObtainedTagName(self): strSQL = "SELECT strTagName FROM techorange_tag WHERE isGot=0" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTagName = [] for rowData in lstRowData: lstStrTagName.append(rowData["strTagName"]) return lstStrTagName #取得所有已完成下載的 Tag 名稱 def fetchallCompletedObtainedTagName(self): strSQL = "SELECT strTagName FROM techorange_tag WHERE isGot=1" lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrTagName = [] for rowData in lstRowData: lstStrTagName.append(rowData["strTagName"]) return lstStrTagName #更新 Tag 為已完成下載狀態 def updateTagStatusIsGot(self, strTagName=None): strSQL = "UPDATE techorange_tag SET isGot=1 WHERE strTagName='%s'"%strTagName self.db.commitSQL(strSQL=strSQL) #儲存 news URL 以及 URL 所對應的 tag def insertNewsUrlAndNewsTagMappingIfNotExists(self, strNewsUrl=None, strTagName=None): #insert news url if not exists strSQL = "SELECT * FROM techorange_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO techorange_news VALUES(NULL, '%s', 0)"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #insert news tag mapping if not exists strSQL = "SELECT * FROM techorange_newstag WHERE strNewsUrl='%s' AND strTagName='%s'"%(strNewsUrl, strTagName) lstRowData = self.db.fetchallSQL(strSQL=strSQL) if len(lstRowData) == 0: strSQL = "INSERT INTO techorange_newstag VALUES(NULL, '%s', '%s')"%(strNewsUrl, strTagName) self.db.commitSQL(strSQL=strSQL) #取得指定 tag 的 news url def fetchallNewsUrlByTagName(self, strTagName=None): strSQL = "SELECT * FROM techorange_newstag WHERE strTagName='%s'"%strTagName lstRowData = self.db.fetchallSQL(strSQL=strSQL) lstStrNewsUrl = [] for rowData in lstRowData: lstStrNewsUrl.append(rowData["strNewsUrl"]) return lstStrNewsUrl #檢查 news 是否已下載 def checkNewsIsGot(self, strNewsUrl=None): isGot = True strSQL = "SELECT * FROM techorange_news WHERE strNewsUrl='%s'"%strNewsUrl lstRowData = self.db.fetchallSQL(strSQL=strSQL) for rowData in lstRowData: if rowData["isGot"] == 0: isGot = False return isGot #更新 news 為已完成下載狀態 def updateNewsStatusIsGot(self, strNewsUrl=None): strSQL = "UPDATE techorange_news SET isGot=1 WHERE strNewsUrl='%s'"%strNewsUrl self.db.commitSQL(strSQL=strSQL) #更新 news 為未完成下載狀態 (指定部分 url ) def updateNewsStatusIsNotGot(self, strNewsUrlPart=None): strSQL = "UPDATE techorange_news SET isGot=0 WHERE strNewsUrl LIKE'%" + strNewsUrlPart + "%'" self.db.commitSQL(strSQL=strSQL) #清除測試資料 (clear table) def clearTestData(self): strSQL = "DELETE FROM techorange_news" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM techorange_tag" self.db.commitSQL(strSQL=strSQL) strSQL = "DELETE FROM techorange_newstag" self.db.commitSQL(strSQL=strSQL)

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null

0

1

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null

0

6

b24437e7a2b052bb60bcdbba9119bb288d776585

64

py

Python

pyanom/__init__.py

ground0state/pyanom

15480b6e0cf4c27603737c81d8006013a98f410d

[ "MIT" ]

5

2021-09-07T14:48:19.000Z

2021-09-25T22:58:39.000Z

pyanom/__init__.py

ground0state/pyanom

15480b6e0cf4c27603737c81d8006013a98f410d

[ "MIT" ]

2

2020-05-21T01:39:33.000Z

2021-09-07T08:27:05.000Z

pyanom/__init__.py

ground0state/pyanom

15480b6e0cf4c27603737c81d8006013a98f410d

[ "MIT" ]

3

2020-05-21T17:36:12.000Z

2021-07-27T08:54:19.000Z

from pyanom.__version__ import __version__ from pyanom import *

21.333333

42

0.84375

8

64

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0.5

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0.125

64

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6

a22b4b458cb9dccb0de671b921f8f40e6a8ba2c9

4,028

py

Python

tests/tasks/util/test_asynctask.py

nhausman1/python-client

b15f76977dc3178634ee8e007b53f613ddd2ac7c

[ "Apache-2.0" ]

13

2017-03-17T15:15:20.000Z

2022-03-14T22:24:10.000Z

tests/tasks/util/test_asynctask.py

nhausman1/python-client

b15f76977dc3178634ee8e007b53f613ddd2ac7c

[ "Apache-2.0" ]

81

2017-01-12T23:06:48.000Z

2022-02-21T18:20:23.000Z

tests/tasks/util/test_asynctask.py

nhausman1/python-client

b15f76977dc3178634ee8e007b53f613ddd2ac7c

[ "Apache-2.0" ]

14

2017-05-25T10:49:13.000Z

2021-12-27T16:39:20.000Z

"""Asynctask test module.""" import time import threading from splitio.tasks.util import asynctask class AsyncTaskTests(object): """AsyncTask test cases.""" def test_default_task_flow(self, mocker): """Test the default execution flow of an asynctask.""" main_func = mocker.Mock() on_init = mocker.Mock() on_stop = mocker.Mock() on_stop_event = threading.Event() task = asynctask.AsyncTask(main_func, 0.5, on_init, on_stop) task.start() time.sleep(1) assert task.running() task.stop(on_stop_event) on_stop_event.wait() assert on_stop_event.is_set() assert 0 < len(main_func.mock_calls) <= 2 assert len(on_init.mock_calls) == 1 assert len(on_stop.mock_calls) == 1 assert not task.running() def test_main_exception_skips_iteration(self, mocker): """Test that an exception in the main func only skips current iteration.""" def raise_exception(): raise Exception('something') main_func = mocker.Mock() main_func.side_effect = raise_exception on_init = mocker.Mock() on_stop = mocker.Mock() on_stop_event = threading.Event() task = asynctask.AsyncTask(main_func, 0.1, on_init, on_stop) task.start() time.sleep(1) assert task.running() task.stop(on_stop_event) on_stop_event.wait() assert on_stop_event.is_set() assert 9 <= len(main_func.mock_calls) <= 10 assert len(on_init.mock_calls) == 1 assert len(on_stop.mock_calls) == 1 assert not task.running() def test_on_init_failure_aborts_task(self, mocker): """Test that if the on_init callback fails, the task never runs.""" def raise_exception(): raise Exception('something') main_func = mocker.Mock() on_init = mocker.Mock() on_init.side_effect = raise_exception on_stop = mocker.Mock() on_stop_event = threading.Event() task = asynctask.AsyncTask(main_func, 0.1, on_init, on_stop) task.start() time.sleep(0.5) assert not task.running() # Since on_init fails, task never starts task.stop(on_stop_event) on_stop_event.wait(1) assert on_stop_event.is_set() assert on_init.mock_calls == [mocker.call()] assert on_stop.mock_calls == [mocker.call()] assert main_func.mock_calls == [] assert not task.running() def test_on_stop_failure_ends_gacefully(self, mocker): """Test that if the on_init callback fails, the task never runs.""" def raise_exception(): raise Exception('something') main_func = mocker.Mock() on_init = mocker.Mock() on_stop = mocker.Mock() on_stop.side_effect = raise_exception on_stop_event = threading.Event() task = asynctask.AsyncTask(main_func, 0.1, on_init, on_stop) task.start() time.sleep(1) task.stop(on_stop_event) on_stop_event.wait(1) assert on_stop_event.isSet() assert on_init.mock_calls == [mocker.call()] assert on_stop.mock_calls == [mocker.call()] assert 9 <= len(main_func.mock_calls) <= 10 def test_force_run(self, mocker): """Test that if the on_init callback fails, the task never runs.""" main_func = mocker.Mock() on_init = mocker.Mock() on_stop = mocker.Mock() on_stop_event = threading.Event() task = asynctask.AsyncTask(main_func, 5, on_init, on_stop) task.start() time.sleep(1) assert task.running() task.force_execution() task.force_execution() task.stop(on_stop_event) on_stop_event.wait(1) assert on_stop_event.isSet() assert on_init.mock_calls == [mocker.call()] assert on_stop.mock_calls == [mocker.call()] assert len(main_func.mock_calls) == 2 assert not task.running()

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null

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1

0

1

0

null

0

6

a24c0115096b3a3bded9bc1bed1395c2340663ee

38

py

Python

Code/Day 13/demo07.py

AndyChiangSH/2021-IT-30days

d001523f1ed80d765fb92c893e3f936010a96c30

[ "MIT" ]

8

2021-10-19T03:35:37.000Z

2022-03-27T09:58:19.000Z

Code/Day 13/demo07.py

AndyChiangSH/2021-IT-30days

d001523f1ed80d765fb92c893e3f936010a96c30

[ "MIT" ]

null

Code/Day 13/demo07.py

AndyChiangSH/2021-IT-30days

d001523f1ed80d765fb92c893e3f936010a96c30

[ "MIT" ]

null

import math print(math.log(1024, 2))

9.5

24

0.710526

7

38

3.857143

0.857143

0

0.151515

0.131579

38

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1

0

1

0

1

0

6

a264eeb5359dbaec7dde65bed2d0836eb6afbfd1

89

py

Python

__init__.py

maminian/caterpillar_mset

e25008a5c82cad0db914058198bf8f257bc2df36

[ "MIT" ]

5

2020-11-19T05:48:46.000Z

2022-03-09T07:44:49.000Z

__init__.py

maminian/caterpillar_mset

e25008a5c82cad0db914058198bf8f257bc2df36

[ "MIT" ]

3

2020-11-24T05:46:16.000Z

2021-12-24T15:15:29.000Z

__init__.py

maminian/caterpillar_mset

e25008a5c82cad0db914058198bf8f257bc2df36

[ "MIT" ]

3

2020-05-11T17:13:38.000Z

2021-05-16T06:11:24.000Z

# Needed if one wants to utilize this as a package. from . import tde from . import mset

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1

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6

a267fd0b5eb6fa653d610e9a0790d9f6cc60df2f

38,007

py

Python

pysmFISH/stitching_package/hybregistration.py

ambrosejcarr/pysmFISH

0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb

[ "MIT" ]

5

2018-05-29T23:03:19.000Z

2022-02-02T02:04:41.000Z

pysmFISH/stitching_package/hybregistration.py

ambrosejcarr/pysmFISH

0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb

[ "MIT" ]

3

2018-12-18T20:18:38.000Z

2019-01-18T22:47:45.000Z

pysmFISH/stitching_package/hybregistration.py

ambrosejcarr/pysmFISH

0eb24355f70c0d5c9013a9407fd56f2e1e9ee3cb

[ "MIT" ]

5

2018-08-10T14:54:54.000Z

2021-10-09T13:32:08.000Z

"""Functions to perform registration between all hybridizations. register_final_images(folder, gene='Nuclei', sub_pic_frac=0.2, use_MPI=False, apply_to_corners=True, apply_warping = True) -- Register stitched images an in all HDF5 file in the folder find_reg_final_image(im_file_1, im_file_n, max_trans, sub_pic_frac, nr_peaks=8) -- Find the transform that registers image n correctly onto image 1. transform_final_image(im_file_n, trans, new_size) -- Transform an image according to trans. transform_data_file(folder, data_name, trans, new_size) -- Transform the corners in the pickled data file align_sub_region(overlap1, overlap2, nr_peaks) -- Determine how much overlap2 should be shifted to fit overlap1, help function for find_reg_final_image """ import numpy as np import h5py import os import skimage.transform as smtf try: from mpi4py import MPI MPI_available = True except ImportError: MPI_available = False import logging import glob # Own imports from . import inout from . import pairwisesingle as ps logger = logging.getLogger(__name__) def register_final_images(folder, gene='Nuclei', sub_pic_frac=0.2, use_MPI=False, apply_to_corners=True, apply_warping = False, region=None, compare_in_seq=False): """Register stitched images an in all HDF5 file in the folder Loops the hybridizations in the HDF5 file, takes the stitched images as indicated by gene and then compares each image to the first image. For the comparison only a small patch of the images is used, the size of this patch can be controlled with "sub_pic_frac". Parameters: ----------- folder: str The name of the folder containing the pickled file with stitching data, needs a trailing slash ("/"). gene: str The gene of which the stitched images are present and should be realigned. Typically this will be 'Nuclei', because the smFISH genes will not have enough signal to align the pictures properly. (Default: 'Nuclei') sub_pic_frac: float The fraction of the size of the original image that should be used to compare images. (Default: 0.2) use_MPI: bool If True open the files in MPI friendly mode, if False open files in normal single processing mode. (Default: False) apply_to_corners: bool Determines if the found registration will be applied to the tile corners in the pickled stitching data file. (Default: True) apply_warping: bool Determines if the found registration will be applied as a warp to the final pictures in the hdf5 file, should not be used with large datasets. (Default: False) region: list List of length four containing ints. The region that should be compared to determine the shift needed for registration. Should be in the order: [y_min, y_max, x_min, x_max]. When region is defined, sub_pic_frac will not be used. By default the code will determine the region itself taking a area around the center of the image with a size determined by sub_pic_frac(Default: None) compare_in_seq: bool Determines if we should compare images in sequence or if we should compare all to the first image. """ if not compare_in_seq: file_name_list, file_1, im_file_1, trans, old_size_list, \ max_trans = \ prepare_for_comparing(folder, gene, compare_in_seq, use_MPI=use_MPI) # Compare each file to file 1: for i in range(1, len(file_name_list)): cur_trans, max_trans, cur_old_size, file_ind = \ get_single_trans(file_name_list, i, gene, im_file_1, max_trans, sub_pic_frac=sub_pic_frac, region=region, use_MPI=use_MPI) trans[file_ind, :] = cur_trans old_size_list[file_ind, :] = cur_old_size # Close the hdf5 file. file_1.close() trans, new_size = correct_trans_and_size(trans, old_size_list, max_trans, compare_in_seq) else: file_name_list, trans_relative, old_size_list, max_trans = \ prepare_for_comparing(folder, gene, compare_in_seq, use_MPI=use_MPI) # Compare each file to previous file: for i in range(1, len(file_name_list)): cur_trans, max_trans, cur_old_size, file_ind = \ get_single_relative_trans(file_name_list, i, gene, max_trans, sub_pic_frac = sub_pic_frac, region = region, use_MPI = use_MPI) trans_relative[file_ind, :] = cur_trans old_size_list[file_ind, :] = cur_old_size trans, new_size = correct_trans_and_size(trans_relative, old_size_list, max_trans, compare_in_seq) logger.debug( 'Files: {} Translations: {}' .format(file_name_list, trans)) # Apply the translations for i in range(len(file_name_list)): if apply_warping: if use_MPI: file_n = h5py.File(file_name_list[i], 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_n = h5py.File(file_name_list[i], 'r+') im_file_n = file_n[gene]['StitchedImage'] transform_final_image(im_file_n, trans[i, :], new_size) file_n.close() if apply_to_corners: data_name = ( os.path.split(file_name_list[i])[1].split(sep='.')[0] + '_' + gene + '_stitching_data') transform_data_file(folder, data_name, trans[i, :], new_size) def prepare_for_comparing(folder, gene, compare_in_seq, use_MPI=False): """ Prepare the file list, first file and init other lists. Parameters: ----------- folder: str The name of the folder containing the pickled file with stitching data, needs a trailing slash ("/"). gene: str The gene of which the stitched images are present and should be realigned. Typically this will be 'Nuclei', because the smFISH genes will not have enough signal to align the pictures properly. (Default: 'Nuclei') compare_in_seq: bool Determines if we should compare images in sequence or if we should compare all to the first image. use_MPI: bool If True open the files in MPI friendly mode, if False open files in normal single processing mode. (Default: False) Returns: -------- file_name_list: list List of strings. List of the sf.hdf5-files in the folder. trans: np.array Array of ints. The array to store the translations, initialized with zeros. old_size_list: np.array Array of ints. The array to store the sizes of the final images, initialized with zeros. max_trans: np.array Array of ints. Variable to store the largest translation found up to now, initialized at zero. Notes: ------ Only returned if compare_in_seq is True: file_1: pointer File handle to the first hdf5 file in the folder. im_file_1: pointer Reference to the group in the first file that contains th final image. """ # Get a list of files in the folder file_name_list = glob.glob(folder + '*.sf.hdf5') file_name_list.sort() logger.debug('Filenames sorted: {}'.format(file_name_list)) # Initialize some variables: trans = np.zeros((len(file_name_list), 2), dtype=int) old_size_list = np.zeros((len(file_name_list), 2), dtype=int) max_trans = np.zeros((1, 2), dtype=int) # Take the first hybridization (keys() seems to give the groups # as a sorted list) im_name_1 = file_name_list[0] logger.debug('im_name_1: {}'.format(im_name_1)) # Open the stitching file and make a list of the hybridizations # present in this file: if use_MPI: file_1 = h5py.File(im_name_1, 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_1 = h5py.File(im_name_1, 'r+') # hyb_name_list = list(stitching_file.keys()) # Get the right group im_file_1 = file_1[gene]['StitchedImage'] # Get the size of the first image in the list, # which will be the reference image without translation. old_size_list[0, :] = im_file_1['final_image'].shape # Make comparisons if not compare_in_seq: return file_name_list, file_1, im_file_1, trans, \ old_size_list, max_trans else: # Get the size for the first image file_1.close() return file_name_list, trans, old_size_list, max_trans def get_single_trans(file_name_list, i, gene, im_file_1, max_trans, sub_pic_frac=0.2, region=None, use_MPI=False): """Get the translation between image 1 and image i. Get the translation between the image in file 1 and file i from file_name_list. Parameters: ----------- file_name_list: list List of strings. List of the sf.hdf5-files in the folder. i: int Index of the current file to compare. gene: str Gene of which the stitched images are present and should be realigned. Typically this will be 'Nuclei', because the smFISH genes will not have enough signal to align the pictures properly. (Default: 'Nuclei') im_file_1: pointer Reference to the group in the first file that contains th final image. max_trans: np.array Variable to store the largest translation found up to now, initialized at zero. sub_pic_frac: float The fraction of the size of the original image that should be used to compare images. (Default: 0.2) region: list List of length four containing ints. The region that should be compared to determine the shift needed for registration. Should be in the order: [y_min, y_max, x_min, x_max]. When region is defined, sub_pic_frac will not be used. By default the code will determine the region itself taking a area around the center of the image with a size determined by sub_pic_frac(Default: None) use_MPI: bool If True open the files in MPI friendly mode, if False open files in normal single processing mode. (Default: False) Returns: -------- cur_trans: np.array Array of ints. Translation found between the two images that are currently being compared. max_trans: np.array Array of ints. The largest translation found up to now. cur_old_size: np.array Array of ints. The sizes of the original final image found in file_name_list at index i. i: int The index of the second image file used for the current comparison (The first image file is file 1). """ # Get the group containing the image we want to compare with. if use_MPI: file_n = h5py.File(file_name_list[i], 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_n = h5py.File(file_name_list[i], 'r+') im_file_n = file_n[gene]['StitchedImage'] # Find the translation cur_trans, max_trans, cur_old_size \ = find_reg_final_image(im_file_1, im_file_n, max_trans, sub_pic_frac, region=region) logger.debug( "max_trans: {}".format(max_trans)) file_n.close() return cur_trans, max_trans, cur_old_size, i def get_single_relative_trans(file_name_list, i, gene, max_trans, sub_pic_frac=0.2, region=None, use_MPI=False): """Get the translation between image i - 1 and image i. Get the translation between the image in file_name_list[i - 1] and file_name_list[i]. Parameters: ----------- file_name_list: list List of strings. List of the sf.hdf5-files in the folder. i: int Index of the second image in the current comparison. gene: str The gene of which the stitched images are present and should be realigned. Typically this will be 'Nuclei', because the smFISH genes will not have enough signal to align the pictures properly. (Default: 'Nuclei') max_trans: np.array Array of ints. Variable to store the largest translation found up to now, initialized at zero. sub_pic_frac: float The fraction of the size of the original image that should be used to compare images. (Default: 0.2) region: list List of length four containing ints. The region that should be compared to determine the shift needed for registration. Should be in the order: [y_min, y_max, x_min, x_max]. When region is defined, sub_pic_frac will not be used. By default the code will determine the region itself taking a area around the center of the image with a size determined by sub_pic_frac (Default: None) use_MPI: bool If True open the files in MPI friendly mode, if False open files in normal single processing mode. (Default: False) Returns: -------- cur_trans: np.array Array of ints. Translation found between the two images that are currently being compared. max_trans: np.array Array of ints. The largest translation found up to now. cur_old_size: np.array The sizes of the original final image found in file_name_list at index i. i: int The index of the second image file used for the current comparison (The first image file is file 1). """ # Get the group containing the image we want to compare with. if use_MPI: file_1 = h5py.File(file_name_list[i - 1], 'r+', driver='mpio', comm=MPI.COMM_WORLD) file_2 = h5py.File(file_name_list[i], 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_1 = h5py.File(file_name_list[i - 1], 'r+') file_2 = h5py.File(file_name_list[i], 'r+') im_file_1 = file_1[gene]['StitchedImage'] im_file_2 = file_2[gene]['StitchedImage'] # Find the translation cur_trans, max_trans, cur_old_size \ = find_reg_final_image(im_file_1, im_file_2, max_trans, sub_pic_frac, region=region) logger.debug("max_trans: {}".format(max_trans)) file_1.close() file_2.close() return cur_trans, max_trans, cur_old_size, i def correct_trans_and_size(trans_relative, old_size_list, max_trans, compare_in_seq): """Correct the translations and the size of the registered images. Parameters: ----------- trans_relative: np.array Array of ints. The array with the non-corrected translation. old_size_list: np.array Array of ints. The array with the sizes of all the final, non-registered images. max_trans: np.array Array of ints. Variable to store the largest translation found up to now. compare_in_seq: bool Determines if we should compare images in sequence or if we should compare all to the first image. Returns: -------- trans: np.array Array of ints. The array with the corrected translations for each image. new_size: np.array Array of length 2 containing ints. The size the images should have after registration. """ if compare_in_seq: # Get the normalized transistions trans = np.cumsum(trans_relative, axis=0) max_trans = np.amax(trans, axis=0) logger.debug(("Comparing in sequence: relative translations: " + "\n {} \n normalized translations: \n{}\n" .format(trans_relative, trans))) logger.debug("max_trans: {}".format(max_trans)) else: trans = trans_relative # Correct translations trans -= max_trans logger.debug('old_size_list: {}' .format(old_size_list)) # Determine final image size: new_size_list = old_size_list + abs(trans) new_size = np.amax(new_size_list, axis=0) logger.debug('new_size_list: {} new_size: {}' .format(new_size_list, new_size)) return trans, new_size def find_reg_final_image(im_file_1, im_file_n, max_trans, sub_pic_frac, region=None, nr_peaks=8): """ Find the transform that registers image n correctly onto image 1. Parameters: im_file_1: pointer HDF5 group reference or file handle, should contain a dataset "final_image" holding image 1. im_name_n: pointer HDF5 group reference or file handle, should contain a dataset "final_image" holding image n. max_trans: np.array Array of length 2 with dtype: int. Largest translation currently found. sub_pic_frac: float The fraction of the size of the original image that should be used to compare images. region: list List of length four containing ints. The region that should be compared to determine the shift needed for registration. Should be in the order: [y_min, y_max, x_min, x_max]. When region is defined, sub_pic_frac will not be used. By default the code will determine the region itself taking a area around the center of the image with a size determined by sub_pic_frac(Default: None) nr_peaks: int The number of peaks used to get the best peaks from the phase correlation matrix. (default: 8) Returns: -------- trans: np.array Array of length 2 containing ints. Translation that projects image n correctly onto image 1. max_trans: np.array Array of shape (1, 2) containing ints. The max_trans value that was passed to this function, replaced by (part of) the current translation if it is larger than max_trans. shape_n: tuple Tuple of python ints. The shape of image n. """ # Get the image shapes shape_1 = im_file_1['final_image'].shape shape_n = im_file_n['final_image'].shape if region is None: # Determine the size of the part of the picture that we want to compare sub_pic_size = (np.array(shape_1) * sub_pic_frac).astype(int, copy=False) logger.debug('sub_pic_size: {}'.format(sub_pic_size)) # Take the center coordinates in the y and x axes center = (int(np.floor(min(shape_1[-2] / 2, shape_n[-2] / 2))), int(np.floor(min(shape_1[-1] / 2, shape_n[-1] / 2)))) start = np.array([center[0], center[1]]) end = np.array([min(start[-2] + sub_pic_size[-2], shape_1[-2], shape_n[-2]), min(start[-1] + sub_pic_size[-1], shape_1[-1], shape_n[-1])]) else: start = np.array([region[0],region[2]]) end = np.array([region[1],region[3]]) logger.debug("Area based on given region: Start: {} " "End: {}".format(start, end)) # Get the region to compare from the pictures if im_file_1['final_image'].ndim == 3: pic_1 = np.amax(im_file_1['final_image'][:, start[0]:end[0], start[1]:end[1]]) else: pic_1 = im_file_1['final_image'][start[0]:end[0], start[1]:end[1]] if im_file_1['final_image'].ndim == 3: pic_n = np.amax(im_file_n['final_image'][:, start[0]:end[0], start[1]:end[1]]) else: pic_n = im_file_n['final_image'][start[0]:end[0], start[1]:end[1]] # Find the best translation trans, best_cov = align_sub_region(pic_1, pic_n, nr_peaks) logger.debug('Found trans: {} \n best covariance: {}' .format(trans, best_cov)) # Adjust max trans if necessary max_trans = np.maximum(max_trans, np.array(trans)) return trans, max_trans, shape_n def transform_final_image(im_file_n, trans, new_size): """ Transform an image according to trans. Parameters: ----------- im_file_n: pointer HDF5 group reference or file handle, should contain a dataset "final_image" holding image n. trans: np.array Array of len 2 containing ints. y and x transform of the image. new_size: tuple Tuple of length 2. The size of the image after the transform. """ # Make the trans matrix trans_matrix = np.eye(3) trans_matrix[1][2] = trans[0] trans_matrix[0][2] = trans[1] # Make a separate dataset for the registered image. logger.debug('new_size {}'.format(new_size)) try: registered_image = im_file_n.require_dataset('reg_image', shape=tuple(new_size), dtype=np.float64) except TypeError as err: logger.debug( ("Incompatible data set for reg_image, deleting old " + "dataset. N.B: Not cleaning up space. \n {}") .format(err)) del im_file_n['reg_image'] registered_image = im_file_n.require_dataset('reg_image', shape=tuple(new_size), dtype=np.float64) # Transform the image registered_image[:, :] = smtf.warp(im_file_n['final_image'], trans_matrix, output_shape=new_size, order=0) def transform_data_file(folder, data_name, trans, new_size): """ Transform the corners in the pickled data file Parameters: ----------- folder: str The name of the folder containing the pickled file with stitching data, needs a trailing slash ("/"). data_name: str Name of the pickled file with the corner coordinates. trans: np.array Array of len 2 containing ints. y and x transform of the image. new_size: tuple Tuple of length 2. The size of the image after the transform. """ # Determine the name to safe the new pickled data file. exp_name = '_'.join(data_name.split('_')[:-2]) # Get the original coordinates loaded_data = inout.load_stitching_coord(folder + data_name) micData = loaded_data['micData'] joining_original = loaded_data['joining'] joining_new = {} # Translate the corners temp_corner_list = [[tile_ind, (corner - trans)] for tile_ind, corner in joining_original['corner_list']] logger.debug( 'temp_corner_list: {} trans: {}'.format(temp_corner_list, trans)) # Place the corners in the joining dictionary. joining_new['corner_list'] = temp_corner_list # Change final image shape of original: joining_new['final_image_shape'] = new_size # Save to a new file inout.save_to_file(folder + exp_name + '_stitching_data_reg', micData=micData, joining=joining_new) def align_sub_region(overlap1, overlap2, nr_peaks): """Determine how much overlap2 should be shifted to fit overlap1. Parameters: ----------- overlap1: np.array 2D numpy array. Patch of the image that should be compared. overlap2: np.array 2D numpy array. Patch of the image that should be compared. nr_peaks: int The number of peaks used to get the best peaks from the phase correlation matrix. Returns: -------- best_trans: np.array Array of len 2 containing ints. Transform that projects overlap2 correctly onto overlap1. best_cov: float The normalized covariance """ plot_order = np.ones((1, 2)) # Calculate possible translations unr_pos_transistions = ps.calculate_pos_shifts( overlap1, overlap2, nr_peaks, 2) logger.debug("Possible translations: {}". format(unr_pos_transistions)) # Do correlation over the found shifts: best_trans, best_cov = ps.find_best_trans(unr_pos_transistions, overlap1, overlap2, plot_order) # Give some feedback logger.info( "Best shift: {} covariance: {}".format(best_trans, best_cov)) return np.array(best_trans,dtype='int16'), best_cov def register_final_images_old(folder, gene='Nuclei', sub_pic_frac=0.2, use_MPI=False, apply_to_corners=True, apply_warping=False, region=None, compare_in_seq=False): """Register stitched images an in all HDF5 file in the folder Loops the hybridizations in the HDF5 file, takes the stitched images as indicated by gene and then compares each image to the first image. For the comparison only a small patch of the images is used, the size of this patch can be controlled with "sub_pic_frac". Parameters: ----------- folder: str The name of the folder containing the pickled file with stitching data, needs a trailing slash ("/"). gene: str The gene of which the stitched images are present and should be realigned. Typically this will be 'Nuclei', because the smFISH genes will not have enough signal to align the pictures properly. (Default: 'Nuclei') sub_pic_frac: float The fraction of the size of the original image that should be used to compare images. (Default: 0.2) use_MPI: bool True open the files in MPI friendly mode, if False open files in normal single processing mode. (Default: False) apply_to_corners: bool Determines if the found registration will be applied to the tile corners in the pickled stitching data file. (Default: True) apply_warping: bool Determines if the found registration will be applied as a warp to the final pictures in the hdf5 file, should not be used with large datasets. (Default: False) region: list List of length four containing ints. The region that should be compared to determine the shift needed for registration. Should be in the order: [y_min, y_max, x_min, x_max]. When region is defined, sub_pic_frac will not be used. By default the code will determine the region itself taking a area around the center of the image with a size determined by sub_pic_frac(Default: None) compare_in_seq: bool Determines if we should compare images in sequence or if we should compare all to the first image. """ # Get a list of files in the folder file_name_list = glob.glob(folder + '*.sf.hdf5') file_name_list.sort() logger.debug('Filenames sorted: {}'.format(file_name_list)) # Initialize some variables: trans = np.zeros((len(file_name_list), 2), dtype=int) old_size_list = np.zeros((len(file_name_list), 2), dtype=int) max_trans = np.zeros((1, 2), dtype=int) # Make comparisons if not compare_in_seq: # Take the first hybridization (keys() seems to give the groups # as a sorted list) im_name_1 = file_name_list[0] logger.debug('im_name_1: {}'.format(im_name_1)) # Open the stitching file and make a list of the hybridizations # present in this file: if use_MPI: file_1 = h5py.File(im_name_1, 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_1 = h5py.File(im_name_1, 'r+') # hyb_name_list = list(stitching_file.keys()) # Get the right group im_file_1 = file_1[gene]['StitchedImage'] old_size_list[0, :] = im_file_1['final_image'].shape # Compare each file to file 1: for i in range(1, len(file_name_list)): # Get the group containing the image we want to compare with. if use_MPI: file_n = h5py.File(file_name_list[i], 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_n = h5py.File(file_name_list[i], 'r+') im_file_n = file_n[gene]['StitchedImage'] # Find the translation trans[i, :], max_trans, old_size_list[i, :] \ = find_reg_final_image(im_file_1, im_file_n, max_trans, sub_pic_frac, region=region) logger.debug( "max_trans: {}".format(max_trans)) file_n.close() # Close the hdf5 file. file_1.close() else: # Init specific array trans_relative = np.zeros((len(file_name_list), 2), dtype=int) # Compare each file to previous file: for i in range(1, len(file_name_list)): # Get the group containing the image we want to compare with. if use_MPI: file_1 = h5py.File(file_name_list[i - 1], 'r+', driver='mpio', comm=MPI.COMM_WORLD) file_2 = h5py.File(file_name_list[i], 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_1 = h5py.File(file_name_list[i - 1], 'r+') file_2 = h5py.File(file_name_list[i], 'r+') im_file_1 = file_1[gene]['StitchedImage'] im_file_2 = file_2[gene]['StitchedImage'] # Get the size of the first image in the list, # which will be the reference image without translation. if (i - 1) == 0: old_size_list[0, :] = im_file_1['final_image'].shape # Find the translation trans_relative[i, :], max_trans, old_size_list[i, :] \ = find_reg_final_image(im_file_1, im_file_2, max_trans, sub_pic_frac, region=region) logger.debug("max_trans: {}".format(max_trans)) file_1.close() file_2.close() # Get the normalized transistions trans = np.cumsum(trans_relative, axis=0) max_trans = np.amax(trans, axis=0) logger.debug(("Comparing in sequence: relative translations: " + "\n {} \n normalized translations: \n{}\n" .format(trans_relative, trans))) logger.debug("max_trans: {}".format(max_trans)) # Correct translations trans -= max_trans logger.debug('old_size_list: {}' .format(old_size_list)) # Determine final image size: new_size_list = old_size_list + abs(trans) new_size = np.amax(new_size_list, axis=0) logger.debug( 'Files: {} Translations: {} new_size_list: {} new_size: {}' .format(file_name_list, trans, new_size_list, new_size)) # Apply the translations for i in range(len(file_name_list)): if apply_warping: if use_MPI: file_n = h5py.File(file_name_list[i], 'r+', driver='mpio', comm=MPI.COMM_WORLD) else: file_n = h5py.File(file_name_list[i], 'r+') im_file_n = file_n[gene]['StitchedImage'] transform_final_image(im_file_n, trans[i, :], new_size) file_n.close() if apply_to_corners: data_name = ( os.path.split(file_name_list[i])[1].split(sep='.')[0] + '_' + gene + '_stitching_data') transform_data_file(folder, data_name, trans[i, :], new_size) def register_final_images_reg_data_only(folder, gene='Nuclei', sub_pic_frac=0.2, use_MPI=False, apply_to_corners=True, apply_warping = False, region=None, compare_in_seq=False): """Register stitched images an in all HDF5 file in the folders. It is modified from register_final_images and saves only the reg_data file with the new coords and nothing in the hdf5 file. Loops the hybridizations in the HDF5 file, takes the stitched images as indicated by gene and then compares each image to the first image. For the comparison only a small patch of the images is used, the size of this patch can be controlled with "sub_pic_frac". Parameters: ----------- folder: str The name of the folder containing the pickled file with stitching data, needs a trailing slash ("/"). gene: str The gene of which the stitched images are present and should be realigned. Typically this will be 'Nuclei', because the smFISH genes will not have enough signal to align the pictures properly. (Default: 'Nuclei') sub_pic_frac: float The fraction of the size of the original image that should be used to compare images. (Default: 0.2) use_MPI: bool If True open the files in MPI friendly mode, if False open files in normal single processing mode. (Default: False) apply_to_corners: bool Determines if the found registration will be applied to the tile corners in the pickled stitching data file. (Default: True) apply_warping: bool Determines if the found registration will be applied as a warp to the final pictures in the hdf5 file, should not be used with large datasets. (Default: False) region: list List of length four containing ints. The region that should be compared to determine the shift needed for registration. Should be in the order: [y_min, y_max, x_min, x_max]. When region is defined, sub_pic_frac will not be used. By default the code will determine the region itself taking a area around the center of the image with a size determined by sub_pic_frac(Default: None) compare_in_seq: bool Determines if we should compare images in sequence or if we should compare all to the first image. """ if not compare_in_seq: file_name_list, file_1, im_file_1, trans, old_size_list, \ max_trans = \ prepare_for_comparing(folder, gene, compare_in_seq, use_MPI=use_MPI) # Compare each file to file 1: for i in range(1, len(file_name_list)): cur_trans, max_trans, cur_old_size, file_ind = \ get_single_trans(file_name_list, i, gene, im_file_1, max_trans, sub_pic_frac=sub_pic_frac, region=region, use_MPI=use_MPI) trans[file_ind, :] = cur_trans old_size_list[file_ind, :] = cur_old_size # Close the hdf5 file. file_1.close() trans, new_size = correct_trans_and_size(trans, old_size_list, max_trans, compare_in_seq) else: file_name_list, trans_relative, old_size_list, max_trans = \ prepare_for_comparing(folder, gene, compare_in_seq, use_MPI=use_MPI) # Compare each file to previous file: for i in range(1, len(file_name_list)): cur_trans, max_trans, cur_old_size, file_ind = \ get_single_relative_trans(file_name_list, i, gene, max_trans, sub_pic_frac = sub_pic_frac, region = region, use_MPI = use_MPI) trans_relative[file_ind, :] = cur_trans old_size_list[file_ind, :] = cur_old_size trans, new_size = correct_trans_and_size(trans_relative, old_size_list, max_trans, compare_in_seq) logger.debug( 'Files: {} Translations: {}' .format(file_name_list, trans)) # Apply the translations for i in range(len(file_name_list)): if apply_to_corners: data_name = ( os.path.split(file_name_list[i])[1].split(sep='.')[0] + '_' + gene + '_stitching_data') transform_data_file(folder, data_name, trans[i, :], new_size)

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Python

tests/test_multitask_gp.py

ivandariojr/core

c4dec054a3e80355ed3812d48ca2bba286584a67

[ "MIT" ]

6

2021-01-26T21:00:24.000Z

2022-02-28T23:57:50.000Z

tests/test_multitask_gp.py

ivandariojr/core

c4dec054a3e80355ed3812d48ca2bba286584a67

[ "MIT" ]

15

2020-01-28T22:49:18.000Z

2021-12-14T08:34:39.000Z

tests/test_multitask_gp.py

ivandariojr/core

c4dec054a3e80355ed3812d48ca2bba286584a67

[ "MIT" ]

6

2019-06-07T21:31:20.000Z

2021-12-13T01:00:02.000Z

from numpy import pi from core.learning.gaussian_process import RBFKernel, GaussianProcess, \ GPScaler, ScaledGaussianProcess, PeriodicKernel, AdditiveKernel, \ AffineDotProductKernel, MultiplicativeKernel, save_gp, load_gp import matplotlib.pyplot as plt import torch as th import pathlib from os import makedirs th.manual_seed(0) def confidence_region(mean, cov_matrix): variance = cov_matrix.diag() std = variance.sqrt() return mean - 2*std, mean + 2*std def test_1dx1dgp(): sigma = 0.2 train_x = th.rand((20,1))*2*pi train_y = th.sin(train_x) + sigma * (th.rand_like(train_x) - 0.5) kernel = RBFKernel(1) gp = GaussianProcess(train_x, train_y, kernel) gp.train_model() test_x = th.linspace(0, 2 * pi, 1000).unsqueeze(1) test_y = th.sin(test_x) y_hat, cov = gp(test_x) lower, upper = confidence_region(y_hat.squeeze(), cov.to_dense().squeeze()) assert (y_hat - test_y).abs().mean().item() < 1e-1 assert (test_y.squeeze() > lower).all() assert (upper > test_y.squeeze()).all() #uncomment plotting for debugging # plt.plot(train_x.squeeze().detach().numpy(), # train_y.detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # plt.plot(test_x.detach().squeeze().numpy(), # test_y.detach().numpy(), # label='True Y') # plt.fill_between(test_x.detach().squeeze().numpy(), # lower.detach().numpy(), # upper.detach().numpy(), alpha=0.5) # plt.plot(test_x.detach().squeeze().numpy(), # y_hat.detach().numpy(), label='Estimate') # plt.legend(loc='upper right') # plt.show() def test_2dx1dgp(): sigma = 0.2 train_X = th.rand((650,2))*2*pi train_Y = th.sin(train_X[:,0])*th.cos(train_X[:,1]) \ + sigma * (th.rand_like(train_X[:,0]) - 0.5) train_Y = train_Y.unsqueeze(1) kernel = RBFKernel(2) gp = GaussianProcess(train_X, train_Y, kernel) gp.train_model() dim_sample = th.linspace(0,2*pi, 100) X, Y = th.meshgrid(dim_sample, dim_sample) x_vec, y_vec = th.flatten(X), th.flatten(Y) test_x = th.stack((x_vec, y_vec), dim=1) y_hat, cov = gp(test_x) lower, upper = confidence_region(y_hat.squeeze(), cov.to_dense().squeeze()) test_y = (th.sin(test_x[:,0])*th.cos(test_x[:,1])).unsqueeze(1) assert (y_hat - test_y).abs().max().item() < sigma assert (test_y.squeeze() > lower).all() assert (upper > test_y.squeeze()).all() #uncomment plotting for debugging # fig = plt.figure() # ax = fig.add_subplot(111, projection='3d') # ax.plot_surface(X.detach().numpy(), Y.detach().numpy(), # (y_hat - test_y).abs().view_as(X).detach().numpy()) # # ax.plot_surface(X.detach().numpy(), Y.detach().numpy(), y_hat.view_as(X).detach().numpy()) # plt.show() def test_gp_save_load(): sigma = 0.2 train_X = th.rand((650,2))*2*pi train_Y = th.sin(train_X[:,0])*th.cos(train_X[:,1]) \ + sigma * (th.rand_like(train_X[:,0]) - 0.5) train_Y = train_Y.unsqueeze(1) kernel = RBFKernel(2) gp_original = GaussianProcess(train_X, train_Y, kernel) gp_original.train_model() root_dir = pathlib.Path(__file__).parent.absolute() save_dir = root_dir / 'data' / 'test_load_save_gp' makedirs(save_dir, exist_ok=True) save_file = save_dir / 'test_save.th' save_gp(gp_original, save_file) gp_loaded = load_gp(save_file, RBFKernel(2)) dim_sample = th.linspace(0,2*pi, 100) X, Y = th.meshgrid(dim_sample, dim_sample) x_vec, y_vec = th.flatten(X), th.flatten(Y) test_x = th.stack((x_vec, y_vec), dim=1) y_hat, cov = gp_loaded(test_x) lower, upper = confidence_region(y_hat.squeeze(), cov.to_dense().squeeze()) test_y = (th.sin(test_x[:,0])*th.cos(test_x[:,1])).unsqueeze(1) assert (y_hat - test_y).abs().max().item() < sigma assert (test_y.squeeze() > lower).all() assert (upper > test_y.squeeze()).all() def test_2d2dgp_multiplicative_periodic(): sigma = 0.5 train_X = th.stack([th.distributions.Uniform(0, 2 * pi * 10).sample((250,)), th.distributions.Uniform(-10 * pi * 100, 10 * pi * 100).sample( (250,))], dim=1) def genYYprime(X): Y = th.stack([ 10 * th.sin(X[:, 0]/10), 10 * th.cos(X[:, 0]/10) * X[:, 1]], dim=1) Y_prime = th.stack([ th.stack([th.cos(X[:, 0] / 10), th.zeros((X.shape[0], ))], dim=1), th.stack([(- th.sin(X[:, 0] / 10) * X[:,1]), 10 * th.cos(X[:, 0] / 10)], dim=1)], dim=1) return Y, Y_prime train_Y, train_Y_prime = genYYprime(train_X) train_Y += th.distributions.Normal(0.0, sigma).sample(train_X.shape) # kernel = RBFKernel(2, ard_num_dims=True) kernel = MultiplicativeKernel( kernels=[PeriodicKernel(2), RBFKernel(1)], active_dims=[[0], [1]]) x_scaler = GPScaler(xmins=th.tensor([0, -10 * pi * 100]), xmaxs=th.tensor([2 * pi * 10, 10 * pi * 100]), wraps=th.tensor([True, False])) y_scaler = GPScaler(xmins=th.tensor([-10, -100 * pi * 100]), xmaxs=th.tensor([10, 100 * pi * 100])) gp = ScaledGaussianProcess(train_X, train_Y, kernel, x_scaler=x_scaler, y_scaler=y_scaler) gp.train_model() x_vec = th.linspace(-10 * pi * 10, 10 * pi * 10, 100) y_vec = th.linspace(-10 * pi * 100, 10 * pi * 100, 100) test_x = th.stack((x_vec, y_vec), dim=1) test_y , test_y_prime = genYYprime(test_x) y_hat, cov = gp(test_x) y_hat_prime, cov_prime = gp.ddx(test_x) lower1, upper1 = confidence_region(y_hat.squeeze()[:, 0], cov[:, :, 0, 0]) lower2, upper2 =confidence_region(y_hat.squeeze()[:, 1], cov[:, :, 1, 1]) lower1_p, upper1_p = confidence_region(y_hat_prime[:, 0, 0], cov_prime[:, :, 0, 0, 0, 0]) lower2_p, upper2_p = confidence_region(y_hat_prime[:, 1, 1], cov_prime[:, :, 1, 1, 1, 1]) # these dont' guarantee true value is in confidence region but its good # enough # GP tests assert (test_y[:, 0] > lower1).all() assert (test_y[:, 1] > lower2).all() assert (test_y[:, 0] < upper1).all() assert (test_y[:, 1] < upper2).all() #big error because the problem is scaled to be very large assert (y_hat[5:-5,:] - test_y[5:-5,:]).abs().mean() < 25 # # derivative of GP tests assert abs(y_hat_prime[:, 0, 0] - test_y_prime[:, 0, 0]).mean() < 1 assert abs(y_hat_prime[:, 1, 1] - test_y_prime[:, 1, 1]).mean() < 1 assert abs(y_hat_prime[:, 1, 0] - test_y_prime[:, 1, 0]).mean() < 10 assert y_hat_prime[:, 0, 1].abs().mean() < 1 # notice the loss of a significant figure assert (y_hat_prime[:, 0, 0] < upper1_p).all() assert (y_hat_prime[:, 0, 0] > lower1_p).all() assert (y_hat_prime[:, 1, 1] < upper2_p).all() assert (y_hat_prime[:, 1, 1] > lower2_p).all() # uncomment plots for debugging # f, axs = plt.subplots(2,2, dpi=200) # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # y_hat[:,0].detach().squeeze().numpy(), label='Estimate') # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # test_y[:,0].detach().squeeze().numpy(), label='True Y') # axs[0][0].legend(loc='upper right') # axs[0][0].plot(train_X[:,0].squeeze().detach().numpy(), # train_Y[:,0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # axs[0][0].fill_between(test_x[:,0].detach().squeeze().numpy(), # lower1.detach().numpy(), # upper1.detach().numpy(), alpha=0.5) # axs[0][0].title.set_text('$10 \\sin(\\frac{x_1}{10})$') # axs[0][1].plot(test_x[:,1].detach().squeeze().numpy(), # y_hat[:,1].detach().squeeze().numpy(), label='Estimate') # axs[0][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y[:, 1].detach().squeeze().numpy(), label='True Y') # axs[0][1].legend(loc='upper right') # axs[0][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2.detach().numpy(), # upper2.detach().numpy(), alpha=0.5) # axs[0][1].title.set_text('$10 \\cos(\\frac{x_1}{10}) x_2$') # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # y_hat_prime[:, 0,0].detach().squeeze().numpy(), # label='Estimate') # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # test_y_prime[:, 0,0].detach().squeeze().numpy(), label='True Y') # axs[1][0].legend(loc='upper right') # axs[1][0].fill_between(test_x[:, 0].detach().squeeze().numpy(), # lower1_p.detach().numpy(), # upper1_p.detach().numpy(), alpha=0.5) # axs[1][0].plot(train_X[:, 0].squeeze().detach().numpy(), # train_Y_prime[:, 0, 0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # axs[1][0].title.set_text('$\\cos(\\frac{x_1}{10})$') # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # y_hat_prime[:, 1, 1].detach().squeeze().numpy(), # label='Estimate') # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y_prime[:, 1,1].detach().squeeze().numpy(), # label='True Y') # axs[1][1].legend(loc='upper right') # axs[1][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2_p.detach().numpy(), # upper2_p.detach().numpy(), alpha=0.5) # axs[1][1].title.set_text('$10 \\cos(\\frac{x_1}{10})$') # plt.show() def test_2d2dgp_scaling_wrapping(): #diagonals should be derivative # off-diagonals should be approximately zero sigma = 0.5 train_X = th.stack([th.distributions.Uniform(0, 2 * pi * 10).sample((250,)), th.distributions.Uniform(0, 2 * pi * 100).sample((250,))], dim=1) train_Y = th.stack([ 10 * th.sin(train_X[:, 0]/10), 100 * th.cos(train_X[:,1]/ 100), train_X[:, 0] * train_X[:,1] ], dim=1) + th.distributions.Normal( 0.0, sigma).sample((train_X.shape[0], 3)) train_Y_prime = th.stack([ th.stack([th.cos(train_X[:, 0] / 10), th.zeros(train_X.shape[0],)], dim=1), th.stack([th.zeros(train_X.shape[0],), -th.sin(train_X[:, 1] / 100)],dim=1), th.stack([train_X[:, 1], train_X[:, 0]], dim=1)], dim=1) # kernel = RBFKernel(2, ard_num_dims=True) kernel = AdditiveKernel(kernels=[ PeriodicKernel(p_prior=2.), PeriodicKernel(p_prior=2.)], active_dims=[ [0], [1]]) x_scaler = GPScaler(xmins=th.tensor([0, 0]), xmaxs=th.tensor([2*pi*10, 2*pi*100])) y_scaler = GPScaler(xmins=th.tensor([-10, -100, 0]), xmaxs=th.tensor([10,100, 4 * pi * pi * 1000])) gp = ScaledGaussianProcess(train_X, train_Y, kernel, x_scaler=x_scaler, y_scaler=y_scaler) gp.train_model() # X, Y = th.meshgrid(dim_sample, dim_sample) x_vec = th.linspace(-4 * pi * 10, 4 * pi * 10, 100) y_vec = th.linspace(-4 * pi * 100, 4 * pi * 100, 100) test_x = th.stack((x_vec, y_vec), dim=1) test_y = th.stack([ 10 * th.sin(test_x[:,0]/10), 100 * th.cos(test_x[:,1] / 100), test_x[:,0] * test_x[:,1] ], dim=1) test_y_prime = th.stack([ th.stack([th.cos(test_x[:, 0] / 10), th.zeros(test_x.shape[0], )], dim=1), th.stack([th.zeros(test_x.shape[0], ), -th.sin(test_x[:, 1] / 100)], dim=1), th.stack([test_x[:, 1], test_x[:, 0]], dim=1)], dim=1) y_hat, cov = gp(test_x) y_hat_prime, cov_prime = gp.ddx(test_x) lower1, upper1 = confidence_region(y_hat.squeeze()[:,0], cov[:,:,0,0]) lower2, upper2 = confidence_region(y_hat.squeeze()[:, 1], cov[:,:,1,1]) lower3, upper3 = confidence_region(y_hat.squeeze()[:, 2], cov[:,:,2,2]) lower1_p , upper1_p = confidence_region(y_hat_prime[:,0,0], cov_prime[:,:,0,0,0,0]) lower2_p , upper2_p = confidence_region(y_hat_prime[:,1,1], cov_prime[:,:,1,1,1,1]) lower3_p, upper3_p = confidence_region(y_hat_prime[:, 2, 1], cov_prime[:, :, 1, 1, 2,2]) # these dont' guarantee true value is in confidence region but its good # enough # GP tests assert (test_y[:,0] > lower1).all() assert (test_y[:,1] > lower2).all() assert (test_y[:, 0] < upper1).all() assert (test_y[:, 1] < upper2).all() assert (y_hat[:,[0,1]] - test_y[:, [0,1]]).abs().mean() < 1 #notice the # loss of a significant figure # # # derivative of GP tests assert abs(y_hat_prime[:, 0, 0] - test_y_prime[:, 0, 0]).mean() < 1e-1 assert abs(y_hat_prime[:, 1, 1] - test_y_prime[:, 1, 1]).mean() < 1e-1 assert y_hat_prime[:, 0, 1].abs().mean() < 1 #notice the loss of a significant figure assert y_hat_prime[:, 1, 0].abs().mean() < 1 #notice the loss of a significant figure assert (y_hat_prime[:, 0, 0] < upper1_p).all() assert (y_hat_prime[:, 0, 0] > lower1_p).all() assert (y_hat_prime[:, 1, 1] < upper2_p).all() assert (y_hat_prime[:, 1, 1] > lower2_p).all() #uncomment plots for debugging # f, axs = plt.subplots(2,3, figsize=(18, 6)) # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # y_hat[:,0].detach().squeeze().numpy(), label='Estimate') # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # test_y[:,0].detach().squeeze().numpy(), label='True Y') # axs[0][0].legend(loc='upper right') # axs[0][0].plot(train_X[:,0].squeeze().detach().numpy(), # train_Y[:,0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # axs[0][0].fill_between(test_x[:,0].detach().squeeze().numpy(), # lower1.detach().numpy(), # upper1.detach().numpy(), alpha=0.5) # axs[0][1].plot(test_x[:,1].detach().squeeze().numpy(), # y_hat[:,1].detach().squeeze().numpy(), label='Estimate') # axs[0][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y[:, 1].detach().squeeze().numpy(), label='True Y') # axs[0][1].legend(loc='upper right') # axs[0][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2.detach().numpy(), # upper2.detach().numpy(), alpha=0.5) # axs[0][1].plot(train_X[:,1].squeeze().detach().numpy(), # train_Y[:,1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[0][2].plot(test_x[:, 1].detach().squeeze().numpy(), # y_hat[:, 2].detach().squeeze().numpy(), label='Estimate') # axs[0][2].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y[:, 2].detach().squeeze().numpy(), label='True Y') # axs[0][2].legend(loc='upper right') # axs[0][2].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower3.detach().numpy(), # upper3.detach().numpy(), alpha=0.5) # axs[0][2].plot(train_X[:, 1].squeeze().detach().numpy(), # train_Y[:, 2].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # y_hat_prime[:, 0,0].detach().squeeze().numpy(), # label='Estimate') # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # test_y_prime[:, 0, 0].detach().squeeze().numpy(), label='True Y') # axs[1][0].legend(loc='upper right') # axs[1][0].fill_between(test_x[:, 0].detach().squeeze().numpy(), # lower1_p.detach().numpy(), # upper1_p.detach().numpy(), alpha=0.5) # axs[1][0].plot(train_X[:, 0].squeeze().detach().numpy(), # train_Y_prime[:, 0, 0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # y_hat_prime[:, 1, 1].detach().squeeze().numpy(), # label='Estimate') # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y_prime[:, 1, 1].detach().squeeze().numpy(), # label='True Y') # axs[1][1].legend(loc='upper right') # axs[1][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2_p.detach().numpy(), # upper2_p.detach().numpy(), alpha=0.5) # axs[1][1].plot(train_X[:, 1].squeeze().detach().numpy(), # train_Y_prime[:, 1, 1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][2].plot(test_x[:, 1].detach().squeeze().numpy(), # y_hat_prime[:, 2, 1].detach().squeeze().numpy(), # label='Estimate') # test_y_prime.shape # axs[1][2].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y_prime[:, 2,1].detach().squeeze().numpy(), # label='True Y') # axs[1][2].legend(loc='upper right') # axs[1][2].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower3_p.detach().numpy(), # upper3_p.detach().numpy(), alpha=0.5) # axs[1][2].plot(train_X[:, 1].squeeze().detach().numpy(), # train_Y_prime[:, 2, 1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # plt.show() def test_2d2dgp_scaling(): #diagonals should be derivative # off-diagonals should be approximately zero sigma = 0.5 train_X = th.stack([th.distributions.Uniform(0, 2 * pi * 10).sample((250,)), th.distributions.Uniform(0, 2 * pi * 100).sample((250,))], dim=1) train_Y = th.stack([ 10 * th.sin(train_X[:, 0]/10), 100 * th.cos(train_X[:,1]/ 100)], dim=1) + th.distributions.Normal(0.0, sigma).sample(train_X.shape) train_Y_prime = th.stack([ th.cat([th.cos(train_X[:, 0]/10).unsqueeze(1), th.zeros(train_X.shape[0], 1)],dim=1), th.cat([th.zeros(train_X.shape[0], 1), -th.sin(train_X[:,1]/100).unsqueeze(1)], dim=1)], dim=1) kernel = RBFKernel(2, ard_num_dims=True) x_scaler = GPScaler(xmins=th.tensor([0, 0]), xmaxs=th.tensor([2*pi*10, 2*pi*100])) y_scaler = GPScaler(xmins=th.tensor([-10, -100]) , xmaxs=th.tensor([10, 100])) gp = ScaledGaussianProcess(train_X, train_Y, kernel, x_scaler=x_scaler, y_scaler=y_scaler) gp.train_model() # X, Y = th.meshgrid(dim_sample, dim_sample) x_vec = th.linspace(0, 2 * pi * 10, 100) y_vec = th.linspace(0, 2 * pi * 100, 100) test_x = th.stack((x_vec, y_vec), dim=1) test_y = th.stack([ 10 * th.sin(test_x[:,0]/10), 100 * th.cos(test_x[:,1] / 100)], dim=1) test_y_prime = th.stack([ th.cos(test_x[:,0]/10), -th.sin(test_x[:,1]/100)], dim=1) y_hat, cov = gp(test_x) y_hat_prime, cov_prime = gp.ddx(test_x) lower1, upper1 = confidence_region(y_hat.squeeze()[:,0], cov[:,:,0,0]) lower2, upper2 = confidence_region(y_hat.squeeze()[:, 1], cov[:,:,1,1]) lower1_p , upper1_p = confidence_region(y_hat_prime[:,0,0], cov_prime[:,:,0,0, 0,0]) lower2_p , upper2_p = confidence_region(y_hat_prime[:,1,1], cov_prime[:,:,1,1, 1, 1]) # these dont' guarantee true value is in confidence region but its good # enough # GP tests assert (test_y[:,0] > lower1).all() assert (test_y[:,1] > lower2).all() assert (test_y[:, 0] < upper1).all() assert (test_y[:, 1] < upper2).all() assert (y_hat - test_y).abs().mean() < 1 #notice the loss of a significant figure # # derivative of GP tests # assert abs(y_hat_prime[:, 0, 0] - test_y_prime[:, 0]).mean() < 1e-1 # assert abs(y_hat_prime[:, 1, 1] - test_y_prime[:, 1]).mean() < 1e-1 assert y_hat_prime[:, 0, 1].abs().mean() < 1 #notice the loss of a significant figure assert y_hat_prime[:, 1, 0].abs().mean() < 1 #notice the loss of a significant figure assert (y_hat_prime[:, 0, 0] < upper1_p).all() assert (y_hat_prime[:, 0, 0] > lower1_p).all() assert (y_hat_prime[:, 1, 1] < upper2_p).all() assert (y_hat_prime[:, 1, 1] > lower2_p).all() #uncomment plots for debugging # f, axs = plt.subplots(2,2) # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # y_hat[:,0].detach().squeeze().numpy(), label='Estimate') # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # test_y[:,0].detach().squeeze().numpy(), label='True Y') # axs[0][0].legend(loc='upper right') # axs[0][0].plot(train_X[:,0].squeeze().detach().numpy(), # train_Y[:,0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # axs[0][0].fill_between(test_x[:,0].detach().squeeze().numpy(), # lower1.detach().numpy(), # upper1.detach().numpy(), alpha=0.5) # axs[0][1].plot(test_x[:,1].detach().squeeze().numpy(), # y_hat[:,1].detach().squeeze().numpy(), label='Estimate') # axs[0][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y[:, 1].detach().squeeze().numpy(), label='True Y') # axs[0][1].legend(loc='upper right') # axs[0][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2.detach().numpy(), # upper2.detach().numpy(), alpha=0.5) # axs[0][1].plot(train_X[:,1].squeeze().detach().numpy(), # train_Y[:,1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # y_hat_prime[:, 0,0].detach().squeeze().numpy(), # label='Estimate') # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # test_y_prime[:, 0].detach().squeeze().numpy(), label='True Y') # axs[1][0].legend(loc='upper right') # axs[1][0].fill_between(test_x[:, 0].detach().squeeze().numpy(), # lower1_p.detach().numpy(), # upper1_p.detach().numpy(), alpha=0.5) # axs[1][0].plot(train_X[:, 0].squeeze().detach().numpy(), # train_Y_prime[:, 0, 0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # y_hat_prime[:, 1, 1].detach().squeeze().numpy(), # label='Estimate') # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y_prime[:, 1].detach().squeeze().numpy(), # label='True Y') # axs[1][1].legend(loc='upper right') # axs[1][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2_p.detach().numpy(), # upper2_p.detach().numpy(), alpha=0.5) # axs[1][1].plot(train_X[:, 1].squeeze().detach().numpy(), # train_Y_prime[:, 1, 1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # plt.show() def test_2d2dgp(): #diagonals should be derivative # off-diagonals should be approximately zero sigma = 0.2 train_X = th.rand((250,2))*2*pi train_Y = th.stack([ th.sin(train_X[:, 0]), th.cos(train_X[:,1])],dim=1) + sigma * (th.rand_like(train_X) - 0.5) train_Y_prime = th.stack([ th.cat([th.cos(train_X[:, 0]).unsqueeze(1), th.zeros(train_X.shape[0], 1)],dim=1), th.cat([th.zeros(train_X.shape[0], 1), -th.sin(train_X[:, 1]).unsqueeze(1)],dim=1)], dim=1) kernel = RBFKernel(2) gp = GaussianProcess(train_X, train_Y, kernel) gp.train_model() dim_sample = th.linspace(0, 2 * pi, 100) # X, Y = th.meshgrid(dim_sample, dim_sample) x_vec, y_vec = dim_sample, dim_sample test_x = th.stack((x_vec, y_vec), dim=1) test_y = th.stack([ th.sin(test_x[:,0]), th.cos(test_x[:,1])], dim=1) test_y_prime = th.stack([ th.cos(test_x[:,0]), -th.sin(test_x[:,1])], dim=1) y_hat, cov = gp(test_x) cov = cov.to_dense() y_hat_prime, cov_prime = gp.ddx(test_x) cov_prime = cov_prime.to_dense() lower1, upper1 = confidence_region(y_hat.squeeze()[:,0], cov[:,:,0, 0]) lower2, upper2 = confidence_region(y_hat.squeeze()[:, 1], cov[:,:,1, 1]) lower1_p , upper1_p = confidence_region(y_hat_prime[:,0,0], cov_prime[:,:,0,0, 0, 0]) lower2_p , upper2_p = confidence_region(y_hat_prime[:,1,1], cov_prime[:,:,1,1, 1, 1]) # these dont' guarantee true value is in confidence region but its good # enough # GP tests assert (test_y[:,0] > lower1).all() assert (test_y[:,1] > lower2).all() assert (test_y[:, 0] < upper1).all() assert (test_y[:, 1] < upper2).all() assert (y_hat - test_y).abs().mean() < 1e-1 # # derivative of GP tests assert abs(y_hat_prime[:, 0, 0] - test_y_prime[:, 0]).mean() < 1e-1 assert abs(y_hat_prime[:, 1, 1] - test_y_prime[:, 1]).mean() < 1e-1 assert y_hat_prime[:, 0, 1].abs().mean() < 1e-1 assert y_hat_prime[:, 1, 0].abs().mean() < 1e-1 # assert (y_hat_prime[:, 0, 0] < upper1_p).all() assert (y_hat_prime[:, 0, 0] > lower1_p).all() assert (y_hat_prime[:, 1, 1] < upper2_p).all() assert (y_hat_prime[:, 1, 1] > lower2_p).all() #uncomment plots for debugging # f, axs = plt.subplots(2,2) # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # y_hat[:,0].detach().squeeze().numpy(), label='Estimate') # axs[0][0].plot(test_x[:,0].detach().squeeze().numpy(), # test_y[:,0].detach().squeeze().numpy(), label='True Y') # axs[0][0].legend(loc='upper right') # axs[0][0].plot(train_X[:,0].squeeze().detach().numpy(), # train_Y[:,0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # axs[0][0].fill_between(test_x[:,0].detach().squeeze().numpy(), # lower1.detach().numpy(), # upper1.detach().numpy(), alpha=0.5) # axs[0][1].plot(test_x[:,1].detach().squeeze().numpy(), # y_hat[:,1].detach().squeeze().numpy(), label='Estimate') # axs[0][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y[:, 1].detach().squeeze().numpy(), label='True Y') # axs[0][1].legend(loc='upper right') # axs[0][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2.detach().numpy(), # upper2.detach().numpy(), alpha=0.5) # axs[0][1].plot(train_X[:,1].squeeze().detach().numpy(), # train_Y[:,1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # y_hat_prime[:, 0,0].detach().squeeze().numpy(), # label='Estimate') # axs[1][0].plot(test_x[:, 0].detach().squeeze().numpy(), # test_y_prime[:, 0].detach().squeeze().numpy(), label='True Y') # axs[1][0].legend(loc='upper right') # axs[1][0].fill_between(test_x[:, 0].detach().squeeze().numpy(), # lower1_p.detach().numpy(), # upper1_p.detach().numpy(), alpha=0.5) # axs[1][0].plot(train_X[:, 0].squeeze().detach().numpy(), # train_Y_prime[:, 0, 0].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # y_hat_prime[:, 1, 1].detach().squeeze().numpy(), # label='Estimate') # axs[1][1].plot(test_x[:, 1].detach().squeeze().numpy(), # test_y_prime[:, 1].detach().squeeze().numpy(), # label='True Y') # axs[1][1].legend(loc='upper right') # axs[1][1].fill_between(test_x[:, 1].detach().squeeze().numpy(), # lower2_p.detach().numpy(), # upper2_p.detach().numpy(), alpha=0.5) # axs[1][1].plot(train_X[:, 1].squeeze().detach().numpy(), # train_Y_prime[:, 1, 1].detach().numpy(), 'o', # color='black', # markersize=5, # fillstyle="none") # plt.show() def test_exp_kernel(): test_X = th.tensor([[7.1,-100.2], [0.5, 12.2]], dtype=th.float64) kernel = RBFKernel(2) kernel._length_scale.data = th.tensor(1.23) kernel._signal_variance.data = th.tensor(4.56) K = kernel(test_X, test_X) dKdx1 = kernel.ddx1(test_X, test_X) dKdx2 = kernel.ddx2(test_X, test_X) d2Kdx1x2 = kernel.d2dx1x2(test_X, test_X) K_expected = th.tensor( [[95.5835, 6.18301e-234], [6.18301e-234, 95.5835]]) dKdx1_expected = th.tensor([[[0, 0], [-3.48642e-234, 5.93748e-233]], [[3.48642e-234, -5.93748e-233], [0, 0]]]) dKdx2_expected = th.tensor([[[0, 0], [3.48642e-234, -5.93748e-233]], [[-3.48642e-234,5.93748e-233], [0, 0]]]) d2Kdx1x2_expected = th.tensor([[[[8.166169912567646, 0], [0, 8.166169912567646]], [[-1.43764e-234,3.34797e-233], [3.34797e-233, -5.69641e-232]]], \ [[[-1.43764e-234, 3.34797e-233], [3.34797e-233, -5.69641e-232]], [[8.166169912567646,0], [0, 8.166169912567646]]]]) th.testing.assert_allclose(K, K_expected) th.testing.assert_allclose(dKdx1, dKdx1_expected, atol=1e-234, rtol=1e-234) th.testing.assert_allclose(dKdx2, dKdx2_expected, atol=1e-234, rtol=1e-234) #Diagonals of first and alst elements are a bit weird. #probably because we are doing a partial not full derivative #even when x1 == x2. Mathematica shortcuts to zero in these cases. th.testing.assert_allclose(d2Kdx1x2, d2Kdx1x2_expected, atol=1e-234, rtol=1e-234) def test_periodic_kernel(): test_X = th.tensor([[7.1,-100.2], [0.5, 12.2]], dtype=th.float64) kernel = PeriodicKernel(p_prior=3) kernel._length_scale.data = th.tensor(1.23) kernel._signal_variance.data = th.tensor(4.56) K = kernel(test_X[:, 0, None], test_X[:, 0, None]) dKdx1 = kernel.ddx1(test_X[:,0,None], test_X[:,0,None]) dKdx2 = kernel.ddx2(test_X[:,0,None], test_X[:,0,None]) d2Kdx1x2 = kernel.d2dx1x2(test_X[:,0,None], test_X[:,0,None]) K_expected = th.tensor([[95.5835, 90.1041], [90.1041, 95.5835]]) dKdx1_expected = th.tensor([[0, -15.3336], [15.3336, 0]]) dKdx2_expected = th.tensor([[0, 15.3336], [-15.3336, 0]]) d2Kdx1x2_expected = th.tensor([[35.8208, 7.82527], [7.82527, 35.8208]]) th.testing.assert_allclose(K, K_expected, atol=1e-6, rtol=1e-6) th.testing.assert_allclose(dKdx1, dKdx1_expected, atol=1e-5, rtol=1e-5) th.testing.assert_allclose(dKdx2, dKdx2_expected, atol=1e-5, rtol=1e-5) th.testing.assert_allclose(d2Kdx1x2, d2Kdx1x2_expected, atol=1e-6, rtol=1e-6) def test_additive_kernel(): test_X = th.tensor([[7.1, -100.2], [0.5, 12.2]], dtype=th.float64) p_kernel = PeriodicKernel(p_prior=3) rbf_kernel = RBFKernel(2) p_kernel._length_scale.data = th.tensor(1.23) p_kernel._signal_variance.data = th.tensor(4.56) rbf_kernel._length_scale.data = th.tensor(1.23) rbf_kernel._signal_variance.data = th.tensor(4.56) kernel = AdditiveKernel(kernels=[p_kernel, rbf_kernel], active_dims=[[0], [0,1]]) K = kernel(test_X) dKdx1 = kernel.ddx1(test_X, test_X) dKdx2 = kernel.ddx2(test_X, test_X) d2Kdx1x2 = kernel.d2dx1x2(test_X, test_X) rbf_K_expected = th.tensor( [[95.5835, 6.18301e-234], [6.18301e-234, 95.5835]]) rbf_dKdx1_expected = th.tensor([[[0, 0], [-3.48642e-234, 5.93748e-233]], [[3.48642e-234, -5.93748e-233], [0, 0]]]) rbf_dKdx2_expected = th.tensor([[[0, 0], [3.48642e-234, -5.93748e-233]], [[-3.48642e-234, 5.93748e-233], [0, 0]]]) rbf_d2Kdx1x2_expected = th.tensor([[[[8.166169912567646, 0], [0, 8.166169912567646]], [[-1.43764e-234, 3.34797e-233], [3.34797e-233, -5.69641e-232]]], \ [[[-1.43764e-234, 3.34797e-233], [3.34797e-233, -5.69641e-232]], [[8.166169912567646, 0], [0, 8.166169912567646]]]]) p_K_expected = th.tensor([[95.5835, 90.1041], [90.1041, 95.5835]]) p_dKdx1_expected = th.zeros_like(rbf_dKdx1_expected) p_dKdx1_expected[:, :,0] = th.tensor([[0, -15.3336], [15.3336, 0]]) p_dKdx2_expected = th.zeros_like(rbf_dKdx1_expected) p_dKdx2_expected[:, :, 0] = th.tensor([[0, 15.3336], [-15.3336, 0]]) p_d2Kdx1x2_expected = th.zeros_like(rbf_d2Kdx1x2_expected) p_d2Kdx1x2_expected[:,:,0,0] = th.tensor([[35.8208, 7.82527], [7.82527, 35.8208]]) K_expected = rbf_K_expected + p_K_expected dKdx1_expected = rbf_dKdx1_expected + p_dKdx1_expected dKdx2_expected = rbf_dKdx2_expected + p_dKdx2_expected d2Kdx1x2_expected = rbf_d2Kdx1x2_expected + p_d2Kdx1x2_expected th.testing.assert_allclose(K, K_expected) th.testing.assert_allclose(dKdx1, dKdx1_expected, atol=1e-5, rtol=1e-5) th.testing.assert_allclose(dKdx2, dKdx2_expected, atol=1e-5, rtol=1e-5) th.testing.assert_allclose(d2Kdx1x2, d2Kdx1x2_expected, atol=1e-234, rtol=1e-5) def test_multiplicative_kernel(): test_X = th.tensor([[7.1, -100.2], [0.5, 12.2]], dtype=th.float64) p_kernel = PeriodicKernel(p_prior=3) rbf_kernel = RBFKernel(1) p_kernel._length_scale.data = th.tensor(1.23) p_kernel._signal_variance.data = th.tensor(4.56) rbf_kernel._length_scale.data = th.tensor(1.23) rbf_kernel._signal_variance.data = th.tensor(4.56) kernel = MultiplicativeKernel(kernels=[p_kernel, rbf_kernel], active_dims=[[0], [1]]) K = kernel(test_X, test_X) dKdx1 = kernel.ddx1(test_X, test_X) dKdx2 = kernel.ddx2(test_X, test_X) d2Kdx1x2 = kernel.d2dx1x2(test_X, test_X) K_expected = th.tensor([[9136.2, 3.58153e-231], [3.58153e-231, 9136.2]]) dKdx1_expected = th.tensor([[[0., 0.], [-6.09493e-232, 3.4393e-230]], [[6.09493e-232, -3.4393e-230], [0., 0.]]]) dKdx2_expected = th.tensor([[[0., 0.], [6.09493e-232, -3.4393e-230]], [[-6.09493e-232, 3.4393e-230], [0., 0.]]]) d2Kdx1x2_expected = th.tensor([[[[3423.88, 0.], [0., 780.551]], [[3.11045e-232, 5.85289e-231], [5.85289e-231, -3.29966e-229]]], \ [[[3.11045e-232, 5.85289e-231], [5.85289e-231, -3.29966e-229]], [[3423.88, 0.], [0., 780.551]]]]) th.testing.assert_allclose(K, K_expected) th.testing.assert_allclose(dKdx1, dKdx1_expected, atol=1e-234, rtol=1e-5) th.testing.assert_allclose(dKdx2, dKdx2_expected, atol=1e-234, rtol=1e-5) th.testing.assert_allclose(d2Kdx1x2, d2Kdx1x2_expected, atol=1e-234, rtol=1e-5) def test_affine_dot_product_kernel(): test_X = th.tensor([[7.1, -100.2, -1., 1.3], [0.5, 12.2, 3, 6.7]], dtype=th.float64) p_kernel = PeriodicKernel(p_prior=3) rbf_kernel = RBFKernel(1) p_kernel._length_scale.data = th.tensor(1.23) p_kernel._signal_variance.data = th.tensor(4.56) rbf_kernel._length_scale.data = th.tensor(1.23) rbf_kernel._signal_variance.data = th.tensor(4.56) sub_kernels = [MultiplicativeKernel(kernels=[p_kernel, rbf_kernel], active_dims=[[0], [1]])]*3 kernel = AffineDotProductKernel(s_idx=[0,1], m_idx=[2,3], kernels=sub_kernels, last_is_unit=True) K = kernel(test_X, test_X) dKdx1 = kernel.ddx1(test_X, test_X) dKdx2 = kernel.ddx2(test_X, test_X) d2Kdx1x2 = kernel.d2dx1x2(test_X, test_X) def test_multiplicative_periodic_consistency(): kernel = MultiplicativeKernel( kernels=[PeriodicKernel(p_prior=1/2, learn_period=False), RBFKernel(2, ard_num_dims=True)], active_dims=[[0], [1, 2]] ) expected_1 = kernel.kernels[0](th.tensor([[-1.]]), th.tensor([[-1.]])) * \ kernel.kernels[1]( th.tensor([[0.5, 0]]), th.tensor([[0.5, 0]])) expected_2 = kernel.kernels[0](th.tensor([[-1.]]), th.tensor([[-2.]])) * \ kernel.kernels[1]( th.tensor([[0.5, 0]]), th.tensor([[0.5, 0]])) actual_1 = kernel(th.tensor([[-1, 0.5, 0]]), th.tensor([[-1, 0.5, 0]])) actual_2 = kernel(th.tensor([[-1, 0.5, 0]]), th.tensor([[-2, 0.5, 0]])) actual_3 = kernel(th.tensor([[1, 0.5, 0]]), th.tensor([[1, 0.5, 0]])) actual_4 = kernel(th.tensor([[1, 0.5, 0]]), th.tensor([[2, 0.5, 0]])) th.testing.assert_allclose(expected_1, expected_2) th.testing.assert_allclose(actual_1, expected_1) th.testing.assert_allclose(actual_2, expected_2) th.testing.assert_allclose(actual_3, actual_4) th.testing.assert_allclose(actual_3, actual_2)

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a297417c5df77528b3521ecdb1d16984df511f38

31,025

py

Python

simplebitcoinfuncs/_doctester.py

maxweisspoker/simplebitcoinfuncs

ad332433dfcc067e86d2e77fa0c8f1a27daffb63

[ "MIT" ]

1

2017-03-18T06:00:51.000Z

simplebitcoinfuncs/_doctester.py

maxweisspoker/simplebitcoinfuncs

ad332433dfcc067e86d2e77fa0c8f1a27daffb63

[ "MIT" ]

null

simplebitcoinfuncs/_doctester.py

maxweisspoker/simplebitcoinfuncs

ad332433dfcc067e86d2e77fa0c8f1a27daffb63

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' I removed all the doctests from everywhere else and put them here. ''' from __future__ import print_function, division, absolute_import try: from __builtin__ import bytes, str, open, super, range, zip, round, int, pow, object, input except ImportError: pass try: from __builtin__ import raw_input as input except: pass from codecs import decode from binascii import hexlify, unhexlify try: ModuleNotFoundError except: ModuleNotFoundError = ImportError try: from .hexhashes import * from .ecmath import * from .base58 import * from .miscfuncs import * from .miscbitcoinfuncs import * from .bitcoin import * from .signandverify import * from .stealth import * from .bip32 import * from .bip39 import * from .electrum1 import * from .electrum2 import * from .rfc6979 import generate_k except Exception as e: if type(e) != ImportError and \ type(e) != ModuleNotFoundError and \ type(e) != ValueError and \ type(e) != SystemError: raise Exception("Unknown problem with imports.") from hexhashes import * from ecmath import * from base58 import * from miscfuncs import * from miscbitcoinfuncs import * from bitcoin import * from signandverify import * from stealth import * from bip32 import * from bip39 import * from electrum1 import * from electrum2 import * from rfc6979 import generate_k def hexhashes_py___doctest(): ''' hexhashes.py tests: >>> sha256('') 'e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855' >>> sha256('aabbccdd') '8d70d691c822d55638b6e7fd54cd94170c87d19eb1f628b757506ede5688d297' >>> sha512('') 'cf83e1357eefb8bdf1542850d66d8007d620e4050b5715dc83f4a921d36ce9ce47d0d13c5d85f2b0ff8318d2877eec2f63b931bd47417a81a538327af927da3e' >>> sha512('aabbccdd') '48e218b30d4ea16305096fe35e84002a0d262eb3853131309423492228980c60238f9eed238285036f22e37c4662e40c80a461000a7aa9a03fb3cb6e4223e83b' >>> sha512d('') '826df068457df5dd195b437ab7e7739ff75d2672183f02bb8e1089fabcf97bd9dc80110cf42dbc7cff41c78ecb68d8ba78abe6b5178dea3984df8c55541bf949' >>> sha512d('aabbccdd') '46561839a3278e5cd3999450c8f89e459aa8c234fbee7935635db777d7dbd654bf7293c84cf64c318be0197a41c622a247a70024ff9d27f392c0d4a4da8d6354' >>> ripemd160('') '9c1185a5c5e9fc54612808977ee8f548b2258d31' >>> ripemd160('aabbccdd') '148164ccf60a825bc3250722074c3426a7f67fcb' >>> hash160('') 'b472a266d0bd89c13706a4132ccfb16f7c3b9fcb' >>> hash160('aabbccdd') 'd6e9254683798a28eabd2626fd573cf2cf3869f9' >>> hash256('') '5df6e0e2761359d30a8275058e299fcc0381534545f55cf43e41983f5d4c9456' >>> hash256('aabbccdd') '6a83c7f1def9386347c206e94c90559f49be557609fc1811bfe311b67ecef8b0' >>> hash512('') '6b4e6c1fe36504e12e6d9716f74250ecb6fefb2a83af8e8edee9caeb3f32ca4683eca58c50faa06afc40a15fdc4c706d296a6f859bfb9b22871d28a500baf7b1' >>> hash512('aabbccdd') '20df4f6c9244b517cb5dd1c3b1e13bb316a45f5b904fc57799b66389947186d266ad611ee282fdea6630da4dbc96015beba2faecc110782015df662c4abf6297' ''' return def ecmath_py___doctest(): ''' Many doctest for ecmath are done in a weird way. That is to ensure the same output for Python 2 and 3, so things like 4 vs 4L don't mess up the tests. >>> modinv(2521213890399410648018095333325722136449021566908310412768334520696982806641) == \ 17465617466841484688650846354295959695753514552349626970717521890536775674935 True >>> modinv(-95700528412413679576195283092455617561285633360671739483652140770588235170392,N) == \ 11411284869303779416452608717884069348175089368882490102158000583211275329323 True >>> x,y = ecadd( \ 4938373901174265576094805690384936437621390742743114714534166734031749709952, \ 23406007515733211420427986631155727216565925582529100160361434981966318828999, \ 11029270422249989266356636372380040023432092195222839243672437607748020962878, \ 12338920660869481789439141094019604918037726829679018934712977981859756778348) >>> x == 83336094426407305185582932726071265758876028986498851406936393497302545717601 True >>> y == 71857134501436534997244054415723847888276629084374532235863885413095164252131 True >>> x,y = ecsubtract( \ 4938373901174265576094805690384936437621390742743114714534166734031749709952, \ 23406007515733211420427986631155727216565925582529100160361434981966318828999, \ 11029270422249989266356636372380040023432092195222839243672437607748020962878, \ 12338920660869481789439141094019604918037726829679018934712977981859756778348) >>> x == 55597633869961612317309410433076836678766403763677101352598043583451378461409 True >>> y == 26239925317332119257936947014113260047893818687460697966446999229620006489892 True >>> x,y = ecdouble( \ 17122607971474055933869599824585174586417044884544686165239805207052395415204, \ 40838023179274613372805173210407024975579475223402894269126337256598864150690) >>> x == 72311113040667355501201059093433510680042205181920994715815687665050367873657 True >>> y == 81202695815007557875587128276299271839897857009219307151962560322569452526782 True >>> x,y = ecmultiply(Gx,Gy,42) >>> x == 115136800820456833737994126771386015026287095034625623644186278108926690779567 True >>> y == 3479535755779840016334846590594739014278212596066547564422106861430200972724 True >>> x,y = ecmultiply( \ 2521213890399410648018095333325722136449021566908310412768334520696982806641, \ 61992791029995100687584613680591045503872148133214804167999634260847801377258, \ 86160004736639257141798190143937095024102878958814199546049053726283481854320) >>> x == 84191613447606291376043707809973780390176222720060740978105574111402634616050 True >>> y == 109474824470067519156060832976900905455196281901984947573736908658291479411212 True >>> pow_mod(int(N//4),int(N//15),P) == \ 85863265686857850576725992990591539765753424982812429250530061375940639195105 True ''' return def base58_py___doctest(): ''' >>> b58e('0000000000000000000000000000000000000000000000000000000000000000') '11111111111111111111111111111111273Yts' >>> b58e('80000000000000000000000000000000000000000000000000000000000000000101') 'KwDiBf89QgGbjEhKnhXJuH7LrciVrZi3qYjgd9M7rFU73sVHnoWn' >>> b58e('80000000000000000000000000000000000000000000000000000000000000000101', False) '3tq8Vmhh9SN5XhjTGSWgx8iKk59XbKG6UH4oqpejRoF9ASt' >>> b58e('') '3QJmnh' >>> b58d('3A5vdSL9MQrKRijvxr8S3V2DQ918XPL1GL') '055c16274562a91d531f6043f86c68d3a0f65be42a' >>> b58d('11111111111111111111111111111111', False) '0000000000000000000000000000000000000000000000000000000000000000' >>> b58d('11111111111111111111111111111111273Yts') '0000000000000000000000000000000000000000000000000000000000000000' # Incorrect checksum, while check is True >>> b58d('11111111111111111111111111111111273YYY') Traceback (most recent call last): ... AssertionError >>> b58d('11111111111111111111111111111111273YYY', False) '00000000000000000000000000000000000000000000000000000000000000002b32d6d1' >>> b58d('') Traceback (most recent call last): ... AssertionError # str() because if unicode, Exception says character u'0' which fails # the doctest >>> b58d(str('XY0Z')) Traceback (most recent call last): ... Exception: Character '0' is not a valid base58 character ''' return def miscfuncs_py___doctest(): ''' >>> strlify(b'aabb') 'aabb' >>> strlify(hexlify(unhexlify("aabb"))) 'aabb' >>> strlify('bb') 'bb' >>> strlify(b'b') 'b' >>> strlify('b') 'b' >>> isitstring(55) False >>> isitstring('Hello') True >>> isitstring(u'Hello') True >>> isitint(4) True >>> isitint(2**256) True >>> isitint(-4) True >>> isitint(0) True >>> isitint('0') False >>> isitint('00') False >>> isitint(unhexlify('00')) False >>> isitint(4.0) False # Doctest for Py3 doesn't properly handle bytes completely, # hence using unhexlify >>> hexstrlify(bytes(unhexlify('bbc7f07e59670ffdbb6bbb'))) 'bbc7f07e59670ffdbb6bbb' >>> hexreverse('a1b2c3d4') 'd4c3b2a1' >>> dechex(4,2) '0004' >>> dechex(0) '00' >>> dechex(0000) '00' >>> dechex(43528704357807084357809435278904235,16) '000862217d6e549c3fdf5c2e5b450bab' ''' return def miscbitcoinfuncs_py___doctest(): ''' >>> oppushdatalen(13) '0d' >>> oppushdatalen(105) '4c69' >>> oppushdatalen(436) '4db401' >>> oppushdatalen(4294967290) '4efaffffff' >>> intfromoppushdatalen('4efaffffff') 4294967290 >>> intfromoppushdatalen('4db401') 436 >>> intfromoppushdatalen('4c69') 105 >>> intfromoppushdatalen('0d') 13 >>> intfromoppushdatalen('4c69dd') Traceback (most recent call last): ... AssertionError >>> intfromoppushdatalen('0daa') Traceback (most recent call last): ... AssertionError >>> tovarint(250) 'fa' >>> tovarint(253) 'fdfd00' >>> tovarint(260) 'fd0401' >>> tovarint(294967296) 'fe00d89411' >>> tovarint(6418473620) 'ff9422927e01000000' >>> numvarintbytes('b5') 1 >>> numvarintbytes('fb') 1 >>> numvarintbytes('fc') 1 >>> numvarintbytes('fd') 3 >>> numvarintbytes('fe') 5 >>> numvarintbytes('ff') 9 >>> numvarintbytes('fd0401') Traceback (most recent call last): ... AssertionError >>> fromvarint('ff9422927e01000000') 6418473620 >>> fromvarint('fe00d89411') 294967296 >>> fromvarint('fd0401') 260 >>> fromvarint('fdfd00') 253 >>> fromvarint('fc') 252 >>> fromvarint('fdfd0005') Traceback (most recent call last): ... AssertionError >>> fromvarint('c9') 201 >>> x = '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' >>> getandstrip_varintdata(x) ('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', 'ffffffff0140420f00000000001976a914ae56b4db13554d321c402db3961187aed1bbed5b88ac00000000') >>> inttoDER(23159624154826860047781259025922852200415127951164078404008335037124850950245) '02203333e1fba07e542a357c45103a2fa62c044af1000d21b54dc9c54de36aef2065' >>> inttoDER(59344652041488171117647191841137823404561998159176754666338830039597703962725) '0221008333e1fba07e542a357c45103a2fa62c044af1000d21b54dc9c54de36aef2065' >>> inttoDER(6783848548763080805863616406882737495015296602382933530988832128704613) '021e00fba07e542a357c45103a2fa62c044af1000d21b54dc9c54de36aef2065' >>> inttoDER(3332975375367798912146238475744224768789742116297740253407570016804965) '021d7ba07e542a357c45103a2fa62c044af1000d21b54dc9c54de36aef2065' >>> inttoLEB128(624485) 'e58e26' >>> LEB128toint('e58e26') 624485 ''' return def bitcoin_py___doctest(): ''' >>> uncompress('03AB27DC61A8D60CEB3A3234E69B818F2DF5B79FD67E0CCFF474B788ACE319FBB8') '04ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb89dff12fbeb8368d30d28bf6c00dd1900c89ba086b19dab33828557418d855267' >>> uncompress('02ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb8') '04ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb86200ed04147c972cf2d74093ff22e6ff37645f794e6254cc7d7aa8bd727aa9c8' >>> compress('04ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb89dff12fbeb8368d30d28bf6c00dd1900c89ba086b19dab33828557418d855267') '03ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb8' >>> compress('04ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb86200ed04147c972cf2d74093ff22e6ff37645f794e6254cc7d7aa8bd727aa9c8') '02ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb8' >>> privtopub('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d') '03ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb8' >>> privtopub('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d', False) '04ab27dc61a8d60ceb3a3234e69b818f2df5b79fd67e0ccff474b788ace319fbb89dff12fbeb8368d30d28bf6c00dd1900c89ba086b19dab33828557418d855267' >>> addprivkeys('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d', \ '5a499293484e3dec9d452d0996d9566613bebfe75b7c49bb205517336254105d') '71d8a7f77f46c99745e9584b3b86e3dc262fdab955a3c7d8ab1b5e3939cc519a' >>> addprivkeys('ff8f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d', \ '5a499293484e3dec9d452d0996d9566613bebfe75b7c49bb205517336254105d') '59d8a7f77f46c99745e9584b3b86e3dd6b80fdd2a65b279ceb48ffac69961059' >>> subtractprivkeys('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d', \ '5a499293484e3dec9d452d0996d9566613bebfe75b7c49bb205517336254105d') 'bd4582d0eeaa4dbe0b5efe380dd4370eb96137d14df3d49e2a438e5f455a7221' >>> multiplypriv('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d', \ '5a499293484e3dec9d452d0996d9566613bebfe75b7c49bb205517336254105d') 'a08be4c08d9820284fc81896b465a08b0a95305cf364517d9d46ec7ae954321e' >>> multiplypub( \ '04eee3998f3546c061cfedd989cc77280ba2777dff4ed437b00d43dd2942dae003a702ba24e6c79ca23f1890249639c2621f897618d51d633b5039f1f3a4f4e7d4', \ '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d') '02fdd25715a72408d662e844027d6deb58b76cb0b9a294ee490191a4ef40df4792' >>> multiplypub('02eee3998f3546c061cfedd989cc77280ba2777dff4ed437b00d43dd2942dae003', \ '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d', False) '04fdd25715a72408d662e844027d6deb58b76cb0b9a294ee490191a4ef40df47923efac534afd12d2fcd07c751ef4f6fac9286045df6e9e29608d56efc403a0438' >>> addpubs('02fdd25715a72408d662e844027d6deb58b76cb0b9a294ee490191a4ef40df4792', \ '02eee3998f3546c061cfedd989cc77280ba2777dff4ed437b00d43dd2942dae003') '024abeabbdd5de7727bbb2ff5251d57310ef2607dab1e2889f4315474778b466a3' >>> subtractpubs( \ '02fdd25715a72408d662e844027d6deb58b76cb0b9a294ee490191a4ef40df4792', \ '02eee3998f3546c061cfedd989cc77280ba2777dff4ed437b00d43dd2942dae003') '02e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c' >>> pubtoaddress('02e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c') '18o5G4us8k5DscDdyFq1nx8iSE7RFy2euv' >>> pubtoaddress(uncompress('02e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c')) '1MtiJXjp3Vr8s1AtgK1veGLNnjhy3PrUxE' >>> validatepubkey('02E3752F728D53E227F789BE951FD899E36295C386F6C249940B5C9C275B4F908C') '02e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c' >>> validatepubkey('04e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c9a50cec685f8e2a1f77b216b60319c5b5da20cb1ad305af39c85c42a78cebf64') '04e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c9a50cec685f8e2a1f77b216b60319c5b5da20cb1ad305af39c85c42a78cebf64' >>> validatepubkey('04e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c9a50cec685f8e2a1f77b216b60319c5b5da20cb1ad305af39c85c42a78cebf65') False >>> validatepubkey('04e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c') False >>> validatepubkey('e3752f728d53e227f789be951fd899e36295c386f6c249940b5c9c275b4f908c') False >>> wiftohex("5KcCmPP68JhjXE3guHwMnA5aiYWvsMbQrpDJYkreLpgGQAroXDh") ('ebf4c9e128721400d4d8ac059c1aff929e9ad121518f744bfedf456592cd1dbd', '80', False) >>> wiftohex("L58NvunVdF8ngQas7okviK5DpN76mFttJsPJTAa7pVSJy1KbUUkL") ('ebf4c9e128721400d4d8ac059c1aff929e9ad121518f744bfedf456592cd1dbd', '80', True) >>> wiftohex("6uUqLX6roU6TbVtWqWRRzSAwMx2E7ctTbwDGL8Dyn1bmyKfS9f8") ('2f43829ce7f2985d4b4de7cbbb99b8d15843ad3f3149879ab20963f2978aeab6', 'b0', False) >>> privtohex('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d') '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d' >>> privtohex('5HzfMyinNsX6ohao4LxY6dssqxy9Tg5unjV1KCt9UCiJRZvq5Gv') '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d' >>> privtohex('Kx1WKbMRHXyrd88AHm68FsmZR82pLjWfzrWcPMSkP4hHuHszrrZK') '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d' >>> privtohex('T3qmmLebguxTPxm2qQ2zUEJwMyg8QpXZp4QsFA5Hx2sTRBUPvfom') '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d' >>> privtohex(10656002286135494676906904972529529473002948329995631005275422314744862228797) '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d' >>> privtohex(unhexlify('178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d')) '178f156436f88baaa8a42b41a4ad8d7612711ad1fa277e1d8ac64705d778413d' >>> privtohex("This is not a private key!") Traceback (most recent call last): ... Exception: Cannot interpret input key. >>> privtohex('T3qmmLebguxTPxm2qQ2zUEJwMyg8QpX') Traceback (most recent call last): ... Exception: Cannot interpret input key. >>> mycoin = Coin(u'ed4cbc48b674f3d3bce9f3f17ec7b9d8c03b5423afefd16a8c098ead535ec206','80','00') >>> mycoin.priv 'ed4cbc48b674f3d3bce9f3f17ec7b9d8c03b5423afefd16a8c098ead535ec206' >>> mycoin.wifc 'L5AzQaAcHxGhZUfzyHQqrJ76YFyzozEHZ1JNLbVQzbUScbPSw1hv' >>> mycoin.wifu '5Kco5tU6jMgMX1qFwtkTvmCoZunYDuqmz6WJyYq8FQfqrMrdMhE' >>> mycoin.addrc '1B7jusx1FY9u7XxsSYzLQtcohzf8sevxE4' >>> mycoin.pubprefix '00' ''' return def signandverify_py___doctest(): ''' >>> h = 'f7011e94125b5bba7f62eb25efe23339eb1637539206c87df3ee61b5ec6b023e' >>> p = 'c05694a7af0e01dceb63e5912a415c28d3fc823ca1fd3fa34d41afde03740466' >>> k = 4 # chosen by fair dice roll, guaranteed to be random >>> sign(h,p,k) '3045022100e493dbf1c10d80f3581e4904930b1404cc6c13900ee0758474fa94abe8c4cd130220598e37e2e66277ef4d0caf0e32d095debb3c744219508cd394b9747e548662b7' >>> h = 'f7011e94125b5bba7f62eb25efe23339eb1637539206c87df3ee61b5ec6b023e' >>> sig = '3045022100e493dbf1c10d80f3581e4904930b1404cc6c13900ee0758474fa94abe8c4cd130220598e37e2e66277ef4d0caf0e32d095debb3c744219508cd394b9747e548662b7' >>> pub = '022587327dabe23ee608d8504d8bc3a341397db1c577370389f94ccd96bb59a077' >>> verify(h,sig,pub) True >>> sig = '3046022100e493dbf1c10d80f3581e4904930b1404cc6c13900ee0758474fa94abe8c4cd13022100a671c81d199d8810b2f350f1cd2f6a1fff7268a495f813682b18ea0e7bafde8a' >>> verify(h,sig,pub) True >>> verify(h,sig,uncompress(pub)) True >>> verify(h,sig,pub,True) Traceback (most recent call last): ... TypeError: High S value. >>> checksigformat('3045022100e493dbf1c10d80f3581e4904930b1404cc6c13900ee0758474fa94abe8c4cd130220598e37e2e66277ef4d0caf0e32d095debb3c744219508cd394b9747e548662b7') True >>> checksigformat('3046022100e493dbf1c10d80f3581e4904930b1404cc6c13900ee0758474fa94abe8c4cd13022100a671c81d199d8810b2f350f1cd2f6a1fff7268a495f813682b18ea0e7bafde8a') True >>> checksigformat('3046022100e493dbf1c10d80f3581e4904930b1404cc6c13900ee0758474fa94abe8c4cd13022100a671c81d199d8810b2f350f1cd2f6a1fff7268a495f813682b18ea0e7bafde8a', \ True) False >>> msg = '"You miss 100% of the shots you don\\'t take. -- Wayne Gretzky"\\n -- Michael Scott' >>> p = 'c05694a7af0e01dceb63e5912a415c28d3fc823ca1fd3fa34d41afde03740466' >>> k = 4 # chosen by fair dice roll, guaranteed to be random >>> signmsg(msg,p,True,k) 'H+ST2/HBDYDzWB5JBJMLFATMbBOQDuB1hHT6lKvoxM0TBxoLMWsgrFmA3CGam/poUZPl/PukXCrYBzuwMW3Tyyo=' >>> msg = '"You miss 100% of the shots you don\\'t take. -- Wayne Gretzky"\\n -- Michael Scott' >>> sig = 'H+ST2/HBDYDzWB5JBJMLFATMbBOQDuB1hHT6lKvoxM0TBxoLMWsgrFmA3CGam/poUZPl/PukXCrYBzuwMW3Tyyo=' >>> x = verifymsg(msg,sig) >>> pub = '022587327dabe23ee608d8504d8bc3a341397db1c577370389f94ccd96bb59a077' >>> x == pub True >>> checkmsgsigformat('H+ST2/HBDYDzWB5JBJMLFATMbBOQDuB1hHT6lKvoxM0TBxoLMWsgrFmA3CGam/poUZPl/PukXCrYBzuwMW3Tyyo=') True >>> checkmsgsigformat('H+ST2/HBDYDzWB5JBJMLFATMbBOQDuB1hHT6lKvoxM0TBxoLMWsgrFmA3CGam/poUZPl/PukXCrYBzuwMW3Tyyo=') True >>> checkmsgsigformat('H+ST2/HBDYDzWB5JBJMLFATMbBOQDuB1hHT6lKvoxM0T+OX0zpTfU6Z/I95lZAWXrSbI3+sK7HVjuJauW2Jidhc=') True >>> checkmsgsigformat('H+ST2/HBDYDzWB5JBJMLFATMbBOQDuB1hHT6lKvoxM0T+OX0zpTfU6Z/I95lZAWXrSbI3+sK7HVjuJauW2Jidhc=',True) False ''' return def rfc6979_generate_k___doctest(): ''' >>> ######## >>> # Test vectors from https://bitcointalk.org/index.php?topic=285142.40 >>> ######## >>> h = sha256(hexlify(b"Satoshi Nakamoto")) >>> p = dechex(1, 32) >>> k = generate_k(p, h) >>> k == 0x8F8A276C19F4149656B280621E358CCE24F5F52542772691EE69063B74F15D15 True >>> h = sha256(hexlify(b"All those moments will be lost in time, like tears in rain. Time to die...")) >>> k = generate_k(p, h) >>> k == 0x38AA22D72376B4DBC472E06C3BA403EE0A394DA63FC58D88686C611ABA98D6B3 True >>> h = sha256(hexlify(b"Satoshi Nakamoto")) >>> p = dechex(int(0xFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFEBAAEDCE6AF48A03BBFD25E8CD0364140),32) >>> k = generate_k(p, h) >>> k == 0x33A19B60E25FB6F4435AF53A3D42D493644827367E6453928554F43E49AA6F90 True >>> h = sha256(hexlify(b"Alan Turing")) >>> p = dechex(int(0xf8b8af8ce3c7cca5e300d33939540c10d45ce001b8f252bfbc57ba0342904181),32) >>> k = generate_k(p, h) >>> k == 0x525A82B70E67874398067543FD84C83D30C175FDC45FDEEE082FE13B1D7CFDF1 True >>> h = sha256(hexlify(b"There is a computer disease that anybody who works " \ + b"with computers knows about. It's a very serious " \ + b"disease and it interferes completely with the work. " \ + b"The trouble with computers is that you 'play' with " \ + b"them!")) >>> p = dechex(int(0xe91671c46231f833a6406ccbea0e3e392c76c167bac1cb013f6f1013980455c2),32) >>> k = generate_k(p, h) >>> k == 0x1F4B84C23A86A221D233F2521BE018D9318639D5B8BBD6374A8A59232D16AD3D True >>> p, z1, z2 = wiftohex("KwDiBf89QgGbjEhKnhXJuH7LrciVrZi3qYjgd9M7rFU73sVHnoWn") >>> h = sha256(hexlify(b"Everything should be made as simple as possible, but not simpler.")) >>> k = generate_k(p, h) >>> sign(h,p,k) '3044022033a69cd2065432a30f3d1ce4eb0d59b8ab58c74f27c41a7fdb5696ad4e6108c902206f807982866f785d3f6418d24163ddae117b7db4d5fdf0071de069fa54342262' >>> p, z1, z2 = wiftohex("L5oLkpV3aqBjhki6LmvChTCV6odsp4SXM6FfU2Gppt5kFLaHLuZ9") >>> h = sha256(hexlify(b"Equations are more important to me, because " \ + b"politics is for the present, but an equation " \ + b"is something for eternity.")) >>> k = generate_k(p, h) >>> sign(h,p,k) '3044022054c4a33c6423d689378f160a7ff8b61330444abb58fb470f96ea16d99d4a2fed022007082304410efa6b2943111b6a4e0aaa7b7db55a07e9861d1fb3cb1f421044a5' >>> p, z1, z2 = wiftohex("L5oLkpV3aqBjhki6LmvChTCV6odsp4SXM6FfU2Gppt5kFLaHLuZ9") >>> h = sha256(hexlify(b"Not only is the Universe stranger than we think, it is stranger than we can think.")) >>> k = generate_k(p, h) >>> sign(h,p,k) '3045022100ff466a9f1b7b273e2f4c3ffe032eb2e814121ed18ef84665d0f515360dab3dd002206fc95f5132e5ecfdc8e5e6e616cc77151455d46ed48f5589b7db7771a332b283' >>> p = '0000000000000000000000000000000000000000000000000000000000000001' >>> h = sha256(hexlify(b"How wonderful that we have met with a paradox. " \ + b"Now we have some hope of making progress.")) >>> k = generate_k(p, h) >>> sign(h,p,k) '3045022100c0dafec8251f1d5010289d210232220b03202cba34ec11fec58b3e93a85b91d3022075afdc06b7d6322a590955bf264e7aaa155847f614d80078a90292fe205064d3' ''' def stealth_py___doctest(): ''' >>> paystealth("vJmvinTgWP1phdFnACjc64U5iMExyv7JcQJVZjMA15MRf2KzmqjSpgDjmj8NxaFfiMBUEjaydmNfLBCcXstVDfkjwRoFQw7rLHWdFk", \ '824dc0ed612deca8664b3d421eaed28827eeb364ae76abc9a5924242ddca290a', 0) ('03e05931191100fa6cd072b1eda63079736464b950d2875e67f2ab2c8af9b07b8d', \ '0600000124025c6fb169b0ff1c95426fa073fadc62f50a6e98482ec8b3f26fb73006009d1c00') >>> receivestealth('af4afaeb40810e5f8abdbb177c31a2d310913f91cf556f5350bca10cbfe8b9ec', \ 'd39758028e201e8edf6d6eec6910ae4038f9b1db3f2d4e2d109ed833be94a026', \ '03b8a715c9432b2b52af9d58aaaf0ccbdefe36d45e158589ecc21ba2f064ebb315') '6134396c3bc9a56ccaf80cd38728e6d3a7751524246e7924b21b08b0bfcc3cc4' ''' return def bip32_py___doctest(): ''' >>> testvector1 = BIP32('000102030405060708090a0b0c0d0e0f') >>> str(testvector1) 'xprv9s21ZrQH143K3QTDL4LXw2F7HEK3wJUD2nW2nRk4stbPy6cq3jPPqjiChkVvvNKmPGJxWUtg6LnF5kejMRNNU3TGtRBeJgk33yuGBxrMPHi' >>> testvector1.child("m/0H/1/2H/2/1000000000") 'xprvA41z7zogVVwxVSgdKUHDy1SKmdb533PjDz7J6N6mV6uS3ze1ai8FHa8kmHScGpWmj4WggLyQjgPie1rFSruoUihUZREPSL39UNdE3BBDu76' >>> BIP32.xprvtoxpub(testvector1.child("m/0H/1/2H/2/1000000000")) 'xpub6H1LXWLaKsWFhvm6RVpEL9P4KfRZSW7abD2ttkWP3SSQvnyA8FSVqNTEcYFgJS2UaFcxupHiYkro49S8yGasTvXEYBVPamhGW6cFJodrTHy' >>> testvector1.wif 'L52XzL2cMkHxqxBXRyEpnPQZGUs3uKiL3R11XbAdHigRzDozKZeW' >>> testvector1["m/0H/1/2H/2/1000000000"].addr '1LZiqrop2HGR4qrH1ULZPyBpU6AUP49Uam' >>> testvector2 = BIP32('fffcf9f6f3f0edeae7e4e1dedbd8d5d2cfccc9c6c3c0bdbab7b4b1aeaba8a5a29f9c999693908d8a8784817e7b7875726f6c696663605d5a5754514e4b484542') >>> testvector2.child("m/0/2147483647'/1/2147483646'/2") 'xprvA2nrNbFZABcdryreWet9Ea4LvTJcGsqrMzxHx98MMrotbir7yrKCEXw7nadnHM8Dq38EGfSh6dqA9QWTyefMLEcBYJUuekgW4BYPJcr9E7j' >>> BIP32(BIP32.xprvtoxpub(testvector2.xprv)).child("m/0/2147483647'/1/2147483646'/2") Traceback (most recent call last): ... Exception: Input path contains hardened derivation. Cannot derive hardened child from public master key. >>> path = 'm/2/4352/0/231/8/0' >>> x = testvector1.child(path) >>> x 'xprvA5hf574kbP5WQsvUYw7z8o7Sp5RmABwvw9wNFdeBotkbYfGedxB8UguRcxFPYVXDQzeb5SETXCCP8aXsyP3u2sNb42XdNZVYFUQ2nptCVUQ' >>> crack_test = BIP32.crack(testvector1.xpub,x,path) >>> crack_test == testvector1.xprv True >>> path = 'm/2/4352/0H/231/8/0' >>> BIP32.crack(testvector1.xpub,testvector1.child(path),path) Traceback (most recent call last): ... Exception: Path input indicates a hardened key. Cannot crack up a level from hardened keys. ''' return def bip39_py___doctest(): ''' >>> x = BIP39("00000000000000000000000000000000") >>> x.en 'abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon abandon about' >>> x.enbip32seed '5eb00bbddcf069084889a8ab9155568165f5c453ccb85e70811aaed6f6da5fc19a5ac40b389cd370d086206dec8aa6c43daea6690f20ad3d8d48b2d2ce9e38e4' >>> x.setpassword('TREZOR') >>> x.enbip32seed 'c55257c360c07c72029aebc1b53c05ed0362ada38ead3e3e9efa3708e53495531f09a6987599d18264c1e1c92f2cf141630c7a3c4ab7c81b2f001698e7463b04' >>> x.hex '00000000000000000000000000000000' ''' return def electrum1_py___doctest(): ''' >>> x = Electrum1('school eventually space front trip delicate drift score surely nine serve again') >>> x.words 'school eventually space front trip delicate drift score surely nine serve again' >>> x.seed '950421e37c371408a14aeb9164d7a559' >>> x.seed == Electrum1.wordstohex(x.words) True >>> x.mpriv '9f8d1ab5da1f3133a87a6dff6daa1f8905906187ed72b6476fdc8a9a9aec68d5' >>> x.mpub '887867b2914527765faed6ac3d7fd1a4c373fda4a7d6350ac9adabc55befe34a50fc0ada9d1a439650653d445c5aad27d52d153cea3cf375578646a2b9820c58' >>> x[4.0][0] '5KJZT97WqVvLXwbbDyaVkGAcjK2AnMWBBy979BhWYbQ2yP7uJvb' >>> x.mpriv == Electrum1.crack(x.mpub,x[4.0][0]) True ''' return def electrum2_py___doctest(): ''' >>> x = Electrum2('ride win pass silver noble position because balcony unveil perfect keen pyramid abuse') >>> str(x) 'ride win pass silver noble position because balcony unveil perfect keen pyramid abuse' >>> x.bip32xpub 'xpub661MyMwAqRbcGEHVXvE19EHH5Bpe7S4YFYXKPNAvCZ982MA1MyzkSAPSTmxWKqHjPsht3BDG2DxBfhiAKwrVzJFzVCTSovCEVXst6LPamzv' >>> x.hex '9af0f368c77311c27aa1cadc8d417ed5cb' >>> x[3][0] 'Kwk1qQYC1NQkYrv2sgedWGEvSggKWMRbwrRTRCfVSbKbCrd2WfmL' >>> x[1.] ('L2cthViuxbGEMiiBcxhAvgtusg13mSXT94ZHv2WuYfmDZbu3q4dx', '02fa3aab7ebc4a45f4e2bf428b113751f0aa31b39110c2f039c46b4da39fa0477b', '1NSuzNYZJBU9G91HdQw9szoAiGuZJyXRWj') >>> x['m/4/8h/0'][2] '18GrpcrjMDTnNtbbgNUuphNS2DhC9YMhPC' >>> y = Electrum2.crack(x.bip32xpub,x[3][0]) >>> y == x.bip32xprv True >>> Electrum2.validate('ride win pass silver noble position because balcony unveil perfect keen pyramid abuse') True >>> Electrum2.validate('ride win pass silver noble position because balcony unveil perfect keen pyramid pyramid') False >>> Electrum2('ride win pass silver noble position because balcony unveil perfect keen pyramid pyramid') Traceback (most recent call last): ... Exception: Word list invalid. ''' return if __name__ == "__main__": import doctest doctest.testmod()

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null

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1

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1

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6

0c5446b85db5107cad16f31eefccc7dc97ee27ce

30

py

Python

virgo/__init__.py

terrykong/pyvirgo

9c6cb8d791446881265a4a0e3f601376c618dadc

[ "MIT" ]

4

2021-05-23T21:07:44.000Z

2021-08-11T00:04:54.000Z

virgo/__init__.py

terrykong/pyvirgo

9c6cb8d791446881265a4a0e3f601376c618dadc

[ "MIT" ]

9

2021-02-22T02:04:36.000Z

2021-05-24T04:53:54.000Z

virgo/__init__.py

terrykong/pyvirgo

9c6cb8d791446881265a4a0e3f601376c618dadc

[ "MIT" ]

1

2021-05-24T05:00:32.000Z

from .load import load, loads

15

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1

0

6

a79a034811e3d1e4d15316455ac143b1a5f362e5

404

py

Python

Pendulum/2d_simulation.py

garlicbutter/Jonathan-Tom

c1696f0a94da46911b3566a3d4f49791e877373f

[ "MIT" ]

2

2021-10-05T04:31:19.000Z

2021-10-05T04:31:26.000Z

Pendulum/2d_simulation.py

garlicbutter/Tom-Jonathan

c1696f0a94da46911b3566a3d4f49791e877373f

[ "MIT" ]

null

Pendulum/2d_simulation.py

garlicbutter/Tom-Jonathan

c1696f0a94da46911b3566a3d4f49791e877373f

[ "MIT" ]

null

import numpy as np # parameters L1, L2 = 0.4, 0.3 # meters m1, m2, m_end = 8, 5, 4 # kg K_e = 10**5 # N/m # F = Kx + Bx' + Mx'' K_imp = [[62500.0, 0.0],[0.0, 62500.0]] B_imp = [[3500.0, 0.0],[0.0, 3500.0]] M_imp = [[100.0, 0.0],[0.0, 100.0]] # PD controller P_PBIC = [[150.0, 0.0] ,[0.0, 170.0]] D_PBIC = [[18.0, 0.0] ,[0.0, 10.0]] P_IMIC = [[0.5, 0.0] ,[0.0, 0.5]] D_IMIC = [[0.1, 0.0] ,[0.0, 0.1]]

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1

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1

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null

0

6

a7b03c434c7ffef8645cebbc0da857591896212f

158

py

Python

utils/models/__init__.py

wufanyou/growth-ring-detection

27989870e12ab149413363a99080f7684db6cf1a

[ "MIT" ]

null

utils/models/__init__.py

wufanyou/growth-ring-detection

27989870e12ab149413363a99080f7684db6cf1a

[ "MIT" ]

null

utils/models/__init__.py

wufanyou/growth-ring-detection

27989870e12ab149413363a99080f7684db6cf1a

[ "MIT" ]

null

from .FPNV1 import FPN as FPN from .FPNV2 import FPN as FPNV2 from .FPNV3 import FPN as FPNV3 from .FPNV4 import FPN as FPNV4 from .FPNV5 import FPN as FPNV5

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null

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1

0

1

0

6

a7be627ec2bf6f24d752f0052e916e6ee33cc1c0

1,722

py

Python

dev_up/categories/warface.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

2

2021-01-10T15:44:41.000Z

2021-01-10T15:59:48.000Z

dev_up/categories/warface.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

null

dev_up/categories/warface.py

lordralinc/dev_up

e035afd386c8a16c574aaa7615c263f1c1369911

[ "MIT" ]

4

2021-01-10T15:45:19.000Z

2021-03-05T20:09:57.000Z

import typing as ty from dev_up import models from dev_up.categories.base import BaseAPICategories class WarfaceAPICategories(BaseAPICategories): def get_info( self, nick: str, type: ty.Union[models.WarfaceGetInfoTypeEnum, str] = models.WarfaceGetInfoTypeEnum.STATISTICS, key: str = None, **kwargs ) -> models.WarfaceGetInfo: """Получает информацию об игроке Warface :param nick: Ник игрока :param type: Тип инфромации, defaults to models.WarfaceGetInfoTypeEnum.STATISTICS :param key: Ключ доступа, defaults to None :return: Информация об игроке. response зависит от переданного type """ return self.api.make_request( method='warface.getInfo', data=dict(nick=nick, type=models.WarfaceGetInfoTypeEnum(type).value, key=key, **kwargs), dataclass=models.WarfaceGetInfo ) async def get_info_async( self, nick: str, type: ty.Union[models.WarfaceGetInfoTypeEnum, str] = models.WarfaceGetInfoTypeEnum.STATISTICS, key: str = None, **kwargs ) -> models.WarfaceGetInfo: """Получает информацию об игроке Warface :param nick: Ник игрока :param type: Тип инфромации, defaults to models.WarfaceGetInfoTypeEnum.STATISTICS :param key: Ключ доступа, defaults to None :return: Информация об игроке. response зависит от переданного type """ return await self.api.make_request_async( method='warface.getInfo', data=dict(nick=nick, type=models.WarfaceGetInfoTypeEnum(type).value, key=key, **kwargs), dataclass=models.WarfaceGetInfo )

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6

3bd49f9c389bc2e87bb10ee416ff9c4d288827e9

49

py

Python

Python/libraries/recognizers-number/recognizers_number/number/japanese/__init__.py

acblacktea/Recognizers-Text

2170b8e35216f3fd56cce98fb33cde5339c9f088

[ "MIT" ]

1

2019-06-19T10:45:24.000Z

Python/libraries/recognizers-number/recognizers_number/number/japanese/__init__.py

AzureMentor/Recognizers-Text

4f18e1d03607cc96e87095d8bf68c481c1b0756f

[ "MIT" ]

null

Python/libraries/recognizers-number/recognizers_number/number/japanese/__init__.py

AzureMentor/Recognizers-Text

4f18e1d03607cc96e87095d8bf68c481c1b0756f

[ "MIT" ]

null

from .extractors import * from .parsers import *

24.5

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0.666667

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0

1

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0

6

ce154a949261c622e9a4b259a1861b315c8a4cbb

257

py

Python

paypalhttp/__init__.py

cameronmoten/paypalhttp_python

72609783230663b8e34c6f0384837db7b166c8f4

[ "MIT" ]

5

2020-04-25T01:07:23.000Z

2021-10-21T21:39:00.000Z

paypalhttp/__init__.py

cameronmoten/paypalhttp_python

72609783230663b8e34c6f0384837db7b166c8f4

[ "MIT" ]

3

2020-07-23T12:07:26.000Z

2021-12-01T18:45:36.000Z

paypalhttp/__init__.py

cameronmoten/paypalhttp_python

72609783230663b8e34c6f0384837db7b166c8f4

[ "MIT" ]

13

2020-03-03T02:35:50.000Z

2022-03-17T18:12:49.000Z

from paypalhttp.environment import Environment from paypalhttp.file import File from paypalhttp.http_client import HttpClient from paypalhttp.http_response import HttpResponse from paypalhttp.http_error import HttpError from paypalhttp.serializers import *

36.714286

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6

ce23a59eb6ab7f23f79d4e76eebb6e0cfabb0fcc

1,486

py

Python

tests/public/calculate_sleep_amount_test.py

Tesshin/CS-Pound

8e40f3a144aa6578e87d30aba0d43cb51756ecdf

[ "MIT" ]

4

2019-01-23T00:57:53.000Z

2021-12-22T14:59:39.000Z

tests/public/calculate_sleep_amount_test.py

Tesshin/CS-Pound

8e40f3a144aa6578e87d30aba0d43cb51756ecdf

[ "MIT" ]

11

2018-10-03T09:12:03.000Z

2022-01-15T01:44:12.000Z

tests/public/calculate_sleep_amount_test.py

Tesshin/CS-Pound

8e40f3a144aa6578e87d30aba0d43cb51756ecdf

[ "MIT" ]

4

2018-10-03T08:45:03.000Z

2020-07-21T09:21:43.000Z

from constants import Variables from library import calculate_sleep_amount class TestClass: def test_on_cooldown_less_than_1_hour(self): Variables.cooldown = True assert calculate_sleep_amount(1) == (-59, 60, True) assert calculate_sleep_amount(3599) == (3539, 60, True) assert calculate_sleep_amount(3600) == (3540, 60, True) assert calculate_sleep_amount(0) == (0, 3600, False) def test_off_cooldown_no_time(self): Variables.cooldown = False assert calculate_sleep_amount(-1) == (-1, 3600, False) assert calculate_sleep_amount(0) == (0, 3600, False) def test_off_cooldown_more_than_2_hours(self): Variables.cooldown = False assert calculate_sleep_amount(7201) == (7201, 1, False) assert calculate_sleep_amount(7200) == (7200, 0, False) def test_off_cooldown_between_1_and_2_hours(self): Variables.cooldown = False assert calculate_sleep_amount(3601) == (3600, 1, False) Variables.cooldown = False assert calculate_sleep_amount(3600) == (3600, 0, False) def test_off_cooldown_less_than_1_hour(self): Variables.cooldown = False assert calculate_sleep_amount(3599) == (3599, 0, False) Variables.cooldown = False assert calculate_sleep_amount(1) == (1, 0, False) def test_off_cooldown_10_hours(self): Variables.cooldown = False assert calculate_sleep_amount(36000) == (36000, 3600, False)

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null

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null

0

1

0

6

ce25ad3fff6d22ac1681cb6ae9967544df9e427f

32

py

Python

hypernlp/nlp/data_process/eda/__init__.py

DataCanvasIO/HyperNLP

3ae565c88b6fc63b664c8fb264dc89c47ff92623

[ "Apache-2.0" ]

3

2021-11-22T04:09:22.000Z

2022-01-10T10:27:28.000Z

hypernlp/nlp/data_process/eda/__init__.py

DataCanvasIO/HyperNLP

3ae565c88b6fc63b664c8fb264dc89c47ff92623

[ "Apache-2.0" ]

null

hypernlp/nlp/data_process/eda/__init__.py

DataCanvasIO/HyperNLP

3ae565c88b6fc63b664c8fb264dc89c47ff92623

[ "Apache-2.0" ]

null

def eda_model(): return None

16

0.6875

5

32

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1

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null

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1

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6

cbef5755ca571f2eea563e5cdb8c61dd1d737fc1

302,726

py

Python

QChemTool/Polarizable_atoms/Polarization_module_HeteroDimer.py

slamavl/QChemTool

b6b17adf6cfa8ac1db47acba93aab1ee49c1be47

[ "MIT" ]

null

QChemTool/Polarizable_atoms/Polarization_module_HeteroDimer.py

slamavl/QChemTool

b6b17adf6cfa8ac1db47acba93aab1ee49c1be47

[ "MIT" ]

1

2018-01-03T12:08:41.000Z

QChemTool/Polarizable_atoms/Polarization_module_HeteroDimer.py

slamavl/QChemTool

b6b17adf6cfa8ac1db47acba93aab1ee49c1be47

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Jan 31 14:33:56 2017 @author: Vladislav Sláma """ import numpy as np from copy import deepcopy from scipy.spatial.distance import pdist,squareform import os from ..QuantumChem.Classes.structure import Structure from ..QuantumChem.calc import identify_molecule from ..QuantumChem.read_mine import read_TrEsp_charges from ..QuantumChem.interaction import charge_charge from ..QuantumChem.positioningTools import project_on_plane, CenterMolecule, fit_plane from ..General.units import conversion_facs_energy, conversion_facs_mass from .Electrostatics_module import PrepareMolecule_1Def as ElStat_PrepareMolecule_1Def from .Electrostatics_module import PrepareMolecule_2Def as ElStat_PrepareMolecule_2Def from ..General.Potential import potential_charge, potential_dipole from ..QuantumChem.Classes.general import Energy as EnergyClass from ..General.UnitsManager import energy_units from ..QuantumChem.calc import GuessBonds from ..QuantumChem.output import OutputMathematica debug=False #============================================================================== # Definition of class for polarizable environment #============================================================================== class Dielectric: ''' Class managing dielectric properties of the material Parameters ---------- coor : numpy.array of real (dimension Nx3) where N is number of atoms origin of density grid polar : numpy.array or list of real (dimension N) Polarizabilities for every atom charge : numpy.array or list of real (dimension N) charges on individual atoms (initial charges) dipole : numpy.array of real (dimension Nx3) dipole on individual atoms (initial dipole) ''' def __init__(self,coor,charge,dipole,AlphaE,Alpha_E,BetaEE,V): self.coor=np.copy(coor) self.polar={} self.polar['AlphaE']=AlphaE self.polar['Alpha_E']=Alpha_E self.polar['BetaEE']=BetaEE self.VinterFG=V self.charge=np.copy(charge) self.dipole=np.copy(dipole) self.Nat=len(coor) def assign_polar(self,pol_type,**kwargs): ''' For now assignment is working only for fluorographene carbons with type 'CF' and defect carbons with type 'CD' Parameters ---------- pol_type : numpy.array or list of str (dimension N) Polarization atomic types for assign of polarizabilities - diferent from atomic types - for example group C-F will be treated as single atom and type will be pol_type='CF'. **kwargs : dict dictionary with three matrixes for every polarizable atom type. For example: kwargs['PolValues']['CF'][0] is Alpha(E) polarizability matrix for atom tyle 'CF'. [1] correspond to Alpha(-E) matrix and [2] to Beta(E,E) Returns ------- polar : numpy.array or list of real (dimension N) Polarizabilities for every atom. 'CF'=1.03595 and 'CD'=1.4 ''' ZeroM=np.zeros((3,3),dtype='f8') PolValues={'CF': [ZeroM,ZeroM,ZeroM], 'CD': [ZeroM,ZeroM,ZeroM],'C': [ZeroM,ZeroM,ZeroM]} for key in list(kwargs.keys()): if key=='PolValues': PolValues=kwargs['PolValues'] if self.Nat!=len(pol_type): raise IOError('Polarization type vector must have the same length as number of atoms') polar={} polar['AlphaE']=np.zeros((len(pol_type),3,3),dtype='f8') polar['Alpha_E']=np.zeros((len(pol_type),3,3),dtype='f8') polar['BetaEE']=np.zeros((len(pol_type),3,3),dtype='f8') for ii in range(len(pol_type)): polar['AlphaE'][ii,:,:]=PolValues[pol_type[ii]][0] polar['Alpha_E'][ii,:,:]=PolValues[pol_type[ii]][1] polar['BetaEE'][ii,:,:]=PolValues[pol_type[ii]][2] return polar def _swap_atoms(self,index1,index2): ''' Function which exchange polarization properties between atoms defined by index1 and atoms defined by index 2 index1 : list or numpy.array of integer (dimension Natoms_change) Indexes of first set of atoms which we would like to swap index2 : list or numpy.array of integer (dimension Natoms_change) Indexes of second set of atoms which we would like to swap ''' if len(index1)!=len(index2): raise IOError('You can swap values only between same number of atoms') for ii in range(len(index1)): # swap charges self.charge[index1[ii]],self.charge[index2[ii]] = self.charge[index2[ii]],self.charge[index1[ii]] # swap dipoles self.dipole[index1[ii],:],self.dipole[index2[ii],:] = self.dipole[index2[ii],:],self.dipole[index1[ii],:] # swap polarizabilities #print(np.shape(self.dipole),index1[ii]) self.polar['AlphaE'][index1[ii],:,:],self.polar['AlphaE'][index2[ii],:,:] = self.polar['AlphaE'][index2[ii],:,:],self.polar['AlphaE'][index1[ii],:,:] self.polar['Alpha_E'][index1[ii],:,:],self.polar['Alpha_E'][index2[ii],:,:] = self.polar['Alpha_E'][index2[ii],:,:],self.polar['Alpha_E'][index1[ii],:,:] self.polar['BetaEE'][index1[ii],:,:],self.polar['BetaEE'][index2[ii],:,:] = self.polar['BetaEE'][index2[ii],:,:],self.polar['BetaEE'][index1[ii],:,:] def _test_2nd_order(self,typ,Estatic=np.zeros(3,dtype='f8'),eps=1): ''' Function for testing of calculation with induced dipoles. Calculate induced dipoles in second order (by induced dipoles). Combined with calc_dipoles_All(typ,NN=1) we should obtain the same dipoles as with calc_dipoles_All(typ,NN=2) Parameters ---------- typ : str ('AlphaE','Alpha_E','BetaEE') Specifies which polarizability is used for calculation of induced atomic dipoles Estatic : numpy.array of real (dimension 3) (optional - init=np.zeros(3,dtype='f8')) External homogeneous electric fiel vectord (orientation and strength) in ATOMIC UNITS. By default there is no electric field eps : real (optional - init=1.0) Relative dielectric polarizability of medium where the dipoles and molecule is present ( by default vacuum with relative permitivity 1.0) Notes ---------- **OK. Definition of Tensor T is right** ''' debug=False R=np.zeros((self.Nat,self.Nat,3),dtype='f8') # mutual distance vectors P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): for jj in range(ii+1,self.Nat): R[ii,jj,:]=self.coor[ii]-self.coor[jj] R[jj,ii,:]=-R[ii,jj,:] RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) # mutual distances unit=np.diag([1]*self.Nat) RR=RR+unit # only for avoiding ddivision by 0 for diagonal elements RR3=np.power(RR,3) RR5=np.power(RR,5) # definition of T tensor T=np.zeros((self.Nat,self.Nat,3,3),dtype='f8') # mutual distance vectors for ii in range(3): T[:,:,ii,ii]=1/RR3[:,:]-3*np.power(R[:,:,ii],2)/RR5 for jj in range(ii+1,3): T[:,:,ii,jj] = -3*R[:,:,ii]*R[:,:,jj]/RR5 T[:,:,jj,ii] = T[:,:,ii,jj] for ii in range(self.Nat): T[ii,ii,:,:]=0.0 # no self interaction of atom i with atom i # calculating induced dipoles in second order Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) if debug and typ=='AlphaE': from ..General.Potential import ElField_dipole # Test first order induced dipoles self.dipole=np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1) if np.allclose(P,self.dipole): print('First order dipoles are the same.') else: print('Problem with first order induced dipoles.') # test induced electric field Elfield=np.zeros(3,dtype='f8') for ii in range(3): Elfield[ii]=np.dot(-T[0,1,ii,:],P[1,:]) print('Electric field at atom 0 induced by dipole at position 1 wT:',Elfield) Elfield=np.zeros(3,dtype='f8') Elfield=ElField_dipole(P[1,:],R[0,1,:]) print('Electric field at atom 0 induced by dipole at position 1 woT:',Elfield) ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) # -P should be 2nd order induced dipoles self.dipole+=(-P) if debug: print('Dipole sum:',np.sum(self.dipole,axis=0)) def _dRcB_BpA(self,index2,charge2,typ,c,eps=1): ''' function which calculate derivation of interaction energy between defect A and defect B defined by index2: d/dRc^{(B)}[Sum_{n} E^{(B)}(Rn).(1/2*Polarizability(n)).E^{(A)}(Rn)] Parameters ---------- index2 : list or numpy.array of integer (dimension N_def_atoms) Atomic indexes of atoms which coresponds to defect B (defect with zero charges) charge2 : numpy array of real (dimension N_def_atoms) Vector of transition charge for every atom of defect B (listed in `index2`) typ : str ('AlphaE','Alpha_E','BetaEE') Specifies which polarizability is used for calculation of induced atomic dipoles c : integer Atomic index specifying along which atom displacement should we calculate derivation eps : real (optional - init=1.0) Relative dielectric polarizability of medium where the dipoles and molecule is present ( by default vacuum with relative permitivity 1.0) Notes ---------- In initial structure transition charges are placed only on atoms from first defect (defect A defined by index1) and zero charges are placed on second defect (defect B defined by index2) ''' # check if atom with index c is in defect B if c in index2: c_indx=np.where(index2==c)[0][0] else: raise IOError('Defined index c is not in defect B') R=np.zeros((self.Nat,self.Nat,3),dtype='f8') # mutual distance vectors P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): for jj in range(ii+1,self.Nat): R[ii,jj,:]=self.coor[ii]-self.coor[jj] R[jj,ii,:]=-R[ii,jj,:] RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) # mutual distances unit=np.diag([1]*self.Nat) RR=RR+unit # only for avoiding ddivision by 0 for diagonal elements RR3=np.power(RR,3) RR5=np.power(RR,5) # definition of T tensor T=np.zeros((self.Nat,self.Nat,3,3),dtype='f8') # mutual distance vectors for ii in range(3): T[:,:,ii,ii]=1/RR3[:,:]-3*np.power(R[:,:,ii],2)/RR5 for jj in range(ii+1,3): T[:,:,ii,jj] = -3*R[:,:,ii]*R[:,:,jj]/RR5 T[:,:,jj,ii] = T[:,:,ii,jj] for ii in range(self.Nat): T[ii,ii,:,:]=0.0 # no self interaction of atom i with atom i # calculating derivation according to atom displacement from defect B Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') # Calculation of electric field generated by defect A for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') # calculate induced dipoles induced by defect A ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) # TODO: check if it shouldnt be res = - charge2[c_indx]*ELFV[c,:] res=charge2[c_indx]*ELFV[c,:] return res def _dR_BpA(self,index1,index2,charge1,charge2,typ,eps=1): ''' function which calculate derivation of interaction energy between defect A and defect B defined by index: \n d/dR[Sum_{n} E^{(B)}(Rn).(1/2*Polarizability(n)).E^{(A)}(Rn)] \n which is -interaction energy ( for derivation of energy, Hamiltonian, we need to take negative value of the result) Parameters ---------- index1 : list or numpy.array of integer (dimension N_def1_atoms) Atomic indexes of atoms which coresponds to first defect (defect with zero charges) index2 : list or numpy.array of integer (dimension N_def2_atoms) Atomic indexes of atoms which coresponds to second defect (defect with zero charges) charge1 : numpy array of real (dimension N_def1_atoms) Vector of transition charges for every atom of defect A (listed in ``index1``) charge2 : numpy array of real (dimension N_def2_atoms) Vector of transition charges for every atom of defect b (listed in ``index2``) typ : str ('AlphaE','Alpha_E','BetaEE') Specifies which polarizability is used for calculation of induced atomic dipoles eps : real (optional - init=1.0) Relative dielectric polarizability of medium where the dipoles and molecule is present ( by default vacuum with relative permitivity 1.0) Notes ---------- **After exiting the function transition charges are the same as in the begining** For calculation of derivation of ApA use ``_dR_BpA(index1,index1, charge1,charge1,typ,eps=1)`` where charges in molecule Dielectric class have to be nonzero for defect with ``index1`` **and zero for the other defect if present**. ''' # TODO: Add posibility to read charges from self.charges: charge1 = self.charges[index1] and charge2 = self.charges[index2] # TODO: Read polarizabilities on the defects and when potting charges to zero put also zero polarizabilities charge1_orig = self.charge[index1] charge2_orig = self.charge[index2] res=np.zeros((self.Nat,3),dtype='f8') # calculation of tensors with interatomic distances R=np.zeros((self.Nat,self.Nat,3),dtype='f8') # mutual distance vectors P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): for jj in range(ii+1,self.Nat): R[ii,jj,:]=self.coor[ii]-self.coor[jj] R[jj,ii,:]=-R[ii,jj,:] RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) # mutual distances unit=np.diag([1]*self.Nat) RR=RR+unit # only for avoiding ddivision by 0 for diagonal elements RR3=np.power(RR,3) RR5=np.power(RR,5) # definition of T tensor T=np.zeros((self.Nat,self.Nat,3,3),dtype='f8') # mutual distance vectors for ii in range(3): T[:,:,ii,ii]=1/RR3[:,:]-3*np.power(R[:,:,ii],2)/RR5 for jj in range(ii+1,3): T[:,:,ii,jj] = -3*R[:,:,ii]*R[:,:,jj]/RR5 T[:,:,jj,ii] = T[:,:,ii,jj] for ii in range(self.Nat): T[ii,ii,:,:]=0.0 # no self interaction of atom i with atom i # Place transition charges only on the first defect (defect A) if index1==index2: self.charge[index1] = charge1 # The folowing is only for polarization with transition density and not polarization by ground state charges and interaction with excited ones # if (charge1==charge2).all(): # self.charge[index1] = charge1 # else: # raise Warning("For calculation of d_ApA same charges have to be inputed.") else: self.charge[index1] = charge1 self.charge[index2] = 0.0 # calculating derivation according to defect B atom displacement Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') # calculate electric field generated by the first defect (defect A) for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') # calculate induced dipoles induced by the first defect (defect A) ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) for ii in range(len(index2)): res[index2[ii],:] -= charge2[ii]*ELFV[index2[ii],:] # calculating derivation with respect to displacement of environment atom for ii in range(self.Nat): if not (ii in index1 or ii in index2): for jj in range(len(index2)): res[ii,:]+=charge2[jj]*np.dot(T[index2[jj],ii,:,:],P[ii,:]) # # swap porarization parameters from defect A to defect B # self._swap_atoms(index1,index2) # Place transition charges only on the second defect (defect B) if index1==index2: self.charge[index2] = charge2 # viz previous case # if (charge1==charge2).all(): # self.charge[index2] = charge2 # else: # raise Warning("For calculation of d_ApA same charges have to be inputed.") else: self.charge[index1] = 0.0 self.charge[index2] = charge2 # calculating derivation according to atom displacement from defect A Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') # Calculate electric field generated by the second defect (defect B) for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') # Calculate induced dipoles, induced by the second defect (defect B) ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) for ii in range(len(index1)): res[index1[ii],:] -= charge1[ii]*ELFV[index1[ii],:] # calculating derivation with respect to displacement of environment atom for ii in range(self.Nat): if not (ii in index1 or ii in index2): for jj in range(len(index1)): res[ii,:]+=charge1[jj]*np.dot(T[index1[jj],ii,:,:],P[ii,:]) # # swap porarization parameters back to original position # self._swap_atoms(index1,index2) # Place transition charges back on both defects self.charge[index1] = charge1_orig self.charge[index2] = charge2_orig return res.reshape(3*self.Nat) def _dR_BppA(self,index1,index2,charge1,charge2,typ,eps=1): ''' function which calculate derivation of second order interaction energy between defect A and defect B defined by index1 resp. index2: \n ``d/dR[Sum_{n} E^{(B)}(Rn).(1/2*Polarizability(n)). Sum_{n'} T(Rn-Rn').(1/2*Polarizability(n')).E^{(A)}(Rn)]`` \n which is -interaction energy ( for derivation of energy, Hamiltonian, we need to take negative value of the result) Parameters ---------- index1 : list or numpy.array of integer (dimension N_def_atoms) Atomic indexes of atoms which coresponds to first defect (defect with zero charges) index2 : list or numpy.array of integer (dimension N_def_atoms) Atomic indexes of atoms which coresponds to second defect (defect with zero charges) charge1 : numpy array of real (dimension N_def1_atoms) Vector of transition charges for every atom of defect A (listed in ``index1``) charge2 : numpy array of real (dimension N_def2_atoms) Vector of transition charges for every atom of defect b (listed in ``index2``) typ : str ('AlphaE','Alpha_E','BetaEE') Specifies which polarizability is used for calculation of induced atomic dipoles eps : real (optional - init=1.0) Relative dielectric polarizability of medium where the dipoles and molecule is present ( by default vacuum with relative permitivity 1.0) Notes ---------- **After exiting the function transition charges are placed on both defects and not only on the first** For calculation of derivation of AppA use ``_dR_BppA(index1,index1, charge1,charge1,typ,eps=1)`` where charges in molecule Dielectric class have to be nonzero for defect with ``index1`` **and zero for the other defect if present**. ''' # TODO: Add posibility to read charges from self.charges: charge1 = self.charges[index1] and charge2 = self.charges[index2] # TODO: Read polarizabilities on the defects and when potting charges to zero put also zero polarizabilities res=np.zeros((self.Nat,3),dtype='f8') # calculation of tensors with interatomic distances R=np.zeros((self.Nat,self.Nat,3),dtype='f8') # mutual distance vectors P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): for jj in range(ii+1,self.Nat): R[ii,jj,:]=self.coor[ii]-self.coor[jj] R[jj,ii,:]=-R[ii,jj,:] RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) # mutual distances unit=np.diag([1]*self.Nat) RR=RR+unit # only for avoiding ddivision by 0 for diagonal elements RR3=np.power(RR,3) RR5=np.power(RR,5) RR7=np.power(RR,7) # definition of T tensor T=np.zeros((self.Nat,self.Nat,3,3),dtype='f8') # mutual distance vectors for ii in range(3): T[:,:,ii,ii]=1/RR3[:,:]-3*np.power(R[:,:,ii],2)/RR5 for jj in range(ii+1,3): T[:,:,ii,jj] = -3*R[:,:,ii]*R[:,:,jj]/RR5 T[:,:,jj,ii] = T[:,:,ii,jj] for ii in range(self.Nat): T[ii,ii,:,:]=0.0 # no self interaction of atom i with atom i # definition of S tensor S=np.zeros((self.Nat,self.Nat,3,3,3),dtype='f8') # mutual distance vectors for ii in range(3): for jj in range(3): for kk in range(3): S[:,:,ii,jj,kk]=-5*R[:,:,ii]*R[:,:,jj]*R[:,:,kk]/RR7 for ii in range(3): for jj in range(3): S[:,:,ii,ii,jj]+=R[:,:,jj]/RR5 S[:,:,ii,jj,ii]+=R[:,:,jj]/RR5 S[:,:,jj,ii,ii]+=R[:,:,jj]/RR5 for ii in range(self.Nat): S[ii,ii,:,:,:]=0.0 # no self interaction of atom i with atom i # Place transition charges only on the first defect (defect A) if index1==index2: if (charge1==charge2).all(): self.charge[index1] = charge1 else: raise Warning("For calculation of d_ApA same charges have to be inputed.") else: self.charge[index1] = charge1 self.charge[index2] = 0.0 # calculating derivation according to atom displacement from defect B Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') # Calculate electric field generated by the first defect (defect A) for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') # Calculate induced dipoles, induced by the first defect (defect A) ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i PA=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): PA[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) for rep in range(2): P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) for ii in range(len(index2)): res[index2[ii],:] += charge2[ii]*ELFV[index2[ii],:] # calculating derivation with respect to displacement of environment atom for ii in range(self.Nat): if not (ii in index1 or ii in index2): for jj in range(len(index2)): res[ii,:] -= charge2[jj]*np.dot(T[index2[jj],ii,:,:],P[ii,:]) # # swap porarization parameters from defect A to defect B # self._swap_atoms(index1,index2) # Place transition charges only on the second defect (defect B) if index1==index2: if (charge1==charge2).all(): self.charge[index2] = charge2 else: raise Warning("For calculation of d_ApA same charges have to be inputed.") else: self.charge[index1] = 0.0 self.charge[index2] = charge2 # calculating derivation according to atom displacement from defect A Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') # Calculate electric field generated by the second defect (defect B) for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') # Calculate induced dipoles, induced by the second defect (defect B) ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i PB=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): PB[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) for rep in range(2): P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) for ii in range(len(index1)): res[index1[ii],:] += charge1[ii]*ELFV[index1[ii],:] # calculating derivation with respect to displacement of environment atom for ii in range(self.Nat): if not (ii in index1 or ii in index2): for jj in range(len(index1)): res[ii,:] -= charge1[jj]*np.dot(T[index1[jj],ii,:,:],P[ii,:]) # + contribution from S tensor for nn in range(self.Nat): for ii in range(3): for kk in range(3): res[nn,:]+=3*PB[nn,ii]*np.dot(S[nn,:,ii,:,kk].T,PA[:,kk]) res[nn,:]+=3*PA[nn,ii]*np.dot(S[nn,:,ii,:,kk].T,PB[:,kk]) # # swap porarization parameters back to original position # self._swap_atoms(index1,index2) # Place transition charges back on both defects self.charge[index1] = charge1 self.charge[index2] = charge2 return res.reshape(3*self.Nat) def _dR_ApEnv(self,index1,charge1,env_coor,env_charge,typ,eps=1): ''' function which calculate derivation of 'interaction energy' between defect A defined by index and environment atoms: \n d/dR[Sum_{n} E^{(A)}(Rn).(1/2*Polarizability(n)).E^{(env)}(Rn)] \n which is -interaction energy ( for derivation of energy, Hamiltonian, we need to take negative value of the result) Parameters ---------- index1 : list or numpy.array of integer (dimension N_def1_atoms) Atomic indexes of atoms which coresponds to first defect charge1 : numpy array of real (dimension N_def1_atoms) Vector of charges (transition, excited, ground, ...) for every atom of defect A (listed in ``index1``) env_coor : numpy.array of real (dimension Nat x 3) Coordninates for every environment atom. env_charge : numpy array or list of real (dimension Nat) Atomic ESP charges for every atom in the environment typ : str ('AlphaE','Alpha_E','BetaEE') Specifies which polarizability is used for calculation of induced atomic dipoles eps : real (optional - init=1.0) Relative dielectric polarizability of medium where the dipoles and molecule is present ( by default vacuum with relative permitivity 1.0) Return ---------- res res_env Notes ---------- **After exiting the function transition charges are the same as in the begining** ''' # TODO: Add posibility to read charges from self.charges: charge1 = self.charges[index1] and charge2 = self.charges[index2] # TODO: Read polarizabilities on the defects and when potting charges to zero put also zero polarizabilities charge1_orig = self.charge[index1] charge_env_orig = env_charge[index1] env_Nat = env_coor.shape[0] res=np.zeros((self.Nat,3),dtype='f8') res_env=np.zeros((env_Nat,3),dtype='f8') MASK = np.zeros(self.Nat,dtype="bool") MASK[index1] = True # Place charges on the defect (defect A) self.charge[index1] = charge1 # zero charges for defect in the environment env_charge[index1] = 0.0 # calculation of tensors with interatomic distances for polarizability class R=np.zeros((self.Nat,self.Nat,3),dtype='f8') # mutual distance vectors P=np.zeros((self.Nat,3),dtype='f8') for ii in range(self.Nat): for jj in range(ii+1,self.Nat): R[ii,jj,:]=self.coor[ii]-self.coor[jj] R[jj,ii,:]=-R[ii,jj,:] RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) # mutual distances unit=np.diag([1]*self.Nat) RR=RR+unit # only for avoiding ddivision by 0 for diagonal elements RR3=np.power(RR,3) RR5=np.power(RR,5) # calculate tensor with interactomic distances between environmnet and polarizability class atoms #R_env2pol=np.zeros((self.Nat,env_Nat,3),dtype='f8') # mutual distance vectors R_pol = np.tile(self.coor,(env_Nat,1,1)) R_pol = np.swapaxes(R_pol,0,1) R_env = np.tile(env_coor,(self.Nat,1,1)) R_env2pol = R_pol - R_env RR_env2pol = np.linalg.norm(R_env2pol,axis=2) RR3_env2pol = np.power(RR_env2pol,3) RR5_env2pol = np.power(RR_env2pol,5) # definition of T tensor T=np.zeros((self.Nat,self.Nat,3,3),dtype='f8') # mutual distance vectors for ii in range(3): T[:,:,ii,ii]=1/RR3[:,:]-3*np.power(R[:,:,ii],2)/RR5 for jj in range(ii+1,3): T[:,:,ii,jj] = -3*R[:,:,ii]*R[:,:,jj]/RR5 T[:,:,jj,ii] = T[:,:,ii,jj] for ii in range(self.Nat): T[ii,ii,:,:]=0.0 # no self interaction of atom i with atom i # definition of T tensor between environment and the polarizability class T_pol2env=np.zeros((env_Nat,self.Nat,3,3),dtype='f8') # mutual distance vectors for ii in range(3): T_pol2env[:,:,ii,ii]=1/(RR3_env2pol.T + np.identity(self.Nat))[:,:]-3*np.power(R_env2pol[:,:,ii],2).T/(RR5_env2pol.T+np.identity(self.Nat)) for jj in range(ii+1,3): T_pol2env[:,:,ii,jj] = -3*R_env2pol[:,:,ii].T*R_env2pol[:,:,jj].T/(RR5_env2pol.T + np.identity(self.Nat)) T_pol2env[:,:,jj,ii] = T_pol2env[:,:,ii,jj] for ii in range(self.Nat): T_pol2env[ii,ii,:,:]=0.0 # no self interaction of atom i with atom i # calculating derivation according to environment atom displacement Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') # calculate electric field generated by the first defect (defect A) for jj in range(3): ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') # calculate induced dipoles induced by the first defect (defect A) ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) for ii in range(3): for n in range(self.nat): if not MASK[n]: res[n,ii] += np.dot(np.dot(env_charge,T_pol2env[:,n,ii,:]),P[n,:]) ELFV=np.zeros((env_Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T_pol2env[:,:,ii,jj],P[:,jj]) for ii in range(3): res_env[:,ii] -= env_charge * ELFV[:,ii] # calculate induced dipoles induced by the environment ESP atomic charges Q=np.meshgrid(env_charge,self.charge)[0] # in columns same charges - in rows environment charges ELF=np.zeros((self.Nat,env_Nat,3),dtype='f8') for jj in range(3): ELF[:,:,jj]=( Q/(RR3_env2pol+np.identity(self.Nat)) )*R_env2pol[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j for ii in range(self.Nat): ELF[ii,ii,:]=np.zeros(3,dtype='f8') ELFV=np.array(np.sum(ELF,axis=1),dtype='f8') # ELFV[i,:] is electric field at position of atom i for ii in range(self.Nat): P[ii,:]=np.dot(self.polar[typ][ii],ELFV[ii,:]) # induced dipoles by environment charge distribution P[index1,:]=0.0 # just for sure for n in range(self.Nat): if not MASK[n]: for jj in range(len(index1)): res[n,:]+=charge1[jj]*np.dot(T[index1[jj],n,:,:],P[n,:]) # calculating derivation according to atom displacement from defect A ELFV=np.zeros((self.Nat,3),dtype='f8') for ii in range(3): for jj in range(3): ELFV[:,ii]+=np.dot(T[:,:,ii,jj],P[:,jj]) for ii in range(len(index1)): res[index1[ii],:] -= charge1[ii]*ELFV[index1[ii],:] # Place original charges back defects and the environment atoms self.charge[index1] = charge1_orig env_charge[index1] = charge_env_orig return res.reshape(3*self.Nat),res_env.reshape(3*self.Nat) # TODO: Add possibility for NN = -err to calculate dipoles until convergence is reached def _calc_dipoles_All(self,typ,Estatic=np.zeros(3,dtype='f8'),NN=60,eps=1,debug=False): ''' Function for calculation induced dipoles of SCF procedure for interaction of molecule with environment. It calculates induced dipoles on individual atoms by static charge distribution and homogeneous electric field. Parameters ---------- typ : str ('AlphaE','Alpha_E','BetaEE') Specifies which polarizability is used for calculation of induced atomic dipoles Estatic : numpy.array of real (dimension 3) (optional - init=np.zeros(3,dtype='f8')) External homogeneous electric fiel vectord (orientation and strength) in ATOMIC UNITS. By default there is no electric field NN : integer (optional - init=60) Number of SCF steps for calculation of induced dipole eps : real (optional - init=1.0) Relative dielectric polarizability of medium where the dipoles and molecule is present ( by default vacuum with relative permitivity 1.0) ''' if debug: import timeit time0 = timeit.default_timer() #R=np.zeros((self.Nat,self.Nat,3),dtype='f8') # mutual distance vectors #P=np.zeros((self.Nat,self.Nat,3),dtype='f8') #for ii in range(self.Nat): # for jj in range(ii+1,self.Nat): # R[ii,jj,:]=self.coor[ii]-self.coor[jj] # R[jj,ii,:]=-R[ii,jj,:] #if debug: # time01 = timeit.default_timer() #RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) # mutual distances R = np.tile(self.coor,(self.Nat,1,1)) R = (np.swapaxes(R,0,1) - R) RR=squareform(pdist(self.coor)) if 0: RR=np.sqrt(np.power(R[:,:,0],2)+np.power(R[:,:,1],2)+np.power(R[:,:,2],2)) RR2=squareform(pdist(self.coor)) print((RR2==RR).all()) # False print(np.allclose(RR2,RR)) # True if not (RR2==RR).all(): print(RR[0,1]) print(pdist(self.coor)[0]) print(RR[0,2]) print(pdist(self.coor)[1]) if debug: time01 = timeit.default_timer() unit=np.diag([1]*self.Nat) RR=RR+unit # only for avoiding ddivision by 0 for diagonal elements RR3=np.power(RR,3) RR5=np.power(RR,5) #mask=[] #for ii in range(len(self.charge)): # if abs(self.charge[ii])>1e-8: # mask.append(ii) mask=(np.abs(self.charge)>1e-8) mask=np.expand_dims(mask, axis=0) MASK=np.dot(mask.T,mask) MASK=np.tile(MASK,(3,1,1)) # np.shape(mask)=(3,N,N) True all indexes where are both non-zero charges MASK=np.rollaxis(MASK,0,3) MASK2=np.diag(np.ones(self.Nat,dtype='bool')) MASK2=np.tile(MASK2,(3,1,1)) MASK2=np.rollaxis(MASK2,0,3) Q=np.meshgrid(self.charge,self.charge)[0] # in columns same charges #ELF=np.zeros((self.Nat,self.Nat,3),dtype='f8') #ELF_Q=(Q/RR3)*np.rollaxis(R,2) #ELF_Q=np.rollaxis(ELF,0,3) if debug: time1 = timeit.default_timer() print('Time spend on preparation of variables in calc_dipoles_All:',time1-time0,'s') for kk in range(NN): # point charge electric field ELF=(Q/RR3)*np.rollaxis(R,2) ELF=np.rollaxis(ELF,0,3) #for jj in range(3): # ELF[:,:,jj]=(Q/RR3)*R[:,:,jj] # ELF[i,j,:] is electric field at position i generated by atom j - on diagonal there are zeros # TODO: Change this procedure because atoms with a charges could be polarized by all atoms with charges - but imput defect charges should be fitted accordingly with polarizable atoms # polarization by static charges only in area without charges: #for ii in mask: # ELF[ii,mask,:]=0.0 ELF[MASK]=0.0 # dipole electric field #for ii in range(self.Nat): # P[ii,:,:]=self.dipole[:,:] P=np.tile(self.dipole[:,:],(self.Nat,1,1)) # P[ii,:,:]=self.dipole[:,:] for ii going through all atoms PR=np.sum(np.multiply(P,R),axis=2) # TODO: This takes One second - make it faster for jj in range(3): ELF[:,:,jj]+=(3*PR/RR5)*R[:,:,jj] ELF[:,:,jj]-=P[:,:,jj]/RR3 #for ii in range(self.Nat): # ELF[ii,ii,:]=np.zeros(3,dtype='f8') ELF[MASK2]=0.0 elf=np.sum(ELF,axis=1)/eps # TODO: Think if this could be done in some efficient way for ii in range(self.Nat): self.dipole[ii,:]=np.dot(self.polar[typ][ii],elf[ii]+Estatic) if debug: print('Dipole sum:',np.sum(self.dipole,axis=0)) if debug: time2 = timeit.default_timer() print('Time spend on calculation in calc_dipoles_All:',time2-time1,'s') print('Calculation vs preparation ratio:',(time2-time1)/(time1-time0)) print('Time for filling coordinate matrix vs all the rest:',(time01-time0)/(time1-time01)) def _get_interaction_energy(self,index,charge=None,debug=False): ''' Function calculates interaction energy between atoms defined in index and the rest of the atoms Parameters ---------- index : list of int (dimension N) List of atoms where we would like to calculate potential and for which we would like to calculate interaction energy with the rest of the system charge : numpy.array of real (dimension Natoms_of_defect) Atomic trasition charges (TrEsp charges) for every atom of one defect defined by `index` Returns ------- InterE : real Interaction energies in atomic units (Hartree) ''' if isinstance(charge,np.ndarray) or isinstance(charge,list): use_orig_charges=False else: if charge==None: use_orig_charges=True else: raise IOError('Unable to determine charges') if use_orig_charges: charge=np.zeros(len(index),dtype='f8') # coppy charges and assign zero charges to those in index AllCharge=np.copy(self.charge) AllDipole=np.copy(self.dipole) for ii in range(self.Nat): if ii in index: if use_orig_charges: charge[np.where(index==ii)[0][0]]=AllCharge[ii] AllCharge[ii]=0.0 AllDipole[ii,:]=np.zeros(3,dtype='f8') InterE=0.0 # TODO: This distance matrix R is calculated many times - it would be faster to have it as global variable # TODO: Check if this filling of whole matrix and then taking only small slice is not slower than two for cycles only through relevant pairs # Fill matrix of interatomic vectors: R = np.tile(self.coor,(self.Nat,1,1)) R = (R - np.swapaxes(R,0,1)) # R[ii,jj,:]=self.coor[jj]-self.coor[ii] # Correct regions with zero distance if (AllCharge[index]==0.0).all(): R[index,index,0]=1.0 # it is small distance but it will be always multiplied by zero and therefore it wont influent total potential else: R[index,index,0]=1e20 # large distance to have a small norm in order not ti influent the total potential (these atoms should be excluded) # Take only slice of the matrix R[:,jj,:] where jj corespond to indexes R=R[:,index,:] pot_charge=potential_charge(AllCharge,R) pot_dipole=potential_dipole(AllDipole,R) # TODO: Move to test part if debug: print('Length of index list:',len(index)) print('Shape of coor matrix:',R.shape) #print('Coor 0,0:',R[0,0]) #print('Coor 0,1:',R[0,1]) #print('Coor 0,2:',R[0,2]) #print('Coor 2,3:',R[2,3]) potential_charge_test=np.zeros(len(index),dtype='f8') potential_dipole_test=np.zeros(len(index),dtype='f8') #print(pot_charge) for jj in range(len(index)): for ii in range(self.Nat): if ii!=index[jj]: R=self.coor[index[jj]]-self.coor[ii] #if jj==0 and ii==0: # print('Coor 0,0:',R) #if jj==1 and ii==0: # print('Coor 0,1:',R) #if jj==2 and ii==0: # print('Coor 0,2:',R) #if jj==3 and ii==2: # print('Coor 2,3:',R) potential_charge_test[jj]+=potential_charge(AllCharge[ii],R) potential_dipole_test[jj]+=potential_dipole(AllDipole[ii],R) #print(potential_test) print(pot_dipole) print(potential_dipole_test) if np.allclose(potential_charge_test,pot_charge): print('Potential generated by charges is the same for old and new calculation') else: raise Warning('Potentials generated by charges are different for both methods') if np.allclose(potential_dipole_test,pot_dipole): print('Potential generated by dipoles is the same for old and new calculation') else: raise Warning('Potentials generated by dipoles are different for both methods') for jj in range(len(index)): potential=0.0 for ii in range(self.Nat): if ii!=index[jj]: R=self.coor[index[jj]]-self.coor[ii] potential+=potential_charge(AllCharge[ii],R) potential+=potential_dipole(AllDipole[ii],R) InterE+=potential*charge[jj] if np.allclose(InterE,np.dot(charge,pot_charge+pot_dipole)): print('Interaction energy is calculated correctly') else: raise Warning('Interaction energy for both methods is different') InterE = np.dot(charge, pot_charge+pot_dipole) return InterE def _fill_Polar_matrix(self,index1,index2,typ='AlphaE',order=80,debug=False): """ Calculate polarization matrix representation for interaction energy calculation. Parameters --------- index1 : list of integer (dimension Natoms_defect1) Indexes of all atoms from the first defect (starting from 0) index2 : list of integer (dimension Natoms_defect2) Indexes of all atoms from the second defect (starting from 0) typ : string (optional init = 'AlphaE') Which polarizability should be used for calculation of induced dipoles. Supported types are: ``'AlphaE'``, ``'Alpha_E'`` and ``'BetaEE'`` order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 Returns ------- PolMAT : numpy array of float (dimension 2x2) Polarizability matrix representation. For ``typ='AlphaE'`` or ``typ='BetaEE': PolMAT[0,0] = -E(1)*induced_dipole(1), PolMAT[0,1] = PolMAT[1,0] = -E(1)*induced_dipole(2) and PolMAT[1,1] = -E(2)*induced_dipole(2). For ``typ='Alpha_E'`` diagonal elements are swapped: PolMAT[0,0] = -E(2)*induced_dipole(2), PolMAT[0,1] = PolMAT[1,0] = -E(1)*induced_dipole(2) and PolMAT[1,1] = -E(1)*induced_dipole(1) dipolesA : numpy array of float (dimension 3) Total induced dipole moment in the environment by the first defect. dipolesB : numpy array of float (dimension 3) Total induced dipole moment in the environment by the second defect. dipoles_polA : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect """ if typ=='BetaEE' and order>1: raise IOError('For calculation with beta polarization maximal order is 1') elif typ=='BetaEE' and order<1: return np.zeros((2,2),dtype='f8') defA_charge=self.charge[index1] defB_charge=self.charge[index2] defA_indx=deepcopy(index1) defB_indx=deepcopy(index2) PolMAT=np.zeros((2,2),dtype='f8') E_TrEsp=self.get_TrEsp_Eng(index1, index2) if debug: print(typ,order) # Polarization by molecule B self.charge[defA_indx]=0.0 self._calc_dipoles_All(typ,NN=order,eps=1,debug=False) dipolesB=np.sum(self.dipole,axis=0) # induced dipoles by second defect (defect B) self.charge[defA_indx]=defA_charge PolMAT[1,1] = self._get_interaction_energy(defB_indx,charge=defB_charge,debug=False) - E_TrEsp PolMAT[0,1] = self._get_interaction_energy(defA_indx,charge=defA_charge,debug=False) - E_TrEsp PolMAT[1,0] = PolMAT[0,1] dipoles_polB = self.dipole.copy() self.dipole=np.zeros((self.Nat,3),dtype='f8') # Polarization by molecule A self.charge[defB_indx]=0.0 self._calc_dipoles_All(typ,NN=order,eps=1,debug=False) dipolesA=np.sum(self.dipole,axis=0) self.charge[defB_indx]=defB_charge PolMAT[0,0] = self._get_interaction_energy(defA_indx,charge=defA_charge,debug=False) - E_TrEsp if debug: print(PolMAT*conversion_facs_energy["1/cm"]) if np.isclose(self._get_interaction_energy(defB_indx,charge=defB_charge,debug=False)-E_TrEsp,PolMAT[1,0]): print('ApB = BpA') else: raise Warning('ApB != BpA') dipoles_polA = self.dipole.copy() self.dipole=np.zeros((self.Nat,3),dtype='f8') if typ=='AlphaE' or typ=='BetaEE' or typ=='Alpha_st': return PolMAT,dipolesA,dipolesB,dipoles_polA,dipoles_polB elif typ=='Alpha_E': PolMAT[[0,1],[0,1]] = PolMAT[[1,0],[1,0]] # Swap AlphaMAT[0,0] with AlphaMAT[1,1] return PolMAT,dipolesA,dipolesB,dipoles_polA,dipoles_polB def _TEST_fill_Polar_matrix(self,index1,index2,typ='AlphaE',order=80,debug=False, out_pot=False): """ Calculate polarization matrix representation for interaction energy calculation. Parameters --------- index1 : list of integer (dimension Natoms_defect1) Indexes of all atoms from the first defect (starting from 0) index2 : list of integer (dimension Natoms_defect2) Indexes of all atoms from the second defect (starting from 0) typ : string (optional init = 'AlphaE') Which polarizability should be used for calculation of induced dipoles. Supported types are: ``'AlphaE'``, ``'Alpha_E'`` and ``'BetaEE'`` order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 Returns ------- PolMAT : numpy array of float (dimension 2x2) Polarizability matrix representation. For ``typ='AlphaE'`` or ``typ='BetaEE': PolMAT[0,0] = -E(1)*induced_dipole(1), PolMAT[0,1] = PolMAT[1,0] = -E(1)*induced_dipole(2) and PolMAT[1,1] = -E(2)*induced_dipole(2). For ``typ='Alpha_E'`` diagonal elements are swapped: PolMAT[0,0] = -E(2)*induced_dipole(2), PolMAT[0,1] = PolMAT[1,0] = -E(1)*induced_dipole(2) and PolMAT[1,1] = -E(1)*induced_dipole(1) dipolesA : numpy array of float (dimension 3) Total induced dipole moment in the environment by the first defect. dipolesB : numpy array of float (dimension 3) Total induced dipole moment in the environment by the second defect. dipoles_polA : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect """ if typ=='BetaEE' and order>1: raise IOError('For calculation with beta polarization maximal order is 1') elif typ=='BetaEE' and order<1: return np.zeros((2,2),dtype='f8') defA_charge=self.charge[index1] defB_charge=self.charge[index2] defA_indx=deepcopy(index1) defB_indx=deepcopy(index2) PolMAT=np.zeros((2,2),dtype='f8') E_TrEsp=self.get_TrEsp_Eng(index1, index2) if debug: print(typ,order) # Polarization by molecule B self.charge[defA_indx]=0.0 self._calc_dipoles_All(typ,NN=order,eps=1,debug=False) dipolesB=np.sum(self.dipole,axis=0) # induced dipoles by second defect (defect B) self.charge[defA_indx]=defA_charge PolMAT[1,1] = self._get_interaction_energy(defB_indx,charge=defB_charge,debug=False) - E_TrEsp PolMAT[0,1] = self._get_interaction_energy(defA_indx,charge=defA_charge,debug=False) - E_TrEsp PolMAT[1,0] = PolMAT[0,1] self.dipole=np.zeros((self.Nat,3),dtype='f8') # Polarization by molecule A self.charge[defB_indx]=0.0 self._calc_dipoles_All(typ,NN=order,eps=1,debug=False) dipolesA=np.sum(self.dipole,axis=0) self.charge[defB_indx]=defB_charge PolMAT[0,0] = self._get_interaction_energy(defA_indx,charge=defA_charge,debug=False) - E_TrEsp if debug: print(PolMAT*conversion_facs_energy["1/cm"]) if np.isclose(self._get_interaction_energy(defB_indx,charge=defB_charge,debug=False)-E_TrEsp,PolMAT[1,0]): print('ApB = BpA') else: raise Warning('ApB != BpA') dipoles_polA = self.dipole.copy() self.dipole=np.zeros((self.Nat,3),dtype='f8') if typ=='AlphaE' or typ=='BetaEE' or typ=='Alpha_st': return PolMAT,dipolesA,dipolesB,dipoles_polA elif typ=='Alpha_E': PolMAT[[0,1],[0,1]] = PolMAT[[1,0],[1,0]] # Swap AlphaMAT[0,0] with AlphaMAT[1,1] return PolMAT,dipolesA,dipolesB,dipoles_polA def get_TrEsp_Eng(self, index1, index2): """ Calculate TrEsp interaction energy for defects (defect-like molecules) in vacuum. Parameters -------- index1 : list of integer (dimension Natoms_defect1) Indexes of all atoms from the first defect (starting from 0) index2 : list of integer (dimension Natoms_defect2) Indexes of all atoms from the second defect (starting from 0) Returns -------- E_TrEsp : float TrEsp interaction energy in ATOMIC UNITS (Hartree) between defect in vacuum. """ defA_coor = self.coor[index1] defB_coor = self.coor[index2] defA_charge = self.charge[index1] defB_charge = self.charge[index2] E_TrEsp = charge_charge(defA_coor,defA_charge,defB_coor,defB_charge)[0] return E_TrEsp # in hartree def get_TrEsp_Dipole(self, index): """ Calculate vacuum transition dipole moment for single defect (from TrEsp charges). Parameters ---------- index : list of integer (dimension Natoms_defect) Indexes of all atoms from the defect (starting from 0) of which transition dipole is calculated Returns -------- Dip_TrEsp : numpy array of float (dimension 3) Transition dipole in ATOMIC UNITS for specified defect (by index) calculated from TrEsp charges """ def_coor = self.coor[index] def_charge = self.charge[index] Dip_TrEsp = np.dot(def_charge,def_coor) return Dip_TrEsp # in AU def get_SingleDefectProperties(self, index, dAVA=0.0, order=80, approx=1.1): ''' Calculate effects of environment such as transition energy shift and transition dipole change for single defect. Parameters ---------- index : list of integer (dimension Natoms_defect) Indexes of all atoms from the defect (starting from 0) for which transition energy and transition dipole is calculated dAVA : float **dAVA = <A|V|A> - <G|V|G>** Difference in electrostatic interaction energy between defect and environment for defect in excited state <A|V|A> and in ground state <G|V|G>. order : integer (optional - init = 80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns ------- Eshift : Energy class Transition energy shift for the defect due to the fluorographene environment calculated from structure with single defect. Units are energy managed TrDip : numpy array of real (dimension 3) Total transition dipole for the defect with environment effects included calculated from structure with single defect (in ATOMIC UNITS) **Neglecting `tilde{Beta(E)}` is not valid approximation. It shoudl be better to neglect Beta(E,-E) to be consistent with approximation for interaction energy** Notes ---------- dip = Alpha(E)*El_field_TrCharge + Alpha(-E)*El_field_TrCharge Then final transition dipole of molecule with environment is calculated according to the approximation: **Approximation 1.1:** dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init(1-1/4*Ind_dip_Beta(E,E)*El_field_TrCharge) **Approximation 1.2:** dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init **Approximation 1.3:** dip_fin = dip - 2*Vinter*Beta(E,E)*El_field_TrCharge + dip_init ''' # Get TrEsp Transition dipole TrDip_TrEsp = np.dot(self.charge[index],self.coor[index,:]) # vacuum transition dipole for single defect charge = self.charge[index] # Calculate polarization matrixes # TODO: Shift this block to separate function self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=order,eps=1,debug=False) dip_AlphaE = np.sum(self.dipole,axis=0) Polar_AlphaE = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_E',NN=order,eps=1,debug=False) dip_Alpha_E = np.sum(self.dipole,axis=0) Polar_Alpha_E = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=order//2,eps=1,debug=False) dip_Beta = np.sum(self.dipole,axis=0) Polar_Beta = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') if approx==1.1: # Calculate transition energy shift Eshift = dAVA + Polar_AlphaE - Polar_Alpha_E Eshift -= (self.VinterFG - dAVA)*Polar_Beta # Calculate transition dipoles for every defect TrDip = TrDip_TrEsp*(1 + Polar_Beta/4) + dip_AlphaE + dip_Alpha_E TrDip -= (self.VinterFG - dAVA)*dip_Beta # Change to energy class with energy_units('AU'): Eshift = EnergyClass(Eshift) return Eshift, TrDip else: raise IOError('Unsupported approximation') def _TEST_Compare_SingleDefectProperties(self, tr_charge, gr_charge, ex_charge, struc, index, dAVA=0.0, order=80, approx=1.1): ''' Calculate effects of environment such as transition energy shift and transition dipole change for single defect. Parameters ---------- index : list of integer (dimension Natoms_defect) Indexes of all atoms from the defect (starting from 0) for which transition energy and transition dipole is calculated dAVA : float **dAVA = <A|V|A> - <G|V|G>** Difference in electrostatic interaction energy between defect and environment for defect in excited state <A|V|A> and in ground state <G|V|G>. order : integer (optional - init = 80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns ------- Eshift : Energy class Transition energy shift for the defect due to the fluorographene environment calculated from structure with single defect. Units are energy managed TrDip : numpy array of real (dimension 3) Total transition dipole for the defect with environment effects included calculated from structure with single defect (in ATOMIC UNITS) **Neglecting `tilde{Beta(E)}` is not valid approximation. It shoudl be better to neglect Beta(E,-E) to be consistent with approximation for interaction energy** Notes ---------- dip = Alpha(E)*El_field_TrCharge + Alpha(-E)*El_field_TrCharge Then final transition dipole of molecule with environment is calculated according to the approximation: **Approximation 1.1:** dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init(1-1/4*Ind_dip_Beta(E,E)*El_field_TrCharge) **Approximation 1.2:** dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init **Approximation 1.3:** dip_fin = dip - 2*Vinter*Beta(E,E)*El_field_TrCharge + dip_init ''' # Get TrEsp Transition dipole TrDip_TrEsp = np.dot(self.charge[index],self.coor[index,:]) # vacuum transition dipole for single defect # Get energy contribution from polarization by transition density self.charge[index] = tr_charge charge = self.charge[index] # Set distance matrix R_elst = np.tile(struc.coor._value,(self.Nat,1,1)) R_pol = np.tile(self.coor,(struc.nat,1,1)) R = (R_elst - np.swapaxes(R_pol,0,1)) # R[ii,jj,:]=self.coor[jj]-self.coor[ii] # Calculate polarization matrixes # TODO: Shift this block to separate function self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) Polar1_AlphaE = self._get_interaction_energy(index,charge=charge,debug=False) pot1_dipole_AlphaE_tr = potential_dipole(self.dipole,R) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=2,eps=1,debug=False) Polar2_AlphaE = self._get_interaction_energy(index,charge=charge,debug=False) Polar2_AlphaE = Polar2_AlphaE - Polar1_AlphaE dip_AlphaE = np.sum(self.dipole,axis=0) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_E',NN=1,eps=1,debug=False) Polar1_Alpha_E = self._get_interaction_energy(index,charge=charge,debug=False) pot1_dipole_Alpha_E_tr = potential_dipole(self.dipole,R) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_E',NN=2,eps=1,debug=False) dip_Alpha_E = np.sum(self.dipole,axis=0) dip_Alpha_E = np.sum(self.dipole,axis=0) Polar2_Alpha_E = self._get_interaction_energy(index,charge=charge,debug=False) Polar2_Alpha_E = Polar2_Alpha_E - Polar1_Alpha_E self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) dip_Beta = np.sum(self.dipole,axis=0) Polar1_Beta_EE = self._get_interaction_energy(index,charge=charge,debug=False) pot1_dipole_betaEE_tr = potential_dipole(self.dipole,R) self.charge[index] = ex_charge charge = self.charge[index] Polar1_Beta_EE_tr_ex = self._get_interaction_energy(index,charge=charge,debug=False) self.charge[index] = gr_charge charge = self.charge[index] Polar1_Beta_EE_tr_gr = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') # Calculate polarization by ground state charge distribution self.charge[index] = gr_charge charge = self.charge[index] self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) Polar1_static_gr = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=2,eps=1,debug=False) Polar2_static_gr = self._get_interaction_energy(index,charge=charge,debug=False) Polar2_static_gr = Polar2_static_gr - Polar1_static_gr self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) Polar1_Beta_EE_gr = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') # Calculate polarization by excited state charge distribution self.charge[index] = ex_charge charge = self.charge[index] self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) Polar1_static_ex = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=2,eps=1,debug=False) Polar2_static_ex = self._get_interaction_energy(index,charge=charge,debug=False) Polar2_static_ex = Polar2_static_ex - Polar1_static_ex self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) Polar1_Beta_EE_ex = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') # Calculate indiced dipole by charge difference between ground and excited state self.charge[index] = ex_charge - gr_charge charge = self.charge[index] self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) pot1_dipole_ex_gr = potential_dipole(self.dipole,R) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=2,eps=1,debug=False) pot2_dipole_ex_gr = potential_dipole(self.dipole,R) pot2_dipole_ex_gr = pot2_dipole_ex_gr - pot1_dipole_ex_gr self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) pot1_dipole_betaEE_ex_gr = potential_dipole(self.dipole,R) self.dipole = np.zeros((self.Nat,3),dtype='f8') # calculate interaction between induced dipoles by transition density with ground and excited charges of the chromophore self.charge[index] = tr_charge self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) pot1_dipole_static_tr = potential_dipole(self.dipole,R) self.charge[index] = ex_charge charge = self.charge[index] Polar1_static_tr_ex = self._get_interaction_energy(index,charge=charge,debug=False) self.charge[index] = gr_charge charge = self.charge[index] Polar1_static_tr_gr = self._get_interaction_energy(index,charge=charge,debug=False) self.charge[index] = tr_charge self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index] = gr_charge charge = self.charge[index] Polar1_AlphaE_tr_gr = self._get_interaction_energy(index,charge=charge,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = tr_charge self._calc_dipoles_All('Alpha_E',NN=1,eps=1,debug=False) self.charge[index] = ex_charge charge = self.charge[index] Polar1_Alpha_E_tr_ex = self._get_interaction_energy(index,charge=charge,debug=False) # Set the variables to initial state self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = tr_charge if approx==1.1: # Calculate transition energy shift Eshift = dAVA + Polar1_AlphaE + Polar2_AlphaE - Polar1_Alpha_E - Polar2_Alpha_E Eshift -= (self.VinterFG - dAVA)*Polar1_Beta_EE # Calculate transition dipoles for every defect TrDip = TrDip_TrEsp*(1 + Polar1_Beta_EE/4) + dip_AlphaE + dip_Alpha_E TrDip -= (self.VinterFG - dAVA)*dip_Beta # Change to energy class with energy_units('AU'): Eshift = EnergyClass(Eshift) dAVA = EnergyClass(dAVA) Polar1_AlphaE = EnergyClass(Polar1_AlphaE) Polar2_AlphaE = EnergyClass(Polar2_AlphaE) Polar1_Alpha_E = EnergyClass(Polar1_Alpha_E) Polar2_Alpha_E = EnergyClass(Polar2_Alpha_E) Polar1_Beta_EE = EnergyClass(Polar1_Beta_EE) Polar1_static_ex_gr = EnergyClass(Polar1_static_ex - Polar1_static_gr) Polar2_static_ex_gr = EnergyClass(Polar2_static_ex - Polar2_static_gr) Polar1_Beta_EE_ex_gr = EnergyClass(Polar1_Beta_EE_ex - Polar1_Beta_EE_gr) Polar1_static_tr_ex = EnergyClass(Polar1_static_tr_ex) Polar1_static_tr_gr = EnergyClass(Polar1_static_tr_gr) Polar1_AlphaE_tr_gr = EnergyClass(Polar1_AlphaE_tr_gr) Polar1_Alpha_E_tr_ex = EnergyClass(Polar1_Alpha_E_tr_ex) Polar1_Beta_EE_tr_ex = EnergyClass(Polar1_Beta_EE_tr_ex) Polar1_Beta_EE_tr_gr = EnergyClass(Polar1_Beta_EE_tr_gr) res_Energy = {'dE_0-1': Eshift, 'dE_elstat(exct-grnd)': dAVA} res_Energy['E_pol1_Alpha(E)'] = Polar1_AlphaE res_Energy['E_pol2_Alpha(E)'] = Polar2_AlphaE res_Energy['E_pol1_Alpha(-E)'] = Polar1_Alpha_E res_Energy['E_pol2_Alpha(-E)'] = Polar2_Alpha_E res_Energy['E_pol1_Beta(E,E)'] = Polar1_Beta_EE res_Energy['E_pol1_static_(exct-grnd)'] = Polar1_static_ex_gr res_Energy['E_pol2_static_(exct-grnd)'] = Polar2_static_ex_gr res_Energy['E_pol1_Beta(E,E)_(exct-grnd)'] = Polar1_Beta_EE_ex_gr res_Energy['E_pol1_static_(trans)_(exct)'] = Polar1_static_tr_ex res_Energy['E_pol1_static_(trans)_(grnd)'] = Polar1_static_tr_gr res_Energy['E_pol1_Alpha(E)_(trans)_(grnd)'] = Polar1_AlphaE_tr_gr res_Energy['E_pol1_Alpha(-E)_(trans)_(exct)'] = Polar1_Alpha_E_tr_ex res_Energy['E_pol1_Beta(E,E)_(trans)_(exct)'] = Polar1_Beta_EE_tr_ex res_Energy['E_pol1_Beta(E,E)_(trans)_(grnd)'] = Polar1_Beta_EE_tr_gr res_Pot = {'Pol2-env_static_(exct-grnd)': pot2_dipole_ex_gr} res_Pot['Pol1-env_static_(exct-grnd)'] = pot1_dipole_ex_gr res_Pot['Pol1-env_Beta(E,E)_(exct-grnd)'] = pot1_dipole_betaEE_ex_gr res_Pot['Pol1-env_Beta(E,E)_(trans)'] = pot1_dipole_betaEE_tr res_Pot['Pol1-env_Alpha(E)_(trans)'] = pot1_dipole_AlphaE_tr res_Pot['Pol1-env_Alpha(-E)_(trans)'] = pot1_dipole_Alpha_E_tr res_Pot['Pol1-env_static_(trans)'] = pot1_dipole_static_tr # with energy_units('1/cm'): # print(Eshift.value,dAVA.value,Polar1_AlphaE.value,Polar2_AlphaE.value,Polar1_AlphaE.value+Polar2_AlphaE.value,Polar1_Alpha_E.value,Polar2_Alpha_E.value,Polar1_Alpha_E.value+Polar2_Alpha_E.value) # return res_Energy, res_Pot, TrDip else: raise IOError('Unsupported approximation') def get_SingleDefectProperties_new(self, gr_charge, ex_charge, FG_elstat, struc, index, E01, dAVA=0.0, order=2, approx=1.1): ''' Calculate effects of environment such as transition energy shift and transition dipole change for single defect. Parameters ---------- index : list of integer (dimension Natoms_defect) Indexes of all atoms from the defect (starting from 0) for which transition energy and transition dipole is calculated dAVA : float **dAVA = <A|V|A> - <G|V|G>** Difference in electrostatic interaction energy between defect and environment for defect in excited state <A|V|A> and in ground state <G|V|G>. order : integer (optional - init = 80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns ------- Eshift : Energy class Transition energy shift for the defect due to the fluorographene environment calculated from structure with single defect. Units are energy managed TrDip : numpy array of real (dimension 3) Total transition dipole for the defect with environment effects included calculated from structure with single defect (in ATOMIC UNITS) **Neglecting `tilde{Beta(E)}` is not valid approximation. It shoudl be better to neglect Beta(E,-E) to be consistent with approximation for interaction energy** Notes ---------- dip = Alpha(E)*El_field_TrCharge + Alpha(-E)*El_field_TrCharge Then final transition dipole of molecule with environment is calculated according to the approximation: **Approximation 1.1:** dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init(1-1/4*Ind_dip_Beta(E,E)*El_field_TrCharge) **Approximation 1.2:** dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init **Approximation 1.3:** dip_fin = dip - 2*Vinter*Beta(E,E)*El_field_TrCharge + dip_init ''' # Get TrEsp Transition dipole TrDip_TrEsp = np.dot(self.charge[index],self.coor[index,:]) # vacuum transition dipole for single defect # Set initial charges tr_charge = self.charge[index] FG_charge_orig = FG_elstat.charge[index] FG_charge = FG_elstat.charge.copy() FG_charge[index] = 0.0 FG_elstat.charge[index] = tr_charge Eelstat_trans=FG_elstat.get_EnergyShift() FG_elstat.charge[index] = FG_charge_orig # Set distance matrix R_elst = np.tile(struc.coor._value,(self.Nat,1,1)) R_pol = np.tile(self.coor,(struc.nat,1,1)) R = (R_elst - np.swapaxes(R_pol,0,1)) # R[ii,jj,:]=self.coor[jj]-self.coor[ii] # TODO: Maybe also exclude connected fluorinesto atoms ii for ii in range(self.Nat): R[ii,ii,:] = 0.0 # self interaction is not permited in potential calculation # Calculate polarization matrixes # TODO: Shift this block to separate function self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=order,eps=1,debug=False) Polar2_AlphaE = self._get_interaction_energy(index,charge=tr_charge,debug=False) dip_AlphaE = np.sum(self.dipole,axis=0) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) Potential = potential_dipole(self.dipole,R) E_Pol1_env_AE_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_E',NN=order,eps=1,debug=False) Polar2_Alpha_E = self._get_interaction_energy(index,charge=tr_charge,debug=False) dip_Alpha_E = np.sum(self.dipole,axis=0) self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('Alpha_E',NN=1,eps=1,debug=False) Potential = potential_dipole(self.dipole,R) E_Pol1_env_A_E_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = ex_charge self._calc_dipoles_All('Alpha_st',NN=order,eps=1,debug=False) Polar2_Alpha_st_ex = self._get_interaction_energy(index,charge=ex_charge,debug=False) Potential = potential_dipole(self.dipole,R) Polar2_env_Alpha_st_ex = np.dot(FG_charge,Potential) dip_Alpha_st_ex = np.sum(self.dipole,axis=0) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = gr_charge self._calc_dipoles_All('Alpha_st',NN=order,eps=1,debug=False) Polar2_Alpha_st_gr = self._get_interaction_energy(index,charge=gr_charge,debug=False) Potential = potential_dipole(self.dipole,R) Polar2_env_Alpha_st_gr = np.dot(FG_charge,Potential) dip_Alpha_st_gr = np.sum(self.dipole,axis=0) # TODO: for pol2-env_static second order is twice and first order is only single times - therefore I need to calculate first and second order separately for environmnet efects self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = ex_charge self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) dip1_Ast_ex = np.sum(self.dipole,axis=0) Potential = potential_dipole(self.dipole,R) Polar1_env_Alpha_st_ex = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = gr_charge self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) dip1_Ast_gr = np.sum(self.dipole,axis=0) Potential = potential_dipole(self.dipole,R) Polar1_env_Alpha_st_gr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = tr_charge self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) Potential = potential_dipole(self.dipole,R) Pol1_env_Alpha_st_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = tr_charge self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) dip_Beta = np.sum(self.dipole,axis=0) Polar1_Beta_EE = self._get_interaction_energy(index,charge=tr_charge,debug=False) #pot1_dipole_betaEE_tr = potential_dipole(self.dipole,R) # needed for transition dipole self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = gr_charge self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) dip1_AE_gr = np.sum(self.dipole,axis=0) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = ex_charge self._calc_dipoles_All('Alpha_E',NN=1,eps=1,debug=False) dip1_A_E_ex = np.sum(self.dipole,axis=0) # Set the variables to initial state self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = tr_charge if approx==1.1: # Calculate transition energy shift Eshift = dAVA + Polar2_AlphaE - Polar2_Alpha_E Eshift -= (self.VinterFG - dAVA)*Polar1_Beta_EE Eshift += Polar2_Alpha_st_ex - Polar2_Alpha_st_gr Eshift += Polar1_env_Alpha_st_ex - Polar1_env_Alpha_st_gr Eshift += 2*(Polar2_env_Alpha_st_ex - Polar1_env_Alpha_st_ex - Polar2_env_Alpha_st_gr + Polar1_env_Alpha_st_gr) Eshift += Eelstat_trans/E01._value * (2*E_Pol1_env_AE_tr + 4*Pol1_env_Alpha_st_tr + 2*E_Pol1_env_A_E_tr) # Calculate transition dipoles for every defect TrDip = TrDip_TrEsp*(1 + Polar1_Beta_EE/2 - 2*(Eelstat_trans/E01._value)*(Eelstat_trans/E01._value) ) TrDip += dip_AlphaE + dip_Alpha_E TrDip -= (self.VinterFG - dAVA)*dip_Beta TrDip += (Eelstat_trans/E01._value)*(dip1_Ast_gr - dip1_Ast_ex) TrDip += (Eelstat_trans/E01._value)*(dip1_AE_gr - dip1_A_E_ex) # TODO: Add term for polarization of environment by environment itself # Change to energy class with energy_units('AU'): Eshift = EnergyClass(Eshift) dAVA = EnergyClass(dAVA) res_Energy = {'dE_0-1': Eshift, 'dE_elstat(exct-grnd)': dAVA} res_Energy['E_pol2_Alpha(E)'] = EnergyClass(Polar2_AlphaE) res_Energy['E_pol2_Alpha(-E)'] = EnergyClass(Polar2_Alpha_E) res_Energy['E_pol1_Beta(E,E)'] = EnergyClass(Polar1_Beta_EE) res_Energy['E_pol2_static_(exct-grnd)'] = EnergyClass(Polar2_Alpha_st_ex - Polar2_Alpha_st_gr) res_Energy['Pol1-env_static_(exct-grnd)'] = EnergyClass(Polar1_env_Alpha_st_ex - Polar1_env_Alpha_st_gr) res_Energy['Pol2-env_static_(exct-grnd)'] = EnergyClass(Polar2_env_Alpha_st_ex - Polar2_env_Alpha_st_gr) res_Energy['Pol1-env_Alpha(E)_(trans)'] = EnergyClass(E_Pol1_env_AE_tr) res_Energy['Pol1-env_Alpha(-E)_(trans)'] = EnergyClass(E_Pol1_env_A_E_tr) res_Energy['Pol1-env_static_(trans)'] = EnergyClass(Pol1_env_Alpha_st_tr) # with energy_units('1/cm'): # print(Eshift.value,dAVA.value,res_Energy['E_pol2_Alpha(E)'].value,res_Energy['E_pol2_Alpha(-E)'].value,res_Energy['E_pol2_static_(exct-grnd)'].value,res_Energy['Pol2-env_static_(exct-grnd)'].value) return Eshift, res_Energy, TrDip else: raise IOError('Unsupported approximation') def get_SingleDefect_derivation(self, gr_charge, ex_charge, FG_elstat, struc, index, E01, order=2, approx=1.1): ''' Calculate derivative of single defect property ''' # Set initial charges tr_charge = self.charge[index] FG_charge_orig = FG_elstat.charge[index] FG_charge = FG_elstat.charge.copy() FG_charge[index] = 0.0 dAVA, dR_dAVA = FG_elstat.get_EnergyShift_and_Derivative() # calculate interaction between transition charges and environment atoms FG_elstat.charge[index] = tr_charge Eelstat_trans=FG_elstat.get_EnergyShift() FG_elstat.charge[index] = FG_charge_orig # Set distance matrix - polarizable atoms x electrostatic atoms R_elst = np.tile(struc.coor._value,(self.Nat,1,1)) R_pol = np.tile(self.coor,(struc.nat,1,1)) R_pol_elst = (R_elst - np.swapaxes(R_pol,0,1)) # R[ii,jj,:]=self.coor[jj]-self.coor[ii] # TODO: Maybe also exclude connected fluorinesto atoms ii for ii in range(self.Nat): R_pol_elst[ii,ii,:] = 0.0 # self interaction is not permited in potential calculation # Negative values are there because we want to calculate dE/dR and not d(El(Rn)*1/2*Alpha*El(Rn))/dR # calculate first order derivation - Polar1_Alpha(E) dR_pol1_AlphaE = -self._dR_BpA(index, index, tr_charge, tr_charge, 'AlphaE') # calculate first order derivation - Polar1_Alpha(-E) dR_pol1_Alpha_E = -self._dR_BpA(index, index, tr_charge, tr_charge, 'Alpha_E') # calculate second order derivation - Polar2_Alpha(E) dR_pol2_AlphaE = -self._dR_BppA(index, index, tr_charge, tr_charge, 'AlphaE') # calculate second order derivation - Polar2_Alpha(-E) dR_pol2_Alpha_E = -self._dR_BppA(index, index, tr_charge, tr_charge, 'Alpha_E') # calculate first order derivation - Polar1_static for excited and ground charges dR_pol1_static_grnd = -self._dR_BpA(index, index, gr_charge, gr_charge, 'Alpha_st') dR_pol1_static_exct = -self._dR_BpA(index, index, ex_charge, ex_charge, 'Alpha_st') # calculate second order derivation - Polar2_static for excited and ground charges dR_pol2_static_grnd = -self._dR_BppA(index, index, gr_charge, gr_charge, 'Alpha_st') dR_pol2_static_exct = -self._dR_BppA(index, index, ex_charge, ex_charge, 'Alpha_st') # calculate first order derivation - Polar1_Beta(E,E) dR_pol1_BetaEE = -self._dR_BpA(index, index, tr_charge, tr_charge, 'BetaEE') # calculate first order derivation of Palar1-env with static polarizability dR_pol1_env_static_ex_gr, dR_pol1_env_static_ex_gr_env = -self._dR_ApEnv(index,ex_charge-gr_charge,FG_elstat.coor,FG_charge,'Alpha_st') # this could be maybe left out: dR_pol1_env_AlphaE_tr, dR_pol1_env_AlphaE_tr_env = -self._dR_ApEnv(index,tr_charge,FG_elstat.coor,FG_charge,'AlphaE') dR_pol1_env_Alpha_E_tr, dR_pol1_env_Alpha_E_tr_env = -self._dR_ApEnv(index,tr_charge,FG_elstat.coor,FG_charge,'Alpha_E') dR_pol1_env_static_tr, dR_pol1_env_static_tr_env = -self._dR_ApEnv(index,tr_charge,FG_elstat.coor,FG_charge,'Alpha_st') # calculate Beta polarizability self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index] = tr_charge self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) Polar1_Beta_EE = self._get_interaction_energy(index,charge=tr_charge,debug=False) # TODO: Add derivation of pol-env # TODO: Split environment contribution and polarizable atoms contribution - both different dimensions if approx==1.1: # Calculate transition energy shift dR_Eshift_env = dR_dAVA dR_Eshift = (dR_pol1_AlphaE + dR_pol2_AlphaE) dR_Eshift -= (dR_pol1_Alpha_E + dR_pol2_Alpha_E) dR_Eshift -= (self.VinterFG - dAVA)*dR_pol1_BetaEE dR_Eshift_env += dR_dAVA*Polar1_Beta_EE dR_Eshift += (dR_pol1_static_exct + dR_pol2_static_exct) dR_Eshift -= (dR_pol1_static_grnd + dR_pol2_static_grnd) dR_Eshift += dR_pol1_env_static_ex_gr dR_Eshift_env += dR_pol1_env_static_ex_gr_env # this could be maybe left out dR_Eshift += Eelstat_trans/E01._value * ( 2*dR_pol1_env_AlphaE_tr + 4*dR_pol1_env_static_tr + 2*dR_pol1_env_Alpha_E_tr) dR_Eshift_env += Eelstat_trans/E01._value * ( 2*dR_pol1_env_AlphaE_tr_env + 4*dR_pol1_env_static_tr_env + 2*dR_pol1_env_Alpha_E_tr_env) # Eshift += 2*(Polar2_env_Alpha_st_ex - Polar1_env_Alpha_st_ex - Polar2_env_Alpha_st_gr + Polar1_env_Alpha_st_gr) return dR_Eshift, dR_Eshift_env else: raise IOError('Unsupported approximation') def get_HeterodimerProperties(self, index1, index2, Eng1, Eng2, dAVA=0.0, dBVB=0.0, order=80, approx=1.1): ''' Calculate effects of the environment for structure with two different defects such as interaction energy, site transition energy shifts and changes in transition dipoles Parameters ---------- index1 : list of integer (dimension Natoms_defect1) Indexes of all atoms from the first defect (starting from 0) index2 : list of integer (dimension Natoms_defect2) Indexes of all atoms from the second defect (starting from 0) Eng1 : float Vacuum transition energy of the first defect in ATOMIC UNITS (Hartree) Eng2 : float Vacuum transition energy of the second defect in ATOMIC UNITS (Hartree) dAVA : float **dAVA = <A|V|A> - <G|V|G>** Difference in electrostatic interaction energy between first defect the and environment for the defect in excited state <A|V|A> and in ground state <G|V|G>. dBVB : float **dBVB = <B|V|B> - <G|V|G>** Difference in electrostatic interaction energy between second defect and the environment for the defect in excited state <B|V|B> and in ground state <G|V|G>. order : integer (optional - init = 80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns ------- J_inter : Energy class Interaction energy with effects of environment included. Units are energy managed Eshift1 : Energy class Transition energy shift for the first defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed Eshift2 : Energy class Transition energy shift for the second defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed TrDip1 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) TrDip2 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) AllDipAE : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Alpha(E) atomic polarizability AllDipA_E : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Alpha(-E) atomic polarizability AllDipBE : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Beta(E,E) atomic polarizability ''' # Get TrEsp interaction energy E_TrEsp = self.get_TrEsp_Eng(index1, index2) # Calculate polarization matrixes PolarMat_AlphaE, dip_AlphaE1, dip_AlphaE2, AllDipAE1, AllDipAE2 = self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=order) PolarMat_Alpha_E, dip_Alpha_E1, dip_Alpha_E2, AllDipA_E1, AllDipA_E2 = self._fill_Polar_matrix(index1,index2,typ='Alpha_E',order=order) PolarMat_Beta, dip_Beta1, dip_Beta2, AllDipBE1, AllDipBE2 = self._fill_Polar_matrix(index1,index2,typ='BetaEE',order=order//2) # calculate new eigenstates and energies HH=np.zeros((2,2),dtype='f8') if Eng1<Eng2: HH[0,0] = Eng1+dAVA HH[1,1] = Eng2+dBVB else: HH[1,1] = Eng1+dAVA HH[0,0] = Eng2+dBVB HH[0,1] = E_TrEsp HH[1,0] = HH[0,1] Energy,Coeff=np.linalg.eigh(HH) d_esp=np.sqrt( E_TrEsp**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # sqrt( (<A|V|B>)**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # Calculate interaction energies if approx==1.1: # Calculate Total polarizability matrix PolarMat = PolarMat_AlphaE + PolarMat_Alpha_E + PolarMat_Beta*(dAVA/2 + dBVB/2 - self.VinterFG) # Calculate interaction energies C1 = Coeff.T[0] E1 = Energy[0] + np.dot(C1, np.dot(PolarMat - d_esp*PolarMat_Beta, C1.T)) C2 = Coeff.T[1] E2 = Energy[1] + np.dot(C2, np.dot(PolarMat + d_esp*PolarMat_Beta, C2.T)) J_inter = np.sqrt( (E2 - E1)**2 - (Eng2 - Eng1)**2 )/2*np.sign(E_TrEsp) # Calculate energy shifts for every defect Eshift1 = dAVA + PolarMat_AlphaE[0,0] - PolarMat_Alpha_E[1,1] Eshift1 -= (self.VinterFG - dAVA)*PolarMat_Beta[0,0] Eshift2 = dBVB + PolarMat_AlphaE[1,1] - PolarMat_Alpha_E[0,0] Eshift2 -= (self.VinterFG - dBVB)*PolarMat_Beta[1,1] # Calculate transition dipoles for every defect TrDip1 = np.dot(self.charge[index1],self.coor[index1,:]) # vacuum transition dipole for single defect TrDip1 = TrDip1*(1 + PolarMat_Beta[0,0]/4) + dip_AlphaE1 + dip_Alpha_E1 TrDip1 -= (self.VinterFG - dAVA)*dip_Beta1 TrDip2 = np.dot(self.charge[index2],self.coor[index2,:]) # vacuum transition dipole for single defect TrDip2 = TrDip2*(1 + PolarMat_Beta[1,1]/4) + dip_AlphaE2 + dip_Alpha_E2 TrDip2 -= (self.VinterFG - dBVB)*dip_Beta2 # Change to energy class with energy_units('AU'): J_inter = EnergyClass(J_inter) Eshift1 = EnergyClass(Eshift1) Eshift2 = EnergyClass(Eshift2) return J_inter, Eshift1, Eshift2, TrDip1, TrDip2, AllDipAE1, AllDipA_E1, AllDipBE1 else: raise IOError('Unsupported approximation') def _TEST_HeterodimerProperties(self, gr_charge1, ex_charge1, gr_charge2, ex_charge2, FG_charge, struc, index1, index2, Eng1, Eng2, dAVA=0.0, dBVB=0.0, order=80, approx=1.1): ''' Calculate effects of the environment for structure with two different defects such as interaction energy, site transition energy shifts and changes in transition dipoles Parameters ---------- index1 : list of integer (dimension Natoms_defect1) Indexes of all atoms from the first defect (starting from 0) index2 : list of integer (dimension Natoms_defect2) Indexes of all atoms from the second defect (starting from 0) Eng1 : float Vacuum transition energy of the first defect in ATOMIC UNITS (Hartree) Eng2 : float Vacuum transition energy of the second defect in ATOMIC UNITS (Hartree) dAVA : float **dAVA = <A|V|A> - <G|V|G>** Difference in electrostatic interaction energy between first defect the and environment for the defect in excited state <A|V|A> and in ground state <G|V|G>. dBVB : float **dBVB = <B|V|B> - <G|V|G>** Difference in electrostatic interaction energy between second defect and the environment for the defect in excited state <B|V|B> and in ground state <G|V|G>. order : integer (optional - init = 80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns ------- J_inter : Energy class Interaction energy with effects of environment included. Units are energy managed Eshift1 : Energy class Transition energy shift for the first defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed Eshift2 : Energy class Transition energy shift for the second defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed TrDip1 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) TrDip2 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) AllDipAE : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Alpha(E) atomic polarizability AllDipA_E : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Alpha(-E) atomic polarizability AllDipBE : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Beta(E,E) atomic polarizability ''' res = {} # Get TrEsp interaction energy E_TrEsp = self.get_TrEsp_Eng(index1, index2) # Calculate polarization matrixes (1-2) PolarMat1_AlphaE, dip_AlphaE1, dip_AlphaE2, AllDipAE1, AllDipAE2 = self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=1) PolarMat1_Alpha_E, dip_Alpha_E1, dip_Alpha_E2, AllDipA_E1, AllDipA_E2 = self._fill_Polar_matrix(index1,index2,typ='Alpha_E',order=1) PolarMat_AlphaE, dip_AlphaE1, dip_AlphaE2, AllDipAE1, AllDipAE2 = self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=2) PolarMat_Alpha_E, dip_Alpha_E1, dip_Alpha_E2, AllDipA_E1, AllDipA_E2 = self._fill_Polar_matrix(index1,index2,typ='Alpha_E',order=2) PolarMat_Beta, dip_Beta1, dip_Beta2, AllDipBE1, AllDipBE2 = self._fill_Polar_matrix(index1,index2,typ='BetaEE',order=order//2) res["E_pol2_A(E)"] = (PolarMat_AlphaE - PolarMat1_AlphaE) * conversion_facs_energy["1/cm"] res["E_pol2_A(-E)"] = (PolarMat_Alpha_E - PolarMat1_Alpha_E) * conversion_facs_energy["1/cm"] res["E_pol2_B(E,E)"] = PolarMat_Beta """ Aditional first order contribution """ # gr_charge1, ex_charge1, gr_charge2, ex_charge2 tr_charge1 = self.charge[index1] tr_charge2 = self.charge[index2] self.charge[index1] = gr_charge1 self.charge[index2] = ex_charge2 PolarMat_Alpha_st_gr_ex, dip_Alpha_st1_gr, dip_Alpha_st2_ex, AllDipA_st1_gr, AllDipA_st2_ex = self._fill_Polar_matrix(index1,index2,typ='Alpha_st',order=1) self.charge[index1] = ex_charge1 self.charge[index2] = gr_charge2 PolarMat_Alpha_st_ex_gr, dip_Alpha_st1_ex, dip_Alpha_st2_gr, AllDipA_st1_ex, AllDipA_st2_gr = self._fill_Polar_matrix(index1,index2,typ='Alpha_st',order=1) # charges for the ground state and excited state are the same => correct # difference between first and second defect is in non symetrical charges - repeat the fit with symmetry constrains PolarMat_Alpha_st = np.zeros((2,2),dtype='f8') PolarMat_Alpha_st[0,0] = np.sum(PolarMat_Alpha_st_ex_gr) # PolarMat_Alpha_st_ex_gr[0,0] + PolarMat_Alpha_st_ex_gr[1,1] + 2*PolarMat_Alpha_st_ex_gr[0,1] PolarMat_Alpha_st[1,1] = np.sum(PolarMat_Alpha_st_gr_ex) # PolarMat_Alpha_st_gr_ex[0,0] + PolarMat_Alpha_st_gr_ex[1,1] + 2*PolarMat_Alpha_st_gr_ex[0,1] # pol1-env #----------------------------------- # Set distance matrix R_elst = np.tile(struc.coor._value,(self.Nat,1,1)) R_pol = np.tile(self.coor,(struc.nat,1,1)) R = (R_elst - np.swapaxes(R_pol,0,1)) # R[ii,jj,:]=self.coor[jj]-self.coor[ii] # if normaly ordered first are carbon atoms and then are fluorine atoms - for carbon atoms same indexes in pol_mol as in struc for ii in range(self.Nat): R[ii,ii,:] = 0.0 # self interaction is not permited in potential calculation # TODO: Maybe also exclude connected fluorinesto atoms ii # Calculate potential of induced dipoles pot1_dipole_Alpha_st1_gr = potential_dipole(AllDipA_st1_gr,R) pot1_dipole_Alpha_st1_ex = potential_dipole(AllDipA_st1_ex,R) pot1_dipole_Alpha_st2_gr = potential_dipole(AllDipA_st2_gr,R) pot1_dipole_Alpha_st2_ex = potential_dipole(AllDipA_st2_ex,R) # calculate interaction energies with environment FG_charge_tmp = FG_charge.charge.copy() FG_charge_tmp[index1] = 0.0 FG_charge_tmp[index2] = 0.0 E_Pol1_env_static_gr1_FG = np.dot(FG_charge_tmp,pot1_dipole_Alpha_st1_gr) E_Pol1_env_static_ex1_FG = np.dot(FG_charge_tmp,pot1_dipole_Alpha_st1_ex) E_Pol1_env_static_gr2_FG = np.dot(FG_charge_tmp,pot1_dipole_Alpha_st2_gr) E_Pol1_env_static_ex2_FG = np.dot(FG_charge_tmp,pot1_dipole_Alpha_st2_ex) PolarMat_Alpha_st[0,0] = 2*( E_Pol1_env_static_ex1_FG + E_Pol1_env_static_gr2_FG ) PolarMat_Alpha_st[1,1] = 2*( E_Pol1_env_static_gr1_FG + E_Pol1_env_static_ex2_FG ) # return transition charges back self.charge[index1] = tr_charge1 self.charge[index2] = tr_charge2 """ Aditional second order contribution - Comparison of magnitudes """ # Calculate polarization matrix A_grnd B_exct self.charge[index1] = gr_charge1 self.charge[index2] = ex_charge2 PolarMat_Beta_gr_ex, dip_Beta1_gr, dip_Beta2_ex, AllDipBE1_gr, AllDipBE2_ex = self._fill_Polar_matrix(index1,index2,typ='BetaEE',order=1) # Calculate polarization matrix A_exct B_grnd self.charge[index1] = ex_charge1 self.charge[index2] = gr_charge2 PolarMat_Beta_ex_gr, dip_Beta1_ex, dip_Beta2_gr, AllDipBE1_ex, AllDipBE2_gr = self._fill_Polar_matrix(index1,index2,typ='BetaEE',order=1) res["E_pol1_B(E,E)_(A_exct,B_grnd)"] = PolarMat_Beta_ex_gr res["E_pol1_B(E,E)_(A_grnd,B_exct)"] = PolarMat_Beta_gr_ex # calculate pol-env for previous: pot1A_dipole_BEE_gr = potential_dipole(AllDipBE1_gr,R) pot1A_dipole_BEE_ex = potential_dipole(AllDipBE1_ex,R) pot1B_dipole_BEE_gr = potential_dipole(AllDipBE2_gr,R) pot1B_dipole_BEE_ex = potential_dipole(AllDipBE2_ex,R) PolarMat_env_Beta_ex = np.zeros((2,2),dtype="f8") PolarMat_env_Beta_gr = np.zeros((2,2),dtype="f8") PolarMat_env_Beta_ex[0,0] = np.dot(FG_charge_tmp,pot1A_dipole_BEE_ex) PolarMat_env_Beta_ex[1,1] = np.dot(FG_charge_tmp,pot1B_dipole_BEE_ex) PolarMat_env_Beta_gr[0,0] = np.dot(FG_charge_tmp,pot1B_dipole_BEE_gr) PolarMat_env_Beta_gr[1,1] = np.dot(FG_charge_tmp,pot1A_dipole_BEE_gr) res["E_pol1-env_B(E,E)_grnd"] = PolarMat_env_Beta_gr res["E_pol1-env_B(E,E)_exct"] = PolarMat_env_Beta_ex # Calculate secon order contribution to the first order quantities self.charge[index1] = gr_charge1 self.charge[index2] = ex_charge2 PolarMat2_Alpha_st_gr_ex, dumm, dumm, AllDipA2_st1_gr, AllDipA2_st2_ex = self._fill_Polar_matrix(index1,index2,typ='Alpha_st',order=2) PolarMat2_Alpha_st_gr_ex = PolarMat2_Alpha_st_gr_ex - PolarMat_Alpha_st_gr_ex self.charge[index1] = ex_charge1 self.charge[index2] = gr_charge2 PolarMat2_Alpha_st_ex_gr, dumm, dumm, AllDipA2_st1_ex, AllDipA2_st2_gr = self._fill_Polar_matrix(index1,index2,typ='Alpha_st',order=2) PolarMat2_Alpha_st_ex_gr = PolarMat2_Alpha_st_ex_gr - PolarMat_Alpha_st_ex_gr res["E_pol2_st_(A_exct,B_grnd)"] = PolarMat2_Alpha_st_ex_gr * conversion_facs_energy["1/cm"] res["E_pol2_st_(A_grnd,B_exct)"] = PolarMat2_Alpha_st_gr_ex * conversion_facs_energy["1/cm"] pot2A_dipole_st_gr = potential_dipole(AllDipA2_st1_gr - AllDipA_st1_gr,R) pot2A_dipole_st_ex = potential_dipole(AllDipA2_st1_ex - AllDipA_st1_ex,R) pot2B_dipole_st_gr = potential_dipole(AllDipA2_st2_gr - AllDipA_st2_gr,R) pot2B_dipole_st_ex = potential_dipole(AllDipA2_st2_ex - AllDipA_st2_ex,R) PolarMat2_env_st_ex = np.zeros((2,2),dtype="f8") PolarMat2_env_st_gr = np.zeros((2,2),dtype="f8") PolarMat2_env_st_ex[0,0] = np.dot(FG_charge_tmp,pot2A_dipole_st_ex) PolarMat2_env_st_ex[1,1] = np.dot(FG_charge_tmp,pot2B_dipole_st_ex) PolarMat2_env_st_gr[0,0] = np.dot(FG_charge_tmp,pot2B_dipole_st_gr) PolarMat2_env_st_gr[1,1] = np.dot(FG_charge_tmp,pot2A_dipole_st_gr) res["E_pol2-env_st_grnd"] = PolarMat2_env_st_gr * conversion_facs_energy["1/cm"] res["E_pol2-env_st_exct"] = PolarMat2_env_st_ex * conversion_facs_energy["1/cm"] # Calculate polarization matrixes A_grnd B_0->1 self.charge[index1] = tr_charge1 self.charge[index2] = np.zeros(len(index2),dtype='f8') self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index1] = np.zeros(len(index1),dtype='f8') E_AB_pol1_tr_gr_1 = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) E_A_pol1_tr_gr = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index1] = np.zeros(len(index1),dtype='f8') self.charge[index2] = tr_charge2 self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index2] = np.zeros(len(index2),dtype='f8') E_AB_pol1_gr_tr_1 = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) E_B_pol1_tr_gr = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) self.charge[index1] = gr_charge1 self.charge[index2] = np.zeros(len(index2),dtype='f8') self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index1] = np.zeros(len(index1),dtype='f8') E_AB_pol1_gr_tr_2 = self._get_interaction_energy(index2,charge=tr_charge2,debug=False) self.charge[index1] = np.zeros(len(index1),dtype='f8') self.charge[index2] = gr_charge2 self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index2] = np.zeros(len(index2),dtype='f8') E_AB_pol1_tr_gr_2 = self._get_interaction_energy(index1,charge=tr_charge1,debug=False) self.dipole = np.zeros((self.Nat,3),dtype='f8') # return transition charges back if (gr_charge1!=gr_charge2).any() : raise IOError("Heterodimer should have the same ground state charges") # return transition charges back if (tr_charge1!=tr_charge2).any() : raise IOError("Heterodimer should have the same transition charges") self.charge[index1] = gr_charge1 self.charge[index2] = tr_charge2 PolarMat_AlphaE_gr_tr, dip_AlphaE1_gr, dip_AlphaE2_tr, AllDipAE1_gr, AllDipAE2_tr = self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=1) E_AB_pol1_gr_tr = PolarMat_AlphaE_gr_tr[0,1] self.charge[index1] = tr_charge1 self.charge[index2] = gr_charge2 PolarMat_AlphaE_gr_tr, dip_AlphaE1_gr, dip_AlphaE2_tr, AllDipAE1_gr, AllDipAE2_tr = self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=1) E_AB_pol1_tr_gr = PolarMat_AlphaE_gr_tr[0,1] res["E_pol1_B(E,E)_(tr_gr,ex)"] = np.zeros((2,2),dtype="f8") self.charge[index1] = tr_charge1 self.charge[index2] = np.zeros(len(index2),dtype='f8') self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) self.charge[index1] = np.zeros(len(index1),dtype='f8') res["E_pol1_B(E,E)_(tr_gr,ex)"][0,0] = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) res["E_pol1_B(E,E)_(tr_gr,ex)"][0,1] = self._get_interaction_energy(index1,charge=ex_charge1,debug=False) self.charge[index1] = np.zeros(len(index2),dtype='f8') self.charge[index2] = tr_charge2 self.dipole = np.zeros((self.Nat,3),dtype='f8') self._calc_dipoles_All('BetaEE',NN=1,eps=1,debug=False) self.charge[index2] = np.zeros(len(index2),dtype='f8') res["E_pol1_B(E,E)_(tr_gr,ex)"][1,0] = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) res["E_pol1_B(E,E)_(tr_gr,ex)"][1,1] = self._get_interaction_energy(index2,charge=ex_charge2,debug=False) # return transition charges back self.charge[index1] = tr_charge1 self.charge[index2] = tr_charge2 # compare electrostatic energies - TEST VAB_0101 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = ex_charge1 VAB_1101 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = gr_charge1 VAB_0001 = self.get_TrEsp_Eng(index1, index2) self.charge[index2] = gr_charge2 VAB_0000 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = ex_charge1 self.charge[index2] = ex_charge2 VAB_1111 = self.get_TrEsp_Eng(index1, index2) self.charge[index2] = gr_charge2 VAB_1100 = self.get_TrEsp_Eng(index1, index2) charge_orig1 = FG_charge.charge[index1] charge_orig2 = FG_charge.charge[index2] FG_charge.charge[index1] = gr_charge1 FG_charge.charge[index2] = 0.0 E_grnd=FG_charge.get_EnergyShift() FG_charge.charge[index1] = ex_charge1 FG_charge.charge[index2] = 0.0 E_exct=FG_charge.get_EnergyShift() FG_charge.charge[index1] = tr_charge1 FG_charge.charge[index2] = 0.0 E_trans=FG_charge.get_EnergyShift() FG_charge.charge[index1] = charge_orig1 FG_charge.charge[index2] = charge_orig2 self.charge[index1] = tr_charge1 self.charge[index2] = tr_charge2 # calculate new eigenstates and energies HH=np.zeros((2,2),dtype='f8') if Eng1<Eng2: HH[0,0] = Eng1+dAVA HH[1,1] = Eng2+dBVB else: HH[1,1] = Eng1+dAVA HH[0,0] = Eng2+dBVB HH[0,1] = E_TrEsp HH[1,0] = HH[0,1] Energy,Coeff=np.linalg.eigh(HH) d_esp=np.sqrt( E_TrEsp**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # sqrt( (<A|V|B>)**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # Calculate interaction energies if approx==1.1: # Calculate Total polarizability matrix PolarMat = PolarMat_AlphaE + PolarMat_Alpha_E + PolarMat_Alpha_st + PolarMat_Beta*(dAVA/2 + dBVB/2 - self.VinterFG) # Calculate interaction energies C1 = Coeff.T[0] E1 = Energy[0] + np.dot(C1, np.dot(PolarMat - d_esp*PolarMat_Beta, C1.T)) C2 = Coeff.T[1] E2 = Energy[1] + np.dot(C2, np.dot(PolarMat + d_esp*PolarMat_Beta, C2.T)) J_inter = np.sqrt( (E2 - E1)**2 - (Eng2 - Eng1)**2 )/2*np.sign(E_TrEsp) # Calculate energy shifts for every defect Eshift1 = dAVA + PolarMat_AlphaE[0,0] - PolarMat_Alpha_E[1,1] Eshift1 -= (self.VinterFG - dAVA)*PolarMat_Beta[0,0] Eshift2 = dBVB + PolarMat_AlphaE[1,1] - PolarMat_Alpha_E[0,0] Eshift2 -= (self.VinterFG - dBVB)*PolarMat_Beta[1,1] # Calculate transition dipoles for every defect TrDip1 = np.dot(self.charge[index1],self.coor[index1,:]) # vacuum transition dipole for single defect TrDip1 = TrDip1*(1 + PolarMat_Beta[0,0]/4) + dip_AlphaE1 + dip_Alpha_E1 TrDip1 -= (self.VinterFG - dAVA)*dip_Beta1 TrDip2 = np.dot(self.charge[index2],self.coor[index2,:]) # vacuum transition dipole for single defect TrDip2 = TrDip2*(1 + PolarMat_Beta[1,1]/4) + dip_AlphaE2 + dip_Alpha_E2 TrDip2 -= (self.VinterFG - dBVB)*dip_Beta2 # Change to energy class with energy_units('AU'): J_inter = EnergyClass(J_inter) Eshift1 = EnergyClass(Eshift1) Eshift2 = EnergyClass(Eshift2) E_pol_static1_ex_gr = EnergyClass(PolarMat_Alpha_st_ex_gr[0,0]-PolarMat_Alpha_st_gr_ex[0,0]) E_pol_static2_ex_gr = EnergyClass(PolarMat_Alpha_st_gr_ex[1,1]-PolarMat_Alpha_st_ex_gr[1,1]) E_pol_env_static1_ex_gr = EnergyClass(E_Pol1_env_static_ex1_FG - E_Pol1_env_static_gr1_FG) E_pol_env_static2_ex_gr = EnergyClass(E_Pol1_env_static_ex2_FG - E_Pol1_env_static_gr2_FG) VAB_0101 = EnergyClass(VAB_0101) VAB_1101 = EnergyClass(VAB_1101) VAB_0001 = EnergyClass(VAB_0001) VAB_0000 = EnergyClass(VAB_0000) VAB_1111 = EnergyClass(VAB_1111) VAB_1100 = EnergyClass(VAB_1100) E_grnd = EnergyClass(E_grnd) E_exct = EnergyClass(E_exct) E_trans = EnergyClass(E_trans) E_AB_pol1_gr_tr = EnergyClass(E_AB_pol1_gr_tr) E_AB_pol1_tr_gr = EnergyClass(E_AB_pol1_tr_gr) E_AB_pol1_gr_tr_1 = EnergyClass(E_AB_pol1_gr_tr_1) E_AB_pol1_tr_gr_1 = EnergyClass(E_AB_pol1_tr_gr_1) E_AB_pol1_gr_tr_2 = EnergyClass(E_AB_pol1_gr_tr_2) E_AB_pol1_tr_gr_2 = EnergyClass(E_AB_pol1_tr_gr_2) E_A_pol1_tr_gr = EnergyClass(E_A_pol1_tr_gr) E_B_pol1_tr_gr = EnergyClass(E_B_pol1_tr_gr) with energy_units("1/cm"): print("EA_pol1_s_ex_gr EA_pol1_env_s_ex_gr EAB_pol1_tr_gr EA_pol1_tr_gr") print(" {:9.4f} {:9.4f} {:9.4f} {:9.4f}".format( E_pol_static1_ex_gr.value, E_pol_env_static1_ex_gr.value, E_AB_pol1_tr_gr.value, E_A_pol1_tr_gr.value)) print(" VAB_0101 VAB_1101 VAB_0001 VAB_0000 VAB_1111 VAB_1100 E_grnd E_exct E_trans") print(VAB_0101.value, VAB_1101.value, VAB_0001.value, VAB_0000.value, VAB_1111.value, VAB_1100.value, E_grnd.value, E_exct.value, E_trans.value) # res["E_pol2_A(E)"] # res["E_pol2_A(-E)"] # res["E_pol2_B(E,E)"] # res["E_pol1_B(E,E)_(A_exct,B_grnd)"] # res["E_pol1_B(E,E)_(A_grnd,B_exct)"] # res["E_pol1-env_B(E,E)_grnd"] # res["E_pol1-env_B(E,E)_exct"] # res["E_pol2_st_(A_exct,B_grnd)"] # res["E_pol2_st_(A_grnd,B_exct)"] # res["E_pol2-env_st_grnd"] # res["E_pol2-env_st_exct"] return J_inter, Eshift1, Eshift2, TrDip1, TrDip2, AllDipAE1, AllDipA_E1, AllDipBE1, res else: raise IOError('Unsupported approximation') def get_HeterodimerProperties_new(self, gr_charge1, ex_charge1, gr_charge2, ex_charge2, FG_elstat, struc, index1, index2, Eng1, Eng2, eps, dAVA=0.0, dBVB=0.0, order=2, approx=1.1): ''' Calculate effects of the environment for structure with two different defects such as interaction energy, site transition energy shifts and changes in transition dipoles Parameters ---------- index1 : list of integer (dimension Natoms_defect1) Indexes of all atoms from the first defect (starting from 0) index2 : list of integer (dimension Natoms_defect2) Indexes of all atoms from the second defect (starting from 0) Eng1 : float Vacuum transition energy of the first defect in ATOMIC UNITS (Hartree) Eng2 : float Vacuum transition energy of the second defect in ATOMIC UNITS (Hartree) dAVA : float **dAVA = <A|V|A> - <G|V|G>** Difference in electrostatic interaction energy between first defect the and environment for the defect in excited state <A|V|A> and in ground state <G|V|G>. dBVB : float **dBVB = <B|V|B> - <G|V|G>** Difference in electrostatic interaction energy between second defect and the environment for the defect in excited state <B|V|B> and in ground state <G|V|G>. order : integer (optional - init = 80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns ------- J_inter : Energy class Interaction energy with effects of environment included. Units are energy managed Eshift1 : Energy class Transition energy shift for the first defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed Eshift2 : Energy class Transition energy shift for the second defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed TrDip1 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) TrDip2 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) AllDipAE : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Alpha(E) atomic polarizability AllDipA_E : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Alpha(-E) atomic polarizability AllDipBE : numpy array of float (dimension Natoms x 3) Induced atomic dipole moments for all atoms in the environment by the first defect with Beta(E,E) atomic polarizability ''' # eps = EnergyClass res = {} # get transition charge tr_charge1 = self.charge[index1] tr_charge2 = self.charge[index2] # Get fluorographene charges charge_orig1 = FG_elstat.charge[index1] charge_orig2 = FG_elstat.charge[index2] FG_charge = FG_elstat.charge.copy() FG_charge[index1] = 0.0 FG_charge[index2] = 0.0 # Set distance matrix for interaction of defects with the environmnet R_elst = np.tile(struc.coor._value,(self.Nat,1,1)) R_pol = np.tile(self.coor,(struc.nat,1,1)) R = (R_elst - np.swapaxes(R_pol,0,1)) # R[ii,jj,:]=self.coor[jj]-self.coor[ii] # if normaly ordered first are carbon atoms and then are fluorine atoms - for carbon atoms same indexes in pol_mol as in struc # TODO: Maybe also exclude connected fluorinesto atoms ii for ii in range(self.Nat): R[ii,ii,:] = 0.0 # self interaction is not permited in potential calculation # Get vaccuum interaction energies (V0100 != V0001) - ground state electron density symmetric to inversion and transition density antisymmetric to inversion - change of sign for some cases E_TrEsp = self.get_TrEsp_Eng(index1, index2) VAB_0101 = E_TrEsp self.charge[index1] = ex_charge1 VAB_1101 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = gr_charge1 VAB_0001 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = tr_charge1 self.charge[index2] = ex_charge2 VAB_0111 = self.get_TrEsp_Eng(index1, index2) self.charge[index2] = gr_charge2 VAB_0100 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = gr_charge1 VAB_0000 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = ex_charge1 VAB_1100 = self.get_TrEsp_Eng(index1, index2) self.charge[index2] = ex_charge2 VAB_1111 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = gr_charge1 VAB_0011 = self.get_TrEsp_Eng(index1, index2) self.charge[index1] = tr_charge1 self.charge[index2] = tr_charge2 # get electroctatic interaction energy of defects with environment FG_elstat.charge[index1] = gr_charge1 FG_elstat.charge[index2] = np.zeros(len(index2),dtype='f8') EA_grnd=FG_elstat.get_EnergyShift() FG_elstat.charge[index1] = ex_charge1 EA_exct=FG_elstat.get_EnergyShift() FG_elstat.charge[index1] = tr_charge1 EA_trans=FG_elstat.get_EnergyShift() FG_elstat.charge[index1] = np.zeros(len(index1),dtype='f8') FG_elstat.charge[index2] = gr_charge2 EB_grnd=FG_elstat.get_EnergyShift() FG_elstat.charge[index2] = ex_charge2 EB_exct=FG_elstat.get_EnergyShift() FG_elstat.charge[index2] = tr_charge2 EB_trans=FG_elstat.get_EnergyShift() # Calculate polarization matrixes for the second order contributions PolarMat_AlphaE, dip_AlphaE1, dip_AlphaE2, AllDipAE1, AllDipAE2 = self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=order) PolarMat_Alpha_E, dip_Alpha_E1, dip_Alpha_E2, AllDipA_E1, AllDipA_E2 = self._fill_Polar_matrix(index1,index2,typ='Alpha_E',order=order) self.charge[index1] = gr_charge1 self.charge[index2] = ex_charge2 PolarMat_Alpha_st_gr_ex, dip_Alpha_st1_gr, dip_Alpha_st2_ex, AllDipA_st1_gr, AllDipA_st2_ex = self._fill_Polar_matrix(index1,index2,typ='Alpha_st',order=order) self.charge[index1] = ex_charge1 self.charge[index2] = gr_charge2 PolarMat_Alpha_st_ex_gr, dip_Alpha_st1_ex, dip_Alpha_st2_gr, AllDipA_st1_ex, AllDipA_st2_gr = self._fill_Polar_matrix(index1,index2,typ='Alpha_st',order=order) res["E_pol2_A(E)"] = PolarMat_AlphaE res["E_pol2_A(-E)"] = PolarMat_Alpha_E PolarMat_Alpha_st = np.zeros((2,2),dtype='f8') PolarMat_Alpha_st[0,0] = np.sum(PolarMat_Alpha_st_ex_gr) # PolarMat_Alpha_st_ex_gr[0,0] + PolarMat_Alpha_st_ex_gr[1,1] + 2*PolarMat_Alpha_st_ex_gr[0,1] PolarMat_Alpha_st[1,1] = np.sum(PolarMat_Alpha_st_gr_ex) # PolarMat_Alpha_st_gr_ex[0,0] + PolarMat_Alpha_st_gr_ex[1,1] + 2*PolarMat_Alpha_st_gr_ex[0,1] # Add Alpha static pol-env contribution pot2_A_dipole_Alpha_st_gr = potential_dipole(AllDipA_st1_gr,R) pot2_A_dipole_Alpha_st_ex = potential_dipole(AllDipA_st1_ex,R) pot2_B_dipole_Alpha_st_gr = potential_dipole(AllDipA_st2_gr,R) pot2_B_dipole_Alpha_st_ex = potential_dipole(AllDipA_st2_ex,R) EA_Pol2_env_static_gr_FG = np.dot(FG_charge,pot2_A_dipole_Alpha_st_gr) EA_Pol2_env_static_ex_FG = np.dot(FG_charge,pot2_A_dipole_Alpha_st_ex) EB_Pol2_env_static_gr_FG = np.dot(FG_charge,pot2_B_dipole_Alpha_st_gr) EB_Pol2_env_static_ex_FG = np.dot(FG_charge,pot2_B_dipole_Alpha_st_ex) PolarMat_Alpha_st[0,0] = 2*( EA_Pol2_env_static_ex_FG + EB_Pol2_env_static_gr_FG ) PolarMat_Alpha_st[1,1] = 2*( EA_Pol2_env_static_gr_FG + EB_Pol2_env_static_ex_FG ) res["E_pol2_A_static"] = PolarMat_Alpha_st # first order electrostatic contribution ElstatMat_1 = np.zeros((2,2), dtype='f8') ElstatMat_1[0,0] = (EA_trans + VAB_0100)**2 - (EB_trans + VAB_1101)**2 ElstatMat_1[1,1] = (EB_trans + VAB_0001)**2 - (EA_trans + VAB_0111)**2 ElstatMat_1[0,1] = (EA_trans + VAB_0100)*(EB_trans + VAB_0001) - (EA_trans + VAB_0111)*(EB_trans + VAB_1101) ElstatMat_1[1,0] = ElstatMat_1[0,1] ElstatMat_1 = ElstatMat_1/eps._value res['E_elstat_1'] = ElstatMat_1 # TODO: This electrostatic contribution should be small print and see if it could be neglected # calculate polarization matrixes for contriutions containing only first order polarizabilities PolarMat_Beta, dip_Beta1, dip_Beta2, AllDipBE1, AllDipBE2 = self._fill_Polar_matrix(index1,index2,typ='BetaEE',order=order//2) PolarMat_Beta_scaled = ( (VAB_1100 - VAB_0000 + VAB_0011 - VAB_0000 + EA_exct - EA_grnd + EB_exct - EB_grnd)/2 - self.VinterFG)*PolarMat_Beta res["E_pol2_B(E,E)"] = PolarMat_Beta res["E_pol2_B(E,E)_scaled"] = PolarMat_Beta_scaled # TODO: Calculate and check contribution from d_epsilon # calculate contribution from 0-1 ground interaction with alpha(E) polarizability self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index1] = tr_charge1 self.charge[index2] = np.zeros(len(index2),dtype='f8') self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index1] = np.zeros(len(index1),dtype='f8') E_AB_pol1_tr_gr = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) E_A_pol1_tr_gr = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) Potential = potential_dipole(self.dipole,R) E_A_pol1_env_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index1] = np.zeros(len(index1),dtype='f8') self.charge[index2] = tr_charge2 self._calc_dipoles_All('AlphaE',NN=1,eps=1,debug=False) self.charge[index2] = np.zeros(len(index1),dtype='f8') E_AB_pol1_gr_tr = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) E_B_pol1_tr_gr = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) Potential = potential_dipole(self.dipole,R) E_B_pol1_env_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') PolarMat_Alpha_tr_gr = np.zeros((2,2),dtype='f8') PolarMat_Alpha_tr_gr[0,0] = E_A_pol1_tr_gr + E_AB_pol1_tr_gr + E_A_pol1_env_tr PolarMat_Alpha_tr_gr[0,1] = E_B_pol1_tr_gr + E_AB_pol1_gr_tr + E_B_pol1_env_tr PolarMat_Alpha_tr_gr[1,0] = PolarMat_Alpha_tr_gr[0,0] PolarMat_Alpha_tr_gr[1,1] = PolarMat_Alpha_tr_gr[0,1] PolarMat_Alpha_tr_gr[0,:] = PolarMat_Alpha_tr_gr[0,:]*( EA_trans + VAB_0100 )/eps._value PolarMat_Alpha_tr_gr[1,:] = PolarMat_Alpha_tr_gr[1,:]*( EB_trans + VAB_0001 )/eps._value res["E_pol2_A(E)_(trans,grnd)"] = PolarMat_Alpha_tr_gr # calculate contribution from 0-1 ground and 0-1 excited interaction with alpha_static polarizability self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index1] = tr_charge1 self.charge[index2] = np.zeros(len(index2),dtype='f8') self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) self.charge[index1] = np.zeros(len(index1),dtype='f8') E_AB_st_pol1_tr_gr = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) E_AB_st_pol1_tr_ex = self._get_interaction_energy(index2,charge=ex_charge2,debug=False) E_A_st_pol1_tr_gr = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) E_A_st_pol1_tr_ex = self._get_interaction_energy(index1,charge=ex_charge1,debug=False) Potential = potential_dipole(self.dipole,R) E_A_st_pol1_env_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') self.charge[index1] = np.zeros(len(index1),dtype='f8') self.charge[index2] = tr_charge2 self._calc_dipoles_All('Alpha_st',NN=1,eps=1,debug=False) self.charge[index2] = np.zeros(len(index1),dtype='f8') E_AB_st_pol1_gr_tr = self._get_interaction_energy(index1,charge=gr_charge1,debug=False) E_AB_st_pol1_ex_tr = self._get_interaction_energy(index1,charge=ex_charge1,debug=False) E_B_st_pol1_tr_gr = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) E_B_st_pol1_tr_ex = self._get_interaction_energy(index2,charge=gr_charge2,debug=False) Potential = potential_dipole(self.dipole,R) E_B_st_pol1_env_tr = np.dot(FG_charge,Potential) self.dipole = np.zeros((self.Nat,3),dtype='f8') PolarMat_static_tr_gr_ex = np.zeros((2,2),dtype='f8') PolarMat_static_tr_gr_ex[0,0] = (EA_trans + VAB_0100)/eps._value * (E_A_st_pol1_tr_ex + E_AB_st_pol1_tr_gr + E_A_st_pol1_env_tr) PolarMat_static_tr_gr_ex[1,0] = (EB_trans + VAB_0001)/eps._value * (E_A_st_pol1_tr_ex + E_AB_st_pol1_tr_gr + E_A_st_pol1_env_tr) PolarMat_static_tr_gr_ex[0,1] = (EA_trans + VAB_0100)/eps._value * (E_B_st_pol1_tr_ex + E_AB_st_pol1_gr_tr + E_B_st_pol1_env_tr) PolarMat_static_tr_gr_ex[1,1] = (EB_trans + VAB_0001)/eps._value * (E_B_st_pol1_tr_ex + E_AB_st_pol1_gr_tr + E_B_st_pol1_env_tr) PolarMat_static_tr_gr_ex[0,0] -= (EB_trans + VAB_1101)/eps._value * (E_AB_st_pol1_ex_tr + E_B_st_pol1_tr_gr + E_B_st_pol1_env_tr) PolarMat_static_tr_gr_ex[0,1] -= (EA_trans + VAB_0111)/eps._value * (E_AB_st_pol1_ex_tr + E_B_st_pol1_tr_gr + E_B_st_pol1_env_tr) PolarMat_static_tr_gr_ex[1,0] -= (EB_trans + VAB_1101)/eps._value * (E_AB_st_pol1_tr_ex + E_A_st_pol1_tr_gr + E_A_st_pol1_env_tr) PolarMat_static_tr_gr_ex[1,1] -= (EA_trans + VAB_0111)/eps._value * (E_AB_st_pol1_tr_ex + E_A_st_pol1_tr_gr + E_A_st_pol1_env_tr) res["E_pol1_A_static"] = PolarMat_static_tr_gr_ex # return charges to original values FG_elstat.charge[index1] = charge_orig1 FG_elstat.charge[index2] = charge_orig2 self.charge[index1] = tr_charge1 self.charge[index2] = tr_charge2 # calculate new eigenstates and energies HH=np.zeros((2,2),dtype='f8') if Eng1<Eng2: HH[0,0] = Eng1+dAVA HH[1,1] = Eng2+dBVB else: HH[1,1] = Eng1+dAVA HH[0,0] = Eng2+dBVB HH[0,1] = E_TrEsp HH[1,0] = HH[0,1] Energy,Coeff=np.linalg.eigh(HH) d_esp=np.sqrt( E_TrEsp**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # sqrt( (<A|V|B>)**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # Calculate interaction energies if approx==1.1: # Calculate Total polarizability matrix PolarMat = PolarMat_AlphaE + PolarMat_Alpha_E + PolarMat_Alpha_st PolarMat += PolarMat_Beta_scaled + ElstatMat_1 + 2*PolarMat_Alpha_tr_gr PolarMat += 2*PolarMat_static_tr_gr_ex # Calculate interaction energies C1 = Coeff.T[0] E1 = Energy[0] + np.dot(C1, np.dot(PolarMat - d_esp*PolarMat_Beta, C1.T)) C2 = Coeff.T[1] E2 = Energy[1] + np.dot(C2, np.dot(PolarMat + d_esp*PolarMat_Beta, C2.T)) J_inter = np.sqrt( (E2 - E1)**2 - (Eng2 - Eng1)**2 )/2*np.sign(E_TrEsp) # Calculate energy shifts for every defect Eshift1 = dAVA + PolarMat_AlphaE[0,0] - PolarMat_Alpha_E[1,1] Eshift1 -= (self.VinterFG - dAVA)*PolarMat_Beta[0,0] Eshift2 = dBVB + PolarMat_AlphaE[1,1] - PolarMat_Alpha_E[0,0] Eshift2 -= (self.VinterFG - dBVB)*PolarMat_Beta[1,1] # Calculate transition dipoles for every defect TrDip1 = np.dot(self.charge[index1],self.coor[index1,:]) # vacuum transition dipole for single defect TrDip1 = TrDip1*(1 + PolarMat_Beta[0,0]/4) + dip_AlphaE1 + dip_Alpha_E1 TrDip1 -= (self.VinterFG - dAVA)*dip_Beta1 TrDip2 = np.dot(self.charge[index2],self.coor[index2,:]) # vacuum transition dipole for single defect TrDip2 = TrDip2*(1 + PolarMat_Beta[1,1]/4) + dip_AlphaE2 + dip_Alpha_E2 TrDip2 -= (self.VinterFG - dBVB)*dip_Beta2 # Change to energy class with energy_units('AU'): J_inter = EnergyClass(J_inter) Eshift1 = EnergyClass(Eshift1) Eshift2 = EnergyClass(Eshift2) res["E_pol2_A(E)"] = EnergyClass(res["E_pol2_A(E)"]) res["E_pol2_A(-E)"] = EnergyClass(res["E_pol2_A(-E)"]) res["E_pol2_A_static"] = EnergyClass(res["E_pol2_A_static"]) res["E_pol2_B(E,E)_scaled"] = EnergyClass(res["E_pol2_B(E,E)_scaled"]) res["E_pol2_A(E)_(trans,grnd)"] = EnergyClass(res["E_pol2_A(E)_(trans,grnd)"]) res["E_pol1_A_static"] = EnergyClass(res["E_pol1_A_static"]) res["E_elstat_1"] = EnergyClass(res["E_elstat_1"]) res["E_pol2_B(E,E)"] = EnergyClass(res["E_pol2_B(E,E)"]) # with energy_units("1/cm"): # print("EA_pol1_s_ex_gr EA_pol1_env_s_ex_gr EAB_pol1_tr_gr EA_pol1_tr_gr") # print(" {:9.4f} {:9.4f} {:9.4f} {:9.4f}".format( # E_pol_static1_ex_gr.value, # E_pol_env_static1_ex_gr.value, # E_AB_pol1_tr_gr.value, # E_A_pol1_tr_gr.value)) # print(" VAB_0101 VAB_1101 VAB_0001 VAB_0000 VAB_1111 VAB_1100 E_grnd E_exct E_trans") # print(VAB_0101.value, VAB_1101.value, VAB_0001.value, VAB_0000.value, VAB_1111.value, VAB_1100.value, E_grnd.value, E_exct.value, E_trans.value) # res["E_pol2_A(E)"] = PolarMat_AlphaE # res["E_pol2_A(-E)"] = PolarMat_Alpha_E # res["E_pol2_A_static"] = PolarMat_Alpha_st # res["E_pol2_B(E,E)"] = PolarMat_Beta # res["E_pol2_B(E,E)_scaled"] = PolarMat_Beta_scaled # res["E_pol2_A(E)_(trans,grnd)"] = PolarMat_Alpha_tr_gr # res["E_pol1_A_static"] = PolarMat_static_tr_gr_ex # res["E_elstat_1"] = ElstatMat_1 return J_inter, Eshift1, Eshift2, TrDip1, TrDip2, AllDipAE1, AllDipA_E1, AllDipBE1, res else: raise IOError('Unsupported approximation') def get_gmm(self,gr_charge, ex_charge, FG_elstat, struc, index, E01, int2cart, freq, red_mass, order=2, approx=1.1, CoarseGrain='C'): """ Calculate coupling strength of the site energy to atomic coordinates. The reult is dimensionless coupling strength and resulting spectral density is defined as \sum_xi {gmm_xi*gmm_xi*\delta(omega-omega_xi)} Parameters ---------- gr_charge : numpy array of real (dimension Natoms_defect) Ground state ESP charges for every atom from the defect ex_charge : numpy array of real (dimension Natoms_defect) Excited state ESP charges for every atom from the defect FG_elstat : Electrostatics class Electrostatic definition of the system (atomic charges, positions, ...). It is possible to use it for calculation of electrostatic interaction energy between defect and environment struc : Structure class Structure definition of the molecule (needed for calculation of derivative of the hamiltonian with respect to atomic coordinates). index : list of integer (dimension Natoms_defect) Indexes of all atoms from the defect (starting from 0) E01 : Energy class Transition energy of isolated defect without environment (calculated by quantum chemistry). Needed for calculation of derivative of hamiltonian with respect to atomic coordinates int2cart : numpy array of real (dimension 3*Nat x Nnormal_modes) transformation matrix from internal to cartesian coordinates. In columns there are normalized normal mode vectors in cartesian coordinates ordered as [dx1,dy1,dz1,dx2,dy2,dz2,dx3,...]. Norm of the whole vector is 1.0 and it is dimensionless freq : numpy array of real (dimension Nnormal_modes) Wavenumbers of individual normal modes (frequency/speed of light - default output from gaussian and AMBER - in both called frequency) in inverse centimeters red_mass : numpy array of real (dimension Nnormal_modes) Reduced masses for every normal mode in AMU (atomic mass units) order : integer (optional - init = 2) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 CoarseGrain : string (optional - init = "C") Possible values are: "plane","C","CF" and "all_atom". Define which level of coarse grained model should be used. If ``CoarseGrain="plane"`` then all atoms are projected on plane defined by nvec and C-F atoms are treated as single atom - for this case polarizabilities defined only in 2D by two numbers. If ``CoarseGrain="C"`` then carbon atoms are center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="CF"`` then center of C-F bonds are used as center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="all_atom"`` all atoms are used as centers polarizability tensor. approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition """ dR_Hmm,dR_env_Hmm = self.get_SingleDefect_derivation(gr_charge, ex_charge, FG_elstat, struc, index, E01, order=2, approx=1.1) # freq is actualy wavenumber (= frequency/speed_of_light). # therefore angluar frequency omega in atomic units = 100/(2*Rydberg_inf) wavenumber in [cm-1] omega_au = freq/conversion_facs_energy["1/cm"] RedMass_au = red_mass/conversion_facs_mass # in atomic units hbar = 1.0, m_e = 1.0, elementary_charge = 1.0, 1/(4*pi*eps_0) = 1.0, speed_of_light = 137 ( fine-structure constant) # pick only carbon atoms from eigenvectors of normal modes (assume that fluorine atoms doesn't influent the result) - in needed if CoarseGrain in ["C","plane"] : indxC = np.where(np.array(struc.at_type) == 'C') index = np.zeros((len(indxC),3),dtype='i8') for ii in range(3): index[:,ii] = index*3+ii index.reshape(3*len(indxC)) int2cart_loc = int2cart[index,:] else: int2cart_loc = int2cart.copy() g_mm = np.dot(int2cart_loc.T,dR_Hmm) + np.dot(int2cart.T,dR_env_Hmm) g_mm = g_mm/(np.sqrt(omega_au*omega_au*omega_au)) g_mm = g_mm/(2*np.sqrt(RedMass_au)) return g_mm # ============================================================================= # OLD AND NOT USED FUNCTION - WILL BE DELETED IN FUTURE # ============================================================================= # def get_selfinteraction_energy(self,debug=False): # ''' Calculates interaction energy between induced dipoles by chromophore # transition charges and transition charges of the same chromophore # # Returns # ------- # InterE : real # Interaction energies in atomic units (Hartree) multiplied by (-1) # correspond to Electric_field_of_TrCharges.Induced_dipole # # Notes # ------- # **By definition it is not an interaction energy but interaction energy # with opposite sign** # # # ''' # # # # coppy charges and assign zero charges to those in index # charge=[] # charge_coor=[] # dipole=[] # dipole_coor=[] # for ii in range(self.Nat): # if self.charge[ii]!=0.0: # charge.append(self.charge[ii]) # charge_coor.append(self.coor[ii]) # elif self.dipole[ii,0]!=0.0 or self.dipole[ii,1]!=0.0 or self.dipole[ii,2]!=0.0: # dipole.append(self.dipole[ii]) # dipole_coor.append(self.coor[ii]) # # charge=np.array(charge,dtype='f8') # charge_coor=np.array(charge_coor,dtype='f8') # dipole=np.array(dipole,dtype='f8') # dipole_coor=np.array(dipole_coor,dtype='f8') # if debug: # print('Charges:') # print(charge) # print('Dipoles self-inter:') # print(dipole) # # if debug: # print('Charge coordinates') # print(charge_coor.shape) # print(charge_coor) # print('Charges:') # print(charge) # # if not charge.any(): # return 0.0 # If all charges are zero interaction is also zero # if not dipole.any(): # print("All induced dipoles are zero - check if you calculating everything correctly") # return 0.0 # If all dipoles are zero interaction is zero # # rr = np.tile(dipole_coor,(charge_coor.shape[0],1,1)) # rr = np.swapaxes(rr,0,1) # dipole coordinate # R = np.tile(charge_coor,(dipole_coor.shape[0],1,1)) # charge coordinate # R = R-rr # R[ii,jj,:]=charge_coor[jj]-dipole_coor[ii] # ## TODO: There is no posibility to have charge and dipole on same atom (correct this) - so far no possibility to have zero R # pot_dipole = potential_dipole(dipole, R) # InterE = -np.dot(charge, pot_dipole) # # if debug: # #calculate interaction energy # InterE2=0.0 # for jj in range(len(charge)): # potential=0.0 # for ii in range(len(dipole)): # R=charge_coor[jj]-dipole_coor[ii] # potential+=potential_dipole(dipole[ii],R) # InterE2-=potential*charge[jj] # # minus is here because we dont want to calculate interaction energy # # but interaction of electric field of transition charges with induced # # dipoles and this is exactly - interaction energy between transition # # charge and dipole # # if np.allclose(InterE,InterE2): # print('Selfinteraction energy is calculated correctly') # else: # raise Warning('Selfinteraction energy for both methods is different') # # return InterE # # def get_InteractionEng(self, index1, index2, Eng1, Eng2, dAVA=0.0, dBVB=0.0, order=80, approx=1.1): # ''' # # dAVA = <A|V|A> - <G|V|G> # dBVB = <B|V|B> - <G|V|G> # ''' # # # Get TrEsp interaction energy # E_TrEsp = self.get_TrEsp_Eng(index1, index2) # # # calculate new eigenstates and energies # HH=np.zeros((2,2),dtype='f8') # if Eng1<Eng2: # HH[0,0] = Eng1+dAVA # HH[1,1] = Eng2+dBVB # else: # HH[1,1] = Eng1+dAVA # HH[0,0] = Eng2+dBVB # HH[0,1] = E_TrEsp # HH[1,0] = HH[0,1] # Energy,Coeff=np.linalg.eigh(HH) # # d_esp=np.sqrt( E_TrEsp**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # sqrt( (<A|V|B>)**2 + ((Eng2-Eng1+dBVB-dAVA)/2)**2 ) # # # PolarMat=np.zeros((2,2),dtype='f8') # if approx==1.1: # # Fill polarization matrix # PolarMat += self._fill_Polar_matrix(index1,index2,typ='AlphaE',order=order) # PolarMat += self._fill_Polar_matrix(index1,index2,typ='Alpha_E',order=order) # BetaMat = self._fill_Polar_matrix(index1,index2,typ='BetaEE',order=order//2) # PolarMat += BetaMat*(dAVA/2 + dBVB/2 - self.VinterFG) # # # Calculate interaction energies # C1 = Coeff.T[0] # E1 = Energy[0] + np.dot(C1, np.dot(PolarMat - d_esp*BetaMat, C1.T)) # C2 = Coeff.T[1] # E2 = Energy[1] + np.dot(C2, np.dot(PolarMat + d_esp*BetaMat, C2.T)) # # J_inter = np.sqrt( (E2 - E1)**2 - (Eng2 - Eng1)**2 )/2*np.sign(E_TrEsp) # # return J_inter # else: # raise IOError('Unsupported approximation') # # # def get_TrDip(self,*args,output_dipoles=False,order=80,approx=1.1): # ''' Function for calculation of transition dipole moment for chromophore # embeded in polarizable atom environment # # Parameters # ---------- # *args : real (optional) # Diference in electrostatic interaction energy between ground and # excited state in ATOMIC UNITS (DE). If not defined it is assumed to # be zero. DE=<A|V|A>-<G|V|G> # output_dipoles : logical (optional - init=False) # If atomic dipoles should be outputed or not. Atomic dipoles are # outputed as `AtDip_Alpha(E)+AtDip_Alpha(-E)-self.VinterFG*AtDip_Beta(E,E) # order : integer (optional - init=80) # Specify how many SCF steps shoudl be used in calculation of induced dipoles # approx : real (optional - init=1.2) # Specifies which approximation should be used. # # **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)`. # With this approximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.1.2**: Approximation 1.1 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.1 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. # With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.2.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # # **Approximation MMpol**: Dipole will be calculated as a original dipole # plus full polarization of the environmnet. # # Returns # ------- # dipole : numpy.array of real (dimension 3) # Transition dipole including the effects from interaction with environment # in ATOMIC UNITS (e*Bohr) # AtDipoles : numpy.array of real (dimension Natoms x 3) (optional) # Induced atomic dipoles defined as: # `AtDip_Alpha(E)+AtDip_Alpha(-E)-self.VinterFG*AtDip_Beta(E,E) # in ATOMIC UNITS (e*Bohr) # # **Neglecting `tilde{Beta(E)}` is not valid approximation. It shoudl be # better to neglect Beta(E,-E) to be consistent with approximation for # interaction energy** # # Notes # ---------- # dip = Alpha(E)*El_field_TrCharge + Alpha(-E)*El_field_TrCharge # Then final transition dipole of molecule with environment is calculated # according to the approximation: # # **Approximation 1.1:** # dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init(1-1/4*Ind_dip_Beta(E,E)*El_field_TrCharge) # **Approximation 1.1.2:** # dip_fin = dip - Vinter*Beta(E,E)*El_field_TrCharge + dip_init(1-1/4*Ind_dip_Beta(E,E)*El_field_TrCharge) # **Approximation 1.2:** # dip_fin = dip - (Vinter-DE)*Beta(E,E)*El_field_TrCharge + dip_init # **Approximation 1.2.2:** # dip_fin = dip - Vinter*Beta(E,E)*El_field_TrCharge + dip_init # **Approximation 1.3:** # dip_fin = dip - 2*Vinter*Beta(E,E)*El_field_TrCharge + dip_init # # ''' # # if approx==1.3: # if not np.array_equal(self.polar['AlphaE'],self.polar['Alpha_E']): # raise Warning('For calculation with Approximation 1.3 Alpha(E) should be equal Alpha(-E)') # # if approx==1.1: # if not np.array_equal(np.zeros((len(self.polar['Alpha_E']),3,3),dtype='f8'),self.polar['Alpha_E']): # print('For calculation with Approximation 1.1 Alpha(-E) should be equal to zero') # # is_elstat=False # if len(args)==1: # DE=args[0] # is_elstat=True # # use_alpha_instead_alphahalf=False # if type(approx)==str and order==2: # if 'MMpol' in approx: # use_alpha_instead_alphahalf=True # # # For MMpol approximation we have to use alpha instead alpha/2 and resulting induced dipoles # # have to be devided by 2. This way we correct the second term in perturbation expansion # if use_alpha_instead_alphahalf: # self.polar['AlphaE']=self.polar['AlphaE']*2 # self.polar['Alpha_E']=self.polar['Alpha_E']*2 # # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability AlphaE for rescaled charges # self._calc_dipoles_All('AlphaE',NN=order) # AtDipoles1=np.copy(self.dipole) # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # if not (approx=='MMpol' and order>2): # # if we calculate with MMpol procedure we use only one polarizability matrix and therefore doesn't have to be calculated # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability Alpha_E for rescaled charges # self._calc_dipoles_All('Alpha_E',NN=order) # AtDipoles2=np.copy(self.dipole) # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # if use_alpha_instead_alphahalf: # self.polar['AlphaE']=self.polar['AlphaE']/2 # self.polar['Alpha_E']=self.polar['Alpha_E']/2 # AtDipoles1=AtDipoles1/2 # AtDipoles2=AtDipoles2/2 # # # calculate induced dipoles with polarizability Beta for rescaled charges # #self._calc_dipoles_All('BetaEE',NN=order//2) # if order>2: # self._calc_dipoles_All('BetaEE',NN=1) # else: # self._calc_dipoles_All('BetaEE',NN=order//2) # AtDipolesBeta=np.copy(self.dipole) # # # calculate transition dipole: # dipole=np.zeros(3,dtype='f8') # for ii in range(self.Nat): # dipole+=self.coor[ii,:]*self.charge[ii] # dipole_tmp=np.copy(dipole) # dipole+=np.sum(AtDipoles1,axis=0) # if not (approx=='MMpol' and order>2): # dipole+=np.sum(AtDipoles2,axis=0) # # # term with Beta polarizability # if approx==1.1 or approx=='MMpol_1.1': # dipole-=self.VinterFG*np.sum(AtDipolesBeta,axis=0) - dipole_tmp*self.get_selfinteraction_energy()/4 # if is_elstat: # dipole+=DE*np.sum(AtDipolesBeta,axis=0) # if approx==1.2 or approx=='MMpol_1.2': # dipole-=self.VinterFG*np.sum(AtDipolesBeta,axis=0) # if is_elstat: # dipole+=DE*np.sum(AtDipolesBeta,axis=0) # elif approx==1.3 or approx=='MMpol_1.3': # dipole-=2*self.VinterFG*np.sum(AtDipolesBeta,axis=0) # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # if output_dipoles: # if approx=='MMpol' and order>2: # return dipole,AtDipoles1 # elif approx=='MMpol': # return dipole,AtDipoles1+AtDipoles2 # else: # return dipole,AtDipoles1+AtDipoles2-self.VinterFG*AtDipolesBeta # else: # return dipole # # # def calculate_EnergyShift(self,index,charge,*args,order=80,output_dipoles=False,approx=1.1): # ''' Function for calculation of transition energy shift for chromophore # embeded in polarizable atom environment # # Parameters # ---------- # **index and charge** : Not used (useful only for structure with more than one defect) # # *args : real (optional) # Diference in electrostatic interaction energy between ground and # excited state in ATOMIC UNITS (DE). If not defined it is assumed to # be zero. DE=<A|V|A>-<G|V|G> # order : integer (optional - init=80) # Specify how many SCF steps shoudl be used in calculation of induced dipoles # output_dipoles : logical (optional - init=False) # If atomic dipoles should be outputed or not. Atomic dipoles are # outputed as `AtDip_Alpha(E)+AtDip_Alpha(-E)-self.VinterFG*AtDip_Beta(E,E) # approx : real (optional - init=1.2) # Specifies which approximation should be used. # # **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)`. # With this approximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.1.2**: Approximation 1.1 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.1 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. # With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.2.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # # **Approximation MMpol**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # # Returns # ------- # Eshift : real # Excitation energy shift in ATOMIC UNITS (Hartree) caused by the # interaction of molecule with polarizable atom environment # AtDipoles : numpy.array of real (dimension Natoms x 3) (optional) # Induced atomic dipoles defined as: # `AtDip_Alpha(E)+AtDip_Alpha(-E)-self.VinterFG*AtDip_Beta(E,E)` # in ATOMIC UNITS (e*Bohr) # # **Neglecting `tilde{Beta(E)}` is not valid approximation. It should be # better to neglect Beta(E,-E) to be consistent with approximation for # interaction energy** # # Notes # ---------- # E = -Ind_dip_Alpha(E)*El_field_TrCharge + Ind_dip_Alpha(-E)*El_field_TrCharge # Then final energy shift E_fin of molecule embeded in environment is calculated # according to the approximation: # # *Approximation 1.1:** # Exactly the same as Approximation 1.2 # *Approximation 1.1.2:** # Exactly the same as Approximation 1.2.2 # **Approximation 1.2:** # E_fin = E + DE + (Vinter-DE)*Ind_dip_Beta(E,E)*El_field_TrCharge # **Approximation 1.2.2:** # E_fin = E + Vinter*Ind_dip_Beta(E,E)*El_field_TrCharge # **Approximation 1.3:** # E_fin = E + DE*(1-2*Ind_dip_Beta(E,E)*El_field_TrCharge) # # ''' # # if approx==1.3: # if not np.array_equal(self.polar['AlphaE'],self.polar['Alpha_E']): # raise Warning('For calculation with Approximation 1.3 Alpha(E) should be equal Alpha(-E)') # # if approx==1.1: # if not np.array_equal(np.zeros((len(self.polar['Alpha_E']),3,3),dtype='f8'),self.polar['Alpha_E']): # print('For calculation with Approximation 1.1 Alpha(-E) should be equal to zero') # # is_elstat=False # if len(args)==1: # DE=args[0] # is_elstat=True # # use_alpha_instead_alphahalf=False # if type(approx)==str and order==2: # if 'MMpol' in approx: # use_alpha_instead_alphahalf=True # # # For MMpol approximation we have to use alpha instead alpha/2 and resulting induced dipoles # # have to be devided by 2. This way we correct the second term in perturbation expansion # if use_alpha_instead_alphahalf: # self.polar['AlphaE']=self.polar['AlphaE']*2 # self.polar['Alpha_E']=self.polar['Alpha_E']*2 # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability AlphaE for rescaled charges # self._calc_dipoles_All('AlphaE',NN=order) # AtDipoles1=np.copy(self.dipole) # if use_alpha_instead_alphahalf: # AtDipoles1=AtDipoles1/2 # self.dipole=self.dipole/2 # #Einter=self._get_interaction_energy(index,charge=charge) ## TODO: Check if with using MMpol procedure it souldn't be 1/2 of selfinteraction energy # Eshift=-self.get_selfinteraction_energy() # # if not (approx=='MMpol' and order>2): # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability Alpha_E for rescaled charges # self._calc_dipoles_All('Alpha_E',NN=order) # AtDipoles2=np.copy(self.dipole) # if use_alpha_instead_alphahalf: # AtDipoles2=AtDipoles2/2 # self.dipole=self.dipole/2 # #Eshift=-self._get_interaction_energy(index,charge=charge) # Eshift+=self.get_selfinteraction_energy() # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability Beta for rescaled charges # #self._calc_dipoles_All('BetaEE',NN=order//2) # if order>2: # self._calc_dipoles_All('BetaEE',NN=1) # else: # self._calc_dipoles_All('BetaEE',NN=order//2) # AtDipolesBeta=np.copy(self.dipole) # #Eshift=-self._get_interaction_energy(index,charge=charge) # # if use_alpha_instead_alphahalf: # self.polar['AlphaE']=self.polar['AlphaE']/2 # self.polar['Alpha_E']=self.polar['Alpha_E']/2 # # if approx==1.2 or approx==1.1 or approx=='MMpol_1.2' or approx=='MMpol_1.1': # if is_elstat: # Eshift+=(self.VinterFG-DE)*self.get_selfinteraction_energy() # Eshift+=DE # else: # Eshift+=self.VinterFG*self.get_selfinteraction_energy() # elif approx==1.3 or approx=='MMpol_1.3': # if is_elstat: # Eshift+=DE*(1-2*self.get_selfinteraction_energy()) # elif approx=='MMpol': # if is_elstat: # Eshift+=DE # if output_dipoles: # if approx=='MMpol': # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # if order>2: # return Eshift,AtDipoles1 # else: # return Eshift,AtDipoles1+AtDipoles2 # else: # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # return Eshift,AtDipoles1+AtDipoles2-self.VinterFG*AtDipolesBeta # else: # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # return Eshift # # # def calculate_InteractionEnergy(self,index,charge,*args,order=80,output_dipoles=False,approx=1.1): # ''' Function for calculation of interaction energies for chromophores # embeded in polarizable atom environment. So far only for symetric homodimer # # Parameters # ---------- # index : list of integer (dimension Natoms_of_defect) # Specify atomic indexes of one defect. For this defect interation energy # with induced dipoles in the environment and also other defect will # be calculated. # charge : numpy.array of real (dimension Natoms_of_defect) # Atomic trasition charges (TrEsp charges) for every atom of one defect # defined by `index` # *args : real (optional) # Diference in electrostatic interaction energy between ground and # excited state in ATOMIC UNITS (DE). If not defined it is assumed to # be zero. DE=<A|V|A>-<G|V|G> # order : integer (optional - init=80) # Specify how many SCF steps shoudl be used in calculation of induced dipoles # output_dipoles : logical (optional - init=False) # If atomic dipoles should be outputed or not. Atomic dipoles are # outputed as `AtDip_Alpha(E)+AtDip_Alpha(-E)-self.VinterFG*AtDip_Beta(E,E) # approx : real (optional - init=1.2) # Specifies which approximation should be used. **Different approximation # than for dipole or energy shift** # # **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and # `Alpha(-E)`. With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.1.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # # **Approximation MMpol**: Interaction energy is calculated as interaction # without environment plus interaction of induced dipoles in the environmnet # with electric field of the second molecule. # # Returns # ------- # Einter : real # Interaction energy in ATOMIC UNITS (Hartree) between two chormophores # embeded in polarizable atom environment. # AtDipoles : numpy.array of real (dimension Natoms x 3) (optional) # Induced atomic dipoles defined as: # `AtDip_Alpha(E)+AtDip_Alpha(-E)-2*self.VinterFG*AtDip_Beta(E,E)` # in ATOMIC UNITS (e*Bohr) # # Notes # ---------- # E = -Ind_dip_Alpha(E)*El_field_TrCharge + Ind_dip_Alpha(-E)*El_field_TrCharge # Then final energy shift E_fin of molecule embeded in environment is calculated # according to the approximation: # # **Approximation 1.1:** # Einter=E_TrEsp*(1+E1Bself)+(self.VinterFG-DE)*E12B+E12AE+E12A_E # **Approximation 1.1.2:** # Einter=E_TrEsp*(1+E1Bself)+self.VinterFG*E12B+E12AE+E12A_E # **Approximation 1.3:** # Einter=E_TrEsp+2*self.VinterFG*E12B+E12AE+E12A_E # # ''' # # debug=False # # if approx==1.2: # raise IOError('Approximation 1.2 for interaction energy calculation not yet supported. Look at Approximation 1.1') # # if approx==1.3: # if not np.array_equal(self.polar['AlphaE'],self.polar['Alpha_E']): # raise Warning('For calculation with Approximation 1.3 Alpha(E) should be equal Alpha(-E)') # # if approx==1.1: # if not np.array_equal(np.zeros((len(self.polar['Alpha_E']),3,3),dtype='f8'),self.polar['Alpha_E']): # print('For calculation with Approximation 1.1 Alpha(-E) should be equal to zero') # # is_elstat=False # if len(args)==1: # DE=args[0] # is_elstat=True # # use_alpha_instead_alphahalf=False # if type(approx)==str and order==2: # if 'MMpol' in approx: # use_alpha_instead_alphahalf=True # # # For MMpol approximation we have to use alpha instead alpha/2 and resulting induced dipoles # # have to be devided by 2. This way we correct the second term in perturbation expansion # if use_alpha_instead_alphahalf: # self.polar['AlphaE']=self.polar['AlphaE']*2 # self.polar['Alpha_E']=self.polar['Alpha_E']*2 # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # TrEsp interaction energy # E_TrEsp=self._get_interaction_energy(index,charge=charge) # #print('TrEsp interaction:',E_TrEsp*conversion_facs_energy["1/cm"]) # # this will put zero charges on index atoms then calculate potential from # # everything else and calculate interaction with charges defined by charges # # original charges and dipoles remain unchanged # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # if not (approx=='MMpol' and order>2): # # calculate induced dipoles with polarizability Beta for rescaled charges # if order>2: # self._calc_dipoles_All('BetaEE',NN=1) # else: # self._calc_dipoles_All('BetaEE',NN=order//2) # #self._calc_dipoles_All('BetaEE',NN=order//2) # AtDipolesBeta=np.copy(self.dipole) # E1Bself=-self.get_selfinteraction_energy() # should be negative for all Beta # E12B=E_TrEsp-self._get_interaction_energy(index,charge=charge) # # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability AlphaE for rescaled charges # if debug==True and order==2: # self._calc_dipoles_All('AlphaE',NN=1) # self._test_2nd_order('AlphaE') # else: # self._calc_dipoles_All('AlphaE',NN=order) # if use_alpha_instead_alphahalf: # self.dipole=self.dipole/2 # AtDipoles1=np.copy(self.dipole) # E12AE=(self._get_interaction_energy(index,charge=charge)-E_TrEsp) # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # if not (approx=='MMpol' and order>2): # # calculate induced dipoles with polarizability AlphaE for rescaled charges # self._calc_dipoles_All('Alpha_E',NN=order) # if use_alpha_instead_alphahalf: # self.dipole=self.dipole/2 # AtDipoles2=np.copy(self.dipole) # E12A_E=(self._get_interaction_energy(index,charge=charge)-E_TrEsp) # # if use_alpha_instead_alphahalf: # self.polar['AlphaE']=self.polar['AlphaE']/2 # self.polar['Alpha_E']=self.polar['Alpha_E']/2 # # # if approx==1.1 or approx=='MMpol_1.1': # if is_elstat: # Einter=E_TrEsp*(1+E1Bself)+(self.VinterFG-DE)*E12B+E12AE+E12A_E # else: # Einter=E_TrEsp*(1+E1Bself)+self.VinterFG*E12B+E12AE+E12A_E # elif approx==1.3 or approx=='MMpol_1.3': # Einter=E_TrEsp+2*self.VinterFG*E12B+E12AE+E12A_E # elif approx=='MMpol': # Einter=E_TrEsp+E12AE # else: # raise IOError('Unknown type of approximation. Alowed types are: 1.1 and 1.3') # # if output_dipoles: # if approx=='MMpol': # if order>2: # return Einter,AtDipoles1 # else: # return Einter,AtDipoles1+AtDipoles2 # else: # return Einter,AtDipoles1+AtDipoles2-2*self.VinterFG*AtDipolesBeta # else: # return Einter # # def _calculate_InteractionEnergy2(self,index,charge,order=80,output_dipoles=False): # ''' Function for calculation of interaction energies for chromophores # embeded in polarizable atom environment. So far only for symetric homodimer # # Induced dipoles, needed for inteaction energy calculation, calculated # at every step of the SCF procedure are for output multiplied by different # factor. First order is multiplied by factor 1, second by factor 3/2, # third by factor of 2, etc. # # **According to latest derivation the rescaling of every SCF step should # not be used and therefore also this function should not be used** # # Notes # ---------- # This function is kept only for maintaining backward compatibility. # # ''' # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # TrEsp interaction energy # E_TrEsp=self._get_interaction_energy(index,charge=charge) # #print('TrEsp interaction:',E_TrEsp*conversion_facs_energy["1/cm"]) # # this will put zero charges on index atoms then calculate potential from # # everything else and calculate interaction with charges defined by charges # # original charges and dipoles remain unchanged # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability Beta for rescaled charges # if order>2: # self._calc_dipoles_All('BetaEE',NN=1) # else: # self._calc_dipoles_All('BetaEE',NN=order//2) # AtDipolesBeta=np.copy(self.dipole) # E1Bself=-self.get_selfinteraction_energy() # should be negative for all Beta # E12B=E_TrEsp-self._get_interaction_energy(index,charge=charge) # # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability AlphaE for rescaled charges # #self._calc_dipoles_All('AlphaE',NN=order) # self.__calc_dipoles_All2('AlphaE',NN=order+2) # AtDipoles1=np.copy(self.dipole) # E12AE=2*(self._get_interaction_energy(index,charge=charge)-E_TrEsp) # # # reset iduced dipoles to zero # self.dipole=np.zeros((self.Nat,3),dtype='f8') # # # calculate induced dipoles with polarizability AlphaE for rescaled charges # #self._calc_dipoles_All('Alpha_E',NN=order) # self.__calc_dipoles_All2('Alpha_E',NN=order+2) # AtDipoles2=np.copy(self.dipole) # E12A_E=2*(self._get_interaction_energy(index,charge=charge)-E_TrEsp) # # # Einter=E_TrEsp*(1+E1Bself)+2*self.VinterFG*E12B+E12AE+E12A_E # # if output_dipoles: # return Einter,AtDipoles1+AtDipoles2-2*self.VinterFG*AtDipolesBeta # else: # return Einter # #def CalculateTrDip(filenames,ShortName,index_all,Dipole_QCH,Dip_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=80,verbose=False,approx=1.1,MathOut=False,**kwargs): # ''' Calculate transition dipole for defect embeded in polarizable atom environment # for all systems given in filenames. # # Parameters # ---------- # filenames : list of dictionary (dimension Nsystems) # In the dictionary there are specified all needed files which contains # nessesary information for transformig the system into Dielectric class. # keys: # `'2def_structure'`: xyz file with system geometry and atom types # `'charge_structure'`: xyz file with defect like molecule geometry for which transition charges were calculated # `charge_grnd`: file with ground state charges for the defect # `'charge_exct'`: file with excited state charges for the defect # `'charge'`: file with transition charges for the defect # ShortName : list of strings # List of short description (name) of individual systems # index_all : list of integers (dimension Nsystems x 6) # There are specified indexes neded for asignment of defect # atoms. First three indexes correspond to center and two main axes of # reference structure (structure which was used for charges calculation) # and the remaining three indexes are corresponding atoms of the defects # on fluorographene system. # Dipole_QCH : list of real (dimension Nsystems) # List of quantum chemistry values of transition dipoles in ATOMIC UNITS # (e*Bohr) for defect in polarizable atom environment # (used for printing comparison - not used for calculation at all) # Dip_all : list of real (dimension Nsystems) # In this variable there will be stored dipoles in ATOMIC UNITS (e*Bohr) # calculated by polarizable atoms method for description of the environment. # AlphaE : numpy.array of real (dimension 2x2) # Atomic polarizability Alpha(E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # Alpha_E : numpy.array of real (dimension 2x2) # Atomic polarizability Alpha(-E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # BetaE : numpy.array of real (dimension 2x2) # Atomic polarizability Beta(E,E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # VinterFG : real # Difference in electrostatic interaction energy between interaction of # excited C-F corse grained atom of fluorographene with all others # fluorographene corse grained atoms in ground state and interaction of # ground state C-F corse grained atom of fluorographene with all others # fluorographene corse grained atoms in ground state. Units are ATOMIC # UNITS (Hartree) # FG_charges : list of real (dimension 2) # [charge on inner fluorographene atom, charge on borded fluorographe carbon] # ChargeType : string # Specifies which method was used for calcultion of ground and excited state # charges for defect atoms. Allowed types are: 'qchem','qchem_all','AMBER' # and 'gaussian'. **'qchem'** - charges calculated by fiting Q-Chem ESP on carbon # atoms. **'qchem_all'** - charges calculated by fiting Q-Chem ESP on all # atoms, only carbon charges are used and same charge is added to all carbon # atoms in order to have neutral molecule. **'AMBER'** and **'gaussian'** # not yet fully implemented. # order : integer (optional - init=80) # Specify how many SCF steps shoudl be used in calculation of induced dipoles # verbose : logical (optional - init=False) # If `True` aditional information about whole proces will be printed # approx : real (optional - init=1.1) # Specifies which approximation should be used. # # **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and # `Alpha(-E)`. With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.1.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. # With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.2.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # **kwargs : dictionary (optional) # Definition of polarizabitity matrixes for defect atoms (if nonzero # polarizability is used) # # Notes # ---------- # Working only for fluorographene system with single defect # # ''' # # for ii in range(len(filenames)): # if verbose: # print('Calculation of dipoles for:',ShortName[ii]) # # # read and prepare molecule # if kwargs: # mol_polar,index1,charge=prepare_molecule_1Def(filenames[ii],index_all[ii],AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,**kwargs) # else: # mol_polar,index1,charge=prepare_molecule_1Def(filenames[ii],index_all[ii],AlphaE,Alpha_E,BetaE,VinterFG,verbose=False) # mol_Elstat,index,charge_grnd,charge_exct=ElStat_PrepareMolecule_1Def(filenames[ii],index_all[ii],FG_charges,ChargeType=ChargeType,verbose=False) # # # calculate <A|V|A>-<G|V|G> # DE=mol_Elstat.get_EnergyShift() # #print('DE:',DE*conversion_facs_energy["1/cm"],'cm-1') # # # calculate transition dipole # TrDip,AtDipoles=mol_polar.get_TrDip(DE,order=order,output_dipoles=True,approx=approx) # # if verbose: # print(' Total transition dipole:',np.sqrt(np.dot(TrDip,TrDip)),'Quantum chemistry dipole:',Dipole_QCH[ii]) # print(ShortName[ii],Dipole_QCH[ii],np.sqrt(np.dot(TrDip,TrDip))) # Dip_all[ii,:]=TrDip[:] # # if MathOut: # # output dipoles to mathematica # Bonds=GuessBonds(mol_polar.coor,bond_length=4.0) # mat_filename="".join(['Pictures/Polar_',ShortName[ii],'.nb']) # OutputMathematica(mat_filename,mol_polar.coor,Bonds,['C']*mol_polar.Nat,scaleDipole=30.0,**{'TrPointCharge': mol_polar.charge,'AtDipole': AtDipoles,'rSphere_dip': 0.5,'rCylinder_dip':0.1}) # #def CalculateEnergyShift(filenames,ShortName,index_all,Eshift_QCH,Eshift_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=80,verbose=False,approx=1.1,MathOut=False,**kwargs): # ''' Calculate transition energy shifts for defect embeded in polarizable atom # environment for all systems given in filenames. # # Parameters # ---------- # filenames : list of dictionary (dimension Nsystems) # In the dictionary there are specified all needed files which contains # nessesary information for transformig the system into Dielectric class. # keys: # `'2def_structure'`: xyz file with system geometry and atom types # `'charge_structure'`: xyz file with defect like molecule geometry for which transition charges were calculated # `charge_grnd`: file with ground state charges for the defect # `'charge_exct'`: file with excited state charges for the defect # `'charge'`: file with transition charges for the defect # ShortName : list of strings # List of short description (name) of individual systems # index_all : list of integers (dimension Nsystems x 6) # There are specified indexes neded for asignment of defect # atoms. First three indexes correspond to center and two main axes of # reference structure (structure which was used for charges calculation) # and the remaining three indexes are corresponding atoms of the defects # on fluorographene system. # Eshift_QCH : list of real (dimension Nsystems) # List of quantum chemistry values of transition energy shifts in INVERSE # CENTIMETERS for defect in polarizable atom environment (used for printing # comparison - not used for calculation at all) # Eshift_all : list of real (dimension Nsystems) # In this variable there will be stored transition energy shifts in ATOMIC # UNITS (Hartree) calculated by polarizable atoms method for description # of the environment. # AlphaE : numpy.array of real (dimension 2x2) # Atomic polarizability Alpha(E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # Alpha_E : numpy.array of real (dimension 2x2) # Atomic polarizability Alpha(-E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # BetaE : numpy.array of real (dimension 2x2) # Atomic polarizability Beta(E,E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # VinterFG : real # Difference in electrostatic interaction energy between interaction of # excited C-F corse grained atom of fluorographene with all others # fluorographene corse grained atoms in ground state and interaction of # ground state C-F corse grained atom of fluorographene with all others # fluorographene corse grained atoms in ground state. Units are ATOMIC # UNITS (Hartree) # FG_charges : list of real (dimension 2) # [charge on inner fluorographene atom, charge on borded fluorographe carbon] # ChargeType : string # Specifies which method was used for calcultion of ground and excited state # charges for defect atoms. Allowed types are: 'qchem','qchem_all','AMBER' # and 'gaussian'. **'qchem'** - charges calculated by fiting Q-Chem ESP on carbon # atoms. **'qchem_all'** - charges calculated by fiting Q-Chem ESP on all # atoms, only carbon charges are used and same charge is added to all carbon # atoms in order to have neutral molecule. **'AMBER'** and **'gaussian'** # not yet fully implemented. # order : integer (optional - init=80) # Specify how many SCF steps shoudl be used in calculation of induced dipoles # verbose : logical (optional - init=False) # If `True` aditional information about whole proces will be printed # approx : real (optional - init=1.1) # Specifies which approximation should be used. # # **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and # `Alpha(-E)`. With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.1.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. # With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.2.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # **kwargs : dictionary (optional) # Definition of polarizabitity matrixes for defect atoms (if nonzero # polarizability is used) # # Notes # ---------- # Working only for system with single defect # # ''' # # for ii in range(len(filenames)): # if verbose: # print('Calculation of excitation energy shift for:',ShortName[ii]) # # # read and prepare molecule # if kwargs: # mol_polar,index1,charge=prepare_molecule_1Def(filenames[ii],index_all[ii],AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,**kwargs) # else: # mol_polar,index1,charge=prepare_molecule_1Def(filenames[ii],index_all[ii],AlphaE,Alpha_E,BetaE,VinterFG,verbose=False) # mol_Elstat,index,charge_grnd,charge_exct=ElStat_PrepareMolecule_1Def(filenames[ii],index_all[ii],FG_charges,ChargeType=ChargeType,verbose=False) # # # calculate <A|V|A>-<G|V|G> # DE=mol_Elstat.get_EnergyShift() # #print('DE:',DE*conversion_facs_energy["1/cm"],'cm-1',DE,'AU') # # # calculate transition dipole # Eshift,AtDipoles=mol_polar.calculate_EnergyShift(index1,charge,DE,order=order,output_dipoles=True,approx=approx) # # if verbose: # print(' Transition enegy shift:',Eshift*conversion_facs_energy["1/cm"],'Quantum chemistry shift:',Eshift_QCH[ii]) # print(ShortName[ii],Eshift_QCH[ii],Eshift*conversion_facs_energy["1/cm"]) # Eshift_all[ii]=Eshift*conversion_facs_energy["1/cm"] # # if MathOut: # # output dipoles to mathematica # Bonds=GuessBonds(mol_polar.coor,bond_length=4.0) # mat_filename="".join(['Pictures/Polar_',ShortName[ii],'.nb']) # OutputMathematica(mat_filename,mol_polar.coor,Bonds,['C']*mol_polar.Nat,scaleDipole=30.0,**{'TrPointCharge': mol_polar.charge,'AtDipole': AtDipoles,'rSphere_dip': 0.5,'rCylinder_dip':0.1}) # #def CalculateInterE(filenames,ShortName,index_all,Energy_QCH,Energy_all,nvec_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=80,verbose=False,approx=1.1,MathOut=False,**kwargs): # ''' Calculate interaction energies between defects embeded in polarizable atom # environment for all systems given in filenames. # # Parameters # ---------- # filenames : list of dictionary (dimension Nsystems) # In the dictionary there are specified all needed files which contains # nessesary information for transformig the system into Dielectric class. # keys: # `'2def_structure'`: xyz file with system geometry and atom types # `'charge_structure'`: xyz file with defect like molecule geometry for which transition charges were calculated # `charge_grnd`: file with ground state charges for the defect # `'charge_exct'`: file with excited state charges for the defect # `'charge'`: file with transition charges for the defect # ShortName : list of strings # List of short description (name) of individual systems # index_all : list of integers (dimension Nsystems x 6) # There are specified indexes neded for asignment of defect # atoms. First three indexes correspond to center and two main axes of # reference structure (structure which was used for charges calculation) # and the remaining three indexes are corresponding atoms of the defects # on fluorographene system. # Energy_QCH : list of real (dimension Nsystems) # List of quantum chemistry values of interaction energies in INVERSE # CENTIMETERS between defects in polarizable atom environment # (used for printing comparison - not used for calculation at all) # Energy_all : list of real (dimension Nsystems) # In this variable there will be stored interaction energies in ATOMIC UNITS # (Hartree) calculated by polarizable atoms method for description of the # environment. # AlphaE : numpy.array of real (dimension 2x2) # Atomic polarizability Alpha(E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # Alpha_E : numpy.array of real (dimension 2x2) # Atomic polarizability Alpha(-E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # BetaE : numpy.array of real (dimension 2x2) # Atomic polarizability Beta(E,E) for C-F corse grained atoms of # fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) # VinterFG : real # Difference in electrostatic interaction energy between interaction of # excited C-F corse grained atom of fluorographene with all others # fluorographene corse grained atoms in ground state and interaction of # ground state C-F corse grained atom of fluorographene with all others # fluorographene corse grained atoms in ground state. Units are ATOMIC # UNITS (Hartree) # FG_charges : list of real (dimension 2) # [charge on inner fluorographene atom, charge on borded fluorographe carbon] # ChargeType : string # Specifies which method was used for calcultion of ground and excited state # charges for defect atoms. Allowed types are: 'qchem','qchem_all','AMBER' # and 'gaussian'. **'qchem'** - charges calculated by fiting Q-Chem ESP on carbon # atoms. **'qchem_all'** - charges calculated by fiting Q-Chem ESP on all # atoms, only carbon charges are used and same charge is added to all carbon # atoms in order to have neutral molecule. **'AMBER'** and **'gaussian'** # not yet fully implemented. # order : integer (optional - init=80) # Specify how many SCF steps shoudl be used in calculation of induced dipoles # verbose : logical (optional - init=False) # If `True` aditional information about whole proces will be printed # approx : real (optional - init=1.1) # Specifies which approximation should be used. # # **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and # `Alpha(-E)`. With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.1.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. # With this apprximation diference in electrostatic interaction energy # between ground and excited state in ATOMIC UNITS (DE) has to be imputed # as `*args` # # **Approximation 1.2.2**: Approximation 1.2 + neglecting difference # in electrostatic interaction between ground and excited state # (imputed as approximation 1.2 but no electrostatic interaction energy # diference - DE is defiend) # # **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also # `Alpha(E)=Alpha(-E)`, however the second one is not condition # **kwargs : dictionary (optional) # Definition of polarizabitity matrixes for defect atoms (if nonzero # polarizability is used) # # Notes # ---------- # Working only for systems with two symetric defects # # ''' # # # for ii in range(len(filenames)): # if verbose: # print('Calculation of interaction energy for:',ShortName[ii]) # # # read and prepare molecule # if kwargs: # mol_polar,index1,index2,charge=prepare_molecule_2Def(filenames[ii],index_all[ii],AlphaE,Alpha_E,BetaE,VinterFG,nvec=nvec_all[ii],verbose=False,**kwargs) # else: # mol_polar,index1,index2,charge=prepare_molecule_2Def(filenames[ii],index_all[ii],AlphaE,Alpha_E,BetaE,VinterFG,nvec=nvec_all[ii],verbose=False) # # calculate <A|V|A>-<G|V|G> # mol_Elstat,at_type=ElStat_PrepareMolecule_2Def(filenames[ii],index_all[ii],FG_charges,ChargeType=ChargeType,verbose=False) # DE=mol_Elstat.get_EnergyShift() # #print('DE:',DE*conversion_facs_energy["1/cm"],'cm-1') # # # calculate interaction energy # Einter,AtDipoles=mol_polar.calculate_InteractionEnergy(index2,charge,DE,order=order,output_dipoles=True,approx=approx) # # if verbose: # print(' Total interaction energy:',Einter*conversion_facs_energy["1/cm"],'Quantum interaction energy:',Energy_QCH[ii]) # # print(ShortName[ii],Energy_QCH[ii],abs(Einter*conversion_facs_energy["1/cm"])) # # Energy_all[ii]=abs(Einter*conversion_facs_energy["1/cm"]) # # if MathOut: # # output dipoles to mathematica # Bonds=GuessBonds(mol_polar.coor,bond_length=4.0) # mat_filename="".join(['Pictures/Polar_',ShortName[ii],'.nb']) # OutputMathematica(mat_filename,mol_polar.coor,Bonds,['C']*mol_polar.Nat,scaleDipole=30.0,**{'TrPointCharge': mol_polar.charge,'AtDipole': AtDipoles,'rSphere_dip': 0.5,'rCylinder_dip':0.1}) #============================================================================== # Definition of fuction for allocation of polarized molecules #============================================================================== def prepare_molecule_1Def(filenames,indx,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,CoarseGrain="plane",**kwargs): ''' Read all informations needed for Dielectric class and transform system with single defect into this class. Useful for calculation of interaction energies, transition site energy shifts and dipole changes. Parameters ---------- filenames : list of dictionary (dimension Nsystems) In the dictionaries there are specified all needed files which contains nessesary information for transformig the system into Dielectric class. keys: `'1def_structure'`: xyz file with system geometry and atom types `'charge_structure'`: xyz file with defect like molecule geometry for which transition charges were calculated `charge_grnd`: file with ground state charges for the defect `'charge_exct'`: file with excited state charges for the defect `'charge'`: file with transition charges for the defect indx : list of integers (dimension Nsystems x 6) For every system there are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the remaining three indexes are corresponding atoms of the defect on fluorographene system. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) CoarseGrain : string (optional init = "plane") Possible values are: "plane","C","CF". Define which level of coarse grained model should be used. If ``CoarseGrain="plane"`` then all atoms are projected on plane defined by nvec and C-F atoms re treated as single atom - for this case polarizabilities defined only in 2D by two numbers. If ``CoarseGrain="C"`` then carbon atoms are center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="CF"`` then center of C-F bonds are used as center for atomic polarizability tensor and again C-F are treated as a single atom. verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed **kwargs : dictionary (optional) Definition of polarizabitity matrixes for defect atoms (if nonzero polarizability is used) Returns ------- mol_polar : Dielectric class Fluorographene with defect in Dielectric class which contains all information needed for calculation of energy shifts and dipole changes for defect embeded in fluorographene index1 : list of integer (dimension Ndefect_atoms) Atom indexes of defect atoms charge : numpy.array of real (dimension Ndefect_atoms) Transition charges for every defect atom. First charge correspond to atom defined by first index in index1 list and so on. struc : Structure class Structure of the fluorographene system with single defects ''' if verbose: print(indx) indx_center_test=indx[0] indx_x_test=indx[1] indx_y_test=indx[2] indx_center1=indx[3] indx_x1=indx[4] indx_y1=indx[5] # Specify files: xyzfile2=filenames['charge_structure'] filenameESP=filenames['charge'] xyzfile=filenames['1def_structure'] if verbose: print(' Reading charges and format to polarization format...') struc_test=Structure() struc_test.load_xyz(xyzfile2) # Structure of molecule used for fitting charges if verbose: print(' Loading molecule...') struc=Structure() struc.load_xyz(xyzfile) # Fluorographene with single defect coor,charge,at_type=read_TrEsp_charges(filenameESP,verbose=False) if verbose: print(' Centering molecule...') struc.center(indx_center1,indx_x1,indx_y1) index1=identify_molecule(struc,struc_test,indx_center1,indx_x1,indx_y1,indx_center_test,indx_x_test,indx_y_test,onlyC=True) if len(index1)!=len(np.unique(index1)): raise IOError('There are repeating elements in index file') # Assign pol types and charges PolCoor,Polcharge,PolType = _prepare_polar_structure_1def(struc,index1,charge,CoarseGrain,verbose=False) # PolType=[] # Polcharge=[] # PolCoor=[] # for ii in range(struc.nat): # if struc.at_type[ii]=='C' and (ii in index1): # Polcharge.append(charge[np.where(index1==ii)[0][0]]) # PolType.append('C') # PolCoor.append(struc.coor._value[ii]) # elif struc.at_type[ii]=='C': # PolType.append('CF') # Polcharge.append(0.0) # PolCoor.append(struc.coor._value[ii]) # PolType=np.array(PolType) # Polcharge=np.array(Polcharge,dtype='f8') # PolCoor=np.array(PolCoor,dtype='f8') # # # project molecule whole system to plane defined by defect # nvec=np.array([0.0,0.0,1.0],dtype='f8') # center=np.array([0.0,0.0,0.0],dtype='f8') # PolCoor=project_on_plane(PolCoor,nvec,center) polar={} polar['AlphaE']=np.zeros((len(PolCoor),3,3),dtype='f8') polar['Alpha_E']=np.zeros((len(PolCoor),3,3),dtype='f8') polar['BetaE']=np.zeros((len(PolCoor),3,3),dtype='f8') mol_polar=Dielectric(PolCoor,Polcharge,np.zeros((len(PolCoor),3),dtype='f8'), polar['AlphaE'],polar['Alpha_E'],polar['BetaE'],VinterFG) ZeroM=np.zeros((3,3),dtype='f8') Polarizability = { 'CF': [AlphaE,Alpha_E,BetaE], 'CD': [AlphaE,Alpha_E,BetaE]} if "Alpha(E)" in kwargs.keys(): AlphaE_def=kwargs['Alpha(E)'] Alpha_E_def=kwargs['Alpha(-E)'] BetaE_def=kwargs['Beta(E,E)'] Polarizability['C'] = [AlphaE_def,Alpha_E_def,BetaE_def] else : Polarizability['C'] = [ZeroM,ZeroM,ZeroM] if "Fpolar" in kwargs.keys(): Polarizability['FC'] = kwargs['Fpolar'] else: Polarizability['FC'] = [ZeroM,ZeroM,ZeroM] mol_polar.polar=mol_polar.assign_polar(PolType,**{'PolValues': Polarizability}) if "Alpha_static" in kwargs.keys(): mol_polar.polar['Alpha_st'] = np.zeros((len(PolCoor),3,3),dtype='f8') if CoarseGrain=="all_atom": Alpha_static=kwargs["Alpha_static"] AlphaF_static=kwargs["AlphaF_static"] else: Alpha_static=kwargs["Alpha_static"] AlphaF_static=ZeroM for ii in range(len(PolType)): if PolType[ii]=='CF': mol_polar.polar['Alpha_st'][ii]=Alpha_static elif PolType[ii]=='FC': mol_polar.polar['Alpha_st'][ii]=AlphaF_static return mol_polar,index1,charge,struc def prepare_molecule_2Def(filenames,indx,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False, def2_charge=True,CoarseGrain="plane",**kwargs): ''' Read all informations needed for Dielectric class and transform system with two same defects into this class. Useful for calculation of interaction energies, transition site energy shifts and dipole changes. Parameters ---------- filenames : dictionary In the dictionary there are specified all needed files which contains nessesary information for transformig the system into Dielectric class. keys: * ``'2def_structure'``: xyz file with FG system with two defects geometry and atom types * ``'charge1_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge1'``: file with transition charges for the first defect (from TrEsp charges fitting) * ``'charge2_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to second defect * ``'charge2'``: file with transition charges for the second defect (from TrEsp charges fitting) indx : list of integers (dimension 9) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the remaining six indexes are corresponding atoms of the defects on fluorographene system (three correspond to first defect and the last three to the second one). AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) def2_charge : logical (init = True) Specifies if transition charges should be placed also to the second defect CoarseGrain : string (optional init = "plane") Possible values are: "plane","C","CF". Define which level of coarse grained model should be used. If ``CoarseGrain="plane"`` then all atoms are projected on plane defined by nvec and C-F atoms re treated as single atom - for this case polarizabilities defined only in 2D by two numbers. If ``CoarseGrain="C"`` then carbon atoms are center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="CF"`` then center of C-F bonds are used as center for atomic polarizability tensor and again C-F are treated as a single atom. verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed **kwargs : dictionary (optional) Definition of polarizabitity matrixes for defect atoms (if nonzero polarizability is used) Returns ------- mol_polar : Dielectric class Fluorographene with two defects in Dielectric class which contains all information needed for calculation of energy shifts, dipole changes and interaction energies for defect homodimer embeded in fluorographene index1 : list of integer (dimension Ndefect_atoms) Atom indexes of first defect atoms index2 : list of integer (dimension Ndefect_atoms) Atom indexes of second defect atoms charge1 : numpy.array of real (dimension Ndefect1_atoms) Transition charges for every atom of the first defect. First charge correspond to atom defined by first index in index1 list and so on. charge2 : numpy.array of real (dimension Ndefect2_atoms) Transition charges for every atom of the second defect. First charge correspond to atom defined by first index in index2 list and so on. struc : Structure class Structure of the fluorographene system with two defects ''' indx_center_test=indx[0] indx_x_test=indx[1] indx_y_test=indx[2] indx_center1=indx[3] indx_x1=indx[4] indx_y1=indx[5] indx_center2=indx[6] indx_x2=indx[7] indx_y2=indx[8] # Specify files: xyzfile_chrg1=filenames['charge1_structure'] filenameESP_chrg1=filenames['charge1'] xyzfile_chrg2=filenames['charge2_structure'] filenameESP_chrg2=filenames['charge2'] xyzfile=filenames['2def_structure'] # Read Transition charges #filenameESP="".join([MolDir,'Perylene_TDDFT_fitted_charges_NoH.out']) if verbose: print(' Reading charges and format to polarization format...') struc1_test=Structure() struc2_test=Structure() struc1_test.load_xyz(xyzfile_chrg1) # Structure of molecule used for fitting charges struc2_test.load_xyz(xyzfile_chrg2) # Structure of molecule used for fitting charges coor,charge1,at_type=read_TrEsp_charges(filenameESP_chrg1,verbose=False) coor,charge2,at_type=read_TrEsp_charges(filenameESP_chrg2,verbose=False) # load molecule - fuorographene with 2 defects if verbose: print(' Loading molecule...') struc=Structure() struc.load_xyz(xyzfile) # Fluorographene with two defects index1=identify_molecule(struc,struc1_test,indx_center1,indx_x1,indx_y1,indx_center_test,indx_x_test,indx_y_test,onlyC=True) index2=identify_molecule(struc,struc2_test,indx_center2,indx_x2,indx_y2,indx_center_test,indx_x_test,indx_y_test,onlyC=True) if len(index1)!=len(np.unique(index1)) or len(index2)!=len(np.unique(index2)): print('index1:') print(index1) print('index2:') print(index2) raise IOError('There are repeating elements in index file') # Assign pol types PolCoor,Polcharge,PolType = _prepare_polar_structure_2def(struc,index1,charge1,index2,charge2,CoarseGrain) # PolType=[] # Polcharge=[] # PolCoor=[] # for ii in range(struc.nat): # if struc.at_type[ii]=='C' and (ii in index1): # Polcharge.append(charge1[np.where(index1==ii)[0][0]]) # PolType.append('C') # PolCoor.append(struc.coor._value[ii]) # elif struc.at_type[ii]=='C' and (ii in index2): # if def2_charge: # Polcharge.append(charge2[np.where(index2==ii)[0][0]]) # else: # Polcharge.append(0.0) # #Polcharge.append(charge[np.where(index2==ii)[0][0]]) # PolType.append('C') # PolCoor.append(struc.coor._value[ii]) # elif struc.at_type[ii]=='C': # PolType.append('CF') # Polcharge.append(0.0) # PolCoor.append(struc.coor._value[ii]) # # PolType=np.array(PolType) # Polcharge=np.array(Polcharge,dtype='f8') # PolCoor=np.array(PolCoor,dtype='f8') # # # project molecule whole system to plane defined by defect # center=np.array([0.0,0.0,0.0],dtype='f8') # PolCoor=project_on_plane(PolCoor,nvec,center) # center projected molecule on plane if verbose: print(' Centering molecule...') PolCoor,Phi,Psi,Chi,center=CenterMolecule(PolCoor,indx_center1,[indx_center1,indx_x1,indx_center2,indx_x2],[indx_center1,indx_y1,indx_center2,indx_y2],print_angles=True) # Do the same transformation also with the structure struc.move(-center[0],-center[1],-center[2]) struc.rotate(Phi,Psi,Chi) polar={} polar['AlphaE']=np.zeros((len(PolCoor),3,3),dtype='f8') polar['Alpha_E']=np.zeros((len(PolCoor),3,3),dtype='f8') polar['BetaE']=np.zeros((len(PolCoor),3,3),dtype='f8') mol_polar=Dielectric(PolCoor,Polcharge,np.zeros((len(PolCoor),3),dtype='f8'), polar['AlphaE'],polar['Alpha_E'],polar['BetaE'],VinterFG) ZeroM=np.zeros((3,3),dtype='f8') Polarizability = { 'CF': [AlphaE,Alpha_E,BetaE], 'CD': [AlphaE,Alpha_E,BetaE]} if "Alpha(E)" in kwargs.keys(): AlphaE_def=kwargs['Alpha(E)'] Alpha_E_def=kwargs['Alpha(-E)'] BetaE_def=kwargs['Beta(E,E)'] Polarizability['C'] = [AlphaE_def,Alpha_E_def,BetaE_def] else : Polarizability['C'] = [ZeroM,ZeroM,ZeroM] if "Fpolar" in kwargs.keys(): Polarizability['FC'] = kwargs['Fpolar'] else: Polarizability['FC'] = [ZeroM,ZeroM,ZeroM] mol_polar.polar=mol_polar.assign_polar(PolType,**{'PolValues': Polarizability}) if "Alpha_static" in kwargs.keys(): mol_polar.polar['Alpha_st'] = np.zeros((len(PolCoor),3,3),dtype='f8') if CoarseGrain=="all_atom": Alpha_static=ZeroM else: Alpha_static=kwargs["Alpha_static"] for ii in range(len(PolType)): if PolType[ii]=='CF': mol_polar.polar['Alpha_st'][ii]=Alpha_static return mol_polar,index1,index2,charge1,charge2,struc def _prepare_polar_structure_1def(struc,index1,charge1,Type,verbose=False): """ Type = "plane","C","CF","all_atom" """ if not Type in ["plane","C","CF","all_atom"]: raise Warning("Unsupported type of coarse graining.") if verbose: print(Type) # Molecule has to be centered and oriented first before this calculation is done # Assign pol types and charges PolType=[] Polcharge=[] PolCoor=[] if Type == "plane" or Type == "C": for ii in range(struc.nat): if struc.at_type[ii]=='C' and (ii in index1): Polcharge.append(charge1[np.where(index1==ii)[0][0]]) PolType.append('C') PolCoor.append(struc.coor._value[ii]) elif struc.at_type[ii]=='C': PolType.append('CF') Polcharge.append(0.0) PolCoor.append(struc.coor._value[ii]) PolType=np.array(PolType) Polcharge=np.array(Polcharge,dtype='f8') PolCoor=np.array(PolCoor,dtype='f8') if Type == "plane": # project molecule whole system to plane defined by defect nvec_test,origin_test = fit_plane(PolCoor) PolCoor=project_on_plane(PolCoor,nvec_test,origin_test) #center=np.array([0.0,0.0,0.0],dtype='f8') #PolCoor=project_on_plane(PolCoor,nvec,center) elif Type == "all_atom": PolCoor = struc.coor._value.copy() for ii in range(struc.nat): if struc.at_type[ii]=='C' and (ii in index1): Polcharge.append(charge1[np.where(index1==ii)[0][0]]) PolType.append('C') elif struc.at_type[ii]=='C': PolType.append('CF') Polcharge.append(0.0) elif struc.at_type[ii]=='F': PolType.append('FC') Polcharge.append(0.0) PolType=np.array(PolType) Polcharge=np.array(Polcharge,dtype='f8') PolCoor=np.array(PolCoor,dtype='f8') elif Type == "CF": connectivity = [] for ii in range(struc.nat): connectivity.append([]) if struc.bonds is None: struc.guess_bonds() for ii in range(len(struc.bonds)): indx1=struc.bonds[ii][0] at1=struc.at_type[indx1] indx2=struc.bonds[ii][1] at2=struc.at_type[indx2] if at1=="C" and at2=="F": connectivity[indx1].append(indx2) elif at2=="C" and at1=="F": connectivity[indx2].append(indx1) for ii in range(struc.nat): if struc.at_type[ii]=='C' and (ii in index1): Polcharge.append(charge1[np.where(index1==ii)[0][0]]) PolType.append('C') PolCoor.append(struc.coor._value[ii]) elif struc.at_type[ii]=='C': PolType.append('CF') Polcharge.append(0.0) # polarizabiliy center will be located at center of C-F bond (or F-C-F for border carbons) count = 1 position = struc.coor._value[ii] for jj in range(len(connectivity[ii])): position += struc.coor._value[ connectivity[ii][jj] ] count += 1 position = position / count PolCoor.append(position) PolType=np.array(PolType) Polcharge=np.array(Polcharge,dtype='f8') PolCoor=np.array(PolCoor,dtype='f8') # TODO: add all atom representation return PolCoor,Polcharge,PolType def _prepare_polar_structure_2def(struc,index1,charge1,index2,charge2,Type,verbose=False): """ Type = "plane","C","CF","all_atom" """ if not Type in ["plane","C","CF","all_atom"]: raise Warning("Unsupported type of coarse graining.") if verbose: print(Type) # Assign pol types PolType=[] Polcharge=[] PolCoor=[] if Type == "plane" or Type == "C": for ii in range(struc.nat): if struc.at_type[ii]=='C' and (ii in index1): Polcharge.append(charge1[np.where(index1==ii)[0][0]]) PolType.append('C') PolCoor.append(struc.coor._value[ii]) elif struc.at_type[ii]=='C' and (ii in index2): Polcharge.append(charge2[np.where(index2==ii)[0][0]]) PolType.append('C') PolCoor.append(struc.coor._value[ii]) elif struc.at_type[ii]=='C': PolType.append('CF') Polcharge.append(0.0) PolCoor.append(struc.coor._value[ii]) PolType=np.array(PolType) Polcharge=np.array(Polcharge,dtype='f8') PolCoor=np.array(PolCoor,dtype='f8') if Type == "plane": # project molecule whole system to plane defined by defect nvec_test,origin_test = fit_plane(PolCoor) PolCoor=project_on_plane(PolCoor,nvec_test,origin_test) #center=np.array([0.0,0.0,0.0],dtype='f8') #PolCoor=project_on_plane(PolCoor,nvec,center) elif Type == "all_atom": PolCoor = struc.coor._value.copy() for ii in range(struc.nat): if struc.at_type[ii]=='C' and (ii in index1): Polcharge.append(charge1[np.where(index1==ii)[0][0]]) PolType.append('C') elif struc.at_type[ii]=='C' and (ii in index2): Polcharge.append(charge2[np.where(index2==ii)[0][0]]) PolType.append('C') elif struc.at_type[ii]=='C': PolType.append('CF') Polcharge.append(0.0) elif struc.at_type[ii]=='F': PolType.append('FC') Polcharge.append(0.0) PolType=np.array(PolType) Polcharge=np.array(Polcharge,dtype='f8') #print(len(PolCoor),len(PolType)) # TODO: TEST this assignment of polarizability centers elif Type == "CF": connectivity = [] for ii in range(struc.nat): connectivity.append([]) if struc.bonds is None: struc.guess_bonds() for ii in range(len(struc.bonds)): indx1=struc.bonds[ii][0] at1=struc.at_type[indx1] indx2=struc.bonds[ii][1] at2=struc.at_type[indx2] if at1=="C" and at2=="F": connectivity[indx1].append(indx2) elif at2=="C" and at1=="F": connectivity[indx2].append(indx1) for ii in range(struc.nat): if struc.at_type[ii]=='C' and (ii in index1): Polcharge.append(charge1[np.where(index1==ii)[0][0]]) PolType.append('C') PolCoor.append(struc.coor._value[ii]) elif struc.at_type[ii]=='C' and (ii in index2): Polcharge.append(charge2[np.where(index2==ii)[0][0]]) PolType.append('C') PolCoor.append(struc.coor._value[ii]) elif struc.at_type[ii]=='C': PolType.append('CF') Polcharge.append(0.0) # polarizabiliy center will be located at center of C-F bond (or F-C-F for border carbons) count = 1 position = struc.coor._value[ii] for jj in range(len(connectivity[ii])): position += struc.coor._value[ connectivity[ii][jj] ] count += 1 position = position / count PolCoor.append(position) PolType=np.array(PolType) Polcharge=np.array(Polcharge,dtype='f8') PolCoor=np.array(PolCoor,dtype='f8') # TODO: add all atom representation return PolCoor,Polcharge,PolType #TODO: Get rid of ShortName def Calc_SingleDef_FGprop(filenames,ShortName,index_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=80,verbose=False,approx=1.1,MathOut=False,CoarseGrain="plane",**kwargs): ''' Calculate energy shifts and transition dipole shifts for single defect embeded in fluorographene Parameters ---------- filenames : dictionary Dictionary with information about all needed files which contains nessesary information for transformig the system into Dielectric class and electrostatic calculations. Keys: * ``'1def_structure'``: xyz file with FG system with single defect geometry and atom types * ``'charge_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge'``: file with transition charges for the defect (from TrEsp charges fitting) * ``'charge_grnd'``: file with ground state charges for the defect (from TrEsp charges fitting) * ``'charge_exct'``: file with excited state charges for the defect (from TrEsp charges fitting) ShortName : string Short description of the system index_all : list of integers (dimension 6) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the last three indexes are corresponding atoms of the defect. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) FG_charges : list of real (dimension 2) [charge on inner fluorographene atom, charge on borded fluorographe carbon] ChargeType : string Specifies which charges should be used for electrostatic calculations (ground and excited state charges) for defect atoms. Allowed types are: ``'qchem'``, ``'qchem_all'``, ``'AMBER'`` and ``'gaussian'``. * ``'qchem'`` - charges calculated by fiting Q-Chem ESP on carbon atoms. * ``'qchem_all'`` - charges calculated by fiting Q-Chem ESP on all atoms, only carbon charges are used and same charge is added to all carbon atoms in order to have neutral molecule. * ``'AMBER'`` - not yet fully implemented. * ``'gaussian'`` - not yet fully implemented. order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns -------- Eshift : Energy class Transition energy shift for the defect due to the fluorographene environment calculated from structure with single defect. Units are energy managed TrDip : numpy array of real (dimension 3) Total transition dipole for the defect with environment effects included calculated from structure with single defect (in ATOMIC UNITS) Notes -------- By comparing QC calculations it was found that energy shift from structure with two defects and with single defect is almost the same. ''' if verbose: print('Calculation of interaction energy for:',ShortName) # read and prepare molecule mol_polar,index1,charge,struc=prepare_molecule_1Def(filenames,index_all,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,CoarseGrain=CoarseGrain,**kwargs) # calculate dAVA = <A|V|A>-<G|V|G> AditInfo={'Structure': struc,'index1': index1} mol_Elstat,index,charge_grnd,charge_exct=ElStat_PrepareMolecule_1Def(filenames,index_all,FG_charges,ChargeType=ChargeType,verbose=False,**AditInfo) dAVA=mol_Elstat.get_EnergyShift() # calculate transition energy shifts and transition dipole change Eshift,TrDip=mol_polar.get_SingleDefectProperties(index1,dAVA=dAVA,order=order,approx=approx) if verbose: with energy_units("1/cm"): print(ShortName,Eshift.value) print(" dipole:",np.linalg.norm(TrDip)) print(" dAVA:",dAVA*conversion_facs_energy["1/cm"],'cm-1') return Eshift, TrDip #TODO: Get rid of ShortName #TODO: Input vacuum transition energies def Calc_Heterodimer_FGprop(filenames,ShortName,index_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=80,verbose=False,approx=1.1,MathOut=False,CoarseGrain="plane",**kwargs): ''' Calculate interaction energies between defects embeded in polarizable atom environment for all systems given in filenames. Possibility of calculate transition energy shifts and transition dipoles. Parameters ---------- filenames : dictionary Dictionary with information about all needed files which contains nessesary information for transformig the system into Dielectric class and electrostatic calculations. Keys: * ``'2def_structure'``: xyz file with FG system with two defects geometry and atom types * ``'charge1_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge1'``: file with transition charges for the first defect (from TrEsp charges fitting) * ``'charge1_grnd'``: file with ground state charges for the first defect (from TrEsp charges fitting) * ``'charge1_exct'``: file with excited state charges for the first defect (from TrEsp charges fitting) * ``'charge2_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to second defect * ``'charge2'``: file with transition charges for the second defect (from TrEsp charges fitting) * ``'charge2_grnd'``: file with ground state charges for the second defect (from TrEsp charges fitting) * ``'charge2_exct'``: file with excited state charges for the second defect (from TrEsp charges fitting) ShortName : string Short description of the system index_all : list of integers (dimension 6) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the next three indexes are corresponding atoms of the first defects on fluorographene system and the last three indexes are corresponding atoms of the second defect. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) FG_charges : list of real (dimension 2) [charge on inner fluorographene atom, charge on borded fluorographe carbon] ChargeType : string Specifies which charges should be used for electrostatic calculations (ground and excited state charges) for defect atoms. Allowed types are: ``'qchem'``, ``'qchem_all'``, ``'AMBER'`` and ``'gaussian'``. * ``'qchem'`` - charges calculated by fiting Q-Chem ESP on carbon atoms. * ``'qchem_all'`` - charges calculated by fiting Q-Chem ESP on all atoms, only carbon charges are used and same charge is added to all carbon atoms in order to have neutral molecule. * ``'AMBER'`` - not yet fully implemented. * ``'gaussian'`` - not yet fully implemented. order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 CoarseGrain : string (optional init = "plane") Possible values are: "plane","C","CF". Define which level of coarse grained model should be used. If ``CoarseGrain="plane"`` then all atoms are projected on plane defined by nvec and C-F atoms re treated as single atom - for this case polarizabilities defined only in 2D by two numbers. If ``CoarseGrain="C"`` then carbon atoms are center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="CF"`` then center of C-F bonds are used as center for atomic polarizability tensor and again C-F are treated as a single atom. verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed approx : real (optional - init=1.1) Specifies which approximation should be used. **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns -------- Einter : Energy class Interaction energy with effects of environment included. Units are energy managed Eshift1 : Energy class Transition energy shift for the first defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed Eshift2 : Energy class Transition energy shift for the second defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed TrDip1 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) TrDip2 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) Notes ---------- No far working only with two symmetric defects - for heterodimer need to input vacuum transition energy for every defect. ''' if verbose: print('Calculation of interaction energy for:',ShortName) # read and prepare molecule mol_polar,index1,index2,charge1,charge2,struc=prepare_molecule_2Def(filenames,index_all,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,def2_charge=True,CoarseGrain=CoarseGrain,**kwargs) # # calculate dAVA = <A|V|A>-<G|V|G> and dBVB = <B|V|B>-<G|V|G> AditInfo={'Structure': struc,'index1': index1,'index2':index2} mol_Elstat,indx1,indx2,charge1_grnd,charge2_grnd,charge1_exct,charge2_exct=ElStat_PrepareMolecule_2Def(filenames,index_all,FG_charges,ChargeType=ChargeType,verbose=False,**AditInfo) dAVA=mol_Elstat.get_EnergyShift(index=index2, charge=charge2_grnd) dBVB=mol_Elstat.get_EnergyShift(index=index1, charge=charge1_grnd) # calculate interaction energy and transition energy shifts Einter,Eshift1,Eshift2,TrDip1,TrDip2,dipAE,dipA_E,dipBE=mol_polar.get_HeterodimerProperties(index1,index2,0.0,0.0,dAVA=dAVA,dBVB=dBVB,order=order,approx=approx) if verbose: with energy_units("1/cm"): print(' Total interaction energy:',Einter.value) print(ShortName,abs(Einter.value),Eshift1.value,Eshift2.value) print("dipole:",np.linalg.norm(TrDip1),np.linalg.norm(TrDip2)) print("dAVA:",dAVA*conversion_facs_energy["1/cm"],"dBVB:",dBVB*conversion_facs_energy["1/cm"]) if MathOut: if not os.path.exists("Pictures"): os.makedirs("Pictures") Bonds = GuessBonds(mol_polar.coor) if CoarseGrain in ["plane","C","CF"]: at_type = ['C']*mol_polar.Nat elif CoarseGrain == "all_atom": at_type = struc.at_type.copy() mat_filename = "".join(['Pictures/Polar_',ShortName,'_AlphaE.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipAE,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) mat_filename = "".join(['Pictures/Polar_',ShortName,'_Alpha_E.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipA_E,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) mat_filename = "".join(['Pictures/Polar_',ShortName,'_BetaE.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipBE,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) return Einter, Eshift1, Eshift2, TrDip1, TrDip2 def TEST_Calc_Heterodimer_FGprop(filenames,ShortName,index_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=80,verbose=False,approx=1.1,MathOut=False,CoarseGrain="plane",**kwargs): ''' Calculate interaction energies between defects embeded in polarizable atom environment for all systems given in filenames. Possibility of calculate transition energy shifts and transition dipoles. Parameters ---------- filenames : dictionary Dictionary with information about all needed files which contains nessesary information for transformig the system into Dielectric class and electrostatic calculations. Keys: * ``'2def_structure'``: xyz file with FG system with two defects geometry and atom types * ``'charge1_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge1'``: file with transition charges for the first defect (from TrEsp charges fitting) * ``'charge1_grnd'``: file with ground state charges for the first defect (from TrEsp charges fitting) * ``'charge1_exct'``: file with excited state charges for the first defect (from TrEsp charges fitting) * ``'charge2_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to second defect * ``'charge2'``: file with transition charges for the second defect (from TrEsp charges fitting) * ``'charge2_grnd'``: file with ground state charges for the second defect (from TrEsp charges fitting) * ``'charge2_exct'``: file with excited state charges for the second defect (from TrEsp charges fitting) ShortName : string Short description of the system index_all : list of integers (dimension 6) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the next three indexes are corresponding atoms of the first defects on fluorographene system and the last three indexes are corresponding atoms of the second defect. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) FG_charges : list of real (dimension 2) [charge on inner fluorographene atom, charge on borded fluorographe carbon] ChargeType : string Specifies which charges should be used for electrostatic calculations (ground and excited state charges) for defect atoms. Allowed types are: ``'qchem'``, ``'qchem_all'``, ``'AMBER'`` and ``'gaussian'``. * ``'qchem'`` - charges calculated by fiting Q-Chem ESP on carbon atoms. * ``'qchem_all'`` - charges calculated by fiting Q-Chem ESP on all atoms, only carbon charges are used and same charge is added to all carbon atoms in order to have neutral molecule. * ``'AMBER'`` - not yet fully implemented. * ``'gaussian'`` - not yet fully implemented. order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 CoarseGrain : string (optional init = "plane") Possible values are: "plane","C","CF". Define which level of coarse grained model should be used. If ``CoarseGrain="plane"`` then all atoms are projected on plane defined by nvec and C-F atoms re treated as single atom - for this case polarizabilities defined only in 2D by two numbers. If ``CoarseGrain="C"`` then carbon atoms are center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="CF"`` then center of C-F bonds are used as center for atomic polarizability tensor and again C-F are treated as a single atom. verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed approx : real (optional - init=1.1) Specifies which approximation should be used. **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns -------- Einter : Energy class Interaction energy with effects of environment included. Units are energy managed Eshift1 : Energy class Transition energy shift for the first defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed Eshift2 : Energy class Transition energy shift for the second defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed TrDip1 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) TrDip2 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) Notes ---------- No far working only with two symmetric defects - for heterodimer need to input vacuum transition energy for every defect. ''' if verbose: print('Calculation of interaction energy for:',ShortName) # read and prepare molecule mol_polar,index1,index2,charge1,charge2,struc=prepare_molecule_2Def(filenames,index_all,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,def2_charge=True,CoarseGrain=CoarseGrain,**kwargs) if (mol_polar.charge[index1] != mol_polar.charge[index2]).any(): raise Warning("Transition charges are not the same - after creation.") # # calculate dAVA = <A|V|A>-<G|V|G> and dBVB = <B|V|B>-<G|V|G> AditInfo={'Structure': struc,'index1': index1,'index2':index2} mol_Elstat,indx1,indx2,charge1_grnd,charge2_grnd,charge1_exct,charge2_exct=ElStat_PrepareMolecule_2Def(filenames,index_all,FG_charges,ChargeType=ChargeType,verbose=False,**AditInfo) dAVA=mol_Elstat.get_EnergyShift(index=index2, charge=charge2_grnd) dBVB=mol_Elstat.get_EnergyShift(index=index1, charge=charge1_grnd) # dAVA=mol_Elstat.get_EnergyShift(index=index2) # dBVB=mol_Elstat.get_EnergyShift(index=index1) if (mol_polar.charge[index1] != mol_polar.charge[index2]).any(): raise Warning("Transition charges are not the same - after elstat.") # calculate interaction energy and transition energy shifts - so far for homodimer Einter,Eshift1,Eshift2,TrDip1,TrDip2,dipAE,dipA_E,dipBE,res=mol_polar._TEST_HeterodimerProperties(charge1_grnd,charge1_exct,charge2_grnd,charge2_exct,mol_Elstat,struc,index1,index2,0.0,0.0,dAVA=dAVA,dBVB=dBVB,order=order,approx=approx) #get_HeterodimerProperties_new(self, gr_charge1, ex_charge1, gr_charge2, ex_charge2, FG_elstat, struc, index1, index2, Eng1, Eng2, eps, dAVA=0.0, dBVB=0.0, order=2, approx=1.1) # res["E_pol2_A(E)"] # res["E_pol2_A(-E)"] # res["E_pol2_B(E,E)"] # res["E_pol1_B(E,E)_(A_exct,B_grnd)"] # res["E_pol1_B(E,E)_(A_grnd,B_exct)"] # res["E_pol1-env_B(E,E)_grnd"] # res["E_pol1-env_B(E,E)_exct"] # res["E_pol2_st_(A_exct,B_grnd)"] # res["E_pol2_st_(A_grnd,B_exct)"] # res["E_pol2-env_st_grnd"] # res["E_pol2-env_st_exct"] # res["E_pol1_B(E,E)_(tr_gr,ex)"] import os if not os.path.isfile("Temp.dat"): text = " pol2_A(E) | pol2_A(-E) | pol2_st_(A_ex,B_gr) | pol2_st_(A_gr,B_ex) | E_pol2-env_st_grnd | E_pol2-env_st_exct | pol1_BEE | pol1_BEE_(A_ex,B_gr) | pol1_BEE_(A_gr,B_ex) | pol1-env_BEE_grnd | pol1-env_BEE_exct | pol1_BEE_(tr_gr,ex) |" os.system("".join(['echo "',text,'" >> Temp.dat'])) text = "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|" os.system("".join(['echo "',text,'" >> Temp.dat'])) # pol2_A(E) | pol2_A(-E) | pol2_st_(A_ex,B_gr) | pol2_st_(A_gr,B_ex) | E_pol2-env_st_grnd | E_pol2-env_st_exct | pol1_BEE | pol1_BEE_(A_ex,B_gr) | pol1_BEE_(A_gr,B_ex) | pol1-env_BEE_grnd |" ii = 0 text="{:21} {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} |".format( ShortName,res["E_pol2_A(E)"][ii,0],res["E_pol2_A(E)"][ii,1],res["E_pol2_A(-E)"][ii,0],res["E_pol2_A(-E)"][ii,1], res["E_pol2_st_(A_exct,B_grnd)"][ii,0],res["E_pol2_st_(A_exct,B_grnd)"][ii,1],res["E_pol2_st_(A_grnd,B_exct)"][ii,0], res["E_pol2_st_(A_grnd,B_exct)"][ii,1],res["E_pol2-env_st_grnd"][ii,0],res["E_pol2-env_st_grnd"][ii,1], res["E_pol2-env_st_exct"][ii,0],res["E_pol2-env_st_exct"][ii,1],res["E_pol2_B(E,E)"][ii,0],res["E_pol2_B(E,E)"][ii,1], res["E_pol1_B(E,E)_(A_exct,B_grnd)"][ii,0],res["E_pol1_B(E,E)_(A_exct,B_grnd)"][ii,1],res["E_pol1_B(E,E)_(A_grnd,B_exct)"][ii,0], res["E_pol1_B(E,E)_(A_grnd,B_exct)"][ii,1],res["E_pol1-env_B(E,E)_grnd"][ii,0],res["E_pol1-env_B(E,E)_grnd"][ii,1], res["E_pol1-env_B(E,E)_exct"][ii,0],res["E_pol1-env_B(E,E)_exct"][ii,1],res["E_pol1_B(E,E)_(tr_gr,ex)"][ii,0],res["E_pol1_B(E,E)_(tr_gr,ex)"][ii,1]) os.system("".join(['echo "',text,'" >> Temp.dat'])) ii = 1 text="{:21} {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} |".format( " ",res["E_pol2_A(E)"][ii,0],res["E_pol2_A(E)"][ii,1],res["E_pol2_A(-E)"][ii,0],res["E_pol2_A(-E)"][ii,1], res["E_pol2_st_(A_exct,B_grnd)"][ii,0],res["E_pol2_st_(A_exct,B_grnd)"][ii,1],res["E_pol2_st_(A_grnd,B_exct)"][ii,0], res["E_pol2_st_(A_grnd,B_exct)"][ii,1],res["E_pol2-env_st_grnd"][ii,0],res["E_pol2-env_st_grnd"][ii,1], res["E_pol2-env_st_exct"][ii,0],res["E_pol2-env_st_exct"][ii,1],res["E_pol2_B(E,E)"][ii,0],res["E_pol2_B(E,E)"][ii,1], res["E_pol1_B(E,E)_(A_exct,B_grnd)"][ii,0],res["E_pol1_B(E,E)_(A_exct,B_grnd)"][ii,1],res["E_pol1_B(E,E)_(A_grnd,B_exct)"][ii,0], res["E_pol1_B(E,E)_(A_grnd,B_exct)"][ii,1],res["E_pol1-env_B(E,E)_grnd"][ii,0],res["E_pol1-env_B(E,E)_grnd"][ii,1], res["E_pol1-env_B(E,E)_exct"][ii,0],res["E_pol1-env_B(E,E)_exct"][ii,1],res["E_pol1_B(E,E)_(tr_gr,ex)"][ii,0],res["E_pol1_B(E,E)_(tr_gr,ex)"][ii,1]) os.system("".join(['echo "',text,'" >> Temp.dat'])) text = "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|" os.system("".join(['echo "',text,' " >> Temp.dat'])) # if (mol_polar.charge[index1] != mol_polar.charge[index2]).any(): # raise Warning("Transition charges are not the same - after polar.") # TODO: For testing output structure and polarization structure - I'm getting different values for first and second defect # struc.output_to_xyz("".join([ShortName,"_structure.xyz"])) # from QChemTool.QuantumChem.output import OutputToXYZ # from QChemTool.General.units import conversion_facs_position # OutputToXYZ(mol_polar.coor*conversion_facs_position["Angstrom"],["C"]*len(mol_polar.coor),"".join([ShortName,"_pol.xyz"])) if verbose: with energy_units("1/cm"): print(' Total interaction energy:',Einter.value) print(ShortName,abs(Einter.value),Eshift1.value,Eshift2.value) print("dipole:",np.linalg.norm(TrDip1),np.linalg.norm(TrDip2)) print("dAVA:",dAVA*conversion_facs_energy["1/cm"],"dBVB:",dBVB*conversion_facs_energy["1/cm"]) if MathOut: if not os.path.exists("Pictures"): os.makedirs("Pictures") Bonds = GuessBonds(mol_polar.coor) if CoarseGrain in ["plane","C","CF"]: at_type = ['C']*mol_polar.Nat elif CoarseGrain == "all_atom": at_type = struc.at_type.copy() # if (mol_polar.charge[index1] != mol_polar.charge[index2]).any(): # raise Warning("Transition charges are not the same - before output.") mat_filename = "".join(['Pictures/Polar_',ShortName,'_AlphaE.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipAE,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) mat_filename = "".join(['Pictures/Polar_',ShortName,'_Alpha_E.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipA_E,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) mat_filename = "".join(['Pictures/Polar_',ShortName,'_BetaE.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipBE,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) return Einter, Eshift1, Eshift2, TrDip1, TrDip2 def Calc_Heterodimer_FGprop_new(filenames,ShortName,E1,E2,index_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=2,verbose=False,approx=1.1,MathOut=False,CoarseGrain="plane",**kwargs): ''' Calculate interaction energies between defects embeded in polarizable atom environment for all systems given in filenames. Possibility of calculate transition energy shifts and transition dipoles. Parameters ---------- filenames : dictionary Dictionary with information about all needed files which contains nessesary information for transformig the system into Dielectric class and electrostatic calculations. Keys: * ``'2def_structure'``: xyz file with FG system with two defects geometry and atom types * ``'charge1_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge1'``: file with transition charges for the first defect (from TrEsp charges fitting) * ``'charge1_grnd'``: file with ground state charges for the first defect (from TrEsp charges fitting) * ``'charge1_exct'``: file with excited state charges for the first defect (from TrEsp charges fitting) * ``'charge2_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to second defect * ``'charge2'``: file with transition charges for the second defect (from TrEsp charges fitting) * ``'charge2_grnd'``: file with ground state charges for the second defect (from TrEsp charges fitting) * ``'charge2_exct'``: file with excited state charges for the second defect (from TrEsp charges fitting) ShortName : string Short description of the system index_all : list of integers (dimension 6) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the next three indexes are corresponding atoms of the first defects on fluorographene system and the last three indexes are corresponding atoms of the second defect. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) FG_charges : list of real (dimension 2) [charge on inner fluorographene atom, charge on borded fluorographe carbon] ChargeType : string Specifies which charges should be used for electrostatic calculations (ground and excited state charges) for defect atoms. Allowed types are: ``'qchem'``, ``'qchem_all'``, ``'AMBER'`` and ``'gaussian'``. * ``'qchem'`` - charges calculated by fiting Q-Chem ESP on carbon atoms. * ``'qchem_all'`` - charges calculated by fiting Q-Chem ESP on all atoms, only carbon charges are used and same charge is added to all carbon atoms in order to have neutral molecule. * ``'AMBER'`` - not yet fully implemented. * ``'gaussian'`` - not yet fully implemented. order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 CoarseGrain : string (optional init = "plane") Possible values are: "plane","C","CF". Define which level of coarse grained model should be used. If ``CoarseGrain="plane"`` then all atoms are projected on plane defined by nvec and C-F atoms re treated as single atom - for this case polarizabilities defined only in 2D by two numbers. If ``CoarseGrain="C"`` then carbon atoms are center for atomic polarizability tensor and again C-F are treated as a single atom. If ``CoarseGrain="CF"`` then center of C-F bonds are used as center for atomic polarizability tensor and again C-F are treated as a single atom. verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed approx : real (optional - init=1.1) Specifies which approximation should be used. **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns -------- Einter : Energy class Interaction energy with effects of environment included. Units are energy managed Eshift1 : Energy class Transition energy shift for the first defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed Eshift2 : Energy class Transition energy shift for the second defect due to fluorographene environment calculated from heterodymer structure. Units are energy managed TrDip1 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) TrDip2 : numpy array of real (dimension 3) Total transition dipole for the first defect with environment effects included calculated from heterodimer structure (in ATOMIC UNITS) Notes ---------- No far working only with two symmetric defects - for heterodimer need to input vacuum transition energy for every defect. ''' if verbose: print('Calculation of interaction energy for:',ShortName) # read and prepare molecule mol_polar,index1,index2,charge1,charge2,struc=prepare_molecule_2Def(filenames,index_all,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,def2_charge=True,CoarseGrain=CoarseGrain,**kwargs) if (mol_polar.charge[index1] != mol_polar.charge[index2]).any(): raise Warning("Transition charges are not the same - after creation.") # # calculate dAVA = <A|V|A>-<G|V|G> and dBVB = <B|V|B>-<G|V|G> AditInfo={'Structure': struc,'index1': index1,'index2':index2} mol_Elstat,indx1,indx2,charge1_grnd,charge2_grnd,charge1_exct,charge2_exct=ElStat_PrepareMolecule_2Def(filenames,index_all,FG_charges,ChargeType=ChargeType,verbose=False,**AditInfo) dAVA=mol_Elstat.get_EnergyShift(index=index2, charge=charge2_grnd) dBVB=mol_Elstat.get_EnergyShift(index=index1, charge=charge1_grnd) # dAVA=mol_Elstat.get_EnergyShift(index=index2) # dBVB=mol_Elstat.get_EnergyShift(index=index1) if (mol_polar.charge[index1] != mol_polar.charge[index2]).any(): raise Warning("Transition charges are not the same - after elstat.") eps = EnergyClass( (E1.value+E2.value)/2 ) # calculate interaction energy and transition energy shifts - so far for homodimer Einter,Eshift1,Eshift2,TrDip1,TrDip2,dipAE,dipA_E,dipBE,res=mol_polar.get_HeterodimerProperties_new(charge1_grnd,charge1_exct,charge2_grnd,charge2_exct,mol_Elstat,struc,index1,index2,0.0,0.0,eps,dAVA=dAVA,dBVB=dBVB,order=order,approx=approx) if verbose: with energy_units("1/cm"): print(' Total interaction energy:',Einter.value) print(ShortName,abs(Einter.value),Eshift1.value,Eshift2.value) print("dipole:",np.linalg.norm(TrDip1),np.linalg.norm(TrDip2)) print("dAVA:",dAVA*conversion_facs_energy["1/cm"],"dBVB:",dBVB*conversion_facs_energy["1/cm"]) if MathOut: if not os.path.exists("Pictures"): os.makedirs("Pictures") Bonds = GuessBonds(mol_polar.coor) if CoarseGrain in ["plane","C","CF"]: at_type = ['C']*mol_polar.Nat elif CoarseGrain == "all_atom": at_type = struc.at_type.copy() mat_filename = "".join(['Pictures/Polar_',ShortName,'_AlphaE.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipAE,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) mat_filename = "".join(['Pictures/Polar_',ShortName,'_Alpha_E.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipA_E,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) mat_filename = "".join(['Pictures/Polar_',ShortName,'_BetaE.nb']) params = {'TrPointCharge': mol_polar.charge,'AtDipole': dipBE,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_polar.coor,Bonds,at_type,scaleDipole=50.0,**params) # res["E_pol2_A(E)"] = PolarMat_AlphaE # res["E_pol2_A(-E)"] = PolarMat_Alpha_E # res["E_pol2_A_static"] = PolarMat_Alpha_st # res["E_pol2_B(E,E)"] = PolarMat_Beta # res["E_pol2_B(E,E)_scaled"] = PolarMat_Beta_scaled # res["E_pol2_A(E)_(trans,grnd)"] = PolarMat_Alpha_tr_gr # res["E_pol1_A_static"] = PolarMat_static_tr_gr_ex # res["E_elstat_1"] = ElstatMat_1 if verbose: if not os.path.isfile("Temp.dat"): text = " pol2_A(E) | pol2_A(-E) | pol2_st | pol2_BEE_scaled | E_pol1-A(E)_tr_gr | E_pol1_st | pol1_BEE | sum_elstat |" os.system("".join(['echo "',text,'" >> Temp.dat'])) text = "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|" os.system("".join(['echo "',text,'" >> Temp.dat'])) with energy_units("1/cm"): ii = 0 text="{:21} {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} |".format( ShortName,res["E_pol2_A(E)"].value[ii,0],res["E_pol2_A(E)"].value[ii,1],res["E_pol2_A(-E)"].value[ii,0],res["E_pol2_A(-E)"].value[ii,1], res["E_pol2_A_static"].value[ii,0],res["E_pol2_A_static"].value[ii,1],res["E_pol2_B(E,E)_scaled"].value[ii,0], res["E_pol2_B(E,E)_scaled"].value[ii,1],res["E_pol2_A(E)_(trans,grnd)"].value[ii,0],res["E_pol2_A(E)_(trans,grnd)"].value[ii,1], res["E_pol1_A_static"].value[ii,0],res["E_pol1_A_static"].value[ii,1],res["E_pol2_B(E,E)"].value[ii,0],res["E_pol2_B(E,E)"].value[ii,1], res["E_elstat_1"].value[ii,0],res["E_elstat_1"].value[ii,1]) os.system("".join(['echo "',text,'" >> Temp.dat'])) ii = 1 text="{:21} {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.3f} {:10.3f} | {:10.6f} {:10.6f} | {:10.6f} {:10.6f} |".format( " ",res["E_pol2_A(E)"].value[ii,0],res["E_pol2_A(E)"].value[ii,1],res["E_pol2_A(-E)"].value[ii,0],res["E_pol2_A(-E)"].value[ii,1], res["E_pol2_A_static"].value[ii,0],res["E_pol2_A_static"].value[ii,1],res["E_pol2_B(E,E)_scaled"].value[ii,0], res["E_pol2_B(E,E)_scaled"].value[ii,1],res["E_pol2_A(E)_(trans,grnd)"].value[ii,0],res["E_pol2_A(E)_(trans,grnd)"].value[ii,1], res["E_pol1_A_static"].value[ii,0],res["E_pol1_A_static"].value[ii,1],res["E_pol2_B(E,E)"].value[ii,0],res["E_pol2_B(E,E)"].value[ii,1], res["E_elstat_1"].value[ii,0],res["E_elstat_1"].value[ii,1]) os.system("".join(['echo "',text,'" >> Temp.dat'])) text = "----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|" os.system("".join(['echo "',text,' " >> Temp.dat'])) return Einter, Eshift1, Eshift2, TrDip1, TrDip2 def TEST_Compare_SingleDef_FGprop(filenames,ShortName,index_all,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=1,verbose=False,approx=1.1,MathOut=False,CoarseGrain="plane",**kwargs): ''' Compare magnitude of individual terms in energy shift calculation for defect in Fluorographene environment (so far only for first order of perturbation expansion -> order = 1) Parameters ---------- filenames : dictionary Dictionary with information about all needed files which contains nessesary information for transformig the system into Dielectric class and electrostatic calculations. Keys: * ``'1def_structure'``: xyz file with FG system with single defect geometry and atom types * ``'charge_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge'``: file with transition charges for the defect (from TrEsp charges fitting) * ``'charge_grnd'``: file with ground state charges for the defect (from TrEsp charges fitting) * ``'charge_exct'``: file with excited state charges for the defect (from TrEsp charges fitting) ShortName : string Short description of the system index_all : list of integers (dimension 6) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the last three indexes are corresponding atoms of the defect. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) FG_charges : list of real (dimension 2) [charge on inner fluorographene atom, charge on borded fluorographe carbon] ChargeType : string Specifies which charges should be used for electrostatic calculations (ground and excited state charges) for defect atoms. Allowed types are: ``'qchem'``, ``'qchem_all'``, ``'AMBER'`` and ``'gaussian'``. * ``'qchem'`` - charges calculated by fiting Q-Chem ESP on carbon atoms. * ``'qchem_all'`` - charges calculated by fiting Q-Chem ESP on all atoms, only carbon charges are used and same charge is added to all carbon atoms in order to have neutral molecule. * ``'AMBER'`` - not yet fully implemented. * ``'gaussian'`` - not yet fully implemented. order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns -------- Eshift : Energy class Transition energy shift for the defect due to the fluorographene environment calculated from structure with single defect. Units are energy managed TrDip : numpy array of real (dimension 3) Total transition dipole for the defect with environment effects included calculated from structure with single defect (in ATOMIC UNITS) Notes -------- By comparing QC calculations it was found that energy shift from structure with two defects and with single defect is almost the same. ''' # read and prepare molecule mol_polar,index1,charge,struc=prepare_molecule_1Def(filenames,index_all,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,CoarseGrain=CoarseGrain,**kwargs) # calculate dAVA = <A|V|A>-<G|V|G> AditInfo={'Structure': struc,'index1': index1,'Output_exct': True} mol_Elstat,index,charge_grnd,charge_exct=ElStat_PrepareMolecule_1Def(filenames,index_all,FG_charges,ChargeType=ChargeType,verbose=False,**AditInfo) dAVA=mol_Elstat.get_EnergyShift() # Calculate interaction with ground state charges mol_Elstat.charge[index] = charge_grnd E_elst_grnd = mol_Elstat.get_EnergyShift() mol_Elstat.charge[index] = charge_exct - charge_grnd # Calculate interaction with excited state charges mol_Elstat.charge[index] = charge_exct E_elst_exct = mol_Elstat.get_EnergyShift() mol_Elstat.charge[index] = charge_exct - charge_grnd # Calculate interaction with transition density mol_Elstat.charge[index] = charge E_elst_trans = mol_Elstat.get_EnergyShift() mol_Elstat.charge[index] = charge_exct - charge_grnd # calculate transition energy shifts and transition dipole change res_Energy, res_Pot, TrDip = mol_polar._TEST_Compare_SingleDefectProperties(charge,charge_grnd,charge_exct,struc,index1,dAVA=dAVA,order=order,approx=approx) charge_FG_grnd = mol_Elstat.charge.copy() charge_FG_grnd[index] = 0.0 E_Pol1_env_static_ex_gr_FG = np.dot(charge_FG_grnd,res_Pot['Pol1-env_static_(exct-grnd)']) E_Pol2_env_static_ex_gr_FG = np.dot(charge_FG_grnd,res_Pot['Pol2-env_static_(exct-grnd)']) E_Pol1_env_BetaEE_ex_gr_FG = np.dot(charge_FG_grnd,res_Pot['Pol1-env_Beta(E,E)_(exct-grnd)']) E_Pol1_env_BetaEE_trans_FG = np.dot(charge_FG_grnd,res_Pot['Pol1-env_Beta(E,E)_(trans)']) E_Pol1_env_AlphaE_trans_FG = np.dot(charge_FG_grnd,res_Pot['Pol1-env_Alpha(E)_(trans)']) E_Pol1_env_Alpha_E_trans_FG = np.dot(charge_FG_grnd,res_Pot['Pol1-env_Alpha(-E)_(trans)']) E_Pol1_env_static_trans_FG = np.dot(charge_FG_grnd,res_Pot['Pol1-env_static_(trans)']) #E_Polar_AlphaE_gr_ex_FG = 0.0 # pot_dipole_gr_ex = potential of induced dipoles induced by difference charges between ground and excited state (gr_charges - ex_charges) with energy_units("AU"): E_elst_trans = EnergyClass(E_elst_trans) E_elst_grnd = EnergyClass(E_elst_grnd) E_elst_exct = EnergyClass(E_elst_exct) E_Pol1_env_static_ex_gr_FG = EnergyClass(E_Pol1_env_static_ex_gr_FG) E_Pol2_env_static_ex_gr_FG = EnergyClass(E_Pol2_env_static_ex_gr_FG) E_Pol1_env_BetaEE_ex_gr_FG = EnergyClass(E_Pol1_env_BetaEE_ex_gr_FG) E_Pol1_env_BetaEE_trans_FG = EnergyClass(E_Pol1_env_BetaEE_trans_FG) E_Pol1_env_AlphaE_trans_FG = EnergyClass(E_Pol1_env_AlphaE_trans_FG) E_Pol1_env_Alpha_E_trans_FG = EnergyClass(E_Pol1_env_Alpha_E_trans_FG) E_Pol1_env_static_trans_FG = EnergyClass(E_Pol1_env_static_trans_FG) if MathOut: if not os.path.exists("Pictures"): os.makedirs("Pictures") Bonds = GuessBonds(mol_polar.coor) struc.guess_bonds() if CoarseGrain in ["plane","C","CF"]: at_type = ['C']*mol_polar.Nat elif CoarseGrain == "all_atom": at_type = struc.at_type.copy() mat_filename = "".join(['Pictures/Charge_',ShortName,'_Exct-Grnd.nb']) params = {'TrPointCharge': mol_Elstat.charge,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_Elstat.coor,struc.bonds,struc.at_type,**params) mol_Elstat.charge[index] = charge mat_filename = "".join(['Pictures/Charge_',ShortName,'_Trans.nb']) params = {'TrPointCharge': mol_Elstat.charge,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_Elstat.coor,struc.bonds,struc.at_type,**params) # res_Pot = {'Pol2-env_static_(exct-grnd)': pot2_dipole_ex_gr} # res_Pot['Pol1-env_static_(exct-grnd)'] = pot1_dipole_ex_gr # res_Pot['Pol1-env_Beta(E,E)_(exct-grnd)'] = pot1_dipole_betaEE_ex_gr # res_Pot['Pol1-env_Beta(E,E)_(trans)'] = pot1_dipole_betaEE_tr # res_Pot['Pol1-env_Alpha(E)_(trans)'] = pot1_dipole_AlphaE_tr # res_Pot['Pol1-env_Alpha(-E)_(trans)'] = pot1_dipole_Alpha_E_tr # res_Pot['Pol1-env_static_(trans)'] = pot1_dipole_static_tr # # res_Energy = {'dE_0-1': Eshift, 'dE_elstat(exct-grnd)': dAVA} # res_Energy['E_pol1_Alpha(E)'] = Polar1_AlphaE # res_Energy['E_pol2_Alpha(E)'] = Polar2_AlphaE # res_Energy['E_pol1_Alpha(-E)'] = Polar1_Alpha_E # res_Energy['E_pol2_Alpha(-E)'] = Polar2_Alpha_E # res_Energy['E_pol1_Beta(E,E)'] = Polar1_Beta_EE # res_Energy['E_pol1_static_(exct-grnd)'] = Polar1_static_ex_gr # res_Energy['E_pol2_static_(exct-grnd)'] = Polar2_static_ex_gr # res_Energy['E_pol1_Beta(E,E)_(exct-grnd)'] = Polar1_Beta_EE_ex_gr # res_Energy['E_pol1_static_(trans)_(exct)'] = Polar1_static_tr_ex # res_Energy['E_pol1_static_(trans)_(grnd)'] = Polar1_static_tr_gr # res_Energy['E_pol1_Alpha(E)_(trans)_(grnd)'] = Polar1_AlphaE_tr_gr # res_Energy['E_pol1_Alpha(-E)_(trans)_(exct)'] = Polar1_Alpha_E_tr_ex # res_Energy['E_pol1_Beta(E,E)_(trans)_(exct-grnd)'] = Polar1_Beta_EE_tr_ex_gr # res_Energy['E_elstat_trans'] = E_elst_trans res_Energy['E_pol1-env_static_(exct-grnd)'] = E_Pol1_env_static_ex_gr_FG res_Energy['E_pol2-env_static_(exct-grnd)'] = E_Pol2_env_static_ex_gr_FG res_Energy['E_pol1-env_Beta(E,E)_(exct-grnd)'] = E_Pol1_env_BetaEE_ex_gr_FG res_Energy['E_pol1-env_Beta(E,E)_(trans)'] = E_Pol1_env_BetaEE_trans_FG res_Energy['E_pol1-env_Alpha(E)_(trans)'] = E_Pol1_env_AlphaE_trans_FG res_Energy['E_pol1-env_Alpha(-E)_(trans)'] = E_Pol1_env_Alpha_E_trans_FG res_Energy['E_pol1-env_static_(trans)'] = E_Pol1_env_static_trans_FG # E_elst_grnd, E_elst_exct return res_Energy, TrDip def Calc_SingleDef_FGprop_new(filenames,ShortName,index_all,E01,AlphaE,Alpha_E,BetaE,VinterFG,FG_charges,ChargeType,order=2,verbose=False,approx=1.1,MathOut=False,CoarseGrain="plane",**kwargs): ''' Compare magnitude of individual terms in energy shift calculation for defect in Fluorographene environment (so far only for first order of perturbation expansion -> order = 1) Parameters ---------- filenames : dictionary Dictionary with information about all needed files which contains nessesary information for transformig the system into Dielectric class and electrostatic calculations. Keys: * ``'1def_structure'``: xyz file with FG system with single defect geometry and atom types * ``'charge_structure'``: xyz file with defect-like molecule geometry for which transition charges were calculated corresponding to first defect * ``'charge'``: file with transition charges for the defect (from TrEsp charges fitting) * ``'charge_grnd'``: file with ground state charges for the defect (from TrEsp charges fitting) * ``'charge_exct'``: file with excited state charges for the defect (from TrEsp charges fitting) ShortName : string Short description of the system index_all : list of integers (dimension 6) There are specified indexes neded for asignment of defect atoms. First three indexes correspond to center and two main axes of reference structure (structure which was used for charges calculation) and the last three indexes are corresponding atoms of the defect. AlphaE : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) Alpha_E : numpy.array of real (dimension 2x2) Atomic polarizability Alpha(-E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) BetaE : numpy.array of real (dimension 2x2) Atomic polarizability Beta(E,E) for C-F corse grained atoms of fluorographene in ATOMIC UNITS (Bohr^2 - because 2D) VinterFG : real Difference in electrostatic interaction energy between interaction of excited C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state and interaction of ground state C-F corse grained atom of fluorographene with all others fluorographene corse grained atoms in ground state. Units are ATOMIC UNITS (Hartree) FG_charges : list of real (dimension 2) [charge on inner fluorographene atom, charge on borded fluorographe carbon] ChargeType : string Specifies which charges should be used for electrostatic calculations (ground and excited state charges) for defect atoms. Allowed types are: ``'qchem'``, ``'qchem_all'``, ``'AMBER'`` and ``'gaussian'``. * ``'qchem'`` - charges calculated by fiting Q-Chem ESP on carbon atoms. * ``'qchem_all'`` - charges calculated by fiting Q-Chem ESP on all atoms, only carbon charges are used and same charge is added to all carbon atoms in order to have neutral molecule. * ``'AMBER'`` - not yet fully implemented. * ``'gaussian'`` - not yet fully implemented. order : integer (optional - init=80) Specify how many SCF steps shoudl be used in calculation of induced dipoles - according to the used model it should be 2 verbose : logical (optional - init=False) If `True` aditional information about whole proces will be printed approx : real (optional - init=1.1) Specifies which approximation should be used. * **Approximation 1.1**: Neglect of `Beta(-E,-E)` and `Beta(-E,E)` and `Alpha(-E)`. * **Approximation 1.2**: Neglect of `Beta(-E,-E)` and `tilde{Beta(E)}`. * **Approximation 1.3**: `Beta(E,E)=Beta(-E,E)=Beta(-E,-E)` and also `Alpha(E)=Alpha(-E)`, however the second one is not condition Returns -------- Eshift : Energy class Transition energy shift for the defect due to the fluorographene environment calculated from structure with single defect. Units are energy managed TrDip : numpy array of real (dimension 3) Total transition dipole for the defect with environment effects included calculated from structure with single defect (in ATOMIC UNITS) Notes -------- By comparing QC calculations it was found that energy shift from structure with two defects and with single defect is almost the same. ''' # read and prepare molecule mol_polar,index1,charge,struc=prepare_molecule_1Def(filenames,index_all,AlphaE,Alpha_E,BetaE,VinterFG,verbose=False,CoarseGrain=CoarseGrain,**kwargs) # calculate dAVA = <A|V|A>-<G|V|G> AditInfo={'Structure': struc,'index1': index1,'Output_exct': True} mol_Elstat,index,charge_grnd,charge_exct=ElStat_PrepareMolecule_1Def(filenames,index_all,FG_charges,ChargeType=ChargeType,verbose=False,**AditInfo) dAVA=mol_Elstat.get_EnergyShift() # dAVA2, dAVA_R = mol_Elstat.get_EnergyShift_and_Derivative() # print(dAVA,dAVA2,dAVA-dAVA2) # calculate transition energy shifts and transition dipole change # res_Energy, res_Pot, TrDip = mol_polar._TEST_Compare_SingleDefectProperties(charge,charge_grnd,charge_exct,struc,index1,dAVA=dAVA,order=order,approx=approx) Eshift,res_Energy,TrDip = mol_polar.get_SingleDefectProperties_new(charge_grnd, charge_exct, mol_Elstat, struc, index1, E01, dAVA=dAVA, order=order, approx=approx) if MathOut: if not os.path.exists("Pictures"): os.makedirs("Pictures") Bonds = GuessBonds(mol_polar.coor) struc.guess_bonds() if CoarseGrain in ["plane","C","CF"]: at_type = ['C']*mol_polar.Nat elif CoarseGrain == "all_atom": at_type = struc.at_type.copy() mat_filename = "".join(['Pictures/Charge_',ShortName,'_Exct-Grnd.nb']) params = {'TrPointCharge': mol_Elstat.charge,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_Elstat.coor,struc.bonds,struc.at_type,**params) mol_Elstat.charge[index] = charge mat_filename = "".join(['Pictures/Charge_',ShortName,'_Trans.nb']) params = {'TrPointCharge': mol_Elstat.charge,'rSphere_dip': 0.5,'rCylinder_dip':0.1} OutputMathematica(mat_filename,mol_Elstat.coor,struc.bonds,struc.at_type,**params) return Eshift, TrDip '''----------------------- TEST PART --------------------------------''' if __name__=="__main__": print(' TESTS') print('-----------------------------------------') ''' Test derivation of energy d/dR ApB ''' # SETUP VERY SIMPLE SYSTEM OF TWO DEFECT ATOMS AND ONE ENVIRONMENT ATOM: coor=np.array([[-1.0,0.0,0.0],[0.0,0.0,0.0],[1.0,0.0,0.0]],dtype='f8') charge_pol=np.array([1.0,0.0,0.0],dtype='f8') dipole=np.zeros((len(coor),3),dtype='f8') AlphaE=np.array([np.zeros((3,3)),[[2.0,0.0,0.0],[0.0,2.0,0.0],[0.0,0.0,0.0]],np.zeros((3,3))],dtype='f8') pol_mol=Dielectric(coor,charge_pol,dipole,AlphaE,AlphaE,AlphaE,0.0) # definition of defect atoms and corresponding charges charge=np.array([1.0],dtype='f8') index1=[0] index2=[2] res_general=pol_mol._dR_BpA(index1,index2,charge,'AlphaE') result=np.zeros((3,3),dtype='f8') result2=np.array([[-4.0,0.0,0.0],[0.0,0.0,0.0],[4.0,0.0,0.0]],dtype='f8').reshape(3*len(coor)) R01=coor[1,:]-coor[0,:] RR01=np.sqrt(np.dot(R01,R01)) R21=coor[1,:]-coor[2,:] RR21=np.sqrt(np.dot(R21,R21)) dn=np.dot(AlphaE[1],R21/(RR21**3)) result[0,:]=charge[0]*charge[0]*(3*np.dot(R01/(RR01**5),dn)*R01-1/(RR01**3)*dn) dn=np.dot(AlphaE[1],R01/(RR01**3)) result[2,:]=charge[0]*charge[0]*(3*np.dot(R21/(RR21**5),dn)*R21-1/(RR21**3)*dn) if np.allclose(res_general,result2): print('Symm _dR_BpA simple system ... OK') else: print('Symm _dR_BpA simple system ... Error') print(' General result: ',res_general) print(' Analytical result:',result2) result3=np.array([[8.0,0.0,0.0],[-8.0,0.0,0.0]],dtype='f8').reshape(6) pol_mol._swap_atoms(index1,index2) res_general=pol_mol._dR_BpA(index2,index2,charge,'AlphaE') if np.allclose(res_general[3:9],result3): print('Symm _dR_ApA simple system ... OK') else: print('Symm _dR_ApA simple system ... Error') print(' General result: ',res_general) print(' Analytical result:',result3) # SETUP NON-SYMETRIC SIMPLE SYSTEM OF TWO DEFECT ATOMS AND ONE ENVIRONMENT ATOM: coor=np.array([[-1.0,0.0,0.0],[0.0,0.0,0.0],[1.0,2.0,0.0]],dtype='f8') charge_pol=np.array([1.0,0.0,0.0],dtype='f8') dipole=np.zeros((len(coor),3),dtype='f8') AlphaE=np.array([np.zeros((3,3)),[[2.0,0.0,0.0],[0.0,2.0,0.0],[0.0,0.0,0.0]],np.zeros((3,3))],dtype='f8') pol_mol=Dielectric(coor,charge_pol,dipole,AlphaE,AlphaE,AlphaE,0.0) # definition of defect atoms and corresponding charges charge=np.array([1.0],dtype='f8') index1=[0] index2=[2] res_general=pol_mol._dR_BpA(index1,index2,charge,'AlphaE') # # result=np.zeros((3,3),dtype='f8') result2=np.array([[-4.0/np.sqrt(5)**3,4.0/np.sqrt(5)**3,0.0], [6*(1/np.sqrt(5)**3-1/np.sqrt(5)**5),-4/np.sqrt(5)**3-12/np.sqrt(5)**5,0.0], [6/np.sqrt(5)**5-2/np.sqrt(5)**3,12/np.sqrt(5)**5,0.0]],dtype='f8').reshape(3*len(coor)) result=np.zeros((3,3),dtype='f8') R01=coor[1,:]-coor[0,:] RR01=np.sqrt(np.dot(R01,R01)) R21=coor[1,:]-coor[2,:] RR21=np.sqrt(np.dot(R21,R21)) dn=np.dot(AlphaE[1],R21/(RR21**3)) result[0,:]=charge[0]*charge[0]*(3*np.dot(R01/(RR01**5),dn)*R01-1/(RR01**3)*dn) dn=np.dot(AlphaE[1],R01/(RR01**3)) result[2,:]=charge[0]*charge[0]*(3*np.dot(R21/(RR21**5),dn)*R21-1/(RR21**3)*dn) #print(result2) #print(result) if np.allclose(res_general,result2): print('non-Symm _dR_BpA simple system ... OK') else: print('non-Symm _dR_BpA simple system ... Error') print(' General result: ',res_general) print(' Analytical result:',result2) result3=np.array([[0.064,0.128,0.0],[-0.064,-0.128,0.0]],dtype='f8').reshape(6) pol_mol._swap_atoms(index1,index2) res_general=pol_mol._dR_BpA(index2,index2,charge,'AlphaE') if np.allclose(res_general[3:9],result3): print('non-Symm _dR_ApA simple system ... OK') else: print('non-Symm _dR_ApA simple system ... Error') print(' General result: ',res_general) print(' Analytical result:',result3) # SETUP LITTLE BIT MORE COMPLICATED SYSTEM OF 2 DEFECT ATOMS AND 2ENVIRONMENT ATOMS for kk in range(2): if kk==0: coor=np.array([[-2.0,0.0,0.0],[-2.0,-1.0,0.0],[0.0,0.0,0.0],[1.0,0.0,0.0],[2.0,0.0,0.0],[2.0,1.0,0.0]],dtype='f8') else: coor=np.array([[-2.0,0.0,0.0],[-2.0,1.0,0.0],[0.0,0.0,0.0],[1.0,0.0,0.0],[2.0,0.0,0.0],[2.0,1.0,0.0]],dtype='f8') charge_pol=np.array([1.0,-1.0,0.0,0.0,0.0,0.0],dtype='f8') dipole=np.zeros((len(coor),3),dtype='f8') AlphaE=np.array([np.zeros((3,3)),np.zeros((3,3)), [[2.0,0.0,0.0],[0.0,2.0,0.0],[0.0,0.0,0.0]], [[2.0,0.0,0.0],[0.0,2.0,0.0],[0.0,0.0,0.0]], np.zeros((3,3)),np.zeros((3,3))],dtype='f8') pol_mol=Dielectric(coor,charge_pol,dipole,AlphaE,AlphaE,AlphaE,0.0) # definition of defect atoms and corresponding charges charge=np.array([1.0,-1.0],dtype='f8') index1=[0,1] index2=[4,5] res_general=pol_mol._dR_BpA(index1,index2,charge,'AlphaE') if kk==0: # for coor[1]=[-2.0,-1.0,0.0] result2=np.array([[-0.1313271490,-0.04854981982,0.0],[0.04798957640,0.07411449339,0.0], [0.0,0.0,0.0],[-0.04637925945,-0.08345754376,0.0], [0.1005284061,0.08560623298,0.0], [0.02918842589,-0.02771336278,0.0]],dtype='f8').reshape(3*len(coor)) else: # for coor[1]=[-2.0,1.0,0.0] result2=np.array([[-0.131327,-0.0485498,0.0],[0.126639,-0.0300095,0.0], [0.0,0.0624526,0.0],[-0.0195464,0.138987,0.0], [0.100528,-0.0856062,0.0],[-0.0762936,-0.037274,0.0]],dtype='f8').reshape(3*len(coor)) if np.allclose(res_general,result2): print('non-Symm _dR_BpA system',kk+1,' ... OK') else: print('non-Symm _dR_BpA system',kk+1,' ... Error') print(' General result: ',res_general) print(' Analytical result:',result2) if kk==1: res_general=pol_mol._dR_BpA(index1,index1,charge,'AlphaE') result3=np.array([[0.0759272,-0.0494062,0.0],[0.00288743,0.0479804,0.0], [-0.0738948,0.0013901,0.0],[-0.00491991,0.00003574515217,0.0]],dtype='f8').reshape(12) if np.allclose(res_general[0:12],result3): print('non-Symm _dR_ApA system',kk+1,' ... OK') else: print('non-Symm _dR_ApA system',kk+1,' ... Error') print(' General result: ',res_general) print(' Analytical result:',result3) ''' Test derivation of energy d/dR BppA ''' # SETUP NON-SYMETRIC SIMPLE SYSTEM OF TWO DEFECT ATOMS AND TWO ENVIRONMENT ATOM: coor=np.array([[-1.0,0.0,0.0],[0.0,0.0,0.0],[0.0,1.0,0.0],[1.0,0.0,0.0]],dtype='f8') charge_pol=np.array([1.0,0.0,0.0,0.0],dtype='f8') dipole=np.zeros((len(coor),3),dtype='f8') AlphaE=np.array([np.zeros((3,3)),[[2.0,0.0,0.0],[0.0,2.0,0.0],[0.0,0.0,0.0]], [[2.0,0.0,0.0],[0.0,2.0,0.0],[0.0,0.0,0.0]],np.zeros((3,3))],dtype='f8') pol_mol=Dielectric(coor,charge_pol,dipole,AlphaE,AlphaE,AlphaE,0.0) # definition of defect atoms and corresponding charges charge=np.array([1.0],dtype='f8') index1=[0] index2=[3] res_general=pol_mol._dR_BppA(index1,index2,charge,'AlphaE') result2=np.array([[3.535533906,-0.7071067812,0.0],[0.0,14.14213562,0.0], [0.0,-12.72792206,0.0],[-3.535533906,-0.7071067812,0.0], ],dtype='f8').reshape(3*len(coor)) if np.allclose(res_general,result2): print('non-Symm _dR_BppA simple system ... OK') else: print('non-Symm _dR_BppA simple system ... Error') print(' General result: ',res_general) print(' Analytical result:',result2) res_general=pol_mol._dR_BppA(index1,index1,charge,'AlphaE') result3=np.array([[-7.071067812,-9.899494937,0.0],[-2.8284271247,-2.8284271247,0.0], [9.899494937,12.72792206,0.0], ],dtype='f8').reshape(9) if np.allclose(res_general[0:9],result3): print('non-Symm _dR_AppA simple system ... OK') else: print('non-Symm _dR_AppA simple system ... Error') print(' General result: ',res_general[0:9]) print(' Analytical result:',result3)

52.33852

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0.005015

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0.003778

0.870045

0.848986

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0.802881

0.782187

0.767407

0

0.030933

0.276825

302,726

5,784

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52.33852

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0.047037

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null

0

1

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null

0

6

0211ba964bfcfda6a3a91c1911c1918cb54f15f1

763

py

Python

gprm/__init__.py

siwill22/GPlatesClassStruggle

713a87ff4f054d3a493ec09e5f310aa3036d3bc5

[ "MIT" ]

7

2020-05-04T03:05:09.000Z

2022-01-28T13:52:53.000Z

gprm/__init__.py

siwill22/GPlatesClassStruggle

713a87ff4f054d3a493ec09e5f310aa3036d3bc5

[ "MIT" ]

null

gprm/__init__.py

siwill22/GPlatesClassStruggle

713a87ff4f054d3a493ec09e5f310aa3036d3bc5

[ "MIT" ]

3

2021-05-23T01:53:52.000Z

2021-09-14T12:21:53.000Z

#import utils #from .GPlatesReconstructionModel.gprm import utils from .GPlatesReconstructionModel import ReconstructionModel from .GPlatesReconstructionModel import ReconstructedPolygonSnapshot from .GPlatesReconstructionModel import PlateTree from .GPlatesReconstructionModel import GPlatesRaster from .GPlatesReconstructionModel import PlateSnapshot from .GPlatesReconstructionModel import MotionPathFeature from .GPlatesReconstructionModel import FlowlineFeature from .GPlatesReconstructionModel import VelocityField from .GPlatesReconstructionModel import SubductionConvergence from .GPlatesReconstructionModel import AgeCodedPointDataset from .GPlatesReconstructionModel import PointDistributionOnSphere from .GPlatesReconstructionModel import CrossSection

50.866667

68

0.908257

55

763

12.6

0.309091

0.562771

0.623377

0.118326

0

0.070773

763

14

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54.5

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1

null

1

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null

0

1

0

1

0

1

0

6

02208259341a6bd919959511e42bd473f447377d

149

py

Python

make_us_rich/pipelines/fetching/__init__.py

ChainYo/make-me-rich

ad3bbc23bef4840f80799e0fd4903767d9a57a72

[ "Apache-2.0" ]

11

2022-02-06T18:01:29.000Z

2022-02-23T15:51:48.000Z

make_us_rich/pipelines/fetching/__init__.py

ChainYo/make-me-rich

ad3bbc23bef4840f80799e0fd4903767d9a57a72

[ "Apache-2.0" ]

null

make_us_rich/pipelines/fetching/__init__.py

ChainYo/make-me-rich

ad3bbc23bef4840f80799e0fd4903767d9a57a72

[ "Apache-2.0" ]

1

2022-02-14T10:41:53.000Z

from .pipeline import create_pipeline from .nodes import fetch_data_to_dataframe __all__ = [ "create_pipeline", "fetch_data_to_dataframe", ]

21.285714

42

0.778523

19

149

5.473684

0.526316

0.269231

0.211538

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149

7

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null

1

0

1

0

null

0

1

0

6

026863faa67c919d45449c13c767272bc0bdde4f

28,921

py

Python

cmws/examples/stacking/render.py

tuananhle7/hmws

175f77a2b386ce5a9598b61c982e053e7ecff8a2

[ "MIT" ]

null

cmws/examples/stacking/render.py

tuananhle7/hmws

175f77a2b386ce5a9598b61c982e053e7ecff8a2

[ "MIT" ]

null

cmws/examples/stacking/render.py

tuananhle7/hmws

175f77a2b386ce5a9598b61c982e053e7ecff8a2

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from cmws import util class Square: def __init__(self, name, color, size): self.name = name self.color = color self.size = size @property def device(self): return self.size.device def __repr__(self): return f"{self.name}(color={self.color.tolist()}, size={self.size.item():.1f})" class LearnableSquare(nn.Module): def __init__(self, name=None, fixed_color=False): super().__init__() if name is None: self.name = "LearnableSquare" else: self.name = name self.fixed_color = fixed_color if not self.fixed_color: self.raw_color = nn.Parameter(torch.randn((3,))) self.raw_size = nn.Parameter(torch.randn(())) @property def device(self): return self.raw_size.device @property def size(self): min_size = 0.01 max_size = 1.0 return self.raw_size.sigmoid() * (max_size - min_size) + min_size @property def color(self): if self.fixed_color: return torch.zeros((3,), device=self.device) else: return self.raw_color.sigmoid() def __repr__(self): return f"{self.name}(color={self.color.tolist()}, size={self.size.item():.1f})" def get_min_edge_distance(square_size, location, point): """Computes shortest distance from a point to the square edge. (batched) Negative if it's inside the square. Positive if it's outside the square. Args square_size [] or [*location_shape] location [*location_shape, 2] point [*point_shape, 2] Returns [*location_shape, *point_shape] """ # Extract device = location.device # [*location_shape] min_x, min_y = location[..., 0], location[..., 1] max_x = min_x + square_size max_y = min_y + square_size location_shape = min_x.shape num_locations = int(torch.tensor(location_shape).prod().long().item()) # [*point_shape] x, y = point[..., 0], point[..., 1] point_shape = x.shape num_points = int(torch.tensor(point_shape).prod().long().item()) # Flatten # [num_locations, 1] min_x, min_y, max_x, max_y = [tmp.view(-1)[:, None] for tmp in [min_x, min_y, max_x, max_y]] # [1, num_points] x, y = [tmp.view(-1)[None] for tmp in [x, y]] # Determine which area the point is in # [num_locations, num_points] # --High level areas up = y >= max_y middle = (y >= min_y) & (y < max_y) bottom = y < min_y left = x < min_x center = (x >= min_x) & (x < max_x) right = x >= max_x # --Use high level areas to define smaller sectors which we're going to work with area_1 = left & up area_2 = center & up area_3 = right & up area_4 = left & middle area_5 = center & middle area_6 = right & middle area_7 = left & bottom area_8 = center & bottom area_9 = right & bottom # Compute min distances # --Init the results # [num_locations, num_points] min_edge_distance = torch.zeros((num_locations, num_points), device=device) # --Compute distances for points in the corners (areas 1, 3, 7, 9) min_edge_distance[area_1] = util.sqrt((x - min_x) ** 2 + (y - max_y) ** 2)[area_1] min_edge_distance[area_3] = util.sqrt((x - max_x) ** 2 + (y - max_y) ** 2)[area_3] min_edge_distance[area_7] = util.sqrt((x - min_x) ** 2 + (y - min_y) ** 2)[area_7] min_edge_distance[area_9] = util.sqrt((x - max_x) ** 2 + (y - min_y) ** 2)[area_9] # --Compute distances for points in the outside strips (areas 2, 4, 6, 8) min_edge_distance[area_2] = (y - max_y)[area_2] min_edge_distance[area_4] = (min_x - x)[area_4] min_edge_distance[area_6] = (x - max_x)[area_6] min_edge_distance[area_8] = (min_y - y)[area_8] # --Compute distances for points inside the square min_edge_distance[area_5] = -torch.min( torch.stack([y - min_y, max_y - y, x - min_x, max_x - x]), dim=0 )[0][area_5] return min_edge_distance.view(*[*location_shape, *point_shape]) def get_render_log_prob(min_edge_distance, blur=1e-4): """ Returns the (log) probability map used for soft rasterization as specified by equation (1) of https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Soft_Rasterizer_A_Differentiable_Renderer_for_Image-Based_3D_Reasoning_ICCV_2019_paper.pdf Also visualized here https://www.desmos.com/calculator/5z95dy2mny Args min_edge_distance [*shape] blur [] (default 1e-4): this is the σ in equation (1) Returns [*shape] """ return F.logsigmoid(-torch.sign(min_edge_distance) * min_edge_distance ** 2 / blur) def get_canvas_xy(num_rows, num_cols, device): """Create xy points on the canvas Args num_rows (int) num_cols (int) Returns canvas_x [num_rows, num_cols] canvas_y [num_rows, num_cols] """ x_range = torch.linspace(-1, 1, steps=num_cols, device=device) y_range = torch.linspace(-1, 1, steps=num_rows, device=device).flip(dims=[0]) # [num_cols, num_rows] canvas_x, canvas_y = torch.meshgrid(x_range, y_range) # [num_rows, num_cols] canvas_x, canvas_y = canvas_x.T, canvas_y.T return canvas_x, canvas_y def init_canvas(device, num_channels=3, num_rows=32, num_cols=32, shape=[]): """Return a white canvas of shape [*shape, num_channels, num_rows, num_cols]""" return torch.ones(*[*shape, num_channels, num_rows, num_cols], device=device) def render_square(square, location, canvas, draw_on_top=False): """Draws a square on a canvas whose xy limits are [-1, 1]. Args square location [2] canvas [num_channels, num_rows, num_cols] draw_on_top (bool): draw squares on top of the canvas, instead of adding it to the canvas Returns new_canvas [num_channels, num_rows, num_cols] """ # Extract # [] min_x, min_y = location max_x = min_x + square.size max_y = min_y + square.size num_channels, num_rows, num_cols = canvas.shape device = location.device # Canvas xy # [num_rows, num_cols] canvas_x, canvas_y = get_canvas_xy(num_rows, num_cols, device) # Draw on canvas new_canvas = canvas.clone() for channel_id in range(num_channels): if draw_on_top: new_canvas[ channel_id, (canvas_x >= min_x) & (canvas_x <= max_x) & (canvas_y >= min_y) & (canvas_y <= max_y), ] = square.color[channel_id] else: new_canvas[ channel_id, (canvas_x >= min_x) & (canvas_x <= max_x) & (canvas_y >= min_y) & (canvas_y <= max_y), ] -= (1 - square.color[channel_id]) new_canvas = new_canvas.clamp(0, 1) return new_canvas def soft_render_square( square, location, background, background_depth=-1e-3, color_sharpness=1e-4, blur=1e-4 ): """Draws a square on a canvas whose xy limits are [-1, 1]. Follows equations (2) and (3) in https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Soft_Rasterizer_A_Differentiable_Renderer_for_Image-Based_3D_Reasoning_ICCV_2019_paper.pdf Args square location [*shape, 2] background [num_channels, num_rows, num_cols] or [*shape, num_channels, num_rows, num_cols] this is the background color C_b in equation (2) background_weight [] (default 1.): ϵ in equation (3) color_sharpness [] (default 1e-4): γ in equation (3) blur [] (default 1e-4): this is the σ in equation (1) Returns new_canvas [*shape, num_channels, num_rows, num_cols] """ # Extract shape = location.shape[:-1] # Init device = location.device if background.ndim > 3: num_channels, num_rows, num_cols = background.shape[-3:] expanded_background = True assert background.shape[:-3] == shape else: num_channels, num_rows, num_cols = background.shape expanded_background = False # Canvas xy # [num_rows, num_cols] canvas_x, canvas_y = get_canvas_xy(num_rows, num_cols, device) canvas_xy = torch.stack([canvas_x, canvas_y], dim=-1) # Get render log prob # --Foreground object (treat depth z = -1) [*shape, num_rows, num_cols] depth = 0 square_render_log_prob = ( get_render_log_prob(get_min_edge_distance(square.size, location, canvas_xy), blur=blur) + depth / color_sharpness ) # --Background [*shape, num_rows, num_cols] background_render_log_prob = ( torch.ones_like(square_render_log_prob) * background_depth / color_sharpness ) # Compute color weight (equation (3)) # [*shape, num_rows, num_cols] square_weight, background_weight = F.softmax( torch.stack([square_render_log_prob, background_render_log_prob]), dim=0 ) # Flatten # [num_samples, num_rows, num_cols] square_weight_flattened = square_weight.view(-1, num_rows, num_cols) background_weight_flattened = background_weight.view(-1, num_rows, num_cols) if expanded_background: background_flattened = background.view(-1, num_channels, num_rows, num_cols) else: background_flattened = background[None] return ( square_weight_flattened[:, None] * square.color[None, :, None, None] + background_weight_flattened[:, None] * background_flattened ).view(*[*shape, num_channels, num_rows, num_cols]) def soft_render_square_batched( square_size, square_color, location, background, background_depth=-1e-3, color_sharpness=1e-4, blur=1e-4, ): """Draws a square on a canvas whose xy limits are [-1, 1]. Follows equations (2) and (3) in https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Soft_Rasterizer_A_Differentiable_Renderer_for_Image-Based_3D_Reasoning_ICCV_2019_paper.pdf Args square_size [*shape] or [] square_color [*shape, 3] or [3] location [*shape, 2] background [num_channels, num_rows, num_cols] or [*shape, num_channels, num_rows, num_cols] this is the background color C_b in equation (2) background_weight [] (default 1.): ϵ in equation (3) color_sharpness [] (default 1e-4): γ in equation (3) blur [] (default 1e-4): this is the σ in equation (1) Returns new_canvas [*shape, num_channels, num_rows, num_cols] """ # Extract shape = location.shape[:-1] # Init device = location.device if background.ndim > 3: num_channels, num_rows, num_cols = background.shape[-3:] expanded_background = True assert background.shape[:-3] == shape else: num_channels, num_rows, num_cols = background.shape expanded_background = False # Canvas xy # [num_rows, num_cols] canvas_x, canvas_y = get_canvas_xy(num_rows, num_cols, device) canvas_xy = torch.stack([canvas_x, canvas_y], dim=-1) # Get render log prob # --Foreground object (treat depth z = -1) [*shape, num_rows, num_cols] depth = 0 square_render_log_prob = ( get_render_log_prob(get_min_edge_distance(square_size, location, canvas_xy), blur=blur) + depth / color_sharpness ) # --Background [*shape, num_rows, num_cols] background_render_log_prob = ( torch.ones_like(square_render_log_prob) * background_depth / color_sharpness ) # Compute color weight (equation (3)) # [*shape, num_rows, num_cols] square_weight, background_weight = F.softmax( torch.stack([square_render_log_prob, background_render_log_prob]), dim=0 ) # Flatten # [num_samples, num_rows, num_cols] square_weight_flattened = square_weight.view(-1, num_rows, num_cols) background_weight_flattened = background_weight.view(-1, num_rows, num_cols) if expanded_background: background_flattened = background.view(-1, num_channels, num_rows, num_cols) else: background_flattened = background[None] if square_color.ndim == 1: square_color_expanded = square_color[None, :, None, None] else: square_color_expanded = square_color.reshape(-1, 3)[:, :, None, None] return ( square_weight_flattened[:, None] * square_color_expanded + background_weight_flattened[:, None] * background_flattened ).view(*[*shape, num_channels, num_rows, num_cols]) def render_square_batched( square_size, square_color, location, background, ): """Draws a square on a canvas whose xy limits are [-1, 1]. Follows equations (2) and (3) in https://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Soft_Rasterizer_A_Differentiable_Renderer_for_Image-Based_3D_Reasoning_ICCV_2019_paper.pdf Args square_size [*shape] or [] square_color [*shape, 3] or [3] location [*shape, 2] background [num_channels, num_rows, num_cols] or [*shape, num_channels, num_rows, num_cols] this is the background color C_b in equation (2) background_weight [] (default 1.): ϵ in equation (3) color_sharpness [] (default 1e-4): γ in equation (3) blur [] (default 1e-4): this is the σ in equation (1) Returns new_canvas [*shape, num_channels, num_rows, num_cols] """ # Extract shape = location.shape[:-1] device = location.device num_elements = int(torch.tensor(shape).prod().long().item()) num_channels, num_rows, num_cols = background.shape[-3:] num_points = num_rows * num_cols # Canvas xy # --Compute # [num_rows, num_cols] canvas_x, canvas_y = get_canvas_xy(num_rows, num_cols, device) # --Flatten # [1, num_points] x, y = [tmp.reshape(-1)[None] for tmp in [canvas_x, canvas_y]] # Compute boundaries # --Compute # [*shape] min_x, min_y = location[..., 0], location[..., 1] max_x = min_x + square_size max_y = min_y + square_size # --Flatten # [num_elements, 1] min_x, min_y, max_x, max_y = [tmp.view(-1)[:, None] for tmp in [min_x, min_y, max_x, max_y]] # Draw on canvas # --Expand background if background.ndim > 3: canvas = background.clone().view(num_elements, num_channels, num_points) assert background.shape[:-3] == shape else: canvas = ( background.clone() .view(1, num_channels, num_points) .expand(num_elements, num_channels, num_points) ) # --Expand square_color if square_color.ndim == 1: square_color_expanded = square_color[None, :, None].expand( num_elements, num_channels, num_points ) else: square_color_expanded = square_color.reshape(-1, 3, 1).expand( num_elements, num_channels, num_points ) # --Compute a mask that indicates whether a point is inside a square # [num_elements, num_channels, num_points] inside_square = ((x >= min_x) & (x <= max_x) & (y >= min_y) & (y <= max_y))[:, None, :].expand( num_elements, num_channels, num_points ) # --Draw inside the square canvas[inside_square] = square_color_expanded[inside_square] return canvas.view(*[*shape, num_channels, num_rows, num_cols]) def render(primitives, stacking_program, raw_locations, num_channels=3, num_rows=32, num_cols=32): # Init device = primitives[0].device # Convert locations = convert_raw_locations(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols) for primitive_id, location in zip(stacking_program, locations): primitive = primitives[primitive_id] canvas = render_square(primitive, location, canvas) return canvas def render_batched( primitives, num_blocks, stacking_program, raw_locations, num_channels=3, num_rows=32, num_cols=32, ): """ Args primitives (list [num_primitives]) num_blocks [*shape] stacking_program (tensor [*shape, max_num_blocks]) raw_locations (tensor [*shape, max_num_blocks]) Returns [*shape, num_channels, num_rows, num_cols] """ # Extract device = primitives[0].device shape = stacking_program.shape[:-1] max_num_blocks = stacking_program.shape[-1] # [num_primitives] square_size = torch.stack([primitive.size for primitive in primitives]) # [num_primitives, 3] square_color = torch.stack([primitive.color for primitive in primitives]) # Convert [*shape, max_num_blocks, 2] locations = convert_raw_locations_batched(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols, shape) for block_id in range(max_num_blocks): # Determine whether this block is drawn # [*shape, 1, 1, 1] is_drawn = (block_id < num_blocks).float()[..., None, None, None] # Draw the block canvas = render_square_batched( square_size[stacking_program[..., block_id]], square_color[stacking_program[..., block_id]], locations[..., block_id, :], canvas, ) * is_drawn + canvas * (1 - is_drawn) return canvas def soft_render( primitives, stacking_program, raw_locations, raw_color_sharpness, raw_blur, num_channels=3, num_rows=32, num_cols=32, ): """ Args primitives (list [num_primitives]) stacking_program (tensor [num_blocks]) raw_locations (tensor [num_blocks]) raw_color_sharpness [] raw_blur [] Returns [num_channels, num_rows, num_cols] """ # Init device = primitives[0].device # Convert locations = convert_raw_locations(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols) for primitive_id, location in zip(stacking_program, locations): primitive = primitives[primitive_id] canvas = soft_render_square( primitive, location, canvas, color_sharpness=get_color_sharpness(raw_color_sharpness), blur=get_blur(raw_blur), ) return canvas def soft_render_batched( primitives, stacking_program, raw_locations, raw_color_sharpness, raw_blur, num_channels=3, num_rows=32, num_cols=32, ): """ Args primitives (list [num_primitives]) stacking_program (tensor [*shape, num_blocks]) raw_locations (tensor [*shape, num_blocks]) raw_color_sharpness [] raw_blur [] Returns [*shape, num_channels, num_rows, num_cols] """ # Extract device = primitives[0].device shape = stacking_program.shape[:-1] num_blocks = stacking_program.shape[-1] # [num_primitives] square_size = torch.stack([primitive.size for primitive in primitives]) # [num_primitives, 3] square_color = torch.stack([primitive.color for primitive in primitives]) # Convert locations = convert_raw_locations_batched(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols, shape) for block_id in range(num_blocks): canvas = soft_render_square_batched( square_size[stacking_program[..., block_id]], square_color[stacking_program[..., block_id]], locations[..., block_id, :], canvas, color_sharpness=get_color_sharpness(raw_color_sharpness), blur=get_blur(raw_blur), ) return canvas def soft_render_variable_num_blocks( primitives, num_blocks, stacking_program, raw_locations, raw_color_sharpness, raw_blur, num_channels=3, num_rows=32, num_cols=32, ): """ Args primitives (list [num_primitives]) num_blocks [*shape] stacking_program (tensor [*shape, max_num_blocks]) raw_locations (tensor [*shape, max_num_blocks]) raw_color_sharpness [] raw_blur [] Returns [*shape, num_channels, num_rows, num_cols] """ # Extract device = primitives[0].device shape = stacking_program.shape[:-1] max_num_blocks = stacking_program.shape[-1] # [num_primitives] square_size = torch.stack([primitive.size for primitive in primitives]) # [num_primitives, 3] square_color = torch.stack([primitive.color for primitive in primitives]) # Convert [*shape, max_num_blocks, 2] locations = convert_raw_locations_batched(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols, shape) for block_id in range(max_num_blocks): # Determine whether this block is drawn # [*shape, 1, 1, 1] is_drawn = (block_id < num_blocks).float()[..., None, None, None] # Draw the block canvas = soft_render_square_batched( square_size[stacking_program[..., block_id]], square_color[stacking_program[..., block_id]], locations[..., block_id, :], canvas, color_sharpness=get_color_sharpness(raw_color_sharpness), blur=get_blur(raw_blur), ) * is_drawn + canvas * (1 - is_drawn) return canvas def convert_raw_locations(raw_locations, stacking_program, primitives): """ Args raw_locations (tensor [num_blocks]) stacking_program (tensor [num_blocks]) primitives (list [num_primitives]) Returns [num_blocks, 2] """ # Extract device = primitives[0].device # Sample the bottom y = torch.tensor(-1.0, device=device) min_x = -0.8 max_x = 0.8 locations = [] for primitive_id, raw_location in zip(stacking_program, raw_locations): size = primitives[primitive_id].size min_x = min_x - size x = raw_location.sigmoid() * (max_x - min_x) + min_x locations.append(torch.stack([x, y])) y = y + size min_x = x max_x = min_x + size return torch.stack(locations) def convert_raw_locations_batched(raw_locations, stacking_program, primitives): """ Args raw_locations (tensor [*shape, num_blocks]) stacking_program (tensor [*shape, num_blocks]) primitives (list [num_primitives]) Returns [*shape, num_blocks, 2] """ # Extract shape = raw_locations.shape[:-1] num_samples = util.get_num_elements(shape) num_blocks = raw_locations.shape[-1] # Flatten # [num_samples, num_blocks] raw_locations_flattened = raw_locations.view(num_samples, num_blocks) stacking_program_flattened = stacking_program.reshape(num_samples, num_blocks) locations_batched = [] for sample_id in range(num_samples): locations_batched.append( convert_raw_locations( raw_locations_flattened[sample_id], stacking_program_flattened[sample_id], primitives, ) ) return torch.stack(locations_batched).view(*[*shape, num_blocks, 2]) def get_color_sharpness(raw_color_sharpness): return raw_color_sharpness.exp() def get_blur(raw_blur): return raw_blur.exp() def convert_raw_locations_top_down(raw_locations, stacking_program, primitives): """ Args raw_locations (tensor [num_blocks]) stacking_program (tensor [num_blocks]) primitives (list [num_primitives]) Returns [num_blocks, 2] """ # Sample the bottom min_x = -0.8 max_x = 0.8 locations = [] for primitive_id, raw_location in zip(stacking_program, raw_locations): size = primitives[primitive_id].size min_x = min_x - size x = raw_location.sigmoid() * (max_x - min_x) + min_x y = -size / 2.0 locations.append(torch.stack([x, y])) min_x = x max_x = min_x + size return torch.stack(locations) def convert_raw_locations_batched_top_down(raw_locations, stacking_program, primitives): """ Args raw_locations (tensor [*shape, num_blocks]) stacking_program (tensor [*shape, num_blocks]) primitives (list [num_primitives]) Returns [*shape, num_blocks, 2] """ # Extract shape = raw_locations.shape[:-1] num_samples = util.get_num_elements(shape) num_blocks = raw_locations.shape[-1] # Flatten # [num_samples, num_blocks] raw_locations_flattened = raw_locations.view(num_samples, num_blocks) stacking_program_flattened = stacking_program.reshape(num_samples, num_blocks) locations_batched = [] for sample_id in range(num_samples): locations_batched.append( convert_raw_locations_top_down( raw_locations_flattened[sample_id], stacking_program_flattened[sample_id], primitives, ) ) return torch.stack(locations_batched).view(*[*shape, num_blocks, 2]) def soft_render_top_down( primitives, num_blocks, stacking_program, raw_locations, raw_color_sharpness, raw_blur, num_channels=3, num_rows=32, num_cols=32, ): """ Args primitives (list [num_primitives]) num_blocks [*shape] stacking_program (tensor [*shape, max_num_blocks]) raw_locations (tensor [*shape, max_num_blocks]) raw_color_sharpness [] raw_blur [] Returns [*shape, num_channels, num_rows, num_cols] """ # Extract device = primitives[0].device shape = stacking_program.shape[:-1] max_num_blocks = stacking_program.shape[-1] # [num_primitives] square_size = torch.stack([primitive.size for primitive in primitives]) # [num_primitives, 3] square_color = torch.stack([primitive.color for primitive in primitives]) # Convert [*shape, max_num_blocks, 2] locations = convert_raw_locations_batched_top_down(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols, shape) for block_id in range(max_num_blocks): # Determine whether this block is drawn # [*shape, 1, 1, 1] is_drawn = (block_id < num_blocks).float()[..., None, None, None] # Draw the block canvas = soft_render_square_batched( square_size[stacking_program[..., block_id]], square_color[stacking_program[..., block_id]], locations[..., block_id, :], canvas, color_sharpness=get_color_sharpness(raw_color_sharpness), blur=get_blur(raw_blur), ) * is_drawn + canvas * (1 - is_drawn) return canvas def render_batched_top_down( primitives, num_blocks, stacking_program, raw_locations, num_channels=3, num_rows=32, num_cols=32, ): """ Args primitives (list [num_primitives]) num_blocks [*shape] stacking_program (tensor [*shape, max_num_blocks]) raw_locations (tensor [*shape, max_num_blocks]) Returns [*shape, num_channels, num_rows, num_cols] """ # Extract device = primitives[0].device shape = stacking_program.shape[:-1] max_num_blocks = stacking_program.shape[-1] # [num_primitives] square_size = torch.stack([primitive.size for primitive in primitives]) # [num_primitives, 3] square_color = torch.stack([primitive.color for primitive in primitives]) # Convert [*shape, max_num_blocks, 2] locations = convert_raw_locations_batched_top_down(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols, shape) for block_id in range(max_num_blocks): # Determine whether this block is drawn # [*shape, 1, 1, 1] is_drawn = (block_id < num_blocks).float()[..., None, None, None] # Draw the block canvas = render_square_batched( square_size[stacking_program[..., block_id]], square_color[stacking_program[..., block_id]], locations[..., block_id, :], canvas, ) * is_drawn + canvas * (1 - is_drawn) return canvas def render_top_down( primitives, stacking_program, raw_locations, num_channels=3, num_rows=32, num_cols=32 ): # Init device = primitives[0].device # Convert locations = convert_raw_locations_top_down(raw_locations, stacking_program, primitives) # Render canvas = init_canvas(device, num_channels, num_rows, num_cols) for primitive_id, location in zip(stacking_program, locations): primitive = primitives[primitive_id] canvas = render_square(primitive, location, canvas, draw_on_top=True) return canvas

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0.050088

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0

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0.243007

28,921

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0.789111

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0

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false

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0.012658

0.137131

0

null

0

1

0

null

0

6

65f4379a10ea774538684f28caff94390ec74965

104

py

Python

rpas/tests.py

geoffreynyaga/ANGA-UTM

8371a51ad27c85d2479bb34d8c4e02ea28465941

[ "Apache-2.0" ]

7

2020-01-18T16:53:41.000Z

2021-12-21T07:02:43.000Z

rpas/tests.py

geoffreynyaga/ANGA-UTM

8371a51ad27c85d2479bb34d8c4e02ea28465941

[ "Apache-2.0" ]

28

2020-01-06T18:36:54.000Z

2022-02-10T10:03:55.000Z

rpas/tests.py

geoffreynyaga/ANGA-UTM

8371a51ad27c85d2479bb34d8c4e02ea28465941

[ "Apache-2.0" ]

3

2020-01-18T16:53:54.000Z

2020-10-26T11:21:41.000Z

from django.test import TestCase # Create your tests here. def test_a_plus_b(): assert 1 == 1

17.333333

33

0.682692

17

104

4

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0.025316

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104

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1

0

6

5a091a9bd9345bb779b53c04f7a3e2c1e7df9d93

377

py

Python

python/testData/refactoring/pullup/properties/Class.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/refactoring/pullup/properties/Class.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/refactoring/pullup/properties/Class.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

from SuperClass import SuperClass class AnyClass(SuperClass): C = 1 def __init__(self): super(AnyClass, self).__init__() @property def new_property(self): return 1 @new_property.setter def new_property(self, value): pass @new_property.deleter def new_property(self): pass def foo(self): pass

14.5

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44

377

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0.431818

0.252294

0.192661

0.247706

0

0.007634

0.30504

377

25

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0

0.3125

0

1

0.3125

false

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null

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null

0

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0

1

0

1

0

6

5a403728f96f58315c8f4f3eb45da15397484d96

24,669

py

Python

scripts/dual_power_law_constrained.py

kevincovey/fit-rossby

d7041d093e4399088797e01d64d33195053631bc

[ "MIT" ]

null

scripts/dual_power_law_constrained.py

kevincovey/fit-rossby

d7041d093e4399088797e01d64d33195053631bc

[ "MIT" ]

null

scripts/dual_power_law_constrained.py

kevincovey/fit-rossby

d7041d093e4399088797e01d64d33195053631bc

[ "MIT" ]

null

# Originally written by Stephanie T. Douglas (2012-2014) # Modified by Kevin Covey (2019) # under the MIT License (see LICENSE.txt for full details) import numpy as np import emcee import matplotlib.pyplot as plt def quantile(x,quantiles): """ Calculates quantiles - taken from DFM's triangle.py """ xsorted = sorted(x) qvalues = [xsorted[int(q * len(xsorted))] for q in quantiles] return list(zip(quantiles,qvalues)) def dual_power_law(parameters,x): """ computes a dual-power law model For x >= turnover, the model values follow a power-law with slope beta_2: y = C + beta_2 log10(x) For x < turnover, the model values are a second power-law with slope beta_1: y = C + (beta_2 - beta_1)*log10(turnover) + beta_1 * log10(x) Inputs and outputs are in log space (ie, saturation level is -3., rather than 10.**(-3.); similar for loglxlbol values) Input ----- parameters : array-like (4) parameters for the model: C (intercept constant), turnover, beta_1, beta_2 Ro : array-like Rossby number values. The model Log L_{whatever}/L_{bol} values will be computed for these Rossby numbers Output ------ : numpy.ndarray (same size as Ro) Model Log L_{whatever}/L_{bol} values corresponding to input Ro """ #save the parameters with intuitive names intercept_constant, turnover, beta_1, beta_2 = parameters[0], parameters[1], parameters[2], parameters[3] #calculate the pivot constant that ensures the two laws meet at the same point pivot_constant = intercept_constant + (beta_2 - beta_1) * np.log10(turnover) #define the Log_LxLbol array and fill with saturated level datapoints Log_LxLbol = np.ones(len(x)) #find unsaturated objects and calculate their Log_LxLbols based on the assumed power law behavior un_sat = np.where(x>=turnover)[0] Log_LxLbol[un_sat] = intercept_constant + beta_2 * np.log10(x[un_sat]) #find saturated points and calculate their Log_LxLbols sat = np.where(x<turnover)[0] Log_LxLbol[sat] = pivot_constant + beta_1 * np.log10(x[sat]) return Log_LxLbol def dual_lnprior_periods_fixSlope(parameters, low_slope, high_slope): """ simple method of setting (flat) priors on model parameters If input parameters are within the priors, a (constant) likelihood is returned; if the input parameters are outside the priors, a negative infinity is returned to indicate an unacceptable fit. Input ----- parameters : array-like (3) parameters for the model: saturation level (expressed as Log L_{whatever}/L_{bol}, turnover_Ro, beta Output ------ : value 0.0 if parameters are within priors; -np.inf if not. """ #print('slope bounds are: ', low_slope, high_slope) intercept_constant, turnover, beta_1, beta_2, lnf = parameters[0], parameters[1], parameters[2], parameters[3], parameters[4] if 20 < intercept_constant < 40 and 2 < turnover < 50 and -4 < beta_1 < 2 and low_slope < beta_2 < high_slope and -10.0 < lnf < 1.0: return 0.0 return -np.inf def dual_lnprior_periods(parameters): """ simple method of setting (flat) priors on model parameters If input parameters are within the priors, a (constant) likelihood is returned; if the input parameters are outside the priors, a negative infinity is returned to indicate an unacceptable fit. Input ----- parameters : array-like (3) parameters for the model: saturation level (expressed as Log L_{whatever}/L_{bol}, turnover_Ro, beta Output ------ : value 0.0 if parameters are within priors; -np.inf if not. """ intercept_constant, turnover, beta_1, beta_2, lnf = parameters[0], parameters[1], parameters[2], parameters[3], parameters[4] if 20 < intercept_constant < 40 and 2 < turnover < 50 and -4 < beta_1 < 2 and -5 < beta_2 < 1 and -10.0 < lnf < 1.0: return 0.0 return -np.inf def dual_lnprior_fixSlope(parameters, low_slope, high_slope): """ simple method of setting (flat) priors on model parameters If input parameters are within the priors, a (constant) likelihood is returned; if the input parameters are outside the priors, a negative infinity is returned to indicate an unacceptable fit. Input ----- parameters : array-like (3) parameters for the model: saturation level (expressed as Log L_{whatever}/L_{bol}, turnover_Ro, beta Output ------ : value 0.0 if parameters are within priors; -np.inf if not. """ #print('slope bounds are: ', low_slope, high_slope) intercept_constant, turnover, beta_1, beta_2, lnf = parameters[0], parameters[1], parameters[2], parameters[3], parameters[4] if -99 < intercept_constant < 100 and 0.05 < turnover < 0.5 and -1 < beta_1 < 1 and low_slope < beta_2 < high_slope and -10.0 < lnf < 1.0: # if 20 < intercept_constant < 40 and 2 < turnover < 50 and -4 < beta_1 < 2 and low_slope < beta_2 < high_slope and -10.0 < lnf < 1.0: return 0.0 return -np.inf def dual_lnprior(parameters): """ simple method of setting (flat) priors on model parameters If input parameters are within the priors, a (constant) likelihood is returned; if the input parameters are outside the priors, a negative infinity is returned to indicate an unacceptable fit. Input ----- parameters : array-like (3) parameters for the model: saturation level (expressed as Log L_{whatever}/L_{bol}, turnover_Ro, beta Output ------ : value 0.0 if parameters are within priors; -np.inf if not. """ intercept_constant, turnover, beta_1, beta_2, lnf = parameters[0], parameters[1], parameters[2], parameters[3], parameters[4] if -99 < intercept_constant < 100 and 0.05 < turnover < 0.5 and -1 < beta_1 < 1 and -4 < beta_2 < -1 and -10.0 < lnf < 1.0: return 0.0 return -np.inf def dual_lnlike(parameters, rossby_no, log_LxLbol ,err_ll): """ Calculates the natural log of the likelihood for a given model fit to a given input dataset (with errors). Input ----- parameters : array-like (4) parameters for the model: saturation level, turnover, beta, multiplicative error inflator rossby_no : array-like Data Rossby number values log_LxLbol : array-like Data activity values (L_{whatever}/L_{bol} - in the original case, LxLbol error_ll : array-like Uncertainties in the data activity values. Output ------ lnprob : float natural log of the likelihood of the model given the data """ intercept_constant, turnover, beta_1, beta_2, lnf = parameters[0], parameters[1], parameters[2], parameters[3], parameters[4] #if ((sat_level>1e-1) or (sat_level<1e-8) or (turnover<0.001) ## stephanie's original method of setting priors; # or (turnover>2) or (beta>2) or (beta<-6)): ## now offloaded to lnprior # return -np.inf model_ll = dual_power_law(parameters, rossby_no) #inv_sigma2 = 1.0/(err_ll**2) ## inverse sigma assuming only quoted errors inv_sigma2 = 1.0/(err_ll**2 + model_ll**2*np.exp(2*lnf)) ## inverse sigma assuming errors are underestimated by some multiplicative factor ln_like = -0.5*(np.sum((log_LxLbol-model_ll)**2*inv_sigma2 - np.log(inv_sigma2))) return ln_like def dual_lnprob_periods_fixed(parameters, rossby_no, log_LxLbol, err_ll, lowSlope, highSlope): """ Calculates the natural log of the probability of a model, given a set of priors, the defined likelihood function, and the observed data Input ----- parameters : array-like (4) parameters for the model: saturation level, turnover, beta, multiplicative error inflator rossby_no : array-like Data Rossby number values log_LxLbol : array-like Data activity values (L_{whatever}/L_{bol} - in the original case, LxLbol error_ll : array-like Uncertainties in the data activity values. Output ------ lnprob : float natural log of the likelihood of the model given the data and the priors (by adding prior and model likelihood terms, which are calculated by lnprior() and lnlike() respectively) """ lp = dual_lnprior_periods_fixSlope(parameters, lowSlope, highSlope) if not np.isfinite(lp): return -np.inf return lp + dual_lnlike(parameters, rossby_no, log_LxLbol, err_ll) def dual_lnprob_periods(parameters, rossby_no, log_LxLbol, err_ll): """ Calculates the natural log of the probability of a model, given a set of priors, the defined likelihood function, and the observed data Input ----- parameters : array-like (4) parameters for the model: saturation level, turnover, beta, multiplicative error inflator rossby_no : array-like Data Rossby number values log_LxLbol : array-like Data activity values (L_{whatever}/L_{bol} - in the original case, LxLbol error_ll : array-like Uncertainties in the data activity values. Output ------ lnprob : float natural log of the likelihood of the model given the data and the priors (by adding prior and model likelihood terms, which are calculated by lnprior() and lnlike() respectively) """ lp = dual_lnprior_periods(parameters) if not np.isfinite(lp): return -np.inf return lp + dual_lnlike(parameters, rossby_no, log_LxLbol, err_ll) def dual_lnprob(parameters, rossby_no, log_LxLbol, err_ll): """ Calculates the natural log of the probability of a model, given a set of priors, the defined likelihood function, and the observed data Input ----- parameters : array-like (4) parameters for the model: saturation level, turnover, beta, multiplicative error inflator rossby_no : array-like Data Rossby number values log_LxLbol : array-like Data activity values (L_{whatever}/L_{bol} - in the original case, LxLbol error_ll : array-like Uncertainties in the data activity values. Output ------ lnprob : float natural log of the likelihood of the model given the data and the priors (by adding prior and model likelihood terms, which are calculated by lnprior() and lnlike() respectively) """ lp = dual_lnprior(parameters) if not np.isfinite(lp): return -np.inf return lp + dual_lnlike(parameters, rossby_no, log_LxLbol, err_ll) def dual_lnprob_fixed(parameters, rossby_no, log_LxLbol, err_ll, low_slope, high_slope): """ Calculates the natural log of the probability of a model, given a set of priors, the defined likelihood function, and the observed data Input ----- parameters : array-like (4) parameters for the model: saturation level, turnover, beta, multiplicative error inflator rossby_no : array-like Data Rossby number values log_LxLbol : array-like Data activity values (L_{whatever}/L_{bol} - in the original case, LxLbol error_ll : array-like Uncertainties in the data activity values. Output ------ lnprob : float natural log of the likelihood of the model given the data and the priors (by adding prior and model likelihood terms, which are calculated by lnprior() and lnlike() respectively) """ #lp = dual_lnprior(parameters) lp = dual_lnprior_fixSlope(parameters, low_slope, high_slope) if not np.isfinite(lp): return -np.inf return lp + dual_lnlike(parameters, rossby_no, log_LxLbol, err_ll) def run_dual_fit_constrained(start_p, data_rossby, data_ll, data_ull, lowSlope, highSlope, nwalkers=256,nsteps=40000): """ Sets up the emcee ensemble sampler, runs it, prints out the results, then returns the samples. Input ----- start_p : (3) starting guesses for the three model parameters saturation level, turnover point, and power-law slope (beta) data_rossby : array-like (ndata) Data Rossby number values data_ll : array-like (ndata) Data activity values (L_{whatever}/L_{bol} - in my case I was using L_{Halpha}/L_{bol}) data_ull : array-like (ndata) Uncertainties in the data activity values. Output ------ samples : array-like (nwalkers*nsteps,3) all the samples from all the emcee walkers, reshaped so there's just one column per parameter """ ndim = 5 p0 = np.zeros((nwalkers,ndim)) # initialize the walkers in a tiny gaussian ball around the starting point for i in range(nwalkers): p0[i] = start_p + (1e-1*np.random.randn(ndim)*start_p) sampler = emcee.EnsembleSampler(nwalkers,ndim,dual_lnprob_fixed, args=[data_rossby,data_ll,data_ull,lowSlope,highSlope]) pos,prob,state=sampler.run_mcmc(p0,nsteps/2) sampler.reset() pos,prob,state=sampler.run_mcmc(pos,nsteps) ic_mcmc = quantile(sampler.flatchain[:,0],[.16,.5,.84]) #sl_mcmc.info() #print(sl_mcmc) to_mcmc = quantile(sampler.flatchain[:,1],[.16,.5,.84]) #print(to_mcmc) beta1_mcmc = quantile(sampler.flatchain[:,2],[.16,.5,.84]) beta2_mcmc = quantile(sampler.flatchain[:,3],[.16,.5,.84]) #print(be_mcmc) var_mcmc = quantile(sampler.flatchain[:,4],[.16,.5,.84]) print('intercept constant={0:.7f} +{1:.7f}/-{2:.7f}'.format( ic_mcmc[1][1],ic_mcmc[1][1]-ic_mcmc[0][1],ic_mcmc[2][1]-ic_mcmc[1][1])) print('turnover={0:.3f} +{1:.3f}/-{2:.3f}'.format( to_mcmc[1][1],to_mcmc[1][1]-to_mcmc[0][1],to_mcmc[2][1]-to_mcmc[1][1])) print('beta1={0:.3f} +{1:.3f}/-{2:.3f}'.format( beta1_mcmc[1][1],beta1_mcmc[1][1]-beta1_mcmc[0][1],beta1_mcmc[2][1]-beta1_mcmc[1][1])) print('beta2={0:.3f} +{1:.3f}/-{2:.3f}'.format( beta2_mcmc[1][1],beta2_mcmc[1][1]-beta2_mcmc[0][1],beta2_mcmc[2][1]-beta2_mcmc[1][1])) print('var={0:.3f} +{1:.3f}/-{2:.3f}'.format( var_mcmc[1][1],var_mcmc[1][1]-var_mcmc[0][1],var_mcmc[2][1]-var_mcmc[1][1])) samples = sampler.flatchain return samples pos,prob,state=sampler.run_mcmc(pos,nsteps) ic_mcmc = quantile(sampler.flatchain[:,0],[.16,.5,.84]) #sl_mcmc.info() #print(sl_mcmc) to_mcmc = quantile(sampler.flatchain[:,1],[.16,.5,.84]) #print(to_mcmc) beta1_mcmc = quantile(sampler.flatchain[:,2],[.16,.5,.84]) beta2_mcmc = quantile(sampler.flatchain[:,3],[.16,.5,.84]) #print(be_mcmc) var_mcmc = quantile(sampler.flatchain[:,4],[.16,.5,.84]) print('intercept constant={0:.7f} +{1:.7f}/-{2:.7f}'.format( ic_mcmc[1][1],ic_mcmc[1][1]-ic_mcmc[0][1],ic_mcmc[2][1]-ic_mcmc[1][1])) print('turnover={0:.3f} +{1:.3f}/-{2:.3f}'.format( to_mcmc[1][1],to_mcmc[1][1]-to_mcmc[0][1],to_mcmc[2][1]-to_mcmc[1][1])) print('beta1={0:.3f} +{1:.3f}/-{2:.3f}'.format( beta1_mcmc[1][1],beta1_mcmc[1][1]-beta1_mcmc[0][1],beta1_mcmc[2][1]-beta1_mcmc[1][1])) print('beta2={0:.3f} +{1:.3f}/-{2:.3f}'.format( beta2_mcmc[1][1],beta2_mcmc[1][1]-beta2_mcmc[0][1],beta2_mcmc[2][1]-beta2_mcmc[1][1])) print('var={0:.3f} +{1:.3f}/-{2:.3f}'.format( var_mcmc[1][1],var_mcmc[1][1]-var_mcmc[0][1],var_mcmc[2][1]-var_mcmc[1][1])) samples = sampler.flatchain return samples def run_dual_fit_periods_constrained(start_p, data_rossby, data_ll, data_ull, lowSlope, highSlope, nwalkers=256,nsteps=10000): """ Sets up the emcee ensemble sampler, runs it, prints out the results, then returns the samples. Input ----- start_p : (3) starting guesses for the three model parameters saturation level, turnover point, and power-law slope (beta) data_rossby : array-like (ndata) Data Rossby number values data_ll : array-like (ndata) Data activity values (L_{whatever}/L_{bol} - in my case I was using L_{Halpha}/L_{bol}) data_ull : array-like (ndata) Uncertainties in the data activity values. Output ------ samples : array-like (nwalkers*nsteps,3) all the samples from all the emcee walkers, reshaped so there's just one column per parameter """ ndim = 5 p0 = np.zeros((nwalkers,ndim)) # initialize the walkers in a tiny gaussian ball around the starting point for i in range(nwalkers): p0[i] = start_p + (1e-1*np.random.randn(ndim)*start_p) sampler = emcee.EnsembleSampler(nwalkers,ndim,dual_lnprob_periods_fixed, args=[data_rossby,data_ll,data_ull,lowSlope,highSlope]) pos,prob,state=sampler.run_mcmc(p0,nsteps/2) sampler.reset() pos,prob,state=sampler.run_mcmc(pos,nsteps) ic_mcmc = quantile(sampler.flatchain[:,0],[.16,.5,.84]) #sl_mcmc.info() #print(sl_mcmc) to_mcmc = quantile(sampler.flatchain[:,1],[.16,.5,.84]) #print(to_mcmc) beta1_mcmc = quantile(sampler.flatchain[:,2],[.16,.5,.84]) beta2_mcmc = quantile(sampler.flatchain[:,3],[.16,.5,.84]) #print(be_mcmc) var_mcmc = quantile(sampler.flatchain[:,4],[.16,.5,.84]) print('intercept constant={0:.7f} +{1:.7f}/-{2:.7f}'.format( ic_mcmc[1][1],ic_mcmc[1][1]-ic_mcmc[0][1],ic_mcmc[2][1]-ic_mcmc[1][1])) print('turnover={0:.3f} +{1:.3f}/-{2:.3f}'.format( to_mcmc[1][1],to_mcmc[1][1]-to_mcmc[0][1],to_mcmc[2][1]-to_mcmc[1][1])) print('beta1={0:.3f} +{1:.3f}/-{2:.3f}'.format( beta1_mcmc[1][1],beta1_mcmc[1][1]-beta1_mcmc[0][1],beta1_mcmc[2][1]-beta1_mcmc[1][1])) print('beta2={0:.3f} +{1:.3f}/-{2:.3f}'.format( beta2_mcmc[1][1],beta2_mcmc[1][1]-beta2_mcmc[0][1],beta2_mcmc[2][1]-beta2_mcmc[1][1])) print('var={0:.3f} +{1:.3f}/-{2:.3f}'.format( var_mcmc[1][1],var_mcmc[1][1]-var_mcmc[0][1],var_mcmc[2][1]-var_mcmc[1][1])) samples = sampler.flatchain return samples def plot_dual_fit(samples,data_rossby,data_ll,data_ull,plotfilename=None,ylabel=r'$L_{X}/L_{bol}$', sampleName=None): """ Plot fit results with data Input ----- samples : array-like (nwalkers*nsteps,3) all the samples from all the emcee walkers, reshaped so there's just one column per parameter data_rossby : array-like (ndata) Data Rossby number values data_ll : array-like (ndata) Data activity values (L_{whatever}/L_{bol} - in my case I was using L_{Halpha}/L_{bol}) data_ull : array-like (ndata) Uncertainties in the data activity values. plotfilename : string (optional; default=None) if not None, the plot will be saved using this filename """ ic_mcmc = quantile(samples[:,0],[.16,.5,.84]) to_mcmc = quantile(samples[:,1],[.16,.5,.84]) beta1_mcmc = quantile(samples[:,2],[.16,.5,.84]) beta2_mcmc = quantile(samples[:,3],[.16,.5,.84]) var_mcmc = quantile(samples[:,4],[.16,.5,.84]) plt.figure() ax = plt.subplot(111) ax.set_xscale('log') #ax.set_yscale('log') # Just trying to reduce the number of plotted points... xl = np.append(np.arange(0.001,0.2,0.001),np.arange(0.2,2.5,0.02)) # xl = np.arange(0.001,2.0,0.005) #for p in list(samples[np.random.randint(len(samples), size=100)]): # ax.plot(xl,rossby_model(p,xl),color='LightGrey') intercept_constant = ic_mcmc[1][1] turnover = to_mcmc[1][1] x = np.asarray([turnover,2.0]) # x = np.arange(turnover,2.0,0.001) #constant = sat_level/(turnover**-1.) #ax.plot(x,constant*(x**-1.),'k--',lw=1.5,label=r'$\beta=\ -1$') #constant = sat_level/(turnover**-2.1) #ax.plot(x,constant*(x**-2.1),'k-.',lw=1.5,label=r'$\beta=\ -2.1$') #constant = sat_level/(turnover**-2.7) #ax.plot(x,constant*(x**-2.7),'k:',lw=2,label=r'$\beta=\ -2.7$') star_color = 'steelblue' ax.errorbar(data_rossby,data_ll,data_ull,color=star_color,fmt='.',capsize=1, ms=2,mec=star_color) #print('parameters for model plot:') #print('xl: ') #print(xl) #print('model inputs: ') #print([sl_mcmc[1][1],to_mcmc[1][1],be_mcmc[1][1]]) #print('model: ') #print( ax.plot(xl,dual_power_law([ic_mcmc[1][1],to_mcmc[1][1],beta1_mcmc[1][1],beta2_mcmc[1][1]],xl), 'k-',lw=2,label=r'$\beta1=\ {0:.2f}$'.format(beta1_mcmc[1][1])+"\n"+r'$\beta2=\ {0:.2f}$'.format(beta2_mcmc[1][1]) ) ax.set_ylabel(ylabel,fontsize='xx-large') ax.set_xlabel('R$_o$',fontsize='x-large') ax.set_xlim(1e-3,2) ax.tick_params(labelsize='x-large') #ax.set_xticklabels((0.001,0.01,0.1,1)) handles, labels = ax.get_legend_handles_labels() new_handles = np.append(handles[-1],handles[0:-1]) new_labels = np.append(labels[-1],labels[0:-1]) if sampleName!=None: ax.legend(new_handles,new_labels,loc=3, title=sampleName) else: ax.legend(new_handles,new_labels,loc=3) if plotfilename!=None: plt.savefig(plotfilename) def plot_dual_fit_periods(samples,data_rossby,data_ll,data_ull,plotfilename=None,ylabel=r'$Log L_{X}$', sampleName=None): """ Plot fit results with data Input ----- samples : array-like (nwalkers*nsteps,3) all the samples from all the emcee walkers, reshaped so there's just one column per parameter data_rossby : array-like (ndata) Data Rossby number values data_ll : array-like (ndata) Data activity values (L_{whatever}/L_{bol} - in my case I was using L_{Halpha}/L_{bol}) data_ull : array-like (ndata) Uncertainties in the data activity values. plotfilename : string (optional; default=None) if not None, the plot will be saved using this filename """ #print(len(data_rossby),len(data_ll), len(data_ull)) ic_mcmc = quantile(samples[:,0],[.16,.5,.84]) to_mcmc = quantile(samples[:,1],[.16,.5,.84]) beta1_mcmc = quantile(samples[:,2],[.16,.5,.84]) beta2_mcmc = quantile(samples[:,3],[.16,.5,.84]) var_mcmc = quantile(samples[:,4],[.16,.5,.84]) plt.figure() ax = plt.subplot(111) ax.set_xscale('log') #ax.set_yscale('log') # Just trying to reduce the number of plotted points... xl = np.append(np.arange(0.05,7,0.01),np.arange(7,160,0.5)) # xl = np.arange(0.001,2.0,0.005) #for p in list(samples[np.random.randint(len(samples), size=100)]): # ax.plot(xl,rossby_model(p,xl),color='LightGrey') intercept_constant = ic_mcmc[1][1] turnover = to_mcmc[1][1] x = np.asarray([turnover,2.0]) # x = np.arange(turnover,2.0,0.001) #constant = sat_level/(turnover**-1.) #ax.plot(x,constant*(x**-1.),'k--',lw=1.5,label=r'$\beta=\ -1$') #constant = sat_level/(turnover**-2.1) #ax.plot(x,constant*(x**-2.1),'k-.',lw=1.5,label=r'$\beta=\ -2.1$') #constant = sat_level/(turnover**-2.7) #ax.plot(x,constant*(x**-2.7),'k:',lw=2,label=r'$\beta=\ -2.7$') star_color = 'steelblue' # ax.errorbar(data_rossby,data_ll,data_ull,color=star_color,fmt='.',capsize=0, # ms=4,mec=star_color) ax.scatter(data_rossby,data_ll,color=star_color) #,fmt='.',capsize=0, # ms=4,mec=star_color) #print('parameters for model plot:') #print('xl: ') #print(xl) #print('model inputs: ') #print([sl_mcmc[1][1],to_mcmc[1][1],be_mcmc[1][1]]) #print('model: ') #print( ax.plot(xl,dual_power_law([ic_mcmc[1][1],to_mcmc[1][1],beta1_mcmc[1][1],beta2_mcmc[1][1]],xl), 'k-',lw=2,label=r'$\beta1=\ {0:.2f}$'.format(beta1_mcmc[1][1])+"\n"+r'$\beta2=\ {0:.2f}$'.format(beta2_mcmc[1][1]) ) ax.set_ylabel(ylabel,fontsize='xx-large') ax.set_xlabel(r'P$_{rot}$',fontsize='x-large') ax.set_xlim(0.05,200) ax.tick_params(labelsize='x-large') #ax.set_xticklabels((0.001,0.01,0.1,1)) handles, labels = ax.get_legend_handles_labels() new_handles = np.append(handles[-1],handles[0:-1]) new_labels = np.append(labels[-1],labels[0:-1]) if sampleName!=None: ax.legend(new_handles,new_labels,loc=3, title=sampleName) else: ax.legend(new_handles,new_labels,loc=3) if plotfilename!=None: plt.savefig(plotfilename) def print_pdf(cropchain,filename,col_names=["sat_level,turnover,beta"]): f = open(filename,"w") f.write("# {}".format(col_names[0])) for cname in col_names[1:]: f.write(",{}".format(cname)) f.write("\n") for i,p in enumerate(cropchain): #print p f.write(str(p[0])) for this_p in p[1:]: f.write(",{}".format(this_p)) f.write("\n") f.close()

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5a440f5e374be50e1a76de06f9f6d2dc745b5e7a

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py

Python

polliwog/line/test_functions.py

algrs/polliwog

faa6531e8e2f7d0b52e928d64a4c1914199c4023

[ "BSD-2-Clause" ]

null

polliwog/line/test_functions.py

algrs/polliwog

faa6531e8e2f7d0b52e928d64a4c1914199c4023

[ "BSD-2-Clause" ]

null

polliwog/line/test_functions.py

algrs/polliwog

faa6531e8e2f7d0b52e928d64a4c1914199c4023

[ "BSD-2-Clause" ]

null

import numpy as np import vg from .functions import project_to_line def test_project_to_line(): p1 = np.array([5.0, 5.0, 4.0]) p2 = np.array([10.0, 10.0, 6.0]) along_line = p2 - p1 common_kwargs = dict(reference_points_of_lines=p1, vectors_along_lines=along_line) np.testing.assert_array_almost_equal( project_to_line(points=p1, **common_kwargs), p1 ) np.testing.assert_array_almost_equal( project_to_line(points=p2, **common_kwargs), p2 ) other_point_on_line = np.array([0.0, 0.0, 2.0]) np.testing.assert_array_almost_equal( project_to_line(points=other_point_on_line, **common_kwargs), other_point_on_line, ) example_perpendicular_displacement = [ k * vg.perpendicular(vg.normalize(along_line), vg.basis.x) for k in [0.1, 0.5, -2.0] ] for point_on_line in [p1, p2, other_point_on_line]: for displacement in example_perpendicular_displacement: np.testing.assert_array_almost_equal( project_to_line(points=point_on_line + displacement, **common_kwargs), point_on_line, ) def test_project_to_line_stacked_points(): p1 = np.array([5.0, 5.0, 4.0]) p2 = np.array([10.0, 10.0, 6.0]) along_line = p2 - p1 common_kwargs = dict(reference_points_of_lines=p1, vectors_along_lines=along_line) other_point_on_line = np.array([0.0, 0.0, 2.0]) example_perpendicular_displacement = [ k * vg.perpendicular(vg.normalize(along_line), vg.basis.x) for k in [0.1, 0.5, -2.0] ] example_points = np.vstack([p1, p2, other_point_on_line]) expected_projected_points = np.vstack([p1, p2, other_point_on_line]) np.testing.assert_array_almost_equal( project_to_line(points=example_points, **common_kwargs), expected_projected_points, ) np.testing.assert_array_almost_equal( project_to_line( points=example_points + example_perpendicular_displacement, **common_kwargs ), expected_projected_points, ) def test_project_to_line_stacked_lines(): p1 = np.array([5.0, 5.0, 4.0]) p2 = np.array([10.0, 10.0, 6.0]) along_line = p2 - p1 common_kwargs = dict( reference_points_of_lines=np.array([p1, p1]), vectors_along_lines=np.array([along_line, along_line]), ) other_point_on_line = np.array([0.0, 0.0, 2.0]) np.testing.assert_array_almost_equal( project_to_line(points=other_point_on_line, **common_kwargs), np.array([other_point_on_line, other_point_on_line]), ) example_perpendicular_displacement = [ k * vg.perpendicular(vg.normalize(along_line), vg.basis.x) for k in [0.1, 0.5, -2.0] ] for point_on_line in [p1, p2, other_point_on_line]: for displacement in example_perpendicular_displacement: np.testing.assert_array_almost_equal( project_to_line(points=point_on_line + displacement, **common_kwargs), np.array([point_on_line, point_on_line]), ) def test_project_to_line_stacked_both(): p1 = np.array([5.0, 5.0, 4.0]) p2 = np.array([10.0, 10.0, 6.0]) along_line = p2 - p1 common_kwargs = dict( reference_points_of_lines=np.array([p1, p1, p1]), vectors_along_lines=np.array([along_line, along_line, along_line]), ) other_point_on_line = np.array([0.0, 0.0, 2.0]) example_perpendicular_displacement = [ k * vg.perpendicular(vg.normalize(along_line), vg.basis.x) for k in [0.1, 0.5, -2.0] ] example_points = np.vstack([p1, p2, other_point_on_line]) expected_projected_points = np.vstack([p1, p2, other_point_on_line]) np.testing.assert_array_almost_equal( project_to_line(points=example_points, **common_kwargs), expected_projected_points, ) np.testing.assert_array_almost_equal( project_to_line( points=example_points + example_perpendicular_displacement, **common_kwargs ), expected_projected_points, )

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null

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6

5a4a914e7782ac523d8f9db863a54b5f8a4af7fc

6,500

py

Python

registry/application/handlers/service_handlers.py

vinthedark/snet-marketplace-service

66ed9d093b00f09d3e28ef4d86c4e4c125037d06

[ "MIT" ]

null

registry/application/handlers/service_handlers.py

vinthedark/snet-marketplace-service

66ed9d093b00f09d3e28ef4d86c4e4c125037d06

[ "MIT" ]

null

registry/application/handlers/service_handlers.py

vinthedark/snet-marketplace-service

66ed9d093b00f09d3e28ef4d86c4e4c125037d06

[ "MIT" ]

null

import json from common.constant import StatusCode from common.exception_handler import exception_handler from common.exceptions import BadRequestException from common.logger import get_logger from common.utils import generate_lambda_response, handle_exception_with_slack_notification, validate_dict, \ validate_dict_list from registry.application.services.service_publisher_service import ServicePublisherService from registry.config import NETWORK_ID, SLACK_HOOK from registry.exceptions import EXCEPTIONS logger = get_logger(__name__) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def verify_service_id(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] query_parameters = event["queryStringParameters"] if "org_uuid" not in path_parameters and "service_id" not in query_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] service_id = query_parameters["service_id"] response = ServicePublisherService(username, org_uuid, None).get_service_id_availability_status(service_id) return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def save_transaction_hash_for_published_service(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] payload = json.loads(event["body"]) if "org_uuid" not in path_parameters and "service_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] service_uuid = path_parameters["service_uuid"] response = ServicePublisherService(username, org_uuid, service_uuid).save_transaction_hash_for_published_service(payload) return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def submit_service_for_approval(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] payload = json.loads(event["body"]) if "org_uuid" not in path_parameters and "service_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] service_uuid = path_parameters["service_uuid"] response = ServicePublisherService(username, org_uuid, service_uuid).submit_service_for_approval(payload) return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def save_service(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] payload = json.loads(event["body"]) if "org_uuid" not in path_parameters and "service_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] service_uuid = path_parameters["service_uuid"] response = ServicePublisherService(username, org_uuid, service_uuid).save_service(payload) return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def create_service(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] payload = json.loads(event["body"]) if "org_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] response = ServicePublisherService(username, org_uuid, None).create_service(payload) return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def get_services_for_organization(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] payload = json.loads(event["body"]) if "org_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] response = ServicePublisherService(username, org_uuid, None).get_services_for_organization(payload) return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def get_service_for_service_uuid(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] if "org_uuid" not in path_parameters and "service_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] service_uuid = path_parameters["service_uuid"] response = ServicePublisherService(username, org_uuid, service_uuid).get_service_for_given_service_uuid() return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True ) @handle_exception_with_slack_notification(SLACK_HOOK=SLACK_HOOK, NETWORK_ID=NETWORK_ID, logger=logger) def publish_service_metadata_to_ipfs(event, context): username = event["requestContext"]["authorizer"]["claims"]["email"] path_parameters = event["pathParameters"] if "org_uuid" not in path_parameters and "service_uuid" not in path_parameters: raise BadRequestException() org_uuid = path_parameters["org_uuid"] service_uuid = path_parameters["service_uuid"] response = ServicePublisherService(username, org_uuid, service_uuid).publish_service_data_to_ipfs() return generate_lambda_response( StatusCode.OK, {"status": "success", "data": response, "error": {}}, cors_enabled=True )

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null

0

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null

0

6

5a800e64085648342c91064d728f42cbb69ec851

34

py

Python

env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_bar.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

11,750

2015-10-12T07:03:39.000Z

2022-03-31T20:43:15.000Z

env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_bar.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

2,951

2015-10-12T00:41:25.000Z

2022-03-31T22:19:26.000Z

env/lib/python3.8/site-packages/plotly/graph_objs/layout/template/data/_bar.py

acrucetta/Chicago_COVI_WebApp

a37c9f492a20dcd625f8647067394617988de913

[ "MIT", "Unlicense" ]

2,623

2015-10-15T14:40:27.000Z

2022-03-28T16:05:50.000Z

from plotly.graph_objs import Bar

17

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34

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null

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1

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1

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6

ce4b79e5ae5ad6d170b58e1a9070ebe19cacb1c4

112

py

Python

app_name/views.py

hotbaby/django-app-skeleton

db965ee14dd377681e14a02a70c258b8c1cb73d8

[ "MIT" ]

null

app_name/views.py

hotbaby/django-app-skeleton

db965ee14dd377681e14a02a70c258b8c1cb73d8

[ "MIT" ]

1

2019-02-12T09:21:19.000Z

app_name/views.py

hotbaby/django-app-skeleton

db965ee14dd377681e14a02a70c258b8c1cb73d8

[ "MIT" ]

null

# encoding: utf8 from . import models from . import filters from . import exceptions from . import serializers

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0

null

0

1

0

1

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1

0

6

ce50714576cefbe2b5c2188eabeef0888f943e06

4,180

py

Python

django_extended/utils/date/format_date_range_html.py

dalou/django-extended

a7ba952ea7089cfb319b4615ae098579c9ab14f9

[ "BSD-3-Clause" ]

1

2015-12-14T17:16:04.000Z

django_extended/utils/date/format_date_range_html.py

dalou/django-extended

a7ba952ea7089cfb319b4615ae098579c9ab14f9

[ "BSD-3-Clause" ]

null

django_extended/utils/date/format_date_range_html.py

dalou/django-extended

a7ba952ea7089cfb319b4615ae098579c9ab14f9

[ "BSD-3-Clause" ]

null

# encoding: utf-8 import datetime from django.utils.safestring import mark_safe def format_date_range_html( start_date=None, end_date=None, start_hour=None, end_hour=None, divider='<br/>'): if end_date and start_date: if start_date.day == end_date.day and \ start_date.month == end_date.month and \ start_date.year == end_date.year: if start_hour and end_hour: return mark_safe( start_date.strftime("Le %A %d %B ") + divider + "de " + start_hour.strftime("%H:%M") + " à " + end_hour.strftime("%H:%M") ) elif start_hour: return mark_safe( start_date.strftime("Le %A %d %B ") + divider + "à partir de " + start_hour.strftime("%H:%M") ) elif end_hour: return mark_safe( start_date.strftime("Le %A %d %B ") + divider + "jusqu'à " + end_hour.strftime("%H:%M") ) else: return mark_safe( start_date.strftime("Le %A %d %B ") + divider + "toute la journée" ) else: if start_hour and end_hour: return mark_safe( start_date.strftime("Du %A %d %B ") + divider + "à " + start_hour.strftime("%H:%M") + divider + end_date.strftime("Jusqu'au %A %d %B ") + divider + "à " + end_hour.strftime("%H:%M") ) elif start_hour: return mark_safe( start_date.strftime("Du %A %d %B ") + divider + "à " + start_hour.strftime("%H:%M") + divider + end_date.strftime("Jusqu'au %A %d %B") ) elif end_hour: return mark_safe( start_date.strftime("Du %A %d %B") + divider + end_date.strftime("Jusqu'au %A %d %B ") + divider + "à " + end_hour.strftime("%H:%M") ) else: return mark_safe( start_date.strftime("Du %A %d %B") + divider + end_date.strftime("Jusqu'au %A %d %B ") + divider ) elif start_date: if start_hour and end_hour: return mark_safe( start_date.strftime("À partir du %A %d %B ") + divider + "de " + start_hour.strftime("%H:%M") + " à " + end_hour.strftime("%H:%M") ) elif start_hour: return mark_safe( start_date.strftime("À partir du %A %d %B ") + divider + "à " + start_hour.strftime("%H:%M") ) elif end_hour: return mark_safe( start_date.strftime("À partir du %A %d %B ") + divider + "jusqu'à " + end_hour.strftime("%H:%M") ) else: return mark_safe( start_date.strftime("À partir du %A %d %B") ) elif end_date: if start_hour and end_hour: return mark_safe( end_date.strftime("Jusqu'au %A %d %B ") + divider + "de " + start_hour.strftime("%H:%M") + " à " + end_hour.strftime("%H:%M") ) elif start_hour: return mark_safe( end_date.strftime("Jusqu'au %A %d %B ") + divider + "à partir de " + start_hour.strftime("%H:%M") ) elif end_hour: return mark_safe( end_date.strftime("Jusqu'au %A %d %B ") + divider + "à " + end_hour.strftime("%H:%M") ) else: return mark_safe( start_date.strftime("Jusqu'au %A %d %B") ) else: if start_hour and end_hour: return mark_safe( "Aujourd'hui " + divider + start_hour.strftime("de %H:%M") + " à " + end_hour.strftime("%H:%M") ) elif start_hour: return mark_safe( "Aujourd'hui " + divider + start_hour.strftime("à %H:%M") ) elif end_hour: return mark_safe( "Aujourd'hui " + divider + end_hour.strftime("jusqu'à %H:%M") ) else: return None

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0.806299

0.804199

0

0.000393

0.391866

4,180

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0

null

0

1

0

null

0

6

ce7b024c3a4fe2d6e4f49c2f4f83d37a2fe4926b

36

py

Python

srfnef/corrections/new_scatter/__init__.py

twj2417/srf

63365cfd75199d70eea2273214a4fa580a9fdf2a

[ "Apache-2.0" ]

null

srfnef/corrections/new_scatter/__init__.py

twj2417/srf

63365cfd75199d70eea2273214a4fa580a9fdf2a

[ "Apache-2.0" ]

null

srfnef/corrections/new_scatter/__init__.py

twj2417/srf

63365cfd75199d70eea2273214a4fa580a9fdf2a

[ "Apache-2.0" ]

null

from .scatter import ScatterCorrect

18

35

0.861111

4

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1

0

0.111111

36

1

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0.96875

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1

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null

0

1

0

1

0

1

0

6

ceb31df1832ffedfcb127492db48f01015d8c27b

94

py

Python

planning_python/utils/helpers.py

daahuang/planning_python

1b9ae0346df66ed8cf2cb80a54a92fd909a578fe

[ "BSD-3-Clause" ]

12

2017-10-18T21:39:20.000Z

2021-11-28T06:36:37.000Z

planning_python/utils/helpers.py

daahuang/planning_python

1b9ae0346df66ed8cf2cb80a54a92fd909a578fe

[ "BSD-3-Clause" ]

null

planning_python/utils/helpers.py

daahuang/planning_python

1b9ae0346df66ed8cf2cb80a54a92fd909a578fe

[ "BSD-3-Clause" ]

6

2017-10-29T05:23:16.000Z

2020-11-17T10:53:41.000Z

import numpy as np def rgb2gray(rgb): return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])

18.8

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94

3.111111

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94

5

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null

0

1

0

null

0

1

0

1

0

6

ceb6bb0f66a3e6969deea1f6aa10921211766226

208

py

Python

base/admin.py

omololevy/study-college

5ce482b4f09314fd370509654337e95ec39c4612

[ "MIT" ]

1

2022-03-21T08:23:19.000Z

base/admin.py

omololevy/study-college

5ce482b4f09314fd370509654337e95ec39c4612

[ "MIT" ]

1

2022-03-21T08:21:27.000Z

base/admin.py

omololevy/study-college

5ce482b4f09314fd370509654337e95ec39c4612

[ "MIT" ]

null

from django.contrib import admin from .models import Cohort, Profile, Module, Discussion admin.site.register(Profile) admin.site.register(Cohort) admin.site.register(Module) admin.site.register(Discussion)

23.111111

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6

cebba6a2e505265209e84617816b66c6606824e8

25,325

py

Python

patternMatching/algorithms.py

yaukwankiu/armor

6c57df82fe3e7761f43f9fbfe4f3b21882c91436

[ "CC0-1.0" ]

1

2015-11-06T06:41:33.000Z

patternMatching/algorithms.py

yaukwankiu/armor

6c57df82fe3e7761f43f9fbfe4f3b21882c91436

[ "CC0-1.0" ]

null

patternMatching/algorithms.py

yaukwankiu/armor

6c57df82fe3e7761f43f9fbfe4f3b21882c91436

[ "CC0-1.0" ]

null

""" matching algorithms for pattern2.DataStreamSets format: def alg(obs, w, obsTime, maxHourDiff, **kwargs): input: obs - observation stream, a pattern.DBZstream object w - wrf (model) stream, another pattern.DBZstream object obsTime - time at which obs is compared with the wrfs, e.g. "20140612.0200' maxHourDiff - the maximal time difference (in hours) between obs and wrfs, e.g. 7 (hours) kwargs - key-worded parameters (a=1, b=2, etc) output: a dict: {'score':score, 'timeShift':timeShift} where score - a real number, representing the alikeness/degree of matching timeShift - a number, in hours, the optimal timeShift for the wrf to do: combinations of - correlations, adjusted correlations, histogram, moments, other features (2014-04-15) """ ####################### # imports import os, time, datetime from datetime import timedelta import numpy as np from armor import pattern dp = pattern.dp # defaultParameters ########################### # functions def normalisedCorrelation(): """ 1 May, 2014 """ pass def plainCorr(obs, wrf, obsTime, maxHourDiff=7, verbose=False): a = obs(obsTime)[0] # getting the DBZ object from the observation stream to be compared T = a.datetime() # getting the time range maxTime = T + timedelta(maxHourDiff * 1./24) minTime = T - timedelta(maxHourDiff * 1./24) maxTime = a.getDataTime(maxTime) # converting it into string format for pattern.DBZ minTime = a.getDataTime(minTime) # converting it into string format for pattern.DBZ if verbose: print "minTime, obsTime, maxTime:", minTime, obsTime, maxTime scores = [] for w in wrf: if w.dataTime > maxTime or w.dataTime < minTime: continue else: if a.matrix.var()==0: # test if it is empty a.load() if w.matrix.var()==0: w.load() score = a.corr(w) # <<<<<<<< key comparison step >>>>>>>>>>>>>> scores.append( {'time':w.dataTime, 'score': score} ) scores.sort(key=lambda v:v['score'], reverse=True) topScore = scores[0]['score'] topScoreTime = scores[0]['time'] timeShift = (a.datetime(topScoreTime) - a.datetime()).total_seconds() # the time difference is a datetime.timedelta object timeShift = 1. * timeShift/3600 # convert it into hours return {'score':topScore, 'timeShift':timeShift} #score - depending on the algorithm; timeShift - in hours def nonstandardKernel(obs, wrf, regions, shiibaAlg="", shiibaArgs={}, obsTime="", maxHourDiff=7, k=24, # number of 10-minute steps to semi-lagrange advect verbose=False, outputFolder= dp.defaultLabReportsFolder +'2014-03-07-filter-matching-scoring-pipeline/', volumevolumeProportionWeight =0., **kwargs #just in case ): """ "Non-standard Kernel matching": see ARMOR RFP 2014 inputs: obs - one observation stream wrf - one wrf (NWP model) stream regions - list of regions of interest (listed in descending order of priority?!) {'name':name, 'points':points, 'weight':weight} regionalWeights - to get a weighed averge if necessary shiibaAlg - pointer to the function for shiiba Regression shiibaArgs- parameters for shiiba regression maxHourDiff - max temporal difference (in hours) you would consider for the WRF to match the OBS outputs: {'score': a list, top weighed average score followed by a list of regional scores 'timeShift': time shift for the top score} USE: from armor import defaultParameters as dp from armor import misc from armor import pattern, pattern2 from armor.patternMatching import pipeline as pp, algorithms from armor.filter import filters hualien4 = misc.getFourCorners(dp.hualienCounty) yilan4 = misc.getFourCorners(dp.yilanCounty) kaohsiung4 = misc.getFourCorners(dp.kaohsiungCounty) regions = [{'name': "hualien", 'points': hualien4, 'weight': 0.5}, {'name': "kaohsiung", 'points':kaohsiung4, 'weight':0.3}, {'name':"yilan", 'points':yilan4, 'weight':0.2}, ] pp.pipeline(filteringAlgorithm = filters.gaussianFilter, filteringAlgorithmArgs = {'sigma':20}, matchingAlgorithm = algorithms.nonstandardKernel, matchingAlgorithmArgs = {'obsTime':"20130829.0300", 'maxHourDiff':7, 'regions':regions} , outputFolder=dp.defaultRootFolder + "labReports/2014-03-07-filter-matching-scoring-pipeline/", toLoad=False) 2014-03-11 """ # codes adapted from plainCorr above timeString = str(int(time.time())) miscRemarks = "" outputFolder += timeString + wrf.name + '/' print "\n\n\n................................................................." print "outputFolder:", outputFolder print "volumevolumeProportionWeight:",volumevolumeProportionWeight os.makedirs(outputFolder) print "sleeping 5 seconds", time.sleep(5) if obsTime == "": obsTime = obs[0].dataTime a = obs(obsTime)[0] # getting the DBZ object from the observation stream to be compared #a.show() #debug T = a.datetime() # getting the time range T_string = a.getDataTime(T) T2 = sorted([v.dataTime for v in obs if v.dataTime>T_string])[0] # the next time, assumed 10 mins apart - or else # or else need to adjust the k for vect.semiLagrange below b = obs(T2)[0] if b.datetime() - a.datetime() > datetime.timedelta(600./86400): # 600 seconds td = b.datetime() - a.datetime() miscRemarks += "\nTime difference between %s and %s is " % (b.name, a.name) miscRemarks += str(td.days) + " days " + str(td.seconds) + "seconds.\n" #b.debug() #show if a.matrix.var()==0: # test if it is empty a.load() if b.matrix.var()==0: b.load() #a.saveImage() #b.saveImage() if shiibaAlg == "": from armor import analysis shiibaAlg = analysis.shiiba maxTime = T + timedelta(maxHourDiff * 1./24) minTime = T - timedelta(maxHourDiff * 1./24) maxTime = a.getDataTime(maxTime) # converting it into string format for pattern.DBZ minTime = a.getDataTime(minTime) # converting it into string format for pattern.DBZ if verbose: print "minTime, obsTime, maxTime:", minTime, obsTime, maxTime # check if there's no corresponing wrf for the time if [v.dataTime for v in wrf if v.dataTime>=minTime and v.dataTime<=maxTime] == []: return {} scores = [] # get the ABLER-Shiiba vector field try: shiibaResults = a.shiibaResultLocalCopy # need to regress at least once!!! except AttributeError: shiibaResults = shiibaAlg(a, b, **shiibaArgs) a.shiibaResultLocalCopy = shiibaResults vect = shiibaResults['vect'] + shiibaResults['mn'] a.backupMatrix('good_copy') a.drawCoast() for R in regions: a.drawRectangularHull(R['points']) a.saveImage(imagePath=outputFolder+ a.name +dp.defaultImageSuffix) a.restoreMatrix('good_copy') b.backupMatrix('good_copy') b.drawCoast() for R in regions: b.drawRectangularHull(R['points']) b.saveImage(imagePath=outputFolder+ b.name +dp.defaultImageSuffix) b.restoreMatrix('good_copy') print "a saved to", outputFolder+ a.name +dp.defaultImageSuffix # debug print "b saved to", outputFolder+ b.name +dp.defaultImageSuffix # debug vect.saveImage(imagePath=outputFolder+ "abler_vector_field" + dp.defaultImageSuffix) # looping a_with_windows = a.copy() a_with_windows.drawCoast() for w in wrf: if w.dataTime > maxTime or w.dataTime < minTime: continue else: if w.matrix.var()==0: # test if it is empty w.load() #w.backupMatrix('good_copy') #################################################################### # matching core # 1. shiiba regression -> find the vector field # 2. semi-lagrangian -> find the extended region # 3. cut out the region in obs # 4. match the appropriate region in wrf regionalScores = [] for R0 in regions: name = R0['name'] points = R0['points'] weight = R0['weight'] # extract the "nonstandard kernel" as a1 points1 = vect.semiLagrange(L=points, k=k, direction=-1, verbose=verbose) # back advection points2 = points + points1 iMax = int(max(v[0] for v in points2)) iMin = int(min(v[0] for v in points2)) jMax = int(max(v[1] for v in points2)) jMin = int(min(v[1] for v in points2)) height = iMax-iMin width = jMax-jMin a1 = a.getWindow(iMin, jMin, height, width) a1.name = a.name + '_' + name a1.imagePath = outputFolder + a1.name + dp.defaultImageSuffix # suffix = ".png" a1.saveImage(imagePath=a1.imagePath) a_with_windows.drawRectangle(iMin, jMin, height, width, newObject=False) # match a1 with a similar rectangle on the wrf, scoring by correlation # we shift the kernel by 1/10 of it's width/height # 4 times left, right, up and down respectively iStep = int(height//10 + 1) jStep = int(width//10 + 1) print "points (corners for the region):", points #debug print "iStep, jStep", iStep, jStep #debug score = 0 shift = (-999,-999) #initialise for i in range(-4*iStep, 4*iStep+1, iStep): for j in range(-4*jStep, 4*jStep+1, jStep): #w.restoreMatrix('good_copy') w1 = w.getWindow(iMin+i, jMin+j, height, width) tempScore = a1.corr(w1) # <<<<<<<< key comparison step >>>>>>>>>>>>>> # adding a step to compare the relative volume, 2014-03-28 proportion = a1.matrix.sum() / w1.matrix.sum() if proportion > 1: proportion = 1./proportion #diffLog = abs(np.log(a1.matrix.sum()) - np.log(w1.matrix.sum())) #tempScore = a1.cov(w1)[0,1] # use straight corr for now, will convert to shiiba or normalised corr later # or can use covariance rather than correlation tempScore = tempScore*(1-volumevolumeProportionWeight ) + proportion*volumevolumeProportionWeight if score < tempScore: score = tempScore # get the highest #scoreTime = w.dataTime shift = (i,j) # this info is probably not needed regionalScores.append({'name' : name, # name of the region 'score' : score, 'shift' : shift, 'weight' : weight, 'upWindRegion': (iMin, jMin, height, width), }) # compute weighed average over regions averageScore = np.sum([v['score']*v['weight'] for v in regionalScores]) # # ##################################################################### scores.append( {'time':w.dataTime, 'score': averageScore, 'regionalScores': regionalScores} ) scores.sort(key=lambda v:v['score'], reverse=True) topScore = scores[0]['score'] topScoreTime = scores[0]['time'] topScoresRegional = scores[0]['regionalScores'] # actually regional scores for the top score timeShift = (a.datetime(topScoreTime) - a.datetime()).total_seconds() # the time difference is a datetime.timedelta object timeShift = 1. * timeShift/3600 # convert it into hours # saving images w = wrf(topScoreTime)[0].copy() # temp image object ######### # 2014-06-26 for R0 in regions: print "extracting window for", R0['name'] name = R0['name'] points = R0['points'] #weight = R0['weight'] # extract the "nonstandard kernel" as a1 iMax = int(max(v[0] for v in points)) iMin = int(min(v[0] for v in points)) jMax = int(max(v[1] for v in points)) jMin = int(min(v[1] for v in points)) height = iMax-iMin width = jMax-jMin print "iMin, jMin=", iMin, jMin print "topScoresRegional:",topScoresRegional #debug upWindRegion = [v['upWindRegion'] for v in topScoresRegional if v['name']==name][0] print "upWindRegion:", upWindRegion w1 = w.getWindow(*upWindRegion) w1.name = w.name + '_' + name + " Upwind Region" w1.imagePath = outputFolder + w.name + "_window_" + name + "_upWindRegion"+ dp.defaultImageSuffix # suffix = ".png" #print w1.imagePath #debug #w1.show() #debug w1.saveImage(imagePath=w1.imagePath) # ######### w.coastDataPath=obs[0].coastDataPath w.drawCoast() a_frames = (a_with_windows.matrix > 999) # hack, getting the window frames for w w.matrix += a_frames * 9999 # hack, getting the window frames for w w.saveImage(imagePath=outputFolder+w.name+dp.defaultImageSuffix) a_with_windows.saveImage(imagePath=outputFolder+ a.name + "_with_windows" + dp.defaultImageSuffix) return {'score':topScore, 'timeShift':timeShift, 'topScoresRegional': topScoresRegional, 'Remarks': "'topScoresRegional' stands for regional scores for the top score", 'miscRemarks': miscRemarks, } #score - depending on the algorithm; timeShift - in hours def shiftedCorr(obs, wrf, regions="", obsTime="", maxHourDiff=7, maxLatDiff=4, maxLongDiff=6, shiftStep = 2, #2014-06-25 verbose=False, outputFolder= dp.defaultLabLogsFolder , volumevolumeProportionWeight =0., **kwargs #just in case ): """ adapted from nonStandardKernel() above 2014-06-24 first applied to 20140312.1100 etc maxLatDiff / maxLongDiff: maximal latitudinal / longitudinal difference between obs frame and wrf frame """ timeString = str(int(time.time())) miscRemarks = "" outputFolder += timeString + wrf.name + '/' print "\n\n\n................................................................." print "outputFolder:", outputFolder print "volumevolumeProportionWeight:",volumevolumeProportionWeight os.makedirs(outputFolder) print "sleeping .5 second", time.sleep(.5) if obsTime == "": obsTime = obs[0].dataTime a = obs(obsTime)[0] # getting the DBZ object from the observation stream to be compared #a.show() #debug if regions == "": regions = [(0, 0, a.matrix.shape[0], a.matrix.shape[1])] # a list of one region consisting of the full array, if none given T = a.datetime() # getting the time range T_string = a.getDataTime(T) T2 = sorted([v.dataTime for v in obs if v.dataTime>T_string])[0] # the next time, assumed 10 mins apart - or else # or else need to adjust the k for vect.semiLagrange below b = obs(T2)[0] if b.datetime() - a.datetime() > datetime.timedelta(600./86400): # 600 seconds td = b.datetime() - a.datetime() miscRemarks += "\nTime difference between %s and %s is " % (b.name, a.name) miscRemarks += str(td.days) + " days " + str(td.seconds) + "seconds.\n" #b.debug() #show if a.matrix.var()==0: # test if it is empty a.load() if b.matrix.var()==0: b.load() #a.saveImage() #b.saveImage() maxTime = T + timedelta(maxHourDiff * 1./24) minTime = T - timedelta(maxHourDiff * 1./24) maxTime = a.getDataTime(maxTime) # converting it into string format for pattern.DBZ minTime = a.getDataTime(minTime) # converting it into string format for pattern.DBZ if verbose: print "minTime, obsTime, maxTime:", minTime, obsTime, maxTime # check if there's no corresponing wrf for the time if [v.dataTime for v in wrf if v.dataTime>=minTime and v.dataTime<=maxTime] == []: return {} scores = [] a.backupMatrix('good_copy') a.drawCoast() for R in regions: a.drawRectangularHull(R['points']) a.saveImage(imagePath=outputFolder+ a.name +dp.defaultImageSuffix) a.restoreMatrix('good_copy') b.backupMatrix('good_copy') b.drawCoast() for R in regions: b.drawRectangularHull(R['points']) b.saveImage(imagePath=outputFolder+ b.name +dp.defaultImageSuffix) b.restoreMatrix('good_copy') print "a saved to", outputFolder+ a.name +dp.defaultImageSuffix # debug print "b saved to", outputFolder+ b.name +dp.defaultImageSuffix # debug # looping a_with_windows = a.copy() a_with_windows.drawCoast() for w in wrf: if w.dataTime > maxTime or w.dataTime < minTime: continue else: if w.matrix.var()==0: # test if it is empty w.load() #w.backupMatrix('good_copy') #################################################################### # matching core # 1. shiiba regression -> find the vector field # 2. semi-lagrangian -> find the extended region # 3. cut out the region in obs # 4. match the appropriate region in wrf regionalScores = [] for R0 in regions: name = R0['name'] points = R0['points'] weight = R0['weight'] # extract the "nonstandard kernel" as a1 iMax = int(max(v[0] for v in points)) iMin = int(min(v[0] for v in points)) jMax = int(max(v[1] for v in points)) jMin = int(min(v[1] for v in points)) height = iMax-iMin width = jMax-jMin a1 = a.getWindow(iMin, jMin, height, width) a1.name = a.name + '_' + name a1.imagePath = outputFolder + a1.name + dp.defaultImageSuffix # suffix = ".png" a1.saveImage(imagePath=a1.imagePath) a_with_windows.drawRectangle(iMin, jMin, height, width, newObject=False) # match a1 with a similar rectangle on the wrf, scoring by correlation # we shift the kernel by 1/10 of it's width/height # 4 times left, right, up and down respectively iStep = shiftStep jStep = shiftStep print "points (corners for the region):", points #debug #print "iStep, jStep", iStep, jStep #debug score = 0 shift = (-999,-999) #initialise for i in range(-maxLatDiff, maxLatDiff+1, iStep): for j in range(-maxLongDiff, maxLongDiff+1, jStep): #w.restoreMatrix('good_copy') w1 = w.getWindow(iMin+i, jMin+j, height, width) tempScore = a1.corr(w1) # <<<<<<<< key comparison step >>>>>>>>>>>>>> # adding a step to compare the relative volume, 2014-03-28 proportion = abs(np.log(a1.matrix.sum() / w1.matrix.sum())) #diffLog = abs(np.log(a1.matrix.sum()) - np.log(w1.matrix.sum())) #tempScore = a1.cov(w1)[0,1] # use straight corr for now, will convert to shiiba or normalised corr later # or can use covariance rather than correlation tempScore = tempScore*(1-volumevolumeProportionWeight ) + proportion*volumevolumeProportionWeight if score < tempScore: score = tempScore # get the highest #scoreTime = w.dataTime shift = (i,j) # this info is probably not needed regionalScores.append({'name' : name, # name of the region 'score' : score, 'shift' : shift, 'weight' : weight, }) # compute weighed average over regions averageScore = np.sum([v['score']*v['weight'] for v in regionalScores]) # # ##################################################################### scores.append( {'time':w.dataTime, 'score': averageScore, 'regionalScores': regionalScores} ) scores.sort(key=lambda v:v['score'], reverse=True) topScore = scores[0]['score'] topScoreTime = scores[0]['time'] topScoresRegional = scores[0]['regionalScores'] # actually regional scores for the top score timeShift = (a.datetime(topScoreTime) - a.datetime()).total_seconds() # the time difference is a datetime.timedelta object timeShift = 1. * timeShift/3600 # convert it into hours # saving images w = wrf(topScoreTime)[0].copy() # temp image object ######### # 2014-06-26 for R0 in regions: print "extracting window for", R0['name'] name = R0['name'] points = R0['points'] #weight = R0['weight'] # extract the "nonstandard kernel" as a1 iMax = int(max(v[0] for v in points)) iMin = int(min(v[0] for v in points)) jMax = int(max(v[1] for v in points)) jMin = int(min(v[1] for v in points)) height = iMax-iMin width = jMax-jMin print "iMin, jMin=", iMin, jMin print "topScoresRegional:",topScoresRegional #debug iShift, jShift = [v['shift'] for v in topScoresRegional if v['name']==name][0] iMin += iShift jMin += jShift print "iShift, jShift=", iShift, jShift w1 = w.getWindow(iMin, jMin, height, width) w1.name = w.name + '_' + name + " with shift: (x, y) = " + str((jShift, iShift)) w1.imagePath = outputFolder + w.name + "_window_" + name + "_with_shift"+ dp.defaultImageSuffix # suffix = ".png" #print w1.imagePath #debug #w1.show() #debug w1.saveImage(imagePath=w1.imagePath) # ######### try: w.coastDataPath=obs[0].coastDataPath w.drawCoast() except: print "can't draw coast for ", w.name a_frames = (a_with_windows.matrix > 999) # hack, getting the window frames for w w.matrix += a_frames * 9999 # hack, getting the window frames for w w.saveImage(imagePath=outputFolder+w.name+dp.defaultImageSuffix) a_with_windows.saveImage(imagePath=outputFolder+ a.name + "_with_windows" + dp.defaultImageSuffix) return {'score':topScore, 'timeShift':timeShift, 'topScoresRegional': topScoresRegional, 'Remarks': "'topScoresRegional' stands for regional scores for the top score", 'miscRemarks': miscRemarks, } #score - depending on the algorithm; timeShift - in hours

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1

0

1

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null

0

1

0

6

0c6850850efc50809b6ea6c3afb4857144ba34cc

97

py

Python

Python/PythonCrashCourse2ndEdition/simple_messages.py

awakun/LearningPython

578f9290c8065df37ade49abe4b0ab4e6b35a1bd

[ "MIT" ]

null

Python/PythonCrashCourse2ndEdition/simple_messages.py

awakun/LearningPython

578f9290c8065df37ade49abe4b0ab4e6b35a1bd

[ "MIT" ]

null

Python/PythonCrashCourse2ndEdition/simple_messages.py

awakun/LearningPython

578f9290c8065df37ade49abe4b0ab4e6b35a1bd

[ "MIT" ]

null

message = 'Take me to your leader.' print(message) message = 'ACK ACK ACK ACK ACK' print(message)

24.25

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16

97

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0.338028

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null

0

1

0

6

0b7aa4beeafad036f9afd2c3dd4d7c0a10e29b3c

228

py

Python

puckdb/exceptions.py

metahockey/aaront_puckdb

915638aaff304321f918d19582d2d1fdc83192e6

[ "Apache-2.0" ]

null

puckdb/exceptions.py

metahockey/aaront_puckdb

915638aaff304321f918d19582d2d1fdc83192e6

[ "Apache-2.0" ]

null

puckdb/exceptions.py

metahockey/aaront_puckdb

915638aaff304321f918d19582d2d1fdc83192e6

[ "Apache-2.0" ]

null

class FilterException(Exception): def __init__(self, message=None): self.message = message def __str__(self): return 'Invalid filter{message}'.format(message=': ' + self.message if self.message else '')

32.571429

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0.675439

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0

null

0

1

0

1

0

6

0b9949e80570fd417feb5fd31228c4b6d01358f8

432

py

Python

pathvalidate/handler.py

thombashi/pathvalidate

44807d66392d911e949056f9ccd3248746b0393f

[ "MIT" ]

113

2016-06-14T06:38:28.000Z

2022-03-29T15:15:19.000Z

pathvalidate/handler.py

thombashi/pathvalidate

44807d66392d911e949056f9ccd3248746b0393f

[ "MIT" ]

21

2016-06-14T03:55:29.000Z

2022-03-21T17:35:50.000Z

pathvalidate/handler.py

thombashi/pathvalidate

44807d66392d911e949056f9ccd3248746b0393f

[ "MIT" ]

12

2016-06-14T06:38:32.000Z

2021-09-01T09:48:54.000Z

""" .. codeauthor:: Tsuyoshi Hombashi <tsuyoshi.hombashi@gmail.com> """ from datetime import datetime from typing import Callable from .error import ValidationError Handler = Callable[[ValidationError], str] def return_null_string(e: ValidationError) -> str: return "" def return_timestamp(e: ValidationError) -> str: return str(datetime.now().timestamp()) def raise_error(e: ValidationError) -> str: raise e

17.28

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0

0.155093

432

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0.2

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null

1

0

null

0

1

0

1

0

6

e7e8c934762b60eb0776c80585ce73028477bafc

7,950

py

Python

attackgraph/training.py

wyz2368/deepRL

b92c7dc9c6dbec5ff217162c4fcce35695eabcbb

[ "MIT" ]

null

attackgraph/training.py

wyz2368/deepRL

b92c7dc9c6dbec5ff217162c4fcce35695eabcbb

[ "MIT" ]

null

attackgraph/training.py

wyz2368/deepRL

b92c7dc9c6dbec5ff217162c4fcce35695eabcbb

[ "MIT" ]

null

from attackgraph import json_op as jp from baselines.common import models from baselines.deepq.deepq import learn_multi_nets, Learner from baselines.common.tf_util import ALREADY_INITIALIZED import os # import copy DIR_def = os.getcwd() + '/defender_strategies/' DIR_att = os.getcwd() + '/attacker_strategies/' def training_att(game, mix_str_def, epoch, retrain = False): if len(mix_str_def) != len(game.def_str): raise ValueError("The length of mix_str_def and def_str does not match while training") # env = copy.deepcopy(game.env) print("training_att mix_str_def is ", mix_str_def) ALREADY_INITIALIZED.clear() env = game.env env.reset_everything() env.set_training_flag(1) env.defender.set_mix_strategy(mix_str_def) env.defender.set_str_set(game.def_str) param_path = os.getcwd() + '/network_parameters/param.json' param = jp.load_json_data(param_path) if retrain: scope = 'att_str_retrain' + str(0) + '.pkl' + '/' else: scope = 'att_str_epoch' + str(epoch) + '.pkl' + '/' learner = Learner() with learner.graph.as_default(): with learner.sess.as_default(): act_att, a_BD = learner.learn_multi_nets( env, network = models.mlp(num_hidden=param['num_hidden'], num_layers=param['num_layers']), lr =param['lr'], total_timesteps=param['total_timesteps_att'], exploration_fraction=param['exploration_fraction_att'], exploration_final_eps=param['exploration_final_eps'], print_freq=param['print_freq'], param_noise=param['param_noise'], gamma=param['gamma'], prioritized_replay=param['prioritized_replay'], checkpoint_freq=param['checkpoint_freq'], scope = scope, epoch = epoch ) print("Saving attacker's model to pickle.") if retrain: act_att.save(os.getcwd() + '/retrain_att/' + 'att_str_retrain' + str(0) + '.pkl', 'att_str_retrain' + str(0) + '.pkl' + '/') else: act_att.save(DIR_att + "att_str_epoch" + str(epoch) + ".pkl", 'att_str_epoch' + str(epoch) + '.pkl' + '/') learner.sess.close() return a_BD def training_def(game, mix_str_att, epoch, retrain = False): if len(mix_str_att) != len(game.att_str): raise ValueError("The length of mix_str_att and att_str does not match while retraining") print("training_def mix_str_att is ", mix_str_att) ALREADY_INITIALIZED.clear() # env = copy.deepcopy(game.env) env = game.env env.reset_everything() env.set_training_flag(0) env.attacker.set_mix_strategy(mix_str_att) env.attacker.set_str_set(game.att_str) param_path = os.getcwd() + '/network_parameters/param.json' param = jp.load_json_data(param_path) if retrain: scope = 'def_str_retrain' + str(0) + '.pkl' + '/' else: scope = 'def_str_epoch' + str(epoch) + '.pkl' + '/' learner = Learner() with learner.graph.as_default(): with learner.sess.as_default(): act_def, d_BD = learner.learn_multi_nets( env, network=models.mlp(num_hidden=param['num_hidden'], num_layers=param['num_layers']), lr=param['lr'], total_timesteps=param['total_timesteps_def'], exploration_fraction=param['exploration_fraction_def'], exploration_final_eps=param['exploration_final_eps'], print_freq=param['print_freq'], param_noise=param['param_noise'], gamma=param['gamma'], prioritized_replay=param['prioritized_replay'], checkpoint_freq=param['checkpoint_freq'], scope = scope, epoch=epoch ) print("Saving defender's model to pickle.") if retrain: act_def.save(os.getcwd() + '/retrain_def/' + 'def_str_retrain' + str(0) + '.pkl', 'def_str_retrain' + str(0) + '.pkl' + '/') else: act_def.save(DIR_def + "def_str_epoch" + str(epoch) + ".pkl", "def_str_epoch" + str(epoch) + '.pkl' + '/') learner.sess.close() return d_BD # for all strategies learned by retraining, the scope index is 0. def training_hado_att(game): param = game.param mix_str_def = game.hado_str(identity=0, param=param) if len(mix_str_def) != len(game.def_str): raise ValueError("The length of mix_str_def and def_str does not match while retraining") # env = copy.deepcopy(game.env) env = game.env env.reset_everything() env.set_training_flag(1) env.defender.set_mix_strategy(mix_str_def) env.defender.set_str_set(game.def_str) param_path = os.getcwd() + '/network_parameters/param.json' param = jp.load_json_data(param_path) learner = Learner(retrain=True, freq=param['retrain_freq']) # TODO: add epoch??? with learner.graph.as_default(): with learner.sess.as_default(): act_att, _ = learner.learn_multi_nets( env, network = models.mlp(num_hidden=param['num_hidden'], num_layers=param['num_layers']), lr =param['lr'], total_timesteps=param['retrain_timesteps'], exploration_fraction=param['exploration_fraction'], exploration_final_eps=param['exploration_final_eps'], print_freq=param['print_freq'], param_noise=param['param_noise'], gamma=param['gamma'], prioritized_replay=param['prioritized_replay'], checkpoint_freq=param['checkpoint_freq'], scope = 'att_str_retrain' + str(0) + '.pkl' + '/', load_path=os.getcwd() + '/retrain_att/' + 'att_str_retrain' + str(0) + '.pkl' ) # print("Saving attacker's model to pickle.") # act_att.save(os.getcwd() + '/retrain_att/' + 'att_str_retrain' + str(epoch) + ".pkl", 'att_str_epoch' + str(epoch) + '.pkl' + '/') learner.sess.close() def training_hado_def(game): param = game.param mix_str_att = game.hado_str(identity=1, param=param) if len(mix_str_att) != len(game.att_str): raise ValueError("The length of mix_str_att and att_str does not match while training") # env = copy.deepcopy(game.env) env = game.env env.reset_everything() env.set_training_flag(0) env.attacker.set_mix_strategy(mix_str_att) env.attacker.set_str_set(game.att_str) param_path = os.getcwd() + '/network_parameters/param.json' param = jp.load_json_data(param_path) learner = Learner(retrain=True, freq=param['retrain_freq']) with learner.graph.as_default(): with learner.sess.as_default(): act_def, _ = learner.learn_multi_nets( env, network=models.mlp(num_hidden=param['num_hidden'], num_layers=param['num_layers']), lr=param['lr'], total_timesteps=param['retrain_timesteps'], exploration_fraction=param['exploration_fraction'], exploration_final_eps=param['exploration_final_eps'], print_freq=param['print_freq'], param_noise=param['param_noise'], gamma=param['gamma'], prioritized_replay=param['prioritized_replay'], checkpoint_freq=param['checkpoint_freq'], scope = 'def_str_retrain' + str(0) + '.pkl' + '/', load_path = os.getcwd() + '/retrain_def/' + 'def_str_retrain' + str(0) + '.pkl' ) # print("Saving defender's model to pickle.") # act_def.save(os.getcwd() + '/retrain_def/' + 'def_str_retrain' + str(epoch) + ".pkl", "def_str_epoch" + str(epoch) + '.pkl' + '/') learner.sess.close()

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f0102a920ff1a58aee641748c8ce080081bbfd27

294

pyde

Python

processing/chapter3/sketch_3_3_L3/sketch_3_3_L3.pyde

brickdonut/2019-fall-polytech-cs

b2830795f35e65ff90cf73e0746551c6efdd1f87

[ "MIT" ]

null

processing/chapter3/sketch_3_3_L3/sketch_3_3_L3.pyde

brickdonut/2019-fall-polytech-cs

b2830795f35e65ff90cf73e0746551c6efdd1f87

[ "MIT" ]

null

processing/chapter3/sketch_3_3_L3/sketch_3_3_L3.pyde

brickdonut/2019-fall-polytech-cs

b2830795f35e65ff90cf73e0746551c6efdd1f87

[ "MIT" ]

null

def setup(): size(500,500) noLoop() def setup(): size(500, 500) smooth() background(255) noLoop() fill(50, 80) stroke(100) strokeWeight(3) def draw(): ellipse(250,200,100,100) ellipse(250-50,250,100,100) ellipse(250+50,250,100,100) ellipse(250,250+50,100,100)

14

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1

0

6

f03f832cd30b77fad02c711f3a04696320130d7e

266

py

Python

tests/drop_packets/fanout/fanout_base.py

shubav/sonic-mgmt

0ff71b907a55489bb4ed7d17b1682380fd459bf2

[ "Apache-2.0" ]

132

2016-10-19T12:34:44.000Z

2022-03-16T09:00:39.000Z

tests/drop_packets/fanout/fanout_base.py

shubav/sonic-mgmt

0ff71b907a55489bb4ed7d17b1682380fd459bf2

[ "Apache-2.0" ]

3,152

2016-09-21T23:05:58.000Z

2022-03-31T23:29:08.000Z

tests/drop_packets/fanout/fanout_base.py

shubav/sonic-mgmt

0ff71b907a55489bb4ed7d17b1682380fd459bf2

[ "Apache-2.0" ]

563

2016-09-20T01:00:15.000Z

2022-03-31T22:43:54.000Z

from abc import ABCMeta, abstractmethod class BaseFanoutHandler(object): __metaclass__ = ABCMeta def __init__(self): pass @abstractmethod def update_config(self): pass @abstractmethod def restore_config(self): pass

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f07891bf831deea713dd1a49588e6b8419cfa9fa

242

py

Python

affpose/ARLAffPose/utils/pose/load_camera_pose.py

UW-Advanced-Robotics-Lab/densefusion

8784af40a954421dab9c9648f2d6a739de4c706c

[ "MIT" ]

1

2021-07-23T05:12:43.000Z

affpose/ARLAffPose/utils/pose/load_camera_pose.py

akeaveny/DenseFusion

8784af40a954421dab9c9648f2d6a739de4c706c

[ "MIT" ]

null

affpose/ARLAffPose/utils/pose/load_camera_pose.py

akeaveny/DenseFusion

8784af40a954421dab9c9648f2d6a739de4c706c

[ "MIT" ]

1

2021-11-16T23:55:11.000Z

import yaml import numpy as np ####################################### ####################################### def load_camera_pose(posegraph_addr): return np.loadtxt(posegraph_addr, dtype=np.float32)[:, 1:] # we exclude the timestamp

24.2

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6

b2e80f54b7e4db840337f6a0d3137a6954c436a1

3,264

py

Python

tests/unit/worker/test_docker_api.py

mateuspontes/fastlane

a77505a344da990ad67cffd0ee0eb830489f7324

[ "MIT" ]

32

2019-02-19T01:37:57.000Z

2022-03-19T22:12:23.000Z

tests/unit/worker/test_docker_api.py

mateuspontes/fastlane

a77505a344da990ad67cffd0ee0eb830489f7324

[ "MIT" ]

15

2019-02-18T17:51:57.000Z

2020-03-20T16:27:38.000Z

tests/unit/worker/test_docker_api.py

mateuspontes/fastlane

a77505a344da990ad67cffd0ee0eb830489f7324

[ "MIT" ]

26

2019-02-14T20:00:11.000Z

2020-01-24T18:12:57.000Z

# Standard Library from json import dumps # 3rd Party from preggy import expect # Fastlane from fastlane.worker.docker.api import validate_hostname def test_validate_hostname1(): expect(validate_hostname("example.com")).to_be_false() def test_validate_hostname2(): expect(validate_hostname("example.com/")).to_be_false() def test_validate_hostname3(): expect(validate_hostname("example.com:abcd")).to_be_false() def test_validate_hostname4(): expect(validate_hostname("example.com:1234")).to_be_true() def test_add_to_blacklist1(client): """Test adding to blacklist without payload returns 400""" with client.application.app_context(): resp = client.post( f"/docker-executor/blacklist" ) expect(resp.status_code).to_equal(400) def test_add_to_blacklist2(client): """Test adding to blacklist without host on payload returns 400""" with client.application.app_context(): resp = client.post( f"/docker-executor/blacklist", data=dumps({ "myparam": "abc" }) ) expect(resp.status_code).to_equal(400) def test_add_to_blacklist3(client): """Test adding to blacklist with valid host on payload returns 400""" with client.application.app_context(): resp = client.post( f"/docker-executor/blacklist", data=dumps({ "host": "example.com" }) ) expect(resp.status_code).to_equal(400) def test_add_to_blacklist4(client): """Test adding to blacklist with valid host on payload returns 200""" with client.application.app_context(): resp = client.post( f"/docker-executor/blacklist", data=dumps({ "host": "example.com:1234" }) ) expect(resp.status_code).to_equal(200) def test_remove_from_blacklist1(client): """Test adding to blacklist without payload returns 400""" with client.application.app_context(): resp = client.delete( f"/docker-executor/blacklist" ) expect(resp.status_code).to_equal(400) def test_remove_from_blacklist2(client): """Test adding to blacklist without host on payload returns 400""" with client.application.app_context(): resp = client.delete( f"/docker-executor/blacklist", data=dumps({ "myparam": "abc" }) ) expect(resp.status_code).to_equal(400) def test_remove_from_blacklist3(client): """Test adding to blacklist with valid host on payload returns 400""" with client.application.app_context(): resp = client.delete( f"/docker-executor/blacklist", data=dumps({ "host": "example.com" }) ) expect(resp.status_code).to_equal(400) def test_remove_from_blacklist4(client): """Test adding to blacklist with valid host on payload returns 200""" with client.application.app_context(): resp = client.delete( f"/docker-executor/blacklist", data=dumps({ "host": "example.com:1234" }) ) expect(resp.status_code).to_equal(200)

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0

1

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null

0

6

65091020a3512ae832d4b196ce83a514d82ecdd4

5,626

py

Python

tests/bam_fixtures.py

holtgrewe/pyhtslib

ad3f313d5aa75a313ab092cfc4331bd57a91d959

[ "BSD-3-Clause" ]

null

tests/bam_fixtures.py

holtgrewe/pyhtslib

ad3f313d5aa75a313ab092cfc4331bd57a91d959

[ "BSD-3-Clause" ]

null

tests/bam_fixtures.py

holtgrewe/pyhtslib

ad3f313d5aa75a313ab092cfc4331bd57a91d959

[ "BSD-3-Clause" ]

1

2020-09-01T22:27:16.000Z

#!/usr/bin/env python """Fixture files for the pyhtslib.bam tests""" import os import py import pytest import pyhtslib.bam_internal as bam_internal import pyhtslib.hts_internal as hts_internal __author__ = 'Manuel Holtgrewe <manuel.holtgrewe@bihealth.de>' # --------------------------------------------------------------------------- # Fixtures # --------------------------------------------------------------------------- @pytest.yield_fixture def header_only_sam(tmpdir): """Copy the header_only.sam file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'header_only.sam') dst = tmpdir.join('header_only.sam') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def header_only_sam_header(header_only_sam): hts_file = hts_internal._hts_open( str(header_only_sam).encode('utf-8'), 'r') hdr = bam_internal._sam_hdr_read(hts_file) yield hdr bam_internal._bam_hdr_destroy(hdr) hts_internal._hts_close(hts_file) @pytest.yield_fixture def header_only_sam_gz(tmpdir): """Copy the header_only.sam.gz file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'header_only.sam.gz') dst = tmpdir.join('header_only.sam.gz') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def header_only_sam_gz_header(header_only_sam_gz): hts_file = hts_internal._hts_open( str(header_only_sam_gz).encode('utf-8'), 'r') hdr = bam_internal._sam_hdr_read(hts_file) yield hdr bam_internal._bam_hdr_destroy(hdr) hts_internal._hts_close(hts_file) @pytest.yield_fixture def header_only_bam(tmpdir): """Copy the header_only.bam file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'header_only.bam') dst = tmpdir.join('header_only.bam') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def header_only_bai(tmpdir): """Copy the header_only.bam.bai file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'header_only.bam.bai') dst = tmpdir.join('header_only.bam.bai') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def six_records_sam(tmpdir): """Copy the six_records.sam file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'six_records.sam') dst = tmpdir.join('six_records.sam') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def six_records_sam_header(six_records_sam): hts_file = hts_internal._hts_open( str(six_records_sam).encode('utf-8'), 'r') hdr = bam_internal._sam_hdr_read(hts_file) yield hdr bam_internal._bam_hdr_destroy(hdr) hts_internal._hts_close(hts_file) @pytest.yield_fixture def six_records_sam_gz(tmpdir): """Copy the six_records.sam.gz file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'six_records.sam.gz') dst = tmpdir.join('six_records.sam.gz') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def six_records_sam_gz_header(six_records_sam_gz): hts_file = hts_internal._hts_open( str(six_records_sam_gz).encode('utf-8'), 'r') hdr = bam_internal._sam_hdr_read(hts_file) yield hdr bam_internal._bam_hdr_destroy(hdr) hts_internal._hts_close(hts_file) @pytest.yield_fixture def six_records_bam(tmpdir): """Copy the six_records.bam file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'six_records.bam') dst = tmpdir.join('six_records.bam') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def six_records_bai(tmpdir): """Copy the six_records.bam.bai file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'six_records.bam.bai') dst = tmpdir.join('six_records.bam.bai') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def two_hundred_sam(tmpdir): """Copy the two_hundred.sam file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'two_hundred.sam') dst = tmpdir.join('two_hundred.sam') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def two_hundred_sam_gz(tmpdir): """Copy the two_hundred.sam.gz file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'two_hundred.sam.gz') dst = tmpdir.join('two_hundred.sam.gz') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def two_hundred_tbi(tmpdir): """Copy the two_hundred.sam.gz.tbi file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'two_hundred.sam.gz.tbi') dst = tmpdir.join('two_hundred.sam.gz.tbi') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def two_hundred_bam(tmpdir): """Copy the two_hundred.bam file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'two_hundred.bam') dst = tmpdir.join('two_hundred.bam') src.copy(dst) yield dst dst.remove() @pytest.yield_fixture def two_hundred_bai(tmpdir): """Copy the two_hundred.bam.bai file to temporary directory.""" src = py.path.local(os.path.dirname(__file__)).join( 'files', 'two_hundred.bam.bai') dst = tmpdir.join('two_hundred.bam.bai') src.copy(dst) yield dst dst.remove()

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6

65233b4c67cf933bda7ee7abb25c6d0fc82af331

48

py

Python

testsuite/modulegraph-dir/renamed_b.py

xoviat/modulegraph2

766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a

[ "MIT" ]

9

2020-03-22T14:48:01.000Z

2021-05-30T12:18:12.000Z

testsuite/modulegraph-dir/renamed_b.py

xoviat/modulegraph2

766d00bdb40e5b2fe206b53a87b1bce3f9dc9c2a

[ "MIT" ]

15

2020-01-06T10:02:32.000Z

2021-05-28T12:22:44.000Z

testsuite/modulegraph-dir/renamed_b.py

ronaldoussoren/modulegraph2

b6ab1766b0098651b51083235ff8a18a5639128b

[ "MIT" ]

4

2020-05-10T18:51:41.000Z

2021-04-07T14:03:12.000Z

import sys as c from package import submod as d

16

31

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0

1

0

1

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6

65494a89d5fd46a719792ccc35be36b97a2c4076

1,050

py

Python

varnish/datadog_checks/varnish/config_models/defaults.py

gaffneyd4/integrations-core

4c7725c9f1be4985381aad9740e7186f16a87976

[ "BSD-3-Clause" ]

null

varnish/datadog_checks/varnish/config_models/defaults.py

gaffneyd4/integrations-core

4c7725c9f1be4985381aad9740e7186f16a87976

[ "BSD-3-Clause" ]

null

varnish/datadog_checks/varnish/config_models/defaults.py

gaffneyd4/integrations-core

4c7725c9f1be4985381aad9740e7186f16a87976

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2021-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) from datadog_checks.base.utils.models.fields import get_default_field_value def shared_service(field, value): return get_default_field_value(field, value) def instance_daemon_host(field, value): return 'localhost' def instance_daemon_port(field, value): return 6082 def instance_empty_default_hostname(field, value): return False def instance_metrics_filter(field, value): return get_default_field_value(field, value) def instance_min_collection_interval(field, value): return 15 def instance_name(field, value): return get_default_field_value(field, value) def instance_secretfile(field, value): return '/etc/varnish/secret' def instance_service(field, value): return get_default_field_value(field, value) def instance_tags(field, value): return get_default_field_value(field, value) def instance_varnishadm(field, value): return get_default_field_value(field, value)

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1,050

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1

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null

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1

0

1

0

6

3308cf3b0db548074d1e2bbb40d6eda871756e34

10,401

py

Python

visulization/weather_piechart.py

Jonas2019/car-accident-analysis

1d900beae0c2428c0c85eec44887012fbc2ab988

[ "MIT" ]

null

visulization/weather_piechart.py

Jonas2019/car-accident-analysis

1d900beae0c2428c0c85eec44887012fbc2ab988

[ "MIT" ]

null

visulization/weather_piechart.py

Jonas2019/car-accident-analysis

1d900beae0c2428c0c85eec44887012fbc2ab988

[ "MIT" ]

null

from pymongo import MongoClient import plotly as py import plotly.graph_objs as go import pandas as pd pyplt = py.offline.plot from plotly.subplots import make_subplots client = MongoClient("mongodb+srv://dbAdmin:cmpt732@cluster732.jfbfw.mongodb.net") db = client.CMPT732 df_weather_condition=pd.DataFrame(list(db['WeatherCondition'].find())) df_weather_wind=pd.DataFrame(list(db['WeatherWind'].find())) #.......................Weather Condition.............................................................................. colors_condition = ['rgb(107,174,214)', 'rgb(8,81,156)', 'rgb(7,40,89)'] specs_condtion = [[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}, {'type':'domain'}]] condition = make_subplots(rows=2, cols=3, specs=specs_condtion, subplot_titles=("Clear & Cloudy", "Fog", "Rain", "Snow", "Storm", "Sand & Dust")) labels_condition = ['1','2','3'] clear_1 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Clear & Cloudy'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Clear & Cloudy'])['Severity']==1])['Counts']) clear_2 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Clear & Cloudy'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Clear & Cloudy'])['Severity']==2])['Counts']) clear_3 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Clear & Cloudy'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Clear & Cloudy'])['Severity']==3])['Counts']) list_clear = [clear_1,clear_2,clear_3] condition.add_trace(go.Pie(labels=labels_condition,values=list_clear,marker={'colors':colors_condition}), 1, 1) fog_1 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Fog'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Fog'])['Severity']==1])['Counts']) fog_2 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Fog'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Fog'])['Severity']==2])['Counts']) fog_3 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Fog'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Fog'])['Severity']==3])['Counts']) list_fog = [fog_1,fog_2,fog_3] condition.add_trace(go.Pie(labels=labels_condition,values=list_fog,marker={'colors':colors_condition}), 1, 2) rain_1 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Rain'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Rain'])['Severity']==1])['Counts']) rain_2 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Rain'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Rain'])['Severity']==2])['Counts']) rain_3 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Rain'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Rain'])['Severity']==3])['Counts']) list_rain = [rain_1,rain_2,rain_3] condition.add_trace(go.Pie(labels=labels_condition,values=list_rain,marker={'colors':colors_condition}), 1, 3) snow_1 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Snow'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Snow'])['Severity']==1])['Counts']) snow_2 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Snow'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Snow'])['Severity']==2])['Counts']) snow_3 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Snow'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Snow'])['Severity']==3])['Counts']) list_snow = [snow_1,snow_2,snow_3] condition.add_trace(go.Pie(labels=labels_condition,values=list_snow,marker={'colors':colors_condition}), 2, 1) storm_1 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Storm'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Storm'])['Severity']==1])['Counts']) storm_2 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Storm'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Storm'])['Severity']==2])['Counts']) storm_3 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Storm'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Storm'])['Severity']==3])['Counts']) list_storm = [storm_1,storm_2,storm_3] condition.add_trace(go.Pie(labels=labels_condition,values=list_storm,marker={'colors':colors_condition}), 2, 2) sand_1 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Sand & Dust'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Sand & Dust'])['Severity']==1])['Counts']) sand_2 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Sand & Dust'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Sand & Dust'])['Severity']==2])['Counts']) sand_3 =int(((df_weather_condition[df_weather_condition['Weather_Condition'] == 'Sand & Dust'])[(df_weather_condition[df_weather_condition['Weather_Condition'] == 'Sand & Dust'])['Severity']==3])['Counts']) list_sand = [sand_1,sand_2,sand_3] condition.add_trace(go.Pie(labels=labels_condition,values=list_sand,marker={'colors':colors_condition}), 2, 3) condition.update_traces(hoverinfo='label+percent+name', textinfo='percent') condition.update_layout(legend_title_text='Severity', autosize=False, width=1000, height=800) condition = go.Figure(condition) condition.show() #pyplt(condition,filename='weather_c.html',image='png') #.......................Wind Direction................................................................................. colors_wind = ['rgb(107,174,214)','rgb(8,81,156)','rgb(7,40,89)' ] specs_wind = [[{'type':'domain'}, {'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}, {'type':'domain'}]] wind = make_subplots(rows=2, cols=3,specs = specs_wind, subplot_titles=("Variable", "Clam", "South", "East", "North", "West")) labels_wind = ['1','2','3'] variable_1 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'Variable'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'Variable'])['Severity']==1])['Counts']) variable_2 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'Variable'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'Variable'])['Severity']==2])['Counts']) variable_3 = int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'Variable'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'Variable'])['Severity']==3])['Counts']) list_variable = [variable_1,variable_2,variable_3] wind.add_trace(go.Pie(labels=labels_wind,values=list_variable,marker={'colors':colors_wind}), 1, 1) clam_1 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'Clam'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'Clam'])['Severity']==1])['Counts']) clam_2 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'Clam'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'Clam'])['Severity']==2])['Counts']) clam_3 = int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'Clam'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'Clam'])['Severity']==3])['Counts']) list_clam = [clam_1,clam_2,clam_3] wind.add_trace(go.Pie(labels=labels_wind,values=list_clam,marker={'colors':colors_wind}), 1, 2) south_1 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'South'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'South'])['Severity']==1])['Counts']) south_2 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'South'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'South'])['Severity']==2])['Counts']) south_3 = int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'South'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'South'])['Severity']==3])['Counts']) list_s = [south_1,south_2,south_3] wind.add_trace(go.Pie(labels=labels_wind,values=list_s,marker={'colors':colors_wind}), 1, 3) east_1 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'East'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'East'])['Severity']==1])['Counts']) east_2 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'East'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'East'])['Severity']==2])['Counts']) east_3 = int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'East'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'East'])['Severity']==3])['Counts']) list_e = [east_1,east_2,east_3] wind.add_trace(go.Pie(labels=labels_wind,values=list_e,marker={'colors':colors_wind}), 2, 1) north_1 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'North'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'North'])['Severity']==1])['Counts']) north_2 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'North'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'North'])['Severity']==2])['Counts']) north_3 = int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'North'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'North'])['Severity']==3])['Counts']) list_n = [north_1,north_2,north_3] wind.add_trace(go.Pie(labels=labels_wind,values=list_n,marker={'colors':colors_wind}), 2, 2) west_1 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'West'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'West'])['Severity']==1])['Counts']) west_2 =int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'West'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'West'])['Severity']==2])['Counts']) west_3 = int(((df_weather_wind[df_weather_wind['Wind_Direction'] == 'West'])[(df_weather_wind[df_weather_wind['Wind_Direction'] == 'West'])['Severity']==3])['Counts']) list_w = [west_1,west_2,west_3] wind.add_trace(go.Pie(labels=labels_wind,values=list_w,marker={'colors':colors_wind}), 2, 3) wind.update_traces(hoverinfo='label+percent+name', textinfo='percent') wind.update_layout(legend_title_text='Severity', autosize=False, width=1000, height=800) wind = go.Figure(wind) #pyplt(wind,filename='weather_w.html',image='png') wind.show()

81.257813

213

0.716181

1,415

10,401

4.89682

0.077032

0.189638

0.103911

0.790879

0.746717

0.722471

0

0.020942

0.058841

10,401

127

214

81.897638

0.686893

0.032593

0

0.083333

0

0.253381

0.005768

0

1

0

false

0

0.052083

0

0.052083

0

null

0

1

0

1

0

null

0

6

333243522f96d41b444dd7f661b36537aa050102

84

py

Python

src/nna/exp/__init__.py

EnisBerk/speech_audio_understanding

2b1ba15a67bb48de7b949a6e5e0205dc5c3e24bd

[ "MIT" ]

2

2019-12-05T22:27:54.000Z

2020-04-05T21:24:50.000Z

src/nna/exp/__init__.py

EnisBerk/speech_audio_understanding

2b1ba15a67bb48de7b949a6e5e0205dc5c3e24bd

[ "MIT" ]

21

2020-01-28T22:53:24.000Z

2022-02-10T02:50:11.000Z

src/nna/exp/__init__.py

speechLabBcCuny/nnaAudiosetClassification

ed61303609a069aac1887ca98116521e09cbd2ee

[ "MIT" ]

null

# import nna.exp.augmentations # import nna.exp.runutils # import nna.exp.modelArchs

28

30

0.797619

12

84

5.583333

0.5

0.402985

0.537313

0

0.095238

84

3

31

28

0.881579

0.928571

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

6

336491240f5779b06deb0a5598deb2cdbee8410d

20,628

py

Python

postgres_backend/data/tests/test_models.py

scripted-adventurer/Custom-Fantasy-Football

334419d46d2142ceec7630d4582bf61e06a4de1a

[ "Unlicense" ]

1

2020-09-12T04:25:19.000Z

postgres_backend/data/tests/test_models.py

scripted-adventurer/Custom-Fantasy-Football

334419d46d2142ceec7630d4582bf61e06a4de1a

[ "Unlicense" ]

null

postgres_backend/data/tests/test_models.py

scripted-adventurer/Custom-Fantasy-Football

334419d46d2142ceec7630d4582bf61e06a4de1a

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- import datetime from freezegun import freeze_time import os import pytz from django.test import TestCase import data.models as models from .setup import TestData from common.current_week import get_current_week from common.hashing import generate_hash class ModelsTest(TestCase): # automatically loaded: # 5 test users (referenced with TestData().user) # all active players and teams (prior to the 2020 season) # all games from 2019 REG 17 # all drives, plays, and play_players from game 10160000-0581-45c0-455c-8dcc2dd0671b fixtures = ['user', 'team', 'player', 'game', 'drive', 'play', 'play_player'] def setUp(self): super().setUp() self.data = TestData() def test_game(self): main = models.Game.objects.get(game_id='10160000-0581-45c0-455c-8dcc2dd0671b') same = models.Game.objects.get(game_id='10160000-0581-45c0-455c-8dcc2dd0671b') different = models.Game.objects.get(game_id='10160000-0581-4680-ba82-12e629d4584f') other = models.Drive.objects.get(id=76757) self.assertEqual(repr(main), "{'model': 'Game', 'game_id': '10160000-0581-45c0-455c-8dcc2dd0671b'}") self.assertEqual(str(main), "{Game '10160000-0581-45c0-455c-8dcc2dd0671b'}") self.assertEqual(main.data_dict(), {'id': '10160000-0581-45c0-455c-8dcc2dd0671b', 'start_time': "2019-12-29 18:00", 'season_type': 'REG', 'season_year': 2019, 'week': 17, 'home_team': 'DET', 'away_team': 'GB', 'home_score': 20, 'away_score': 23}) self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_drive(self): main = models.Drive.objects.get(id=76756) same = models.Drive.objects.get(id=76756) different = models.Drive.objects.get(id=76757) other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), "{'model': 'Drive', 'game_id': " "'10160000-0581-45c0-455c-8dcc2dd0671b', 'drive_id': 14}") self.assertEqual(str(main), "{Drive 14 from Game '10160000-0581-45c0-455c-8dcc2dd0671b'}") self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_play(self): main = models.Play.objects.get(id=544462) same = models.Play.objects.get(id=544462) different = models.Play.objects.get(id=544463) other = models.Drive.objects.get(id=76757) self.assertEqual(repr(main), "{'model': 'Play', 'game_id': '10160000-0581-45c0-455c-8dcc2dd0671b', " "'drive_id': 14, 'play_id': 2945}") self.assertEqual(str(main), "{Play 2945 from Drive 14 from Game '10160000-0581-45c0-455c-8dcc2dd0671b'}") self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_play_player(self): main = models.PlayPlayer.objects.get(id=1277030) same = models.PlayPlayer.objects.get(id=1277030) different = models.PlayPlayer.objects.get(id=1277031) other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), "{'model': 'PlayPlayer', 'player': '3200524f-4433-9293-a3cf-ad7758d03003', " "'game_id': '10160000-0581-45c0-455c-8dcc2dd0671b', 'drive_id': 14, " "'play_id': 2945}") self.assertEqual(str(main), "{Player '3200524f-4433-9293-a3cf-ad7758d03003' from Play 2945 from Drive " "14 from Game '10160000-0581-45c0-455c-8dcc2dd0671b'}") self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_player(self): main = models.Player.objects.get( player_id='3200524f-4433-9293-a3cf-ad7758d03003') same = models.Player.objects.get( player_id='3200524f-4433-9293-a3cf-ad7758d03003') different = models.Player.objects.get( player_id='3200434f-5570-9400-e1ae-f835abb5963e') other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), "{'model': 'Player', 'player_id': " "'3200524f-4433-9293-a3cf-ad7758d03003'}") self.assertEqual(str(main), "{Aaron Rodgers QB GB}") self.assertEqual(main.data_dict(), {'id': '3200524f-4433-9293-a3cf-ad7758d03003', 'name': 'Aaron Rodgers', 'team': 'GB', 'position': 'QB', 'status': 'ACT'}) self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) # Sunday during Packers 2019 bye week @freeze_time("2019-11-17 20:00:00") def test_player_is_locked_bye_week(self): gb_qb = models.Player.objects.get( player_id='3200434f-5570-9400-e1ae-f835abb5963e') self.assertEqual(gb_qb.is_locked(), False) # Saturday during Week 17 2019 @freeze_time("2019-12-28 12:00:00") def test_player_is_locked_neither(self): gb_qb = models.Player.objects.get( player_id='3200434f-5570-9400-e1ae-f835abb5963e') sea_qb = models.Player.objects.get( player_id='32005749-4c77-7781-795c-94c753706d1d') self.assertEqual(gb_qb.is_locked(), False) self.assertEqual(sea_qb.is_locked(), False) # Sunday afternoon during Week 17 2019 @freeze_time("2019-12-29 18:30:00") def test_player_is_locked_one(self): gb_qb = models.Player.objects.get( player_id='3200434f-5570-9400-e1ae-f835abb5963e') sea_qb = models.Player.objects.get( player_id='32005749-4c77-7781-795c-94c753706d1d') self.assertEqual(gb_qb.is_locked(), True) self.assertEqual(sea_qb.is_locked(), False) # Monday during Week 1 2019 @freeze_time("2019-12-30 12:00:00") def test_player_is_locked_both(self): gb_qb = models.Player.objects.get( player_id='3200434f-5570-9400-e1ae-f835abb5963e') sea_qb = models.Player.objects.get( player_id='32005749-4c77-7781-795c-94c753706d1d') self.assertEqual(gb_qb.is_locked(), True) self.assertEqual(sea_qb.is_locked(), True) def test_team(self): main = models.Team.objects.get(team_id='GB') same = models.Team.objects.get(team_id='GB') different = models.Team.objects.get(team_id='CHI') other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), "{'model': 'Team', 'team_id': 'GB'}") self.assertEqual(str(main), "{Green Bay Packers}") self.assertEqual(main.data_dict(), {'id': 'GB', 'name': 'Green Bay Packers'}) self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_league_basic(self): self.data.create('League', name='test_league_basic_0', password='password') self.data.create('League', name='test_league_basic_1', password='password') main = models.League.objects.get(name=self.data.league[0].name) same = models.League.objects.get(name=self.data.league[0].name) different = self.data.league[1] other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), f"{{'model': 'League', 'name': '{self.data.league[0].name}'}}") self.assertEqual(str(main), f"{{League {self.data.league[0].name}}}") self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_league_additional(self): password_hash = generate_hash('password') lineup_settings = {'K': 1, 'QB': 1, 'RB': 2, 'TE': 1, 'WR': 2} scoring_settings = [ {'name': 'passing yards', 'field': 'passing_yds', 'conditions': [], 'multiplier': .04}, {'name': 'fg made 40-49 yards', 'field': 'kicking_fgm', 'conditions': [ {'field': 'kicking_fgm_yds', 'comparison': '>=', 'value': 40}, {'field': 'kicking_fgm_yds', 'comparison': '<', 'value': 50}], 'multiplier': 2.0}, {'name': 'fg made 50+ yards', 'field': 'kicking_fgm', 'conditions': [ {'field': 'kicking_fgm_yds', 'comparison': '>=', 'value': 50}], 'multiplier': 3.0}] new_scoring_settings = [ {'name': 'rushing yards', 'field': 'rushing_yds', 'conditions': [], 'multiplier': .1}] # league 0 is blank, league 1 has standard settings self.data.create('League', name='test_league_additional_0', password=password_hash) self.data.create('League', name='test_league_additional_1', password=password_hash, qb=1, rb=2, wr=2, te=1, k=1) for stat in scoring_settings: conditions = True if stat['conditions'] else False self.data.create('LeagueStat', league=self.data.league[1], name=stat['name'], field=stat['field'], conditions=conditions, multiplier=stat['multiplier']) self.data.create('StatCondition', league_stat=self.data.leaguestat[1], field='kicking_fgm_yds', comparison='>=', value=40) self.data.create('StatCondition', league_stat=self.data.leaguestat[1], field='kicking_fgm_yds', comparison='<', value=50) self.data.create('StatCondition', league_stat=self.data.leaguestat[2], field='kicking_fgm_yds', comparison='>=', value=50) self.data.create('Member', league=self.data.league[1], user=self.data.user[0], admin=True) self.data.create('Member', league=self.data.league[1], user=self.data.user[1]) self.data.create('Member', league=self.data.league[1], user=self.data.user[2]) self.assertEqual(self.data.league[0].correct_password('password'), True) self.assertEqual(self.data.league[0].correct_password('incorrect'), False) self.assertEqual(self.data.league[0].get_lineup_settings(), {}) self.assertEqual(self.data.league[0].get_scoring_settings(), []) self.assertEqual(self.data.league[0].get_members(), []) self.assertEqual(self.data.league[1].get_lineup_settings(), lineup_settings) self.assertEqual(self.data.league[1].get_scoring_settings(), scoring_settings) self.assertEqual(self.data.league[1].get_members(), [self.data.user[0].username, self.data.user[1].username, self.data.user[2].username]) self.data.league[1].set_lineup_settings(lineup_settings) self.data.league[1].set_scoring_settings(scoring_settings) self.data.league[1].set_password('new_password') # check all changes were made self.data.league[1] = models.League.objects.get(name='test_league_additional_1') self.assertEqual(self.data.league[1].get_lineup_settings(), lineup_settings) self.assertEqual(self.data.league[1].get_scoring_settings(), scoring_settings) self.assertEqual(self.data.league[1].correct_password('new_password'), True) # check updating stats deletes old stats self.data.league[1].set_scoring_settings(new_scoring_settings) self.data.league[1] = models.League.objects.get(name='test_league_additional_1') self.assertEqual(self.data.league[1].get_scoring_settings(), new_scoring_settings) def test_league_stat(self): password_hash = generate_hash('password') self.data.create('League', name='test_league_stat_0', password=password_hash) self.data.create('LeagueStat', league=self.data.league[0], name='passing yards', field='passing_yds', multiplier=.04) self.data.create('LeagueStat', league=self.data.league[0], name='rushing tds', field='rushing_tds', multiplier=6) main = models.LeagueStat.objects.get(league=self.data.league[0], name='passing yards') same = models.LeagueStat.objects.get(league=self.data.league[0], name='passing yards') different = models.LeagueStat.objects.get(league=self.data.league[0], name='rushing tds') other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), ("{'model': 'LeagueStat', 'league': 'test_league_stat_0', 'name': " + "'passing yards'}")) self.assertEqual(str(main), "{Stat 'passing yards' from League 'test_league_stat_0'}") self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_stat_condition(self): password_hash = generate_hash('password') self.data.create('League', name='test_stat_condition_0', password=password_hash) self.data.create('LeagueStat', league=self.data.league[0], name='fg bonus (40-49)', field='kicking_fgm', conditions=True, multiplier=1) self.data.create('StatCondition', league_stat=self.data.leaguestat[0], field='kicking_fgm_yds', comparison='>=', value=40) self.data.create('StatCondition', league_stat=self.data.leaguestat[0], field='kicking_fgm_yds', comparison='<', value=50) main = models.StatCondition.objects.get(league_stat=self.data.leaguestat[0], field='kicking_fgm_yds', comparison='>=', value=40) same = models.StatCondition.objects.get(league_stat=self.data.leaguestat[0], field='kicking_fgm_yds', comparison='>=', value=40) different = models.StatCondition.objects.get(league_stat=self.data.leaguestat[0], field='kicking_fgm_yds', comparison='<', value=50) other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), ("{'model': 'StatCondition', 'league': 'test_stat_condition_0', " "'stat': 'fg bonus (40-49)', 'field': 'kicking_fgm_yds', " "'comparison': '>=', 'value': 40}")) self.assertEqual(str(main), ("{Condition kicking_fgm_yds>=40 for 'fg bonus (40-49)' in League " "'test_stat_condition_0'}")) self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_member_basic(self): password_hash = generate_hash('password') self.data.create('League', name='test_member_basic_0', password=password_hash) self.data.create('Member', user=self.data.user[0], league=self.data.league[0]) self.data.create('Member', user=self.data.user[1], league=self.data.league[0]) main = models.Member.objects.get(user=self.data.user[0], league=self.data.league[0]) same = models.Member.objects.get(user=self.data.user[0], league=self.data.league[0]) different = models.Member.objects.get(user=self.data.user[1], league=self.data.league[0]) other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), ("{'model': 'Member', 'username': 'test_user_0', 'league': " + "'test_member_basic_0'}")) self.assertEqual(str(main), ("{User 'test_user_0' in League 'test_member_basic_0'}")) self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False) def test_member_additional(self): password_hash = generate_hash('password') self.data.create('League', name='test_member_additional_0', password=password_hash) self.data.create('Member', user=self.data.user[0], league=self.data.league[0]) self.data.create('Member', user=self.data.user[1], league=self.data.league[0]) # past self.data.create('Lineup', member=self.data.member[1], season_type='REG', season_year=2019, week=17, player_id='3200524f-4433-9293-a3cf-ad7758d03003') self.data.create('Lineup', member=self.data.member[1], season_type='REG', season_year=2019, week=17, player_id='3200434f-5570-9400-e1ae-f835abb5963e') # current season_year, season_type, week = get_current_week() self.data.create('Lineup', member=self.data.member[1], season_type=season_type, season_year=season_year, week=week, player_id='3200524f-4433-9293-a3cf-ad7758d03003') self.data.create('Lineup', member=self.data.member[1], season_type=season_type, season_year=season_year, week=week, player_id='32005749-4c77-7781-795c-94c753706d1d') # no lineup self.assertEqual(self.data.member[0].get_lineup('REG', 2019, 17), []) self.assertEqual(self.data.member[0].get_lineup(), []) # existing lineup past existing_lineup = self.data.member[1].get_lineup('REG', 2019, 17) self.assertEqual(existing_lineup[0]['id'], '3200524f-4433-9293-a3cf-ad7758d03003') self.assertEqual(existing_lineup[1]['id'], '3200434f-5570-9400-e1ae-f835abb5963e') # existing lineup current existing_lineup = self.data.member[1].get_lineup() self.assertEqual(existing_lineup[0]['id'], '3200524f-4433-9293-a3cf-ad7758d03003') self.assertEqual(existing_lineup[1]['id'], '32005749-4c77-7781-795c-94c753706d1d') # lineup add previous week self.data.member[0].lineup_add('3200524f-4433-9293-a3cf-ad7758d03003', 'REG', 2019, 17) previous_lineup = self.data.member[0].get_lineup('REG', 2019, 17) self.assertEqual(previous_lineup[0]['name'], 'Aaron Rodgers') # lineup add current week self.data.member[0].lineup_add('3200434f-5570-9400-e1ae-f835abb5963e') current_lineup = self.data.member[0].get_lineup() self.assertEqual(current_lineup[0]['team'], 'MIN') # lineup delete previous week - player doesn't exist self.data.member[0].lineup_delete('32005749-4c77-7781-795c-94c753706d1d', 'REG', 2019, 17) previous_lineup = self.data.member[0].get_lineup('REG', 2019, 17) self.assertEqual(previous_lineup[0]['position'], 'QB') # lineup delete previous week - player exists self.data.member[0].lineup_delete('3200524f-4433-9293-a3cf-ad7758d03003', 'REG', 2019, 17) self.assertEqual(self.data.member[0].get_lineup('REG', 2019, 17), []) # lineup delete current week - player doesn't exist self.data.member[0].lineup_delete('32005749-4c77-7781-795c-94c753706d1d') current_lineup = self.data.member[0].get_lineup() self.assertEqual(current_lineup[0]['status'], 'ACT') # lineup delete current week - player exists self.data.member[0].lineup_delete('3200434f-5570-9400-e1ae-f835abb5963e') self.assertEqual(self.data.member[0].get_lineup(), []) def test_lineup(self): password_hash = generate_hash('password') self.data.create('League', name='test_lineup_0', password=password_hash) self.data.create('Member', user=self.data.user[0], league=self.data.league[0]) self.data.create('Lineup', member=self.data.member[0], season_type='REG', season_year=2019, week=17, player_id='3200524f-4433-9293-a3cf-ad7758d03003') self.data.create('Lineup', member=self.data.member[0], season_type='REG', season_year=2019, week=17, player_id='3200434f-5570-9400-e1ae-f835abb5963e') main = models.Lineup.objects.get(member=self.data.member[0], season_type='REG', season_year=2019, week=17, player_id='3200524f-4433-9293-a3cf-ad7758d03003') same = models.Lineup.objects.get(member=self.data.member[0], season_type='REG', season_year=2019, week=17, player_id='3200524f-4433-9293-a3cf-ad7758d03003') different = models.Lineup.objects.get(member=self.data.member[0], season_type='REG', season_year=2019, week=17, player_id='3200434f-5570-9400-e1ae-f835abb5963e') other = models.Play.objects.get(id=544463) self.assertEqual(repr(main), ("{'model': 'Lineup', 'user': 'test_user_0', 'league': " "'test_lineup_0', 'season_year': 2019, 'season_type': 'REG', " "'week': 17, 'player_id': '3200524f-4433-9293-a3cf-ad7758d03003'}")) self.assertEqual(str(main), ("{Player '3200524f-4433-9293-a3cf-ad7758d03003' for User 'test_user_0' " "in League 'test_lineup_0' for 'REG' 2019 week 17}")) self.assertEqual((main == same), True) self.assertEqual(hash(main) == hash(same), True) self.assertEqual((main == different), False) self.assertEqual(hash(main) == hash(different), False) self.assertEqual(main == other, False)

50.558824

104

0.688142

2,770

20,628

5.002166

0.079061

0.069861

0.041426

0.024899

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0.813727

0.763063

0.709945

0.641527

0.625433

0

0.097975

0.145482

20,628

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105

50.558824

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0.492997

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0.05042

false

0.072829

0.02521

0

0.081232

0

null

0

1

0

1

0

null

0

1

0

6

68a14d7fa3e81bbd515e23671b5ee3bd4e88ef9d

1,892

py

Python

sorting/quick_sort.py

nomadkitty/cs_unit_assessment_prep

4361be48636f583b1fafe33f2432a195f29a4f95

[ "MIT" ]

null

sorting/quick_sort.py

nomadkitty/cs_unit_assessment_prep

4361be48636f583b1fafe33f2432a195f29a4f95

[ "MIT" ]

null

sorting/quick_sort.py

nomadkitty/cs_unit_assessment_prep

4361be48636f583b1fafe33f2432a195f29a4f95

[ "MIT" ]

null

# What kind of input will we get? # We expect a list def quicksort(data): # check if data has 1 or 0 elements # (base case) a side only contains a single element if len(data) <= 1: return data # Partition the data # Start by choosing a pivot (choose the first item in the list) pivot = data[0] # We need to create storage for the LHS and RHS left = [] right = [] # We need to loop through each item for current in data[1:]: # if it's smaller or equal, add to LHS storage if current <= pivot: left.append(current) # if its' bigger, add to RHS storage else: right.append(current) # (recursive case) Recursively Quick Sort LHS and RHS until return quicksort(left) + [pivot] + quicksort(right) quicksort([2, 5, 7, 1, 3, 4, 6, 9, 8]) # # helper version: # def partition(data): # # Partition the data # # Start by choosing a pivot (choose the first item in the list) # pivot = data[0] # # We need to create storage for the LHS and the RHS # left = [] # right = [] # # # We need to loop through each item # for current in data[1:]: # # if it's smaller or equal, append to left # if current <= pivot: # left.append(current) # # if it's bigger, add to RHS storage # else: # right.append(current) # # return left, right, pivot # # # # What kind of input will we get? # # We expect a list # def quicksort(data): # # check if data has 1 or 0 elements # # (base case) a side only contains a single element # if len(data) <= 1: # return data # # left, right, pivot = partition(data) # # # (recursive case) Recursively Quick Sort LHS and RHS until # return quicksort(left) + [pivot] + quicksort(right) # # # print(quicksort([2,5,7,1,3,4,6,9,8]))

28.666667

69

0.589323

278

1,892

4.010791

0.280576

0.017937

0.0287

0.026906

0.898655

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0.762332

0

0.021228

0.302854

1,892

65

70

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0.824109

0.734144

0

1

0.076923

false

0

0.230769

0

null

0

1

0

null

0

6

68ab9a85fc8fba1cd0584d3b46b3327e0c54ff6f

5,030

py

Python

tests/test_cli.py

Red-Lex/tpkutils

5f3694451a1759548af579b689f478cefc633252

[ "BSD-3-Clause" ]

85

2016-09-21T19:29:06.000Z

2022-03-29T10:31:33.000Z

tests/test_cli.py

Red-Lex/tpkutils

5f3694451a1759548af579b689f478cefc633252

[ "BSD-3-Clause" ]

28

2016-09-05T23:55:16.000Z

2022-01-17T11:21:23.000Z

tests/test_cli.py

Red-Lex/tpkutils

5f3694451a1759548af579b689f478cefc633252

[ "BSD-3-Clause" ]

18

2018-08-30T21:27:10.000Z

2022-03-17T13:13:47.000Z

import os import sqlite3 import pytest from click.testing import CliRunner from tpkutils.cli import cli from pymbtiles import MBtiles @pytest.fixture(scope="function") def runner(): return CliRunner() def test_export_mbtiles(runner, tmpdir): tpk = "tests/data/states_filled.tpk" mbtiles = str(tmpdir.join("test.mbtiles")) result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles]) assert result.exit_code == 0 assert os.path.exists(mbtiles) with sqlite3.connect(mbtiles) as db: cursor = db.cursor() # # Verify zoom levels present cursor.execute("select distinct zoom_level from tiles order by zoom_level") zoom_levels = {x[0] for x in cursor.fetchall()} assert zoom_levels == {0, 1, 2, 3, 4} cursor.close() def test_export_mbtiles_zoom(runner, tmpdir): tpk = "tests/data/states_filled.tpk" mbtiles = str(tmpdir.join("test.mbtiles")) result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles, "--zoom", "0,1"]) assert result.exit_code == 0 assert os.path.exists(mbtiles) with sqlite3.connect(mbtiles) as db: cursor = db.cursor() # Verify zoom levels present cursor.execute("select distinct zoom_level from tiles order by zoom_level") zoom_levels = {x[0] for x in cursor.fetchall()} assert zoom_levels == {0, 1} cursor.close() def test_export_mbtiles_existing_output(runner, tmpdir): tpk = "tests/data/states_filled.tpk" mbtiles = str(tmpdir.join("test.mbtiles")) result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles]) assert result.exit_code == 0 assert os.path.exists(mbtiles) result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles]) assert result.exit_code == 1 assert "Output exists and overwrite is false" in result.output result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles, "--overwrite"]) assert result.exit_code == 0 assert os.path.exists(mbtiles) def test_export_mbtiles_tile_bounds(runner, tmpdir): tpk = "tests/data/states_filled.tpk" mbtiles_filename = str(tmpdir.join("test.mbtiles")) result = runner.invoke( cli, ["export", "mbtiles", tpk, mbtiles_filename, "-z", "0", "--tile-bounds"] ) assert result.exit_code == 0 print(result.output) assert os.path.exists(mbtiles_filename) with MBtiles(mbtiles_filename) as mbtiles: assert mbtiles.zoom_range() == (0, 0) assert mbtiles.meta["bounds"] == "-180.000000,-85.051129,180.000000,85.051129" def test_export_mbtiles_verbosity(runner, tmpdir): tpk = "tests/data/states_filled.tpk" mbtiles = str(tmpdir.join("test.mbtiles")) result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles, "-v"]) assert result.exit_code == 0 # assert 'INFO:tpkutils' in result.output # not working w/ pytest mbtiles = str(tmpdir.join("test2.mbtiles")) result = runner.invoke(cli, ["export", "mbtiles", tpk, mbtiles, "-v", "-v"]) assert result.exit_code == 0 # assert 'DEBUG:tpkutils' in result.output # not working w/ pytest def test_export_disk(runner, tmpdir): tpk = "tests/data/states_filled.tpk" path = str(tmpdir.join("tiles")) result = runner.invoke(cli, ["export", "disk", tpk, path]) assert result.exit_code == 0 assert os.path.exists(path) assert os.path.exists(os.path.join(path, "0/0/0.png")) def test_export_disk_zoom(runner, tmpdir): tpk = "tests/data/states_filled.tpk" path = str(tmpdir.join("tiles")) result = runner.invoke(cli, ["export", "disk", tpk, path, "--zoom", "1"]) assert result.exit_code == 0 assert os.path.exists(path) assert os.path.exists(os.path.join(path, "1/0/0.png")) assert not os.path.exists(os.path.join(path, "0/0/0.png")) def test_export_disk_existing_output(runner, tmpdir): tpk = "tests/data/states_filled.tpk" path = str(tmpdir.join("tiles")) result = runner.invoke(cli, ["export", "disk", tpk, path]) assert result.exit_code == 0 assert os.path.exists(path) result = runner.invoke(cli, ["export", "disk", tpk, path]) assert result.exit_code == 1 assert "Output directory must be empty" in result.output def test_export_disk_scheme(runner, tmpdir): tpk = "tests/data/states_filled.tpk" path = str(tmpdir.join("tiles")) result = runner.invoke(cli, ["export", "disk", tpk, path, "--scheme", "xyz"]) assert result.exit_code == 0 assert os.path.exists(path) assert os.path.exists(os.path.join(path, "1/0/1.png")) assert not os.path.exists(os.path.join(path, "1/0/0.png")) def test_export_disk_drop_empty(runner, tmpdir): tpk = "tests/data/states_filled.tpk" path = str(tmpdir.join("tiles")) result = runner.invoke(cli, ["export", "disk", tpk, path, "--drop-empty"]) assert result.exit_code == 0 assert os.path.exists(path) assert os.path.exists(os.path.join(path, "4/2/6.png")) assert not os.path.exists(os.path.join(path, "4/2/7.png"))

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0.009524

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null

0

1

0

null

0

6

d7ad80ed2e6ae498cd702997c567607e6410170e

126

py

Python

app/admin/__init__.py

cmumford/asset-tracker

8356870116478f947ea0c3231ac368320428709a

[ "MIT" ]

null

app/admin/__init__.py

cmumford/asset-tracker

8356870116478f947ea0c3231ac368320428709a

[ "MIT" ]

null

app/admin/__init__.py

cmumford/asset-tracker

8356870116478f947ea0c3231ac368320428709a

[ "MIT" ]

null

from flask import Blueprint admin_blueprint = Blueprint('admin', __name__, template_folder='templates') from . import routes

25.2

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126

6.333333

0.666667

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0.111111

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0

null

0

1

0

1

0

6

d7b9cfba72c41cc3f7d365caab097660cd376aec

19

py

Python

flow_next/models/__init__.py

chenwenxiao/DOI

14bdedd0b1b886efe77737cfb62695f03ee17c58

[ "MIT" ]

1

2021-08-13T22:14:10.000Z

flow_next/models/__init__.py

chenwenxiao/DOI

14bdedd0b1b886efe77737cfb62695f03ee17c58

[ "MIT" ]

null

flow_next/models/__init__.py

chenwenxiao/DOI

14bdedd0b1b886efe77737cfb62695f03ee17c58

[ "MIT" ]

null

from . import glow

9.5

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null

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1

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1

0

1

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6

0bdadefd8b5366911f1422d6e9f14e9e8ddec257

2,396

py

Python

model2/models/__init__.py

alibell/DMLI_Gleason_Score_Challenge

53d3f60d884088a25bd2658fca4c8928c2828c49

[ "MIT" ]

null

model2/models/__init__.py

alibell/DMLI_Gleason_Score_Challenge

53d3f60d884088a25bd2658fca4c8928c2828c49

[ "MIT" ]

null

model2/models/__init__.py

alibell/DMLI_Gleason_Score_Challenge

53d3f60d884088a25bd2658fca4c8928c2828c49

[ "MIT" ]

null

from torchvision.models import efficientnet_b0 from torch import nn, optim from torch.utils.data import Dataset, DataLoader import pandas as pd import numpy as np import openslide import hashlib import random import torch import os import pickle from torchvision import transforms from torch import nn, optim from torch.nn.utils import clip_grad_norm_ from tqdm import tqdm from PIL import Image class patchClassifier (nn.Module): def __init__ (self): super().__init__() self.backbone = efficientnet_b0(pretrained=True) classifier_input = self.backbone.classifier[1].in_features self.backbone.classifier[1] = nn.Sequential( nn.Linear(classifier_input, 4) ) for parameter in self.backbone.parameters(): parameter.requires_grad = True self.network = self.backbone self.criterion = nn.CrossEntropyLoss() self.optim = optim.AdamW(self.parameters()) def forward(self, x): predictions = self.network(x) y_hat = predictions return y_hat def predict(self, x): self.eval() with torch.no_grad(): y_hat = self.forward(x) return y_hat def fit(self, x, y): self.train() self.optim.zero_grad() y_hat = self.forward(x) loss = self.criterion(y_hat, y) loss.backward() self.optim.step() loss_ = loss.detach().cpu().item() return loss_ class tilesClassifier (nn.Module): def __init__ (self): super().__init__() self.backbone = efficientnet_b0(pretrained=True) classifier_input = self.backbone.classifier[1].in_features self.backbone.classifier[1] = nn.Sequential( nn.Linear(classifier_input, 6) ) for parameter in self.backbone.parameters(): parameter.requires_grad = True self.softmax = nn.Softmax(dim=0) self.network = self.backbone self.criterion = nn.CrossEntropyLoss() self.optim = optim.AdamW(self.parameters()) def forward(self, x): predictions = self.network(x) y_hat = predictions return y_hat def predict(self, x): self.eval() with torch.no_grad(): y_hat = self.forward(x) y_hat = self.softmax(y_hat) return y_hat def fit(self, x, y): self.train() self.optim.zero_grad() y_hat = self.forward(x) loss = self.criterion(y_hat, y) loss.backward() self.optim.step() loss_ = loss.detach().cpu().item() return loss_

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0

null

0

1

0

1

0

null

0

6

0be9eea3fb4468b68f455e905decfb437ef7b071

35

py

Python

authz/controller/__init__.py

nimatbt/Auth-Microservice

449fd7c3210822d6c59940f817c978fd1715a876

[ "Apache-2.0" ]

null

authz/controller/__init__.py

nimatbt/Auth-Microservice

449fd7c3210822d6c59940f817c978fd1715a876

[ "Apache-2.0" ]

null

authz/controller/__init__.py

nimatbt/Auth-Microservice

449fd7c3210822d6c59940f817c978fd1715a876

[ "Apache-2.0" ]

null

from authz.controller import apiv1

17.5

34

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1

0

null

0

1

0

1

0

1

0

6

0bf22fa01a81ec04ca96eead71fe3e518f54e99b

21,050

py

Python

test-http/src/test/org_user_tests/org_as_org_admin.py

wizedkyle/cve-services

3e63f2d0f3328d542bc39727300a91bd3acecfef

[ "CC0-1.0" ]

9

2019-05-22T17:28:38.000Z

2019-08-22T15:55:07.000Z

test-http/src/test/org_user_tests/org_as_org_admin.py

wizedkyle/cve-services

3e63f2d0f3328d542bc39727300a91bd3acecfef

[ "CC0-1.0" ]

2

2019-08-22T04:18:46.000Z

2019-09-09T10:45:29.000Z

test-http/src/test/org_user_tests/org_as_org_admin.py

wizedkyle/cve-services

3e63f2d0f3328d542bc39727300a91bd3acecfef

[ "CC0-1.0" ]

2

2019-07-09T01:57:24.000Z

2019-08-14T04:23:04.000Z

# Tests in this file use an Org admin user provided by a Pytest fixture. The # tests here should be a subset of the secretariat tests, since the CNA of last # resort should always be able to perform any root CNA functionality in # addition to functionality reserved for the CNA of last resort. import json import requests import uuid from src import env, utils from src.test.org_user_tests.org import (ORG_URL, create_new_user_with_new_org_by_uuid, create_new_user_with_new_org_by_shortname, post_new_org_user, post_new_org) from src.utils import response_contains, response_contains_json ### GET /org #### def test_org_admin_get_all_orgs(org_admin_headers): """ services api rejects requests for all orgs by non-secretariat users """ res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY') #### GET /org/:identifier #### def test_org_admin_get_mitre_org(org_admin_headers): """ services api rejects requests for secretariat by non-secretariat users """ res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/mitre', # the secretariat's org headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_another_org(org_admin_headers): """ services api rejects requests for any org by another org user """ different_org = str(uuid.uuid4()) # name of an org res = post_new_org(different_org, different_org) # create an org assert res.status_code == 200 res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{different_org}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_own_org(org_admin_headers): """ services api allows org admins to get their own org's document """ org = org_admin_headers["CVE-API-ORG"] res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}', headers=org_admin_headers ) assert res.status_code == 200 response_contains(res, org_admin_headers['CVE-API-ORG']) #### GET /org/:shortname/id_quota #### def test_org_admin_get_secretariat_id_quota_info(org_admin_headers): """ services api rejects requests for secretariat by non-secretariat users """ res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/mitre/id_quota', # the secretariat's org headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_another_org_id_quota_info(org_admin_headers): """ services api rejects requests for any org by another org user """ different_org = str(uuid.uuid4()) # name of an org res = post_new_org(different_org, different_org) # create an org assert res.status_code == 200 res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{different_org}/id_quota', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_own_id_quota_info(org_admin_headers): """ services api allows org admins to get info about their org's quota """ org = org_admin_headers["CVE-API-ORG"] res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/id_quota', headers=org_admin_headers ) assert res.status_code == 200 id_quota = json.loads(res.content.decode())['id_quota'] assert id_quota >= 0 assert id_quota <= 100000 #### GET /org/:shortname/user/:username #### def test_org_admin_get_mitre_user_info(org_admin_headers): """ services api prevents org users from viewing secretariat user info """ res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/mitre/user/{env.AWG_USER_NAME}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_another_org_user_info(org_admin_headers): """ services api prevents org admin users from viewing another org user's info """ org, user = create_new_user_with_new_org_by_uuid() res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_own_user_info(org_admin_headers): """ services api allows org admin to get its own user info """ org = org_admin_headers['CVE-API-ORG'] user = org_admin_headers['CVE-API-USER'] res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}', headers=org_admin_headers ) assert res.status_code == 200 response_contains(res, user) #### GET /org/:shortname/users #### def test_org_admin_get_mitre_users_info(org_admin_headers): """ services api prevents org users from viewing secretariat users info """ res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/mitre/users', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_another_org_users_info(org_admin_headers): """ services api prevents org admin users from viewing all other org's user info """ org = str(uuid.uuid4()) res = post_new_org(org, org) assert res.status_code == 200 res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/users', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_get_own_users_info(org_admin_headers): """ services api allows org admin to get its own users info """ org = org_admin_headers['CVE-API-ORG'] user = org_admin_headers['CVE-API-USER'] res = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/users', headers=org_admin_headers ) assert res.status_code == 200 assert len(json.loads(res.content.decode())['users']) >= 1 response_contains(res, user) #### POST /org #### def test_org_admin_cannot_create_another_org(org_admin_headers): """ services api does not allow org admins to create other orgs """ res = requests.post( f'{env.AWG_BASE_URL}{ORG_URL}', headers=org_admin_headers, params={'short_name': str(uuid.uuid4())} ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY') def test_org_admin_cannot_update_org(org_admin_headers): """ services api does not allow org admins to update their own orgs """ res = requests.post( f'{env.AWG_BASE_URL}{ORG_URL}', headers=org_admin_headers, params={'name': str(uuid.uuid4())} ) assert res.status_code == 403 response_contains_json(res, 'error', 'SECRETARIAT_ONLY') #### POST /org/:shortname/user #### def test_org_admin_cannot_create_user_for_another_org(org_admin_headers): """ services api prevents org admins from creating a user with conflicts in the organization the user belongs to (org in path is diff from org in json body) """ org = str(uuid.uuid4()) res = post_new_org(org, org) assert res.status_code == 200 res = requests.post( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user', headers=org_admin_headers, json={'username':'BLARG', 'org_UUID': 'test'} ) assert res.status_code == 400 response_contains_json(res, 'error', 'SHORTNAME_MISMATCH') def test_org_admin_cannot_create_user_for_another_org(org_admin_headers): """ services api prevents org admins from creating users for other orgs """ org = str(uuid.uuid4()) res = post_new_org(org, org) assert res.status_code == 200 res = requests.post( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user', headers=org_admin_headers, json={'username':'BLARG'} ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_ORG_ADMIN_OR_SECRETARIAT') def test_org_admin_cannot_create_existen_user(org_admin_headers): """ services api prevents org admins from creating existing users """ user = str(uuid.uuid4()) org = org_admin_headers['CVE-API-ORG'] res = post_new_org_user(org, user) assert res.status_code == 200 res = requests.post( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user', headers=org_admin_headers, json={'username':user} ) assert res.status_code == 400 response_contains_json(res, 'error', 'USER_EXISTS') #### PUT /org/:shortname/user/:username #### def test_org_admin_cannot_update_user_org_dne(org_admin_headers): """ services api prevents org admins from updating a user from an org that doesn't exist """ user = org_admin_headers['CVE-API-USER'] org = str(uuid.uuid4()) res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}', headers=org_admin_headers ) assert res.status_code == 404 response_contains_json(res, 'error', 'ORG_DNE_PARAM') def test_org_admin_cannot_update_user_dne(org_admin_headers): """ services api prevents org admins from updating a user that doesn't exist """ user = str(uuid.uuid4()) org = org_admin_headers['CVE-API-ORG'] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}', headers=org_admin_headers ) assert res.status_code == 404 response_contains_json(res, 'error', 'USER_DNE') def test_org_admin_cannot_update_user_for_another_org(org_admin_headers): """ services api prevents org admins from updating a user from a diff org """ org, user = create_new_user_with_new_org_by_uuid() res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') # Admins can't change user's org # def test_org_admin_cannot_update_user_new_shortname_dne(org_admin_headers): # """ services api prevents org admins from updating a user's org that doesn't exist """ # org = org_admin_headers['CVE-API-ORG'] # user = org_admin_headers['CVE-API-USER'] # org_shortname = str(uuid.uuid4()) # res = requests.put( # f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?org_shortname={org_shortname}', # headers=org_admin_headers # ) # assert res.status_code == 404 # response_contains_json(res, 'error', 'ORG_DNE') # Admins can't change user's org # def test_org_admin_cannot_update_duplicate_user_with_new_shortname_and_username(org_admin_headers): # """ services api prevents org admins from updating a user's org and username if that user already exist """ # org1 = org_admin_headers['CVE-API-ORG'] # user1 = org_admin_headers['CVE-API-USER'] # org2, user2 = create_new_user_with_new_org_by_uuid() # res = requests.put( # f'{env.AWG_BASE_URL}{ORG_URL}/{org1}/user/{user1}?org_shortname={org2}&new_username={user2}', # headers=org_admin_headers # ) # assert res.status_code == 403 # response_contains_json(res, 'error', 'DUPLICATE_USERNAME') def test_org_admin_cannot_update_duplicate_user_with_new_username(org_admin_headers): """ services api prevents org admins from updating a user's username if that user already exist """ org = org_admin_headers['CVE-API-ORG'] user1 = org_admin_headers['CVE-API-USER'] user2 = str(uuid.uuid4()) res = post_new_org_user(org, user2) # creating a user with same org as admin org user assert res.status_code == 200 res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user1}?new_username={user2}', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'DUPLICATE_USERNAME') # Admin users aren't able to update a users org # def test_org_admin_cannot_update_duplicate_user_with_new_shortname(org_admin_headers): # """ services api prevents org admins from updating a user's org if that user already exist """ # user = org_admin_headers['CVE-API-USER'] # org1 = org_admin_headers['CVE-API-ORG'] # org2 = str(uuid.uuid4()) # res = create_new_user_with_new_org_by_shortname(org2, user) # creating a user with same username as org admin user's username # res = requests.put( # f'{env.AWG_BASE_URL}{ORG_URL}/{org1}/user/{user}?org_shortname={org2}', # headers=org_admin_headers # ) # assert res.status_code == 403 # response_contains_json(res, 'error', 'NOT_ALLOWED_TO_CHANGE_ORGANIZATION') def test_org_admin_update_same_org_user_state_sn_un(org_admin_headers): """ allows admin users to update a user's active state and user username """ org = org_admin_headers['CVE-API-ORG'] user = str(uuid.uuid4()) res = post_new_org_user(org, user) # creating a user with same org as admin org user assert res.status_code == 200 new_shortname = str(uuid.uuid4()) # used in query new_username = str(uuid.uuid4()) # used in query res = post_new_org(new_shortname, new_shortname) # create new org assert res.status_code == 200 res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?new_username={new_username}&active=false', headers=org_admin_headers ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['active'] == False assert json.loads(res.content.decode())['updated']['username'] == new_username assert json.loads(res.content.decode())['updated']['username'] is not None def test_org_admin_update_same_org_user_roles_name(org_admin_headers): """ allows admin users to update a user's name, add role, and remove role """ org = org_admin_headers['CVE-API-ORG'] user = str(uuid.uuid4()) res = post_new_org_user(org, user) # creating a user with same org as admin org user assert res.status_code == 200 res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?active_roles.add=admin', # adding role headers=org_admin_headers ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['authority']['active_roles'] == ["ADMIN"] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?active_roles.remove=admin', # removing role headers=org_admin_headers ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['authority']['active_roles'] == [] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?name.first=t&name.last=e&name.middle=s&name.suffix=t', # updating name headers=org_admin_headers ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['name']['first'] == 't' assert json.loads(res.content.decode())['updated']['name']['last'] == 'e' assert json.loads(res.content.decode())['updated']['name']['middle'] == 's' assert json.loads(res.content.decode())['updated']['name']['suffix'] == 't' # Admin users can't change org? # def test_org_admin_update_own_user_state_sn_un(org_admin_headers): # """ allows admin users to update its own active state, org shortname, and user username """ # org = org_admin_headers['CVE-API-ORG'] # user = org_admin_headers['CVE-API-USER'] # new_shortname = str(uuid.uuid4()) # used in query # new_username = str(uuid.uuid4()) # used in query # res = post_new_org(new_shortname, new_shortname) # create new org # assert res.status_code == 200 # res = requests.put( # f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?org_shortname={new_shortname}&new_username={new_username}&active=false', # headers=org_admin_headers # ) # assert res.status_code == 200 # assert json.loads(res.content.decode())['updated']['active'] == False # assert json.loads(res.content.decode())['updated']['username'] == new_username # assert json.loads(res.content.decode())['updated']['username'] is not None def test_org_admin_update_own_user_roles_name(org_admin_headers): """ allows admin users to update its own name and remove role """ org = org_admin_headers['CVE-API-ORG'] user = org_admin_headers['CVE-API-USER'] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?active_roles.remove=admin', # removing role headers=org_admin_headers ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['authority']['active_roles'] == [] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?active_roles.add=admin', # adding role headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_ORG_ADMIN_OR_SECRETARIAT') # cannot add role because org admin doesn't have "ADMIN" role anymore res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?active_roles.add=admin', # adding "ADMIN" role back to org admin user headers=utils.BASE_HEADERS ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['authority']['active_roles'] == ["ADMIN"] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}?name.first=t&name.last=e&name.middle=s&name.suffix=t', # updating name headers=org_admin_headers ) assert res.status_code == 200 assert json.loads(res.content.decode())['updated']['name']['first'] == 't' assert json.loads(res.content.decode())['updated']['name']['last'] == 'e' assert json.loads(res.content.decode())['updated']['name']['middle'] == 's' assert json.loads(res.content.decode())['updated']['name']['suffix'] == 't' #### PUT /org/:shortname/user/:username/reset_secret #### def test_org_admin_reset_secret_org_dne(org_admin_headers): org = str(uuid.uuid4()) user = str(uuid.uuid4()) res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}/reset_secret', headers=org_admin_headers ) assert res.status_code == 404 response_contains_json(res, 'error', 'ORG_DNE_PARAM') def test_org_admin_reset_secret_org_dne(org_admin_headers): org = org_admin_headers['CVE-API-ORG'] user = str(uuid.uuid4()) res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}/reset_secret', headers=org_admin_headers ) assert res.status_code == 404 response_contains_json(res, 'error', 'USER_DNE') def test_org_admin_reset_diff_org_secret(org_admin_headers): """ services api prevents admin users to reset the secret of users of different org""" org, user = create_new_user_with_new_org_by_uuid() res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}/reset_secret', headers=org_admin_headers ) assert res.status_code == 403 response_contains_json(res, 'error', 'NOT_SAME_ORG_OR_SECRETARIAT') def test_org_admin_reset_same_org_secret(org_admin_headers): """ services api allows admin users to reset the secret of users of same org""" org = org_admin_headers['CVE-API-ORG'] user = str(uuid.uuid4()) res = post_new_org_user(org, user) assert res.status_code == 200 res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}/reset_secret', headers=org_admin_headers ) assert res.status_code == 200 response_contains(res, 'API-secret') def test_org_admin_reset_own_secret(org_admin_headers): """ services api allows admin users to reset their own secret """ org = org_admin_headers['CVE-API-ORG'] user = org_admin_headers['CVE-API-USER'] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}/reset_secret', headers=org_admin_headers ) assert res.status_code == 200 response_contains(res, 'API-secret') def test_admin_role_preserved_after_resetting_own_secret(org_admin_headers): """ admin user's role remains after resetting own secret """ org = org_admin_headers['CVE-API-ORG'] user = org_admin_headers['CVE-API-USER'] res = requests.put( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}/reset_secret', headers=org_admin_headers ) secret = json.loads(res.content.decode())["API-secret"] assert res.status_code == 200 headers2 = org_admin_headers headers2['CVE-API-KEY'] = secret response_contains(res, 'API-secret') res2 = requests.get( f'{env.AWG_BASE_URL}{ORG_URL}/{org}/user/{user}', headers=headers2 ) assert res2.status_code == 200 assert json.loads(res2.content.decode())["authority"]["active_roles"][0] == "ADMIN" # admin role still remains after changing secret

41.518738

165

0.694394

3,100

21,050

4.425806

0.059032

0.08688

0.114796

0.072012

0.888848

0.862609

0.840452

0.828717

0.803061

0.794825

0

0.012433

0.17848

21,050

506

166

41.600791

0.780952

0.287363

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0

0.005831

0.218337

0.153647

0

0.195335

1

0.090379

false

0

0.017493

0

0.107872

0

null

0

1

0

null

0

6

04225a4e35825be5624b09a7800199cc2339ca2d

34

py

Python

attack_lookup/__init__.py

curated-intel/attack-lookup

301a384abc58ea931930dba492b90be871218b92

[ "MIT" ]

20

2021-11-25T20:16:30.000Z

2022-03-19T22:44:58.000Z

attack_lookup/__init__.py

curated-intel/attack-lookup

301a384abc58ea931930dba492b90be871218b92

[ "MIT" ]

null

attack_lookup/__init__.py

curated-intel/attack-lookup

301a384abc58ea931930dba492b90be871218b92

[ "MIT" ]

null

from .mapping import AttackMapping

34

0.882353

4

34

7.5

1

0

0.088235

34

1

34

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

043066db8fa6a05c0dd14189ceee4dcbd59fc7b3

191

py

Python

src/assignments/main_assignment2.py

acc-cosc-1336/cosc-1336-spring-2018-BluishSilver-1

cc4f066fc5d3c88007bc9e3bf0739f1384388086

[ "MIT" ]

null

src/assignments/main_assignment2.py

acc-cosc-1336/cosc-1336-spring-2018-BluishSilver-1

cc4f066fc5d3c88007bc9e3bf0739f1384388086

[ "MIT" ]

null

src/assignments/main_assignment2.py

acc-cosc-1336/cosc-1336-spring-2018-BluishSilver-1

cc4f066fc5d3c88007bc9e3bf0739f1384388086

[ "MIT" ]

null

from assignment2 import faculty_evaluation_result '''Write code to call the faculty_evaluation_result function with data of your choice''' print(faculty_evaluation_result(5,10,15,20,25,30))

38.2

88

0.827225

30

191

5.066667

0.8

0.335526

0.453947

0

0.069364

0.094241

191

4

89

47.75

0.809249

0

1

0

true

0

0.5

0

0.5

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

044ed63ad861c0d6477b762cb463573ed3f242ca

1,754

py

Python

tests/test_config.py

tsudd/docker-python-test

1c866e475ae9c9ccb0c3f6d823609fa95ebe5adb

[ "MIT" ]

1

2021-04-24T12:29:57.000Z

tests/test_config.py

tsudd/docker-python-test

1c866e475ae9c9ccb0c3f6d823609fa95ebe5adb

[ "MIT" ]

null

tests/test_config.py

tsudd/docker-python-test

1c866e475ae9c9ccb0c3f6d823609fa95ebe5adb

[ "MIT" ]

1

2021-05-14T14:02:52.000Z

DICT_RESULT = "{\'sosediOptions\': {\'marke\\\\\":tName\': \'Sosedi\', \'goodsURL\': \'https://sosedi.by/sales/\'," \ " \'goodFields\': {\'pri ce \': True, \'priceBack\': \'priceBack\', \'sale\': " \ "[22, 5.7, \'nice\', None]}}, \'greenOptions\': {\'ff\': 22.8, \'goodsURL\': " \ "\'https://www.green-market.by/shares\', \'headers\': {\'accept-encoding\': \'gzip, deflate, br\'," \ " \'x-requested-with\': \'XMLHttpRequest\'}, " \ "\'formData\': \'page={0}&cat=\', " \ "\'goodHTMLSection\': {\'class\': \'stock-preview-item\'}}, \'damn\': \'wtf\'}" JSON_FILE = """{ "sosediOptions": { "marke\\\":tName": "Sosedi", "goodsURL": "https://sosedi.by/sales/", "goodFields": { "pri ce ": true, "priceBack": "priceBack", "sale": [22, 5.7, "nice", null] } }, "greenOptions": { "ff": 22.8, "goodsURL": "https://www.green-market.by/shares",\ "headers": { "accept-encoding": "gzip, deflate, br", "x-requested-with": "XMLHttpRequest" }, "formData": "page={0}&cat=", "goodHTMLSection": { "class": "stock-preview-item" } }, "damn": "wtf" }""" DATA_DICT = {"cool": ["228", "nice"], "gogo": {"good": True, "nice": 229, "dont": {"lol": 20.9}}, "next": None} PARSED_DICT = "{ \"cool\": [ \"228\", \"nice\" ], \"gogo\": { \"good\": true," \ " \"nice\": 229, \"dont\": { \"lol\": 20.9 } }, \"next\": null }" def sum_two_elements(a=0, b=0): rez = a + b print_equation(a, b, rez) return rez def print_equation(a, b, c): print(f"{a} + {b} = {c}") def fib_nums(n): if n < 1: return 1 return fib_nums(n - 1) + fib_nums(n - 2)

33.09434

117

0.474344

189

1,754

4.343915

0.433862

0.063337

0.029233

0.070646

0.791717

0

0.030234

0.245724

1,754

52

118

33.730769

0.590325

0

0.641391

0.038198

0

1

0.068182

false

0

0.136364

0.068182

0

null

0

1

0

1

0

null

0

6

f09646606337a26ae61fdac6d82a65fd88ea3ada

1,920

py

Python

tests/fixtures/test_abstract/content_02_expected.py

elifesciences/elife-tools

ee345bf0e6703ef0f7e718355e85730abbdfd117

[ "MIT" ]

9

2015-04-16T08:13:31.000Z

2020-05-18T14:03:06.000Z

tests/fixtures/test_abstract/content_02_expected.py

elifesciences/elife-tools

ee345bf0e6703ef0f7e718355e85730abbdfd117

[ "MIT" ]

310

2015-02-11T00:30:09.000Z

2021-07-14T23:58:50.000Z

tests/fixtures/test_abstract/content_02_expected.py

elifesciences/elife-tools

ee345bf0e6703ef0f7e718355e85730abbdfd117

[ "MIT" ]

9

2015-02-04T01:21:28.000Z

2021-06-15T12:50:47.000Z

from collections import OrderedDict expected = u"To estimate the proportion of rotavirus gastroenteritis (RVGE) among children aged less than 5\u2005years who had been diagnosed with acute gastroenteritis (AGE) and admitted to hospitals and emergency rooms (ERs). The seasonal distribution of RVGE and most prevalent rotavirus (RV) strains was also assessed. A cross-sectional hospital-based surveillance study. 5 reference paediatric hospitals across Abidjan. Children aged less than 5\u2005years, who were hospitalised/visiting ERs for WHO-defined AGE, were enrolled. Written informed consent was obtained from parents/guardians before enrolment. Children who acquired nosocomial infection were excluded from the study. The proportion of RVGE among AGE hospitalisations and ER visits was expressed with 95% exact CI. Stool samples were collected from all enrolled children and were tested for the presence of RV using an enzyme immunoassay. RV-positive samples were serotyped using reverse transcriptase-PCR. Of 357 enrolled children (mean age 13.6\xb111.14\u2005months), 332 were included in the final analyses; 56.3% (187/332) were hospitalised and 43.7% (145/332) were admitted to ERs. The proportion of RVGE hospitalisations and ER visits among all AGE cases was 30.1% (95% CI 23.6% to 37.3%) and 26.9% (95% CI 19.9% to 34.9%), respectively. Ninety-five children (28.6%) were RV positive; the highest number of RVGE cases was observed in children aged 6\u201311\u2005months. The number of GE cases peaked in July and August 2008; the highest percentage of RV-positive cases was observed in January 2008. G1P[8] wild-type and G8P[6] were the most commonly detected strains. RVGE causes substantial morbidity among children under 5\u2005years of age and remains a health concern in the Republic of Ivory Coast, where implementation of prevention strategies such as vaccination might help to reduce disease burden."

480

1,882

0.808333

306

1,920

5.071895

0.539216

0.015464

0.028995

0.025773

0.043814

0

0.062006

0.143229

1,920

3

1,883

640

0.881459

0

0.5

0.972917

0.036458

0

1

0

false

0

0.5

0

0.5

0

null

0

1

0

1

0

null

0

1

0

6

f0b39f6a7bde2e1c109819e047cfab68df60ba6a

33

py

Python

candex/__init__.py

wknoben/candex

0f73a2075a28d75dcce50fa8ee3d6890776f4223

[ "Apache-2.0" ]

2

2020-06-16T16:42:17.000Z

2021-01-22T10:19:35.000Z

candex/__init__.py

goosefall/candex

b7efc001aa00176f76c3b7735d06fa43fed7072b

[ "Apache-2.0" ]

null

candex/__init__.py

goosefall/candex

b7efc001aa00176f76c3b7735d06fa43fed7072b

[ "Apache-2.0" ]

1

2021-04-12T05:15:10.000Z

from .functions import lat_lon_2D

33

0.878788

6

33

4.5

1

0

0.033333

0.090909

33

1

33

0.866667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

f0d24db0264558a5af7231c3cba51dc3e415a75f

9,270

py

Python

tests/test_rules.py

mweltin/sticks

84f6f82bece0ac8b47824545ebdbf4c1e52d193c

[ "MIT" ]

null

tests/test_rules.py

mweltin/sticks

84f6f82bece0ac8b47824545ebdbf4c1e52d193c

[ "MIT" ]

null

tests/test_rules.py

mweltin/sticks

84f6f82bece0ac8b47824545ebdbf4c1e52d193c

[ "MIT" ]

null

import rules.rules as rules import environment.env as env import unittest class RulesTestCase(unittest.TestCase): def test_has_winner_opponent_wins(self): value = [[0, 0], [0, 1]] act = rules.has_winner(value) self.assertEqual(env.Players.opponent, act) def test_has_winner_when_agent_wins(self): value = [[1, 0], [0, 0]] act = rules.has_winner(value) self.assertEqual(env.Players.agent, act) def test_has_winner_when_there_is_no_winner(self): value = [[1, 0], [0, 1]] act = rules.has_winner(value) self.assertFalse(act) self.assertNotEqual(act, env.Players.agent) def test_can_swap_returns_true_if_one_hand_is_empty_and_the_other_has_and_even_number(self): value = [0, 2] act = rules.can_swap(value) self.assertTrue(act) def test_can_swap_false_if_one_hand_is_empty_and_the_other_has_and_odd_number(self): value = [0, 3] act = rules.can_swap(value) self.assertFalse(act) def test_can_swap_false_if_both_hands_are_not_empty(self): value = [1, 3] act = rules.can_swap(value) self.assertFalse(act) def test_swap_returns_an_array_of_two_elements_and_both_elements_are_half_of_largest_element_in_input_array(self): val = 4 value = [0, val] act = rules.swap(value) self.assertEqual([val / 2, val / 2], act) def test_get_opponent_player_index(self): active_player_index = 0 opponent_index = rules.get_opponent_player_index(active_player_index) self.assertEqual(1, opponent_index) active_player_index = 1 opponent_index = rules.get_opponent_player_index(active_player_index) self.assertEqual(0, opponent_index) def test_take_turn_allows_for_swap(self): state = [[0, 4], [1, 1]] active_player_index = 0 action = env.action_table[0] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual([2, 2], new_state[active_player_index]) def test_take_turn_handles_left_to_left_for_both_players(self): state = [[2, 1], [1, 2]] expected_state = [[2, 1], [3, 2]] active_player_index = 0 action = env.action_table[1] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) state = [[2, 1], [1, 2]] expected_state = [[3, 1], [1, 2]] active_player_index = 1 action = env.action_table[1] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) def test_take_turn_handles_left_to_right_for_both_players(self): state = [[2, 1], [1, 2]] expected_state = [[2, 1], [1, 4]] active_player_index = 0 action = env.action_table[2] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) state = [[2, 1], [1, 2]] expected_state = [[2, 2], [1, 2]] active_player_index = 1 action = env.action_table[2] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) def test_take_turn_handles_right_to_left_for_both_players(self): state = [[2, 1], [1, 2]] expected_state = [[2, 1], [2, 2]] active_player_index = 0 action = env.action_table[4] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) state = [[2, 1], [1, 2]] expected_state = [[4, 1], [1, 2]] active_player_index = 1 action = env.action_table[4] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) def test_take_turn_handles_right_to_right_for_both_players(self): state = [[2, 1], [1, 2]] expected_state = [[2, 1], [1, 3]] active_player_index = 0 action = env.action_table[3] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) state = [[2, 1], [1, 2]] expected_state = [[2, 3], [1, 2]] active_player_index = 1 action = env.action_table[3] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) def test_take_turn_handles_case_when_outcome_is_above_five(self): state = [[4, 1], [1, 4]] expected_state = [[4, 1], [1, 3]] active_player_index = 0 action = env.action_table[2] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) def test_take_turn_handles_case_when_outcome_is_equal_five(self): state = [[4, 1], [1, 1]] expected_state = [[4, 1], [1, 0]] active_player_index = 0 action = env.action_table[2] new_state = rules.take_turn(state, active_player_index, action) self.assertEqual(expected_state, new_state) def test_get_valid_actions_identifies_when_swap_is_valid(self): state = [[4, 0], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertIn(env.action_table.index([env.Actions.SWAP]), valid_moves) state = [[4, 1], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.SWAP]), valid_moves) def test_get_valid_actions_identifies_when_right_right_is_valid(self): state = [[4, 1], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertTrue(env.action_table.index([env.Actions.RIGHT, env.Actions.RIGHT]), valid_moves) def test_get_valid_actions_identifies_when_right_right_is_not_valid(self): state = [[4, 1], [1, 0]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.RIGHT, env.Actions.RIGHT]), valid_moves) state = [[4, 0], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.RIGHT, env.Actions.RIGHT]), valid_moves) def test_get_valid_actions_identifies_when_right_left_is_valid(self): state = [[4, 1], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertIn(env.action_table.index([env.Actions.RIGHT, env.Actions.LEFT]), valid_moves) def test_get_valid_actions_identifies_when_right_left_is_not_valid(self): state = [[4, 0], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.RIGHT, env.Actions.LEFT]), valid_moves) state = [[4, 1], [0, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.RIGHT, env.Actions.LEFT]), valid_moves) def test_get_valid_actions_identifies_when_left_left_is_valid(self): state = [[4, 1], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertIn(env.action_table.index([env.Actions.LEFT, env.Actions.LEFT]), valid_moves) def test_get_valid_actions_identifies_when_left_left_is_not_valid(self): state = [[4, 1], [0, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.LEFT, env.Actions.LEFT]), valid_moves) state = [[0, 1], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.LEFT, env.Actions.LEFT]), valid_moves) def test_get_valid_actions_identifies_when_left_right_is_valid(self): state = [[4, 1], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertIn(env.action_table.index([env.Actions.LEFT, env.Actions.RIGHT]), valid_moves) def test_get_valid_actions_identifies_when_left_right_is_not_valid(self): state = [[4, 1], [3, 0]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.LEFT, env.Actions.RIGHT]), valid_moves) state = [[0, 2], [1, 1]] active_player_index = 0 valid_moves = rules.get_valid_actions(state, active_player_index) self.assertNotIn(env.action_table.index([env.Actions.LEFT, env.Actions.RIGHT]), valid_moves) if __name__ == '__main__': unittest.TestLoader.sortTestMethodsUsing = None unittest.main()

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f0e4411dfe73f22993a6e64393101095d5514d94

190

py

Python

jupyter-lab-serverless/config.py

u2takey/jupyter-lab-serverless

e43b3e64afb5643fb72d901b395d72add3a7be2f

[ "Apache-2.0" ]

6

2020-03-13T23:58:31.000Z

2021-08-29T07:33:29.000Z

jupyter-lab-serverless/config.py

u2takey/jupyter-lab-serverless

e43b3e64afb5643fb72d901b395d72add3a7be2f

[ "Apache-2.0" ]

3

2021-08-05T03:07:04.000Z

2022-03-25T21:34:03.000Z

jupyter-lab-serverless/config.py

u2takey/jupyter-lab-serverless

e43b3e64afb5643fb72d901b395d72add3a7be2f

[ "Apache-2.0" ]

1

2020-04-06T16:30:28.000Z

from traitlets.config import Configurable class LatexConfig(Configurable): """ A Configurable that declares the configuration options for the FunctionHandler. """ pass

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f0fe40fb3c6ff17e19d1a5d5c98983c0b83ba8d7

34

py

Python

test/login.py

MeiBianChuiDi/gz02

5c839a85f1409c0abdd9542c4f4d1c20f66a1a28

[ "MIT" ]

null

test/login.py

MeiBianChuiDi/gz02

5c839a85f1409c0abdd9542c4f4d1c20f66a1a28

[ "MIT" ]

null

test/login.py

MeiBianChuiDi/gz02

5c839a85f1409c0abdd9542c4f4d1c20f66a1a28

[ "MIT" ]

null

num =1 num1=10 num2=20 num3=30

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9bd63c4830840b56cb87afbd7ddb5ceec428af67

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py

Python

tests/test_taskpane.py

pyro-team/bkt-toolbox

bbccba142a81ca0a46056f2bcda75899979158a5

[ "MIT" ]

12

2019-05-31T02:57:26.000Z

2022-03-26T09:40:50.000Z

tests/test_taskpane.py

mrflory/bkt-toolbox

bbccba142a81ca0a46056f2bcda75899979158a5

[ "MIT" ]

27

2021-11-27T16:33:19.000Z

2022-03-27T17:47:26.000Z

tests/test_taskpane.py

pyro-team/bkt-toolbox

bbccba142a81ca0a46056f2bcda75899979158a5

[ "MIT" ]

3

2019-06-12T10:59:20.000Z

2020-04-21T15:13:50.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import import unittest import bkt XMLNS = ' xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"' XMLNS_FR = ' xmlns="urn:fluent-ribbon"' class TaskpaneBaseObjectTest(unittest.TestCase): def test_XamlPropertyElement(self): bkt.taskpane.TaskPaneControl.no_id = True # default XamlPropertyElement b = bkt.taskpane.XamlPropertyElement() self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<NotSpecified.NotSpecified' + XMLNS + ' />') #self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<NotSpecified.Resources xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" />') # specifying property name failed b = bkt.taskpane.XamlPropertyElement(property_name="PropertyName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<NotSpecified.PropertyName' + XMLNS + ' />') # specifying type name failed b = bkt.taskpane.XamlPropertyElement(type_name="TypeName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TypeName.NotSpecified' + XMLNS + ' />') # specifying type name and property name failed b = bkt.taskpane.XamlPropertyElement(type_name="TypeName", property_name="PropertyName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TypeName.PropertyName' + XMLNS + ' />') # specifying type name at xml-generation failed b = bkt.taskpane.XamlPropertyElement(property_name="PropertyName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml("TypeName")), u'<TypeName.PropertyName' + XMLNS + ' />') bkt.taskpane.TaskPaneControl.no_id = False def test_XamlPropertyElement_fixed_type(self): bkt.taskpane.TaskPaneControl.no_id = True myclass = type("myclassname", (bkt.taskpane.XamlPropertyElement,), {'_type_name': 'FixedTypeName'}) # Definition of XamlPropertyElement with fixed type name failed b = myclass() self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<FixedTypeName.NotSpecified' + XMLNS + ' />') # specifying property name failed b = myclass(property_name="PropertyName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<FixedTypeName.PropertyName' + XMLNS + ' />') # type name should be overwritable b = myclass(type_name="TypeName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TypeName.NotSpecified' + XMLNS + ' />') # type name and property name should be overwritable b = myclass(type_name="TypeName", property_name="PropertyName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TypeName.PropertyName' + XMLNS + ' />') bkt.taskpane.TaskPaneControl.no_id = False def test_XamlPropertyElement_fixed_property(self): bkt.taskpane.TaskPaneControl.no_id = True myclass = type("myclassname", (bkt.taskpane.XamlPropertyElement,), {'_property_name': 'FixedPropertyName'}) # Definition of XamlPropertyElement with fixed property name failed b = myclass() self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<NotSpecified.FixedPropertyName' + XMLNS + ' />') # property name should be overwritable b = myclass(property_name="PropertyName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<NotSpecified.PropertyName' + XMLNS + ' />') # specifying type name failed b = myclass(type_name="TypeName") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TypeName.FixedPropertyName' + XMLNS + ' />') bkt.taskpane.TaskPaneControl.no_id = False def test_XamlPropertyElements(self): self.maxDiff = None bkt.taskpane.TaskPaneControl.no_id = True # simple usage of XamlPropertyElementGenerator failed b = bkt.taskpane.XamlPropertyElements.Resources() self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<NotSpecified.Resources' + XMLNS + ' />') # specification of type name failed b = bkt.taskpane.XamlPropertyElements.Resources("Button") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button.Resources' + XMLNS + ' />') # specification of type name failed b = bkt.taskpane.XamlPropertyElements.Resources(type_name="Button") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button.Resources' + XMLNS + ' />') # property name should be overwritable b = bkt.taskpane.XamlPropertyElements.Resources(type_name="Button", property_name="Overwritten") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button.Overwritten' + XMLNS + ' />') bkt.taskpane.TaskPaneControl.no_id = False def test_XamlPropertyElement_Attribute(self): bkt.taskpane.TaskPaneControl.no_id = True # usage of XamlPropertyElement as attribute failed b = bkt.taskpane.TaskPaneControl(resources=bkt.taskpane.XamlPropertyElement(property_name="PropertyName")) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <TaskPaneControl.PropertyName />\r\n</TaskPaneControl>') # usage of XamlPropertyElementGenerator as attribute failed b = bkt.taskpane.TaskPaneControl(resources=bkt.taskpane.XamlPropertyElements.Resources()) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <TaskPaneControl.Resources />\r\n</TaskPaneControl>') # usage of XamlPropertyElementGenerator with other xml-namespace failed b = bkt.taskpane.FluentRibbon.Button(resources=bkt.taskpane.XamlPropertyElements.Resources()) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button' + XMLNS_FR + '>\r\n <Button.Resources />\r\n</Button>') # type name should not be overwritable b = bkt.taskpane.TaskPaneControl(resources=bkt.taskpane.XamlPropertyElement(type_name="TypeName", property_name="PropertyName")) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <TaskPaneControl.PropertyName />\r\n</TaskPaneControl>') # type name should not be overwritable b = bkt.taskpane.TaskPaneControl(resources=bkt.taskpane.XamlPropertyElements.Resources(type_name="TypeName")) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <TaskPaneControl.Resources />\r\n</TaskPaneControl>') bkt.taskpane.TaskPaneControl.no_id = False def test_XamlPropertyElement_Child(self): bkt.taskpane.TaskPaneControl.no_id = True # usage of XamlPropertyElement as child-element faild b = bkt.taskpane.TaskPaneControl(children=[bkt.taskpane.XamlPropertyElement(type_name="TypeName", property_name="PropertyName")]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <TypeName.PropertyName />\r\n</TaskPaneControl>') # usage of XamlPropertyElementGenerator as child-element failed b = bkt.taskpane.TaskPaneControl(children=[bkt.taskpane.XamlPropertyElements.Resources("TypeName")]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <TypeName.Resources />\r\n</TaskPaneControl>') # no default type_name if child definition is used # type name should have no fallback if child definition is used b = bkt.taskpane.TaskPaneControl(children=[bkt.taskpane.XamlPropertyElement(property_name="PropertyName")]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <NotSpecified.PropertyName />\r\n</TaskPaneControl>') # type name should have no fallback if child definition is used b = bkt.taskpane.TaskPaneControl(children=[bkt.taskpane.XamlPropertyElements.Resources()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<TaskPaneControl' + XMLNS + '>\r\n <NotSpecified.Resources />\r\n</TaskPaneControl>') bkt.taskpane.TaskPaneControl.no_id = False def test_WPFRibbon(self): bkt.taskpane.TaskPaneControl.no_id = True # definition of RibbonButton failed b = bkt.taskpane.Ribbon.RibbonButton() self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<RibbonButton xmlns="clr-namespace:System.Windows.Controls.Ribbon;assembly=System.Windows.Controls.Ribbon" />') bkt.taskpane.TaskPaneControl.no_id = False def test_FluentRibbon(self): bkt.taskpane.TaskPaneControl.no_id = True bkt.taskpane.FluentRibbonControl.no_id = True # definition of FluentRibbon-Button failed b = bkt.taskpane.FluentRibbon.Button() self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button xmlns="urn:fluent-ribbon" />') bkt.taskpane.FluentRibbonControl.no_id = False def test_FluentRibbon_ScreenTip(self): bkt.taskpane.TaskPaneControl.no_id = True bkt.taskpane.FluentRibbonControl.no_id = True # Button with simple tooltip attribute failed b = bkt.taskpane.FluentRibbon.Button(tool_tip="Tooltip text") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button ToolTip="Tooltip text"' + XMLNS_FR + ' />') # Definition ToolTip-Property-Element failed b = bkt.taskpane.ToolTip("Button") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button.ToolTip' + XMLNS_FR + ' />') # Definition of Screentip-Element failed b = bkt.taskpane.FluentRibbon.ScreenTip(text="screentip text") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<ScreenTip Text="screentip text"' + XMLNS_FR + ' />') # Definition of Screentip-Element failed b = bkt.taskpane.FluentRibbon.ScreenTip(text="screentip text", title="screentip title", disable_reason="This button is diabled because ...", help_topic="Info for additional help") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<ScreenTip DisableReason="This button is diabled because ..." HelpTopic="Info for additional help" Text="screentip text" Title="screentip title"' + XMLNS_FR + ' />') # Screentip-attribute should be parsed to ScreenTip-object b = bkt.taskpane.FluentRibbon.Button(screentip="Screentip text") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button' + XMLNS_FR + '>\r\n <Button.ToolTip>\r\n <ScreenTip IsRibbonAligned="False" Text="Screentip text" />\r\n </Button.ToolTip>\r\n</Button>') # Screentip-attribute should be parsed to ScreenTip-object b = bkt.taskpane.FluentRibbon.Button(screentip="Screentip text", screentip_title="Title", disable_reason="This button is diabled because ...", help_topic="Info for additional help") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button' + XMLNS_FR + '>\r\n <Button.ToolTip>\r\n <ScreenTip DisableReason="This button is diabled because ..." HelpTopic="Info for additional help" IsRibbonAligned="False" Text="Screentip text" Title="Title" />\r\n </Button.ToolTip>\r\n</Button>') # Screentip definition should overwrite tooltip b = bkt.taskpane.FluentRibbon.Button(tool_tip="Tooltip text", screentip="Screentip text") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button' + XMLNS_FR + '>\r\n <Button.ToolTip>\r\n <ScreenTip IsRibbonAligned="False" Text="Screentip text" />\r\n </Button.ToolTip>\r\n</Button>') # Definition of screentip through tooltip-attribute failed b = bkt.taskpane.FluentRibbon.Button(tool_tip=bkt.taskpane.FluentRibbon.ScreenTip(text="Screentip text")) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button' + XMLNS_FR + '>\r\n <Button.ToolTip>\r\n <ScreenTip Text="Screentip text" />\r\n </Button.ToolTip>\r\n</Button>') bkt.taskpane.FluentRibbonControl.no_id = False def test_FluentRibbon_Image(self): bkt.taskpane.TaskPaneControl.no_id = True bkt.taskpane.FluentRibbonControl.no_id = True b = bkt.taskpane.FluentRibbon.Button(image="test_image") self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Button Icon="{StaticResource test_image}"' + XMLNS_FR + ' />') def test_ExpanderStackPanel(self): bkt.taskpane.TaskPaneControl.no_id = True bkt.taskpane.FluentRibbonControl.no_id = True b = bkt.taskpane.Expander(auto_stack=True, children=[bkt.taskpane.Wpf.Button()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Expander IsExpanded="false"'+XMLNS+'>\r\n <StackPanel Orientation="Vertical">\r\n <Button />\r\n </StackPanel>\r\n</Expander>') b = bkt.taskpane.Expander(auto_wrap=True, children=[bkt.taskpane.Wpf.Button()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Expander IsExpanded="false"'+XMLNS+'>\r\n <WrapPanel Orientation="Horizontal">\r\n <Button />\r\n </WrapPanel>\r\n</Expander>') b = bkt.taskpane.Expander(auto_stack=True, header="Test Header", children=[bkt.taskpane.Wpf.Button()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Expander Header="Test Header" IsExpanded="false"'+XMLNS+'>\r\n <StackPanel Orientation="Vertical">\r\n <Button />\r\n </StackPanel>\r\n</Expander>') b = bkt.taskpane.Expander(auto_stack=True, is_expanded=True, children=[bkt.taskpane.Wpf.Button()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<Expander IsExpanded="true"'+XMLNS+'>\r\n <StackPanel Orientation="Vertical">\r\n <Button />\r\n </StackPanel>\r\n</Expander>') def test_Group(self): bkt.taskpane.TaskPaneControl.no_id = True bkt.taskpane.FluentRibbonControl.no_id = True self.maxDiff = None b = bkt.taskpane.Group(auto_wrap=True, children=[bkt.taskpane.Wpf.Button()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<StackPanel Orientation="Vertical"'+XMLNS+'>\r\n <Grid Margin="0,10,0,5">\r\n <Grid.ColumnDefinitions>\r\n <ColumnDefinition Width="*" />\r\n </Grid.ColumnDefinitions>\r\n <Border Height="1" Background="{StaticResource BKTDivider}" HorizontalAlignment="Stretch" SnapsToDevicePixels="True" Margin="7,3,10,3" />\r\n </Grid>\r\n <WrapPanel Orientation="Horizontal">\r\n <Button />\r\n </WrapPanel>\r\n</StackPanel>') b = bkt.taskpane.Group(auto_wrap=True, show_separator=False, children=[bkt.taskpane.Wpf.Button()]) self.assertEqual(bkt.xml.WpfXMLFactory.to_string(b.wpf_xml()), u'<StackPanel Orientation="Vertical"'+XMLNS+'>\r\n <WrapPanel Orientation="Horizontal">\r\n <Button />\r\n </WrapPanel>\r\n</StackPanel>')

60.144531

507

0.68942

1,815

15,397

5.7427

0.088705

0.083373

0.074259

0.086635

0.875468

0.843807

0.819438

0.794013

0.767821

0.716109

0

0.00157

0.172826

15,397

255

508

60.380392

0.816818

0.11827

0

0.378788

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0.090909

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0.117465

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0.318182

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0.090909

false

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0.022727

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0.121212

0

null

0

1

0

null

0

6

50127204a0103d3932d88899f06339333fdbd9a7

61

py

Python

ch1/Ex_1.3.4.1/task06.py

jiayushe/cpbook-code

c3fb85a1acc5f31e15879741e4c826684243fddf

[ "UPL-1.0" ]

1,441

2018-12-03T23:46:17.000Z

2022-03-29T06:36:43.000Z

ch1/Ex_1.3.4.1/task06.py

jiayushe/cpbook-code

c3fb85a1acc5f31e15879741e4c826684243fddf

[ "UPL-1.0" ]

53

2018-12-11T13:50:35.000Z

2022-03-20T04:30:39.000Z

ch1/Ex_1.3.4.1/task06.py

jiayushe/cpbook-code

c3fb85a1acc5f31e15879741e4c826684243fddf

[ "UPL-1.0" ]

420

2018-12-04T11:22:08.000Z

2022-03-27T15:25:33.000Z

from bisect import bisect_left print(v == bisect_left(L, v))

20.333333

30

0.754098

11

61

4

0.636364

0.454545

0

0.131148

61

2

31

30.5

0.830189

0

1

0

true

0

0.5

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

50362b903c5cb464a79f3ff5a05c636e9dd2277c

131

py

Python

src/masonite_permission/providers/__init__.py

yubarajshrestha/masonite-permission

5807b80a50b94526efbc03f0933d3960087a7e54

[ "MIT" ]

4

2022-03-15T13:52:37.000Z

2022-03-17T05:26:54.000Z

src/masonite_permission/providers/__init__.py

yubarajshrestha/masonite-permission

5807b80a50b94526efbc03f0933d3960087a7e54

[ "MIT" ]

2

2022-03-15T06:36:59.000Z

2022-03-15T09:41:47.000Z

src/masonite_permission/providers/__init__.py

yubarajshrestha/masonite-permission

5807b80a50b94526efbc03f0933d3960087a7e54

[ "MIT" ]

null

# flake8: noqa: E501 from .PermissionProvider import PermissionProvider from .PermissionGateProvider import PermissionGateProvider

32.75

58

0.870229

11

131

10.363636

0.636364

0

0.033613

0.091603

131

3

59

43.666667

0.92437

0.137405

0

1

0

true

0

1

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6