xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

a70fa092ee7f331e87d92b7787bb5d1e947646ef

145

py

Python

tests/year_2018/test_day5a.py

vanillaSlice/advent-of-code

3f31be38c598040ec6032bc9b24856005e070c21

[ "MIT" ]

null

tests/year_2018/test_day5a.py

vanillaSlice/advent-of-code

3f31be38c598040ec6032bc9b24856005e070c21

[ "MIT" ]

null

tests/year_2018/test_day5a.py

vanillaSlice/advent-of-code

3f31be38c598040ec6032bc9b24856005e070c21

[ "MIT" ]

null

from src.year_2018.day5a import alchemical_reduction def test_alchemical_reduction(): assert alchemical_reduction('dabAcCaCBAcCcaDA') == 10

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597fcecd294c73cd5b956ee515c7e723d3f6b313

108

py

Python

app/back/mongo/data/collect/railroads/__init__.py

jgphilpott/polyplot

c46861174ee5881dadffbfb2278d555462523547

[ "MIT" ]

5

2021-05-17T14:17:14.000Z

2021-12-14T12:54:32.000Z

app/back/mongo/data/collect/railroads/__init__.py

jgphilpott/iGraph

2a91ba57e4950856a83d3a109753f8f2badee829

[ "MIT" ]

8

2020-02-09T02:48:41.000Z

2021-05-16T04:57:02.000Z

app/back/mongo/data/collect/railroads/__init__.py

jgphilpott/iGraph

2a91ba57e4950856a83d3a109753f8f2badee829

[ "MIT" ]

2

2016-09-12T03:48:16.000Z

2019-05-04T14:15:19.000Z

from back.mongo.data.collect.railroads.model import * from back.mongo.data.collect.railroads.mongo import *

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59d788748f42ce83db0db23e063efdc2b259c905

47,712

py

Python

tests/test_service.py

rscohn2/irrigation_unlimited

d6e5ff56325628e203624346bc5264ac92743b5e

[ "MIT" ]

null

tests/test_service.py

rscohn2/irrigation_unlimited

d6e5ff56325628e203624346bc5264ac92743b5e

[ "MIT" ]

null

tests/test_service.py

rscohn2/irrigation_unlimited

d6e5ff56325628e203624346bc5264ac92743b5e

[ "MIT" ]

null

"""Test integration_blueprint setup process.""" from unittest.mock import patch import pytest from datetime import datetime, timedelta import homeassistant.core as ha from homeassistant.config import load_yaml_config_file from homeassistant.setup import async_setup_component from homeassistant.const import SERVICE_RELOAD from tests.const import MOCK_CONFIG from tests.iu_test_support import ( quiet_mode, begin_test, run_for, run_for_1_tick, run_until, finish_test, test_config_dir, check_summary, ) from custom_components.irrigation_unlimited.irrigation_unlimited import ( IUCoordinator, ) from custom_components.irrigation_unlimited.const import ( DOMAIN, COORDINATOR, SERVICE_CANCEL, SERVICE_DISABLE, SERVICE_ENABLE, SERVICE_MANUAL_RUN, SERVICE_TIME_ADJUST, SERVICE_TOGGLE, ) from custom_components.irrigation_unlimited.__init__ import CONFIG_SCHEMA quiet_mode() async def test_service_adjust_time( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history ): """Test adjust_time service call.""" full_path = test_config_dir + "service_adjust_time.yaml" config = CONFIG_SCHEMA(load_yaml_config_file(full_path)) await async_setup_component(hass, DOMAIN, config) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] start_time = await begin_test(1, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "percentage": 50}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%50.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(2, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "percentage": 200}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(3, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "percentage": 0}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%0.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(4, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "actual": "00:30"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "=0:30:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(5, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "increase": "00:05"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "+0:05:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(6, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "decrease": "00:05"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "-0:05:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(7, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "reset": None}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(8, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "percentage": 100, "minimum": "00:20", }, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%100.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(9, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "percentage": 100, "maximum": "00:05", }, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%100.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(10, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 50}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%50.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%50.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(11, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 200}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%200.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(12, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 0}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%0.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%0.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(13, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "actual": "00:30"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "=0:30:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "=0:30:00" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(14, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "increase": "00:05"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "+0:05:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "+0:05:00" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(15, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "decrease": "00:05"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "-0:05:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "-0:05:00" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(16, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "reset": None}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(17, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 100, "minimum": "00:20", }, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%100.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%100.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(18, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 100, "maximum": "00:05", }, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%100.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%100.0" await finish_test(hass, coordinator, start_time, True) check_summary(full_path, coordinator) async def test_service_enable_disable( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history ): """Test enable/disable service call.""" full_path = test_config_dir + "service_enable_disable.yaml" config = CONFIG_SCHEMA(load_yaml_config_file(full_path)) await async_setup_component(hass, DOMAIN, config) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] # Zone 1 off start_time = await begin_test(1, coordinator) await hass.services.async_call( DOMAIN, SERVICE_DISABLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True await finish_test(hass, coordinator, start_time, True) # Zone 1 on start_time = await begin_test(2, coordinator) await hass.services.async_call( DOMAIN, SERVICE_ENABLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True await finish_test(hass, coordinator, start_time, True) # Zone 1 off, zone 2 on start_time = await begin_test(3, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True await finish_test(hass, coordinator, start_time, True) # Double toggle: zone 1 on, zone 2 off start_time = await begin_test(4, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z2"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False await finish_test(hass, coordinator, start_time, True) # All off start_time = await begin_test(5, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False await finish_test(hass, coordinator, start_time, True) # All back on start_time = await begin_test(6, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z2"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True await finish_test(hass, coordinator, start_time, True) # Controller 1 off start_time = await begin_test(7, coordinator) await hass.services.async_call( DOMAIN, SERVICE_DISABLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "blocked" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "blocked" assert s.attributes["enabled"] == True await finish_test(hass, coordinator, start_time, True) # Controller 1 off, zone 1 on, zone 2 off start_time = await begin_test(8, coordinator) await hass.services.async_call( DOMAIN, SERVICE_ENABLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) await hass.services.async_call( DOMAIN, SERVICE_DISABLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z2"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "blocked" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "blocked" assert s.attributes["enabled"] == False await finish_test(hass, coordinator, start_time, True) # Controller 1 on, zone 1 still on, zone 2 still off start_time = await begin_test(9, coordinator) await hass.services.async_call( DOMAIN, SERVICE_ENABLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False await finish_test(hass, coordinator, start_time, True) # Toggle controller 1 start_time = await begin_test(10, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "disabled" assert s.attributes["enabled"] == False s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "blocked" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "blocked" assert s.attributes["enabled"] == False await finish_test(hass, coordinator, start_time, True) # Toggle controller 1 & zone 2 (All back on) start_time = await begin_test(11, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m"}, True, ) await hass.services.async_call( DOMAIN, SERVICE_TOGGLE, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z2"}, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_m") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["status"] == "off" assert s.attributes["enabled"] == True await finish_test(hass, coordinator, start_time, True) check_summary(full_path, coordinator) async def test_service_manual_run( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history ): """Test manual_run service call.""" full_path = test_config_dir + "service_manual_run.yaml" config = CONFIG_SCHEMA(load_yaml_config_file(full_path)) await async_setup_component(hass, DOMAIN, config) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] start_time = await begin_test(1, coordinator) await hass.services.async_call( DOMAIN, SERVICE_MANUAL_RUN, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1", "time": "00:10"}, True, ) await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(2, coordinator) await hass.services.async_call( DOMAIN, SERVICE_MANUAL_RUN, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "time": "00:10"}, True, ) await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(3, coordinator) await hass.services.async_call( DOMAIN, SERVICE_MANUAL_RUN, { "entity_id": "binary_sensor.irrigation_unlimited_c2_m", "time": "00:20", "sequence_id": 1, }, True, ) await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(4, coordinator) await hass.services.async_call( DOMAIN, SERVICE_MANUAL_RUN, { "entity_id": "binary_sensor.irrigation_unlimited_c2_m", "time": "00:01", "sequence_id": 1, }, True, ) await finish_test(hass, coordinator, start_time, True) check_summary(full_path, coordinator) async def test_service_cancel( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history ): """Test cancel service call.""" full_path = test_config_dir + "service_cancel.yaml" config = CONFIG_SCHEMA(load_yaml_config_file(full_path)) await async_setup_component(hass, DOMAIN, config) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] start_time = await begin_test(1, coordinator) next_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:11:45+00:00"), True, ) await hass.services.async_call( DOMAIN, SERVICE_CANCEL, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z1"}, True, ) await finish_test(hass, coordinator, next_time, True) start_time = await begin_test(2, coordinator) next_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:11:45+00:00"), True, ) await hass.services.async_call( DOMAIN, SERVICE_CANCEL, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m"}, True, ) await finish_test(hass, coordinator, next_time, True) check_summary(full_path, coordinator) async def test_service_reload( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history, ): """Test reload service call.""" full_path = test_config_dir + "service_reload.yaml" await async_setup_component(hass, DOMAIN, CONFIG_SCHEMA(MOCK_CONFIG)) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] with patch( "homeassistant.core.Config.path", return_value=full_path, ): await hass.services.async_call( DOMAIN, SERVICE_RELOAD, None, True, ) start_time = await begin_test(1, coordinator) await finish_test(hass, coordinator, start_time, True) check_summary(full_path, coordinator) async def test_service_reload_error( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history, ): """Test reload service call on a bad config file.""" full_path = test_config_dir + "service_reload_error.yaml" await async_setup_component(hass, DOMAIN, CONFIG_SCHEMA(MOCK_CONFIG)) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] with patch( "homeassistant.core.Config.path", return_value=full_path, ): with pytest.raises(KeyError, match="controllers"): await hass.services.async_call( DOMAIN, SERVICE_RELOAD, None, True, ) async def test_service_adjust_time_while_running( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history ): """Test adjust_time service call while sequence is running.""" full_path = test_config_dir + "service_adjust_time_while_running.yaml" config = CONFIG_SCHEMA(load_yaml_config_file(full_path)) await async_setup_component(hass, DOMAIN, config) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] # Start a sequence start_time = await begin_test(1, coordinator) next_time = await run_for( hass, coordinator, start_time, timedelta(minutes=28), True ) # Hit zone 4 with adjustment midway through sequence await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_z4", "percentage": 200}, True, ) await finish_test(hass, coordinator, next_time, True) # Run next test which should be 200% start_time = await begin_test(2, coordinator) await finish_test(hass, coordinator, start_time, True) # Reset adjustments await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "reset": None}, True, ) # Start a sequence start_time = await begin_test(3, coordinator) next_time = await run_for( hass, coordinator, start_time, timedelta(minutes=28), True ) # Hit controller with adjustment halfway through sequence await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, {"entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 200}, True, ) await finish_test(hass, coordinator, next_time, True) # Run next test which should be 200% start_time = await begin_test(4, coordinator) await finish_test(hass, coordinator, start_time, True) check_summary(full_path, coordinator) async def test_service_adjust_time_sequence( hass: ha.HomeAssistant, skip_start, skip_dependencies, skip_history ): """Test adjust_time service call on a sequence.""" full_path = test_config_dir + "service_adjust_time_sequence.yaml" config = CONFIG_SCHEMA(load_yaml_config_file(full_path)) await async_setup_component(hass, DOMAIN, config) await hass.async_block_till_done() coordinator: IUCoordinator = hass.data[DOMAIN][COORDINATOR] start_time = await begin_test(1, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "percentage": 50, }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:06:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%50.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:12:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%50.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%50.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:20:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%50.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(2, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "percentage": 200, }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:11:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%200.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:30:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%200.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 07:01:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%200.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(3, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "percentage": 0, }, True, ) await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(4, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "actual": "00:30", }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:07:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "=0:30:00" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:16:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "=0:30:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "=0:30:00" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:29:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "=0:30:00" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(5, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "increase": "00:05", }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:08:20+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "+0:05:00" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:19:40+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "+0:05:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "+0:05:00" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:35:45+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "+0:05:00" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(6, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "decrease": "00:05", }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:07:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "-0:05:00" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:16:20+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "-0:05:00" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "-0:05:00" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:29:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "-0:05:00" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(7, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "reset": None, }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:10:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "None" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:21:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "None" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "None" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:32:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "None" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(8, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "percentage": 100, "minimum": "00:50", }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:09:10+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:22:40+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:49:50+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(9, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "percentage": 100, "maximum": "00:20", }, True, ) start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:07:40+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:12:40+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:22:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "None" assert s.attributes["current_adjustment"] == "%100.0" await finish_test(hass, coordinator, start_time, True) start_time = await begin_test(10, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "percentage": 50, }, True, ) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "percentage": 200, }, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "%200.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:06:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%50.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:12:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%50.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%50.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:20:30+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%50.0" await finish_test(hass, coordinator, start_time, True) # Test follows on from above. Timing should be 200% after sequence reset start_time = await begin_test(11, coordinator) await hass.services.async_call( DOMAIN, SERVICE_TIME_ADJUST, { "entity_id": "binary_sensor.irrigation_unlimited_c1_m", "sequence_id": 1, "reset": None, }, True, ) start_time = await run_for_1_tick(hass, coordinator, start_time, True) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "%200.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:11:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z1") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%200.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 06:30:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z2") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%200.0" s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z3") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%200.0" start_time = await run_until( hass, coordinator, start_time, datetime.fromisoformat("2021-01-04 07:01:00+00:00"), True, ) s = hass.states.get("binary_sensor.irrigation_unlimited_c1_z4") assert s.attributes["adjustment"] == "%200.0" assert s.attributes["current_adjustment"] == "%200.0" await finish_test(hass, coordinator, start_time, True) check_summary(full_path, coordinator)

36.421374

87

0.666206

5,920

47,712

5.094088

0.030912

0.06685

0.107106

0.17578

0.965879

0.961203

0.95855

0.956826

0.944557

0.939782

0

0.038632

0.215501

47,712

1,309

88

36.449198

0.767059

0.012848

0

0.820234

0

0.251824

0.150367

0

0.158863

1

0

false

0

0.010033

0

0.010033

0

null

0

1

0

1

0

null

0

8

abef25a32a5aba633bf1d41f1954baf30bfdbc3a

9,000

py

Python

make_morse_dict.py

huwns/classical_cryptography

683a1e884c4b56f537800fabc6a7216837a9338a

[ "BSD-3-Clause" ]

1

2021-09-19T06:32:09.000Z

make_morse_dict.py

huwns/classical_cryptography

683a1e884c4b56f537800fabc6a7216837a9338a

[ "BSD-3-Clause" ]

null

make_morse_dict.py

huwns/classical_cryptography

683a1e884c4b56f537800fabc6a7216837a9338a

[ "BSD-3-Clause" ]

null

from bs4 import BeautifulSoup as bs # Reference(https://www.google.co.jp/ime/-.-.html) GOOGLE_MORSE = ''' <div class="morse-table"> <h3>和文モールス符号表</h3> <table> <tbody><tr> <td>ア</td> <td>イ</td> <td>ウ</td> <td>エ</td> <td>オ</td> </tr> <tr> <td>カ</td> <td>キ</td> <td>ク</td> <td>ケ</td> <td>コ</td> </tr> <tr> <td>サ</td> <td>シ</td> <td>ス</td> <td>セ</td> <td>ソ</td> </tr> <tr> <td>タ</td> <td>チ</td> <td>ツ</td> <td>テ</td> <td>ト</td> </tr> <tr> <td>ナ</td> <td>ニ</td> <td>ヌ</td> <td>ネ</td> <td>ノ</td> </tr> <tr> <td>ハ</td> <td>ヒ</td> <td>フ</td> <td>ヘ</td> <td>ホ</td> </tr> <tr> <td>マ</td> <td>ミ</td> <td>ム</td> <td>メ</td> <td>モ</td> </tr> <tr> <td>ヤ</td> <td></td> <td>ユ</td> <td></td> <td>ヨ</td> </tr> <tr> <td>ラ</td> <td>リ</td> <td>ル</td> <td>レ</td> <td>ロ</td> </tr> <tr> <td>ワ</td> <td>ヰ</td> <td></td> <td>ヱ</td> <td>ヲ</td> </tr> <tr> <td>ン</td> <td>長音 (ー)</td> <td>濁点 (゛)</td> <td>半濁点 (゜)</td> <td>区切り点 (、)</td> </tr><tr> </tr></tbody></table> 「っ」や「ゃゅょ」のような促音・拗音は、従来の和文モールス符号ではサポートされていませんが、Google 日本語入力モールスバージョンでは、大きい「つ」や「やゆよ」の後に半濁点符号を打つことで、促音・拗音を入力することができます。 </div> ''' def google_morse_parser(msg): parsed_dict = {} soup = bs(msg, 'html.parser') soup_td = soup.find('table').find_all('td') for i in soup_td: if i.text is not None: if '(' in i.text: key = i.text.split('(')[1].split(')')[0] else: key = i.text soup_span = i.find_all('span') value = '' for span in soup_span: dahdit = span.get('class') if dahdit == ['dah']: value += '-' elif dahdit == ['dit']: value += '.' parsed_dict[key] = value return parsed_dict if __name__ == '__main__': print(google_morse_parser(GOOGLE_MORSE))

78.947368

281

0.531111

1,291

9,000

3.687839

0.099148

0.446965

0.581601

0.362949

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9,000

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78.947368

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null

1

0

1

null

0

12

f9efeda9feb696d04d440d6cb4f1b6bf54a93a9c

2,175

py

Python

NodeDefender/mail/icpe.py

CTSNE/NodeDefender

24e19f53a27d3b53e599cba8b1448f8f16c0bd5e

[ "MIT" ]

4

2016-09-23T17:51:05.000Z

2017-03-14T02:52:26.000Z

NodeDefender/mail/icpe.py

CTSNE/NodeDefender

24e19f53a27d3b53e599cba8b1448f8f16c0bd5e

[ "MIT" ]

1

2016-09-22T11:32:33.000Z

2017-11-14T10:00:24.000Z

NodeDefender/mail/icpe.py

CTSNE/NodeDefender

24e19f53a27d3b53e599cba8b1448f8f16c0bd5e

[ "MIT" ]

4

2016-10-09T19:05:16.000Z

2020-05-14T04:00:30.000Z

from flask_mail import Message from flask import render_template, url_for import NodeDefender import smtplib @NodeDefender.decorators.mail_enabled @NodeDefender.decorators.celery_task def new_icpe(icpe, host, port): icpe = NodeDefender.db.icpe.get(icpe) if icpe is None: return False mqtt = NodeDefender.db.mqtt.get_sql(host, port) if mqtt is None: return False msg = Message('iCPE {} found on MQTT {}'.format(icpe.mac_address, mqtt.host), sender='noreply@nodedefender.com', recipients=\ [group.email for group in mqtt.groups ]) url = url_for('node_view.nodes_list') msg.body = render_template('mail/icpe/new_icpe.txt', icpe = icpe, mqtt = mqtt, url = url) try: NodeDefender.mail.mail.send(msg) except smtplib.SMTPRecipientsRefused: NodeDefender.mail.logger.error("Unable to send email for: {}".\ format(icpe.mac_address)) except smtplib.SMTPAuthenticationError: NodeDefender.mail.logger.error("Authentication Error when sending email") return True @NodeDefender.decorators.mail_enabled @NodeDefender.decorators.celery_task def icpe_enabled(icpe, host, port): icpe = NodeDefender.db.icpe.get(icpe) if icpe is None: return False mqtt = NodeDefender.db.mqtt.get_sql(host, port) if mqtt is None: return False msg = Message('iCPE {} Enabled from MQTT {}'.format(icpe.mac_address, mqtt.host), sender='noreply@nodedefender.com', recipients=\ [group.email for group in mqtt.groups ]) url = url_for('node_view.nodes_list') msg.body = render_template('mail/icpe/icpe_enabled.txt', icpe = icpe, mqtt = mqtt, url = url) try: NodeDefender.mail.mail.send(msg) except smtplib.SMTPRecipientsRefused: NodeDefender.mail.logger.error("Unable to send email for: {}".\ format(icpe.mac_address)) except smtplib.SMTPAuthenticationError: NodeDefender.mail.logger.error("Authentication Error when sending email") return True

38.157895

85

0.652414

260

2,175

5.361538

0.238462

0.068867

0.034433

0.04878

0.895265

0.797704

0

0.248736

2,175

56

86

38.839286

0.853121

0

0.8

0

0.148046

0.044138

0

1

0.04

false

0

0.08

0

0.24

0

null

0

1

0

1

0

null

0

7

e660fef23e34a2f0463beaefe3ef06636f8985e6

141

py

Python

boa3_test/test_sc/interop_test/policy/ImportPolicy.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

25

2020-07-22T19:37:43.000Z

2022-03-08T03:23:55.000Z

boa3_test/test_sc/interop_test/policy/ImportPolicy.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

419

2020-04-23T17:48:14.000Z

2022-03-31T13:17:45.000Z

boa3_test/test_sc/interop_test/policy/ImportPolicy.py

hal0x2328/neo3-boa

6825a3533384cb01660773050719402a9703065b

[ "Apache-2.0" ]

15

2020-05-21T21:54:24.000Z

2021-11-18T06:17:24.000Z

from boa3.builtin import public from boa3.builtin.interop import policy @public def main() -> int: return policy.get_exec_fee_factor()

17.625

39

0.765957

21

141

5

0.714286

0.152381

0.285714

0

0.016667

0.148936

141

7

40

20.142857

0.858333

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

7

0516bf72f23822c378113dc81216c407c6668113

60,534

py

Python

delicatessen/estimating_equations/regression.py

pzivich/Deli

761aa51c6949334b59fffb185be4266177454b6c

[ "MIT" ]

null

delicatessen/estimating_equations/regression.py

pzivich/Deli

761aa51c6949334b59fffb185be4266177454b6c

[ "MIT" ]

null

delicatessen/estimating_equations/regression.py

pzivich/Deli

761aa51c6949334b59fffb185be4266177454b6c

[ "MIT" ]

null

import warnings import numpy as np from delicatessen.utilities import logit, inverse_logit, identity ################################################################# # Basic Regression Estimating Equations def ee_regression(theta, X, y, model, weights=None): r"""Default stacked estimating equation for regression, with available options including: linear, logistic, and Poisson regression. The general estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - g(X_i^T \theta)) X_i = 0 where :math:`g` indicates a general transformation function. For linear regression, :math:`g` is the identity function. Logistic regression uses the expit, or the inverse-logit function, :math:`expit(u) = 1 / (1 + exp(u))`. Finally, Poisson regression is :math:`g(u) = \exp(u)`. Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughout, these user-defined functions are defined as ``psi``. Here, :math:`\theta` corresponds to the coefficients in the corresponding regression model Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). model : str Type of regression model to estimate. Options are ``'linear'`` (linear regression), ``'logistic'`` (logistic regression), and ``'poisson'`` (Poisson regression). weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y Examples -------- Construction of a estimating equation(s) with ``ee_regression`` should be done similar to the following >>> import numpy as np >>> import pandas as pd >>> from scipy.stats import logistic >>> from delicatessen import MEstimator >>> from delicatessen.estimating_equations import ee_regression Some generic data to estimate the regression models >>> n = 500 >>> data = pd.DataFrame() >>> data['X'] = np.random.normal(size=n) >>> data['Z'] = np.random.normal(size=n) >>> data['Y1'] = 0.5 + 2*data['X'] - 1*data['Z'] + np.random.normal(loc=0, size=n) >>> data['Y2'] = np.random.binomial(n=1, p=logistic.cdf(0.5 + 2*data['X'] - 1*data['Z']), size=n) >>> data['Y3'] = np.random.poisson(np.exp(lam=0.5 + 2*data['X'] - 1*data['Z']), size=n) >>> data['C'] = 1 Note that ``C`` here is set to all 1's. This will be the intercept in the regression. To start, we will demonstrate linear regression for the outcome ``Y1``. Defining psi, or the stacked estimating equations >>> def psi(theta): >>> return ee_regression(theta=theta, X=data[['C', 'X', 'Z']], y=data['Y1'], model='linear') Calling the M-estimation procedure (note that ``init`` requires 3 values, since ``X.shape[1] = 3``). >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0.,]) >>> estr.estimate() Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Next, we can estimate the parameters for a logistic regression model as follows >>> def psi(theta): >>> return ee_regression(theta=theta, X=data[['C', 'X', 'Z']], y=data['Y2'], model='logistic') >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0.,]) >>> estr.estimate() Finally, we can estimate the parameters for a Poisson regression model as follows >>> def psi(theta): >>> return ee_regression(theta=theta, X=data[['C', 'X', 'Z']], y=data['Y3'], model='poisson') >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0.,]) >>> estr.estimate() Additionally, weighted versions of all the previous models can be estimated by specifying the optional ``weights`` argument. References ---------- Boos DD, & Stefanski LA. (2013). M-estimation (estimating equations). In Essential Statistical Inference (pp. 297-337). Springer, New York, NY. """ # Preparation of input shapes and object types X, y, beta = _prep_inputs_(X=X, y=y, theta=theta, penalty=None) # Determining transformation function to use for the regression model transform = _model_transform_(model=model) # Looking up corresponding transformation pred_y = transform(np.dot(X, beta)) # Generating predicted values via speedy matrix calculation # Allowing for a weighted linear model w = _generate_weights_(weights=weights, n_obs=X.shape[0]) # Output b-by-n matrix return w*((y - pred_y) * X).T # Return weighted regression score function def ee_linear_regression(theta, X, y, weights=None): r"""Default stacked estimating equation for linear regression without the homoscedastic assumption. Note ---- The function ``ee_linear_regression`` is deprecated. Please use ``ee_regression`` instead. The estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - X_i^T \theta) X_i = 0 Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Here, theta corresponds to the coefficients in a linear regression model Note ---- For complex regression problems, the optimizer behind the scenes is not particularly robust (unlike functions specializing in solely OLS). Therefore, optimization of OLS via a separate functionality can be done then those estimated parameters are fed forward as the initial values (which should result in a more stable optimization). Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y References ---------- Boos DD, & Stefanski LA. (2013). M-estimation (estimating equations). In Essential Statistical Inference (pp. 297-337). Springer, New York, NY. """ warnings.warn("Regression estimating equations should be implemented using `ee_regression`. The specific type of " "regression estimating equations will be removed in v1.0", DeprecationWarning) return ee_regression(theta=theta, X=X, y=y, model='linear', weights=weights) def ee_logistic_regression(theta, X, y, weights=None): r"""Default stacked estimating equation for logistic regression. Note ---- The function ``ee_linear_regression`` is deprecated. Please use ``ee_regression`` instead. The estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - expit(X_i^T \theta)) X_i = 0 where expit, or the inverse logit is .. math:: expit(u) = 1 / (1 + exp(u)) Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Here, theta corresponds to the coefficients in a logistic regression model, and therefore are the log-odds. Note ---- For complex regression problems, the optimizer behind the scenes is not particularly robust (unlike functions specializing in solely logistic regression). Therefore, optimization of logistic regression via a separate functionality can be done then those estimated parameters are fed forward as the initial values (which should result in a more stable optimization). Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. The Y values should all be 0 or 1. No missing data should be included (missing data may cause unexpected behavior). weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y References ---------- Boos DD, & Stefanski LA. (2013). M-estimation (estimating equations). In Essential Statistical Inference (pp. 297-337). Springer, New York, NY. """ warnings.warn("Regression estimating equations should be implemented using `ee_regression`. The specific type of " "regression estimating equations will be removed in v1.0", DeprecationWarning) return ee_regression(theta=theta, X=X, y=y, model='logistic', weights=weights) def ee_poisson_regression(theta, X, y, weights=None): r"""Default stacked estimating equation for Poisson regression. Note ---- The function ``ee_linear_regression`` is deprecated. Please use ``ee_regression`` instead. The estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - \exp(X_i^T \theta)) X_i = 0 Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Note ---- For complex regression problems, the optimizer behind the scenes is not particularly robust (unlike functions specializing in solely logistic regression). Therefore, optimization of logistic regression via a separate functionality can be done then those estimated parameters are fed forward as the initial values (which should result in a more stable optimization). Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. The Y values should all be 0 or 1. No missing data should be included (missing data may cause unexpected behavior). weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y """ warnings.warn("Regression estimating equations should be implemented using `ee_regression`. The specific type of " "regression estimating equations will be removed in v1.0", DeprecationWarning) return ee_regression(theta=theta, X=X, y=y, model='poisson', weights=weights) ################################################################# # Robust Regression Estimating Equations def ee_robust_regression(theta, X, y, model, k, weights=None): r"""Default stacked estimating equation for robust regression. Specifically, robust linear regression is robust to outlying observations of the outcome variable (``y``). Currently, only linear regression is supported by ``ee_robust_regression``. The estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n \psi_k(Y_i - X_i^T \theta) X_i = 0 where k indicates the upper and lower bounds. Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). model : str Type of regression model to estimate. Options only include ``'linear'`` (linear regression). k : int, float Value to set the symmetric maximum upper and lower bounds on the difference between the observations and predicted values weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y Examples -------- Construction of a estimating equation(s) with ``ee_robust_regression`` should be done similar to the following >>> import numpy as np >>> import pandas as pd >>> from delicatessen import MEstimator >>> from delicatessen.estimating_equations import ee_robust_regression Some generic data to estimate a robust linear regression model >>> n = 100 >>> data = pd.DataFrame() >>> data['X'] = np.random.normal(size=n) >>> data['Z'] = np.random.normal(size=n) >>> data['Y'] = 0.5 + 2*data['X'] - 1*data['Z'] + np.random.normal(loc=0, scale=3, size=n) >>> data['C'] = 1 Note that ``C`` here is set to all 1's. This will be the intercept in the regression. Defining psi, or the stacked estimating equations >>> def psi(theta): >>> return ee_robust_regression(theta=theta, X=data[['C', 'X', 'Z']], y=data['Y'], model='linear', k=3) Calling the M-estimation procedure (note that ``init`` has 3 values now, since ``X.shape[1] = 3``). >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0.,]) >>> estr.estimate() Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() References ---------- Boos DD, & Stefanski LA. (2013). M-estimation (estimating equations). In Essential Statistical Inference (pp. 297-337). Springer, New York, NY. """ # Preparation of input shapes and object types X, y, beta = _prep_inputs_(X=X, y=y, theta=theta, penalty=None) # Allowing for a weighted linear model w = _generate_weights_(weights=weights, n_obs=X.shape[0]) # Determining transformation function to use for the regression model transform = _model_transform_(model=model, # Looking up corresponding transformation assert_linear_model=True) # ... and make sure it is a linear model pred_y = transform(np.dot(X, beta)) # Generating predicted values # Generating predictions and applying Huber function for robust pred_error = np.clip(y - pred_y, -k, k) # Output b-by-n matrix return w*(pred_error * X).T # Score function def ee_robust_linear_regression(theta, X, y, k, weights=None): r"""Default stacked estimating equation for robust linear regression. Note ---- The function ``ee_robust_linear_regression`` is deprecated. Please use ``ee_robust_regression`` instead. The estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n \psi_k(Y_i - X_i^T \theta) X_i = 0 where k indicates the upper and lower bounds. Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- All provided estimating equations are meant to be wrapped inside a user-specified function. Throughtout, these user-defined functions are defined as ``psi``. Here, theta corresponds to the coefficients in a robust linear regression model Note ---- For complex regression problems, the optimizer behind the scenes is not particularly robust (unlike functions specializing in solely OLS). Therefore, optimization of OLS via a separate functionality can be done then those estimated parameters are fed forward as the initial values (which should result in a more stable optimization). Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). k : int, float Value to set the symmetric maximum upper and lower bounds on the difference between the observations and predicted values weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y References ---------- Boos DD, & Stefanski LA. (2013). M-estimation (estimating equations). In Essential Statistical Inference (pp. 297-337). Springer, New York, NY. """ warnings.warn("Robust regression estimating equations should be implemented using `ee_robust_regression`. The " "specific type of regression estimating equations will be removed in v1.0", DeprecationWarning) return ee_robust_regression(theta=theta, X=X, y=y, model='linear', k=k, weights=weights) ################################################################# # Penalized Regression Estimating Equations def ee_ridge_regression(theta, y, X, model, penalty, weights=None, center=0.): r"""Default stacked estimating equation for ridge linear regression. Ridge regression applies an L2-regularization through a squared magnitude penalty. The estimating equation is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - X_i^T \theta) X_i - \lambda \theta = 0 Here, theta is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- The 'strength' of the penalty term is indicated by :math:`\lambda`, which is the ``penalty`` argument scaled (or divided by) the number of observations. Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). model : str Type of regression model to estimate. Options are ``'linear'`` (linear regression), ``'logistic'`` (logistic regression), and ``'poisson'`` (Poisson regression). penalty : int, float, ndarray, list, vector Penalty term to apply to all coefficients (if only a integer or float is provided) or the corresponding coefficient (if a list or vector of integers or floats is provided). Note that the penalty term should either consists of a single value or b values (to match the length of ``theta``). weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. center : int, float, ndarray, list, vector, optional Center or reference value to penalized estimated coefficients towards. Default is zero, which penalized coefficients towards the null. Other center values can be specified for all coefficients (by providing an integer or float) or covariate-specific centering values (by providing a vector of values of the same length as X). Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y Examples -------- Construction of a estimating equation(s) with ``ee_ridge_regression`` should be done similar to the following >>> import numpy as np >>> import pandas as pd >>> from scipy.stats import logistic >>> from delicatessen import MEstimator >>> from delicatessen.estimating_equations import ee_ridge_regression Some generic data to estimate a linear regresion model >>> n = 500 >>> data = pd.DataFrame() >>> data['V'] = np.random.normal(size=n) >>> data['W'] = np.random.normal(size=n) >>> data['X'] = data['W'] + np.random.normal(scale=0.25, size=n) >>> data['Z'] = np.random.normal(size=n) >>> data['Y1'] = 0.5 + 2*data['W'] - 1*data['Z'] + np.random.normal(loc=0, size=n) >>> data['Y2'] = np.random.binomial(n=1, p=logistic.cdf(0.5 + 2*data['W'] - 1*data['Z']), size=n) >>> data['Y3'] = np.random.poisson(lam=np.exp(1 + 2*data['W'] - 1*data['Z']), size=n) >>> data['C'] = 1 Note that ``C`` here is set to all 1's. This will be the intercept in the regression. Defining psi, or the stacked estimating equations. Note that the penalty is a list of values. Here, we are *not* penalizing the intercept (which is generally recommended when the intercept is unlikely to be zero). The remainder of covariates have a penalty of 10 applied. >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y1'] >>> return ee_ridge_regression(theta=theta, X=x, y=y, model='linear', penalty=penalty_vals) Calling the M-estimation procedure (note that ``init`` has 5 values now, since ``X.shape[1] = 5``). >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0., 0., 0.]) >>> estr.estimate(solver='lm') Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Next, we can estimate the parameters for a logistic regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y2'] >>> return ee_ridge_regression(theta=theta, X=x, y=y, model='logistic', penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0., 0., 0.]) >>> estr.estimate(solver='lm') Finally, we can estimate the parameters for a Poisson regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y3'] >>> return ee_ridge_regression(theta=theta, X=x, y=y, model='poisson', penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0., 0., 0.]) >>> estr.estimate(solver='lm') Additionally, weighted versions of all the previous models can be estimated by specifying the optional ``weights`` argument. References ---------- Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical Statistics, 7(3), 397-416. Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132. """ # Preparation of input shapes and object types X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center) # Determining transformation function to use for the regression model transform = _model_transform_(model=model) # Looking up corresponding transformation pred_y = transform(np.dot(X, beta)) # Generating predicted values # Allowing for a weighted penalized regression model w = _generate_weights_(weights=weights, n_obs=X.shape[0]) # Creating penalty term for ridge regression (bridge with gamma=2 is the special case of ridge) penalty_terms = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=2, center=center) # Output b-by-n matrix return w*(((y - pred_y) * X).T - penalty_terms[:, None]) # Score function with penalty term subtracted off def ee_lasso_regression(theta, y, X, model, penalty, epsilon=3.e-3, weights=None, center=0.): r"""Default stacked estimating equation for an approximate LASSO (least absolute shrinkage and selection operator) regressor. LASSO regression applies an L1-regularization through a magnitude penalty. The estimating equation for the approximate LASSO is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - X_i^T \theta) X_i - (1 + \epsilon) | \theta |^{\epsilon} sgn(\theta) = 0 Here, we are using an approximation based on the bridge penalty. For the bridge penalty, LASSO is the special case where :math:`\epsilon = 0`. By making :math:`\epsilon > 0`, we can approximate the LASSO. See the rest of the documentation for further details. Note ---- LASSO is not strictly convex. Therefore, root-finding may be difficult. To get around this issue, ``ee_lasso_regression`` uses an approximation to LASSO. Additionally, the approximate LASSO will not result in coefficients of exactly zero, but coefficients will be shrunk to near zero. Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- The 'strength' of the penalty term is indicated by :math:`\lambda`, which is the ``penalty`` argument scaled (or divided by) the number of observations. Note ---- Root-finding for ``ee_lasso_regression`` can be difficult. In general, it is recommended to use the Leverberg-Marquette algorithm (``MEstimator.estimate(solver='lm')``), increase the number of iterations, and possibly run some pre-washing for starting values. Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). model : str Type of regression model to estimate. Options are ``'linear'`` (linear regression), ``'logistic'`` (logistic regression), and ``'poisson'`` (Poisson regression). penalty : int, float, ndarray, list, vector Penalty term to apply to all coefficients (if only a integer or float is provided) or the corresponding coefficient (if a list or vector of integers or floats is provided). Note that the penalty term should either consists of a single value or b values (to match the length of ``theta``). epsilon : float, optional Approximation error to use for the LASSO approximation. LASSO is the case where ``epsilon=0``. However, the lack of strict convexity of the penalty may causes issues for root-finding. Using an approximation described by Fu (2003) is used instead. Instead, ``epsilon`` is set to be slightly larger than 1. Notice that ``epsilon`` must be > 0. Default argument is 0.003, which results in a bridge penalty of 1.0003. weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. center : int, float, ndarray, list, vector, optional Center or reference value to penalized estimated coefficients towards. Default is zero, which penalized coefficients towards the null. Other center values can be specified for all coefficients (by providing an integer or float) or covariate-specific centering values (by providing a vector of values of the same length as X). Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y Examples -------- Construction of a estimating equation(s) with ``ee_lasso_regression`` should be done similar to the following >>> import numpy as np >>> import pandas as pd >>> from scipy.stats import logistic >>> from delicatessen import MEstimator >>> from delicatessen.estimating_equations import ee_lasso_regression Some generic data to estimate a linear regresion model >>> n = 500 >>> data = pd.DataFrame() >>> data['V'] = np.random.normal(size=n) >>> data['W'] = np.random.normal(size=n) >>> data['X'] = data['W'] + np.random.normal(scale=0.25, size=n) >>> data['Z'] = np.random.normal(size=n) >>> data['Y1'] = 0.5 + 2*data['W'] - 1*data['Z'] + np.random.normal(loc=0, size=n) >>> data['Y2'] = np.random.binomial(n=1, p=logistic.cdf(0.5 + 2*data['W'] - 1*data['Z']), size=n) >>> data['Y3'] = np.random.poisson(lam=np.exp(1 + 2*data['W'] - 1*data['Z']), size=n) >>> data['C'] = 1 Note that ``C`` here is set to all 1's. This will be the intercept in the regression. Defining psi, or the stacked estimating equations. Note that the penalty is a list of values. Here, we are *not* penalizing the intercept (which is generally recommended when the intercept is unlikely to be zero). The remainder of covariates have a penalty of 10 applied. >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y1'] >>> return ee_lasso_regression(theta=theta, X=x, y=y, model='linear', penalty=penalty_vals) Calling the M-estimation procedure (note that ``init`` has 5 values now, since ``X.shape[1] = 5``). Additionally, we set the maximum number of iterations to be much larger. >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=20000) Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Next, we can estimate the parameters for a logistic regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y2'] >>> return ee_lasso_regression(theta=theta, X=x, y=y, model='logistic', penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=20000) Finally, we can estimate the parameters for a Poisson regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y3'] >>> return ee_lasso_regression(theta=theta, X=x, y=y, model='poisson', penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=20000) Additionally, weighted versions of all the previous models can be estimated by specifying the optional ``weights`` argument. References ---------- Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical Statistics, 7(3), 397-416. Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132. """ # Preparation of input shapes and object types X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center) # Determining transformation function to use for the regression model transform = _model_transform_(model=model) # Looking up corresponding transformation pred_y = transform(np.dot(X, beta)) # Generating predicted values # Allowing for a weighted penalized regression model w = _generate_weights_(weights=weights, n_obs=X.shape[0]) # Creating penalty term for ridge regression (bridge with gamma=2 is the special case of ridge) if epsilon < 0: raise ValueError("epsilon must be greater than zero for the approximate LASSO") penalty_terms = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=1+epsilon, center=center) # Output b-by-n matrix return w*(((y - pred_y) * X).T - penalty_terms[:, None]) # Score function with penalty term subtracted off def ee_elasticnet_regression(theta, y, X, model, penalty, ratio, epsilon=3.e-3, weights=None, center=0.): r"""Default stacked estimating equation for Elastic-net regression. Elastic-net applies both L1- and L2-regularization at a pre-specified ratio. Notice that the L1 penalty is based on an approximation. See ``ee_lasso_regression`` for further details on the approximation for the L1 penalty. The estimating equation for Elastic-net with the approximate L1 penalty is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - X_i^T \theta) X_i - r (1 + \epsilon) | \theta |^{\epsilon} sgn(\theta) - (1-r) 2 | \theta |^{1} sgn(\theta) = 0 where :math:`r` is the ratio for the L1 vs L2 penalty. Here, we are using an approximation based on the bridge penalty. For the bridge penalty, LASSO is the special case where :math:`\gamma = 1`. By making :math:`\epsilon > 0`, we can approximate the LASSO. The ridge penalty is the bridge penalty where :math:`\gamma = 2`, which can be evaluated directly. Note ---- LASSO is not strictly convex. Therefore, root-finding may be difficult. To get around this issue, ``ee_elasticnet_regression`` uses an approximation to LASSO. Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- The 'strength' of the penalty term is indicated by :math:`\lambda`, which is the ``penalty`` argument scaled (or divided by) the number of observations. Note ---- Root-finding for ``ee_elasticnet_regression`` can be difficult when the L1 penalty is set to be stronger. In general, it is recommended to use the Leverberg-Marquette algorithm (``MEstimator.estimate(solver='lm')``). Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). model : str Type of regression model to estimate. Options are ``'linear'`` (linear regression), ``'logistic'`` (logistic regression), and ``'poisson'`` (Poisson regression). penalty : int, float, ndarray, list, vector Penalty term to apply to all coefficients (if only a integer or float is provided) or the corresponding coefficient (if a list or vector of integers or floats is provided). Note that the penalty term should either consists of a single value or b values (to match the length of ``theta``). ratio : float Ratio for the L1 vs L2 penalty in Elastic-net. The ratio must be be :math:`0 \ge r \ge 1`. Setting ``ratio=1`` results in LASSO and ``ratio=0`` results in ridge regression. epsilon : float, optional Approximation error to use for the LASSO approximation. LASSO is the case where ``epsilon=0``. However, the lack of strict convexity of the penalty may causes issues for root-finding. Using an approximation described by Fu (2003) is used instead. Instead, ``epsilon`` is set to be slightly larger than 1. Notice that ``epsilon`` must be > 0. Default argument is 0.003, which results in a bridge penalty of 1.0003. weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. center : int, float, ndarray, list, vector, optional Center or reference value to penalized estimated coefficients towards. Default is zero, which penalized coefficients towards the null. Other center values can be specified for all coefficients (by providing an integer or float) or covariate-specific centering values (by providing a vector of values of the same length as X). Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y Examples -------- Construction of a estimating equation(s) with ``ee_elasticnet_regression`` should be done similar to the following >>> import numpy as np >>> import pandas as pd >>> from scipy.stats import logistic >>> from delicatessen import MEstimator >>> from delicatessen.estimating_equations import ee_elasticnet_regression Some generic data to estimate a linear regresion model >>> n = 500 >>> data = pd.DataFrame() >>> data['V'] = np.random.normal(size=n) >>> data['W'] = np.random.normal(size=n) >>> data['X'] = data['W'] + np.random.normal(scale=0.25, size=n) >>> data['Z'] = np.random.normal(size=n) >>> data['Y1'] = 0.5 + 2*data['W'] - 1*data['Z'] + np.random.normal(loc=0, size=n) >>> data['Y2'] = np.random.binomial(n=1, p=logistic.cdf(0.5 + 2*data['W'] - 1*data['Z']), size=n) >>> data['Y3'] = np.random.poisson(lam=np.exp(1 + 2*data['W'] - 1*data['Z']), size=n) >>> data['C'] = 1 Note that ``C`` here is set to all 1's. This will be the intercept in the regression. Defining psi, or the stacked estimating equations. Note that the penalty is a list of values. Here, we are *not* penalizing the intercept (which is generally recommended when the intercept is unlikely to be zero). The remainder of covariates have a penalty of 10 applied. >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y1'] >>> return ee_elasticnet_regression(theta=theta, X=x, y=y, model='linear', ratio=0.5, penalty=penalty_vals) Calling the M-estimation procedure (note that ``init`` has 5 values now, since ``X.shape[1] = 5``). >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate() Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Next, we can estimate the parameters for a logistic regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y2'] >>> return ee_elasticnet_regression(theta=theta, X=x, y=y, model='logistic', ratio=0.5, penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=20000) Finally, we can estimate the parameters for a Poisson regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y3'] >>> return ee_elasticnet_regression(theta=theta, X=x, y=y, model='poisson', ratio=0.5, penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=20000) Additionally, weighted versions of all the previous models can be estimated by specifying the optional ``weights`` argument. References ---------- Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical Statistics, 7(3), 397-416. Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132. """ # Preparation of input shapes and object types X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center) # Determining transformation function to use for the regression model transform = _model_transform_(model=model) # Looking up corresponding transformation pred_y = transform(np.dot(X, beta)) # Generating predicted values # Allowing for a weighted penalized regression model w = _generate_weights_(weights=weights, n_obs=X.shape[0]) # Creating penalty term for ridge regression (bridge with gamma=2 is the special case of ridge) if epsilon < 0: raise ValueError("epsilon must be greater than zero for the approximate LASSO") if not 0 <= ratio <= 1: raise ValueError("The elastic-net penalty is only defined for 0 <= ratio <= 1. The input L1:L2 ratio was " + str(ratio)) penalty_l1 = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=1+epsilon, center=center) penalty_l2 = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=2, center=center) penalty_terms = ratio*penalty_l1 + (1-ratio)*penalty_l2 # Output b-by-n matrix return w * (((y - pred_y) * X).T - penalty_terms[:, None]) # Score function with penalty term subtracted off def ee_bridge_regression(theta, y, X, model, penalty, gamma, weights=None, center=0.): r"""Default stacked estimating equation for bridge penalized regression. The bridge penalty is a generalization of penalized regression, that includes L1 and L2-regularization as special cases. The estimating equation for bridge penalized regression is .. math:: \sum_i^n \psi(Y_i, X_i, \theta) = \sum_i^n (Y_i - X_i^T \theta) X_i - \gamma | \theta |^{\gamma - 1} sgn(\theta) = 0 For the bridge penalty, LASSO is the special case where :math:`\gamma = 1` and ridge regression is :math:`\gamma = 2`. While the bridge penalty is defined for :math:`\gamma > 0`, the provided estimating equation only supports :math:`\gamma \ge 1`. Additionally, LASSO is not strictly convex, so :math:`\gamma = 1` is not generally recommended. Instead, an approximate LASSO can be accomplished by setting :math:`\gamma` to be slightly larger than 1 (as done in ``ee_lasso_regression``. Here, :math:`\theta` is a 1-by-b array, where b is the distinct covariates included as part of X. For example, if X is a 3-by-n matrix, then theta will be a 1-by-3 array. The code is general to allow for an arbitrary number of X's (as long as there is enough support in the data). Note ---- The 'strength' of the penalty term is indicated by :math:`\lambda`, which is the ``penalty`` argument scaled (or divided by) the number of observations. Note ---- Root-finding for ``ee_bridge_regression`` can be difficult when :math:`2 > \gamma > 1`. In general, it is recommended to use the Leverberg-Marquette algorithm (``MEstimator.estimate(solver='lm')``). Parameters ---------- theta : ndarray, list, vector Theta in this case consists of b values. Therefore, initial values should consist of the same number as the number of columns present. This can easily be accomplished generally by ``[0, ] * X.shape[1]``. X : ndarray, list, vector 2-dimensional vector of n observed values for b variables. No missing data should be included (missing data may cause unexpected behavior). y : ndarray, list, vector 1-dimensional vector of n observed values. No missing data should be included (missing data may cause unexpected behavior). model : str Type of regression model to estimate. Options are ``'linear'`` (linear regression), ``'logistic'`` (logistic regression), and ``'poisson'`` (Poisson regression). penalty : int, float, ndarray, list, vector Penalty term to apply to all coefficients (if only a integer or float is provided) or the corresponding coefficient (if a list or vector of integers or floats is provided). Note that the penalty term should either consists of a single value or b values (to match the length of ``theta``). gamma : float Hyperparameter for the bridge penalty, defined for :math:`\gamma > 0`. However, only :math:`\gamma \ge 1` are supported. If :math:`\gamma = 1`, then the bridge penalty is LASSO. If :math:`\gamma = 2`, then the bridge penalty is ridge. weights : ndarray, list, vector, None, optional 1-dimensional vector of n weights. No missing weights should be included. Default is None, which assigns a weight of 1 to all observations. center : int, float, ndarray, list, vector, optional Center or reference value to penalized estimated coefficients towards. Default is zero, which penalized coefficients towards the null. Other center values can be specified for all coefficients (by providing an integer or float) or covariate-specific centering values (by providing a vector of values of the same length as X). Returns ------- array : Returns a b-by-n NumPy array evaluated for the input theta and y Examples -------- Construction of a estimating equation(s) with ``ee_bridge_regression`` should be done similar to the following >>> import numpy as np >>> import pandas as pd >>> from scipy.stats import logistic >>> from delicatessen import MEstimator >>> from delicatessen.estimating_equations import ee_bridge_regression Some generic data to estimate a linear bridge regresion model >>> n = 500 >>> data = pd.DataFrame() >>> data['V'] = np.random.normal(size=n) >>> data['W'] = np.random.normal(size=n) >>> data['X'] = data['W'] + np.random.normal(scale=0.25, size=n) >>> data['Z'] = np.random.normal(size=n) >>> data['Y1'] = 0.5 + 2*data['W'] - 1*data['Z'] + np.random.normal(loc=0, size=n) >>> data['Y2'] = np.random.binomial(n=1, p=logistic.cdf(0.5 + 2*data['W'] - 1*data['Z']), size=n) >>> data['Y3'] = np.random.poisson(lam=np.exp(1 + 2*data['W'] - 1*data['Z']), size=n) >>> data['C'] = 1 Note that ``C`` here is set to all 1's. This will be the intercept in the regression. Defining psi, or the stacked estimating equations. Note that the penalty is a list of values. Here, we are *not* penalizing the intercept (which is generally recommended when the intercept is unlikely to be zero). The remainder of covariates have a penalty of 10 applied. >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y'] >>> return ee_bridge_regression(theta=theta, X=x, y=y, model='linear', gamma=2.3, penalty=penalty_vals) Calling the M-estimation procedure (note that ``init`` has 5 values now, since ``X.shape[1] = 5``). >>> estr = MEstimator(stacked_equations=psi, init=[0., 0., 0., 0., 0.]) >>> estr.estimate() Inspecting the parameter estimates, variance, and confidence intervals >>> estr.theta >>> estr.variance >>> estr.confidence_intervals() Next, we can estimate the parameters for a logistic regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y2'] >>> return ee_bridge_regression(theta=theta, X=x, y=y, model='logistic', gamma=2.3, penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=5000) Finally, we can estimate the parameters for a Poisson regression model as follows >>> penalty_vals = [0., 10., 10., 10., 10.] >>> def psi(theta): >>> x, y = data[['C', 'V', 'W', 'X', 'Z']], data['Y3'] >>> return ee_bridge_regression(theta=theta, X=x, y=y, model='poisson', gamma=2.3, penalty=penalty_vals) >>> estr = MEstimator(stacked_equations=psi, init=[0.01, 0.01, 0.01, 0.01, 0.01]) >>> estr.estimate(solver='lm', maxiter=5000) Additionally, weighted versions of all the previous models can be estimated by specifying the optional ``weights`` argument. References ---------- Fu WJ. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical Statistics, 7(3), 397-416. Fu WJ. (2003). Penalized estimating equations. Biometrics, 59(1), 126-132. """ # Preparation of input shapes and object types X, y, beta, penalty, center = _prep_inputs_(X=X, y=y, theta=theta, penalty=penalty, center=center) # Determining transformation function to use for the regression model transform = _model_transform_(model=model) # Looking up corresponding transformation pred_y = transform(np.dot(X, beta)) # Generating predicted values # Allowing for a weighted penalized regression model w = _generate_weights_(weights=weights, n_obs=X.shape[0]) # Creating penalty term for ridge regression (bridge with gamma=2 is the special case of ridge) penalty_terms = _bridge_penalty_(theta=theta, n_obs=X.shape[0], penalty=penalty, gamma=gamma, center=center) # Output b-by-n matrix return w * (((y - pred_y) * X).T - penalty_terms[:, None]) # Score function with penalty term subtracted off ################################################################# # Utility functions for regression equations def _prep_inputs_(X, y, theta, penalty=None, center=None): """Internal use function to simplify variable transformations for regression. This function is used on the inputs to ensure they are the proper shapes Parameters ---------- X : ndarray y : ndarray theta : ndarray penalty : ndarray, None, optiona Returns ------- transformed parameters """ X = np.asarray(X) # Convert to NumPy array y = np.asarray(y)[:, None] # Convert to NumPy array and ensure correct shape for matrix algebra beta = np.asarray(theta)[:, None] # Convert to NumPy array and ensure correct shape for matrix algebra if penalty is None: # Return the transformed objects return X, y, beta else: # Convert penalty term then return all penalty = np.asarray(penalty) # Convert to NumPy array center = np.asarray(center) # Convert to NumPy array return X, y, beta, penalty, center def _model_transform_(model, assert_linear_model=False): """Internal use function to simplify the checking procedure for the model form to use. Takes the input string and returns the corresponding function for the variable transformation. Parameters ---------- model : str Model identifier to calculate the transformation for Returns ------- function """ # Checking object type (and convert to lower-case) if isinstance(model, str): # If string, convert to lower-case for internal handling model = model.lower() else: raise ValueError("The model argument must be a str object.") # forcing model to be 'linear' (used by ee_robust_regression) if assert_linear_model and model != 'linear': raise ValueError("The selected estimating equation only supports linear regression.") # Process the model transformations if model == 'linear': # If linear regression transform = identity # ... no transformation needed elif model == 'logistic': # If logistic regression transform = inverse_logit # ... expit (inverse_logit) transformation elif model == 'poisson': # If Poisson regression transform = np.exp # ... exponential transformation else: # Else results in error raise ValueError("Invalid input:", model, ". Please select: 'linear', 'logistic', or 'poisson'.") return transform def _generate_weights_(weights, n_obs): """Internal use function to return the weights assigned to each observation. Returns a vector of 1's when no weights are provided. Otherwise, converts provided vector into a numpy array. Parameters ---------- weights : None, ndarray, list Vector of weights, or None if no weights are provided n_obs : int Number of observations in the data Returns ------- ndarray """ if weights is None: # If weights is unspecified w = np.ones(n_obs) # ... assign weight of 1 to all observations else: # Otherwise w = np.asarray(weights) # ... set weights as input vector return w def _bridge_penalty_(theta, gamma, penalty, n_obs, center): r"""Internal use function to calculate the corresponding penalty term. The penalty term formula is based on the bridge penalty, where LASSO is :math:`\gamma = 1` and ridge is :math:`\gamma = 2`. The penalty term is defined for :math:`\gamma > 0` but :math:`\gamma < 1` requires special optimization. Note ---- All penalties are scaled by the number of observations. The penalty term for the score function (first derivative) is: .. math:: \lambda \gamma | \theta |^{\gamma - 1} sgn(\theta) where :math:`\lambda` is the (scaled) penalty, :math:`\gamma` is the hyperparameter for the bridge penalty, and :math:`\theta` are the regression coefficients. Parameters ---------- theta : ndarray, list, vector Regression coefficients to penalize. ``theta`` in this case consists of b values. gamma : float, int Hyperparameter for the bridge penalty, defined for :math:`\gamma > 0`. Notice that :math:`\gamma = 1` corresponds to LASSO, and :math:`\gamma = 2` corresponds to ridge. penalty : int, float, ndarray, list, vector Penalty term to apply to all coefficients (if only a integer or float is provided) or the corresponding coefficient (if a list or vector of integers or floats is provided). Note that the penalty term should either consists of a single value or b values (to match the length of ``theta``). n_obs : int Number of observations. Used to rescale the penalty terms Returns ------- ndarray """ # Checking the penalty term is non-negative if penalty.size != 1: if penalty.shape[0] != len(theta): raise ValueError("The penalty term must be either a single number or the same length as theta.") if center.size != 1: if center.shape[0] != len(theta): raise ValueError("The center term must be either a single number or the same length as theta.") # Checking a valid hyperparameter is being provided # if gamma <= 0: # raise ValueError("L_{gamma} is not defined for `gamma` > 0") if gamma < 1: raise ValueError("L_{gamma} for `gamma` < 1 is not currently able to be supported with estimating equations " "evaluated using numerical methods.") # Calculating the penalties penalty_scaled = penalty / (gamma * n_obs) penalty_terms = penalty_scaled * gamma * (np.abs(theta - center)**(gamma-1)) * np.sign(theta - center) return penalty_terms

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null

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7

054d9ecfaf7a0846daea50baa25931160e3e9a7a

5,307

py

Python

tests/test_testproblems.py

don-alejandrino/rt_opt

f2639ad16a16281755c78d8934e7b8ad51730736

[ "MIT" ]

1

2021-08-12T08:14:47.000Z

tests/test_testproblems.py

don-alejandrino/rt_opt

f2639ad16a16281755c78d8934e7b8ad51730736

[ "MIT" ]

null

tests/test_testproblems.py

don-alejandrino/rt_opt

f2639ad16a16281755c78d8934e7b8ad51730736

[ "MIT" ]

null

import unittest import numpy as np from rt_opt import testproblems as tp from rt_opt import testproblems_shifted as tps def run_test(cls, testProb): if isinstance(testProb.min.x, tuple): if isinstance(testProb.min.f, tuple): for val in testProb.min.x: cls.assertGreater(testProb.f(val), testProb.min.f[0]) cls.assertLess(testProb.f(val), testProb.min.f[1]) else: for val in testProb.min.x: cls.assertAlmostEqual(testProb.f(val), testProb.min.f, delta=np.finfo(float).eps) else: if isinstance(testProb.min.f, tuple): cls.assertGreater(testProb.f(testProb.min.x), testProb.min.f[0]) cls.assertLess(testProb.f(testProb.min.x), testProb.min.f[1]) else: cls.assertAlmostEqual(testProb.f(testProb.min.x), testProb.min.f, delta=np.finfo(float).eps) class Test_testproblems_2D(unittest.TestCase): def test_Ackley(self): testProb = tp.Ackley() run_test(self, testProb) def test_Beale(self): testProb = tp.Beale() run_test(self, testProb) def test_GoldsteinPrice(self): testProb = tp.GoldsteinPrice() run_test(self, testProb) def test_Booth(self): testProb = tp.Booth() run_test(self, testProb) def test_Bukin6(self): testProb = tp.Bukin6() run_test(self, testProb) def test_Matyas(self): testProb = tp.Matyas() run_test(self, testProb) def test_Levi13(self): testProb = tp.Levi13() run_test(self, testProb) def test_Himmelblau(self): testProb = tp.Himmelblau() run_test(self, testProb) def test_ThreeHumpCamel(self): testProb = tp.ThreeHumpCamel() run_test(self, testProb) def test_Easom(self): testProb = tp.Easom() run_test(self, testProb) def test_CrossInTray(self): testProb = tp.CrossInTray() run_test(self, testProb) def test_Eggholder(self): testProb = tp.Eggholder() run_test(self, testProb) def test_Hoelder(self): testProb = tp.Hoelder() run_test(self, testProb) def test_McCormick(self): testProb = tp.McCormick() run_test(self, testProb) def test_Schaffer2(self): testProb = tp.Schaffer2() run_test(self, testProb) def test_Schaffer4(self): testProb = tp.Schaffer4() run_test(self, testProb) class Test_testproblems_shifted_2D(unittest.TestCase): def test_Ackley(self): testProb = tps.Ackley() run_test(self, testProb) def test_Beale(self): testProb = tps.Beale() run_test(self, testProb) def test_GoldsteinPrice(self): testProb = tps.GoldsteinPrice() run_test(self, testProb) def test_Booth(self): testProb = tps.Booth() run_test(self, testProb) def test_Bukin6(self): testProb = tps.Bukin6() run_test(self, testProb) def test_Matyas(self): testProb = tps.Matyas() run_test(self, testProb) def test_Levi13(self): testProb = tps.Levi13() run_test(self, testProb) def test_Himmelblau(self): testProb = tps.Himmelblau() run_test(self, testProb) def test_ThreeHumpCamel(self): testProb = tps.ThreeHumpCamel() run_test(self, testProb) def test_Easom(self): testProb = tps.Easom() run_test(self, testProb) def test_CrossInTray(self): testProb = tps.CrossInTray() run_test(self, testProb) def test_Eggholder(self): testProb = tps.Eggholder() run_test(self, testProb) def test_Hoelder(self): testProb = tps.Hoelder() run_test(self, testProb) def test_McCormick(self): testProb = tps.McCormick() run_test(self, testProb) def test_Schaffer2(self): testProb = tps.Schaffer2() run_test(self, testProb) def test_Schaffer4(self): testProb = tps.Schaffer4() run_test(self, testProb) class Test_testproblems_nD(unittest.TestCase): def setUp(self): self.n_dims = 100 def test_Rastrigin(self): testProb = tp.Rastrigin(self.n_dims) run_test(self, testProb) def test_Sphere(self): testProb = tp.Sphere(self.n_dims) run_test(self, testProb) def test_Rosenbrock(self): testProb = tp.Rosenbrock(self.n_dims) run_test(self, testProb) def test_StyblinskiTang(self): testProb = tp.StyblinskiTang(self.n_dims) run_test(self, testProb) class Test_testproblems_shifted_nD(unittest.TestCase): def setUp(self): self.n_dims = 100 def test_Rastrigin(self): testProb = tps.Rastrigin(self.n_dims) run_test(self, testProb) def test_Sphere(self): testProb = tps.Sphere(self.n_dims) run_test(self, testProb) def test_Rosenbrock(self): testProb = tps.Rosenbrock(self.n_dims) run_test(self, testProb) def test_StyblinskiTang(self): testProb = tps.StyblinskiTang(self.n_dims) run_test(self, testProb) if __name__ == '__main__': unittest.main()

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0.138191

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1

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false

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0

null

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1

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1

0

null

0

1

0

8

55332d0ed3b15f531ee890479270f850bfec1c24

45

py

Python

enhance_me/mirnet/models/__init__.py

soumik12345/enhance-me

c0f9bcb6d4eb46030e90d47e58059f8624f5cf7a

[ "MIT" ]

1

2022-02-01T23:20:19.000Z

enhance_me/mirnet/models/__init__.py

soumik12345/enhance-me

c0f9bcb6d4eb46030e90d47e58059f8624f5cf7a

[ "MIT" ]

2

2021-11-27T08:45:47.000Z

2021-11-28T08:45:59.000Z

enhance_me/mirnet/models/__init__.py

soumik12345/enhance-me

c0f9bcb6d4eb46030e90d47e58059f8624f5cf7a

[ "MIT" ]

null

from .mirnet_model import build_mirnet_model

22.5

44

0.888889

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null

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0

1

0

1

0

7

55906b196788b6e4705f0ff0aa6ec524fd5ae2cf

6,827

py

Python

physical_multiagent_env/utils/maps.py

fxnnxc/physical_multiagent_env

9324d105372da6911de97640e70fb68cde337d61

[ "Apache-2.0" ]

null

physical_multiagent_env/utils/maps.py

fxnnxc/physical_multiagent_env

9324d105372da6911de97640e70fb68cde337d61

[ "Apache-2.0" ]

1

2021-04-29T02:14:14.000Z

2021-04-29T02:14:54.000Z

physical_multiagent_env/utils/maps.py

fxnnxc/physical_multiagent_env

9324d105372da6911de97640e70fb68cde337d61

[ "Apache-2.0" ]

null

class GridMap1: def __init__(self): self.init_position = [5,5,0] self.target_position = [8,8,0] self.agent_position = [1,1,0] self.width = 10 self.height = 10 self.map1 = [[1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], # 5 [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap2: def __init__(self): self.init_position = [5,5,0] self.target_position = [8,4,0] self.agent_position = [1,4,0] self.width = 10 self.height = 10 self.map1 = [[1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,1, 1,0,0,0,1], # 5 [1,0,1,1,0, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap3: def __init__(self): self.init_position = [5,5,0] self.target_position = [4,4,0] self.agent_position = [2,4,0] self.width = 10 self.height = 10 self.map1 = [[1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,1,1,1, 1,1,1,0,1], [1,0,1,1,0, 0,1,1,0,1], # 5 [1,0,1,1,0, 0,1,1,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap4: def __init__(self): self.init_position = [5,5,0] self.target_position = [1,1,0] self.agent_position = [1,2,0] self.width = 10 self.height = 10 self.map1 = [[1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], # 5 [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap5: def __init__(self): self.init_position = [5,5,0] self.target_position = [1,1,0] self.agent_position = [1,2,0] self.width = 10 self.height = 10 self.map1 = [[1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,1,1, 1,1,0,0,1], [1,0,0,1,1, 1,1,0,0,1], # 5 [1,0,0,1,1, 1,1,0,0,1], [1,0,0,1,1, 1,1,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap6: def __init__(self): self.init_position = [5,5,0] self.target_position = [1,1,0] self.agent_position = [1,2,0] self.width = 10 self.height = 10 self.map1 = [ [1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap7: def __init__(self): self.init_position = [5,5,0] self.target_position = [4.5,1,0] self.agent_position = [4.5,8,0] self.width = 10 self.height = 10 self.map1 = [ [1,1,1,1,1, 1,1,1,1,1], # 1 [1,1,1,1,1, 1,1,1,1,1], [1,1,1,1,1, 1,1,1,1,1], [1,1,1,1,1, 1,1,1,1,1], [1,0,0,0,0, 0,0,0,0,1], # 5 [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], [1,1,1,1,1, 1,1,1,1,1], [1,1,1,1,1, 1,1,1,1,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap8: def __init__(self): self.init_position = [5,5,0] self.target_position = [1,1,0] self.agent_position = [2,1,0] self.width = 10 self.height = 10 self.map1 = [ [1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 0,0,0,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,1,1,0, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1]) class GridMap9: def __init__(self): self.init_position = [5,5,0] self.target_position = [1,1,0] self.agent_position = [1,2,0] self.width = 10 self.height = 10 self.map1 = [ [1,1,1,1,1, 1,1,1,1,1], # 1 [1,0,0,0,0, 1,1,1,1,1], [1,1,1,1,0, 1,1,1,1,1], [1,1,0,1,0, 0,0,0,0,1], [1,0,0,1,1, 1,1,0,0,1], [1,0,0,1,1, 0,0,0,0,1], [1,0,1,1,1, 0,1,1,0,1], [1,0,1,1,1, 0,1,1,0,1], [1,0,0,0,0, 0,0,0,0,1], [1,1,1,1,1, 1,1,1,1,1], # 10 ] self.num_obstacles = sum([sum(b) for b in self.map1])

36.31383

61

0.347883

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559b30510fe04c8f92c357f2ff2e8b1648728a8f

92

py

Python

inac8hr/loaders/__init__.py

th-bunratta/8hr.insomniac

5173500a1ad7197096d513b38258aa65b035fcf3

[ "BSD-3-Clause" ]

null

inac8hr/loaders/__init__.py

th-bunratta/8hr.insomniac

5173500a1ad7197096d513b38258aa65b035fcf3

[ "BSD-3-Clause" ]

null

inac8hr/loaders/__init__.py

th-bunratta/8hr.insomniac

5173500a1ad7197096d513b38258aa65b035fcf3

[ "BSD-3-Clause" ]

null

from inac8hr.loaders.image_loader import * from inac8hr.loaders.dirs import GameDirectories

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e9cd60a0d3fd3e847360c58817c8890eb0f158c9

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py

Python

src/PatchMatch/__init__.py

Emmanuel-Ezenwere/Deep-Image-Analogy-PyTorch

4ce14cd0b7c2d49ec4ab2dd1356aa7163d7ffae2

[ "MIT" ]

null

src/PatchMatch/__init__.py

Emmanuel-Ezenwere/Deep-Image-Analogy-PyTorch

4ce14cd0b7c2d49ec4ab2dd1356aa7163d7ffae2

[ "MIT" ]

null

src/PatchMatch/__init__.py

Emmanuel-Ezenwere/Deep-Image-Analogy-PyTorch

4ce14cd0b7c2d49ec4ab2dd1356aa7163d7ffae2

[ "MIT" ]

1

2020-04-13T13:20:24.000Z

from .PatchMatchSimple import PatchMatch as PatchMatchSimple from .PatchMatchOrig import PatchMatch as PatchMatchOrig

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75a4619bab7d849ff78dcc9cda9d3d66789470f5

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py

Python

sdk/python/pulumi_azure/containerservice/registry_task.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

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

109

2018-06-18T00:19:44.000Z

2022-02-20T05:32:57.000Z

sdk/python/pulumi_azure/containerservice/registry_task.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

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

663

2018-06-18T21:08:46.000Z

2022-03-31T20:10:11.000Z

sdk/python/pulumi_azure/containerservice/registry_task.py

henriktao/pulumi-azure

f1cbcf100b42b916da36d8fe28be3a159abaf022

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

41

2018-07-19T22:37:38.000Z

2022-03-14T10:56:26.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs from ._inputs import * __all__ = ['RegistryTaskArgs', 'RegistryTask'] @pulumi.input_type class RegistryTaskArgs: def __init__(__self__, *, container_registry_id: pulumi.Input[str], agent_pool_name: Optional[pulumi.Input[str]] = None, agent_setting: Optional[pulumi.Input['RegistryTaskAgentSettingArgs']] = None, base_image_trigger: Optional[pulumi.Input['RegistryTaskBaseImageTriggerArgs']] = None, docker_step: Optional[pulumi.Input['RegistryTaskDockerStepArgs']] = None, enabled: Optional[pulumi.Input[bool]] = None, encoded_step: Optional[pulumi.Input['RegistryTaskEncodedStepArgs']] = None, file_step: Optional[pulumi.Input['RegistryTaskFileStepArgs']] = None, identity: Optional[pulumi.Input['RegistryTaskIdentityArgs']] = None, is_system_task: Optional[pulumi.Input[bool]] = None, log_template: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, platform: Optional[pulumi.Input['RegistryTaskPlatformArgs']] = None, registry_credential: Optional[pulumi.Input['RegistryTaskRegistryCredentialArgs']] = None, source_triggers: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, timeout_in_seconds: Optional[pulumi.Input[int]] = None, timer_triggers: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]]] = None): """ The set of arguments for constructing a RegistryTask resource. :param pulumi.Input[str] container_registry_id: The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. :param pulumi.Input[str] agent_pool_name: The name of the dedicated Container Registry Agent Pool for this Container Registry Task. :param pulumi.Input['RegistryTaskAgentSettingArgs'] agent_setting: A `agent_setting` block as defined below. :param pulumi.Input['RegistryTaskBaseImageTriggerArgs'] base_image_trigger: A `base_image_trigger` block as defined below. :param pulumi.Input['RegistryTaskDockerStepArgs'] docker_step: A `docker_step` block as defined below. :param pulumi.Input[bool] enabled: Should this Container Registry Task be enabled? Defaults to `true`. :param pulumi.Input['RegistryTaskEncodedStepArgs'] encoded_step: A `encoded_step` block as defined below. :param pulumi.Input['RegistryTaskFileStepArgs'] file_step: A `file_step` block as defined below. :param pulumi.Input['RegistryTaskIdentityArgs'] identity: A `identity` block as defined below. :param pulumi.Input[bool] is_system_task: Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. :param pulumi.Input[str] log_template: The template that describes the run log artifact. :param pulumi.Input[str] name: The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. :param pulumi.Input['RegistryTaskPlatformArgs'] platform: A `platform` block as defined below. :param pulumi.Input['RegistryTaskRegistryCredentialArgs'] registry_credential: One `registry_credential` block as defined below. :param pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]] source_triggers: One or more `source_trigger` blocks as defined below. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Container Registry Task. :param pulumi.Input[int] timeout_in_seconds: The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. :param pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]] timer_triggers: One or more `timer_trigger` blocks as defined below. """ pulumi.set(__self__, "container_registry_id", container_registry_id) if agent_pool_name is not None: pulumi.set(__self__, "agent_pool_name", agent_pool_name) if agent_setting is not None: pulumi.set(__self__, "agent_setting", agent_setting) if base_image_trigger is not None: pulumi.set(__self__, "base_image_trigger", base_image_trigger) if docker_step is not None: pulumi.set(__self__, "docker_step", docker_step) if enabled is not None: pulumi.set(__self__, "enabled", enabled) if encoded_step is not None: pulumi.set(__self__, "encoded_step", encoded_step) if file_step is not None: pulumi.set(__self__, "file_step", file_step) if identity is not None: pulumi.set(__self__, "identity", identity) if is_system_task is not None: pulumi.set(__self__, "is_system_task", is_system_task) if log_template is not None: pulumi.set(__self__, "log_template", log_template) if name is not None: pulumi.set(__self__, "name", name) if platform is not None: pulumi.set(__self__, "platform", platform) if registry_credential is not None: pulumi.set(__self__, "registry_credential", registry_credential) if source_triggers is not None: pulumi.set(__self__, "source_triggers", source_triggers) if tags is not None: pulumi.set(__self__, "tags", tags) if timeout_in_seconds is not None: pulumi.set(__self__, "timeout_in_seconds", timeout_in_seconds) if timer_triggers is not None: pulumi.set(__self__, "timer_triggers", timer_triggers) @property @pulumi.getter(name="containerRegistryId") def container_registry_id(self) -> pulumi.Input[str]: """ The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. """ return pulumi.get(self, "container_registry_id") @container_registry_id.setter def container_registry_id(self, value: pulumi.Input[str]): pulumi.set(self, "container_registry_id", value) @property @pulumi.getter(name="agentPoolName") def agent_pool_name(self) -> Optional[pulumi.Input[str]]: """ The name of the dedicated Container Registry Agent Pool for this Container Registry Task. """ return pulumi.get(self, "agent_pool_name") @agent_pool_name.setter def agent_pool_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "agent_pool_name", value) @property @pulumi.getter(name="agentSetting") def agent_setting(self) -> Optional[pulumi.Input['RegistryTaskAgentSettingArgs']]: """ A `agent_setting` block as defined below. """ return pulumi.get(self, "agent_setting") @agent_setting.setter def agent_setting(self, value: Optional[pulumi.Input['RegistryTaskAgentSettingArgs']]): pulumi.set(self, "agent_setting", value) @property @pulumi.getter(name="baseImageTrigger") def base_image_trigger(self) -> Optional[pulumi.Input['RegistryTaskBaseImageTriggerArgs']]: """ A `base_image_trigger` block as defined below. """ return pulumi.get(self, "base_image_trigger") @base_image_trigger.setter def base_image_trigger(self, value: Optional[pulumi.Input['RegistryTaskBaseImageTriggerArgs']]): pulumi.set(self, "base_image_trigger", value) @property @pulumi.getter(name="dockerStep") def docker_step(self) -> Optional[pulumi.Input['RegistryTaskDockerStepArgs']]: """ A `docker_step` block as defined below. """ return pulumi.get(self, "docker_step") @docker_step.setter def docker_step(self, value: Optional[pulumi.Input['RegistryTaskDockerStepArgs']]): pulumi.set(self, "docker_step", value) @property @pulumi.getter def enabled(self) -> Optional[pulumi.Input[bool]]: """ Should this Container Registry Task be enabled? Defaults to `true`. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enabled", value) @property @pulumi.getter(name="encodedStep") def encoded_step(self) -> Optional[pulumi.Input['RegistryTaskEncodedStepArgs']]: """ A `encoded_step` block as defined below. """ return pulumi.get(self, "encoded_step") @encoded_step.setter def encoded_step(self, value: Optional[pulumi.Input['RegistryTaskEncodedStepArgs']]): pulumi.set(self, "encoded_step", value) @property @pulumi.getter(name="fileStep") def file_step(self) -> Optional[pulumi.Input['RegistryTaskFileStepArgs']]: """ A `file_step` block as defined below. """ return pulumi.get(self, "file_step") @file_step.setter def file_step(self, value: Optional[pulumi.Input['RegistryTaskFileStepArgs']]): pulumi.set(self, "file_step", value) @property @pulumi.getter def identity(self) -> Optional[pulumi.Input['RegistryTaskIdentityArgs']]: """ A `identity` block as defined below. """ return pulumi.get(self, "identity") @identity.setter def identity(self, value: Optional[pulumi.Input['RegistryTaskIdentityArgs']]): pulumi.set(self, "identity", value) @property @pulumi.getter(name="isSystemTask") def is_system_task(self) -> Optional[pulumi.Input[bool]]: """ Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. """ return pulumi.get(self, "is_system_task") @is_system_task.setter def is_system_task(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "is_system_task", value) @property @pulumi.getter(name="logTemplate") def log_template(self) -> Optional[pulumi.Input[str]]: """ The template that describes the run log artifact. """ return pulumi.get(self, "log_template") @log_template.setter def log_template(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "log_template", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def platform(self) -> Optional[pulumi.Input['RegistryTaskPlatformArgs']]: """ A `platform` block as defined below. """ return pulumi.get(self, "platform") @platform.setter def platform(self, value: Optional[pulumi.Input['RegistryTaskPlatformArgs']]): pulumi.set(self, "platform", value) @property @pulumi.getter(name="registryCredential") def registry_credential(self) -> Optional[pulumi.Input['RegistryTaskRegistryCredentialArgs']]: """ One `registry_credential` block as defined below. """ return pulumi.get(self, "registry_credential") @registry_credential.setter def registry_credential(self, value: Optional[pulumi.Input['RegistryTaskRegistryCredentialArgs']]): pulumi.set(self, "registry_credential", value) @property @pulumi.getter(name="sourceTriggers") def source_triggers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]]]: """ One or more `source_trigger` blocks as defined below. """ return pulumi.get(self, "source_triggers") @source_triggers.setter def source_triggers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]]]): pulumi.set(self, "source_triggers", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A mapping of tags which should be assigned to the Container Registry Task. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="timeoutInSeconds") def timeout_in_seconds(self) -> Optional[pulumi.Input[int]]: """ The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. """ return pulumi.get(self, "timeout_in_seconds") @timeout_in_seconds.setter def timeout_in_seconds(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "timeout_in_seconds", value) @property @pulumi.getter(name="timerTriggers") def timer_triggers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]]]: """ One or more `timer_trigger` blocks as defined below. """ return pulumi.get(self, "timer_triggers") @timer_triggers.setter def timer_triggers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]]]): pulumi.set(self, "timer_triggers", value) @pulumi.input_type class _RegistryTaskState: def __init__(__self__, *, agent_pool_name: Optional[pulumi.Input[str]] = None, agent_setting: Optional[pulumi.Input['RegistryTaskAgentSettingArgs']] = None, base_image_trigger: Optional[pulumi.Input['RegistryTaskBaseImageTriggerArgs']] = None, container_registry_id: Optional[pulumi.Input[str]] = None, docker_step: Optional[pulumi.Input['RegistryTaskDockerStepArgs']] = None, enabled: Optional[pulumi.Input[bool]] = None, encoded_step: Optional[pulumi.Input['RegistryTaskEncodedStepArgs']] = None, file_step: Optional[pulumi.Input['RegistryTaskFileStepArgs']] = None, identity: Optional[pulumi.Input['RegistryTaskIdentityArgs']] = None, is_system_task: Optional[pulumi.Input[bool]] = None, log_template: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, platform: Optional[pulumi.Input['RegistryTaskPlatformArgs']] = None, registry_credential: Optional[pulumi.Input['RegistryTaskRegistryCredentialArgs']] = None, source_triggers: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, timeout_in_seconds: Optional[pulumi.Input[int]] = None, timer_triggers: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]]] = None): """ Input properties used for looking up and filtering RegistryTask resources. :param pulumi.Input[str] agent_pool_name: The name of the dedicated Container Registry Agent Pool for this Container Registry Task. :param pulumi.Input['RegistryTaskAgentSettingArgs'] agent_setting: A `agent_setting` block as defined below. :param pulumi.Input['RegistryTaskBaseImageTriggerArgs'] base_image_trigger: A `base_image_trigger` block as defined below. :param pulumi.Input[str] container_registry_id: The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. :param pulumi.Input['RegistryTaskDockerStepArgs'] docker_step: A `docker_step` block as defined below. :param pulumi.Input[bool] enabled: Should this Container Registry Task be enabled? Defaults to `true`. :param pulumi.Input['RegistryTaskEncodedStepArgs'] encoded_step: A `encoded_step` block as defined below. :param pulumi.Input['RegistryTaskFileStepArgs'] file_step: A `file_step` block as defined below. :param pulumi.Input['RegistryTaskIdentityArgs'] identity: A `identity` block as defined below. :param pulumi.Input[bool] is_system_task: Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. :param pulumi.Input[str] log_template: The template that describes the run log artifact. :param pulumi.Input[str] name: The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. :param pulumi.Input['RegistryTaskPlatformArgs'] platform: A `platform` block as defined below. :param pulumi.Input['RegistryTaskRegistryCredentialArgs'] registry_credential: One `registry_credential` block as defined below. :param pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]] source_triggers: One or more `source_trigger` blocks as defined below. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Container Registry Task. :param pulumi.Input[int] timeout_in_seconds: The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. :param pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]] timer_triggers: One or more `timer_trigger` blocks as defined below. """ if agent_pool_name is not None: pulumi.set(__self__, "agent_pool_name", agent_pool_name) if agent_setting is not None: pulumi.set(__self__, "agent_setting", agent_setting) if base_image_trigger is not None: pulumi.set(__self__, "base_image_trigger", base_image_trigger) if container_registry_id is not None: pulumi.set(__self__, "container_registry_id", container_registry_id) if docker_step is not None: pulumi.set(__self__, "docker_step", docker_step) if enabled is not None: pulumi.set(__self__, "enabled", enabled) if encoded_step is not None: pulumi.set(__self__, "encoded_step", encoded_step) if file_step is not None: pulumi.set(__self__, "file_step", file_step) if identity is not None: pulumi.set(__self__, "identity", identity) if is_system_task is not None: pulumi.set(__self__, "is_system_task", is_system_task) if log_template is not None: pulumi.set(__self__, "log_template", log_template) if name is not None: pulumi.set(__self__, "name", name) if platform is not None: pulumi.set(__self__, "platform", platform) if registry_credential is not None: pulumi.set(__self__, "registry_credential", registry_credential) if source_triggers is not None: pulumi.set(__self__, "source_triggers", source_triggers) if tags is not None: pulumi.set(__self__, "tags", tags) if timeout_in_seconds is not None: pulumi.set(__self__, "timeout_in_seconds", timeout_in_seconds) if timer_triggers is not None: pulumi.set(__self__, "timer_triggers", timer_triggers) @property @pulumi.getter(name="agentPoolName") def agent_pool_name(self) -> Optional[pulumi.Input[str]]: """ The name of the dedicated Container Registry Agent Pool for this Container Registry Task. """ return pulumi.get(self, "agent_pool_name") @agent_pool_name.setter def agent_pool_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "agent_pool_name", value) @property @pulumi.getter(name="agentSetting") def agent_setting(self) -> Optional[pulumi.Input['RegistryTaskAgentSettingArgs']]: """ A `agent_setting` block as defined below. """ return pulumi.get(self, "agent_setting") @agent_setting.setter def agent_setting(self, value: Optional[pulumi.Input['RegistryTaskAgentSettingArgs']]): pulumi.set(self, "agent_setting", value) @property @pulumi.getter(name="baseImageTrigger") def base_image_trigger(self) -> Optional[pulumi.Input['RegistryTaskBaseImageTriggerArgs']]: """ A `base_image_trigger` block as defined below. """ return pulumi.get(self, "base_image_trigger") @base_image_trigger.setter def base_image_trigger(self, value: Optional[pulumi.Input['RegistryTaskBaseImageTriggerArgs']]): pulumi.set(self, "base_image_trigger", value) @property @pulumi.getter(name="containerRegistryId") def container_registry_id(self) -> Optional[pulumi.Input[str]]: """ The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. """ return pulumi.get(self, "container_registry_id") @container_registry_id.setter def container_registry_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "container_registry_id", value) @property @pulumi.getter(name="dockerStep") def docker_step(self) -> Optional[pulumi.Input['RegistryTaskDockerStepArgs']]: """ A `docker_step` block as defined below. """ return pulumi.get(self, "docker_step") @docker_step.setter def docker_step(self, value: Optional[pulumi.Input['RegistryTaskDockerStepArgs']]): pulumi.set(self, "docker_step", value) @property @pulumi.getter def enabled(self) -> Optional[pulumi.Input[bool]]: """ Should this Container Registry Task be enabled? Defaults to `true`. """ return pulumi.get(self, "enabled") @enabled.setter def enabled(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "enabled", value) @property @pulumi.getter(name="encodedStep") def encoded_step(self) -> Optional[pulumi.Input['RegistryTaskEncodedStepArgs']]: """ A `encoded_step` block as defined below. """ return pulumi.get(self, "encoded_step") @encoded_step.setter def encoded_step(self, value: Optional[pulumi.Input['RegistryTaskEncodedStepArgs']]): pulumi.set(self, "encoded_step", value) @property @pulumi.getter(name="fileStep") def file_step(self) -> Optional[pulumi.Input['RegistryTaskFileStepArgs']]: """ A `file_step` block as defined below. """ return pulumi.get(self, "file_step") @file_step.setter def file_step(self, value: Optional[pulumi.Input['RegistryTaskFileStepArgs']]): pulumi.set(self, "file_step", value) @property @pulumi.getter def identity(self) -> Optional[pulumi.Input['RegistryTaskIdentityArgs']]: """ A `identity` block as defined below. """ return pulumi.get(self, "identity") @identity.setter def identity(self, value: Optional[pulumi.Input['RegistryTaskIdentityArgs']]): pulumi.set(self, "identity", value) @property @pulumi.getter(name="isSystemTask") def is_system_task(self) -> Optional[pulumi.Input[bool]]: """ Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. """ return pulumi.get(self, "is_system_task") @is_system_task.setter def is_system_task(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "is_system_task", value) @property @pulumi.getter(name="logTemplate") def log_template(self) -> Optional[pulumi.Input[str]]: """ The template that describes the run log artifact. """ return pulumi.get(self, "log_template") @log_template.setter def log_template(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "log_template", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def platform(self) -> Optional[pulumi.Input['RegistryTaskPlatformArgs']]: """ A `platform` block as defined below. """ return pulumi.get(self, "platform") @platform.setter def platform(self, value: Optional[pulumi.Input['RegistryTaskPlatformArgs']]): pulumi.set(self, "platform", value) @property @pulumi.getter(name="registryCredential") def registry_credential(self) -> Optional[pulumi.Input['RegistryTaskRegistryCredentialArgs']]: """ One `registry_credential` block as defined below. """ return pulumi.get(self, "registry_credential") @registry_credential.setter def registry_credential(self, value: Optional[pulumi.Input['RegistryTaskRegistryCredentialArgs']]): pulumi.set(self, "registry_credential", value) @property @pulumi.getter(name="sourceTriggers") def source_triggers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]]]: """ One or more `source_trigger` blocks as defined below. """ return pulumi.get(self, "source_triggers") @source_triggers.setter def source_triggers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskSourceTriggerArgs']]]]): pulumi.set(self, "source_triggers", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ A mapping of tags which should be assigned to the Container Registry Task. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="timeoutInSeconds") def timeout_in_seconds(self) -> Optional[pulumi.Input[int]]: """ The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. """ return pulumi.get(self, "timeout_in_seconds") @timeout_in_seconds.setter def timeout_in_seconds(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "timeout_in_seconds", value) @property @pulumi.getter(name="timerTriggers") def timer_triggers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]]]: """ One or more `timer_trigger` blocks as defined below. """ return pulumi.get(self, "timer_triggers") @timer_triggers.setter def timer_triggers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['RegistryTaskTimerTriggerArgs']]]]): pulumi.set(self, "timer_triggers", value) class RegistryTask(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, agent_pool_name: Optional[pulumi.Input[str]] = None, agent_setting: Optional[pulumi.Input[pulumi.InputType['RegistryTaskAgentSettingArgs']]] = None, base_image_trigger: Optional[pulumi.Input[pulumi.InputType['RegistryTaskBaseImageTriggerArgs']]] = None, container_registry_id: Optional[pulumi.Input[str]] = None, docker_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskDockerStepArgs']]] = None, enabled: Optional[pulumi.Input[bool]] = None, encoded_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskEncodedStepArgs']]] = None, file_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskFileStepArgs']]] = None, identity: Optional[pulumi.Input[pulumi.InputType['RegistryTaskIdentityArgs']]] = None, is_system_task: Optional[pulumi.Input[bool]] = None, log_template: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, platform: Optional[pulumi.Input[pulumi.InputType['RegistryTaskPlatformArgs']]] = None, registry_credential: Optional[pulumi.Input[pulumi.InputType['RegistryTaskRegistryCredentialArgs']]] = None, source_triggers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskSourceTriggerArgs']]]]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, timeout_in_seconds: Optional[pulumi.Input[int]] = None, timer_triggers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskTimerTriggerArgs']]]]] = None, __props__=None): """ Manages a Container Registry Task. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe") example_registry = azure.containerservice.Registry("exampleRegistry", resource_group_name=example_resource_group.name, location=example_resource_group.location, sku="Basic") example_registry_task = azure.containerservice.RegistryTask("exampleRegistryTask", container_registry_id=example_registry.id, platform=azure.containerservice.RegistryTaskPlatformArgs( os="Linux", ), docker_step=azure.containerservice.RegistryTaskDockerStepArgs( dockerfile_path="Dockerfile", context_path="https://github.com/<user name>/acr-build-helloworld-node#main", context_access_token="<github personal access token>", image_names=["helloworld:{{.Run.ID}}"], )) ``` ## Import Container Registry Tasks can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:containerservice/registryTask:RegistryTask example /subscriptions/12345678-1234-9876-4563-123456789012/resourceGroups/group1/providers/Microsoft.ContainerRegistry/registries/registry1/tasks/task1 ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] agent_pool_name: The name of the dedicated Container Registry Agent Pool for this Container Registry Task. :param pulumi.Input[pulumi.InputType['RegistryTaskAgentSettingArgs']] agent_setting: A `agent_setting` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskBaseImageTriggerArgs']] base_image_trigger: A `base_image_trigger` block as defined below. :param pulumi.Input[str] container_registry_id: The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. :param pulumi.Input[pulumi.InputType['RegistryTaskDockerStepArgs']] docker_step: A `docker_step` block as defined below. :param pulumi.Input[bool] enabled: Should this Container Registry Task be enabled? Defaults to `true`. :param pulumi.Input[pulumi.InputType['RegistryTaskEncodedStepArgs']] encoded_step: A `encoded_step` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskFileStepArgs']] file_step: A `file_step` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskIdentityArgs']] identity: A `identity` block as defined below. :param pulumi.Input[bool] is_system_task: Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. :param pulumi.Input[str] log_template: The template that describes the run log artifact. :param pulumi.Input[str] name: The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. :param pulumi.Input[pulumi.InputType['RegistryTaskPlatformArgs']] platform: A `platform` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskRegistryCredentialArgs']] registry_credential: One `registry_credential` block as defined below. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskSourceTriggerArgs']]]] source_triggers: One or more `source_trigger` blocks as defined below. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Container Registry Task. :param pulumi.Input[int] timeout_in_seconds: The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskTimerTriggerArgs']]]] timer_triggers: One or more `timer_trigger` blocks as defined below. """ ... @overload def __init__(__self__, resource_name: str, args: RegistryTaskArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Manages a Container Registry Task. ## Example Usage ```python import pulumi import pulumi_azure as azure example_resource_group = azure.core.ResourceGroup("exampleResourceGroup", location="West Europe") example_registry = azure.containerservice.Registry("exampleRegistry", resource_group_name=example_resource_group.name, location=example_resource_group.location, sku="Basic") example_registry_task = azure.containerservice.RegistryTask("exampleRegistryTask", container_registry_id=example_registry.id, platform=azure.containerservice.RegistryTaskPlatformArgs( os="Linux", ), docker_step=azure.containerservice.RegistryTaskDockerStepArgs( dockerfile_path="Dockerfile", context_path="https://github.com/<user name>/acr-build-helloworld-node#main", context_access_token="<github personal access token>", image_names=["helloworld:{{.Run.ID}}"], )) ``` ## Import Container Registry Tasks can be imported using the `resource id`, e.g. ```sh $ pulumi import azure:containerservice/registryTask:RegistryTask example /subscriptions/12345678-1234-9876-4563-123456789012/resourceGroups/group1/providers/Microsoft.ContainerRegistry/registries/registry1/tasks/task1 ``` :param str resource_name: The name of the resource. :param RegistryTaskArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(RegistryTaskArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, agent_pool_name: Optional[pulumi.Input[str]] = None, agent_setting: Optional[pulumi.Input[pulumi.InputType['RegistryTaskAgentSettingArgs']]] = None, base_image_trigger: Optional[pulumi.Input[pulumi.InputType['RegistryTaskBaseImageTriggerArgs']]] = None, container_registry_id: Optional[pulumi.Input[str]] = None, docker_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskDockerStepArgs']]] = None, enabled: Optional[pulumi.Input[bool]] = None, encoded_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskEncodedStepArgs']]] = None, file_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskFileStepArgs']]] = None, identity: Optional[pulumi.Input[pulumi.InputType['RegistryTaskIdentityArgs']]] = None, is_system_task: Optional[pulumi.Input[bool]] = None, log_template: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, platform: Optional[pulumi.Input[pulumi.InputType['RegistryTaskPlatformArgs']]] = None, registry_credential: Optional[pulumi.Input[pulumi.InputType['RegistryTaskRegistryCredentialArgs']]] = None, source_triggers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskSourceTriggerArgs']]]]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, timeout_in_seconds: Optional[pulumi.Input[int]] = None, timer_triggers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskTimerTriggerArgs']]]]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = RegistryTaskArgs.__new__(RegistryTaskArgs) __props__.__dict__["agent_pool_name"] = agent_pool_name __props__.__dict__["agent_setting"] = agent_setting __props__.__dict__["base_image_trigger"] = base_image_trigger if container_registry_id is None and not opts.urn: raise TypeError("Missing required property 'container_registry_id'") __props__.__dict__["container_registry_id"] = container_registry_id __props__.__dict__["docker_step"] = docker_step __props__.__dict__["enabled"] = enabled __props__.__dict__["encoded_step"] = encoded_step __props__.__dict__["file_step"] = file_step __props__.__dict__["identity"] = identity __props__.__dict__["is_system_task"] = is_system_task __props__.__dict__["log_template"] = log_template __props__.__dict__["name"] = name __props__.__dict__["platform"] = platform __props__.__dict__["registry_credential"] = registry_credential __props__.__dict__["source_triggers"] = source_triggers __props__.__dict__["tags"] = tags __props__.__dict__["timeout_in_seconds"] = timeout_in_seconds __props__.__dict__["timer_triggers"] = timer_triggers super(RegistryTask, __self__).__init__( 'azure:containerservice/registryTask:RegistryTask', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, agent_pool_name: Optional[pulumi.Input[str]] = None, agent_setting: Optional[pulumi.Input[pulumi.InputType['RegistryTaskAgentSettingArgs']]] = None, base_image_trigger: Optional[pulumi.Input[pulumi.InputType['RegistryTaskBaseImageTriggerArgs']]] = None, container_registry_id: Optional[pulumi.Input[str]] = None, docker_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskDockerStepArgs']]] = None, enabled: Optional[pulumi.Input[bool]] = None, encoded_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskEncodedStepArgs']]] = None, file_step: Optional[pulumi.Input[pulumi.InputType['RegistryTaskFileStepArgs']]] = None, identity: Optional[pulumi.Input[pulumi.InputType['RegistryTaskIdentityArgs']]] = None, is_system_task: Optional[pulumi.Input[bool]] = None, log_template: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, platform: Optional[pulumi.Input[pulumi.InputType['RegistryTaskPlatformArgs']]] = None, registry_credential: Optional[pulumi.Input[pulumi.InputType['RegistryTaskRegistryCredentialArgs']]] = None, source_triggers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskSourceTriggerArgs']]]]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, timeout_in_seconds: Optional[pulumi.Input[int]] = None, timer_triggers: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskTimerTriggerArgs']]]]] = None) -> 'RegistryTask': """ Get an existing RegistryTask resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] agent_pool_name: The name of the dedicated Container Registry Agent Pool for this Container Registry Task. :param pulumi.Input[pulumi.InputType['RegistryTaskAgentSettingArgs']] agent_setting: A `agent_setting` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskBaseImageTriggerArgs']] base_image_trigger: A `base_image_trigger` block as defined below. :param pulumi.Input[str] container_registry_id: The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. :param pulumi.Input[pulumi.InputType['RegistryTaskDockerStepArgs']] docker_step: A `docker_step` block as defined below. :param pulumi.Input[bool] enabled: Should this Container Registry Task be enabled? Defaults to `true`. :param pulumi.Input[pulumi.InputType['RegistryTaskEncodedStepArgs']] encoded_step: A `encoded_step` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskFileStepArgs']] file_step: A `file_step` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskIdentityArgs']] identity: A `identity` block as defined below. :param pulumi.Input[bool] is_system_task: Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. :param pulumi.Input[str] log_template: The template that describes the run log artifact. :param pulumi.Input[str] name: The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. :param pulumi.Input[pulumi.InputType['RegistryTaskPlatformArgs']] platform: A `platform` block as defined below. :param pulumi.Input[pulumi.InputType['RegistryTaskRegistryCredentialArgs']] registry_credential: One `registry_credential` block as defined below. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskSourceTriggerArgs']]]] source_triggers: One or more `source_trigger` blocks as defined below. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A mapping of tags which should be assigned to the Container Registry Task. :param pulumi.Input[int] timeout_in_seconds: The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['RegistryTaskTimerTriggerArgs']]]] timer_triggers: One or more `timer_trigger` blocks as defined below. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _RegistryTaskState.__new__(_RegistryTaskState) __props__.__dict__["agent_pool_name"] = agent_pool_name __props__.__dict__["agent_setting"] = agent_setting __props__.__dict__["base_image_trigger"] = base_image_trigger __props__.__dict__["container_registry_id"] = container_registry_id __props__.__dict__["docker_step"] = docker_step __props__.__dict__["enabled"] = enabled __props__.__dict__["encoded_step"] = encoded_step __props__.__dict__["file_step"] = file_step __props__.__dict__["identity"] = identity __props__.__dict__["is_system_task"] = is_system_task __props__.__dict__["log_template"] = log_template __props__.__dict__["name"] = name __props__.__dict__["platform"] = platform __props__.__dict__["registry_credential"] = registry_credential __props__.__dict__["source_triggers"] = source_triggers __props__.__dict__["tags"] = tags __props__.__dict__["timeout_in_seconds"] = timeout_in_seconds __props__.__dict__["timer_triggers"] = timer_triggers return RegistryTask(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="agentPoolName") def agent_pool_name(self) -> pulumi.Output[Optional[str]]: """ The name of the dedicated Container Registry Agent Pool for this Container Registry Task. """ return pulumi.get(self, "agent_pool_name") @property @pulumi.getter(name="agentSetting") def agent_setting(self) -> pulumi.Output[Optional['outputs.RegistryTaskAgentSetting']]: """ A `agent_setting` block as defined below. """ return pulumi.get(self, "agent_setting") @property @pulumi.getter(name="baseImageTrigger") def base_image_trigger(self) -> pulumi.Output[Optional['outputs.RegistryTaskBaseImageTrigger']]: """ A `base_image_trigger` block as defined below. """ return pulumi.get(self, "base_image_trigger") @property @pulumi.getter(name="containerRegistryId") def container_registry_id(self) -> pulumi.Output[str]: """ The ID of the Container Registry that this Container Registry Task resides in. Changing this forces a new Container Registry Task to be created. """ return pulumi.get(self, "container_registry_id") @property @pulumi.getter(name="dockerStep") def docker_step(self) -> pulumi.Output[Optional['outputs.RegistryTaskDockerStep']]: """ A `docker_step` block as defined below. """ return pulumi.get(self, "docker_step") @property @pulumi.getter def enabled(self) -> pulumi.Output[Optional[bool]]: """ Should this Container Registry Task be enabled? Defaults to `true`. """ return pulumi.get(self, "enabled") @property @pulumi.getter(name="encodedStep") def encoded_step(self) -> pulumi.Output[Optional['outputs.RegistryTaskEncodedStep']]: """ A `encoded_step` block as defined below. """ return pulumi.get(self, "encoded_step") @property @pulumi.getter(name="fileStep") def file_step(self) -> pulumi.Output[Optional['outputs.RegistryTaskFileStep']]: """ A `file_step` block as defined below. """ return pulumi.get(self, "file_step") @property @pulumi.getter def identity(self) -> pulumi.Output[Optional['outputs.RegistryTaskIdentity']]: """ A `identity` block as defined below. """ return pulumi.get(self, "identity") @property @pulumi.getter(name="isSystemTask") def is_system_task(self) -> pulumi.Output[Optional[bool]]: """ Whether this Container Registry Task is a system task. Changing this forces a new Container Registry Task to be created. Defaults to `false`. """ return pulumi.get(self, "is_system_task") @property @pulumi.getter(name="logTemplate") def log_template(self) -> pulumi.Output[Optional[str]]: """ The template that describes the run log artifact. """ return pulumi.get(self, "log_template") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ The name which should be used for this Container Registry Task. Changing this forces a new Container Registry Task to be created. """ return pulumi.get(self, "name") @property @pulumi.getter def platform(self) -> pulumi.Output[Optional['outputs.RegistryTaskPlatform']]: """ A `platform` block as defined below. """ return pulumi.get(self, "platform") @property @pulumi.getter(name="registryCredential") def registry_credential(self) -> pulumi.Output[Optional['outputs.RegistryTaskRegistryCredential']]: """ One `registry_credential` block as defined below. """ return pulumi.get(self, "registry_credential") @property @pulumi.getter(name="sourceTriggers") def source_triggers(self) -> pulumi.Output[Optional[Sequence['outputs.RegistryTaskSourceTrigger']]]: """ One or more `source_trigger` blocks as defined below. """ return pulumi.get(self, "source_triggers") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ A mapping of tags which should be assigned to the Container Registry Task. """ return pulumi.get(self, "tags") @property @pulumi.getter(name="timeoutInSeconds") def timeout_in_seconds(self) -> pulumi.Output[Optional[int]]: """ The timeout of this Container Registry Task in seconds. The valid range lies from 300 to 28800. Defaults to 3600. """ return pulumi.get(self, "timeout_in_seconds") @property @pulumi.getter(name="timerTriggers") def timer_triggers(self) -> pulumi.Output[Optional[Sequence['outputs.RegistryTaskTimerTrigger']]]: """ One or more `timer_trigger` blocks as defined below. """ return pulumi.get(self, "timer_triggers")

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Python

Examples/Cell_Free_System/Code/functions_learning.py

amirpandi/METIS

8fc39fdc598c7e4564d45cd431df4dc964c8e77f

[ "MIT" ]

1

2022-01-08T08:37:09.000Z

Examples/Cell_Free_System/Code/functions_learning.py

amirpandi/METIS

8fc39fdc598c7e4564d45cd431df4dc964c8e77f

[ "MIT" ]

3

2022-01-08T08:51:19.000Z

2022-03-23T12:54:16.000Z

Examples/Cell_Free_System/Code/functions_learning.py

amirpandi/METIS

8fc39fdc598c7e4564d45cd431df4dc964c8e77f

[ "MIT" ]

1

2022-01-05T20:13:03.000Z

def active_learning(regressor, gold_regressor, allowed_conc, test_size = 100, steps = 10, verbose=0): ## first step if verbose: print('step: 1') # make first dataset X_train_1 = random_input(allowed_conc, test_size) # first fit regressor.fit(X_train_1, gold_regressor.predict(X_train_1)) # save results result = pd.DataFrame(X_train_1) result['gold_yield'] = gold_regressor.predict(X_train_1) result['pred_yield'] = 0.0 # not available but choose 0.0 to avoid further error result['step'] = 'step_1' ## next steps loop for step in range(steps-1): if verbose>=2: print('step: ',step+2) # make i th dataset X_train_1_1 = random_input(allowed_conc, 100000) df_1 = pd.DataFrame(X_train_1_1) df_1['pred_yield'] = regressor.predict(X_train_1_1) df_1 = df_1.sort_values(['pred_yield'], ascending=False) X_train_2 = df_1.iloc[0:test_size,0:11].values # save and add results temp_result = pd.DataFrame(X_train_2) temp_result['gold_yield'] = gold_regressor.predict(X_train_2) temp_result['pred_yield'] = df_1.iloc[0:test_size,11:12].values temp_result['step'] = 'step_{}'.format(step+2) result = pd.concat([result, temp_result], ignore_index=True) # update and refit regressor regressor.fit(result.iloc[:,0:11].values, result.iloc[:,11].values) return result, regressor def bayesian_optimization(regressors_list, gold_regressor, allowed_conc, exploitation=1, exploration=1, test_size=100, steps=10, verbose=0): ## first step if verbose: print('step: 1') # make first dataset X_train_1 = random_input(allowed_conc, test_size) # first fit for regressor in regressors_list: regressor.fit(X_train_1, gold_regressor.predict(X_train_1)) # save results result = pd.DataFrame(X_train_1) result['gold_yield'] = gold_regressor.predict(X_train_1) result['pred_yield'] = 0.0 # not available but choose 0.0 to avoid further error result['step'] = 'step_1' ## next steps loop for step in range(steps-1): if verbose>=2: print('step: ',step+2) # make i th dataset X_train_1_1 = random_input(allowed_conc, 100000) df_1 = pd.DataFrame(X_train_1_1) #upper Confidence Bound for index, regressor in enumerate(regressors_list): df_1['pred_yield_{}'.format(index)] = regressor.predict(X_train_1_1) df_1['regressors_std'] = df_1[[str(i) for i in df_1.columns if 'pred_yield' in str(i)]].std(axis=1) df_1['mean_vote'] = df_1[[str(i) for i in df_1.columns if 'pred_yield' in str(i)]].mean(axis=1) df_1['UCB'] = exploitation * df_1['mean_vote']+ exploration * df_1['regressors_std'] df_1 = df_1.sort_values(['UCB'], ascending=False) X_train_2 = df_1.iloc[0:test_size,0:11].values # save and add results temp_result = pd.DataFrame(X_train_2) temp_result['gold_yield'] = gold_regressor.predict(X_train_2) #temp_result['pred_yield'] = df_1.iloc[0:test_size,11:12].values temp_result['pred_yield'] = df_1.mean_vote[0:test_size].values temp_result['step'] = 'step_{}'.format(step+2) result = pd.concat([result, temp_result], ignore_index=True) # update and refit regressor regressor.fit(result.iloc[:,0:11].values, result.iloc[:,11].values) return result, regressor

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f9351c58e5a536a929a7d4619c14279b864574a9

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py

Python

torchir/networks/__init__.py

BDdeVos/TorchIR

80ea1045c1029182cada3cfa23a0693dd206cbcc

[ "MIT" ]

9

2021-11-02T18:43:54.000Z

2022-02-19T15:27:55.000Z

torchir/networks/__init__.py

BDdeVos/TorchIR

80ea1045c1029182cada3cfa23a0693dd206cbcc

[ "MIT" ]

null

torchir/networks/__init__.py

BDdeVos/TorchIR

80ea1045c1029182cada3cfa23a0693dd206cbcc

[ "MIT" ]

4

2022-02-07T11:44:15.000Z

2022-03-23T14:13:02.000Z

from torchir.networks.dirnet import DIRNet from torchir.networks.globalnet import AIRNet, RigidIRNet from torchir.networks.unet import UNet

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py

Python

malaya_speech/config/fastspeech2.py

ishine/malaya-speech

fd34afc7107af1656dff4b3201fa51dda54fde18

[ "MIT" ]

111

2020-08-31T04:58:54.000Z

2022-03-29T15:44:18.000Z

malaya_speech/config/fastspeech2.py

ishine/malaya-speech

fd34afc7107af1656dff4b3201fa51dda54fde18

[ "MIT" ]

14

2020-12-16T07:27:22.000Z

2022-03-15T17:39:01.000Z

malaya_speech/config/fastspeech2.py

ishine/malaya-speech

fd34afc7107af1656dff4b3201fa51dda54fde18

[ "MIT" ]

29

2021-02-09T08:57:15.000Z

2022-03-12T14:09:19.000Z

config = { 'n_speakers': 1, 'encoder_hidden_size': 384, 'encoder_num_hidden_layers': 4, 'encoder_num_attention_heads': 2, 'encoder_attention_head_size': 192, 'encoder_intermediate_size': 1024, 'encoder_intermediate_kernel_size': 3, 'encoder_hidden_act': 'mish', 'decoder_hidden_size': 384, 'decoder_num_hidden_layers': 4, 'decoder_num_attention_heads': 2, 'decoder_attention_head_size': 192, 'decoder_intermediate_size': 1024, 'decoder_intermediate_kernel_size': 3, 'decoder_hidden_act': 'mish', 'variant_prediction_num_conv_layers': 2, 'variant_predictor_filter': 256, 'variant_predictor_kernel_size': 3, 'variant_predictor_dropout_rate': 0.5, 'num_mels': 80, 'hidden_dropout_prob': 0.2, 'attention_probs_dropout_prob': 0.1, 'max_position_embeddings': 2048, 'initializer_range': 0.02, 'output_attentions': False, 'output_hidden_states': False, } config_v2 = { 'n_speakers': 1, 'encoder_hidden_size': 256, 'encoder_num_hidden_layers': 3, 'encoder_num_attention_heads': 2, 'encoder_attention_head_size': 16, 'encoder_intermediate_size': 1024, 'encoder_intermediate_kernel_size': 3, 'encoder_hidden_act': 'mish', 'decoder_hidden_size': 256, 'decoder_num_hidden_layers': 3, 'decoder_num_attention_heads': 2, 'decoder_attention_head_size': 16, 'decoder_intermediate_size': 1024, 'decoder_intermediate_kernel_size': 3, 'decoder_hidden_act': 'mish', 'variant_prediction_num_conv_layers': 2, 'variant_predictor_filter': 256, 'variant_predictor_kernel_size': 3, 'variant_predictor_dropout_rate': 0.5, 'num_mels': 80, 'hidden_dropout_prob': 0.2, 'attention_probs_dropout_prob': 0.1, 'max_position_embeddings': 2048, 'initializer_range': 0.02, 'output_attentions': False, 'output_hidden_states': False, }

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py

Python

Python/Tests/TestData/Grammar/ImportStmt.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/Grammar/ImportStmt.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

Python/Tests/TestData/Grammar/ImportStmt.py

nanshuiyu/pytools

9f9271fe8cf564b4f94e9456d400f4306ea77c23

[ "Apache-2.0" ]

null

import sys import sys, fob import sys as oar import sys as oar, fob as baz import sys.fob import sys.fob as oar

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7

34b21aad623e158de3c561970739153499a8cf0d

27,502

py

Python

bevodevo/policies/mlps.py

riveSunder/bevodevo

d45ec97b825489a9e94f79843e7169daa9491264

[ "MIT" ]

4

2020-12-02T22:28:29.000Z

2020-12-28T05:42:06.000Z

bevodevo/policies/mlps.py

riveSunder/bevodevo

d45ec97b825489a9e94f79843e7169daa9491264

[ "MIT" ]

5

2020-12-27T16:43:42.000Z

2021-11-11T21:00:15.000Z

bevodevo/policies/mlps.py

riveSunder/bevodevo

d45ec97b825489a9e94f79843e7169daa9491264

[ "MIT" ]

null

from abc import ABC, abstractmethod from collections import OrderedDict from functools import reduce import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import gym import matplotlib.pyplot as plt from bevodevo.policies.base import Policy class MLPPolicy(nn.Module): def __init__(self, args, discrete=False, use_grad=False): super(MLPPolicy, self).__init__() self.use_grad = use_grad # architecture params self.input_dim = args["dim_x"] self.action_dim = args["dim_y"] self.hid_dims = args["dim_h"] self.hid_dims = [self.hid_dims] if type(self.hid_dims) is not list else self.hid_dims self.activations = nn.ReLU #args["activations"] self.discrete = discrete self.use_bias = False self.var = 1.e-2 if type(self.activations) == list: if len(self.activations) <= (len(self.hid_dims)+1): # use no activation after list of act fns are used up for ii in range(len(self.activations), len(self.hid_dims)+1): # identity function for layers missing activations self.activations.append(lambda x: x) elif len(self.activations) >= (len(self.hid_dims)+1): print("warning: activation list has {} functions but MLP has only {} layers"\ .format(len(self.activations), len(self.hid_dims)+1)) print("... truncating action function list") self.activations = self.activations[:len(self.hid_dims)] else: self.activations = [self.activations] * len(self.hid_dims) if self.discrete: pass #self.activations.append(lambda x: x) else: self.activations.append(nn.Tanh) self.init_params() if args["params"] is not None: self.set_params(args["params"]) def init_params(self): self.layers = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.input_dim, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.activations[0]())\ ])) for jj in range(1, len(self.hid_dims)-1): self.layers.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.layers.add_module("activation{}".format(jj), self.activations[jj]()) self.layers.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.action_dim, bias=self.use_bias)) if self.discrete: pass else: self.layers.add_module("output_activation",\ self.activations[-1]()) for param in self.layers.parameters(): param.requires_grad = self.use_grad self.num_params = self.get_params().shape[0] def forward(self, x): if type(x) is not torch.Tensor: x = torch.tensor(x) x = x.to(torch.float32) #if True in [p.is_cuda for p in self.parameters()]: # x = x.to(torch.device("cuda")) if len(x.shape) == 1: x = x.unsqueeze(0) x = self.layers(x) return x def get_action(self, x): y = self.forward(x) if self.discrete: act = torch.argmax(y, dim=-1) else: act = y return act.detach().cpu().numpy() def get_params(self): params = np.array([]) for param in self.layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def set_params(self, my_params): param_start = 0 for name, param in self.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), requires_grad=self.use_grad), \ requires_grad=self.use_grad) def reset(self): pass class HebbianMLP(MLPPolicy): def __init__(self, args, discrete=False, use_grad=False, plastic=True): self.plastic = plastic self.lr_layers = None self.e_min = -1. self.e_max = 1. super(HebbianMLP, self).__init__(args, discrete, use_grad) self.set_traces() def set_traces(self): self.dim_list = [self.input_dim] self.dim_list.extend(self.hid_dims) self.dim_list.append(self.action_dim) if self.plastic: self.init_traces() else: self.clear_nodes() def init_traces(self): # clear node activations, start at 0 everywhere self.clear_nodes() # initialize learning rate values self.lr_layers = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.input_dim, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.activations[0]())\ ])) self.eligibility_layers = [torch.zeros(self.input_dim, self.hid_dims[0])] for jj in range(1, len(self.hid_dims)-1): self.lr_layers.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.lr_layers.add_module("activation{}".format(jj), self.activations[jj]()) self.eligibility_layers.append(torch.zeros(self.hid_dims[jj], self.hid_dims[jj+1])) self.lr_layers.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.action_dim, bias=self.use_bias)) self.eligibility_layers.append(torch.zeros(self.hid_dims[-1], self.action_dim)) if self.discrete: pass else: self.lr_layers.add_module("output_activation",\ self.activations[-1]()) for param in self.lr_layers.parameters(): param.requires_grad = self.use_grad self.num_params = self.get_params().shape[0] def clear_nodes(self): self.nodes = [torch.zeros(1,elem) for elem in self.dim_list] def clear_traces(self): if self.plastic: self.eligibility_layers = [0.0 * elem for elem in self.eligibility_layers] def forward(self, x): if type(x) is not torch.Tensor: x = torch.tensor(x) x = x.to(torch.float32) if len(x.shape) == 1: x = x.unsqueeze(0) trace_count = 0 self.nodes[trace_count] = x.clone()#.squeeze() for name, module in self.layers.named_modules(): if "layer" in name: trace_count += 1 x = module(x) self.nodes[trace_count] = x.clone() elif "activation" in name: x = module(x) if self.plastic: self.update() return x def update(self): num_layers = len(list(self.layers.named_parameters())) layer_count = 0 for lr_param, param in zip(list(self.lr_layers.named_parameters()), list(self.layers.named_parameters())): layer_dim_x, layer_dim_y = param[1].shape[1], param[1].shape[0] self.eligibility_layers[layer_count] += torch.matmul(self.nodes[layer_count].T, self.nodes[layer_count+1]) self.eligibility_layers[layer_count] = torch.clamp(self.eligibility_layers[layer_count], min=self.e_min, max=self.e_max) for ii in range(layer_dim_x): for jj in range(layer_dim_y): param[1][jj,ii] = param[1][jj,ii] + lr_param[1][jj,ii] * self.eligibility_layers[layer_count][ii,jj] layer_count += 1 def get_params(self): params = np.array([]) for param in self.layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) if self.lr_layers is not None and self.plastic: for param in self.lr_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def set_params(self, my_params): param_start = 0 for name, param in self.layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), requires_grad=self.use_grad), \ requires_grad=self.use_grad) if self.plastic: for name, param in self.lr_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), requires_grad=self.use_grad), \ requires_grad=self.use_grad) def reset(self): self.clear_nodes() self.clear_traces() class HebbianMetaMLP(HebbianMLP): def __init__(self, args, discrete=False, use_grad=False): super(HebbianMetaMLP, self).__init__(args, discrete, use_grad) self.plastic = True self.reset() def get_params(self): params = np.array([]) if self.lr_layers is not None and self.plastic: for param in self.lr_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def set_params(self, my_params): param_start = 0 for name, param in self.lr_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) def reset(self): self.init_params() self.clear_nodes() self.clear_traces() class ABCHebbianMLP(HebbianMLP): def __init__(self, args, discrete=False, use_grad=False, plastic=True): super(ABCHebbianMLP, self).__init__(args, discrete, use_grad, plastic) def init_traces(self): # clear node activations, start at 0 everywhere self.clear_nodes() # learning rules are encoded by lr, A, B, C. # \delta W_{ij} = lr * (A * o_i*o_j + B * o_i + C * o_j) # initialize learning rate values self.lr_layers = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.input_dim, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.activations[0]())\ ])) # Hebbian coefficient A self.a_layers = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.input_dim, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.activations[0]())\ ])) # pre-synaptic coefficient B self.b_layers = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.input_dim, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.activations[0]())\ ])) # post-synaptic coefficient C self.c_layers = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.input_dim, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.activations[0]())\ ])) self.eligibility_layers = [torch.zeros(self.input_dim, self.hid_dims[0])] for jj in range(1, len(self.hid_dims)-1): self.lr_layers.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.a_layers.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.b_layers.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.c_layers.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.eligibility_layers.append(torch.zeros(self.hid_dims[jj], self.hid_dims[jj+1])) self.lr_layers.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.action_dim, bias=self.use_bias)) self.a_layers.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.action_dim, bias=self.use_bias)) self.b_layers.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.action_dim, bias=self.use_bias)) self.c_layers.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.action_dim, bias=self.use_bias)) self.eligibility_layers.append(torch.zeros(self.hid_dims[-1], self.action_dim)) for params in [self.lr_layers.parameters(), self.a_layers.parameters(), \ self.b_layers.parameters(), self.c_layers.parameters()]: for param in params: #self.lr_layers.parameters(): param.requires_grad = self.use_grad self.num_params = self.get_params().shape[0] def update(self): num_layers = len(list(self.layers.named_parameters())) layer_count = 0 for lr_param, param, A, B, C in zip(\ list(self.lr_layers.named_parameters()),\ list(self.layers.named_parameters()),\ list(self.a_layers.named_parameters()),\ list(self.b_layers.named_parameters()),\ list(self.c_layers.named_parameters())): layer_dim_x, layer_dim_y = param[1].shape[1], param[1].shape[0] self.eligibility_layers[layer_count] += torch.matmul(self.nodes[layer_count].T, self.nodes[layer_count+1]) self.eligibility_layers[layer_count] = torch.clamp(self.eligibility_layers[layer_count], min=self.e_min, max=self.e_max) for ii in range(layer_dim_x): for jj in range(layer_dim_y): param[1][jj,ii] = torch.clamp(param[1][jj,ii] + lr_param[1][jj,ii] \ * (\ A[1][jj,ii] * self.eligibility_layers[layer_count][ii,jj] \ +B[1][jj,ii] * self.nodes[layer_count][:,ii] \ +C[1][jj,ii] * self.nodes[layer_count+1][:,jj] \ ), min=-10, max=10) layer_count += 1 def get_params(self): params = np.array([]) for param in self.layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) if self.lr_layers is not None and self.plastic: for param in self.lr_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) for param in self.a_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) for param in self.b_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) for param in self.c_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def set_params(self, my_params): param_start = 0 for name, param in self.layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), requires_grad=self.use_grad), \ requires_grad=self.use_grad) if self.plastic: for name, param in self.lr_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape),\ requires_grad=self.use_grad), \ requires_grad=self.use_grad) for name, param in self.a_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) for name, param in self.b_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) for name, param in self.c_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) class ABCHebbianMetaMLP(ABCHebbianMLP): def __init__(self, args, discrete=False, use_grad=False): super(ABCHebbianMetaMLP, self).__init__(args, discrete, use_grad) self.plastic = True self.set_traces() def get_params(self): params = np.array([]) if self.lr_layers is not None and self.plastic: for param in self.lr_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) for param in self.a_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) for param in self.b_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) for param in self.c_layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def set_params(self, my_params): param_start = 0 if self.plastic: for name, param in self.lr_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape),\ requires_grad=self.use_grad), \ requires_grad=self.use_grad) for name, param in self.a_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) for name, param in self.b_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) for name, param in self.c_layers.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), \ requires_grad=self.use_grad), \ requires_grad=self.use_grad) def reset(self): self.init_params() self.clear_nodes() self.clear_traces() class CPPNHebbianMLP(HebbianMLP): def __init__(self, args, discrete=False, use_grad=False): super(CPPNHebbianMLP, self).__init__(args, discrete, use_grad) self.plastic = False self.set_traces() self.init_cppn() self.set_params(self.get_cppn_params()) def init_cppn(self): self.cppn_in = 6 self.cppn_out = 2 self.cppn_h = [32] self.cppn_act = [nn.LeakyReLU, lambda x: x] self.cppn_out_act = [nn.Tanh, nn.Sigmoid] self.cppn = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.cppn_in, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.cppn_act[0]())\ ])) for jj in range(1, len(self.cppn_h)-1): self.cppn.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.cppn.add_module("activation{}".format(jj), self.cppn_act[jj]()) self.cppn.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.cppn_out, bias=self.use_bias)) for param in self.cppn.parameters(): param.requires_grad = self.use_grad self.num_params = self.get_cppn_params().shape[0] def build_mlp(self): num_layers = len(list(self.layers.named_parameters())) trace_count = 0 for layer_num, param in enumerate(list(self.layers.named_parameters())): layer_dim_x, layer_dim_y = param[1].shape[1], param[1].shape[0] for ii in range(layer_dim_x): for jj in range(layer_dim_y): cppn_input = torch.Tensor([layer_num/num_layers - 0.5,\ ii/layer_dim_x - 0.5, \ jj/ layer_dim_y - 0.5,\ self.nodes[trace_count][0,ii],\ self.nodes[trace_count+1][0,jj],\ param[1][jj,ii]\ ])\ .unsqueeze(0) weight = self.cppn.forward(cppn_input) param[1][jj,ii] = torch.tanh(weight[:,0]) * torch.sigmoid(weight[:,1]) trace_count += 1 def get_action(self, x): self.build_mlp() y = self.forward(x) if self.discrete: act = torch.argmax(y, dim=-1) else: act = y return act.detach().cpu().numpy() def set_params(self, my_params): # set the cppn params, which are then used to set the mlp params param_start = 0 for name, param in self.cppn.named_parameters(): param.requires_grad = False param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), requires_grad=self.use_grad), \ requires_grad=self.use_grad) # self.build_mlp() def get_cppn_params(self): params = np.array([]) for param in self.cppn.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def get_params(self): params = np.array([]) for param in self.layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params class CPPNMLPPolicy(MLPPolicy): def __init__(self, args, discrete=False, use_grad=False): super(CPPNMLPPolicy, self).__init__(args, discrete, use_grad) """ CPPN input (3) by dim_h (32) by output (2) input is the layer + weight coordinate for each weight output is a gating function and weight strength, the product of these ouputs defines weight values The CPPN defines the MLP, which is the actually policy dim_x () by dim_hp (64) by dim_y dim_x and dim_y are the observation space and action space dimensions. """ self.use_bias = False self.init_cppn() self.set_params(self.get_cppn_params()) def init_cppn(self): self.cppn_in = 3 self.cppn_out = 2 self.cppn_h = [32] self.cppn_act = [nn.LeakyReLU, lambda x: x] self.cppn_out_act = [nn.Tanh, nn.Sigmoid] self.cppn = nn.Sequential(OrderedDict([\ ("layer0", nn.Linear(self.cppn_in, self.hid_dims[0], bias=self.use_bias)),\ ("activation_0", self.cppn_act[0]())\ ])) for jj in range(1, len(self.cppn_h)-1): self.cppn.add_module("layer{}".format(jj),\ nn.Linear(self.hid_dims[jj], self.hid_dims[jj+1], bias=self.use_bias)) self.cppn.add_module("activation{}".format(jj), self.cppn_act[jj]()) self.cppn.add_module("output_layer",\ nn.Linear(self.hid_dims[-1], self.cppn_out, bias=self.use_bias)) for param in self.cppn.parameters(): param.requires_grad = self.use_grad self.num_params = self.get_cppn_params().shape[0] def build_mlp(self): num_layers = len(list(self.layers.named_parameters())) for layer_num, param in enumerate(list(self.layers.named_parameters())): layer_dim_x, layer_dim_y = param[1].shape[1], param[1].shape[0] for ii in range(layer_dim_x): for jj in range(layer_dim_y): cppn_input = torch.Tensor([layer_num/num_layers - 0.5,\ ii/layer_dim_x - 0.5, \ jj/ layer_dim_y - 0.5])\ .unsqueeze(0) weight = self.cppn.forward(cppn_input) param[1][jj,ii] = torch.tanh(weight[:,0]) * torch.sigmoid(weight[:,1]) def set_params(self, my_params): # set the cppn params, which are then used to set the mlp params param_start = 0 for name, param in self.cppn.named_parameters(): param_stop = param_start + reduce(lambda x,y: x*y, param.shape) param[:] = torch.nn.Parameter(torch.tensor(\ my_params[param_start:param_stop].reshape(param.shape), requires_grad=self.use_grad), \ requires_grad=self.use_grad) # self.build_mlp() def get_cppn_params(self): params = np.array([]) for param in self.cppn.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params def get_params(self): params = np.array([]) for param in self.layers.named_parameters(): params = np.append(params, param[1].detach().numpy().ravel()) return params if __name__ == "__main__": # run tests args = {} args["dim_x"] = 6 args["dim_y"] = 1 args["dim_h"] = 16 args["params"] = None temp = MLPPolicy(args) temp = HebbianMLP(args) temp = ABCHebbianMLP(args) temp = CPPNHebbianMLP(args) temp = CPPNMLPPolicy(args) print("OK")

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1

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null

0

7

34c67bba4ac7fc80130acdcb490d4af6f2cff1e0

165

py

Python

directory_components/forms/__init__.py

MichaelWalker/directory-components

9dac4e9d7fd477cd272e09440f2c9b7d1ef76e1e

[ "MIT" ]

2

2019-06-24T20:22:23.000Z

2019-07-26T12:51:31.000Z

directory_components/forms/__init__.py

MichaelWalker/directory-components

9dac4e9d7fd477cd272e09440f2c9b7d1ef76e1e

[ "MIT" ]

278

2018-02-21T11:49:46.000Z

2021-09-16T08:27:54.000Z

directory_components/forms/__init__.py

MichaelWalker/directory-components

9dac4e9d7fd477cd272e09440f2c9b7d1ef76e1e

[ "MIT" ]

3

2019-05-02T15:26:26.000Z

2020-02-18T17:47:57.000Z

from directory_components.forms.fields import * # NOQA from directory_components.forms.forms import * # NOQA from directory_components.forms.widgets import * # NOQA

41.25

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8

9ba5a2d15a754fb66e76293e2f656b5c47d8893e

3,531

py

Python

tests/test_networks.py

brunocroh/wifidog-auth-flask

a215cd9ccebc061723a56ce05db62821354f6785

[ "MIT" ]

18

2016-04-19T08:04:46.000Z

2021-12-15T06:45:04.000Z

tests/test_networks.py

brunocroh/wifidog-auth-flask

a215cd9ccebc061723a56ce05db62821354f6785

[ "MIT" ]

3

2016-12-02T14:40:52.000Z

2022-01-15T01:03:45.000Z

tests/test_networks.py

brunocroh/wifidog-auth-flask

a215cd9ccebc061723a56ce05db62821354f6785

[ "MIT" ]

8

2015-11-30T13:21:44.000Z

2018-12-31T05:56:41.000Z

from tests import TestCase class TestNetworks(TestCase): def test_networks_index_as_anonymous(self): self.assertLogin('/networks') def test_networks_index_as_gateway(self): self.login('main-gateway1@example.com', 'admin') self.assertRedirect('/networks') def test_networks_index_as_network(self): self.login('main-network@example.com', 'admin') self.assertRedirect('/networks') def test_networks_index_as_super(self): self.login('super-admin@example.com', 'admin') html = self.assertOk('/networks') networks = html.findall('//table[@id="networks"]/tbody/tr') self.assertEqual(2, len(networks)) self.assertEqual('main-network', networks[0].get('data-id')) def test_networks_new_as_anonymous(self): self.assertLogin('/networks/new') def test_networks_new_as_gateway(self): self.login('main-gateway1@example.com', 'admin') self.assertForbidden('/networks/new') def test_networks_new_as_network(self): self.login('main-network@example.com', 'admin') self.assertForbidden('/networks/new') def test_networks_new_as_super(self): self.login('super-admin@example.com', 'admin') response = self.client.get('/networks/new') self.assertEqual(200, response.status_code) def test_networks_store_as_anonymous(self): self.assertLoginPost('/networks/new', data={'id': 'network', 'title': 'Network'}) def test_networks_store_as_gateway(self): self.login('main-gateway1@example.com', 'admin') self.assertForbiddenPost('/networks/new', data={'id': 'network', 'title': 'Network'}) def test_networks_store_as_network(self): self.login('main-network@example.com', 'admin') self.assertForbiddenPost('/networks/new', data={'id': 'network', 'title': 'Network'}) def test_networks_store_as_super(self): self.login('super-admin@example.com', 'admin') response = self.client.post('/networks/new', data={'id': 'network', 'title': 'Network'}, follow_redirects=True) self.assertEqual(200, response.status_code) def test_networks_edit_as_anonymous(self): self.assertLogin('/networks/main-network') def test_networks_edit_as_gateway(self): self.login('main-gateway1@example.com', 'admin') self.assertForbidden('/networks/main-network') def test_networks_edit_as_network(self): self.login('main-network@example.com', 'admin') self.assertForbidden('/networks/main-network') def test_networks_edit_as_super(self): self.login('super-admin@example.com', 'admin') response = self.client.get('/networks/main-network') self.assertEqual(200, response.status_code) def test_networks_update_as_anonymous(self): self.assertLoginPost('/networks/main-network', {'id': 'network', 'title': 'Network'}) def test_networks_update_as_gateway(self): self.login('main-gateway1@example.com', 'admin') self.assertForbiddenPost('/networks/main-network') def test_networks_update_as_network(self): self.login('main-network@example.com', 'admin') self.assertForbiddenPost('/networks/main-network') def test_networks_update_as_super(self): self.login('super-admin@example.com', 'admin') response = self.client.post('/networks/main-network', data={'id': 'network', 'title': 'Network'}, follow_redirects=True) self.assertEqual(200, response.status_code)

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128

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0.073087

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1

0.3125

false

0

0.015625

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0

null

0

1

0

null

0

1

0

7

fd63a3e7beeeb837f486e5465ff5cc824eb15c4e

214,536

py

Python

Account/app/tests/test_sdk.py

TamSzaGot/mydata-sdk

9c8afb75077f0b993819aa534b904501a8112f76

[ "MIT" ]

4

2018-04-21T00:46:40.000Z

2019-12-03T13:52:03.000Z

Account/app/tests/test_sdk.py

TamSzaGot/mydata-sdk

9c8afb75077f0b993819aa534b904501a8112f76

[ "MIT" ]

1

2019-01-09T10:45:23.000Z

Account/app/tests/test_sdk.py

TamSzaGot/mydata-sdk

9c8afb75077f0b993819aa534b904501a8112f76

[ "MIT" ]

4

2018-04-21T01:12:12.000Z

2020-09-24T06:19:29.000Z

# -*- coding: utf-8 -*- """ Test Cases for Internal API __author__ = "Jani Yli-Kantola" __copyright__ = "" __credits__ = ["Harri Hirvonsalo", "Aleksi Palomäki"] __license__ = "MIT" __version__ = "1.3.0" __maintainer__ = "Jani Yli-Kantola" __contact__ = "https://github.com/HIIT/mydata-stack" __status__ = "Development" """ import unittest from base64 import b64encode from random import randint from flask import json from app import create_app from app.tests.controller import is_json, validate_json, account_create, default_headers, \ generate_sl_init_sink, generate_sl_init_source, gen_jwk_key, generate_sl_payload, \ generate_sl_store_payload, generate_sls_store_payload, generate_signed_ssr_store_payload, generate_consent_payload, \ generate_consent_status_payload, generate_consent_status_payload_signed from app.tests.schemas.schema_account import schema_account_create, schema_account_auth, schema_account_get, schema_account_sdk_info from app.tests.schemas.schema_authorisation import schema_give_consent, schema_consent_status_change, \ schema_consent_listing, schema_consent_status_listing, schema_consent_status, schema_consent from app.tests.schemas.schema_data_connection import schema_authorisation_token_data from app.tests.schemas.schema_error import schema_request_error_detail_as_str, schema_request_error_detail_as_dict from app.tests.schemas.schema_service_linking import schema_slr_init, schema_slr_sign, \ schema_slr_store, schema_slr_listing, schema_slr, schema_slr_status_listing, schema_slr_status, schema_surrogate from app.tests.schemas.schema_system import schema_db_clear, system_running, schema_sdk_auth, schema_system_status class SdkTestCase(unittest.TestCase): API_PREFIX_INTERNAL = "/account/api/v1.3/internal" API_PREFIX_EXTERNAL = "/account/api/v1.3/external" SDK_USERNAME = "test_sdk" SDK_PASSWORD = "test_sdk_pw" # Operator info OPERATOR_ID = str(randint(100, 1000)) OPERATOR_KEY_OBJECT, OPERATOR_KEY_PRIVATE_JSON, OPERATOR_KEY_PUBLIC_JSON, OPERATOR_KID = gen_jwk_key(prefix="operator") OPERATOR_KEY_PUBLIC = json.loads(OPERATOR_KEY_PUBLIC_JSON) OPERATOR_KEY_PRIVATE = json.loads(OPERATOR_KEY_PRIVATE_JSON) # Sink Service SINK_SERVICE_ID = "srv_sink-" + str(randint(100, 1000)) SINK_SURROGATE_ID = "sink-surrogate-" + str(randint(100, 1000)) SINK_KEY_OBJECT, SINK_KEY_PRIVATE_JSON, SINK_KEY_PUBLIC_JSON, SINK_KID = gen_jwk_key(prefix="srv_sink") SINK_KEY_PRIVATE = json.loads(SINK_KEY_PRIVATE_JSON) SINK_KEY_PUBLIC = json.loads(SINK_KEY_PUBLIC_JSON) # Source Service SOURCE_SERVICE_ID = "srv_source-" + str(randint(100, 1000)) SOURCE_SURROGATE_ID = "source-surrogate-" + str(randint(100, 1000)) SOURCE_KEY_OBJECT, SOURCE_KEY_PRIVATE_JSON, SOURCE_KEY_PUBLIC_JSON, SOURCE_KID = gen_jwk_key(prefix="srv_sink") SOURCE_KEY_PRIVATE = json.loads(SOURCE_KEY_PRIVATE_JSON) SOURCE_KEY_PUBLIC = json.loads(SOURCE_KEY_PUBLIC_JSON) def setUp(self): """ TestCase Set Up :return: """ app = create_app() app.config['TESTING'] = True app = app.test_client() self.app = app def tearDown(self): """ TestCase Tear Down :return: """ pass ########## ########## def test_system_running(self): """ Test system running :return: """ url = '/' response = self.app.get(url) unittest.TestCase.assertEqual(self, response.status_code, 200) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, system_running)) ########## ########## def test_system_status(self): """ Test system running :return: """ url = '/system/status/' response = self.app.get(url) unittest.TestCase.assertEqual(self, response.status_code, 200) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_system_status)) ########## ########## def test_system_routes(self): """ Test system running :return: """ url = '/system/routes/' response = self.app.get(url) unittest.TestCase.assertEqual(self, response.status_code, 200) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) ########## ########## def test_sdk_auth(self): """ SDK authentication :return: """ request_headers = default_headers request_headers['Authorization'] = 'Basic ' + b64encode("{0}:{1}".format(self.SDK_USERNAME, self.SDK_PASSWORD)) url = self.API_PREFIX_INTERNAL + '/auth/sdk/' response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_sdk_auth)) response_json = json.loads(response.data) api_key = response_json["Api-Key-Sdk"] return api_key ########## ########## def test_clear_db_positive(self): """ Test database clearing :return: """ response = self.app.get('/system/db/clear/') unittest.TestCase.assertEqual(self, response.status_code, 200) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_db_clear)) ########## ########## def test_account_create_positive(self): """ Test Account creation. Positive case :return: """ account_json, account_username, account_password = account_create() response = self.app.post(self.API_PREFIX_EXTERNAL + '/accounts/', data=account_json, headers=default_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_account_create)) return account_username, account_password ########## ########## def test_account_authentication(self): """ Test user authentication :return: """ account_username, account_password = self.test_account_create_positive() request_headers = default_headers request_headers['Authorization'] = 'Basic ' + b64encode("{0}:{1}".format(account_username, account_password)) url = self.API_PREFIX_EXTERNAL + '/auth/user/' response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_account_auth)) response_json = json.loads(response.data) api_key = response_json["Api-Key-User"] account_id = response_json["account_id"] return api_key, account_id ########## ########## def test_account_fetch(self): """ Fetch Account entry :return: """ account_api_key, account_id = self.test_account_authentication() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) url = self.API_PREFIX_EXTERNAL + "/accounts/" + str(account_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_account_get)) ########## ########## def test_account_delete(self): """ Test user deletion :return: """ account_api_key, account_id = self.test_account_authentication() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) url = self.API_PREFIX_EXTERNAL + "/accounts/" + str(account_id) + "/" response = self.app.delete(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 204, msg=response.data) ########## ########## def test_sdk_account_info(self): """ Verify User-API-Key belongs to specified user :return: account_id, account_api_key, sdk_api_key, slr_id """ account_api_key, account_id = self.test_account_authentication() sdk_api_key = self.test_sdk_auth() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/auth/sdk/account/" + str(account_id) + "/info/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_account_sdk_info)) return account_id, account_api_key, sdk_api_key ########## ########## def test_slr_init_sink(self): """ Test Sink SLR init :return: account_id, account_api_key, sdk_api_key, slr_id """ account_api_key, account_id = self.test_account_authentication() sdk_api_key = self.test_sdk_auth() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/sink/" payload, code, slr_id, pop_key = generate_sl_init_sink() response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_init)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_init_sink_misformatted(self): """ Test Sink SLR init with misformatted pop_key :return: """ account_api_key, account_id = self.test_account_authentication() sdk_api_key = self.test_sdk_auth() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/sink/" payload, code, slr_id, pop_key = generate_sl_init_sink(misformatted_payload=True) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) ########## ########## def test_slr_init_sink_duplicate(self): """ Test Sink SLR init duplicate :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id_original = self.test_slr_init_sink() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/sink/" payload, code, slr_id, pop_key = generate_sl_init_sink(slr_id=slr_id_original) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 409, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_init_source(self): """ Test Sink SLR init :return: account_id, account_api_key, sdk_api_key, slr_id """ account_api_key, account_id = self.test_account_authentication() sdk_api_key = self.test_sdk_auth() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/source/" payload, code, slr_id = generate_sl_init_source() response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_init)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_init_source_misformatted(self): """ Test Source SLR init with misformatted pop_key :return: """ account_api_key, account_id = self.test_account_authentication() sdk_api_key = self.test_sdk_auth() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/source/" payload, code, slr_id = generate_sl_init_source(misformatted_payload=True) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) ########## ########## def test_slr_init_source_duplicate(self): """ Test Source SLR init duplicate :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id_original = self.test_slr_init_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/source/" payload, code, slr_id = generate_sl_init_source(slr_id=slr_id_original) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 409, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_sign_sink(self): """ Test Sink SLR signing :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id = self.test_slr_init_sink() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/" payload = generate_sl_payload( slr_id=slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SINK_SERVICE_ID, surrogate_id=self.SINK_SURROGATE_ID ) response = self.app.patch(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_sign)) return account_id, account_api_key, sdk_api_key, slr_id, response.data ########## ########## def test_slr_sign_sink_malformed(self): """ Test Sink malformed SLR signing :return: """ account_id, account_api_key, sdk_api_key, slr_id = self.test_slr_init_sink() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/" payload = generate_sl_payload( slr_id=slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SINK_SERVICE_ID, surrogate_id=self.SINK_SURROGATE_ID, misformatted_payload=True ) response = self.app.patch(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) ########## ########## def test_slr_sign_sink_wrong_id(self): """ Test Sink SLR signing with wrong SLR id :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id = self.test_slr_init_sink() slr_id = "wrong-" + slr_id request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/" payload = generate_sl_payload( slr_id=slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SINK_SERVICE_ID, surrogate_id=self.SINK_SURROGATE_ID ) response = self.app.patch(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id, response.data ########## ########## def test_slr_store_sink(self): """ Test Sink SLR storing :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_sink() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SINK_SURROGATE_ID, service_key=self.SINK_KEY_OBJECT, service_kid=self.SINK_KID ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_store)) return account_id, account_api_key, sdk_api_key, slr_id, response.data ########## ########## def test_slr_store_sink_malformed(self): """ Test Sink SLR storing - Malformed :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_sink() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SINK_SURROGATE_ID, service_key=self.SINK_KEY_OBJECT, service_kid=self.SINK_KID, misformatted_payload=True ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, slr_id, response.data ########## ########## def test_slr_store_sink_malformed_signature(self): """ Test Sink SLR storing - Signature verification fails :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_sink() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SINK_SURROGATE_ID, service_key=self.SINK_KEY_OBJECT, service_kid=self.SINK_KID, misformatted_signature=True ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_sign_source(self): """ Test Source SLR signing :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id = self.test_slr_init_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/" payload = generate_sl_payload( slr_id=slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SOURCE_SERVICE_ID, surrogate_id=self.SOURCE_SURROGATE_ID ) response = self.app.patch(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_sign)) return account_id, account_api_key, sdk_api_key, slr_id, response.data ########## ########## def test_slr_sign_source_malformed(self): """ Test Source malformed SLR signing :return: """ account_id, account_api_key, sdk_api_key, slr_id = self.test_slr_init_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/" payload = generate_sl_payload( slr_id=slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SOURCE_SERVICE_ID, surrogate_id=self.SOURCE_SURROGATE_ID, misformatted_payload=True ) response = self.app.patch(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) ########## ########## def test_slr_sign_source_wrong_id(self): """ Test Source SLR signing with wrong SLR id :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id = self.test_slr_init_source() slr_id = "wrong-" + slr_id request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/" payload = generate_sl_payload( slr_id=slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SOURCE_SERVICE_ID, surrogate_id=self.SOURCE_SURROGATE_ID ) response = self.app.patch(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id, response.data ########## ########## def test_slr_store_source(self): """ Test Source SLR storing :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_source() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SOURCE_SURROGATE_ID, service_key=self.SOURCE_KEY_OBJECT, service_kid=self.SOURCE_KID ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_store)) return account_id, account_api_key, sdk_api_key, slr_id, ssr_id ########## ########## def test_slr_store_source_malformed(self): """ Test Source SLR storing - Malformed :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_source() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SOURCE_SURROGATE_ID, service_key=self.SOURCE_KEY_OBJECT, service_kid=self.SOURCE_KID, misformatted_payload=True ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_store_source_malformed_signature(self): """ Test Source SLR storing - Signature verification fails :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_source() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SOURCE_SURROGATE_ID, service_key=self.SOURCE_KEY_OBJECT, service_kid=self.SOURCE_KID, misformatted_signature=True ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_slr_store_wrong_id(self): """ Test SLR storing with wrong ID :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slr_data = self.test_slr_sign_source() slr_data = json.loads(slr_data) request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/store/" payload, ssr_id = generate_sl_store_payload( slr_id=slr_id, slr_signed=slr_data['data'], surrogate_id=self.SINK_SURROGATE_ID, service_key=self.SOURCE_KEY_OBJECT, service_kid=self.SOURCE_KID ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing(self): """ Test Fetch SLR listing :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_listing)) # ID verification verification_id_array = [slr_id] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr(self): """ Test Fetch SLR :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id = self.test_fetch_slr_listing() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr)) # ID verification verification_id_array = [slr_id] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_wrong_id(self): """ Test Fetch SLR with wrong slr id :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) slr_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id_wrong) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_status_listing(self): """ Test Fetch SLR status listing :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_status_listing)) response_data_dict = json.loads(response.data) slsr_id = response_data_dict['data'][0]['id'] # ID verification verification_id_array = [ssr_id] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id, slsr_id ########## ########## def test_fetch_slr_status_listing_wrong_id(self): """ Test Fetch SLR status listing with wrong slr_id :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) slr_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id_wrong) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_status(self): """ Test Fetch SLR status by ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_status_listing() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id) + "/statuses/" + str(slsr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_status)) # ID verification verification_id_array = [slsr_id] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id, slsr_id ########## ########## def test_fetch_slr_status_wrong_id(self): """ Test Fetch SLR status by wrong ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_status_listing() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) slrs_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id) + "/statuses/" + str(slrs_id_wrong) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id, slsr_id ########## ########## def test_fetch_slr_last_status(self): """ Test Fetch SLR last status :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_status_listing() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_status)) response_data_dict = json.loads(response.data) slsr_id_from_response = response_data_dict['data']['id'] # ID verification verification_id_array = [slsr_id] id_to_verify = str(response_data_dict['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id, slsr_id_from_response ########## ########## def test_fetch_slr_last_status_wrong_id(self): """ Test Fetch SLR last status with wrong slr id :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_status_listing() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) slr_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(slr_id_wrong) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id, slsr_id ########## ########## def test_fetch_slr_listing_for_service(self): """ Test Fetch SLR listing for Service :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_listing)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_surrogate_id(self): """ Test Fetch SLR listing for Service with Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?surrogate_id=" + str(self.SOURCE_SURROGATE_ID) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_listing)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_account_id(self): """ Test Fetch SLR listing for Service with Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?account_id=" + str(account_id) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_listing)) # ID verification verification_id_array = [slr_id] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_account_id_and_surrogate_id(self): """ Test Fetch SLR listing for Service with Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?account_id=" + str(account_id) + "&surrogate_id=" + str(self.SOURCE_SURROGATE_ID) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_listing)) # ID verification verification_id_array = [slr_id] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_account_id_and_surrogate_id_wrong_surrogate_id(self): """ Test Fetch SLR listing for Service with Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?account_id=" + str(account_id) + "&surrogate_id=" + str(self.SINK_SURROGATE_ID) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_account_id_and_surrogate_id_wrong_account_id(self): """ Test Fetch SLR listing for Service with Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?account_id=" + str(slr_id) + "&surrogate_id=" + str(self.SOURCE_SURROGATE_ID) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_account_id_and_surrogate_id_wrong_account_id_and_surrogate_id(self): """ Test Fetch SLR listing for Service with Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?account_id=" + str(slr_id) + "&surrogate_id=" + str(self.SINK_SURROGATE_ID) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_with_wrong_surrogate_id(self): """ Test Fetch SLR listing for Service with wrong Surrogate ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/?surrogate_id=" + str(self.SINK_SURROGATE_ID) response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_listing_for_service_wrong_service_id(self): """ Test Fetch SLR listing for Service with wrong ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) service_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/services/" + service_id_wrong + "/servicelinks/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_for_service(self): """ Test Fetch SLR for Service :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(slr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr)) # ID verification verification_id_array = [slr_id] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_for_service_wrong_service_id(self): """ Test Fetch SLR for Service with wrong Service ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) service_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/services/" + service_id_wrong + "/servicelinks/" + str(slr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_slr_for_service_wrong_link_id(self): """ Test Fetch SLR for Service with wrong Link ID :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) slr_id_wrong = str(randint(100, 10000)) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(slr_id_wrong) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_ssr_store_source(self): """ Test Source SSR storing :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_last_status() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/statuses/" payload = generate_sls_store_payload( slr_id=slr_id, surrogate_id=self.SOURCE_SURROGATE_ID, prev_record_id=slsr_id, status="Removed" ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_status)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_ssr_store_source_malformed(self): """ Test Source SSR storing with malformed payload :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_last_status() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/statuses/" payload = generate_sls_store_payload( slr_id=slr_id, surrogate_id=self.SOURCE_SURROGATE_ID, prev_record_id=slsr_id, status="Removed", misformatted_payload=True ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_ssr_store_source_signed(self): """ Test Source SSR storing with signed SSR :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_last_status() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/statuses/signed/" payload = generate_signed_ssr_store_payload( slr_id=slr_id, surrogate_id=self.SOURCE_SURROGATE_ID, prev_record_id=slsr_id, status="Removed", operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 201, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_slr_status)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_ssr_store_source_signed_malformed(self): """ Test Source SSR storing with signed SSR with malformed payload :return: account_id, account_api_key, sdk_api_key, slr_id, response.data """ account_id, account_api_key, sdk_api_key, slr_id, slsr_id = self.test_fetch_slr_last_status() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + slr_id + "/statuses/signed/" payload = generate_signed_ssr_store_payload( slr_id=slr_id, surrogate_id=self.SOURCE_SURROGATE_ID, prev_record_id=slsr_id, status="Removed", operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT, misformatted_payload=True ) response = self.app.post(url, data=payload, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 400, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, slr_id ########## ########## def test_fetch_surrogate_object(self): """ Test Fetch Surrogate object :return: account_id, account_api_key, sdk_api_key, slr_id """ account_id, account_api_key, sdk_api_key, slr_id, ssr_id = self.test_slr_store_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/surrogates/" + str(self.SOURCE_SURROGATE_ID) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_surrogate)) return account_id, account_api_key, sdk_api_key, slr_id ################################################################################# # # # Complete flow testing with Service Linking, Authorisation and Data Connection # # # ################################################################################# ########## ########## def test_for_account_link_services(self): """ Link two services for same Account :return: account_id, user_api_key, sdk_api_key, source_slr_id, sink_slr_id """ # Create and Authenticate Account account_api_key, account_id = self.test_account_authentication() # Authenticate Operator-SDK sdk_api_key = self.test_sdk_auth() # Authentication for following requests request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) request_headers['Api-Key-User'] = str(account_api_key) # Service Link Init for Source Service source_slr_init_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/source/" source_slr_init_payload, source_slr_code, source_slr_id = generate_sl_init_source() source_slr_init_response = self.app.post(source_slr_init_url, data=source_slr_init_payload, headers=request_headers) unittest.TestCase.assertEqual(self, source_slr_init_response.status_code, 201, msg=source_slr_init_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=source_slr_init_response.data), msg=source_slr_init_response.data) unittest.TestCase.assertTrue(self, validate_json(source_slr_init_response.data, schema_slr_init)) # Service Link Init for Sink Service sink_slr_init_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/init/sink/" sink_slr_init_payload, sink_slr_code, sink_slr_id, sink_slr_pop_key = generate_sl_init_sink() sink_slr_init_response = self.app.post(sink_slr_init_url, data=sink_slr_init_payload, headers=request_headers) unittest.TestCase.assertEqual(self, sink_slr_init_response.status_code, 201, msg=sink_slr_init_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=sink_slr_init_response.data), msg=sink_slr_init_response.data) unittest.TestCase.assertTrue(self, validate_json(sink_slr_init_response.data, schema_slr_init)) # Account Owner's signature for Service Link of Source Service source_slr_sign_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/" source_slr_sign_payload = generate_sl_payload( slr_id=source_slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SOURCE_SERVICE_ID, surrogate_id=self.SOURCE_SURROGATE_ID ) source_slr_sign_response = self.app.patch(source_slr_sign_url, data=source_slr_sign_payload, headers=request_headers) unittest.TestCase.assertEqual(self, source_slr_sign_response.status_code, 201, msg=source_slr_sign_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=source_slr_sign_response.data), msg=source_slr_sign_response.data) unittest.TestCase.assertTrue(self, validate_json(source_slr_sign_response.data, schema_slr_sign)) # Account Owner's signature for Service Link of Sink Service sink_slr_sign_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(sink_slr_id) + "/" sink_slr_sign_payload = generate_sl_payload( slr_id=sink_slr_id, operator_id=self.OPERATOR_ID, operator_key=self.OPERATOR_KEY_PUBLIC, service_id=self.SINK_SERVICE_ID, surrogate_id=self.SINK_SURROGATE_ID ) sink_slr_sign_response = self.app.patch(sink_slr_sign_url, data=sink_slr_sign_payload, headers=request_headers) unittest.TestCase.assertEqual(self, sink_slr_sign_response.status_code, 201, msg=sink_slr_sign_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=sink_slr_sign_response.data), msg=sink_slr_sign_response.data) unittest.TestCase.assertTrue(self, validate_json(sink_slr_sign_response.data, schema_slr_sign)) # Store Service Link of Source Service source_slr_store_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/store/" source_slr_store_payload, ssr_id = generate_sl_store_payload( slr_id=source_slr_id, slr_signed=json.loads(source_slr_sign_response.data)['data'], surrogate_id=self.SOURCE_SURROGATE_ID, service_key=self.SOURCE_KEY_OBJECT, service_kid=self.SOURCE_KID ) source_slr_store_response = self.app.post(source_slr_store_url, data=source_slr_store_payload, headers=request_headers) unittest.TestCase.assertEqual(self, source_slr_store_response.status_code, 201, msg=source_slr_store_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=source_slr_store_response.data), msg=source_slr_store_response.data) unittest.TestCase.assertTrue(self, validate_json(source_slr_store_response.data, schema_slr_store)) source_ssr_id = json.loads(source_slr_store_response.data)['data']['ssr']['id'] # Store Service Link of Sink Service sink_slr_store_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + sink_slr_id + "/store/" sink_slr_store_payload, ssr_id = generate_sl_store_payload( slr_id=sink_slr_id, slr_signed=json.loads(sink_slr_sign_response.data)['data'], surrogate_id=self.SINK_SURROGATE_ID, service_key=self.SINK_KEY_OBJECT, service_kid=self.SINK_KID ) sink_slr_store_response = self.app.post(sink_slr_store_url, data=sink_slr_store_payload, headers=request_headers) unittest.TestCase.assertEqual(self, sink_slr_store_response.status_code, 201, msg=sink_slr_store_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=sink_slr_store_response.data), msg=sink_slr_store_response.data) unittest.TestCase.assertTrue(self, validate_json(sink_slr_store_response.data, schema_slr_store)) sink_ssr_id = json.loads(sink_slr_store_response.data)['data']['ssr']['id'] return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id ########## ########## def test_for_account_give_consent(self): """ Give Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ # Give Consent account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 201, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_give_consent)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_multiple(self): """ Give Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ # Give Consent account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) count = 0 source_cr_id_array = [] source_csr_id_array = [] sink_cr_id_array = [] sink_csr_id_array = [] for i in range(0, 3): count += 1 give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 201, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_give_consent)) source_cr_id_array.append(source_cr_id) source_csr_id_array.append(source_csr_id) sink_cr_id_array.append(sink_cr_id) sink_csr_id_array.append(sink_csr_id) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_give_consent_malformed(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=True ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_wrong_source_surrogate_id(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id="wrong-id", source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_unknown_sink_surrogate_id(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id="unknown", sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_unknown_source_slr_id(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, #source_slr_id=source_slr_id, source_slr_id="unknown", operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_wrong_sink_slr_id(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id="wrong_id", sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_wrong_source_cr_id_pair(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False, source_cr_id_fault=True, sink_cr_id_fault=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_wrong_sink_cr_id_pair(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False, source_cr_id_fault=False, sink_cr_id_fault=True ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self, validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_surrogate_id_mismatch_source(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str( account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False, source_cr_id_fault=False, sink_cr_id_fault=False, source_surrogate_id_fault=True, sink_surrogate_id_fault=False ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self,validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_give_consent_surrogate_id_mismatch_sink(self): """ Give Consent - With incorrect payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id = self.test_for_account_link_services() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) give_consent_url = self.API_PREFIX_INTERNAL + "/accounts/" + str( account_id) + "/servicelinks/" + source_slr_id + "/" + sink_slr_id + "/consents/" give_consent_payload, source_cr_id, source_csr_id, sink_cr_id, sink_csr_id = generate_consent_payload( source_surrogate_id=self.SOURCE_SURROGATE_ID, source_slr_id=source_slr_id, operator_id=self.OPERATOR_ID, source_subject_id=self.SOURCE_SERVICE_ID, sink_pop_key=self.SINK_KEY_PUBLIC, operator_pub_key=self.OPERATOR_KEY_PUBLIC, sink_surrogate_id=self.SINK_SURROGATE_ID, sink_slr_id=sink_slr_id, sink_subject_id=self.SINK_SERVICE_ID, misformatted_payload=False, source_cr_id_fault=False, sink_cr_id_fault=False, source_surrogate_id_fault=False, sink_surrogate_id_fault=True ) give_consent_response = self.app.post(give_consent_url, data=give_consent_payload, headers=request_headers) unittest.TestCase.assertEqual(self, give_consent_response.status_code, 400, msg=give_consent_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=give_consent_response.data), msg=give_consent_response.data) unittest.TestCase.assertTrue(self,validate_json(give_consent_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_source(self): """ Change Consent Status - Source Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=False, cr_id_fault=False ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 201, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_consent_status_change)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_sink(self): """ Change Consent Status - Sink Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id, sink_csr_id_new """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + sink_cr_id + "/statuses/" consent_status_change_payload, sink_csr_id_new = generate_consent_status_payload( surrogate_id=self.SINK_SURROGATE_ID, cr_id=sink_cr_id, consent_status="Paused", prev_record_id=sink_csr_id, misformatted_payload=False, cr_id_fault=False ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 201, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_consent_status_change)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id, sink_csr_id_new ########## ########## def test_for_account_change_consent_status_incorrect_cr_id(self): """ Change Consent Status - Faulty payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=False, cr_id_fault=True ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 400, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_incorrect_payload(self): """ Change Consent Status - Faulty payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=True, cr_id_fault=False ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 400, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_unknown_consent(self): """ Change Consent Status - Faulty payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) source_cr_id = "unknown-" + source_cr_id # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=False, cr_id_fault=False ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 400, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_signed_source(self): """ Change Consent Status by Operator - Source Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/signed/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload_signed( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=False, cr_id_fault=False, operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 201, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_consent_status_change)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_signed_sink(self): """ Change Consent Status by Operator - Sink Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id, sink_csr_id_new """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_signed_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + sink_cr_id + "/statuses/signed/" consent_status_change_payload, sink_csr_id_new = generate_consent_status_payload_signed( surrogate_id=self.SINK_SURROGATE_ID, cr_id=sink_cr_id, consent_status="Paused", prev_record_id=sink_csr_id, misformatted_payload=False, cr_id_fault=False, operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 201, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_consent_status_change)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id, sink_csr_id_new ########## ########## def test_for_account_change_consent_status_signed_incorrect_payload(self): """ Change Consent Status by Operator - Faulty payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/signed/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload_signed( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=True, cr_id_fault=False, operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 400, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_request_error_detail_as_dict)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_signed_incorrect_cr_id(self): """ Change Consent Status by Operator - Faulty payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/signed/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload_signed( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=False, cr_id_fault=True, operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 400, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_change_consent_status_signed_unknown_cr_id(self): """ Change Consent Status by Operator - Faulty payload :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_give_consent() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) source_cr_id = "unknown-" + source_cr_id # Change Consent Status of Source Service consent_status_change_url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + source_cr_id + "/statuses/signed/" consent_status_change_payload, source_csr_id_new = generate_consent_status_payload_signed( surrogate_id=self.SOURCE_SURROGATE_ID, cr_id=source_cr_id, consent_status="Paused", prev_record_id=source_csr_id, misformatted_payload=False, cr_id_fault=False, operator_kid=self.OPERATOR_KID, operator_key=self.OPERATOR_KEY_OBJECT ) consent_status_change_response = self.app.post(consent_status_change_url, data=consent_status_change_payload, headers=request_headers) unittest.TestCase.assertEqual(self, consent_status_change_response.status_code, 400, msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, is_json(json_object=consent_status_change_response.data), msg=consent_status_change_response.data) unittest.TestCase.assertTrue(self, validate_json(consent_status_change_response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consents_by_link(self): """ Test Consents :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = source_cr_id_array for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id ########## ########## def test_for_account_fetch_consents_by_link_with_consent_pairs(self): """ Test Consents :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/?get_consent_pair=True" count *= 2 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = source_cr_id_array + sink_cr_id_array for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consents_by_link_wrong_slr_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_ssr_id) + "/consents/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consents_by_link_with_consent_pairs_wrong_slr_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_ssr_id) + "/consents/?get_consent_pair=True" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consents_by_link_wrong_account_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(sink_slr_id) + "/servicelinks/" + str(source_slr_id) + "/consents/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id ########## ########## def test_for_account_fetch_consent_by_link(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id_array[0]) + "?get_consent_pair=False" count = 1 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_cr_id_array[0]] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_link_with_consent_pair(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id_array[0]) + "?get_consent_pair=True" count = 2 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_cr_id_array[0], sink_cr_id_array[0]] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_link_wrong_slr_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_ssr_id) + "/consents/" + str(source_cr_id_array[0]) + "/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_link_with_consent_pairs_wrong_slr_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_ssr_id) + "/consents/" + str(source_cr_id_array[0]) + "/?get_consent_pair=True" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_link_wrong_account_id(self): """ Test Consent - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(sink_slr_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id_array[0]) + "/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_last_consent_by_link(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/last/?get_consent_pair=False" count = 1 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_cr_id_array[-1]] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_last_consent_by_link_with_consent_pair(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/last/?get_consent_pair=True" count = 2 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_cr_id_array[-1], sink_cr_id_array[-1]] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_last_consent_by_link_wrong_slr_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_ssr_id) + "/consents/last/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_last_consent_by_link_with_consent_pairs_wrong_slr_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_ssr_id) + "/consents/last/?get_consent_pair=True" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_last_consent_by_link_wrong_account_id(self): """ Test Consent - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(sink_slr_id) + "/servicelinks/" + str(source_slr_id) + "/consents/last/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consents(self): """ Test Consents :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" count *= 2 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = source_cr_id_array + sink_cr_id_array for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consents_wrong_account_id(self): """ Test Consents - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(sink_slr_id) + "/consents/?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id ########## ########## def test_for_account_fetch_consent_by_account(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id_array[0]) + "/?get_consent_pair=False" count = 1 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_cr_id_array[0]] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_account_with_consent_pair(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id_array[0]) + "?get_consent_pair=True" count = 2 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_cr_id_array[0], sink_cr_id_array[0]] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_account_with_wrong_consent_id(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(sink_slr_id) + "?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_by_account_with_wrong_account_id(self): """ Test Consent :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(source_slr_id) + "/consents/" + str(source_cr_id_array[0]) + "?get_consent_pair=False" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_consent(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status_listing)) # ID verification verification_id_array = [source_csr_id, source_csr_id_new] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_consent_with_status_filter(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id) + "/statuses/?status_id=" + str(source_csr_id) count = 1 response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status_listing)) unittest.TestCase.assertEqual(self, len(json.loads(response.data)['data']), count, msg="Response array is containing {} objects instead of {} expexted objects".format(len(json.loads(response.data)['data']), count)) # ID verification verification_id_array = [source_csr_id_new] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_consent_with_wrong_consent_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(sink_slr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_consent_with_status_filter_faulty(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id) + "/statuses/?status_id=faulty_id" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_status_by_account_and_consent(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(source_csr_id) +"/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status)) # ID verification verification_id_array = [source_csr_id] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_status_by_account_and_consent_wrong_consent_status_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(sink_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_link_and_consent(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status_listing)) # ID verification verification_id_array = [source_csr_id, source_csr_id_new] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_link_and_consent_wrong_link_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(sink_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_statuses_by_account_and_link_and_consent_wrong_consent_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(sink_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_status_by_account_and_link_and_consent(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(source_csr_id_new) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status)) # ID verification verification_id_array = [source_csr_id_new] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_status_by_account_and_link_and_consent_wrong_link_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(sink_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(source_csr_id_new) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_status_by_account_and_link_and_consent_wrong_consent_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(sink_cr_id) + "/statuses/" + str(source_csr_id_new) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_status_by_account_and_link_and_consent_wrong_status_id(self): """ Test Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(sink_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_last_status_by_account_and_consent(self): """ Test Last Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status)) # ID verification verification_id_array = [source_csr_id_new] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_last_status_by_account_and_consent_with_wrong_consent_id(self): """ Test Last Consent Status - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/consents/" + str(source_ssr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_last_status_by_account_and_consent_with_wrong_account_id(self): """ Test Last Consent Status - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_last_status_by_account_and_link_and_consent(self): """ Test Last Consent Status :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status)) # ID verification verification_id_array = [source_csr_id_new] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_last_status_by_account_and_link_and_consent_with_wrong_consent_id(self): """ Test Last Consent Status - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(account_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_ssr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_account_fetch_consent_last_status_by_account_and_link_and_consent_with_wrong_account_id(self): """ Test Last Consent Status - Invalid IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-User'] = str(account_api_key) request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/accounts/" + str(source_slr_id) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 403, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ## # TEST CASES FOR SERVICES ########## ########## def test_for_service_fetch_consents(self): """ Consent listing for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_listing)) # ID verification verification_id_array = [source_cr_id] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consents_wrong_service_id(self): """ Consent listing for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SINK_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consents_wrong_link_id(self): """ Consent listing for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(sink_slr_id) + "/consents/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent(self): """ Consent for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent)) # ID verification verification_id_array = [source_cr_id] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_wrong_service_id(self): """ Consent for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SINK_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_wrong_link_id(self): """ Consent for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(sink_slr_id) + "/consents/" + str(source_cr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_wrong_consent_id(self): """ Consent for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(sink_cr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_statuses(self): """ Consent Statuses for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status_listing)) # ID verification verification_id_array = [source_csr_id, source_csr_id_new] for record_object in json.loads(response.data)['data']: id_to_verify = str(record_object['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_statuses_wrong_service_id(self): """ Consent Statuses for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SINK_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_statuses_wrong_link_id(self): """ Consent Statuses for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(sink_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_statuses_wrong_consent_id(self): """ Consent Statuses for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(sink_cr_id) + "/statuses/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_status(self): """ Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(source_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status)) # ID verification verification_id_array = [source_csr_id] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_status_wrong_service_id(self): """ Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SINK_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(source_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_status_wrong_link_id(self): """ Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(sink_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(source_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_status_wrong_consent_id(self): """ Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(sink_cr_id) + "/statuses/" + str(source_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_consent_status_wrong_consent_status_id(self): """ Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/" + str(sink_csr_id) + "/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_last_consent_status(self): """ Last Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_consent_status)) # ID verification verification_id_array = [source_csr_id_new] id_to_verify = str(json.loads(response.data)['data']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_last_consent_status_wrong_service_id(self): """ Last Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SINK_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_last_consent_status_wrong_link_id(self): """ Last Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(sink_slr_id) + "/consents/" + str(source_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_for_service_fetch_last_consent_status_wrong_consent_id(self): """ Last Consent Status for Service :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id = self.test_for_account_change_consent_status_source() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/services/" + str(self.SOURCE_SERVICE_ID) + "/servicelinks/" + str(source_slr_id) + "/consents/" + str(sink_cr_id) + "/statuses/last/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, source_cr_id, source_csr_id, source_csr_id_new, sink_slr_id, sink_ssr_id, sink_cr_id, sink_csr_id ########## ########## def test_authorisation_token_data(self): """ Authorisation token data :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/consents/" + str(sink_cr_id_array[0]) + "/authorisationtoken/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 200, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_authorisation_token_data)) # ID verification ## Source's Consent Record verification_id_array = [source_cr_id_array[0]] id_to_verify = str(json.loads(response.data)['data']['consent_record']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="Source's Consent Record ID {} not one of {}".format(id_to_verify, verification_id_array)) ## Sink's Service Link Record verification_id_array = [sink_slr_id] id_to_verify = str(json.loads(response.data)['data']['service_link_record']['id']) unittest.TestCase.assertIn(self, id_to_verify, verification_id_array, msg="Sink's Service Link Record ID {} not one of {}".format(id_to_verify, verification_id_array)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count ########## ########## def test_authorisation_token_data_wrong_consent_id(self): """ Authorisation token data - Faulty IDs :return: account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count """ account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count = self.test_for_account_give_consent_multiple() request_headers = default_headers request_headers['Api-Key-Sdk'] = str(sdk_api_key) url = self.API_PREFIX_INTERNAL + "/consents/" + str(source_cr_id_array[0]) + "/authorisationtoken/" response = self.app.get(url, headers=request_headers) unittest.TestCase.assertEqual(self, response.status_code, 404, msg=response.data) unittest.TestCase.assertTrue(self, is_json(json_object=response.data), msg=response.data) unittest.TestCase.assertTrue(self, validate_json(response.data, schema_request_error_detail_as_str)) return account_id, account_api_key, sdk_api_key, source_slr_id, source_ssr_id, sink_slr_id, sink_ssr_id, source_cr_id_array, source_csr_id_array, sink_cr_id_array, sink_csr_id_array, count if __name__ == '__main__': unittest.main()

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null

0

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b5ce103b1e939ac31cca4fcb1f9f75aa952842cb

150

py

Python

rman_operators/__init__.py

ian-hsieh/RenderManForBlender

c827f029f4cbbd1fcc71ed8d3694fc5ac58cc468

[ "MIT" ]

12

2019-05-03T21:58:15.000Z

2022-02-24T07:02:21.000Z

rman_operators/__init__.py

ian-hsieh/RenderManForBlender

c827f029f4cbbd1fcc71ed8d3694fc5ac58cc468

[ "MIT" ]

4

2019-03-07T18:20:16.000Z

2020-09-24T21:53:15.000Z

rman_operators/__init__.py

ian-hsieh/RenderManForBlender

c827f029f4cbbd1fcc71ed8d3694fc5ac58cc468

[ "MIT" ]

3

2019-05-25T01:17:09.000Z

2019-09-13T14:43:12.000Z

from . import rman_operators_printer def register(): rman_operators_printer.register() def unregister(): rman_operators_printer.unregister()

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b5e38c7b2f37f77f94a6733875de87bb7dd90a18

43

py

Python

utils/__init__.py

12doge-LEO/imiTGBot

2be263e77bac61232b7a55c0d0159a9fdd0e7b47

[ "MIT" ]

null

utils/__init__.py

12doge-LEO/imiTGBot

2be263e77bac61232b7a55c0d0159a9fdd0e7b47

[ "MIT" ]

null

utils/__init__.py

12doge-LEO/imiTGBot

2be263e77bac61232b7a55c0d0159a9fdd0e7b47

[ "MIT" ]

null

from .audio_cache import update_audio_cache

43

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7

bd41592bf06539b32b241df692280394e2bfa7d7

956

py

Python

jade_utils/dask_tools/__init__.py

tam203/jade_utils

f717229444bd2f18c94e78c9cc659a9ac1650fa4

[ "BSD-3-Clause" ]

null

jade_utils/dask_tools/__init__.py

tam203/jade_utils

f717229444bd2f18c94e78c9cc659a9ac1650fa4

[ "BSD-3-Clause" ]

2

2018-09-25T08:59:09.000Z

jade_utils/dask_tools/__init__.py

tam203/jade_utils

f717229444bd2f18c94e78c9cc659a9ac1650fa4

[ "BSD-3-Clause" ]

1

2021-04-10T23:57:42.000Z

"""Tools for working with dask.""" def update_worker_memory(cluster, new_limit): cluster.pod_template.spec.containers[0].resources.limits["memory"] = new_limit cluster.pod_template.spec.containers[0].resources.requests["memory"] = new_limit if '--memory-limit' in cluster.pod_template.spec.containers[0].args: index = cluster.pod_template.spec.containers[0].args.index('--memory-limit') cluster.pod_template.spec.containers[0].args[index + 1] = new_limit return cluster def update_worker_cpu(cluster, new_limit): cluster.pod_template.spec.containers[0].resources.limits["cpu"] = new_limit cluster.pod_template.spec.containers[0].resources.requests["cpu"] = new_limit if '--nthreads' in cluster.pod_template.spec.containers[0].args: index = cluster.pod_template.spec.containers[0].args.index('--nthreads') cluster.pod_template.spec.containers[0].args[index + 1] = new_limit return cluster

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null

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8

1f9a80523da6daa5d13eb1c6a821d016c111480e

2,305

py

Python

cdisp/helpers.py

felippebarbosa/cdisp

d9a612c252495ab017bffccdd7e82bbb555e07dd

[ "BSL-1.0" ]

null

cdisp/helpers.py

felippebarbosa/cdisp

d9a612c252495ab017bffccdd7e82bbb555e07dd

[ "BSL-1.0" ]

null

cdisp/helpers.py

felippebarbosa/cdisp

d9a612c252495ab017bffccdd7e82bbb555e07dd

[ "BSL-1.0" ]

null

#-*- coding: utf-8 -*- """ Helper functions """ import numpy # module for array manipulation def Df_fine(x, t): """Function for computing first order numerical derivatives using 3rd order derivative calculation (more precise and slower) Usage ------ The function returns y = dx/dt. Parameters ------ x: 1-dimensional array t: 1-dimensional array preferably with the same length as x """ ####### if type(x) <> 'numpy.ndarray': x = numpy.array(x) # convert to numpy array N = x.shape[0] # length of the original array df = [] # initial derivative empyy list for k in range(N): # loop for calculation if k == 0: # first point case dx = x[k + 1] - x[k] dt = t[k + 1] - t[k] elif k == N - 1: # last point case dx = x[k] - x[k - 1] dt = t[k] - t[k - 1] elif k == 1 or k == N - 2: # second and second-to-last cases dx = x[k + 1] - x[k - 1] dt = t[k + 1] - t[k - 1] else: # remaining cases dx = -x[k + 2] + 8*x[k + 1] - 8*x[k - 1] + x[k - 2] dt = 3*(t[k + 2] - t[k - 2]) df.append(Ddata/Dvar) # add point to the list return numpy.array(df) def Df(x, t): """Function for computing first order numerical derivatives Usage ------ The function returns y = dx/dt. Parameters ------ x: 1-dimensional array t: 1-dimensional array preferably with the same length as x """ ####### if type(x) <> 'numpy.ndarray': x = numpy.array(x) # convert to numpy array N = x.shape[0] # length of the original array df = [] # initial derivative empyy list for k in range(N): # loop for calculation if k == 0: # first point case dx = x[k + 1] - x[k] dt = t[k + 1] - t[k] elif k == N - 1: # last point case dx = x[k] - x[k - 1] dt = t[k] - t[k - 1] else: # remaining cases dx = x[k + 1] - x[k - 1] dt = t[k + 1] - t[k - 1] df.append(dx/dt) # add point to the list return numpy.array(df)

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8

1fbb87a2f29fc734a30131b212b93ebc41ef8a20

185

py

Python

platform/hwconf_data/efm32hg/modules/PIN/PIN_Snippets.py

lenloe1/v2.7

9ac9c4a7bb37987af382c80647f42d84db5f2e1d

[ "Zlib" ]

null

platform/hwconf_data/efm32hg/modules/PIN/PIN_Snippets.py

lenloe1/v2.7

9ac9c4a7bb37987af382c80647f42d84db5f2e1d

[ "Zlib" ]

1

2020-08-25T02:36:22.000Z

platform/hwconf_data/efm32hg/modules/PIN/PIN_Snippets.py

lenloe1/v2.7

9ac9c4a7bb37987af382c80647f42d84db5f2e1d

[ "Zlib" ]

1

2020-08-25T01:56:04.000Z

""" Generated from a template """ import efm32hg.PythonSnippet.RuntimeModel as RuntimeModel from efm32hg.modules.PIN.PIN_Defs import PORT_PINS def activate_runtime(): pass

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7

1fc035e5c5221f5032b0f3463b27b45a2fc128a5

193

py

Python

bookworm/platform_services/speech_engines.py

xingkong0113/bookworm

7214067f48e7a951198806a1f9170e3fd8fc0cce

[ "MIT" ]

36

2020-11-15T03:21:39.000Z

2022-03-05T01:11:26.000Z

bookworm/platform_services/speech_engines.py

xingkong0113/bookworm

7214067f48e7a951198806a1f9170e3fd8fc0cce

[ "MIT" ]

90

2020-10-06T14:46:07.000Z

2022-03-31T03:03:34.000Z

bookworm/platform_services/speech_engines.py

xingkong0113/bookworm

7214067f48e7a951198806a1f9170e3fd8fc0cce

[ "MIT" ]

20

2020-09-30T17:40:44.000Z

2022-03-17T19:59:53.000Z

# coding: utf-8 from . import PLATFORM if PLATFORM == "win32": from ._win32.speech_engines import TTS_ENGINES elif PLATFORM == "linux": from ._linux.speech_engines import TTS_ENGINES

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7

1fc7aa9c9db8139983023c3a38b1b4c3e4c9e8ad

8,042

py

Python

main.py

SoloGuardiaN/Project-Zork

63f617f87710fe9246be6314f8ff3ddbe5699199

[ "MIT" ]

null

main.py

SoloGuardiaN/Project-Zork

63f617f87710fe9246be6314f8ff3ddbe5699199

[ "MIT" ]

null

main.py

SoloGuardiaN/Project-Zork

63f617f87710fe9246be6314f8ff3ddbe5699199

[ "MIT" ]

null

import random # Floor 1 - 3 floor3 = ['sword', 'stairs down', 'nothing', 'boss monster', 'prize'] floor2 = ['magic stone', 'stairs up', 'monster', 'stairs down', 'nothing'] floor1 = ['nothing', 'sword', 'monster', 'stairs up', 'sword']# inventory, user room, and user floor variables inventory = [0, 0, 0] user_room = 0 user_floor = floor1 game_over = 0 last = '' print("Welcome unfortunate victim, this is a test of your skills in combat and how fast you can think on your feet. Currently you are locked in a warehouse there are 3 normal 'guests' and one very very special 'guest'. Can you defeat the 'guests' and retrieve the key to escape? Which room on this floor would you like to go to? Or maybe you'd like to go to a different floor? Type 'help' for the commands.\n")# if statements for game function while game_over == 0: if user_floor[user_room] == 'nothing': print("This room has nothing in it.") elif user_floor[user_room] == 'sword': print("This room has a sword in it!") elif user_floor[user_room] == 'magic stone': print("This room has magic stones in it!") elif user_floor[user_room] == 'stairs up': print("This room has stairs going up.") elif user_floor[user_room] == 'stairs down': print("This room has stairs going down.") elif user_floor[user_room] == 'monster': print("There is a monster in the room with you.") elif user_floor[user_room] == 'boss monster': print("The boss looks at you.") elif user_floor[user_room] == 'prize': print("Congrats, you obtained the prize of finishing the game") game_over = 1 x = input("What do you do?") if x == 'help': print("left, right, up, down, grab, fight, help, end") elif x == 'left': if user_floor[user_room] == 'monster' and(last == 'left' or last == 'grab' or last == 'up' or last == 'down'): print("You can pass the monster without a fight.") elif user_room > -1: user_room -= 1 else : print("You run straight into a wall.") elif x == 'right': if user_floor[user_room] == 'monster' or user_floor[user_room] == 'boss monster'import random # Floor 1 - 3 floor3 = ['sword', 'stairs down', 'nothing', 'boss monster', 'prize'] floor2 = ['magic stone', 'stairs up', 'monster', 'stairs down', 'nothing'] floor1 = ['nothing', 'sword', 'monster', 'stairs up', 'sword']# inventory, user room, and user floor variables inventory = [0, 0, 0] user_room = 0 user_floor = floor1 game_over = 0 last = '' print("Welcome unfortunate victim, this is a test of your skills in combat and how fast you can think on your feet. Currently you are locked in a warehouse there are 3 normal 'guests' and one very very special 'guest'. Can you defeat the 'guests' and retrieve the key to escape? Which room on this floor would you like to go to? Or maybe you'd like to go to a different floor? Type 'help' for the commands.\n")# if statements for game function while game_over == 0: if user_floor[user_room] == 'nothing': print("This room has nothing in it.") elif user_floor[user_room] == 'sword': print("This room has a sword in it!") elif user_floor[user_room] == 'magic stone': print("This room has magic stones in it!") elif user_floor[user_room] == 'stairs up': print("This room has stairs going up.") elif user_floor[user_room] == 'stairs down': print("This room has stairs going down.") elif user_floor[user_room] == 'monster': print("There is a monster in the room with you.") elif user_floor[user_room] == 'boss monster': print("The boss looks at you.") elif user_floor[user_room] == 'prize': print("Congrats, you obtained the prize of finishing the game") game_over = 1 x = input("What do you do?") if x == 'help': print("left, right, up, down, grab, fight, help, end") elif x == 'left': if user_floor[user_room] == 'monster' and(last == 'left' or last == 'grab' or last == 'up' or last == 'down'): print("You can pass the monster without a fight.") elif user_room > -1: user_room -= 1 else : print("You run straight into a wall.") elif x == 'right': if user_floor[user_room] == 'monster' or user_floor[user_room] == 'boss monster' and(last == 'right' or last == 'grab' or last == 'up' or last == 'down'): print("You can pass the monster without a fight.") elif user_room < 5: user_room += 1 else : print("You run straight into a wall") elif x == 'up': if user_floor[user_room] == 'stairs up': if user_floor == floor1: user_floor = floor2 print("You went up the stairs") elif user_floor == floor2: user_floor = floor3 print("You went up the stairs") elif x == 'down': if user_floor[user_room] == 'stairs down': if user_floor == floor2: user_floor = floor1 print("You went down the stairs") elif user_floor == floor3: user_floor = floor2 print("You went down the stairs") elif x == 'grab': if user_floor[user_room] == 'sword': slots = 0 var = 0 while var == 0: if inventory[slots] == 'sword' or inventory[slots] == 'magic stone': slots += 1 else : inventory[slots] = "sword" print("You picked up a sword") var = 1 user_floor[user_room] = "nothing" if user_floor[user_room] == 'magic stone': slots = 0 var = 0 while var == 0: if inventory[slots] == 'sword' or inventory[slots] == 'magic stone': slots += 1 else : inventory[slots] = "magic stone" print("You picked up a magic stone") var = 1 user_floor[user_room] = "nothing" elif x == 'fight': if user_floor[user_room] == 'monster': slots = 2 var = 0 while var == 0: if inventory[slots] == '0' or inventory[slots] == 'magic stone': slots -= 1 else : user_floor[user_room] = 'nothing' inventory[slots] = 0 print("You killed the monster, but broke your sword in the process") var = 1 elif user_floor[user_room] == 'boss monster': slots = 2 var = 0 while var == 0: if inventory[slots] == '0' or inventory[slots] == 'sword': slots -= 1 else : user_floor[user_room] = 'nothing' inventory[slots] = 0 print("You killed the boss, but broke your magic stone in the process") var = 1 elif x == 'end': print("You killed yourself. How pityful.") game_over = 1 else : print("That is not a command.") last = x and(last == 'right' or last == 'grab' or last == 'up' or last == 'down'): print("You can pass the monster without a fight.") elif user_room < 5: user_room += 1 else : print("You run straight into a wall") elif x == 'up': if user_floor[user_room] == 'stairs up': if user_floor == floor1: user_floor = floor2 print("You went up the stairs") elif user_floor == floor2: user_floor = floor3 print("You went up the stairs") elif x == 'down': if user_floor[user_room] == 'stairs down': if user_floor == floor2: user_floor = floor1 print("You went down the stairs") elif user_floor == floor3: user_floor = floor2 print("You went down the stairs") elif x == 'grab': if user_floor[user_room] == 'sword': slots = 0 var = 0 while var == 0: if inventory[slots] == 'sword' or inventory[slots] == 'magic stone': slots += 1 else : inventory[slots] = "sword" print("You picked up a sword") var = 1 user_floor[user_room] = "nothing" if user_floor[user_room] == 'magic stone': slots = 0 var = 0 while var == 0: if inventory[slots] == 'sword' or inventory[slots] == 'magic stone': slots += 1 else : inventory[slots] = "magic stone" print("You picked up a magic stone") var = 1 user_floor[user_room] = "nothing" elif x == 'fight': if user_floor[user_room] == 'monster': slots = 2 var = 0 while var == 0: if inventory[slots] == '0' or inventory[slots] == 'magic stone': slots -= 1 else : user_floor[user_room] = 'nothing' inventory[slots] = 0 print("You killed the monster, but broke your sword in the process") var = 1 elif user_floor[user_room] == 'boss monster': slots = 2 var = 0 while var == 0: if inventory[slots] == '0' or inventory[slots] == 'sword': slots -= 1 else : user_floor[user_room] = 'nothing' inventory[slots] = 0 print("You killed the boss, but broke your magic stone in the process") var = 1 elif x == 'end': print("You killed yourself. How pityful.") game_over = 1 else : print("That is not a command.") last = x

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null

0

1

0

1

0

null

0

1

0

8

95200020b103a63680eccbbca94a28ccc056ada1

21,225

py

Python

bank_bot/tests/test_hacking_module.py

Tengro/larp_bankbot

22d5ea49d5f507da74fb3b1f106c24ad52cb9e68

[ "MIT" ]

3

2019-07-27T15:20:49.000Z

2019-10-14T13:10:55.000Z

bank_bot/tests/test_hacking_module.py

Tengro/larp_bankbot

22d5ea49d5f507da74fb3b1f106c24ad52cb9e68

[ "MIT" ]

1

2021-06-01T23:55:12.000Z

bank_bot/tests/test_hacking_module.py

Tengro/larp_bankbot

22d5ea49d5f507da74fb3b1f106c24ad52cb9e68

[ "MIT" ]

null

import pytest from bank_bot.banking_system.client_factory import BankingClientFactory from bank_bot.banking_system.banking_system_class_based import BankingClient from bank_bot.banking_system.user_class import User from bank_bot.banking_system import UserError, TransactionError, HackerError from bank_bot.settings import NO_USER_DATA, NO_TRANSACTIONS_FOUND, DEFAULT_FINANCES, ATTRIBUTE_UPDATE_MESSAGE, NO_MESSAGES_FOUND from bank_bot.banking_system.transaction_class import Transaction from bank_bot.banking_system.message_class import Message def test_hacker_validation(database, mock_message): character_hash = User.create_user(2, 2, "Test user", database) client = BankingClientFactory(database).create_client(mock_message) with pytest.raises(HackerError): client.hacker_validation(0) User.update_db_value(character_hash, "hacker_level", 1, database) client = BankingClientFactory(database).create_client(mock_message) with pytest.raises(HackerError): client.hacker_validation(2) client.hacker_validation(0) def test_hack_inspect_user(database, mock_message): User.create_admin(1, 1, database) character_hash = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash, "hacker_level", 1, database) client = BankingClientFactory(database).create_client(mock_message) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_4 = User.create_user(4, 4, "Test user 4", database) User.update_db_value(character_hash_4, "hacker_defence", 1, database) user2 = client.get_user_by_user_hash(character_hash_2) user4 = client.get_user_by_user_hash(character_hash_4) with pytest.raises(UserError): client.hack_inspect_user("/hack 1234567890") resulting_data, chat_id, show_hack = client.hack_inspect_user(f'/hack {character_hash_2}') assert resulting_data == user2.hack_result assert chat_id == user2.chat_id assert not show_hack with pytest.raises(HackerError): client.hack_inspect_user(f'/hack 0000000000') resulting_data, chat_id, show_hack = client.hack_inspect_user(f'/hack {character_hash_4}') assert resulting_data == user4.hack_result assert chat_id == user4.chat_id assert show_hack def test_hack_inspect_transactions(database, mock_message): User.create_admin(1, 1, database) character_hash_1 = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash_1, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_3 = User.create_user(4, 4, "Test user 3", database) character_hash_4 = User.create_user(5, 5, "Test user 4", database) User.update_db_value(character_hash_3, "hacker_defence", 1, database) client = BankingClientFactory(database).create_client(mock_message) results, chat_id, show_hack = client.hack_inspect_transactions(f"/hack_history_sent {character_hash_2}", True) assert results == NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_transactions(f"/hack_history_recieved {character_hash_2}", False) assert results == NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_transactions(f"/hack_history_recieved {character_hash_3}", False) assert results == NO_TRANSACTIONS_FOUND assert show_hack Transaction.create_transaction(character_hash_2, character_hash_4, 100, database) Transaction.create_transaction(character_hash_2, character_hash_3, 100, database) Transaction.create_transaction(character_hash_2, "0000000000", 100, database) results, chat_id, show_hack = client.hack_inspect_transactions(f"/hack_history_sent {character_hash_2}", True) assert results != NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_transactions(f"/hack_history_recieved {character_hash_2}", False) assert results == NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_transactions(f"/hack_history_recieved {character_hash_3}", False) assert results != NO_TRANSACTIONS_FOUND assert show_hack with pytest.raises(HackerError): client.hack_inspect_transactions(f"/hack_history_recieved 0000000000", False) def test_hack_inspect_all_transactions(database, mock_message): User.create_admin(1, 1, database) character_hash_1 = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash_1, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_3 = User.create_user(4, 4, "Test user 3", database) character_hash_4 = User.create_user(5, 5, "Test user 4", database) User.update_db_value(character_hash_3, "hacker_defence", 1, database) client = BankingClientFactory(database).create_client(mock_message) results, chat_id, show_hack = client.hack_inspect_all_transactions(f"/hack_history_all {character_hash_2}") assert results == NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_all_transactions(f"/hack_history_all {character_hash_3}") assert results == NO_TRANSACTIONS_FOUND assert show_hack Transaction.create_transaction(character_hash_2, character_hash_4, 100, database) Transaction.create_transaction(character_hash_2, character_hash_3, 100, database) Transaction.create_transaction(character_hash_2, "0000000000", 100, database) results, chat_id, show_hack = client.hack_inspect_all_transactions(f"/hack_history_all {character_hash_2}") assert results != NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_all_transactions(f"/hack_history_all {character_hash_3}") assert results != NO_TRANSACTIONS_FOUND assert show_hack with pytest.raises(HackerError): client.hack_inspect_all_transactions(f"/hack_history_all 0000000000") def test_hack_inspect_pair_transactions(database, mock_message): User.create_admin(1, 1, database) character_hash_1 = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash_1, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_3 = User.create_user(4, 4, "Test user 3", database) character_hash_4 = User.create_user(5, 5, "Test user 4", database) User.update_db_value(character_hash_3, "hacker_defence", 1, database) User.update_db_value(character_hash_4, "hacker_defence", 2, database) client = BankingClientFactory(database).create_client(mock_message) results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history(f"/hack_history_pair {character_hash_2} {character_hash_3}") assert results == NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history(f"/hack_history_pair {character_hash_4} {character_hash_3}") assert results == NO_TRANSACTIONS_FOUND assert show_hack with pytest.raises(HackerError): client.hack_inspect_pair_history(f"/hack_history_pair {character_hash_4} 0000000000") Transaction.create_transaction(character_hash_2, character_hash_4, 100, database) Transaction.create_transaction(character_hash_2, character_hash_3, 100, database) Transaction.create_transaction(character_hash_2, "0000000000", 100, database) Transaction.create_transaction(character_hash_3, character_hash_4, 100, database) results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history(f"/hack_history_pair {character_hash_2} {character_hash_3}") assert results != NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history(f"/hack_history_pair {character_hash_2} 0000000000") assert results != NO_TRANSACTIONS_FOUND assert not show_hack results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history(f"/hack_history_pair {character_hash_4} {character_hash_3}") assert results != NO_TRANSACTIONS_FOUND assert show_hack def test_hack_inspect_messages(database, mock_message): User.create_admin(1, 1, database) character_hash_1 = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash_1, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_3 = User.create_user(4, 4, "Test user 3", database) character_hash_4 = User.create_user(5, 5, "Test user 4", database) User.update_db_value(character_hash_3, "hacker_defence", 1, database) client = BankingClientFactory(database).create_client(mock_message) results, chat_id, show_hack = client.hack_inspect_messages(f"/hack_messages_history_sent {character_hash_2}", True) assert results == NO_MESSAGES_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_messages(f"/hack_messages_history_recieved {character_hash_2}", False) assert results == NO_MESSAGES_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_messages(f"/hack_messages_history_recieved {character_hash_3}", False) assert results == NO_MESSAGES_FOUND assert show_hack Message.create_message(character_hash_2, character_hash_4, "100", database) Message.create_message(character_hash_2, character_hash_3, "100", database) Message.create_message(character_hash_2, "0000000000", "100", database) results, chat_id, show_hack = client.hack_inspect_messages(f"/hack_messages_history_sent {character_hash_2}", True) assert results != NO_MESSAGES_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_messages(f"/hack_messages_history_recieved {character_hash_2}", False) assert results == NO_MESSAGES_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_messages(f"/hack_messages_history_recieved {character_hash_3}", False) assert results != NO_MESSAGES_FOUND assert show_hack with pytest.raises(HackerError): client.hack_inspect_messages(f"/hack_messages_history_recieved 0000000000", False) def test_hack_inspect_all_messages(database, mock_message): User.create_admin(1, 1, database) character_hash_1 = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash_1, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_3 = User.create_user(4, 4, "Test user 3", database) character_hash_4 = User.create_user(5, 5, "Test user 4", database) User.update_db_value(character_hash_3, "hacker_defence", 1, database) client = BankingClientFactory(database).create_client(mock_message) results, chat_id, show_hack = client.hack_inspect_all_messages(f"/hack_messages_history_all {character_hash_2}") assert results == NO_MESSAGES_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_all_messages(f"/hack_messages_history_all {character_hash_3}") assert results == NO_MESSAGES_FOUND assert show_hack Message.create_message(character_hash_2, character_hash_4, "100", database) Message.create_message(character_hash_2, character_hash_3, "100", database) Message.create_message(character_hash_2, "0000000000", "100", database) results, chat_id, show_hack = client.hack_inspect_all_messages(f"/hack_messages_history_all {character_hash_2}") assert results != NO_MESSAGES_FOUND assert not show_hack results, chat_id, show_hack = client.hack_inspect_all_messages(f"/hack_messages_history_all {character_hash_3}") assert results != NO_MESSAGES_FOUND assert show_hack with pytest.raises(HackerError): client.hack_inspect_all_messages(f"/hack_messages_history_all 0000000000") def test_hack_inspect_pair_messages(database, mock_message): User.create_admin(1, 1, database) character_hash_1 = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash_1, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_3 = User.create_user(4, 4, "Test user 3", database) character_hash_4 = User.create_user(5, 5, "Test user 4", database) User.update_db_value(character_hash_3, "hacker_defence", 1, database) User.update_db_value(character_hash_4, "hacker_defence", 2, database) client = BankingClientFactory(database).create_client(mock_message) results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history_messages(f"/hack_messages_history_pair {character_hash_2} {character_hash_3}") assert results == NO_MESSAGES_FOUND assert not show_hack results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history_messages(f"/hack_messages_history_pair {character_hash_4} {character_hash_3}") assert results == NO_MESSAGES_FOUND assert show_hack with pytest.raises(HackerError): client.hack_inspect_pair_history_messages(f"/hack_messages_history_pair {character_hash_4} 0000000000") Message.create_message(character_hash_2, character_hash_4, "100", database) Message.create_message(character_hash_2, character_hash_3, "100", database) Message.create_message(character_hash_2, "0000000000", "100", database) Message.create_message(character_hash_3, character_hash_4, "100", database) results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history_messages(f"/hack_messages_history_pair {character_hash_2} {character_hash_3}") assert results != NO_MESSAGES_FOUND assert not show_hack results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history_messages(f"/hack_messages_history_pair {character_hash_2} 0000000000") assert results != NO_MESSAGES_FOUND assert not show_hack results, chat_id, hash_1, chat_2_id, hash_2, show_hack = client.hack_inspect_pair_history_messages(f"/hack_messages_history_pair {character_hash_4} {character_hash_3}") assert results != NO_MESSAGES_FOUND assert show_hack def test_hack_send_hacked_message(database, mock_message): User.create_admin(1, 1, database) character_hash = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash, "hacker_level", 1, database) client = BankingClientFactory(database).create_client(mock_message) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_4 = User.create_user(4, 4, "Test user 4", database) User.update_db_value(character_hash_4, "hacker_defence", 1, database) user2 = client.get_user_by_user_hash(character_hash_2) user4 = client.get_user_by_user_hash(character_hash_4) with pytest.raises(UserError): client.prepare_hacker_message("/h@ck_message 1234567890 OLOLO") chat_id, sent_message, show_hack = client.prepare_hacker_message(f'/h@ck_message {character_hash_2} OLOLO') assert sent_message == "OLOLO" assert chat_id == user2.chat_id assert not show_hack with pytest.raises(HackerError): client.hack_inspect_user(f'/h@ck_message 0000000000 OLOLO') chat_id, sent_message, show_hack= client.prepare_hacker_message(f'/h@ck_message {character_hash_4} OLOLO') assert sent_message == "OLOLO" assert chat_id == user4.chat_id assert show_hack def test_create_hacked_transaction(database, mock_message): User.create_admin(1, 1, database) character_hash = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_4 = User.create_user(4, 4, "Test user 4", database) User.update_db_value(character_hash_4, "hacker_defence", 1, database) client = BankingClientFactory(database).create_client(mock_message) double_amount = DEFAULT_FINANCES * 2 half_amount = DEFAULT_FINANCES / 2 user2 = client.get_user_by_user_hash(character_hash_2) user1 = client.get_user_by_user_hash(character_hash) assert user2.finances == DEFAULT_FINANCES assert user1.finances == DEFAULT_FINANCES with pytest.raises(TransactionError): client.create_hacker_transaction(f"/h@ck_theft {character_hash_2} {double_amount}") with pytest.raises(TransactionError): client.create_hacker_transaction(f"/h@ck_theft {character_hash} {half_amount}") with pytest.raises(TransactionError): client.create_hacker_transaction(f"/h@ck_theft {character_hash_2} notanumber") with pytest.raises(TransactionError): client.create_hacker_transaction(f"/h@ck_theft {character_hash_2} 0") with pytest.raises(UserError): client.create_hacker_transaction(f"/h@ck_theft 1234567890 {half_amount}") with pytest.raises(HackerError): client.create_hacker_transaction(f"/h@ck_theft 0000000000 {half_amount}") hacker_chat_id, hacker_hash, victim_chat_id, transaction_message, show_hack = client.create_hacker_transaction(f"/h@ck_theft {character_hash_2} {half_amount}") user2 = client.get_user_by_user_hash(character_hash_2) user1 = client.get_user_by_user_hash(character_hash) assert user2.finances == DEFAULT_FINANCES - half_amount assert user1.finances == DEFAULT_FINANCES + half_amount assert hacker_chat_id == user1.chat_id assert victim_chat_id == user2.chat_id assert not show_hack hacker_chat_id, hacker_hash, victim_chat_id, transaction_message, show_hack = client.create_hacker_transaction(f"/h@ck_theft {character_hash_4} {half_amount}") user2 = client.get_user_by_user_hash(character_hash_4) user = client.get_user_by_user_hash(character_hash) assert hacker_chat_id == user1.chat_id assert victim_chat_id == user2.chat_id assert user2.finances == DEFAULT_FINANCES - half_amount assert user.finances == DEFAULT_FINANCES + half_amount + half_amount assert show_hack def test_create_hacked_transaction_other(database, mock_message): User.create_admin(1, 1, database) character_hash = User.create_user(2, 2, "Test user", database) User.update_db_value(character_hash, "hacker_level", 1, database) character_hash_2 = User.create_user(3, 3, "Test user 2", database) character_hash_4 = User.create_user(4, 4, "Test user 4", database) character_hash_5 = User.create_user(5, 5, "Test user 5", database) User.update_db_value(character_hash_4, "hacker_defence", 1, database) client = BankingClientFactory(database).create_client(mock_message) double_amount = DEFAULT_FINANCES * 2 half_amount = DEFAULT_FINANCES / 2 user2 = client.get_user_by_user_hash(character_hash_2) user1 = client.get_user_by_user_hash(character_hash) assert user2.finances == DEFAULT_FINANCES assert user1.finances == DEFAULT_FINANCES with pytest.raises(TransactionError): client.create_hacker_transaction_other(f"/h@ck_theft_other {character_hash_2} {character_hash_5} {double_amount}") with pytest.raises(TransactionError): client.create_hacker_transaction_other(f"/h@ck_theft_other {character_hash} {character_hash_5} {half_amount}") with pytest.raises(TransactionError): client.create_hacker_transaction_other(f"/h@ck_theft_other {character_hash_2} {character_hash_5} notanumber") with pytest.raises(TransactionError): client.create_hacker_transaction_other(f"/h@ck_theft_other {character_hash_2} {character_hash_5} 0") with pytest.raises(UserError): client.create_hacker_transaction_other(f"/h@ck_theft_other 1234567890 {character_hash_5} {half_amount}") with pytest.raises(HackerError): client.create_hacker_transaction_other(f"/h@ck_theft_other 0000000000 {character_hash_5} {half_amount}") hacker_hash, victim_chat_id, profiteer_chat_id, transaction_message, show_hack = client.create_hacker_transaction_other(f"/h@ck_theft_other {character_hash_2} {character_hash_5} {half_amount}") user2 = client.get_user_by_user_hash(character_hash_2) user5 = client.get_user_by_user_hash(character_hash_5) assert user2.finances == DEFAULT_FINANCES - half_amount assert user5.finances == DEFAULT_FINANCES + half_amount assert hacker_hash == client.user.character_hash assert victim_chat_id == user2.chat_id assert not show_hack hacker_hash, victim_chat_id, profiteer_chat_id, transaction_message, show_hack = client.create_hacker_transaction_other(f"/h@ck_theft_other {character_hash_4} {character_hash_5} {half_amount}") user4 = client.get_user_by_user_hash(character_hash_4) user5 = client.get_user_by_user_hash(character_hash_5) assert hacker_hash == client.user.character_hash assert victim_chat_id == user4.chat_id assert user4.finances == DEFAULT_FINANCES - half_amount assert user5.finances == DEFAULT_FINANCES + half_amount + half_amount assert show_hack

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Python

flask_opentracing/__init__.py

aaronluoq/python-flask

74bfe8bcd00eee9ce75a15c1634fda4c5d5f26ca

[ "BSD-3-Clause" ]

136

2016-08-24T17:57:45.000Z

2022-03-17T03:43:19.000Z

flask_opentracing/__init__.py

aaronluoq/python-flask

74bfe8bcd00eee9ce75a15c1634fda4c5d5f26ca

[ "BSD-3-Clause" ]

43

2016-12-21T19:11:33.000Z

2021-06-16T09:10:16.000Z

flask_opentracing/__init__.py

aaronluoq/python-flask

74bfe8bcd00eee9ce75a15c1634fda4c5d5f26ca

[ "BSD-3-Clause" ]

45

2016-09-04T03:23:25.000Z

2022-03-12T20:38:18.000Z

from .tracing import FlaskTracing # noqa from .tracing import FlaskTracing as FlaskTracer # noqa, deprecated

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py

Python

go/vumitools/tests/test_utils.py

lynnUg/vumi-go

852f906c46d5d26940bd6699f11488b73bbc3742

[ "BSD-3-Clause" ]

null

go/vumitools/tests/test_utils.py

lynnUg/vumi-go

852f906c46d5d26940bd6699f11488b73bbc3742

[ "BSD-3-Clause" ]

null

go/vumitools/tests/test_utils.py

lynnUg/vumi-go

852f906c46d5d26940bd6699f11488b73bbc3742

[ "BSD-3-Clause" ]

null

from twisted.internet.defer import inlineCallbacks from vumi.tests.helpers import VumiTestCase, MessageHelper from go.vumitools.utils import MessageMetadataDictHelper, MessageMetadataHelper from go.vumitools.tests.helpers import VumiApiHelper class TestMessageMetadataDictHelper(VumiTestCase): def setUp(self): self.msg_helper = self.add_helper(MessageHelper()) def mk_msg(self, go_metadata=None, optout_metadata=None): helper_metadata = {} if go_metadata is not None: helper_metadata['go'] = go_metadata if optout_metadata is not None: helper_metadata['optout'] = optout_metadata return self.msg_helper.make_inbound( "hi", helper_metadata=helper_metadata) def mk_md(self, message=None, go_metadata=None, optout_metadata=None): if message is None: message = self.mk_msg(go_metadata, optout_metadata) return MessageMetadataDictHelper(message['helper_metadata']) def test_is_sensitive(self): md = self.mk_md() self.assertFalse(md.is_sensitive()) md = self.mk_md(go_metadata={'sensitive': True}) self.assertTrue(md.is_sensitive()) def test_has_user_account(self): md = self.mk_md() self.assertFalse(md.has_user_account()) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertTrue(md.has_user_account()) def test_get_account_key(self): md = self.mk_md() self.assertRaises(KeyError, md.get_account_key) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertEqual(md.get_account_key(), 'user-1') def test_get_conversation_key(self): md = self.mk_md() self.assertRaises(KeyError, md.get_conversation_key) md = self.mk_md(go_metadata={'conversation_key': 'conv-1'}) self.assertEqual(md.get_conversation_key(), 'conv-1') def test_get_conversation_info(self): md = self.mk_md() self.assertEqual(md.get_conversation_info(), None) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertEqual(md.get_conversation_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'conversation_type': 'dummy', }) self.assertEqual(md.get_conversation_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'conversation_type': 'dummy', 'conversation_key': 'conv-1', }) self.assertEqual(md.get_conversation_info(), { 'user_account': 'user-1', 'conversation_type': 'dummy', 'conversation_key': 'conv-1', }) def test_set_conversation_info(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_conversation_info('dummy', 'conv-1') self.assertEqual(msg['helper_metadata']['go'], { 'conversation_type': 'dummy', 'conversation_key': 'conv-1', }) def test_set_user_account(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_user_account('user-1') self.assertEqual(msg['helper_metadata']['go'], { 'user_account': 'user-1', }) def test_get_router_key(self): md = self.mk_md() self.assertRaises(KeyError, md.get_router_key) md = self.mk_md(go_metadata={'router_key': 'router-1'}) self.assertEqual(md.get_router_key(), 'router-1') def test_get_router_info(self): md = self.mk_md() self.assertEqual(md.get_router_info(), None) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertEqual(md.get_router_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'router_type': 'dummy', }) self.assertEqual(md.get_router_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'router_type': 'dummy', 'router_key': 'router-1', }) self.assertEqual(md.get_router_info(), { 'user_account': 'user-1', 'router_type': 'dummy', 'router_key': 'router-1', }) def test_set_router_info(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_router_info('dummy', 'router-1') self.assertEqual(msg['helper_metadata']['go'], { 'router_type': 'dummy', 'router_key': 'router-1', }) @inlineCallbacks def test_add_conversation_metadata(self): md = self.mk_md() self.assertEqual(md._go_metadata, {}) vumi_helper = yield self.add_helper(VumiApiHelper()) user_helper = yield vumi_helper.make_user(u'user') conv = yield user_helper.create_conversation(u'bulk_message') md.add_conversation_metadata(conv) self.assertEqual(md._go_metadata, { 'user_account': user_helper.account_key, 'conversation_type': conv.conversation_type, 'conversation_key': conv.key, }) @inlineCallbacks def test_add_router_metadata(self): md = self.mk_md() self.assertEqual(md._go_metadata, {}) vumi_helper = yield self.add_helper(VumiApiHelper()) user_helper = yield vumi_helper.make_user(u'user') router = yield user_helper.create_router(u'keyword') md.add_router_metadata(router) self.assertEqual(md._go_metadata, { 'user_account': user_helper.account_key, 'router_type': router.router_type, 'router_key': router.key, }) class TestMessageMetadataHelper(VumiTestCase): @inlineCallbacks def setUp(self): self.vumi_helper = yield self.add_helper(VumiApiHelper()) self.user_helper = yield self.vumi_helper.make_user(u'user') self.msg_helper = self.add_helper(MessageHelper()) def mk_msg(self, go_metadata=None, optout_metadata=None): helper_metadata = {} if go_metadata is not None: helper_metadata['go'] = go_metadata if optout_metadata is not None: helper_metadata['optout'] = optout_metadata return self.msg_helper.make_inbound( "hi", helper_metadata=helper_metadata) def mk_md(self, message=None, go_metadata=None, optout_metadata=None): if message is None: message = self.mk_msg(go_metadata, optout_metadata) return MessageMetadataHelper(self.vumi_helper.get_vumi_api(), message) def test_is_sensitive(self): md = self.mk_md() self.assertFalse(md.is_sensitive()) md = self.mk_md(go_metadata={'sensitive': True}) self.assertTrue(md.is_sensitive()) def test_has_user_account(self): md = self.mk_md() self.assertFalse(md.has_user_account()) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertTrue(md.has_user_account()) def test_get_account_key(self): md = self.mk_md() self.assertRaises(KeyError, md.get_account_key) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertEqual(md.get_account_key(), 'user-1') def test_get_user_api(self): md = self.mk_md() self.assertRaises(KeyError, md.get_user_api) md = self.mk_md( go_metadata={'user_account': self.user_helper.account_key}) user_api = md.get_user_api() self.assertEqual( user_api.user_account_key, self.user_helper.account_key) def test_get_conversation_key(self): md = self.mk_md() self.assertRaises(KeyError, md.get_conversation_key) md = self.mk_md(go_metadata={'conversation_key': 'conv-1'}) self.assertEqual(md.get_conversation_key(), 'conv-1') @inlineCallbacks def test_get_conversation(self): md = self.mk_md() self.assertRaises(KeyError, md.get_conversation) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertRaises(KeyError, md.get_conversation) conversation = yield self.user_helper.create_conversation( u'bulk_message') md = self.mk_md(go_metadata={ 'user_account': self.user_helper.account_key, 'conversation_key': conversation.key, }) md_conv = yield md.get_conversation() self.assertEqual(md_conv.key, conversation.key) @inlineCallbacks def test_clear_object_cache(self): conversation = yield self.user_helper.create_conversation( u'bulk_message') md = self.mk_md(go_metadata={ 'user_account': conversation.user_account.key, 'conversation_key': conversation.key, }) md.set_tag(["pool", "tag"]) self.assertEqual(md._store_objects, {}) md_conv = yield md.get_conversation() tag_info = yield md.get_tag_info() self.assertEqual(md._store_objects, { 'conversation': md_conv, 'tag_info': tag_info, }) md.clear_object_cache() self.assertEqual(md._store_objects, {}) @inlineCallbacks def test_conversation_caching(self): md = self.mk_md() self.assertRaises(KeyError, md.get_conversation) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertRaises(KeyError, md.get_conversation) conversation = yield self.user_helper.create_conversation( u'bulk_message') md = self.mk_md(go_metadata={ 'user_account': conversation.user_account.key, 'conversation_key': conversation.key, }) md_conv = yield md.get_conversation() self.assertEqual(md_conv.key, conversation.key) self.assertEqual(md_conv.status, conversation.status) # Modify the conversation and get it from md again, making sure we # still have cached data. conversation.set_status_starting() yield conversation.save() md_conv2 = yield md.get_conversation() self.assertIdentical(md_conv, md_conv2) self.assertNotEqual(md_conv2.status, conversation.status) # Clear the stored object cache and get the conversation from md again, # making sure we have new data now. md.clear_object_cache() md_conv3 = yield md.get_conversation() self.assertEqual(md_conv3.key, conversation.key) self.assertEqual(md_conv3.status, conversation.status) self.assertNotIdentical(md_conv, md_conv3) def test_get_conversation_info(self): md = self.mk_md() self.assertEqual(md.get_conversation_info(), None) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertEqual(md.get_conversation_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'conversation_type': 'dummy', }) self.assertEqual(md.get_conversation_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'conversation_type': 'dummy', 'conversation_key': 'conv-1', }) self.assertEqual(md.get_conversation_info(), { 'user_account': 'user-1', 'conversation_type': 'dummy', 'conversation_key': 'conv-1', }) def test_set_conversation_info(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_conversation_info('dummy', 'conv-1') self.assertEqual(msg['helper_metadata']['go'], { 'conversation_type': 'dummy', 'conversation_key': 'conv-1', }) def test_set_user_account(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_user_account('user-1') self.assertEqual(msg['helper_metadata']['go'], { 'user_account': 'user-1', }) def test_is_optout_message(self): md = self.mk_md() self.assertFalse(md.is_optout_message()) md = self.mk_md(optout_metadata={"optout": True}) self.assertTrue(md.is_optout_message()) def test_get_router_key(self): md = self.mk_md() self.assertRaises(KeyError, md.get_router_key) md = self.mk_md(go_metadata={'router_key': 'router-1'}) self.assertEqual(md.get_router_key(), 'router-1') @inlineCallbacks def test_get_router(self): md = self.mk_md() self.assertRaises(KeyError, md.get_router) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertRaises(KeyError, md.get_router) router = yield self.user_helper.create_router(u'keyword') md = self.mk_md(go_metadata={ 'user_account': self.user_helper.account_key, 'router_key': router.key, }) md_router = yield md.get_router() self.assertEqual(md_router.key, router.key) @inlineCallbacks def test_router_caching(self): md = self.mk_md() self.assertRaises(KeyError, md.get_router) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertRaises(KeyError, md.get_router) router = yield self.user_helper.create_router(u'keyword') md = self.mk_md(go_metadata={ 'user_account': router.user_account.key, 'router_key': router.key, }) md_router = yield md.get_router() self.assertEqual(md_router.key, router.key) self.assertEqual(md_router.status, router.status) # Modify the router and get it from md again, making sure we # still have cached data. router.set_status_starting() yield router.save() md_router2 = yield md.get_router() self.assertIdentical(md_router, md_router2) self.assertNotEqual(md_router2.status, router.status) # Clear the stored object cache and get the router from md again, # making sure we have new data now. md.clear_object_cache() md_router3 = yield md.get_router() self.assertEqual(md_router3.key, router.key) self.assertEqual(md_router3.status, router.status) self.assertNotIdentical(md_router, md_router3) def test_get_router_info(self): md = self.mk_md() self.assertEqual(md.get_router_info(), None) md = self.mk_md(go_metadata={'user_account': 'user-1'}) self.assertEqual(md.get_router_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'router_type': 'dummy', }) self.assertEqual(md.get_router_info(), None) md = self.mk_md(go_metadata={ 'user_account': 'user-1', 'router_type': 'dummy', 'router_key': 'router-1', }) self.assertEqual(md.get_router_info(), { 'user_account': 'user-1', 'router_type': 'dummy', 'router_key': 'router-1', }) def test_set_router_info(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_router_info('dummy', 'router-1') self.assertEqual(msg['helper_metadata']['go'], { 'router_type': 'dummy', 'router_key': 'router-1', }) def test_set_tag(self): msg = self.mk_msg() md = self.mk_md(msg) md.set_tag(["pool", "tagname"]) self.assertEqual(msg['helper_metadata']['tag'], { 'tag': ["pool", "tagname"], }) def test_rewrap(self): msg = self.mk_msg() md = self.mk_md(msg) # The metadata wrapper creates the 'go' metadata self.assertEqual(msg['helper_metadata']['go'], {}) # We create a new wrapper around the same message object and make sure # the cached message store objects are still there in the new one. new_md = self.mk_md(msg) self.assertNotEqual(md, new_md) self.assertIdentical(md._store_objects, new_md._store_objects) self.assertIdentical(md._go_metadata, new_md._go_metadata) # We create a new wrapper around the a copy of the message object and # make sure the message store object cache is empty, but the metadata # remains. other_md = self.mk_md(msg.copy()) self.assertNotIdentical(md, other_md) self.assertEqual({}, other_md._store_objects) self.assertEqual(md._go_metadata, other_md._go_metadata) def test_get_tag_info_no_tag(self): md = self.mk_md() self.assertEqual(None, md.tag) self.assertRaises(ValueError, md.get_tag_info) def test_get_tagpool_metadata_no_tag(self): md = self.mk_md() self.assertEqual(None, md.tag) self.assertRaises(ValueError, md.get_tagpool_metadata) @inlineCallbacks def test_get_tag_info(self): md = self.mk_md() md.set_tag(["pool", "tagname"]) tag_info = yield md.get_tag_info() self.assertEqual(("pool", "tagname"), tag_info.tag) @inlineCallbacks def test_tag_info_caching(self): md = self.mk_md() md.set_tag(["pool", "tagname"]) self.assertEqual({}, md._store_objects) tag_info = yield md.get_tag_info() self.assertEqual(("pool", "tagname"), tag_info.tag) self.assertEqual({'tag_info': tag_info}, md._store_objects) # Stash a fake thing in the cache to make sure that what we get is # actually the thing in the cache. md._store_objects['tag_info'] = "I am the cached tag_info" cached_tag_info = yield md.get_tag_info() self.assertEqual(cached_tag_info, "I am the cached tag_info") @inlineCallbacks def test_get_tagpool_metadata(self): yield self.vumi_helper.setup_tagpool("pool", ["tagname"], metadata={ "foo": "bar", }) md = self.mk_md() md.set_tag(["pool", "tagname"]) tagpool_metadata = yield md.get_tagpool_metadata() self.assertEqual({"foo": "bar"}, tagpool_metadata) @inlineCallbacks def test_tagpool_metadata_caching(self): yield self.vumi_helper.setup_tagpool("pool", ["tagname"], metadata={ "foo": "bar", }) md = self.mk_md() md.set_tag(["pool", "tagname"]) self.assertEqual({}, md._store_objects) tagpool_metadata = yield md.get_tagpool_metadata() self.assertEqual({"foo": "bar"}, tagpool_metadata) self.assertEqual( {'tagpool_metadata': tagpool_metadata}, md._store_objects) # Stash a fake thing in the cache to make sure that what we get is # actually the thing in the cache. md._store_objects['tagpool_metadata'] = "I am the cached metadata" cached_tagpool_metadata = yield md.get_tagpool_metadata() self.assertEqual(cached_tagpool_metadata, "I am the cached metadata")

37.566866

79

0.627012

2,351

18,821

4.740961

0.055296

0.053293

0.05096

0.0637

0.817962

0.776781

0.754531

0.723668

0.715234

0.693163

0

0.004833

0.252431

18,821

500

80

37.642

0.787349

0.047553

0

0.78744

0

0.111409

0

0.23913

1

0.103865

false

0

0.009662

0

0.128019

0

null

0

1

0

1

0

null

0

7

1f093df8504af1904df82708b160c2b0ec920c88

20,554

py

Python

tests/benchmarks/unpack_sequence.py

jacebrowning/voc

7bc84e8a870674d300ad5083748cf6b826e7fb68

[ "BSD-3-Clause" ]

null

tests/benchmarks/unpack_sequence.py

jacebrowning/voc

7bc84e8a870674d300ad5083748cf6b826e7fb68

[ "BSD-3-Clause" ]

2

2018-09-26T12:52:52.000Z

2018-09-27T13:51:29.000Z

tests/benchmarks/unpack_sequence.py

jacebrowning/voc

7bc84e8a870674d300ad5083748cf6b826e7fb68

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python """Microbenchmark for Python's sequence unpacking.""" import time def do_unpacking(iterations, to_unpack): times = [] for _ in range(iterations): t0 = time.time() # Should be 400 unpackings, but MethodCodeTooLarge # TODO Look into why this code is so big a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack """ a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack a, b, c, d, e, f, g, h, i, j = to_unpack """ t1 = time.time() times.append(t1 - t0) return times def test_tuple_unpacking(iterations): x = tuple(range(10)) return do_unpacking(iterations, x) def test_list_unpacking(iterations): x = range(10) return do_unpacking(iterations, x) def test_all(iterations): tuple_data = test_tuple_unpacking(iterations) list_data = test_list_unpacking(iterations) return [x + y for (x, y) in zip(tuple_data, list_data)] if __name__ == "__main__": import sys loops = int(sys.argv[1]) times = test_all(loops) print("Time elapsed: " + str(sum(times)) + " sec")

46.188764

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0.399679

4,926

20,554

1.580999

0.01421

0.411916

0.154083

0.205444

0.935285

0.924499

0

0.000974

0.400409

20,554

444

60

46.292793

0.630964

0.00759

0

0.871166

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0.003405

0

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1

0.02454

false

0

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0

1

null

1

0

1

0

1

0

null

0

10

1f368c2100fa3033fd16e2888fca891530eef26e

109

py

Python

MiddleKit/Design/MySQLPythonGenerator.py

PeaceWorksTechnologySolutions/w4py

74f5a03a63f1a93563502b908474aefaae2abda2

[ "MIT" ]

18

2016-08-01T20:15:59.000Z

2019-12-24T16:00:03.000Z

MiddleKit/Design/MySQLPythonGenerator.py

WebwareForPython/w4py

bba08f5974d49f5da7e88abe3eeda1037d0824a3

[ "MIT" ]

6

2016-09-13T05:48:45.000Z

2020-01-09T18:29:12.000Z

MiddleKit/Design/MySQLPythonGenerator.py

WebwareForPython/w4py

bba08f5974d49f5da7e88abe3eeda1037d0824a3

[ "MIT" ]

6

2016-09-16T14:32:29.000Z

2020-01-03T18:52:16.000Z

from SQLPythonGenerator import SQLPythonGenerator class MySQLPythonGenerator(SQLPythonGenerator): pass

18.166667

49

0.853211

8

109

11.625

0.75

0

0.119266

109

5

50

21.8

0.96875

0

1

0

true

0.333333

0

0.666667

0

1

0

1

null

0

1

0

null

0

1

0

1

0

7

1f5329be89cab1e1995b2487d3838dda997e5db4

139

py

Python

tests/parser/true_negation.1.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

tests/parser/true_negation.1.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

tests/parser/true_negation.1.test.py

veltri/DLV2

944aaef803aa75e7ec51d7e0c2b0d964687fdd0e

[ "Apache-2.0" ]

null

input = """ true | -a :- not b. true | b :- not -a. -true. """ output = """ true | -a :- not b. true | b :- not -a. -true. """

10.692308

21

0.395683

20

139

2.75

0.3

0.181818

0.290909

0.327273

0.8

0

0.330935

139

12

22

11.583333

0.591398

0

0.8

0

0.763359

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

1

0

null

0

9

1f93705b9815513d4df36a31fc46c08bf3d4a4f3

90

py

Python

examples/simple.py

nitred/imdb-wiki-dataset

3424ead8c190fd5f1b53e2aba2caf5c163ca0003

[ "MIT" ]

null

examples/simple.py

nitred/imdb-wiki-dataset

3424ead8c190fd5f1b53e2aba2caf5c163ca0003

[ "MIT" ]

null

examples/simple.py

nitred/imdb-wiki-dataset

3424ead8c190fd5f1b53e2aba2caf5c163ca0003

[ "MIT" ]

null

"""Basic functionality.""" import imdb_wiki_dataset print(imdb_wiki_dataset.__version__)

18

36

0.822222

11

90

6

0.727273

0.242424

0.454545

0

0.066667

90

4

37

22.5

0.785714

0.222222

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

7

2f57ae488f95d3c86d7881d335bfe2557e0359d9

1,148

py

Python

src/models/chainlink.py

Dragonfly-Capital/oracles.club.server

092dc1e6d205ceb475cd65f9b1c3e4aa6ef588dd

[ "MIT" ]

7

2020-04-28T02:17:51.000Z

2020-09-23T17:39:38.000Z

src/models/chainlink.py

Dragonfly-Capital/oracles.club.server

092dc1e6d205ceb475cd65f9b1c3e4aa6ef588dd

[ "MIT" ]

1

2020-08-10T19:39:12.000Z

src/models/chainlink.py

Dragonfly-Capital/oracles.club.server

092dc1e6d205ceb475cd65f9b1c3e4aa6ef588dd

[ "MIT" ]

2

2020-05-10T09:39:47.000Z

2020-07-27T18:12:23.000Z

from .create_db import db class ChainlinkETH(db.Model): __tablename__ = 'chainlink' id = db.Column('id', db.Integer, primary_key=True) blocknumber = db.Column('blocknumber', db.Integer) timestamp = db.Column('timestamp', db.Integer) price = db.Column('price', db.Float) def __repr__(self): return '{}, {}, {}'.format(self.blocknumber, self.timestamp, self.price) class ChainlinkBTC(db.Model): __tablename__ = 'chainlinkbtc' id = db.Column('id', db.Integer, primary_key=True) blocknumber = db.Column('blocknumber', db.Integer) timestamp = db.Column('timestamp', db.Integer) price = db.Column('price', db.Float) def __repr__(self): return '{}, {}, {}'.format(self.blocknumber, self.timestamp, self.price) class ChainlinkBAT(db.Model): __tablename__ = 'chainlinkbat' id = db.Column('id', db.Integer, primary_key=True) blocknumber = db.Column('blocknumber', db.Integer) timestamp = db.Column('timestamp', db.Integer) price = db.Column('price', db.Float) def __repr__(self): return '{}, {}, {}'.format(self.blocknumber, self.timestamp, self.price)

32.8

80

0.663763

137

1,148

5.357664

0.189781

0.13079

0.065395

0.049046

0.80654

0

0.173345

1,148

34

81

33.764706

0.773446

0

0.72

0

0.125436

0

1

0.12

false

0

0.04

0.12

1

0

null

0

1

0

1

0

null

0

1

0

8

2f828a8122993bb35a5603c99b290b1d88a36bbe

198

py

Python

books/admin.py

adilmohak/django_book_sharing

6d47cb131524dc761becb7d432b7cc75064c4f58

[ "MIT" ]

13

2021-03-26T05:39:58.000Z

2021-10-13T22:03:46.000Z

books/admin.py

adilmohak/django_book_sharing

6d47cb131524dc761becb7d432b7cc75064c4f58

[ "MIT" ]

1

2021-03-26T05:42:47.000Z

2021-04-24T17:33:26.000Z

books/admin.py

adilmohak/django_book_sharing

6d47cb131524dc761becb7d432b7cc75064c4f58

[ "MIT" ]

2

2021-03-26T05:54:59.000Z

2021-03-26T09:03:46.000Z

from django.contrib import admin from .models import Book, Genres, Review, Booklist admin.site.register(Book) admin.site.register(Booklist) admin.site.register(Genres) admin.site.register(Review)

22

50

0.808081

28

198

5.714286

0.428571

0.225

0.425

0.3125

0

0.085859

198

8

51

24.75

0.883978

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

85f13528fb29dd37cf4ff04ccd41d5c434f96cd4

181

py

Python

app/utils.py

arezi/invest-monitor

d6dda558be0f13b731a3127ead60695f04fcdf16

[ "MIT" ]

null

app/utils.py

arezi/invest-monitor

d6dda558be0f13b731a3127ead60695f04fcdf16

[ "MIT" ]

null

app/utils.py

arezi/invest-monitor

d6dda558be0f13b731a3127ead60695f04fcdf16

[ "MIT" ]

null

import os def get_app_base_path(): return os.path.dirname(os.path.realpath(__file__)) #def get_static_folder_path(): # return os.path.join(get_app_base_path(), "static")

18.1

55

0.734807

29

181

4.137931

0.482759

0.15

0.166667

0.233333

0

0.127072

181

9

56

20.111111

0.759494

0.458564

0

1

0.333333

true

0

0.333333

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

8

c807b5f4d75139d7e7769488aa2248c239b523b9

15,687

py

Python

manila/tests/share/drivers/infortrend/fake_infortrend_manila_data.py

gouthampacha/manila

4b7ba9b99d272663f519b495668715fbf979ffbc

[ "Apache-2.0" ]

159

2015-01-02T09:35:15.000Z

2022-01-04T11:51:34.000Z

manila/tests/share/drivers/infortrend/fake_infortrend_manila_data.py

gouthampacha/manila

4b7ba9b99d272663f519b495668715fbf979ffbc

[ "Apache-2.0" ]

6

2021-02-11T16:09:43.000Z

2022-03-15T09:56:25.000Z

manila/tests/share/drivers/infortrend/fake_infortrend_manila_data.py

gouthampacha/manila

4b7ba9b99d272663f519b495668715fbf979ffbc

[ "Apache-2.0" ]

128

2015-01-05T22:52:28.000Z

2021-12-29T14:00:58.000Z

# Copyright (c) 2019 Infortrend Technology, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. class InfortrendManilaTestData(object): fake_share_id = ['4d6984fd-8572-4467-964f-24936a8c4ea2', # NFS 'a7b933e6-bb77-4823-a86f-f2c3ab41a8a5'] # CIFS fake_id = ['iftt8862-2226-0126-7610-chengweichou', '987c8763-3333-4444-5555-666666666666'] fake_share_nfs = { 'share_id': fake_share_id[0], 'availability_zone': 'nova', 'terminated_at': 'datetime.datetime(2017, 5, 8, 8, 27, 25)', 'availability_zone_id': 'fd32d76d-b5a8-4c5c-93d7-8f09fc2a8ad3', 'updated_at': 'datetime.datetime(2017, 5, 8, 8, 27, 25)', 'share_network_id': None, 'export_locations': [], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': '5a0aa06e-1c57-4996-be46-b81e360e8866', 'size': 30, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': '172.27.112.223:/share-pool-01/LV-1/' + fake_share_id[0], 'display_description': None, 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': 'datetime.datetime(2017, 5, 8, 8, 23, 33)', 'scheduled_at': 'datetime.datetime(2017, 5, 8, 8, 23, 29)', 'status': 'deleting', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': 'compute@ift-manila#share-pool-01', 'access_rules_status': 'active', 'display_name': 'nfs-01', 'name': 'share-5a0aa06e-1c57-4996-be46-b81e360e8866', 'created_at': 'datetime.datetime(2017, 5, 8, 8, 23, 29)', 'share_proto': 'NFS', 'is_public': False, 'source_cgsnapshot_member_id': None } fake_share_cifs = { 'share_id': fake_share_id[1], 'availability_zone': 'nova', 'terminated_at': None, 'availability_zone_id': 'fd32d76d-b5a8-4c5c-93d7-8f09fc2a8ad3', 'updated_at': 'datetime.datetime(2017, 5, 9, 2, 28, 35)', 'share_network_id': None, 'export_locations': [], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': 'aac4fe64-7a9c-472a-b156-9adbb50b4d29', 'size': 50, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': None, 'display_description': None, 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': None, 'scheduled_at': 'datetime.datetime(2017, 5, 9, 2, 28, 35)', 'status': 'creating', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': 'compute@ift-manila#share-pool-01', 'access_rules_status': 'active', 'display_name': 'cifs-01', 'name': 'share-aac4fe64-7a9c-472a-b156-9adbb50b4d29', 'created_at': 'datetime.datetime(2017, 5, 9, 2, 28, 35)', 'share_proto': 'CIFS', 'is_public': False, 'source_cgsnapshot_member_id': None } fake_share_cifs_no_host = { 'share_id': fake_share_id[1], 'availability_zone': 'nova', 'terminated_at': None, 'availability_zone_id': 'fd32d76d-b5a8-4c5c-93d7-8f09fc2a8ad3', 'updated_at': 'datetime.datetime(2017, 5, 9, 2, 28, 35)', 'share_network_id': None, 'export_locations': [], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': 'aac4fe64-7a9c-472a-b156-9adbb50b4d29', 'size': 50, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': None, 'display_description': None, 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': None, 'scheduled_at': 'datetime.datetime(2017, 5, 9, 2, 28, 35)', 'status': 'creating', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': '', 'access_rules_status': 'active', 'display_name': 'cifs-01', 'name': 'share-aac4fe64-7a9c-472a-b156-9adbb50b4d29', 'created_at': 'datetime.datetime(2017, 5, 9, 2, 28, 35)', 'share_proto': 'CIFS', 'is_public': False, 'source_cgsnapshot_member_id': None } fake_non_exist_share = { 'share_id': fake_id[0], 'availability_zone': 'nova', 'terminated_at': 'datetime.datetime(2017, 5, 8, 8, 27, 25)', 'availability_zone_id': 'fd32d76d-b5a8-4c5c-93d7-8f09fc2a8ad3', 'updated_at': 'datetime.datetime(2017, 5, 8, 8, 27, 25)', 'share_network_id': None, 'export_locations': [], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': fake_id[1], 'size': 30, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': '172.27.112.223:/share-pool-01/LV-1/' + fake_id[0], 'display_description': None, 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': 'datetime.datetime(2017, 5, 8, 8, 23, 33)', 'scheduled_at': 'datetime.datetime(2017, 5, 8, 8, 23, 29)', 'status': 'available', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': 'compute@ift-manila#share-pool-01', 'access_rules_status': 'active', 'display_name': 'nfs-01', 'name': 'share-5a0aa06e-1c57-4996-be46-b81e360e8866', 'created_at': 'datetime.datetime(2017, 5, 8, 8, 23, 29)', 'share_proto': 'NFS', 'is_public': False, 'source_cgsnapshot_member_id': None } fake_access_rules_nfs = [{ 'share_id': fake_share_id[0], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 8, 41, 21)', 'updated_at': None, 'access_type': 'ip', 'access_to': '172.27.1.1', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': 'fa60b50f-1428-44a2-9931-7e31f0c5b033'}, { 'share_id': fake_share_id[0], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 8, 45, 37)', 'updated_at': None, 'access_type': 'ip', 'access_to': '172.27.1.2', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': '9bcdd5e6-11c7-4f8f-939c-84fa2f3334bc' }] fake_rule_ip_1 = [{ 'share_id': fake_share_id[0], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 8, 41, 21)', 'updated_at': None, 'access_type': 'ip', 'access_to': '172.27.1.1', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': 'fa60b50f-1428-44a2-9931-7e31f0c5b033' }] fake_rule_ip_2 = [{ 'share_id': fake_share_id[0], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 8, 45, 37)', 'updated_at': None, 'access_type': 'ip', 'access_to': '172.27.1.2', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': '9bcdd5e6-11c7-4f8f-939c-84fa2f3334bc' }] fake_access_rules_cifs = [{ 'share_id': fake_share_id[1], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 9, 39, 18)', 'updated_at': None, 'access_type': 'user', 'access_to': 'user02', 'access_level': 'ro', 'instance_mappings': [], 'deleted_at': None, 'id': '6e8bc969-51c9-4bbb-8e8b-020dc5fec81e'}, { 'share_id': fake_share_id[1], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 9, 38, 59)', 'updated_at': None, 'access_type': 'user', 'access_to': 'user01', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': '0cd9926d-fac4-4122-a523-538e98752e78' }] fake_rule_user01 = [{ 'share_id': fake_share_id[1], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 9, 38, 59)', 'updated_at': None, 'access_type': 'user', 'access_to': 'user01', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': '0cd9926d-fac4-4122-a523-538e98752e78' }] fake_rule_user02 = [{ 'share_id': fake_share_id[1], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 9, 39, 18)', 'updated_at': None, 'access_type': 'user', 'access_to': 'user02', 'access_level': 'ro', 'instance_mappings': [], 'deleted_at': None, 'id': '6e8bc969-51c9-4bbb-8e8b-020dc5fec81e' }] fake_rule_user03 = [{ 'share_id': fake_id[0], 'deleted': 'False', 'created_at': 'datetime.datetime(2017, 5, 9, 9, 39, 18)', 'updated_at': None, 'access_type': 'user', 'access_to': 'user03', 'access_level': 'rw', 'instance_mappings': [], 'deleted_at': None, 'id': fake_id[1] }] fake_share_for_manage_nfs = { 'share_id': '419ab73c-c0fc-4e73-b56a-70756e0b6d27', 'availability_zone': None, 'terminated_at': None, 'availability_zone_id': None, 'updated_at': None, 'share_network_id': None, 'export_locations': [{ 'uuid': '0ebd59e4-e65e-4fda-9457-320375efd0be', 'deleted': 0, 'created_at': 'datetime.datetime(2017, 5, 10, 10, 0, 3)', 'updated_at': 'datetime.datetime(2017, 5, 10, 10, 0, 3)', 'is_admin_only': False, 'share_instance_id': 'd3cfe195-85cf-41e6-be4f-a96f7e7db192', 'path': '172.27.112.223:/share-pool-01/LV-1/test-folder', 'el_metadata': {}, 'deleted_at': None, 'id': 83 }], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': '615ac1ed-e808-40b5-8d7b-87018c6f66eb', 'size': None, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': '172.27.112.223:/share-pool-01/LV-1/test-folder', 'display_description': '', 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': None, 'scheduled_at': 'datetime.datetime(2017, 5, 10, 9, 22, 5)', 'status': 'manage_starting', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': 'compute@ift-manila#share-pool-01', 'access_rules_status': 'active', 'display_name': 'test-manage', 'name': 'share-615ac1ed-e808-40b5-8d7b-87018c6f66eb', 'created_at': 'datetime.datetime(2017, 5, 10, 9, 22, 5)', 'share_proto': 'NFS', 'is_public': False, 'source_cgsnapshot_member_id': None } def _get_fake_share_for_manage(self, location=''): return { 'share_id': '419ab73c-c0fc-4e73-b56a-70756e0b6d27', 'availability_zone': None, 'terminated_at': None, 'availability_zone_id': None, 'updated_at': None, 'share_network_id': None, 'export_locations': [{ 'uuid': '0ebd59e4-e65e-4fda-9457-320375efd0be', 'deleted': 0, 'created_at': 'datetime.datetime(2017, 5, 10, 10, 0, 3)', 'updated_at': 'datetime.datetime(2017, 5, 10, 10, 0, 3)', 'is_admin_only': False, 'share_instance_id': 'd3cfe195-85cf-41e6-be4f-a96f7e7db192', 'path': location, 'el_metadata': {}, 'deleted_at': None, 'id': 83 }], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': '615ac1ed-e808-40b5-8d7b-87018c6f66eb', 'size': None, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': location, 'display_description': '', 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': None, 'scheduled_at': 'datetime.datetime(2017, 5, 10, 9, 22, 5)', 'status': 'manage_starting', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': 'compute@ift-manila#share-pool-01', 'access_rules_status': 'active', 'display_name': 'test-manage', 'name': 'share-615ac1ed-e808-40b5-8d7b-87018c6f66eb', 'created_at': 'datetime.datetime(2017, 5, 10, 9, 22, 5)', 'share_proto': 'NFS', 'is_public': False, 'source_cgsnapshot_member_id': None } fake_share_for_manage_cifs = { 'share_id': '3a1222d3-c981-490a-9390-4d560ced68eb', 'availability_zone': None, 'terminated_at': None, 'availability_zone_id': None, 'updated_at': None, 'share_network_id': None, 'export_locations': [{ 'uuid': '0ebd59e4-e65e-4fda-9457-320375efd0de', 'deleted': 0, 'created_at': 'datetime.datetime(2017, 5, 11, 10, 10, 3)', 'updated_at': 'datetime.datetime(2017, 5, 11, 10, 10, 3)', 'is_admin_only': False, 'share_instance_id': 'd3cfe195-85cf-41e6-be4f-a96f7e7db192', 'path': '\\\\172.27.113.209\\test-folder-02', 'el_metadata': {}, 'deleted_at': None, 'id': 87 }], 'share_server_id': None, 'snapshot_id': None, 'deleted_at': None, 'id': 'd156baf7-5422-4c9b-8c78-ee7943d000ec', 'size': None, 'replica_state': None, 'user_id': '4944594433f0405588928a4212964658', 'export_location': '\\\\172.27.113.209\\test-folder-02', 'display_description': '', 'consistency_group_id': None, 'project_id': '0e63326c50a246ac81fa1a0c8e003d5b', 'launched_at': None, 'scheduled_at': 'datetime.datetime(2017, 5, 11, 3, 7, 59)', 'status': 'manage_starting', 'share_type_id': '23d8c637-0192-47fa-b921-958f22ed772f', 'deleted': 'False', 'host': 'compute@ift-manila#share-pool-01', 'access_rules_status': 'active', 'display_name': 'test-manage-02', 'name': 'share-d156baf7-5422-4c9b-8c78-ee7943d000ec', 'created_at': 'datetime.datetime(2017, 5, 11, 3, 7, 59)', 'share_proto': 'CIFS', 'is_public': False, 'source_cgsnapshot_member_id': None }

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78

0.56601

1,741

15,687

4.867892

0.160253

0.029027

0.078584

0.096047

0.889676

0.875752

0.872802

0.862183

0.860413

0.84413

0

0.162787

0.274367

15,687

408

79

38.448529

0.581745

0.039906

0

0.851064

0

0.005319

0.54221

0.237437

0

1

0.00266

false

0

0.00266

0.047872

0

null

0

1

0

1

0

null

0

7

c80fef0ac54b4f7eaf4ce042cb6f476835195b2f

193

py

Python

test/convert_meshes.py

mskim99/Pix2Vox_modify

0cc28e2c9a4a86c25e570317d2dd296bb8565ff7

[ "MIT" ]

null

test/convert_meshes.py

mskim99/Pix2Vox_modify

0cc28e2c9a4a86c25e570317d2dd296bb8565ff7

[ "MIT" ]

null

test/convert_meshes.py

mskim99/Pix2Vox_modify

0cc28e2c9a4a86c25e570317d2dd296bb8565ff7

[ "MIT" ]

null

import meshio mesh = meshio.read('I:\Program/Pix2Vox-master/voxel_log/voxel_process/gv_mha_000000_up.vtu') mesh.write("I:\Program/Pix2Vox-master/voxel_log/voxel_process/gv_mha_000000_up.obj")

38.6

92

0.823834

33

193

4.515152

0.545455

0.107383

0.201342

0.281879

0.724832

0

0.075269

0.036269

193

5

93

38.6

0.725806

0

0.721649

0

1

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false

0

0.333333

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0

1

0

1

null

0

1

0

11

c81b046a72fff213eb2d2b705f4e5dbc330d712f

5,909

py

Python

hacker_earth/python_problems/tictactoe.py

Faraaz54/python_training_problems

24c7b42daaf54366759e1d7c4b42f9936316e94b

[ "MIT" ]

null

hacker_earth/python_problems/tictactoe.py

Faraaz54/python_training_problems

24c7b42daaf54366759e1d7c4b42f9936316e94b

[ "MIT" ]

null

hacker_earth/python_problems/tictactoe.py

Faraaz54/python_training_problems

24c7b42daaf54366759e1d7c4b42f9936316e94b

[ "MIT" ]

null

for _ in xrange(int(raw_input())): board = [] count = 0 pos = [] winnable = 'NO' for i in range(4): rows = [i for i in raw_input()] board.append(rows) for row in board: print " ".join(row) for row in board: for place in row: if place == 'x' or place == 'o': count += 1 if count == 0: print winnable if count % 2 == 0: bat_piece = 'x' else: bat_piece = 'o' for row in range(0,4): for col in range(0,4): if board[row][col] == bat_piece: pos.append((row, col)) print pos for positions in pos: row_1, col_1 = positions if winnable == 'YES': break #right_row if col_1 + 1 in range(0,4) and col_1 + 2 in range(0,4): if board[row_1][col_1 + 1] == bat_piece and board[row_1][col_1 + 2] == '.': winnable = 'YES' elif board[row_1][col_1 + 1] == '.' and board[row_1][col_1 + 2] == bat_piece: winnable = 'YES' #left_row elif col_1 - 1 in range(0,4) and col_1 - 2 in range(0,4): if board[row_1][col_1 - 1] == bat_piece and board[row_1][col_1 - 2] == '.': winnable = 'YES' elif board[row_1][col_1 - 1] == '.' and board[row_1][col_1 - 2] == bat_piece: winnable = 'YES' #below elif row_1 + 1 in range(0,4) and row_1 + 2 in range(0,4): if board[row_1 + 1][col_1] == bat_piece and board[row_1 + 2][col_1] == '.': winnable = 'YES' elif board[row_1 + 1][col_1] == '.' and board[row_1 + 2][col_1] == bat_piece: winnable = 'YES' #above elif row_1 - 1 in range(0,4) and row_1 - 2 in range(0,4): if board[row_1 - 1][col_1] == bat_piece and board[row_1 - 2][col_1] == '.': winnable = 'YES' if board[row_1 - 1][col_1] == '.' and board[row_1 - 2][col_1] == bat_piece: winnable = 'YES' #left_diagonal elif row_1 - 1 in range(0,4) and col_1 - 1 in range(0,4): if board[row_1 - 1][col_1 - 1] == bat_piece and board[row_1 + 1][col_1 + 1] == '.': winnable = 'YES' elif board[row_1 - 1][col_1 - 1] == '.' and board[row_1 + 1][col_1 + 1] == bat_piece: winnable = 'YES' #left_diagonal elif row_1 - 1 in range(0, 4) and col_1 - 1 in range(0, 4): if row_1 - 2 in range(0, 4) and col_1 - 2 in range(0, 4): if board[row_1 - 1][col_1 - 1] == bat_piece and board[row_1 - 2][col_1 -2] == '.': winnable = 'YES' elif board[row_1 - 1][col_1 - 1] == '.' and board[row_1 - 2][col_1 - 2] == bat_piece: winnable = 'YES' #right_diagonal elif row_1 - 1 in range(0,4) and col_1 + 1 in range(0,4): if board[row_1 - 1][col_1 + 1] == bat_piece and board[row_1 + 1][col_1 - 1] == '.': winnable = 'YES' elif board[row_1 - 1][col_1 + 1] == '.' and board[row_1 + 1][col_1 - 1] == bat_piece: winnable = 'YES' #right_diagonal elif row_1 - 1 in range(0, 4) and col_1 + 1 in range(0, 4): if row_1 - 2 in range(0, 4) and col_1 + 2 in range(0, 4): if board[row_1 - 1][col_1 + 1] == bat_piece and board[row_1 - 2][col_1 + 2] == '.': winnable = 'YES' elif board[row_1 - 1][col_1 + 1] == '.' and board[row_1 - 2][col_1 + 2] == bat_piece: winnable = 'YES' #left_below_diagonal elif row_1 + 1 in range(0,4) and col_1 - 1 in range(0,4): if board[row_1 + 1][col_1 - 1] == bat_piece and board[row_1 - 1][col_1 + 1] == '.': winnable = 'YES' elif board[row_1 + 1][col_1 - 1] == '.' and board[row_1 - 1][col_1 + 1] == bat_piece: winnable = 'YES' #left_below_diagonal elif row_1 + 1 in range(0, 4) and col_1 - 1 in range(0, 4): if row_1 + 2 in range(0, 4) and col_1 - 2 in range(0, 4): if board[row_1 + 1][col_1 - 1] == bat_piece and board[row_1 + 2][col_1 - 2] == '.': winnable = 'YES' elif board[row_1 + 1][col_1 - 1] == '.' and board[row_1 + 2][col_1 - 2] == bat_piece: winnable = 'YES' #right_below_diagonal elif row_1 + 1 in range(0,4) and col_1 + 1 in range(0,4): if board[row_1 + 1][col_1 + 1] == bat_piece and board[row_1 - 1][col_1 - 1] == '.': winnable = 'YES' elif board[row_1 + 1][col_1 + 1] == '.' and board[row_1 - 1][col_1 - 1] == bat_piece: winnable = 'YES' #right_below_diagonal elif row_1 + 1 in range(0, 4) and col_1 + 1 in range(0, 4) and row_1 + 2 in range(0, 4) and col_1 + 2 in range(0, 4): if board[row_1 + 1][col_1 + 1] == bat_piece and board[row_1 + 2][col_1 + 2] == '.': print 'YES' elif board[row_1 + 1][col_1 + 1] == '.' and board[row_1 + 2][col_1 + 2] == bat_piece: print 'YES' #print winnable '''if row_1 + 2 in range(0, 4) and col_1 + 2 in range(0, 4):'''

36.701863

126

0.43273

867

5,909

2.746251

0.04729

0.063839

0.181436

0.136077

0.841663

0.832843

0.831163

0

0.096659

0.43273

5,909

160

127

36.93125

0.613663

0.029277

0

0.347826

0

0.019318

0

null

0

null

0.054348

0

null

0

1

0

1

0

null

0

1

0

8

c82a1d72cb68d521be5c5e33dcb4438b218cac18

40,046

py

Python

stat_analysis/pouring/utils.py

asaran/gaze-LfD

964635d9bf7b208abe35d40b2bf791b05b8a0c3b

[ "MIT" ]

1

2022-02-16T15:35:58.000Z

stat_analysis/pouring/utils.py

asaran/gaze-LfD

964635d9bf7b208abe35d40b2bf791b05b8a0c3b

[ "MIT" ]

null

stat_analysis/pouring/utils.py

asaran/gaze-LfD

964635d9bf7b208abe35d40b2bf791b05b8a0c3b

[ "MIT" ]

null

import cv2 import ast from bisect import bisect_left from numpy import ones,vstack from numpy.linalg import lstsq import numpy as np import math import rosbag import math import os import gzip def takeClosest(myList, myNumber): """ Assumes myList is sorted. Returns closest value to myNumber. If two numbers are equally close, return the smallest number. """ pos = bisect_left(myList, myNumber) if pos == 0: return myList[0], 0 if pos == len(myList): return myList[-1], len(myList)-1 before = myList[pos - 1] after = myList[pos] if after - myNumber < myNumber - before: return after, pos else: return before, pos-1 def read_json(data_dir): data = [] files = os.listdir(data_dir) for file in files: if (file.endswith("json.gz")): with gzip.open(data_dir+'/'+file, "rb") as f: data=f.readlines() for r in range(len(data)): row = data[r] data[r] = ast.literal_eval(row.strip('\n')) vid2ts = {} # dictionary mapping video time to time stamps in json right_eye_pd, left_eye_pd, gp = {}, {}, {} # dicts mapping ts to pupil diameter and gaze points (2D) for both eyes for d in data: if 'vts' in d and d['s']==0: if d['vts'] == 0: vid2ts[d['vts']] = d['ts'] else: vid2ts[d['vts']] = d['ts'] if 'pd' in d and d['s']==0 and d['eye']=='right': right_eye_pd[d['ts']] = d['pd'] if 'pd' in d and d['s']==0 and d['eye']=='left': left_eye_pd[d['ts']] = d['pd'] if 'gp' in d and d['s']==0 : gp[d['ts']] = d['gp'] #list of 2 coordinates print('read json') # map vts to ts all_vts = sorted(vid2ts.keys()) a = all_vts[0] model = [] for i in range(1,len(all_vts)): points = [(a,vid2ts[a]),(all_vts[i],vid2ts[all_vts[i]])] x_coords, y_coords = zip(*points) A = vstack([x_coords, ones(len(x_coords))]).T m, c = lstsq(A, y_coords)[0] model.append((m,c)) return data, gp, model, all_vts def color_dist(color1, color2): r1,g1,b1 = color1 r2,g2,b2 = color2 color_d = pow(r1-r2,2) + pow(g1-g2,2) + pow(b1-b2,2) mean_rgb = ((r1+r2)/2, (g1+g2)/2, (b1+b2)/2) return color_d, mean_rgb def pixel_dist(p1,p2): x1, y1 = p1 x2, y2 = p2 d = pow(x1-x2,2) + pow(y1-y2,2) return math.sqrt(d) def is_known_color(color): known_colors = { 'red': [[170,190],[65,180],[60,90]], 'green': [[95,105],[85,115],[65,95]], 'yellow': [[10,25],[130,160],[110,150]] } lower_red = np.array([170,65,60]) upper_red = np.array([190,180,90]) lower_yellow = np.array([10,130,110]) upper_yellow = np.array([25,160,150]) h,s,v = color for color in known_colors.keys(): if h>color[0][0] and h<color[0][1]: if s>color[1][0] and s<color[1][1]: if v>color[2][0] and v<color[2][1]: return color return None # returns a list of frame indices corresponding to the annotated KF for video demonstrations def get_video_keyframes(user_id, video_file, video_kf_file): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) length = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) print('read video file') vidcap.release() cv2.destroyAllWindows() # read video files with open(video_kf_file) as f: content = f.readlines() # you may also want to remove whitespace characters like `\n` at the end of each line content = [x.strip() for x in content] # print(content) print('read text file') # find segmentation points in video file keyframes = { 'Start': [], 'Reaching': [], 'Grasping': [], 'Close': [], 'Open': [], 'Transport': [], 'Pouring': [], 'Return': [], 'Release': [], 'Stop': [] } kf_type = { 1: 'Start', 2: 'Reaching', 3: 'Grasping', 4: 'Transport', 5: 'Pouring', 6: 'Return', 7: 'Release', 8: 'Reaching', 9: 'Grasping', 10: 'Transport', 11: 'Pouring', 12: 'Return', 13: 'Release', 14: 'Stop' } for kf in content: data = kf.split(' ') # print(data) user = data[0] if(user == user_id): for i in range(1,len(data)): d = data[i] # print(d) if(d=='end'): frame_idx = length else: kf_time = float(d) frame_idx = math.floor(kf_time*fps) k = kf_type[i] keyframes[k].append(frame_idx) print('Found start and stop keyframe indices') return keyframes def get_video_keyframe_labels(user_id, video_file, video_kf_file): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) length = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) print('read video file') vidcap.release() cv2.destroyAllWindows() # read video files with open(video_kf_file) as f: content = f.readlines() # you may also want to remove whitespace characters like `\n` at the end of each line content = [x.strip() for x in content] # print(content) print('read text file') all_keyframe_indices = [] keyframes = {} kf_type = { 1: 'Start', 2: 'Reaching', 3: 'Grasping', 4: 'Transport', 5: 'Pouring', 6: 'Return', 7: 'Release', 8: 'Reaching', 9: 'Grasping', 10: 'Transport', 11: 'Pouring', 12: 'Return', 13: 'Release', 14: 'Stop' } for kf in content: data = kf.split(' ') # print(data) user = data[0] if(user == user_id): for i in range(1,len(data)): d = data[i] # print(d) if(d=='end'): frame_idx = length else: kf_time = float(d) frame_idx = math.floor(kf_time*fps) k = kf_type[i] # The same frame_idx can have multiple kf_types if(frame_idx not in keyframes or k!='Stop'): keyframes[frame_idx] = k all_keyframe_indices.append(frame_idx) print('Found start and stop keyframe indices') return keyframes, all_keyframe_indices # returns a list of rgb color values for gaze point for each video frame def get_color_timeline(data, video_file, keep_saccades): timeline = [] vid2ts = {} # dictionary mapping video time to time stamps in json right_eye_pd, left_eye_pd, gp = {}, {}, {} # dicts mapping ts to pupil diameter and gaze points (2D) for both eyes for d in data: if 'vts' in d and d['s']==0: if d['vts'] == 0: vid2ts[d['vts']] = d['ts'] else: vid2ts[d['vts']] = d['ts'] if 'pd' in d and d['s']==0 and d['eye']=='right': right_eye_pd[d['ts']] = d['pd'] if 'pd' in d and d['s']==0 and d['eye']=='left': left_eye_pd[d['ts']] = d['pd'] if 'gp' in d and d['s']==0 : gp[d['ts']] = d['gp'] #list of 2 coordinates print('read json') # map vts to ts all_vts = sorted(vid2ts.keys()) a = all_vts[0] model = [] for i in range(1,len(all_vts)): points = [(a,vid2ts[a]),(all_vts[i],vid2ts[all_vts[i]])] x_coords, y_coords = zip(*points) A = vstack([x_coords,ones(len(x_coords))]).T m, c = lstsq(A, y_coords)[0] model.append((m,c)) a = all_vts[i] vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() print('reading video file') last_fixation_color =(0,0,0) all_ts = sorted(gp.keys()) count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds gaze_pts = [] while success: frame_ts = int((count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts, _ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) gaze_pts.append(gaze_coords) b, g, r = img[gaze_coords[1]-1][gaze_coords[0]-1] instant_color = [r/255.0,g/255.0,b/255.0] timeline.append(instant_color) count += 1 success, img = vidcap.read() vidcap.release() cv2.destroyAllWindows() saccade_indices = [] if not keep_saccades: timeline, saccade_indices = remove_saccades(gaze_pts, timeline, fps) return timeline, saccade_indices def get_kt_keyframes_labels(all_vts, model, gp, video_file, bag_file): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() print('reading video file') keyframes = {} last_fixation_color =(0,0,0) all_ts = sorted(gp.keys()) count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds videoframe2trackerts = [] gaze_pts = [] while success: frame_ts = int((count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts, _ = takeClosest(all_ts,ts) videoframe2trackerts.append(tracker_ts) count += 1 success, img = vidcap.read() vidcap.release() cv2.destroyAllWindows() # find segmentation points on bagfile all_keyframe_indices = [] gripper = {} record_k = False bag = rosbag.Bag(bag_file) print(bag_file) # get the start time for KT recording start= False frame_idx = None if bag.get_message_count('/gaze_tracker')!=0: # gaze_tracker topic was recorded for topic, msg, t in bag.read_messages(topics=['/gaze_tracker','/log_KTframe','/joint_states','/vector/right_gripper/stat']): if (topic=='/log_KTframe'): if("Recorded keyframe" in msg.data): record_k = True if 'Reaching' in msg.data: kf_type = 'Reaching' elif 'Grasping' in msg.data: kf_type = 'Grasping' elif 'Transport' in msg.data: kf_type = 'Transport' elif 'Pouring' in msg.data: kf_type = 'Pouring' elif 'Return' in msg.data: kf_type = 'Return' elif 'Release' in msg.data: kf_type = 'Release' else: kf_type = 'Other' if("Open" in msg.data): record_k = True kf_type = 'Open' if("Close" in msg.data): record_k = True kf_type = 'Close' if (topic == '/gaze_tracker'): if('gp' in msg.data): gaze_msg = msg.data s = gaze_msg.find('"ts":') e = gaze_msg.find(',') gaze_ts = gaze_msg[s+5:e] tracker_ts, frame_idx = takeClosest(videoframe2trackerts,int(gaze_ts)) if(record_k == True): all_keyframe_indices.append(frame_idx) keyframes[frame_idx] = kf_type record_k = False if (topic == '/joint_states') and not start and frame_idx!=None: start = True keyframes[frame_idx] = 'Start' bag.close() return keyframes, all_keyframe_indices def get_kt_keyframes(all_vts, model, gp, video_file, bag_file): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() print('reading video file') last_fixation_color =(0,0,0) all_ts = sorted(gp.keys()) count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds videoframe2trackerts = [] gaze_pts = [] while success: frame_ts = int((count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts, _ = takeClosest(all_ts,ts) videoframe2trackerts.append(tracker_ts) count += 1 success, img = vidcap.read() vidcap.release() cv2.destroyAllWindows() # find segmentation points on bagfile all_keyframe_indices = [] record_k = False bag = rosbag.Bag(bag_file) print(bag_file) if bag.get_message_count('/gaze_tracker')!=0: # gaze_tracker topic was recorded for topic, msg, t in bag.read_messages(topics=['/gaze_tracker','/log_KTframe']): if (topic=='/log_KTframe'): if("Recorded keyframe" in msg.data): record_k = True if 'Reaching' in msg.data: kf_type = 'Reaching' elif 'Grasping' in msg.data: kf_type = 'Grasping' elif 'Transport' in msg.data: kf_type = 'Transport' elif 'Pouring' in msg.data: kf_type = 'Pouring' elif 'Return' in msg.data: kf_type = 'Return' elif 'Release' in msg.data: kf_type = 'Release' else: kf_type = 'Other' if("Open" in msg.data): record_k = True kf_type = 'Open' if("Close" in msg.data): record_k = True kf_type = 'Close' if (topic == '/gaze_tracker'): if(record_k == True): if('gp' in msg.data): gaze_msg = msg.data s = gaze_msg.find('"ts":') e = gaze_msg.find(',') gaze_ts = gaze_msg[s+5:e] tracker_ts, frame_idx = takeClosest(videoframe2trackerts,int(gaze_ts)) all_keyframe_indices.append(frame_idx) record_k = False bag.close() return all_keyframe_indices def find_saccades(gaze_pts, fps): speed = [] saccade_indices = [] speed.append(0) dt = 1.0/fps for i in range(1,len(gaze_pts)): g = gaze_pts[i] prev_g = gaze_pts[i-1] s = (math.sqrt(math.pow(g[0]-prev_g[0],2)+math.pow(g[1]-prev_g[1],2)))/dt if s>800: saccade_indices.append(i) return saccade_indices def remove_saccades(gaze_pts, color_timeline, fps): speed = [] saccade_indices = [] speed.append(0) dt = 1.0/fps for i in range(1,len(gaze_pts)): g = gaze_pts[i] prev_g = gaze_pts[i-1] s = (math.sqrt(math.pow(g[0]-prev_g[0],2)+math.pow(g[1]-prev_g[1],2)))/dt if s>200: color_timeline[i] = [1.0, 1.0, 1.0] saccade_indices.append(i) return color_timeline, saccade_indices def get_cumulative_gaze_dist(data, video_file): vid2ts = {} # dictionary mapping video time to time stamps in json right_eye_pd, left_eye_pd, gp = {}, {}, {} # dicts mapping ts to pupil diameter and gaze points (2D) for both eyes for d in data: if 'vts' in d and d['s']==0: if d['vts'] == 0: vid2ts[d['vts']] = d['ts'] else: vid2ts[d['vts']] = d['ts'] if 'pd' in d and d['s']==0 and d['eye']=='right': right_eye_pd[d['ts']] = d['pd'] if 'pd' in d and d['s']==0 and d['eye']=='left': left_eye_pd[d['ts']] = d['pd'] if 'gp' in d and d['s']==0 : gp[d['ts']] = d['gp'] #list of 2 coordinates print('read json') # map vts to ts all_vts = sorted(vid2ts.keys()) a = all_vts[0] model = [] for i in range(1,len(all_vts)): points = [(a,vid2ts[a]),(all_vts[i],vid2ts[all_vts[i]])] x_coords, y_coords = zip(*points) A = vstack([x_coords,ones(len(x_coords))]).T m, c = lstsq(A, y_coords)[0] model.append((m,c)) a = all_vts[i] vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() print('reading video file') last_fixation_color =(0,0,0) all_ts = sorted(gp.keys()) count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds gaze_pts = [] current_dist = 0 cumulative_dist = [0] tracker_ts, _ = takeClosest(all_ts,all_vts[0]) gx_p, gy_p = gp[tracker_ts] while success: frame_ts = int((count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts, _ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) gaze_pts.append(gaze_coords) gx, gy = gaze_coords d = math.sqrt(math.pow(gx-gx_p,2)+math.pow(gy-gy_p,2)) current_dist = current_dist + d cumulative_dist.append(current_dist) gx_p, gy_p = gx, gy count += 1 success, img = vidcap.read() vidcap.release() cv2.destroyAllWindows() return cumulative_dist def get_color_name(hsv): color_ranges = { 'red': [[161,140,70],[184,255,255]], 'green': [[36,64,28],[110,155,220]], 'yellow': [[0,90,100],[32,180,180]], 'blue': [[94,111,34],[118,165,136]], 'black': [[0,0,0],[180,255,40]], 'white': [[0,0,170],[180,255,255]] } color_val = { 'black': (0,0,0), 'white': (255,255,255), 'red': (0,0,255), 'green': (0,255,0), 'yellow': (0,255,255), 'blue': (255,0,0), 'pasta': (0,215,225) } h,s,v = hsv color = '' value = None for i, (n,r) in enumerate(color_ranges.items()): if h>=r[0][0] and h<=r[1][0]: if s>=r[0][1] and s<=r[1][1]: if v>=r[0][2] and v<=r[1][2]: color = n value = color_val[n] pasta_color_range = [[0,30,0],[40,130,100]] p = pasta_color_range if color=='': if h>=p[0][0] and h<=p[1][0]: if s>=p[0][1] and s<=p[1][1]: if v>=p[0][2] and v<=p[1][2]: color = 'pasta' value = color_val['pasta'] return color, value def get_color_name_from_hist(gaze_coords, img_hsv, radius): color_hist ={ 'blue': 0, 'yellow': 0, 'red': 0, 'green': 0, 'black': 0, 'pasta': 0, 'other': 0 } color_val = { 'black': (0,0,0), 'red': (0,0,255), 'green': (0,255,0), 'yellow': (0,255,255), 'pasta': (0,255,255), 'blue': (255,0,0), 'other': (192,192,192) } x, y = gaze_coords hsv = img_hsv[y-1][x-1] h,s,v = hsv color = '' value = None # pixels in the image which lie inside a circle of given radius min_x, max_x = max(0,x-radius), min(1920, x+radius) min_y, max_y = max(0,y-radius), min(1080, y+radius) for i,j in zip(range(min_x,max_x), range(min_y,max_y)): d = math.pow((i-x),2)+ math.pow((j-y),2) if d<= math.pow(radius,2): curr_hsv= img_hsv[j][i] current_color, _ = get_color_name(curr_hsv) if current_color in color_hist.keys(): color_hist[current_color] += 1 else: color_hist['other'] += 1 max_val = 0 max_color = '' for key,val in color_hist.items(): if val>max_val: max_val = val max_color = key # do not assign other color if relevant colors are present second_max_val = 0 second_max_color = '' if max_color=='other': # print('***other***') for key,val in color_hist.items(): if key=='other': continue else: if val>second_max_val: second_max_val = val second_max_color = key if second_max_val>5: max_color = second_max_color max_val = second_max_val # print(max_color, second_max_val) value = color_val[max_color] return max_color, value def get_color_name_from_hist_ignore_black(gaze_coords, img_hsv, radius): color_hist ={ 'blue': 0, 'yellow': 0, 'red': 0, 'green': 0, 'black': 0, 'pasta': 0, 'other': 0 } color_val = { 'black': (0,0,0), 'red': (0,0,255), 'green': (0,255,0), 'yellow': (0,255,255), 'pasta': (0,255,255), 'blue': (255,0,0), 'other': (192,192,192) } x, y = gaze_coords hsv = img_hsv[y-1][x-1] h,s,v = hsv color = '' value = None # pixels in the image which lie inside a circle of given radius min_x, max_x = max(0,x-radius), min(1920, x+radius) min_y, max_y = max(0,y-radius), min(1080, y+radius) for i,j in zip(range(min_x,max_x), range(min_y,max_y)): d = math.pow((i-x),2)+ math.pow((j-y),2) if d<= math.pow(radius,2): curr_hsv= img_hsv[j][i] current_color, _ = get_color_name(curr_hsv) if current_color in color_hist.keys(): color_hist[current_color] += 1 else: color_hist['other'] += 1 max_val = 0 max_color = '' for key,val in color_hist.items(): # print(val) if val>max_val: max_val = val max_color = key # tie break black with other colors second_max_val = 0 second_max_color = '' if max_color=='black': # print('***other***') for key,val in color_hist.items(): if key=='black': continue else: if val>second_max_val: second_max_val = val second_max_color = key if second_max_val>50: max_color = second_max_color max_val = second_max_val # do not assign other color if relevant colors are present second_max_val = 0 second_max_color = '' if max_color=='other': # print('***other***') for key,val in color_hist.items(): if key=='other': continue else: if val>second_max_val: second_max_val = val second_max_color = key if second_max_val>5: max_color = second_max_color max_val = second_max_val # print(max_color, second_max_val) value = color_val[max_color] return max_color, value # returns a list of rgb color values for gaze point for each video frame def get_hsv_color_timeline(data, video_file): timeline = [] vid2ts = {} # dictionary mapping video time to time stamps in json right_eye_pd, left_eye_pd, gp = {}, {}, {} # dicts mapping ts to pupil diameter and gaze points (2D) for both eyes for d in data: if 'vts' in d and d['s']==0: if d['vts'] == 0: vid2ts[d['vts']] = d['ts'] else: #vid_time = d['ts'] - d['vts'] vid2ts[d['vts']] = d['ts'] if 'pd' in d and d['s']==0 and d['eye']=='right': right_eye_pd[d['ts']] = d['pd'] if 'pd' in d and d['s']==0 and d['eye']=='left': left_eye_pd[d['ts']] = d['pd'] if 'gp' in d and d['s']==0 : gp[d['ts']] = d['gp'] #list of 2 coordinates print('read json') # map vts to ts all_vts = sorted(vid2ts.keys()) a = all_vts[0] model = [] for i in range(1,len(all_vts)): points = [(a,vid2ts[a]),(all_vts[i],vid2ts[all_vts[i]])] x_coords, y_coords = zip(*points) A = vstack([x_coords,ones(len(x_coords))]).T m, c = lstsq(A, y_coords)[0] model.append((m,c)) a = all_vts[i] vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() print('reading video file') last_fixation_color =(0,0,0) all_ts = sorted(gp.keys()) count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds gaze_pts = [] while success: img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) frame_ts = int((count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts, _ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) gaze_pts.append(gaze_coords) h,s,v = img_hsv[gaze_coords[1]-1][gaze_coords[0]-1] instant_color = [h, s, v] timeline.append(instant_color) count += 1 success, img = vidcap.read() vidcap.release() cv2.destroyAllWindows() saccade_indices = [] saccade_indices = find_saccades(gaze_pts, fps) return timeline, saccade_indices, fps # map vts to ts all_vts = sorted(vid2ts.keys()) a = all_vts[0] model = [] for i in range(1,len(all_vts)): points = [(a,vid2ts[a]),(all_vts[i],vid2ts[all_vts[i]])] x_coords, y_coords = zip(*points) A = vstack([x_coords,ones(len(x_coords))]).T m, c = lstsq(A, y_coords)[0] model.append((m,c)) a = all_vts[i] vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() print('reading video file') last_fixation_color =(0,0,0) all_ts = sorted(gp.keys()) count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds gaze_pts = [] while success: img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) frame_ts = int((count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts, _ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) gaze_pts.append(gaze_coords) h,s,v = img_hsv[gaze_coords[1]-1][gaze_coords[0]-1] instant_color = [h, s, v] timeline.append(instant_color) count += 1 success, img = vidcap.read() vidcap.release() cv2.destroyAllWindows() saccade_indices = [] saccade_indices = find_saccades(gaze_pts, fps) return timeline, saccade_indices, fps def filter_fixations(video_file, model, gp, all_vts, demo_type, saccade_indices, start_idx, end_idx): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() fourcc = cv2.VideoWriter_fourcc(*'XVID') KT_fixation_count = { 'red': 0, 'yellow': 0, 'blue': 0, 'green': 0, 'black': 0, 'other': 0, 'pasta': 0 } fixation_count = KT_fixation_count all_ts = sorted(gp.keys()) total_count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds window = [] win_size = 3 radius = 100 valid_count = 0 while success: if total_count<start_idx or total_count>end_idx: total_count += 1 success, img = vidcap.read() continue frame_ts = int((total_count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts,_ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) color_name, color_value = get_color_name_from_hist(gaze_coords, img_hsv, radius) window.append(color_name) if(len(window)>win_size): del window[0] font = cv2.FONT_HERSHEY_SIMPLEX if total_count not in saccade_indices: # might be a fixation fixation = True for det_c in window: if det_c!=color_name: fixation=False if(fixation): fixation_count[color_name] += 1 valid_count += 1 total_count += 1 success, img = vidcap.read() cv2.destroyAllWindows() for f in fixation_count: if(valid_count!=0): fixation_count[f] = fixation_count[f]*100.0/valid_count else: fixation_count[f] = -1 return fixation_count def filter_fixation_counts(video_file, model, gp, all_vts, demo_type, saccade_indices, start_idx, end_idx): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() fourcc = cv2.VideoWriter_fourcc(*'XVID') KT_fixation_count = { 'red': 0, 'yellow': 0, 'blue': 0, 'green': 0, 'black': 0, 'other': 0, 'pasta': 0 } fixation_count = KT_fixation_count all_ts = sorted(gp.keys()) total_count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds window = [] win_size = 3 radius = 100 valid_count = 0 while success: if total_count<start_idx or total_count>end_idx: total_count += 1 success, img = vidcap.read() continue frame_ts = int((total_count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts,_ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) color_name, color_value = get_color_name_from_hist(gaze_coords, img_hsv, radius) window.append(color_name) if(len(window)>win_size): del window[0] font = cv2.FONT_HERSHEY_SIMPLEX if total_count not in saccade_indices: # might be a fixation fixation = True for det_c in window: if det_c!=color_name: fixation=False if(fixation): fixation_count[color_name] += 1 valid_count += 1 total_count += 1 success, img = vidcap.read() cv2.destroyAllWindows() for f in fixation_count: if(valid_count==0): fixation_count[f] = -1 return fixation_count def filter_fixations_ignore_black(video_file, model, gp, all_vts, demo_type, saccade_indices, keyframe_indices, keyframes): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() fourcc = cv2.VideoWriter_fourcc(*'XVID') KT_fixation_count = { 'red': 0, 'yellow': 0, 'blue': 0, 'green': 0, 'black': 0, 'other': 0, 'pasta': 0 } fixation_count = KT_fixation_count target_objects = { 'Reaching': ['green','yellow'], 'Grasping': ['green', 'yellow'], 'Open': ['green', 'yellow'], 'Close': ['green', 'yellow'], 'Transport': ['red', 'blue'], 'Pouring': ['red', 'blue'], 'Return': ['red','blue'], 'Release': ['red','blue'] } start_idx, end_idx = keyframe_indices[0], keyframe_indices[-1] all_ts = sorted(gp.keys()) total_count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds window = [] win_size = 3 radius = 100 valid_count = 0 current_action = 'Reaching' while success: if total_count<start_idx or total_count>end_idx: total_count += 1 success, img = vidcap.read() continue first_act = True # get current KF segment if demo_type=='k': for i in range(0,len(keyframe_indices)-1): if total_count>keyframe_indices[i] and\ total_count<=keyframe_indices[i+1]: if keyframes[keyframe_indices[i+1]]!= 'Other': current_action = keyframes[keyframe_indices[i+1]] elif demo_type == 'v': for i in range(1,len(keyframe_indices)-1): if total_count>=keyframe_indices[i] and\ total_count<keyframe_indices[i+1]: current_action = keyframes[keyframe_indices[i]] if current_action == 'Open' and first_act==True and demo_type=='k': first_act = False if current_action == 'Release' and first_act==True and demo_type=='v': first_act = False frame_ts = int((total_count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts,_ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) color_name, color_value = get_color_name_from_hist_ignore_black(gaze_coords, img_hsv, radius) if color_name == 'pasta': if first_act: color_name = target_objects[current_action][0] else: color_name = target_objects[current_action][1] window.append(color_name) if(len(window)>win_size): del window[0] font = cv2.FONT_HERSHEY_SIMPLEX if total_count not in saccade_indices: # might be a fixation fixation = True for det_c in window: if det_c!=color_name: fixation=False if(fixation): fixation_count[color_name] += 1 valid_count += 1 total_count += 1 success, img = vidcap.read() cv2.destroyAllWindows() for f in fixation_count: if(valid_count!=0): fixation_count[f] = fixation_count[f]*100.0/valid_count else: fixation_count[f] = -1 return fixation_count def filter_fixations_with_timeline(video_file, model, gp, all_vts, demo_type, saccade_indices, start_idx, end_idx): vidcap = cv2.VideoCapture(video_file) fps = vidcap.get(cv2.CAP_PROP_FPS) success, img = vidcap.read() fourcc = cv2.VideoWriter_fourcc(*'XVID') fixation_list, fixation_idx_list = [], [] all_ts = sorted(gp.keys()) total_count = 0 imgs = [] # list of image frames frame2ts = [] # corresponding list of video time stamp values in microseconds window = [] win_size = 3 radius = 100 valid_count = 0 while success: if total_count<start_idx or total_count>end_idx: total_count += 1 success, img = vidcap.read() continue frame_ts = int((total_count/fps)*1000000) frame2ts.append(frame_ts) less = [a for a in all_vts if a<=frame_ts] idx = len(less)-1 if idx<len(model): m,c = model[idx] else: m,c = model[len(model)-1] ts = m*frame_ts + c tracker_ts,_ = takeClosest(all_ts,ts) gaze = gp[tracker_ts] gaze_coords = (int(gaze[0]*1920), int(gaze[1]*1080)) img_hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV) color_name, color_value = get_color_name_from_hist(gaze_coords, img_hsv, radius) window.append(color_name) if(len(window)>win_size): del window[0] font = cv2.FONT_HERSHEY_SIMPLEX if total_count not in saccade_indices: fixation = True for det_c in window: if det_c!=color_name: fixation=False if(fixation): b,g,r = color_value c_val = [r/255.0, g/255.0, b/255.0] if(color_name != 'other'): fixation_list.append(c_val) fixation_idx_list.append(valid_count) valid_count += 1 total_count += 1 success, img = vidcap.read() cv2.destroyAllWindows() return fixation_list, fixation_idx_list def get_step_kf_indices(keyframes, keyframe_indices): valid_types = ['Reaching', 'Grasping', 'Transport', 'Pouring', 'Return', 'Release'] step_kf_indices = [] kf_type = keyframes[keyframe_indices[0]] if(kf_type!= keyframes[keyframe_indices[1]] and kf_type in valid_types): step_kf_indices.append(keyframe_indices[0]) last_kf_type = '' for i in range(1,len(keyframe_indices)-1): kf = keyframe_indices[i] kf_type = keyframes[kf] prev_kf_type = keyframes[keyframe_indices[i-1]] next_kf_type = keyframes[keyframe_indices[i+1]] if kf_type=='Close': kf_type = 'Grasping' if kf_type=='Open': kf_type = 'Release' if prev_kf_type=='Open': prev_kf_type = 'Release' if prev_kf_type=='Close': prev_kf_type = 'Grasping' # the last KF in a sequence of identical labels is a segmentation KF if (prev_kf_type==kf_type and next_kf_type!=kf_type) and (kf_type in valid_types): step_kf_indices.append(kf) if (prev_kf_type!=kf_type and next_kf_type!=kf_type) and (kf_type in valid_types): step_kf_indices.append(kf) kf_type = keyframes[keyframe_indices[-1]] if(kf_type!= keyframes[keyframe_indices[-2]] and kf_type in valid_types): step_kf_indices.append(keyframe_indices[-1]) return step_kf_indices

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c85589033f6ff58614faa7424a530aceeec041d5

34

py

Python

myQuiz.py

blulady/python

65d8e99f6411cf79be0353abc99a2677dfeebe11

[ "bzip2-1.0.6" ]

null

myQuiz.py

blulady/python

65d8e99f6411cf79be0353abc99a2677dfeebe11

[ "bzip2-1.0.6" ]

null

myQuiz.py

blulady/python

65d8e99f6411cf79be0353abc99a2677dfeebe11

[ "bzip2-1.0.6" ]

1

2020-09-11T16:05:46.000Z

import budget; print(calcBills())

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0

1

0

1

0

7

23c0f106aff8d53d787f8eb1342e73c1c17ee7e1

7,361

py

Python

api/device.py

icarus213/iotku

4aa70002b88ad160861b2b9cd14f5d20706a6643

[ "MIT" ]

8

2018-07-06T10:40:53.000Z

2019-07-31T09:12:10.000Z

api/device.py

echobots/iotku

4aa70002b88ad160861b2b9cd14f5d20706a6643

[ "MIT" ]

null

api/device.py

echobots/iotku

4aa70002b88ad160861b2b9cd14f5d20706a6643

[ "MIT" ]

7

2018-07-06T11:02:48.000Z

2021-01-06T06:32:01.000Z

from flask import Blueprint, request, session, jsonify, url_for from . import api, iotku #------------------DEVICE------------------------- @api.route('/api/device/name', methods=['GET']) def device_name(): content = request.args if not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) elif not content.get('device_id'): return jsonify({'result':False,'reason':"Invalid format"}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result':False,'reason':'Device ID not found'}) else: return jsonify({'result':device.get('device_name')}) @api.route('/api/device/time_added', methods=['GET']) def device_time_added(): content = request.args if not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) elif not content.get('device_id'): return jsonify({'result':False,'reason':"Invalid format"}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result':False,'reason':'Device ID not found'}) else: return jsonify({'result':device.get('time_added')}) @api.route('/api/device/total_sensor', methods=['GET']) def device_total_sensor(): content = request.args if not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) elif not content.get('device_id'): return jsonify({'result':False,'reason':"Invalid format"}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result':False,'reason':'Device ID not found'}) else: return jsonify({'result':device.get('total_sensor')}) @api.route('/api/device/sensor_list', methods=['GET']) def device_sensor_list(): content = request.args if not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) elif not content.get('device_id'): return jsonify({'result':False,'reason':"Invalid format"}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result':False,'reason':'Device ID not found'}) else: sensors = device.get_sensor_list() sensor_id = [x.get('sensor_id') for x in sensors] sensor_name = [x.get('sensor_name') for x in sensors] sensor_list = [{'sensor_id':x,'sensor_name':y} for x,y in zip(sensor_id, sensor_name)] return jsonify({'result':sensor_list}) @api.route('/api/device/add_sensor', methods=['POST']) def device_add_sensor(): content = request.get_json(silent=True) if not content: return jsonify({'result': False, 'reason': 'Invalid format'}) elif not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) elif not all(x in content.keys() for x in ["device_id","sensor_id","sensor_name"]): return jsonify({'result': False, 'reason': 'Invalid format'}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result': False, 'reason': "Device ID not found"}) else: sensor_id, sensor_name = content["sensor_id"],content["sensor_name"] if device.find_sensor(sensor_id): return jsonify({'result': False, 'reason': "Sensor ID exists"}) else: device.add_sensor(sensor_id,sensor_name) return jsonify({'result': True}) @api.route('/api/device/remove_sensor', methods=['POST']) def device_remove_sensor(): content = request.get_json(silent=True) if not content: return jsonify({'result': False, 'reason': 'Invalid format'}) elif not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) elif not all(x in content.keys() for x in ["device_id","sensor_id"]): return jsonify({'result': False, 'reason': 'Invalid format'}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result': False, 'reason': "Device ID not found"}) else: sensor_id = content["sensor_id"] if device.find_sensor(sensor_id): device.remove_sensor(sensor_id) return jsonify({'result': True}) else: return jsonify({'result': False, 'reason': "Sensor not found"}) @api.route('/api/device/command', methods=['GET']) def device_command(): content = request.args if not 'device_id' in content.keys(): if not all(x in session.keys() for x in ["logged_in","device_id"]): return jsonify({'result':False,'reason':'Not logged in / Invalid login type / Invalid format'}) else: device_id = session['device_id'] user = iotku.find_user(api_key=session["api_key"]) device = user.find_device(device_id) if not device: return jsonify({'result': False, 'reason': "Invalid Device ID. Please relogin"}) else: command = device.get('command') return jsonify({'result': command}) else: if not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result': False, 'reason': "Invalid Device ID"}) else: command = device.get('command') return jsonify({'result': command}) @api.route('/api/device/command_history', methods=['GET']) def device_command_history(): content = request.args if not 'device_id' in content.keys(): if not all(x in session.keys() for x in ["logged_in","device_id"]): return jsonify({'result':False,'reason':'Not logged in / Invalid login type / Invalid format'}) else: device_id = session['device_id'] user = iotku.find_user(api_key=session["api_key"]) device = user.find_device(device_id) if not device: return jsonify({'result': False, 'reason': "Invalid Device ID. Please relogin"}) else: command = device.get('command_history') return jsonify({'result': command}) else: if not all(x in session.keys() for x in ["logged_in","email"]): return jsonify({'result':False,'reason':'Not logged in / Unauthorized'}) else: device_id = content['device_id'] user = iotku.find_user(email=session["email"]) device = user.find_device(device_id) if not device: return jsonify({'result': False, 'reason': "Invalid Device ID"}) else: command = device.get('command_history') return jsonify({'result': command}) #------------------/DEVICE-------------------------

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101

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7

23fcb04d48793ef2b46693e6ab203b3cf719565d

71,815

py

Python

great_international/migrations/0048_capitalinvestrelatedsubsectors_internationalsubsectorpage.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2018-03-20T11:19:07.000Z

2021-10-05T07:53:11.000Z

great_international/migrations/0048_capitalinvestrelatedsubsectors_internationalsubsectorpage.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

802

2018-02-05T14:16:13.000Z

2022-02-10T10:59:21.000Z

great_international/migrations/0048_capitalinvestrelatedsubsectors_internationalsubsectorpage.py

uktrade/directory-cms

8c8d13ce29ea74ddce7a40f3dd29c8847145d549

[ "MIT" ]

6

2019-01-22T13:19:37.000Z

2019-07-01T10:35:26.000Z

# Generated by Django 2.2.2 on 2019-07-16 13:48 import core.model_fields import core.validators from django.db import migrations, models import django.db.models.deletion import great_international.panels.great_international import modelcluster.fields class Migration(migrations.Migration): dependencies = [ ('export_readiness', '0051_auto_20190627_1424'), ('wagtailcore', '0041_group_collection_permissions_verbose_name_plural'), ('wagtailimages', '0001_squashed_0021'), ('great_international', '0047_investregionlandingpage_investsectorpage'), ] operations = [ migrations.CreateModel( name='InternationalSubSectorPage', fields=[ ('page_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=True, serialize=False, to='wagtailcore.Page')), ('service_name', models.CharField(choices=[('FIND_A_SUPPLIER', 'Find a Supplier'), ('EXPORT_READINESS', 'Export Readiness'), ('INVEST', 'Invest'), ('COMPONENTS', 'Components'), ('GREAT_INTERNATIONAL', 'Great International')], db_index=True, max_length=100, null=True)), ('uses_tree_based_routing', models.BooleanField(default=False, help_text="Allow this page's URL to be determined by its slug, and the slugs of its ancestors in the page tree.", verbose_name='tree-based routing enabled')), ('heading', models.CharField(max_length=255, verbose_name='Sector name')), ('heading_en_gb', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_de', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_ja', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_zh_hans', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_fr', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_es', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_pt', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('heading_ar', models.CharField(max_length=255, null=True, verbose_name='Sector name')), ('sub_heading', models.TextField(blank=True)), ('sub_heading_en_gb', models.TextField(blank=True, null=True)), ('sub_heading_de', models.TextField(blank=True, null=True)), ('sub_heading_ja', models.TextField(blank=True, null=True)), ('sub_heading_zh_hans', models.TextField(blank=True, null=True)), ('sub_heading_fr', models.TextField(blank=True, null=True)), ('sub_heading_es', models.TextField(blank=True, null=True)), ('sub_heading_pt', models.TextField(blank=True, null=True)), ('sub_heading_ar', models.TextField(blank=True, null=True)), ('heading_teaser', models.TextField(blank=True, verbose_name='Introduction')), ('heading_teaser_en_gb', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_de', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_ja', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_zh_hans', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_fr', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_es', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_pt', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('heading_teaser_ar', models.TextField(blank=True, null=True, verbose_name='Introduction')), ('section_one_body', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_en_gb', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_de', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_ja', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_zh_hans', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_fr', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_es', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_pt', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_body_ar', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='3 unique selling points markdown')), ('section_one_image_caption', models.CharField(blank=True, max_length=255, verbose_name='Image caption')), ('section_one_image_caption_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption')), ('section_one_image_caption_company', models.CharField(blank=True, max_length=255, verbose_name='Image caption attribution')), ('section_one_image_caption_company_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('section_one_image_caption_company_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Image caption attribution')), ('statistic_1_number', models.CharField(blank=True, max_length=255)), ('statistic_1_number_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_number_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading', models.CharField(blank=True, max_length=255)), ('statistic_1_heading_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_heading_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint', models.CharField(blank=True, max_length=255)), ('statistic_1_smallprint_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_1_smallprint_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number', models.CharField(blank=True, max_length=255)), ('statistic_2_number_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_number_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading', models.CharField(blank=True, max_length=255)), ('statistic_2_heading_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_heading_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint', models.CharField(blank=True, max_length=255)), ('statistic_2_smallprint_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_2_smallprint_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number', models.CharField(blank=True, max_length=255)), ('statistic_3_number_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_number_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading', models.CharField(blank=True, max_length=255)), ('statistic_3_heading_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_heading_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint', models.CharField(blank=True, max_length=255)), ('statistic_3_smallprint_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_3_smallprint_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number', models.CharField(blank=True, max_length=255)), ('statistic_4_number_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_number_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading', models.CharField(blank=True, max_length=255)), ('statistic_4_heading_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_heading_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint', models.CharField(blank=True, max_length=255)), ('statistic_4_smallprint_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_4_smallprint_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number', models.CharField(blank=True, max_length=255)), ('statistic_5_number_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_number_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading', models.CharField(blank=True, max_length=255)), ('statistic_5_heading_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_heading_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint', models.CharField(blank=True, max_length=255)), ('statistic_5_smallprint_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_5_smallprint_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number', models.CharField(blank=True, max_length=255)), ('statistic_6_number_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_number_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading', models.CharField(blank=True, max_length=255)), ('statistic_6_heading_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_heading_ar', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint', models.CharField(blank=True, max_length=255)), ('statistic_6_smallprint_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_de', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_ja', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_fr', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_es', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_pt', models.CharField(blank=True, max_length=255, null=True)), ('statistic_6_smallprint_ar', models.CharField(blank=True, max_length=255, null=True)), ('section_two_heading', models.CharField(blank=True, max_length=255, verbose_name='Spotlight')), ('section_two_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight')), ('section_two_teaser', models.TextField(blank=True, verbose_name='Spotlight summary')), ('section_two_teaser_en_gb', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_de', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_ja', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_zh_hans', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_fr', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_es', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_pt', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_teaser_ar', models.TextField(blank=True, null=True, verbose_name='Spotlight summary')), ('section_two_subsection_one_heading', models.CharField(blank=True, max_length=255, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 1 heading')), ('section_two_subsection_one_body', models.TextField(blank=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_en_gb', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_de', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_ja', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_zh_hans', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_fr', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_es', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_pt', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_one_body_ar', models.TextField(blank=True, null=True, verbose_name='Spotlight 1 body')), ('section_two_subsection_two_heading', models.CharField(blank=True, max_length=255, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 2 heading')), ('section_two_subsection_two_body', models.TextField(blank=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_en_gb', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_de', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_ja', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_zh_hans', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_fr', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_es', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_pt', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_two_body_ar', models.TextField(blank=True, null=True, verbose_name='Spotlight 2 body')), ('section_two_subsection_three_heading', models.CharField(blank=True, max_length=255, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Spotlight 3 heading')), ('section_two_subsection_three_body', models.TextField(blank=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_en_gb', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_de', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_ja', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_zh_hans', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_fr', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_es', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_pt', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('section_two_subsection_three_body_ar', models.TextField(blank=True, null=True, verbose_name='Spotlight 3 body')), ('case_study_title', models.CharField(blank=True, max_length=255)), ('case_study_title_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_de', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_ja', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_fr', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_es', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_pt', models.CharField(blank=True, max_length=255, null=True)), ('case_study_title_ar', models.CharField(blank=True, max_length=255, null=True)), ('case_study_description', models.TextField(blank=True)), ('case_study_description_en_gb', models.TextField(blank=True, null=True)), ('case_study_description_de', models.TextField(blank=True, null=True)), ('case_study_description_ja', models.TextField(blank=True, null=True)), ('case_study_description_zh_hans', models.TextField(blank=True, null=True)), ('case_study_description_fr', models.TextField(blank=True, null=True)), ('case_study_description_es', models.TextField(blank=True, null=True)), ('case_study_description_pt', models.TextField(blank=True, null=True)), ('case_study_description_ar', models.TextField(blank=True, null=True)), ('case_study_cta_text', models.TextField(blank=True, verbose_name='Case study link text')), ('case_study_cta_text_en_gb', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_de', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_ja', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_zh_hans', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_fr', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_es', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_pt', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('case_study_cta_text_ar', models.TextField(blank=True, null=True, verbose_name='Case study link text')), ('section_three_heading', models.CharField(blank=True, max_length=255, verbose_name='Fact sheets heading')), ('section_three_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheets heading')), ('section_three_teaser', models.TextField(blank=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_en_gb', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_de', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_ja', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_zh_hans', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_fr', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_es', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_pt', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_teaser_ar', models.TextField(blank=True, null=True, verbose_name='Fact sheets teaser')), ('section_three_subsection_one_heading', models.CharField(blank=True, max_length=255, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 1 heading')), ('section_three_subsection_one_teaser', models.TextField(blank=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_en_gb', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_de', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_ja', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_zh_hans', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_fr', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_es', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_pt', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_teaser_ar', models.TextField(blank=True, null=True, verbose_name='Fact sheet 1 teaser')), ('section_three_subsection_one_body', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_en_gb', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_de', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_ja', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_zh_hans', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_fr', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_es', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_pt', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_one_body_ar', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 1 body')), ('section_three_subsection_two_heading', models.CharField(blank=True, max_length=255, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_en_gb', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_de', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_ja', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_zh_hans', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_fr', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_es', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_pt', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_heading_ar', models.CharField(blank=True, max_length=255, null=True, verbose_name='Fact sheet 2 heading')), ('section_three_subsection_two_teaser', models.TextField(blank=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_en_gb', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_de', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_ja', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_zh_hans', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_fr', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_es', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_pt', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_teaser_ar', models.TextField(blank=True, null=True, verbose_name='Fact sheet 2 teaser')), ('section_three_subsection_two_body', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_en_gb', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_de', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_ja', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_zh_hans', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_fr', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_es', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_pt', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('section_three_subsection_two_body_ar', core.model_fields.MarkdownField(blank=True, null=True, validators=[core.validators.slug_hyperlinks], verbose_name='Fact sheet 2 body')), ('project_opportunities_title', models.CharField(blank=True, max_length=255)), ('project_opportunities_title_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_de', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_ja', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_fr', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_es', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_pt', models.CharField(blank=True, max_length=255, null=True)), ('project_opportunities_title_ar', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text', models.CharField(blank=True, max_length=255)), ('related_opportunities_cta_text_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_de', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_ja', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_fr', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_es', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_pt', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_text_ar', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link', models.CharField(blank=True, max_length=255)), ('related_opportunities_cta_link_en_gb', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_de', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_ja', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_zh_hans', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_fr', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_es', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_pt', models.CharField(blank=True, max_length=255, null=True)), ('related_opportunities_cta_link_ar', models.CharField(blank=True, max_length=255, null=True)), ('case_study_cta_page', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_cta_page_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page', verbose_name='Case study link URL')), ('case_study_image', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('case_study_image_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_ar', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_de', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_en_gb', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_es', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_fr', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_ja', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_pt', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('hero_image_zh_hans', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image')), ('related_page_one', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_one_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_three_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('related_page_two_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailcore.Page')), ('section_one_image', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_one_image_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Image for unique selling points')), ('section_two_subsection_one_icon', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_one_icon_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 1 icon')), ('section_two_subsection_three_icon', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_three_icon_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 3 icon')), ('section_two_subsection_two_icon', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_ar', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_de', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_en_gb', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_es', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_fr', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_ja', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_pt', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('section_two_subsection_two_icon_zh_hans', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image', verbose_name='Spotlight 2 icon')), ('tags', modelcluster.fields.ParentalManyToManyField(blank=True, to='export_readiness.Tag')), ], options={ 'abstract': False, }, bases=('wagtailcore.page', great_international.panels.great_international.BaseInternationalSectorPagePanels), ), migrations.CreateModel( name='CapitalInvestRelatedSubSectors', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('sort_order', models.IntegerField(blank=True, editable=False, null=True)), ('page', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_ar', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_de', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_en_gb', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_es', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_fr', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_ja', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_pt', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('page_zh_hans', modelcluster.fields.ParentalKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='related_sub_sectors', to='great_international.CapitalInvestOpportunityPage')), ('related_sub_sector', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='great_international.InternationalSubSectorPage')), ], options={ 'ordering': ['sort_order'], 'abstract': False, }, ), ]

128.241071

285

0.704324

9,217

71,815

5.18238

0.019421

0.093832

0.072353

0.140183

0.967006

0.962756

0.960286

0.958255

0.943977

0.916405

0

0.020254

0.161248

71,815

559

286

128.470483

0.77274

0.000627

0

0.0217

1

0.001808

0.29208

0.174562

0

1

0

false

0

0.01085

0

0.016275

0.097649

0

null

0

1

0

1

0

1

0

null

0

8

7b0ffc813113ffbeeca98ab0b5134d769a1c489f

98

py

Python

neuroBN/inference/map_exact/__init__.py

Centiment-io/neuroBN

0863efd03f5cc79a2084efcc592d34969c16d4a4

[ "Apache-2.0" ]

1

2018-09-04T09:32:07.000Z

neuroBN/inference/map_exact/__init__.py

Centiment-io/neuroBN

0863efd03f5cc79a2084efcc592d34969c16d4a4

[ "Apache-2.0" ]

null

neuroBN/inference/map_exact/__init__.py

Centiment-io/neuroBN

0863efd03f5cc79a2084efcc592d34969c16d4a4

[ "Apache-2.0" ]

2

2019-10-03T21:23:09.000Z

2020-03-21T11:12:56.000Z

from neuroBN.inference.map_exact.ilp_map import * from neuroBN.inference.map_exact.ve_map import *

49

0.846939

16

98

4.9375

0.5

0.278481

0.506329

0.582278

0.708861

0

0.071429

98

2

50

49

0.868132

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

7b2fc71f61ed14ab76058fa7d5ea0cd6905d3377

96

py

Python

naplib/io/__init__.py

gavinmischler/naplib-python

8cd7a0fc700f1c07243169ec42fc087955885adc

[ "MIT" ]

1

2022-03-02T20:54:23.000Z

naplib/io/__init__.py

gavinmischler/gavlib

cacf9180b1442e4aed98b6182d586747a6d6ef90

[ "MIT" ]

null

naplib/io/__init__.py

gavinmischler/gavlib

cacf9180b1442e4aed98b6182d586747a6d6ef90

[ "MIT" ]

null

from .fileio import load, save, import_outstruct __all__ = ['load','save','import_outstruct']

19.2

48

0.739583

12

96

5.416667

0.583333

0.246154

0.430769

0.707692

0

0.114583

96

4

49

24

0.764706

0

0.252632

0

1

0

false

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

9e5bc86bff1888faa8a491e73ffebbdbc46f2de0

6,139

py

Python

drsa/functions.py

collinprather/DRSA-PyTorch

071148fa81188dd02793ccd90c7812a3f53bbf8b

[ "Apache-2.0" ]

9

2020-06-18T22:06:20.000Z

2022-03-07T12:02:19.000Z

drsa/functions.py

collinprather/DRSA-PyTorch

071148fa81188dd02793ccd90c7812a3f53bbf8b

[ "Apache-2.0" ]

3

2020-05-25T18:30:07.000Z

2021-09-28T02:52:46.000Z

drsa/functions.py

collinprather/DRSA-PyTorch

071148fa81188dd02793ccd90c7812a3f53bbf8b

[ "Apache-2.0" ]

2

2020-06-06T08:16:36.000Z

2020-09-11T07:33:46.000Z

# AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/00_functions.ipynb (unless otherwise specified). __all__ = ['survival_rate', 'event_rate', 'event_time', 'log_survival_rate', 'log_event_rate', 'log_event_time', 'event_time_loss', 'event_rate_loss'] # Cell import torch # Internal Cell def assert_correct_input_shape(h): if len(h.shape) != 3: raise ValueError(f"h is of shape {h.shape}. It is expected that h is of shape (batch size, sequence_length, 1), as this is most amenable to use in training neural nets with pytorch.") def assert_correct_output_shape(q, batch_size): if q.shape != torch.Size([batch_size, 1]): raise ValueError(f"q is of shape {q.shape}. It is expected that q is of shape (batch_size, 1)") # Cell def survival_rate(h): """ Given the predicted conditional hazard rate, this function estimates the survival rate. *input*: * `h`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)`, as this is most amenable to use in training neural nets with pytorch. _output_: * `s`: - type: `torch.tensor` - estimated survival rate at time t. - note: `s.shape == (batch_size, 1)` """ assert_correct_input_shape(h) s = (1-h).prod(dim=1) return s # Cell def event_rate(h): """ Given the predicted conditional hazard rate, this function estimates the event rate. *input*: * `h`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)`, as this is most amenable to use in training neural nets with pytorch. _output_: * `w`: - type: `torch.tensor` - estimated survival rate at time t. - note: `w.shape == (batch_size, 1)` """ assert_correct_input_shape(h) w = 1-survival_rate(h) return w # Cell def event_time(h): """ Given the predicted conditional hazard rate, this function estimates the probability that the event occurs at time t. *input*: * `h`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)`, as this is most amenable to use in training neural nets with pytorch. _output_: * `p`: - type: `torch.tensor` - estimated probability of event at time t. - note: `p.shape == (batch_size, 1)` """ assert_correct_input_shape(h) p = h[:, -1, :] * survival_rate(h[:, :-1, :]) return p # Cell def log_survival_rate(h): """ Given the predicted conditional hazard rate, this function estimates the log survival rate. *input*: * `h`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)`, as this is most amenable to use in training neural nets with pytorch. _output_: * `s`: - type: `torch.tensor` - estimated log survival rate at time t. - note: `s.shape == (batch_size, 1)` """ assert_correct_input_shape(h) s = (1-h).log().sum(dim=1) return s # Cell def log_event_rate(h): """ Given the predicted conditional hazard rate, this function estimates the log event rate. *input*: * `h`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)`, as this is most amenable to use in training neural nets with pytorch. _output_: * `w`: - type: `torch.tensor` - estimated log survival rate at time t. - note: `w.shape == (batch_size, 1)` """ assert_correct_input_shape(h) # w = event_rate(h).log() # numerically unstable, darn probabilities w = (1 - log_survival_rate(h).exp()).log() # numerically stable return w # Cell def log_event_time(h): """ Given the predicted conditional hazard rate, this function estimates the log probability that the event occurs at time t. *input*: * `h`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)`, as this is most amenable to use in training neural nets with pytorch. _output_: * `p`: - type: `torch.tensor` - estimated log probability of event at time t. - note: `p.shape == (batch_size, 1)` """ assert_correct_input_shape(h) p = torch.log(h[:, -1, :]) + log_survival_rate(h[:, :-1, :]) return p # Cell def event_time_loss(input, target=None): """ Loss function applied to uncensored data in order to optimize the PDF of the true event time, z input: * `input`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)` * `target`: - unused, only present to mimic pytorch loss functions output: * `evt_loss`: - type: `torch.tensor` - Loss associated with how wrong each predicted probability was at each time step """ assert_correct_input_shape(input) evt_loss = -log_event_time(input).mean(dim=0).squeeze() return evt_loss # Cell def event_rate_loss(input, target=None): """ Loss function applied to uncensored data in order to optimize the CDF of the true event time, z input: * `input`: - type: `torch.tensor`, - predicted conditional hazard rate, at each observed time step. - note: `h.shape == (batch size, 1, 1)` * `target`: - unused, only present to mimic pytorch loss functions output: * `evr_loss`: - type: `torch.tensor` - Loss associated with how cumulative predicted probabilities differ from the ground truth labels. """ assert_correct_input_shape(input) evr_loss = -log_event_rate(input).mean(dim=0).squeeze() return evr_loss

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0.143541

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0.060565

0.810229

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0.73755

0.700942

0

0.008863

0.264864

6,139

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192

30.093137

0.814314

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false

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null

0

1

0

1

0

null

0

1

0

8

9e61a896c0cce22507cb60f8634873dd1d870de3

154

py

Python

whitenoise/generators/__init__.py

James1345/white-noise

25bad49b69950a59660e14292a1b1819a4e26bd2

[ "MIT" ]

4

2019-01-11T17:05:09.000Z

2022-03-05T19:57:22.000Z

whitenoise/generators/__init__.py

James1345/white-noise

25bad49b69950a59660e14292a1b1819a4e26bd2

[ "MIT" ]

2

2019-07-02T23:14:46.000Z

2019-07-12T00:30:41.000Z

whitenoise/generators/__init__.py

James1345/white-noise

25bad49b69950a59660e14292a1b1819a4e26bd2

[ "MIT" ]

1

2019-07-02T21:57:06.000Z

import inspect from whitenoise.generators.simple import * from whitenoise.generators.list import * from whitenoise.generators.generator import generator

25.666667

53

0.850649

18

154

7.277778

0.444444

0.320611

0.549618

0.458015

0

0.097403

154

5

54

30.8

0.942446

0

1

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true

0

1

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null

1

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1

0

null

0

1

0

1

0

1

0

8

9ea475e2ccfc0b24631364f9707359ed84b7cb3d

14,009

py

Python

swagger_client/api/project_api.py

radon-h2020/radon-ctt-cli

3120b748c73e99d81d0cac5037e393229577d640

[ "Apache-2.0" ]

null

swagger_client/api/project_api.py

radon-h2020/radon-ctt-cli

3120b748c73e99d81d0cac5037e393229577d640

[ "Apache-2.0" ]

null

swagger_client/api/project_api.py

radon-h2020/radon-ctt-cli

3120b748c73e99d81d0cac5037e393229577d640

[ "Apache-2.0" ]

null

# coding: utf-8 """ RADON CTT Server API This is API of the RADON Continuous Testing Tool (CTT) Server: <a href=\"https://github.com/radon-h2020/radon-ctt\">https://github.com/radon-h2020/radon-ctt<a/> # noqa: E501 OpenAPI spec version: 1.0.0-oas3 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from swagger_client.api_client import ApiClient class ProjectApi(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def create_project(self, **kwargs): # noqa: E501 """Creates a project # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_project(async_req=True) >>> result = thread.get() :param async_req bool :param POSTProject body: :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.create_project_with_http_info(**kwargs) # noqa: E501 else: (data) = self.create_project_with_http_info(**kwargs) # noqa: E501 return data def create_project_with_http_info(self, **kwargs): # noqa: E501 """Creates a project # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_project_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :param POSTProject body: :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['body'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method create_project" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'body' in params: body_params = params['body'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/project', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def delete_project(self, project_uuid, **kwargs): # noqa: E501 """Delete a project # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_project(project_uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uuid: UUID of the project to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.delete_project_with_http_info(project_uuid, **kwargs) # noqa: E501 else: (data) = self.delete_project_with_http_info(project_uuid, **kwargs) # noqa: E501 return data def delete_project_with_http_info(self, project_uuid, **kwargs): # noqa: E501 """Delete a project # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_project_with_http_info(project_uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uuid: UUID of the project to delete (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['project_uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_project" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_uuid' is set if ('project_uuid' not in params or params['project_uuid'] is None): raise ValueError("Missing the required parameter `project_uuid` when calling `delete_project`") # noqa: E501 collection_formats = {} path_params = {} if 'project_uuid' in params: path_params['project_uuid'] = params['project_uuid'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/project/{project_uuid}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_project_by_uuid(self, project_uuid, **kwargs): # noqa: E501 """Retrieve a project # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_project_by_uuid(project_uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uuid: UUID of the project to return (required) :return: Project If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_project_by_uuid_with_http_info(project_uuid, **kwargs) # noqa: E501 else: (data) = self.get_project_by_uuid_with_http_info(project_uuid, **kwargs) # noqa: E501 return data def get_project_by_uuid_with_http_info(self, project_uuid, **kwargs): # noqa: E501 """Retrieve a project # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_project_by_uuid_with_http_info(project_uuid, async_req=True) >>> result = thread.get() :param async_req bool :param str project_uuid: UUID of the project to return (required) :return: Project If the method is called asynchronously, returns the request thread. """ all_params = ['project_uuid'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_project_by_uuid" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'project_uuid' is set if ('project_uuid' not in params or params['project_uuid'] is None): raise ValueError("Missing the required parameter `project_uuid` when calling `get_project_by_uuid`") # noqa: E501 collection_formats = {} path_params = {} if 'project_uuid' in params: path_params['project_uuid'] = params['project_uuid'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/project/{project_uuid}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Project', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_projects(self, **kwargs): # noqa: E501 """Get a list of all projects # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_projects(async_req=True) >>> result = thread.get() :param async_req bool :return: list[Project] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async_req'): return self.get_projects_with_http_info(**kwargs) # noqa: E501 else: (data) = self.get_projects_with_http_info(**kwargs) # noqa: E501 return data def get_projects_with_http_info(self, **kwargs): # noqa: E501 """Get a list of all projects # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_projects_with_http_info(async_req=True) >>> result = thread.get() :param async_req bool :return: list[Project] If the method is called asynchronously, returns the request thread. """ all_params = [] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in six.iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_projects" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 return self.api_client.call_api( '/project', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Project]', # noqa: E501 auth_settings=auth_settings, async_req=params.get('async_req'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

35.920513

178

0.602113

1,616

14,009

4.952351

0.097153

0.044983

0.02799

0.035987

0.916781

0.907785

0.884918

0.883169

0.855304

0

0.015786

0.308159

14,009

389

179

36.012853

0.809946

0.319652

0

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0

0.16157

0.037768

0

1

0.043478

false

0

0.019324

0

0.125604

0

null

0

1

0

1

0

null

0

7

7b6132d882f953bc7ad7edac798ce0e0d987d7b7

11,696

py

Python

src/models/kernels.py

mfkiwl/precise_gps

e30c6355447424cb69549feb85c9393b10eae7aa

[ "MIT" ]

null

src/models/kernels.py

mfkiwl/precise_gps

e30c6355447424cb69549feb85c9393b10eae7aa

[ "MIT" ]

null

src/models/kernels.py

mfkiwl/precise_gps

e30c6355447424cb69549feb85c9393b10eae7aa

[ "MIT" ]

null

import numpy as np import tensorflow as tf import gpflow import tensorflow_probability as tfp from src.models.initialization import * from src.models.base_kernel import BaseKernel class ARD(BaseKernel, gpflow.kernels.Kernel): """ Own implementation of the squared exponential kernel with ard property. Should workthe same way as gpflow.kernels.SquaredExponential(ARD = True). Lengthscales and variance can be randomized. This should be handled when initializing the kernel. Args: variance (float) : kernel variance which scales the whole kernel lengthscales (numpy array) : list of lengthscales (should match the dimension of the input) """ def __init__(self, **kwargs): super().__init__() randomized = kwargs["randomized"] dim = kwargs["dim"] if not randomized: lengthscales = np.ones(dim) variance = 1.0 else: lengthscales = np.random.uniform(0.5,2,dim) variance = 1.0 self.variance = gpflow.Parameter( variance, transform = gpflow.utilities.positive()) self.lengthscales = gpflow.Parameter( lengthscales, transform = gpflow.utilities.positive()) self.dim = dim def K_diag(self, X) -> tf.Tensor: """ Returns the diagonal vector when X1 == X2 (used in the background of gpflow) """ return self.variance * tf.ones_like(X[:,0]) def K(self, X1, X2=None) -> tf.Tensor: """ Returns the squared exponential ard kernel. Args: X1 (numpy array) : shaped N x D X2 (numpy array) : shaped M x D (D denotes the number of dimensions of the input) """ if X2 is None: X2 = X1 # Precision is the inverse squared of the lengthscales P = tf.linalg.diag(self.lengthscales**2) X11 = tf.squeeze( tf.expand_dims(X1,axis = 1) @ P @ tf.expand_dims(X1,axis = -1),-1) X22 = tf.transpose( tf.squeeze( tf.expand_dims(X2,axis = 1) @ P @ tf.expand_dims(X2,axis = -1), -1)) X12 = X1 @ P @ tf.transpose(X2) K = self.variance * tf.exp(-0.5 * (X11 - 2*X12 + X22)) return K def precision(self) -> tf.Tensor: return tf.linalg.diag(self.lengthscales**(2)) class ARD_gpflow(BaseKernel, gpflow.kernels.SquaredExponential): def __init__(self, **kwargs): randomized = kwargs["randomized"] dim = kwargs["dim"] if not randomized: lengthscales = np.ones(dim) variance = 1.0 else: lengthscales = np.random.uniform(0.5,3,dim) variance = 1.0 super().__init__(variance, lengthscales) def precision(self) -> tf.Tensor: return tf.linalg.diag(self.lengthscales**(-2)) class FullGaussianKernel(BaseKernel, gpflow.kernels.Kernel): """ Implementation of the full Gaussian kernel which introduces also the off-diagonal covariates of the precision matrix. Randomizing the initialization should be handled outside of this class. Args: variance (float) : signal variance which scales the whole kernel L (numpy array) : vector representation of L, where LL^T = P : precision """ def __init__(self, **kwargs): super().__init__() randomized = kwargs["randomized"] dim = kwargs["dim"] if not randomized: L = np.ones((dim*(dim+1))//2) variance = 1.0 else: L = init_precision(dim) variance = 1.0 self.variance = gpflow.Parameter( variance, transform = gpflow.utilities.positive()) self.L = gpflow.Parameter(L) self.dim = dim def K_diag(self, X) -> tf.Tensor: """ Returns the diagonal vector when X1 == X2 (used in the background of gpflow) """ return self.variance * tf.ones_like(X[:,0]) def K(self, X1, X2=None) -> tf.Tensor: """ Returns the full Gaussian kernel. Args: X1 (numpy array) : shaped N x D X2 (numpy array) : shaped M x D (D denotes the number of dimensions of the input) """ if X2 is None: X2 = X1 #L = tfp.math.fill_triangular(self.L) # matrix representation of L #A = X1 @ L #B = X2 @ L P = self.precision() X11 = tf.squeeze( tf.expand_dims(X1,axis = 1) @ P @ tf.expand_dims(X1,axis = -1),-1) X22 = tf.transpose( tf.squeeze( tf.expand_dims(X2,axis = 1) @ P @ tf.expand_dims(X2,axis = -1), -1)) X12 = X1 @ P @ tf.transpose(X2) # kernel (N,1) - (N,M) + (1,M) K = self.variance*tf.exp(-0.5 * (X11 - 2*X12 + X22)) return K def precision(self) -> tf.Tensor: L = tfp.math.fill_triangular(self.L) return L@tf.transpose(L) class LowRankFullGaussianKernel(BaseKernel, gpflow.kernels.Kernel): """ Implementation of the full Gaussian kernel which introduces also the off-diagonal covariates of the precision matrix. Randomizing the initialization should be handled outside of this class. Args: variance (float) : signal variance which scales the whole kernel L (numpy array) : vector representation of L, where LL^T = P : precision """ def __init__(self, **kwargs): super().__init__() randomized = kwargs["randomized"] dim = kwargs["dim"] rank = kwargs["rank"] if not randomized: L = np.ones((dim*(dim+1))//2) variance = 1.0 else: L = init_lowrank_precision(dim, rank) variance = 1.0 self.length = L.shape[0] self.variance = gpflow.Parameter( variance, transform = gpflow.utilities.positive()) self.L = gpflow.Parameter(L) self.rank = rank def K_diag(self, X) -> tf.Tensor: """ Returns the diagonal vector when X1 == X2 (used in the background of gpflow) """ return self.variance * tf.ones_like(X[:,0]) def K(self, X1, X2=None) -> tf.Tensor: """ Returns the full Gaussian kernel. Args: X1 (numpy array) : shaped N x D X2 (numpy array) : shaped M x D (D denotes the number of dimensions of the input) """ if X2 is None: X2 = X1 P = self.precision() X11 = tf.squeeze( tf.expand_dims(X1,axis = 1) @ P @ tf.expand_dims(X1,axis = -1),-1) X22 = tf.transpose( tf.squeeze( tf.expand_dims(X2,axis = 1) @ P @ tf.expand_dims(X2,axis = -1), -1)) X12 = X1 @ P @ tf.transpose(X2) K = self.variance * tf.exp(-0.5 * (X11 - 2*X12 + X22)) return K def precision(self) -> tf.Tensor: L = fill_lowrank_triangular(self.L, self.rank, self.length) return tf.transpose(L)@L class SGHMC_Full(BaseKernel, gpflow.kernels.Kernel): """ Implementation of the full Gaussian kernel which introduces also the off-diagonal covariates of the precision matrix. Randomizing the initialization should be handled outside of this class. Args: variance (float) : signal variance which scales the whole kernel L (numpy array) : vector representation of L, where LL^T = P : precision """ def __init__(self, **kwargs): super().__init__() randomized = kwargs["randomized"] dim = kwargs["dim"] if not randomized: L = np.ones((dim*(dim+1))//2) variance = 0.0 else: L = init_precision(dim, "wishart") variance = np.random.randn() self.variance = tf.Variable(variance, dtype = tf.float64, trainable = True) self.L = tf.Variable(L, dtype = tf.float64, trainable = False) self.dim = dim def K_diag(self, X) -> tf.Tensor: """ Returns the diagonal vector when X1 == X2 (used in the background of gpflow) """ return tf.exp(self.variance) * tf.ones_like(X[:,0]) def K(self, X1, X2=None) -> tf.Tensor: """ Returns the full Gaussian kernel. Args: X1 (numpy array) : shaped N x D X2 (numpy array) : shaped M x D (D denotes the number of dimensions of the input) """ if X2 is None: X2 = X1 L = tfp.math.fill_triangular(self.L) # matrix representation of L A = X1 @ L B = X2 @ L X11 = tf.squeeze( tf.expand_dims(A, axis = 1) @ tf.expand_dims(A, axis = -1), axis = -1) # (N, 1) X22 = tf.transpose( tf.squeeze( tf.expand_dims(B, axis = 1) @ tf.expand_dims(B, axis = -1), axis = -1)) # (1,M) X12 = A @ tf.transpose(B) # (N,M) K = tf.exp(self.variance)*tf.exp(-0.5 * (X11 - 2*X12 + X22)) return K def precision(self) -> tf.Tensor: L = tfp.math.fill_triangular(self.L) return L@tf.transpose(L) class SGHMC_ARD(BaseKernel, gpflow.kernels.Kernel): """ Own implementation of the squared exponential kernel with ard property. Should work the same way as gpflow.kernels.SquaredExponential(ARD = True). Lengthscales and variance can be randomized. This should be handled when initializing the kernel. Args: variance (float) : kernel variance which scales the whole kernel lengthscales (numpy array) : list of lengthscales (should match the dimension of the input) """ def __init__(self, **kwargs): super().__init__() randomized = kwargs["randomized"] dim = kwargs["dim"] if not randomized: L = np.ones(dim) variance = 0.0 else: L = np.random.randn(dim) variance = np.random.randn() self.variance = tf.Variable(variance, dtype = tf.float64, trainable = True) self.L = tf.Variable(L, dtype = tf.float64, trainable = False) self.dim = dim def K_diag(self, X) -> tf.Tensor: """ Returns the diagonal vector when X1 == X2 (used in the background of gpflow) """ return tf.exp(self.variance) * tf.ones_like(X[:,0]) def K(self, X1, X2=None) -> tf.Tensor: """ Returns the squared exponential ard kernel. Args: X1 (numpy array) : shaped N x D X2 (numpy array) : shaped M x D (D denotes the number of dimensions of the input) """ if X2 is None: X2 = X1 # Precision is the inverse squared of the lengthscales P = tf.linalg.diag(self.L**(2)) X11 = tf.squeeze( tf.expand_dims(X1,axis = 1) @ P @ tf.expand_dims(X1,axis = -1),-1) X22 = tf.transpose( tf.squeeze( tf.expand_dims(X2,axis = 1) @ P @ tf.expand_dims(X2,axis = -1), -1)) # (1,M) X12 = X1 @ P @ tf.transpose(X2) # (N,M) # kernel (N,1) - (N,M) + (1,M) K = tf.exp(self.variance) * tf.exp(-0.5 * (X11 - 2*X12 + X22)) return K def precision(self) -> tf.Tensor: return tf.linalg.diag(self.L**(2))

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null

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1

0

1

0

null

0

7

7bc05932cc891fe4e995171e9505ee992063abe8

9,315

py

Python

src/tf_transformers/utils/convert/convert_mt5.py

s4sarath/tf-transformers

361f7b01c7816034ddfc8661f8b6a967835bc1de

[ "Apache-2.0" ]

2

2021-03-31T17:48:16.000Z

2021-08-22T11:52:19.000Z

src/tf_transformers/utils/convert/convert_mt5.py

Vibha111094/tf-transformers

f26d440a4de0557e0e481279bfd70a732aaa8825

[ "Apache-2.0" ]

null

src/tf_transformers/utils/convert/convert_mt5.py

Vibha111094/tf-transformers

f26d440a4de0557e0e481279bfd70a732aaa8825

[ "Apache-2.0" ]

null

import tensorflow as tf from absl import logging logging.set_verbosity("INFO") def convert_mt5_hf_to_tf_transformers(model_hf, model_tf_transformers, config): # Encoder Side # From vars (Transformer variables) from_model_vars = [ "tfm_t5model/encoder/block_._{}/layer_._0/SelfAttention/q/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._0/SelfAttention/k/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._0/SelfAttention/v/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._0/SelfAttention/o/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._0/layer_norm/weight:0", "tfm_t5model/encoder/block_._{}/layer_._1/DenseReluDense/wi_0/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._1/DenseReluDense/wo/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._1/DenseReluDense/wi_1/kernel:0", "tfm_t5model/encoder/block_._{}/layer_._1/layer_norm/weight:0", ] to_model_vars = [ "tf_transformers/mt5_encoder/transformer/layer_{}/self_attention/query/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/self_attention/key/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/self_attention/value/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/self_attention_output/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/pre_attention_norm/weight:0", "tf_transformers/mt5_encoder/transformer/layer_{}/intermediate/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/output/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/intermediate2/kernel:0", "tf_transformers/mt5_encoder/transformer/layer_{}/self_attention_layer_norm/weight:0", ] # Simple Assertion # assert len(from_model_vars) == len(to_model_vars) mapping_dict = {} for index in range(len(from_model_vars)): for i in range(config["num_hidden_layers"]): mapping_dict[from_model_vars[index].format(i)] = to_model_vars[index].format(i) # Only Layer 0 mapping_dict[ "tfm_t5model/encoder/block_._0/layer_._0/SelfAttention/relative_attention_bias/embeddings:0" ] = "tf_transformers/mt5_encoder/transformer/layer_0/self_attention/relative_attention_bias/embeddings:0" # Word Embedding mapping_dict["shared/shared/weight:0"] = "tf_transformers/mt5_encoder/word_embeddings/embeddings:0" # Final Layer Norm weight mapping_dict[ "tfm_t5model/encoder/final_layer_norm/weight:0" ] = "tf_transformers/mt5_encoder/last_layer_norm/weight:0" from_to_variable_dict = {var.name: var for var in model_hf.variables} # del model_hf logging.info("Deleteing huggingface model for saving memory") tf_transformers_model_index_dict = {} for index, var in enumerate(model_tf_transformers.variables): tf_transformers_model_index_dict[var.name] = index # legacy_ai <-- hub assigned_map = [] assigned_map_values = [] for original_var, legacy_var in mapping_dict.items(): index = tf_transformers_model_index_dict[legacy_var] # If not in mapping_dict, then mostly it is from attention layer if "query/kernel:0" in legacy_var or "key/kernel:0" in legacy_var or "value/kernel:0" in legacy_var: # hub (2D) to tf_transformers (3D) model_tf_transformers.variables[index].assign( tf.reshape( from_to_variable_dict.get(original_var), ( config["embedding_size"], config["num_attention_heads"], config["attention_head_size"], ), ) ) assigned_map.append((original_var, legacy_var)) continue model_tf_transformers.variables[index].assign(from_to_variable_dict.get(original_var)) assigned_map.append((original_var, legacy_var)) logging.info("Done assigning ENCODER variables weights {}".format(len(assigned_map))) # Decoder Side # From vars (Transformer variables) from_model_vars = [ "tfm_t5model/decoder/block_._{}/layer_._0/SelfAttention/q/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._0/SelfAttention/k/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._0/SelfAttention/v/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._0/SelfAttention/o/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._0/layer_norm/weight:0", "tfm_t5model/decoder/block_._{}/layer_._1/EncDecAttention/q/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._1/EncDecAttention/k/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._1/EncDecAttention/v/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._1/EncDecAttention/o/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._1/layer_norm/weight:0", "tfm_t5model/decoder/block_._{}/layer_._2/DenseReluDense/wi_0/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._2/DenseReluDense/wo/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._2/DenseReluDense/wi_1/kernel:0", "tfm_t5model/decoder/block_._{}/layer_._2/layer_norm/weight:0", ] to_model_vars = [ "tf_transformers/mt5_decoder/transformer/layer_{}/self_attention/query/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/self_attention/key/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/self_attention/value/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/self_attention_output/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/pre_attention_norm/weight:0", "tf_transformers/mt5_decoder/transformer/layer_{}/cross_attention/query/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/cross_attention/key/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/cross_attention/value/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/cross_attention_output/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/pre_cross_attention_norm/weight:0", "tf_transformers/mt5_decoder/transformer/layer_{}/intermediate/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/output/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/intermediate2/kernel:0", "tf_transformers/mt5_decoder/transformer/layer_{}/self_attention_layer_norm/weight:0", ] # Simple Assertion assert len(from_model_vars) == len(to_model_vars) mapping_dict = {} for index in range(len(from_model_vars)): for i in range(config["num_hidden_layers"]): mapping_dict[from_model_vars[index].format(i)] = to_model_vars[index].format(i) # Only Layer 0 mapping_dict[ "tfm_t5model/decoder/block_._0/layer_._0/SelfAttention/relative_attention_bias/embeddings:0" ] = "tf_transformers/mt5_decoder/transformer/layer_0/self_attention/relative_attention_bias/embeddings:0" mapping_dict[ "tfm_t5model/decoder/block_._0/layer_._1/EncDecAttention/relative_attention_bias/embeddings:0" ] = "tf_transformers/mt5_decoder/transformer/layer_0/cross_attention/relative_attention_bias/embeddings:0" # Final Layer Norm weight mapping_dict[ "tfm_t5model/decoder/final_layer_norm/weight:0" ] = "tf_transformers/mt5_decoder/last_layer_norm/weight:0" from_to_variable_dict = {var.name: var for var in model_hf.variables} # del model_hf logging.info("Deleteing huggingface model for saving memory") tf_transformers_model_index_dict = {} for index, var in enumerate(model_tf_transformers.variables): tf_transformers_model_index_dict[var.name] = index # legacy_ai <-- hub assigned_map = [] assigned_map_values = [] for original_var, legacy_var in mapping_dict.items(): index = tf_transformers_model_index_dict[legacy_var] # If not in mapping_dict, then mostly it is from attention layer if "query/kernel:0" in legacy_var or "key/kernel:0" in legacy_var or "value/kernel:0" in legacy_var: # hub (2D) to tf_transformers (3D) model_tf_transformers.variables[index].assign( tf.reshape( from_to_variable_dict.get(original_var), ( config["embedding_size"], config["num_attention_heads"], config["attention_head_size"], ), ) ) assigned_map.append((original_var, legacy_var)) continue if ( original_var == "tfm_t5model/decoder/block_._0/layer_._1/EncDecAttention/relative_attention_bias/embeddings:0" ): if original_var not in from_to_variable_dict: model_tf_transformers.variables[index].assign(tf.zeros_like(model_tf_transformers.variables[index])) assigned_map.append((original_var, legacy_var)) continue model_tf_transformers.variables[index].assign(from_to_variable_dict.get(original_var)) assigned_map.append((original_var, legacy_var)) logging.info("Done assigning DECODER variables weights {}".format(len(assigned_map)))

50.901639

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0.954794

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0.670641

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0.185829

9,315

182

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0

null

0

1

0

1

null

0

7

c8fbdf24c52d2db22f56373e69c8956c66b3dc26

5,338

py

Python

tests/functional/conftest.py

dmulyalin/scrapli_netconf

7c9e5e74a1afac7955177db759e54d2211637d42

[ "MIT" ]

61

2020-05-17T19:57:25.000Z

2022-03-30T01:10:32.000Z

tests/functional/conftest.py

dmulyalin/scrapli_netconf

7c9e5e74a1afac7955177db759e54d2211637d42

[ "MIT" ]

79

2020-05-17T20:22:05.000Z

2022-03-02T14:37:28.000Z

tests/functional/conftest.py

dmulyalin/scrapli_netconf

7c9e5e74a1afac7955177db759e54d2211637d42

[ "MIT" ]

6

2021-01-07T16:45:28.000Z

2022-02-11T19:31:49.000Z

import time import pytest from scrapli_netconf.driver.async_driver import AsyncNetconfDriver from scrapli_netconf.driver.sync_driver import NetconfDriver NETCONF_1_0_DEVICE_TYPES = ["cisco_iosxe_1_0", "juniper_junos_1_0"] NETCONF_1_1_DEVICE_TYPES = ["cisco_iosxe_1_1", "cisco_iosxr_1_1"] NETCONF_ALL_VERSIONS_DEVICE_TYPES = NETCONF_1_0_DEVICE_TYPES + NETCONF_1_1_DEVICE_TYPES @pytest.fixture(scope="session") def real_valid_ssh_key_path(test_data_path): return f"{test_data_path}/files/vrnetlab_key" @pytest.fixture(scope="session", params=(True, False), ids=("compressed", "uncompressed")) def use_compressed_parser(request): yield request.param @pytest.fixture( scope="session", params=NETCONF_1_0_DEVICE_TYPES, ) def device_type_1_0(request): yield request.param @pytest.fixture( scope="session", params=NETCONF_1_1_DEVICE_TYPES, ) def device_type_1_1(request): yield request.param @pytest.fixture( scope="session", params=NETCONF_ALL_VERSIONS_DEVICE_TYPES, ) def device_type(request): yield request.param @pytest.fixture(scope="class", params=["system", "ssh2", "paramiko"]) def transport(request): yield request.param @pytest.fixture(scope="session", params=["password"]) def auth_type(request): yield request.param @pytest.fixture(scope="function") def sync_conn_1_0( test_devices_dict, real_valid_ssh_key_path, device_type_1_0, auth_type, transport ): device = test_devices_dict[device_type_1_0].copy() if auth_type == "key": device.pop("auth_password") device["auth_private_key"] = real_valid_ssh_key_path conn = NetconfDriver(**device, transport=transport) yield conn, device_type_1_0 if conn.isalive(): conn.close() # slow down connections since the lab vms can be slow sometimes time.sleep(1) if "cisco_iosxr" in device_type_1_0: # doubly true for xr vm! time.sleep(2) @pytest.fixture(scope="function") async def async_conn_1_0(test_devices_dict, real_valid_ssh_key_path, device_type_1_0, auth_type): device = test_devices_dict[device_type_1_0].copy() device["transport"] = "asyncssh" if auth_type == "key": device.pop("auth_password") device["auth_private_key"] = real_valid_ssh_key_path conn = AsyncNetconfDriver(**device) yield conn, device_type_1_0 if conn.isalive(): await conn.close() # slow down connections since the lab vms can be slow sometimes time.sleep(1) if "cisco_iosxr" in device_type_1_0: # doubly true for xr vm! time.sleep(2) @pytest.fixture(scope="function") def sync_conn_1_1( test_devices_dict, real_valid_ssh_key_path, device_type_1_1, auth_type, transport ): device = test_devices_dict[device_type_1_1].copy() if auth_type == "key": device.pop("auth_password") device["auth_private_key"] = real_valid_ssh_key_path conn = NetconfDriver(**device, transport=transport) yield conn, device_type_1_1 if conn.isalive(): conn.close() # slow down connections since the lab vms can be slow sometimes time.sleep(1) if "cisco_iosxr" in device_type_1_1: # doubly true for xr vm! time.sleep(2) @pytest.fixture(scope="function") async def async_conn_1_1(test_devices_dict, real_valid_ssh_key_path, device_type_1_1, auth_type): device = test_devices_dict[device_type_1_1].copy() device["transport"] = "asyncssh" if auth_type == "key": device.pop("auth_password") device["auth_private_key"] = real_valid_ssh_key_path conn = AsyncNetconfDriver(**device) yield conn, device_type_1_1 if conn.isalive(): await conn.close() # slow down connections since the lab vms can be slow sometimes time.sleep(1) if "cisco_iosxr" in device_type_1_1: # doubly true for xr vm! time.sleep(2) @pytest.fixture(scope="function") def sync_conn( test_devices_dict, real_valid_ssh_key_path, device_type, auth_type, transport, use_compressed_parser, ): device = test_devices_dict[device_type].copy() if auth_type == "key": device.pop("auth_password") device["auth_private_key"] = real_valid_ssh_key_path conn = NetconfDriver(**device, transport=transport, use_compressed_parser=use_compressed_parser) yield conn, device_type if conn.isalive(): conn.close() # slow down connections since the lab vms can be slow sometimes time.sleep(1) if "cisco_iosxr" in device_type: # doubly true for xr vm! time.sleep(2) @pytest.fixture(scope="function") async def async_conn(test_devices_dict, real_valid_ssh_key_path, device_type, auth_type): device = test_devices_dict[device_type].copy() device["transport"] = "asyncssh" if auth_type == "key": device.pop("auth_password") device["auth_private_key"] = real_valid_ssh_key_path conn = AsyncNetconfDriver(**device) yield conn, device_type if conn.isalive(): await conn.close() # slow down connections since the lab vms can be slow sometimes time.sleep(1) if "cisco_iosxr" in device_type: # doubly true for xr vm! time.sleep(2)

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0

null

0

1

0

null

0

1

0

7

c8ffda9081edf28454df980d743689c5f0530743

22,246

py

Python

padqc/steps/patterns.py

qis-unipr/padqc

94599db20711dc755b53425951fa3cb15b749f64

[ "Apache-2.0" ]

null

padqc/steps/patterns.py

qis-unipr/padqc

94599db20711dc755b53425951fa3cb15b749f64

[ "Apache-2.0" ]

null

padqc/steps/patterns.py

qis-unipr/padqc

94599db20711dc755b53425951fa3cb15b749f64

[ "Apache-2.0" ]

1

2021-02-18T22:11:18.000Z

from padqc.gates import Cx, Hadamard from padqc.q_graph import Graph, Node from padqc.steps import TransformationStep class Patterns(TransformationStep): """ Transformation step for specific two-qubit gate patterns. """ def __init__(self): super().__init__() self._num_qubits = None self._wires_to_id = {} self._id_to_wires = {} self._layers = None self._extra_layers = None self._skip = [] self.patterns = 0 def run(self, q_circuit): """Executes the transformation step. Args: q_circuit (q_circuit.QCircuit): the circuit on which to run the step """ self._num_qubits = q_circuit.q_graph.n_qubits i = 0 for q_reg in q_circuit.q_graph.q_registers.values(): for q in range(q_reg['dim']): self._wires_to_id[(q_reg['id'], q)] = i self._id_to_wires[i] = (q_reg['id'], q) i += 1 self.find_pattern(q_circuit) q_circuit.patterns = self.patterns def find_pattern(self, q_circuit): """Finds specific two-qubit gate patterns in *q_circuit* Args: q_circuit (q_circuit.QCircuit): the circuit into which to search for patterns """ q_graph = q_circuit.q_graph new_graph = Graph() for register in q_graph.q_registers: new_graph._add_q_register(register, q_circuit.q_regs[register][1]) for register in q_graph.c_registers: new_graph._add_c_register(register, q_circuit.c_regs[register][1]) # get dag layers self._layers = [layer for layer in q_circuit.q_graph.layers()] # this is the list of new layers for the nearest-neighbor CNOT sequences self._extra_layers = {l: [] for l in range(len(self._layers))} # loop through all layers for i, layer in enumerate(self._layers): if i != 0: # add nearest-neighbor CNOT sequences in the right layer for node in self._extra_layers[i - 1]: new_graph._append_node(node.type, node.gate) # check all gates in the layer for node in layer: temp = None # do not add gates that have been used in the transformation process if node in self._skip: continue # every cnot could be the starting point for a CNOT cascade elif node.name == 'cx': # check for a CNOT cascade # print('Checking Cascade') temp = self.check_cascade(node, i) if temp is not None: self._skip.extend(temp) # print('Found Cascade') self.patterns += 1 else: # check for an inverted CNOT cascade # print('Checking Inverse Cascade') temp = self.check_inverse_cascade(node, i) if temp is not None: self._skip.extend(temp) # print('Found Inverse Cascade') self.patterns += 1 else: # apply the CNOT if no cascade was found self._skip.append(node) new_graph._append_node(node.type, node.gate) else: if node.type == 'gate': self._skip.append(node) new_graph._append_node(node.type, node.gate) q_circuit.q_graph = new_graph def check_cascade(self, node, layer_id): """Starting from *q_node*, searches for CNOT cascades and transform them into nearest-neighbor CNOT sequences. Args: layer_id (int): the layer index node (q_graph.Node): the node from which to start searching for a CNOT cascade Returns: list: a list of nodes to be skipped as they are part of an already transformed CNOT cascade """ target = self._wires_to_id[node.q_args[1]] control = self._wires_to_id[node.q_args[0]] controls = [control] skip = [node] # qubits already added to the CNOT sequence used = set() used.add(target) used.add(control) # qubits that cannot be used anymore off_limits = set() before = {} after = [] # flag to identify the direction of the cascade descending = False if control > target: descending = True count = 1 last_layer = layer_id double_break = False # loop through layers until a max limit is reached while count < min([2 * self._num_qubits, len(self._layers) - layer_id]): for node in self._layers[layer_id + count]: if node in self._skip: for qarg in node.q_args: if self._wires_to_id[qarg] == target: double_break = True break else: if node.name == 'cx': # print('CX: ', node.q_args) g_control = self._wires_to_id[node.q_args[0]] g_target = self._wires_to_id[node.q_args[1]] if g_control == target: double_break = True break if g_control in off_limits or g_target in off_limits: off_limits.add(g_control) off_limits.add(g_target) if g_control not in used: used.add(g_control) if g_target not in used: used.add(g_target) continue # chek that the CNOT is part of the cascade a = (g_target == target and g_control not in controls and g_control not in used) b = (descending is True and g_control > target) \ or (descending is False and g_control < target) if a and b: # print('Adding to Cascade') controls.append(g_control) used.add(g_control) skip.append(node) # check if the CNOT interrupts the cascade elif g_target != target and g_control != target: # remember to put the CNOT after the transformation if g_target not in used and g_control not in used: if last_layer < layer_id + count: last_layer = layer_id + count # updates used and off limits qubits when necessary else: off_limits.add(g_control) off_limits.add(g_target) if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 if g_control not in used: used.add(g_control) if g_target not in used: used.add(g_target) else: # break the loop if the CNOT interrupts the cascade double_break = True break else: # ignore gates acting on off limits qubits double_continue = False for qarg in node.q_args: if self._wires_to_id[qarg] in off_limits: double_continue = True continue if double_continue is True: continue # for special multi-qubits gates, update used and off limits qubits properly, # break the loop if necessary if node.name in ["barrier", "snapshot", "save", "load", "noise"]: qargs = [self._wires_to_id[qarg] for qarg in node.q_args] if target in qargs: if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 double_break = True break u = [] not_u = [] for qarg in qargs: if qarg in used: off_limits.add(qarg) u.append(qarg) else: not_u.append(qarg) if len(u) == len(qargs): # the transformation must be applied before this gate if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 elif len(u) == 0: # the transformation must be applied after this gate if last_layer < layer_id + count: last_layer = layer_id + count else: # the transformation must be applied before this gate if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 for qarg in not_u + u: used.add(qarg) off_limits.add(qarg) else: # print(node.name, node.q_args) # check if one-qubits gates either interrupt the cascade, # can be applied after or before qarg = self._wires_to_id[node.q_args[0]] if qarg == target: after.append(node) skip.append(node) double_break = True break if qarg not in used: # print('Before') if qarg not in before: before[qarg] = [] before[qarg].append(node) else: # print('After') after.append(node) skip.append(node) count += 1 if double_break is True: break # if a cascade was found if len(controls) > 1: if descending is True: controls = sorted(controls) else: controls = sorted(controls, reverse=True) # apply all gates that were encountered before the cascade for u in before: for node in before[u]: self._extra_layers[last_layer].append(node) # apply the transformation for i in range(len(controls) - 1, 0, -1): self._extra_layers[last_layer].append(Node(type='gate', gate=Cx(self._id_to_wires[controls[i]], self._id_to_wires[controls[i - 1]]))) self._extra_layers[last_layer].append( Node(type='gate', gate=Cx(self._id_to_wires[controls[0]], self._id_to_wires[target]))) for i in range(len(controls) - 1): self._extra_layers[last_layer].append( Node(type='gate', gate=Cx(self._id_to_wires[controls[i + 1]], self._id_to_wires[controls[i]]))) # apply all gates that were encountered after the cascade for node in after: self._extra_layers[last_layer].append(node) else: skip = None return skip def check_inverse_cascade(self, node, layer_id): """Starting from *q_node*, searches for inverted CNOT cascades and transforms them into nearest-neighbor CNOT sequences. Args: layer_id (int): the layer kindex node (q_graph.Node): the node from which to start searching for an inverted CNOT cascade Returns: list: a list of nodes to be skipped as they are part of an already transformed inverted CNOT cascade """ target = self._wires_to_id[node.q_args[1]] control = self._wires_to_id[node.q_args[0]] targets = [target] skip = [node] # qubits already added to the CNOT sequence used = set() used.add(target) used.add(control) # qubits that cannot be used anymore off_limits = set() before = {} after = [] # flag to identify the direction of the cascade descending = False if target > control: descending = True count = 1 last_layer = layer_id double_break = False # loop through layers until a max limit is reached while count < min([2 * self._num_qubits, len(self._layers) - layer_id]): for node in self._layers[layer_id + count]: if node in self._skip: for qarg in node.q_args: if self._wires_to_id[qarg] == control: double_break = True break else: if node.name == 'cx': g_control = self._wires_to_id[node.q_args[0]] g_target = self._wires_to_id[node.q_args[1]] if g_target == control: double_break = True break if g_control in off_limits or g_target in off_limits: if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 off_limits.add(g_control) off_limits.add(g_target) if g_control not in used: used.add(g_control) if g_target not in used: used.add(g_target) continue # chek that the CNOT is part of the cascade a = (g_control == control and g_target not in targets and g_target not in used) b = (descending is True and g_target > control) or ( descending is False and g_target < control) if a and b: targets.append(g_target) used.add(g_target) skip.append(node) # check if the CNOT interrupts the cascade elif g_control != control and g_target != control: # remember to put the CNOT after the transformation if g_control not in used and g_target not in used: if last_layer < layer_id + count: last_layer = layer_id + count # updates used and off limits qubits when necessary else: off_limits.add(g_control) off_limits.add(g_target) if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 if g_control not in used: used.add(g_control) if g_target not in used: used.add(g_target) else: # break the loop if the CNOT interrupts the cascade double_break = True break else: # ignore gates acting on off limits qubits double_continue = False for qarg in node.q_args: if self._wires_to_id[qarg] in off_limits: double_continue = True continue if double_continue is True: continue # for special multi-qubits gates, update used and off limits qubits properly, # break the loop if necessary if node.name in ["barrier", "snapshot", "save", "load", "noise"]: qargs = [self._wires_to_id[qarg] for qarg in node.q_args] if control in qargs: if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 double_break = True break u = [] not_u = [] for qarg in qargs: if qarg in used: off_limits.add(qarg) u.append(qarg) else: not_u.append(qarg) if len(u) == len(qargs): # the transformation must be applied before this gate if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 elif len(u) == 0: # the transformation must be applied after this gate if last_layer < layer_id + count: last_layer = layer_id + count else: # the transformation must be applied before this gate if last_layer > layer_id + count - 1: last_layer = layer_id + count - 1 for qarg in not_u + u: used.add(qarg) off_limits.add(qarg) else: # check if one-qubits gates either interrupt the cascade, # can be applied after or before qarg = self._wires_to_id[node.q_args[0]] if qarg == control: after.append(node) skip.append(node) double_break = True break if qarg not in used: if qarg not in before: before[qarg] = [] before[qarg].append(node) skip.append(node) else: after.append(node) skip.append(node) count += 1 if double_break is True: break # if an inverse cascade was found if len(targets) > 1: if descending is True: targets = sorted(targets) else: targets = sorted(targets, reverse=True) # apply all gates that were encountered before the cascade for u in before: for node in before[u]: self._extra_layers[last_layer].append(node) # apply the transformation self._extra_layers[last_layer].append( Node(type='gate', gate=Hadamard(self._id_to_wires[control]))) for t in targets: self._extra_layers[last_layer].append(Node(type='gate', gate=Hadamard(self._id_to_wires[t]))) for i in range(len(targets) - 1, 0, -1): self._extra_layers[last_layer].append(Node(type='gate', gate=Cx(self._id_to_wires[targets[i]], self._id_to_wires[ targets[i - 1]]))) self._extra_layers[last_layer].append( Node(type='gate', gate=Cx(self._id_to_wires[targets[0]], self._id_to_wires[control]))) for i in range(len(targets) - 1): self._extra_layers[last_layer].append( Node(type='gate', gate=Cx(self._id_to_wires[targets[i + 1]], self._id_to_wires[targets[i]]))) self._extra_layers[last_layer].append( Node(type='gate', gate=Hadamard(self._id_to_wires[control]))) for t in targets: self._extra_layers[last_layer].append(Node(type='gate', gate=Hadamard(self._id_to_wires[t]))) # apply all gates that were encountered after the cascade for node in after: self._extra_layers[last_layer].append(node) else: skip = None return skip

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0.703617

0.689615

0

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22,246

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1

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null

0

7

cd9e19ca52f3066816b62a13c3caaa394eb62fbb

2,549

py

Python

AllMotorTest.py

vvzen/Kitronik-Pico-Robotics-Board-MicroPython

fba59af843929e319164949d52ff7f293d2ef499

[ "MIT" ]

8

2021-05-08T14:34:05.000Z

2022-03-01T23:43:41.000Z

AllMotorTest.py

vvzen/Kitronik-Pico-Robotics-Board-MicroPython

fba59af843929e319164949d52ff7f293d2ef499

[ "MIT" ]

1

2021-05-31T21:17:27.000Z

2021-06-07T13:09:09.000Z

AllMotorTest.py

vvzen/Kitronik-Pico-Robotics-Board-MicroPython

fba59af843929e319164949d52ff7f293d2ef499

[ "MIT" ]

2

2021-05-14T08:56:59.000Z

2021-05-14T16:33:21.000Z

#AllMotorTest.py # test code that ramps each motor 0-100-0 then changes direction and does it again. #all motors run at once, but with staggered timings import PicoRobotics import utime board = PicoRobotics.KitronikPicoRobotics() directions = ["f","r"] while True: for direction in directions: for speed in range(0,25): board.motorOn(1, direction, speed) board.motorOn(2, direction, 25-speed) board.motorOn(3, direction, 50-speed) board.motorOn(4, direction, 75-speed) utime.sleep_ms(100) #ramp speed over 25x100ms => approx 2.5 second. for speed in range(0,25): board.motorOn(1, direction, 25+speed) board.motorOn(2, direction, speed) board.motorOn(3, direction, 25-speed) board.motorOn(4, direction, 50-speed) utime.sleep_ms(100) for speed in range(0,25): board.motorOn(1, direction, 50+speed) board.motorOn(2, direction, 25+speed) board.motorOn(3, direction, speed) board.motorOn(4, direction, 25-speed) utime.sleep_ms(100) for speed in range(0,25): board.motorOn(1, direction, 75+speed) board.motorOn(2, direction, 50+speed) board.motorOn(3, direction, 25+speed) board.motorOn(4, direction, speed) utime.sleep_ms(100) for speed in range(0,25): board.motorOn(1, direction, 100-speed) board.motorOn(2, direction, 75+speed) board.motorOn(3, direction, 50+speed) board.motorOn(4, direction, 25+speed) utime.sleep_ms(100) for speed in range(0,25): board.motorOn(1, direction, 75-speed) board.motorOn(2, direction, 100-speed) board.motorOn(3, direction, 75+speed) board.motorOn(4, direction, 50+speed) utime.sleep_ms(100) for speed in range(0,25): board.motorOn(1, direction, 50-speed) board.motorOn(2, direction, 75-speed) board.motorOn(3, direction, 100-speed) board.motorOn(4, direction, 75+speed) utime.sleep_ms(100) for speed in range(0,25): board.motorOn(1, direction, 25-speed) board.motorOn(2, direction, 50-speed) board.motorOn(3, direction, 75-speed) board.motorOn(4, direction, 100-speed) utime.sleep_ms(100)

39.215385

84

0.573558

319

2,549

4.557994

0.175549

0.264099

0.280605

0.082531

0.814993

0.795048

0

0.087709

0.320126

2,549

64

85

39.828125

0.751298

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1

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1

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null

0

8

cdf739aca2929bd13a7b52cdc1525c92ad3ebb45

7,935

py

Python

england/Databricks/CCU002_03-D11-outcomes_dose1.py

BHFDSC/CCU002_03

e525441cf5c8de20e28ce51e12ddf7737109dfce

[ "Apache-2.0" ]

null

england/Databricks/CCU002_03-D11-outcomes_dose1.py

BHFDSC/CCU002_03

e525441cf5c8de20e28ce51e12ddf7737109dfce

[ "Apache-2.0" ]

null

england/Databricks/CCU002_03-D11-outcomes_dose1.py

BHFDSC/CCU002_03

e525441cf5c8de20e28ce51e12ddf7737109dfce

[ "Apache-2.0" ]

null

# Databricks notebook source # MAGIC %md # CCU002_03-D11-outcomes_dose1 # MAGIC # MAGIC **Description** This notebook determines outcomes for the analysis. # MAGIC # MAGIC **Author(s)** Venexia Walker # COMMAND ---------- # MAGIC %md ## Clear cache # COMMAND ---------- # MAGIC %sql # MAGIC CLEAR CACHE # COMMAND ---------- # MAGIC %md ## Define functions # COMMAND ---------- # Define create table function by Sam Hollings # Source: Workspaces/dars_nic_391419_j3w9t_collab/DATA_CURATION_wrang000_functions def create_table(table_name:str, database_name:str='dars_nic_391419_j3w9t_collab', select_sql_script:str=None) -> None: """Will save to table from a global_temp view of the same name as the supplied table name (if no SQL script is supplied) Otherwise, can supply a SQL script and this will be used to make the table with the specificed name, in the specifcied database.""" spark.conf.set("spark.sql.legacy.allowCreatingManagedTableUsingNonemptyLocation","true") if select_sql_script is None: select_sql_script = f"SELECT * FROM global_temp.{table_name}" spark.sql(f"""CREATE TABLE {database_name}.{table_name} AS {select_sql_script} """) spark.sql(f"ALTER TABLE {database_name}.{table_name} OWNER TO {database_name}") def drop_table(table_name:str, database_name:str='dars_nic_391419_j3w9t_collab', if_exists=True): if if_exists: IF_EXISTS = 'IF EXISTS' else: IF_EXISTS = '' spark.sql(f"DROP TABLE {IF_EXISTS} {database_name}.{table_name}") # COMMAND ---------- # MAGIC %md ## Specify outcomes # COMMAND ---------- outcomes = ['myocarditis','pericarditis'] # COMMAND ---------- index_date = '2020-12-08' # COMMAND ---------- # MAGIC %md ## GDPPR # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_gdppr_" + codelist + " AS SELECT NHS_NUMBER_DEID, min(DATE) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, DATE FROM dars_nic_391419_j3w9t_collab.ccu002_03_gdppr_dars_nic_391419_j3w9t WHERE CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' AND terminology=='SNOMED')) WHERE DATE>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- # MAGIC %md ## HES APC # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_first_hesapc_" + codelist + " AS SELECT NHS_NUMBER_DEID, MIN(EPISTART) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, EPISTART FROM dars_nic_391419_j3w9t_collab.ccu002_03_hes_apc_longformat WHERE CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' AND TERMINOLOGY='ICD10') AND (SOURCE='DIAG_3_01' OR SOURCE='DIAG_4_01')) WHERE EPISTART>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_any_hesapc_" + codelist + " AS SELECT NHS_NUMBER_DEID, MIN(EPISTART) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, EPISTART FROM dars_nic_391419_j3w9t_collab.ccu002_03_hes_apc_longformat WHERE CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' AND TERMINOLOGY='ICD10')) WHERE EPISTART>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- # MAGIC %md ## SUS # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_first_sus_" + codelist + " AS SELECT NHS_NUMBER_DEID, MIN(EPISODE_START_DATE) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, EPISODE_START_DATE FROM dars_nic_391419_j3w9t_collab.ccu002_03_sus_longformat WHERE ((CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10')) OR (LEFT(CODE,3) IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10'))) AND SOURCE='PRIMARY_DIAGNOSIS_CODE') WHERE EPISODE_START_DATE>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_any_sus_" + codelist + " AS SELECT NHS_NUMBER_DEID, MIN(EPISODE_START_DATE) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, EPISODE_START_DATE FROM dars_nic_391419_j3w9t_collab.ccu002_03_sus_longformat WHERE ((CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10')) OR (LEFT(CODE,3) IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10')))) WHERE EPISODE_START_DATE>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- # MAGIC %md ## Deaths # COMMAND ---------- # DEATHS for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_first_deaths_" + codelist + " AS SELECT NHS_NUMBER_DEID, MIN(DATE) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, DATE FROM dars_nic_391419_j3w9t_collab.ccu002_03_deaths_longformat WHERE ((CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10')) OR (LEFT(CODE,3) IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10'))) AND SOURCE='S_UNDERLYING_COD_ICD10') WHERE DATE>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- # DEATHS for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_any_deaths_" + codelist + " AS SELECT NHS_NUMBER_DEID, MIN(DATE) AS out_dose1_" + codelist + " FROM (SELECT NHS_NUMBER_DEID, DATE FROM dars_nic_391419_j3w9t_collab.ccu002_03_deaths_longformat WHERE ((CODE IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10')) OR (LEFT(CODE,3) IN (SELECT code FROM dars_nic_391419_j3w9t_collab.ccu002_03_codelists WHERE name = '" + codelist + "' and terminology=='ICD10')))) WHERE DATE>='" + index_date + "' GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- # MAGIC %md ## Combine data sources # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_first_" + codelist + " AS SELECT NHS_NUMBER_DEID, min(out_dose1_" + codelist + ") AS out_dose1_first_" + codelist + " FROM (SELECT * FROM global_temp.ccu002_03_out_dose1_gdppr_" + codelist + " UNION ALL SELECT * FROM global_temp.ccu002_03_out_dose1_first_hesapc_" + codelist + " UNION ALL SELECT * FROM global_temp.ccu002_03_out_dose1_first_deaths_" + codelist + " UNION ALL SELECT * FROM global_temp.ccu002_03_out_dose1_first_sus_" + codelist + ") GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- for codelist in outcomes: sql("CREATE OR REPLACE GLOBAL TEMP VIEW ccu002_03_out_dose1_any_" + codelist + " AS SELECT NHS_NUMBER_DEID, min(out_dose1_" + codelist + ") AS out_dose1_any_" + codelist + " FROM (SELECT * FROM global_temp.ccu002_03_out_dose1_gdppr_" + codelist + " UNION ALL SELECT * FROM global_temp.ccu002_03_out_dose1_any_hesapc_" + codelist + " UNION ALL SELECT * FROM global_temp.ccu002_03_out_dose1_any_deaths_" + codelist + " UNION ALL SELECT * FROM global_temp.ccu002_03_out_dose1_any_sus_" + codelist + ") GROUP BY NHS_NUMBER_DEID") # COMMAND ---------- # MAGIC %md ## Save as tables # COMMAND ---------- for codelist in outcomes: drop_table('ccu002_03_out_dose1_first_'+codelist) # COMMAND ---------- for codelist in outcomes: create_table('ccu002_03_out_dose1_first_'+codelist) # COMMAND ---------- for codelist in outcomes: drop_table('ccu002_03_out_dose1_any_'+codelist) # COMMAND ---------- for codelist in outcomes: create_table('ccu002_03_out_dose1_any_'+codelist)

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7

a834267d3d1320be877a6e6edf376c222b8cb0bc

5,204

py

Python

staging/staging/validate_input.py

lexis-project/ddi-service-apis

9e96c4159154d70613b1977a8ea28374c038b463

[ "Apache-2.0" ]

null

staging/staging/validate_input.py

lexis-project/ddi-service-apis

9e96c4159154d70613b1977a8ea28374c038b463

[ "Apache-2.0" ]

null

staging/staging/validate_input.py

lexis-project/ddi-service-apis

9e96c4159154d70613b1977a8ea28374c038b463

[ "Apache-2.0" ]

null

from . import generate_config from . import verifyMetadata import logging import yaml with open("/etc/staging_api/system.yml") as file: systems = yaml.load(file, Loader=yaml.FullLoader) def validate_staging_input_body(input_data): try: logging.info(input_data["source_system"]) logging.info(input_data["target_system"]) logging.info(input_data["source_path"]) logging.info(input_data["target_path"]) logging.info(input_data["encryption"]) logging.info(input_data["compression"]) source_system = input_data["source_system"] target_system = input_data["target_system"] source_type = generate_config.get_type(source_system) target_type = generate_config.get_type(target_system) except KeyError: raise KeyError( "Input is not valid. Please check source and target information") # Encryption and compression flags validation if "encryption" not in input_data or "compression" not in input_data: raise KeyError( "Input is not valid. Check encryption and compression values") if input_data["encryption"] != "yes" and input_data["encryption"] != "no": raise KeyError( "Input is not valid. Check encryption values. Allowed values are yes or no") if input_data["compression"] != "yes" and input_data["compression"] != "no": raise KeyError( "Input is not valid. Check compression values. Allowed values are yes or no") if source_type == "HPC" or target_type == "HPC": try: logging.info(input_data["task_id"]) logging.info(input_data["job_id"]) logging.info(input_data["heappe_url"]) except KeyError: raise KeyError( "Input is not valid. HEAppE job and task id are required") elif target_type == "iRODS": try: logging.info(input_data["metadata"]) e = verifyMetadata.verifyMetadataForUpload(input_data["metadata"]) if e is not None: raise Exception("Metadata is not valid: " + e) except KeyError: raise KeyError("Input is not valid. Metadata are required") try: logging.info(systems["systems"][source_system]) logging.info(systems["systems"][target_system]) except KeyError: raise (KeyError("Source or target doesn't exist!")) def validate_deletion_input_body(input_data): try: logging.info(input_data["target_system"]) logging.info(input_data["target_path"]) target_system = input_data["target_system"] target_type = generate_config.get_type(target_system) except KeyError: raise KeyError("Input is not valid. Please check target information") if target_type == "HPC": try: logging.info(input_data["task_id"]) logging.info(input_data["job_id"]) logging.info(input_data["heappe_url"]) except KeyError: raise KeyError( "Input is not valid. HEAppE job and task id are required") try: logging.info(systems["systems"][target_system]) except KeyError: raise KeyError("Target doesn't exist!") def validate_replication_input_body(input_data): try: logging.info(input_data["source_system"]) logging.info(input_data["source_path"]) logging.info(input_data["target_system"]) source_system = input_data["source_system"] target_system = input_data["target_system"] except KeyError: raise KeyError( "Input is not valid. Please check source or target information") try: logging.info(systems["systems"][source_system]) logging.info(systems["systems"][target_system]) except KeyError: raise (Exception("Source or Target doesn't exist!")) def validate_pid_assignment_input_body(input_data): try: logging.info(input_data["source_system"]) logging.info(input_data["source_path"]) source_system = input_data["source_system"] except KeyError: raise KeyError("Input is not valid. Please check target information") try: logging.info(systems["systems"][source_system]) except KeyError: raise (Exception("Source doesn't exist!")) def validate_replication_status_input_body(input_data): try: logging.info(input_data["target_system"]) logging.info(input_data["target_path"]) target_system = input_data["target_system"] except KeyError: raise KeyError("Input is not valid. Please check target information") try: logging.info(systems["systems"][target_system]) except KeyError: raise (Exception("Target doesn't exist!")) def validate_data_size_input_body(input_data): try: logging.info(input_data["target_system"]) logging.info(input_data["target_path"]) target_system = input_data["target_system"] except KeyError: raise KeyError("Input is not valid. Please check target information") try: logging.info(systems["systems"][target_system]) except KeyError: raise KeyError("Target doesn't exist!")

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0.144665

0.80862

0.786317

0.739904

0.736588

0.646474

0.637734

0

0.234243

5,204

134

90

38.835821

0.832622

0.008263

0

0.705882

1

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1

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null

0

7

b548dedf9e64e6e024ddb5ee1cb820bdb5ed3145

24,050

py

Python

test/test_GraceHandler.py

josiah-wolf-oberholtzer/consort

6c7d511835d5ad883ad1ad52ae9cd48c4a7b5571

[ "MIT" ]

9

2015-02-11T09:35:40.000Z

2019-04-29T23:57:49.000Z

test/test_GraceHandler.py

josiah-wolf-oberholtzer/consort

6c7d511835d5ad883ad1ad52ae9cd48c4a7b5571

[ "MIT" ]

2

2016-02-07T18:54:47.000Z

2017-08-10T01:38:01.000Z

test/test_GraceHandler.py

josiah-wolf-oberholtzer/consort

6c7d511835d5ad883ad1ad52ae9cd48c4a7b5571

[ "MIT" ]

1

2019-05-13T12:37:15.000Z

import abjad import collections import consort from abjad.tools import rhythmmakertools from abjad.tools import systemtools from abjad.tools import templatetools segment_metadata = collections.OrderedDict( segment_count=2, segment_number=1, ) def test_GraceHandler_01(): music_specifier = consort.MusicSpecifier( grace_handler=consort.GraceHandler( counts=(1,), ), rhythm_maker=rhythmmakertools.NoteRhythmMaker( tie_specifier=rhythmmakertools.TieSpecifier( tie_across_divisions=False, ), ), ) segment_maker = consort.SegmentMaker( discard_final_silence=True, desired_duration_in_seconds=4, omit_stylesheets=True, score_template=templatetools.GroupedRhythmicStavesScoreTemplate( staff_count=2, ), settings=( consort.MusicSetting( timespan_maker=consort.TaleaTimespanMaker( initial_silence_talea=rhythmmakertools.Talea( counts=(0, 1), denominator=4, ), playing_groupings=(2,), ), v1=music_specifier, v2=music_specifier, ), ), tempo=abjad.MetronomeMark((1, 4), 60), permitted_time_signatures=((4, 4),), ) lilypond_file, metadata = segment_maker(segment_metadata=segment_metadata) assert format(lilypond_file) == abjad.String.normalize( r''' \version "2.19.65" \language "english" #(ly:set-option 'relative-includes #t) \score { \context Score = "Grouped Rhythmic Staves Score" << \tag #'time \context TimeSignatureContext = "Time Signature Context" { { \tempo 4=60 \time 4/4 s1 * 1 } } \context StaffGroup = "Grouped Rhythmic Staves Staff Group" << \context RhythmicStaff = "Staff 1" { \context Voice = "Voice 1" { { % [Voice 1] Measure 1 { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } { { \afterGrace r4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } } { { c'4 } } } } \context RhythmicStaff = "Staff 2" { \context Voice = "Voice 2" { { % [Voice 2] Measure 1 { \afterGrace r4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } } { { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } { { r4 } } } } >> >> } ''') def test_GraceHandler_02(): music_specifier = consort.MusicSpecifier( grace_handler=consort.GraceHandler( counts=(1, 2, 3), ), rhythm_maker=rhythmmakertools.NoteRhythmMaker( tie_specifier=rhythmmakertools.TieSpecifier( tie_across_divisions=False, ), ), ) segment_maker = consort.SegmentMaker( discard_final_silence=True, desired_duration_in_seconds=4, omit_stylesheets=True, score_template=templatetools.GroupedRhythmicStavesScoreTemplate( staff_count=2, ), settings=( consort.MusicSetting( timespan_maker=consort.TaleaTimespanMaker( initial_silence_talea=rhythmmakertools.Talea( counts=(1,), denominator=4, ), playing_groupings=(3,), ), v1=music_specifier, v2=music_specifier, ), ), tempo=abjad.MetronomeMark((1, 4), 60), permitted_time_signatures=((4, 4),), ) lilypond_file, metadata = segment_maker(segment_metadata=segment_metadata) assert format(lilypond_file) == abjad.String.normalize( r''' \version "2.19.65" \language "english" #(ly:set-option 'relative-includes #t) \score { \context Score = "Grouped Rhythmic Staves Score" << \tag #'time \context TimeSignatureContext = "Time Signature Context" { { \tempo 4=60 \time 4/4 s1 * 1 } } \context StaffGroup = "Grouped Rhythmic Staves Staff Group" << \context RhythmicStaff = "Staff 1" { \context Voice = "Voice 1" { { % [Voice 1] Measure 1 { \afterGrace r4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } } { { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } } } \context RhythmicStaff = "Staff 2" { \context Voice = "Voice 2" { { % [Voice 2] Measure 1 { \afterGrace r4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } } { { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } } } >> >> } ''') def test_GraceHandler_03(): music_specifier = consort.MusicSpecifier( grace_handler=consort.GraceHandler( counts=(0, 2, 4), ), rhythm_maker=rhythmmakertools.NoteRhythmMaker( tie_specifier=rhythmmakertools.TieSpecifier( tie_across_divisions=False, ), ), ) segment_maker = consort.SegmentMaker( discard_final_silence=True, desired_duration_in_seconds=4, omit_stylesheets=True, score_template=templatetools.GroupedRhythmicStavesScoreTemplate( staff_count=2, ), settings=( consort.MusicSetting( timespan_maker=consort.TaleaTimespanMaker( initial_silence_talea=rhythmmakertools.Talea( counts=(1,), denominator=4, ), playing_groupings=(3,), ), v1=music_specifier, v2=music_specifier, ), ), tempo=abjad.MetronomeMark((1, 4), 60), permitted_time_signatures=((4, 4),), ) lilypond_file, metadata = segment_maker(segment_metadata=segment_metadata) assert format(lilypond_file) == abjad.String.normalize( r''' \version "2.19.65" \language "english" #(ly:set-option 'relative-includes #t) \score { \context Score = "Grouped Rhythmic Staves Score" << \tag #'time \context TimeSignatureContext = "Time Signature Context" { { \tempo 4=60 \time 4/4 s1 * 1 } } \context StaffGroup = "Grouped Rhythmic Staves Staff Group" << \context RhythmicStaff = "Staff 1" { \context Voice = "Voice 1" { { % [Voice 1] Measure 1 { r4 } } { { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } } } \context RhythmicStaff = "Staff 2" { \context Voice = "Voice 2" { { % [Voice 2] Measure 1 { \afterGrace r4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } } { { c'4 } { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 c'16 c'16 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } } } >> >> } ''') def test_GraceHandler_04(): music_specifier = consort.MusicSpecifier( grace_handler=consort.GraceHandler( only_if_preceded_by_silence=True, ), rhythm_maker=rhythmmakertools.NoteRhythmMaker( tie_specifier=rhythmmakertools.TieSpecifier( tie_across_divisions=False, ), ), ) segment_maker = consort.SegmentMaker( discard_final_silence=True, desired_duration_in_seconds=4, omit_stylesheets=True, score_template=templatetools.GroupedRhythmicStavesScoreTemplate( staff_count=1, ), settings=( consort.MusicSetting( timespan_maker=consort.TaleaTimespanMaker( initial_silence_talea=rhythmmakertools.Talea( counts=(1,), denominator=4, ), playing_groupings=(2,), ), v1=music_specifier, ), ), tempo=abjad.MetronomeMark((1, 4), 60), permitted_time_signatures=((4, 4),), ) lilypond_file, metadata = segment_maker(segment_metadata=segment_metadata) assert format(lilypond_file) == abjad.String.normalize( r''' \version "2.19.65" \language "english" #(ly:set-option 'relative-includes #t) \score { \context Score = "Grouped Rhythmic Staves Score" << \tag #'time \context TimeSignatureContext = "Time Signature Context" { { \tempo 4=60 \time 4/4 s1 * 1 } } \context StaffGroup = "Grouped Rhythmic Staves Staff Group" << \context RhythmicStaff = "Staff 1" { \context Voice = "Voice 1" { { % [Voice 1] Measure 1 { \afterGrace r4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } } { { c'4 } { c'4 } } { { r4 } } } } >> >> } ''') def test_GraceHandler_05(): music_specifier = consort.MusicSpecifier( grace_handler=consort.GraceHandler( only_if_preceded_by_nonsilence=True, ), rhythm_maker=rhythmmakertools.NoteRhythmMaker( tie_specifier=rhythmmakertools.TieSpecifier( tie_across_divisions=False, ), ), ) segment_maker = consort.SegmentMaker( discard_final_silence=True, desired_duration_in_seconds=4, omit_stylesheets=True, score_template=templatetools.GroupedRhythmicStavesScoreTemplate( staff_count=1, ), settings=( consort.MusicSetting( timespan_maker=consort.TaleaTimespanMaker( initial_silence_talea=rhythmmakertools.Talea( counts=(1,), denominator=4, ), playing_groupings=(2,), ), v1=music_specifier, ), ), tempo=abjad.MetronomeMark((1, 4), 60), permitted_time_signatures=((4, 4),), ) lilypond_file, metadata = segment_maker(segment_metadata=segment_metadata) assert format(lilypond_file) == abjad.String.normalize( r''' \version "2.19.65" \language "english" #(ly:set-option 'relative-includes #t) \score { \context Score = "Grouped Rhythmic Staves Score" << \tag #'time \context TimeSignatureContext = "Time Signature Context" { { \tempo 4=60 \time 4/4 s1 * 1 } } \context StaffGroup = "Grouped Rhythmic Staves Staff Group" << \context RhythmicStaff = "Staff 1" { \context Voice = "Voice 1" { { % [Voice 1] Measure 1 { r4 } } { { \afterGrace c'4 { \override Flag.stroke-style = #"grace" \override Script.font-size = #0.5 c'16 \revert Flag.stroke-style \revert Script.font-size } } { c'4 } } { { r4 } } } } >> >> } ''')

38.603531

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1,282

24,050

5.516381

0.095164

0.045249

0.067873

0.052036

0.96366

0.957721

0.943298

0

0.034384

0.63842

24,050

622

79

38.665595

0.778864

0

0.84456

0

0.025907

1

0.025907

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0

0.031088

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0.056995

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null

0

1

0

1

0

1

0

null

0

8

b57f017b8a4b52fbfa3a6c5def47add616840bb6

219

py

Python

tests/conftest.py

SurajDonthi/auto-labeling-pipeline

3c334d973faae0cb5ef66d30fd85d4bcfbac8a6b

[ "MIT" ]

31

2020-11-01T15:10:59.000Z

2022-03-17T06:27:39.000Z

tests/conftest.py

SurajDonthi/auto-labeling-pipeline

3c334d973faae0cb5ef66d30fd85d4bcfbac8a6b

[ "MIT" ]

9

2020-12-06T05:03:34.000Z

2021-12-07T14:06:36.000Z

tests/conftest.py

SurajDonthi/auto-labeling-pipeline

3c334d973faae0cb5ef66d30fd85d4bcfbac8a6b

[ "MIT" ]

12

2021-02-19T08:49:44.000Z

2021-10-21T22:46:18.000Z

import pathlib import pytest @pytest.fixture def data_path(): return pathlib.Path(__file__).parent / 'data' @pytest.fixture def cassettes_path(): return pathlib.Path(__file__).parent / 'fixtures/cassettes'

15.642857

63

0.744292

27

219

5.666667

0.444444

0.169935

0.20915

0.27451

0.405229

0

0.141553

219

13

64

16.846154

0.81383

0

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0

0.100457

0

1

0.25

true

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

7

a9448445e229c9e0782f946102c093146c6476c2

646,125

py

Python

lauetoolsnn/utils_lauenn.py

ravipurohit1991/lauetoolsnn

6cc413fb60872297c9ca7a202dd9dd596d4a9a5b

[ "MIT" ]

null

lauetoolsnn/utils_lauenn.py

ravipurohit1991/lauetoolsnn

6cc413fb60872297c9ca7a202dd9dd596d4a9a5b

[ "MIT" ]

null

lauetoolsnn/utils_lauenn.py

ravipurohit1991/lauetoolsnn

6cc413fb60872297c9ca7a202dd9dd596d4a9a5b

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Nov 6 14:47:33 2021 @author: PURUSHOT Functions for lauetoolsneuralnetwork """ __author__ = "Ravi raj purohit PURUSHOTTAM RAJ PUROHIT, CRG-IF BM32 @ ESRF" import warnings warnings.filterwarnings('ignore') import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import logging logger = logging.getLogger() old_level = logger.level logger.setLevel(100) # import matplotlib import matplotlib.pyplot as plt # matplotlib.use('Qt5Agg') # matplotlib.rcParams.update({'font.size': 14}) from mpl_toolkits.axes_grid1 import make_axes_locatable import numpy as np from random import random as rand1 from math import acos import time import enum import functools import math from numpy import pi, dot import scipy # from scipy.spatial.transform import Rotation as R import _pickle as cPickle import configparser from skimage.transform import (hough_line, hough_line_peaks) # ============================================================================= # Additonal networkx module import networkx as nx # ============================================================================= ## LaueTools import try: from lauetools import dict_LaueTools as dictLT from lauetools import IOLaueTools as IOLT from lauetools import generaltools as GT from lauetools import CrystalParameters as CP from lauetools import lauecore as LT from lauetools import LaueGeometry as Lgeo from lauetools import readmccd as RMCCD from lauetools import FitOrient as FitO from lauetools import findorient as FindO from lauetools import IOimagefile as IOimage except: import lauetoolsnn.lauetools.dict_LaueTools as dictLT import lauetoolsnn.lauetools.IOLaueTools as IOLT import lauetoolsnn.lauetools.generaltools as GT import lauetoolsnn.lauetools.CrystalParameters as CP import lauetoolsnn.lauetools.lauecore as LT import lauetoolsnn.lauetools.LaueGeometry as Lgeo import lauetoolsnn.lauetools.readmccd as RMCCD import lauetoolsnn.lauetools.FitOrient as FitO import lauetoolsnn.lauetools.findorient as FindO import lauetoolsnn.lauetools.IOimagefile as IOimage from collections import OrderedDict from math import cos, radians, sin, sqrt import fractions import collections import random, itertools import re ## Keras import tensorflow_keras = True try: import tensorflow as tf import keras from keras.models import Sequential from tensorflow.keras.callbacks import Callback from keras.layers import Dense, Activation, Dropout from tensorflow.keras.utils import to_categorical from keras.regularizers import l2 # from tf.keras.layers.normalization import BatchNormalization except: print("tensorflow not loaded") tensorflow_keras = False try: from wyckpos import wp, eqhkl_default, eqhkl_custom, sgrp_sym, sgrp_name,\ sgrp_params except: from lauetoolsnn.wyckpos import wp, eqhkl_default, eqhkl_custom, sgrp_sym, sgrp_name,\ sgrp_params ## for faster binning of histogram ## C version of hist # from fast_histogram import histogram1d import h5py ## GPU Nvidia drivers needs to be installed! Ughh ## if wish to use only CPU set the value to -1 else set it to 0 for GPU ## CPU training is suggested (as the model requires more RAM) try: # Disable all GPUS tf.config.set_visible_devices([], 'GPU') visible_devices = tf.config.get_visible_devices() for device in visible_devices: assert device.device_type != 'GPU' except: # Invalid device or cannot modify virtual devices once initialized. pass os.environ['CUDA_VISIBLE_DEVICES'] = '-1' def resource_path(relative_path, verbose=0): """ Get absolute path to resource, works for dev and for PyInstaller """ base_path = os.path.dirname(__file__) if verbose: print("Base path of the library: ",base_path) return os.path.join(base_path, relative_path) metricsNN = [ keras.metrics.FalseNegatives(name="fn"), keras.metrics.FalsePositives(name="fp"), keras.metrics.TrueNegatives(name="tn"), keras.metrics.TruePositives(name="tp"), keras.metrics.Precision(name="precision"), keras.metrics.Recall(name="accuracy"), ] ACCEPTABLE_FORMATS = [".npz"] gui_state = np.random.randint(1e6) DIGITS = int(abs(np.log10(1e-08))) CST_ENERGYKEV = 12.398 ACCEPTABLE_FORMATS = [".npz"] hklcond_group = re.compile(r'([-hkil0-9]+): ([-+hklnor1-8=\s,]+)(?:, |$)') DEG = np.pi / 180.0 dist_threshold = 50 # residues_threshold=0.5 # nb_spots_global_threshold=8 # option_global = "v2" # use_om_user = True # nb_spots_consider = 100 ##v1 same as strains ##v2 ambigious spots all ##v3 ambigious spots with uniqueness # if you wish to plot the training and testing dataset images plot_images = False try: from adjustText import adjust_text except: plot_images = False def call_global(): global residues_threshold, nb_spots_global_threshold, option_global, \ use_om_user, nb_spots_consider, path_user_OM, intensity_threshold, \ FitPixelDev_global123, boxsize, softmax_threshold_global123, cap_matchrate123,\ strain_free_parameters, additional_expression ## read a test config file and update the variables. config_setting = configparser.ConfigParser() filepath = resource_path('settings.ini') config_setting.read(filepath) residues_threshold = float(config_setting.get('CALLER', 'residues_threshold')) nb_spots_global_threshold = int(float(config_setting.get('CALLER', 'nb_spots_global_threshold'))) option_global = config_setting.get('CALLER', 'option_global') use_om_user = config_setting.get('CALLER', 'use_om_user') == "true" nb_spots_consider = int(float(config_setting.get('CALLER', 'nb_spots_consider'))) path_user_OM = config_setting.get('CALLER', 'path_user_OM') intensity_threshold = int(float(config_setting.get('CALLER', 'intensity'))) boxsize = int(float(config_setting.get('CALLER', 'boxsize'))) FitPixelDev_global123 = int(float(config_setting.get('CALLER', 'pixdev'))) softmax_threshold_global123 = float(config_setting.get('CALLER', 'cap_softmax')) cap_matchrate123 = float(config_setting.get('CALLER', 'cap_mr')) strain_free_parameters = config_setting.get('CALLER', 'strain_free_parameters').split(",") additional_expression = config_setting.get('CALLER', 'additional_expression').split(",") if cap_matchrate123 < 1: cap_matchrate123 = cap_matchrate123 *100.0 def rmv_freq_class(freq_rmv = 0, elements="all", freq_rmv1 = 0, elements1="all", save_directory="", material_=None, material1_=None, write_to_console=None, progress=None, qapp=None): classhkl0 = np.load(save_directory+"//grain_classhkl_angbin.npz")["arr_0"] if write_to_console != None: write_to_console("First material index length: " + str(len(classhkl0))) ind_mat = np.array([ij for ij in range(len(classhkl0))]) if material_ != material1_: classhkl1 = np.load(save_directory+"//grain_classhkl_angbin1.npz")["arr_0"] if write_to_console != None: write_to_console("Second material index length: " + str(len(classhkl1))) pre_ind = ind_mat[-1] + 1 ind_mat1 = np.array([pre_ind+ij for ij in range(len(classhkl1))]) classhkl = np.vstack((classhkl0, classhkl1)) else: classhkl = classhkl0 # ind_mat = None ind_mat1 = None elements1 = "all" freq_rmv1 = 0 angbins = np.load(save_directory+"//grain_classhkl_angbin.npz")["arr_1"] loc = np.array([ij for ij in range(len(classhkl))]) trainy_ = array_generatorV2(save_directory+"//training_data", 0, progress, qapp) if material_ != material1_: ## split trainy_ for two materials index trainy_mat0 = [] trainy_mat1 = [] for ijnode in trainy_: if ijnode in ind_mat: trainy_mat0.append(ijnode) elif ijnode in ind_mat1: trainy_mat1.append(ijnode) trainy_mat0 = np.array(trainy_mat0) trainy_mat1 = np.array(trainy_mat1) else: trainy_mat0 = trainy_ trainy_mat1 = None if write_to_console != None: write_to_console("Class ID and frequency; check for data imbalance and select \ appropriate LOSS function for training the model") ## lets extract the least common occuring classes to simply the training dataset if elements == "all": most_common0 = collections.Counter(trainy_mat0).most_common() else: most_common0 = collections.Counter(trainy_mat0).most_common()[:elements] if material_ != material1_: if elements1 =="all": most_common1 = collections.Counter(trainy_mat1).most_common() else: most_common1 = collections.Counter(trainy_mat1).most_common()[:elements1] else: most_common1 = [] most_common = most_common0 + most_common1 print(most_common) class_present = [most_common[i][0] for i in range(len(most_common))] rmv_indices = [] count = 0 for i in loc: if i not in class_present: rmv_indices.append(i) elif i in class_present: ind_ = np.where(np.array(class_present)==i)[0] ij = most_common[ind_[0]] if material_ != material1_: if (ij[0] in ind_mat) and (ij[1] <= freq_rmv): rmv_indices.append(int(ij[0])) if (ij[0] in ind_mat1) and (ij[1] <= freq_rmv1): rmv_indices.append(int(ij[0])) else: if (ij[1] <= freq_rmv): rmv_indices.append(int(ij[0])) else: if write_to_console != None: write_to_console("Something Fishy in Remove Freq Class module") if material_ != material1_: for i in rmv_indices: if i in ind_mat: indd = np.where(ind_mat == i)[0] ind_mat = np.delete(ind_mat, indd, axis=0) elif i in ind_mat1: indd = np.where(ind_mat1 == i)[0] ind_mat1 = np.delete(ind_mat1, indd, axis=0) else: for i in rmv_indices: if i in ind_mat: indd = np.where(ind_mat == i)[0] ind_mat = np.delete(ind_mat, indd, axis=0) loc_new = np.delete(loc, rmv_indices) occurances = [most_common[i][1] for i in range(len(most_common)) if int(most_common[i][0]) in loc_new] occurances = np.array(occurances) class_weight = {} class_weight_temp = {} count = 0 for i in loc_new: for ij in most_common: if int(ij[0]) == i: class_weight[count] = int(np.max(occurances)/ij[1]) ##+99 a quick hack to influence the weights class_weight_temp[int(ij[0])] = int(np.max(occurances)/ij[1]) count += 1 for occ in range(len(most_common)): if int(most_common[occ][0]) in loc_new: if write_to_console != None: if int(most_common[occ][0]) == -100: write_to_console("Unclassified HKL (-100); occurance : "+str(most_common[occ][1])+\ ": NN_weights : 0.0") else: write_to_console("HKL : " +str(classhkl[int(most_common[occ][0])])+"; occurance : "+\ str(most_common[occ][1])+\ ": NN_weights : "+ str(class_weight_temp[int(most_common[occ][0])])) if write_to_console != None: write_to_console(str(len(rmv_indices))+ " classes removed from the classHKL object [removal frequency: "+\ str(freq_rmv)+"] (before:"+str(len(classhkl))+", now:"+str(len(classhkl)-len(rmv_indices))+")") print(str(len(rmv_indices))+ " classes removed from the classHKL object [removal frequency: "+\ str(freq_rmv)+"] (before:"+str(len(classhkl))+", now:"+str(len(classhkl)-len(rmv_indices))+")") classhkl = np.delete(classhkl, rmv_indices, axis=0) ## save the altered classHKL object if material_ != material1_: np.savez_compressed(save_directory+'//MOD_grain_classhkl_angbin.npz', classhkl, angbins, loc_new, rmv_indices, freq_rmv, len(ind_mat), len(ind_mat1)) else: np.savez_compressed(save_directory+'//MOD_grain_classhkl_angbin.npz', classhkl, angbins, loc_new, rmv_indices, freq_rmv) with open(save_directory + "//class_weights.pickle", "wb") as output_file: cPickle.dump([class_weight], output_file) if write_to_console != None: write_to_console("Saved class weights data") def array_generator(path_, batch_size, n_classes, loc_new, write_to_console=None, tocategorical=True): """ Assign a new class to data that is removed (to include in the training anyway) """ array_pairs = get_path(path_, ver=0) random.shuffle(array_pairs) zipped = itertools.cycle(array_pairs) while True: temp_var = False for bs in range(batch_size): array_path = next(zipped) obj = np.load(array_path) trainX = obj["arr_0"] loc1 = obj["arr_1"] if len(trainX) == 0 or len(loc1) == 0: if write_to_console != None: write_to_console("Skipping File: "+ array_path+"; No data is found") if bs == 0: temp_var = True continue ## remove the non frequent class and rearrange the data loc1_new = [] loc1_new_rmv = [] for k, i in enumerate(loc1): temp_loc = np.where(loc_new==i)[0] if len(temp_loc) == 1: loc1_new.append(temp_loc) else: loc1_new_rmv.append(k) loc1_new = np.array(loc1_new).ravel() loc1_new_rmv = np.array(loc1_new_rmv).ravel() if len(trainX) != len(loc1_new): if len(loc1_new_rmv) > 0: trainX = np.delete(trainX, loc1_new_rmv, axis=0) if bs == 0 or temp_var: trainX1 = np.copy(trainX) trainY1 = np.copy(loc1_new) else: trainX1 = np.vstack((trainX1, trainX)) trainY1 = np.hstack((trainY1, loc1_new)) ## To normalize the size of one hot encoding count = 0 if np.min(trainY1) != 0: trainY1 = np.append(trainY1, 0) count += 1 if np.max(trainY1) != (n_classes-1): trainY1 = np.append(trainY1, n_classes-1) count += 1 if tocategorical: trainY1 = to_categorical(trainY1) if count == 1: trainY1 = np.delete(trainY1, [len(trainY1)-1] ,axis=0) elif count == 2: trainY1 = np.delete(trainY1, [len(trainY1)-1,len(trainY1)-2] ,axis=0) yield trainX1, trainY1 def vali_array(path_, batch_size, n_classes, loc_new, write_to_console=None, tocategorical=True): array_pairs = get_path(path_, ver=0) random.shuffle(array_pairs) zipped = itertools.cycle(array_pairs) temp_var = False for bs in range(batch_size): array_path = next(zipped) obj = np.load(array_path) trainX = obj["arr_0"] loc1 = obj["arr_1"] if len(trainX) == 0 or len(loc1) == 0: if write_to_console != None: write_to_console("Skipping File: "+ array_path+"; No data is found") if bs == 0: temp_var = True continue ## remove the non frequent class and rearrange the data loc1_new = [] loc1_new_rmv = [] for k, i in enumerate(loc1): temp_loc = np.where(loc_new==i)[0] if len(temp_loc) == 1: loc1_new.append(temp_loc) else: loc1_new_rmv.append(k) loc1_new = np.array(loc1_new).ravel() loc1_new_rmv = np.array(loc1_new_rmv).ravel() if len(trainX) != len(loc1_new): if len(loc1_new_rmv) > 0: trainX = np.delete(trainX, loc1_new_rmv, axis=0) if bs == 0 or temp_var: trainX1 = trainX trainY1 = loc1_new else: trainX1 = np.vstack((trainX1, trainX)) trainY1 = np.hstack((trainY1, loc1_new)) count = 0 if np.min(trainY1) != 0: trainY1 = np.append(trainY1, 0) count += 1 if np.max(trainY1) != (n_classes-1): trainY1 = np.append(trainY1, n_classes-1) count += 1 if tocategorical: trainY1 = to_categorical(trainY1) if count == 1: trainY1 = np.delete(trainY1, [len(trainY1)-1] ,axis=0) elif count == 2: trainY1 = np.delete(trainY1, [len(trainY1)-1,len(trainY1)-2] ,axis=0) return trainX1, trainY1 def get_path(path_, ver=0): image_files = [] for dir_entry in os.listdir(path_): if os.path.isfile(os.path.join(path_, dir_entry)) and \ os.path.splitext(dir_entry)[1] in ACCEPTABLE_FORMATS: file_name, file_extension = os.path.splitext(dir_entry) image_files.append((file_name, file_extension, os.path.join(path_, dir_entry))) return_value = [] for image_file, _, image_full_path in image_files: if image_file == "grain_classhkl_angbin": continue if image_file == "grain_classhkl_angbin1": continue if ver == 1 and image_file == "grain_init": continue if ver == 1 and image_file == "grain_init1": continue return_value.append((image_full_path)) return return_value def array_generator_verify(path_, batch_size, n_classes, loc_new, write_to_console=None): array_pairs = get_path(path_, ver=1) random.shuffle(array_pairs) zipped = itertools.cycle(array_pairs) while True: temp_var = False for bs in range(batch_size): array_path = next(zipped) obj = np.load(array_path) loc1 = obj["arr_1"] if len(loc1) == 0: if write_to_console !=None: write_to_console("Skipping File: "+ array_path+"; No data is found") if bs == 0: temp_var = True continue ## remove the non frequent class and rearrange the data loc1_new = [] for k, i in enumerate(loc1): temp_loc = np.where(loc_new==i)[0] if len(temp_loc) == 1: loc1_new.append(temp_loc) loc1_new = np.array(loc1_new).ravel() if bs == 0 or temp_var: trainY1 = np.copy(loc1_new) else: trainY1 = np.hstack((trainY1, loc1_new)) return trainY1 def create_additional_data(path_, write_to_console=None, material=None, material1=None): """array_generator_verify(self.save_directory+"//training_data", batch_size, len(self.classhkl), self.loc_new, self.write_to_console) if generate_additional_data==True""" array_pairs = get_path(path_, ver=1) for ijk in array_pairs: if ijk.split("\\")[-1].startswith(material+"_grain_"): obj = np.load(ijk) loc1 = obj["arr_1"] for kji in array_pairs: pass#TODO # random.shuffle(array_pairs) # zipped = itertools.cycle(array_pairs) # while True: # temp_var = False # for bs in range(batch_size): # array_path = next(zipped) # obj = np.load(array_path) # loc1 = obj["arr_1"] # if len(loc1) == 0: # if write_to_console !=None: # write_to_console("Skipping File: "+ array_path+"; No data is found") # if bs == 0: # temp_var = True # continue # ## remove the non frequent class and rearrange the data # loc1_new = [] # for k, i in enumerate(loc1): # temp_loc = np.where(loc_new==i)[0] # if len(temp_loc) == 1: # loc1_new.append(temp_loc) # loc1_new = np.array(loc1_new).ravel() # if bs == 0 or temp_var: # trainY1 = np.copy(loc1_new) # else: # trainY1 = np.hstack((trainY1, loc1_new)) # return trainY1 # open each npz file and combine two grains to form another Laue pattern ##save all data then open them and combine into one laue pattern --> better for two phase material # s_tth, s_chi, s_miller_ind, _, _, _, \ # ori_mat, ori_mat1 # s_tth = np.array([item for sublist in l_tth for item in sublist]) # s_chi = np.array([item for sublist in l_chi for item in sublist]) # s_miller_ind = np.array([item for sublist in l_miller_ind for item in sublist]) # s_posx = np.array([item for sublist in l_posx for item in sublist]) # s_posy = np.array([item for sublist in l_posy for item in sublist]) # s_E = np.array([item for sublist in l_E for item in sublist]) # s_intensity=np.array([item for sublist in l_intensity for item in sublist]) # if sortintensity: # indsort = np.argsort(s_intensity)[::-1] # s_tth=np.take(s_tth, indsort) # s_chi=np.take(s_chi, indsort) # s_miller_ind=np.take(s_miller_ind, indsort, axis=0) # s_posx=np.take(s_posx, indsort) # s_posy=np.take(s_posy, indsort) # s_E=np.take(s_E, indsort) # s_intensity=np.take(s_intensity, indsort) def array_generatorV2(path_, ver=1, progress=None, qapp=None): array_pairs = get_path(path_, ver=ver) random.shuffle(array_pairs) if progress !=None: progress.setMaximum(len(array_pairs)) for bs in range(len(array_pairs)): loc1 = np.load(array_pairs[bs])["arr_1"] if bs == 0: trainY1 = loc1 if bs > 0: trainY1 = np.hstack((trainY1, loc1)) if progress !=None: progress.setValue(bs+1) if qapp !=None: qapp.processEvents() return trainY1 def printProgressBar(iteration, total, prefix = '', suffix = 'Complete', decimals = 1, length = 50, fill = '█', printEnd = "\r"): percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total))) filledLength = int(length * iteration // total) bar = fill * filledLength + '-' * (length - filledLength) print(f'\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd) # Print New Line on Complete if iteration == total: print() def mse_images(pathA, pathB, ix, iy, ccd_label, progressbar=False, iteration=None, total=None): # the 'Mean Squared Error' between the two images is the # sum of the squared difference between the two images; # NOTE: the two images must have the same dimension imageA, _, _ = IOimage.readCCDimage(pathA, stackimageindex=-1, CCDLabel=ccd_label, dirname=None, verbose=0) imageB, _, _ = IOimage.readCCDimage(pathB, stackimageindex=-1, CCDLabel=ccd_label, dirname=None, verbose=0) err = np.sum((imageA.astype("int") - imageB.astype("int")) ** 2) err /= float(imageA.shape[0] * imageA.shape[1]) if progressbar: printProgressBar(iteration, total-1) return err, ix, iy class LoggingCallback(Callback): """Callback that logs message at end of epoch. """ def __init__(self, print_fcn, progress_func, qapp, model, fn_model): Callback.__init__(self) self.print_fcn = print_fcn self.progress_func = progress_func self.batch_count = 0 self.qapp = qapp self.model = model self.model_name = fn_model def on_batch_end(self, batch, logs={}): self.batch_count += 1 self.progress_func.setValue(self.batch_count) self.qapp.processEvents() def on_epoch_end(self, epoch, logs={}): msg = "{Epoch: %i} %s" % (epoch, ", ".join("%s: %f" % (k, v) for k, v in logs.items())) self.print_fcn(msg) model_json = self.model.to_json() with open(self.model_name+".json", "w") as json_file: json_file.write(model_json) # serialize weights to HDF5 self.model.save_weights(self.model_name+"_"+str(epoch)+".h5") def model_arch_general(n_bins, n_outputs, kernel_coeff = 0.0005, bias_coeff = 0.0005, lr=None, verbose=1, write_to_console=None): """ Very simple and straight forward Neural Network with few hyperparameters straighforward RELU activation strategy with cross entropy to identify the HKL Tried BatchNormalization --> no significant impact Tried weighted approach --> not better for HCP Trying Regularaization l2(0.001) means that every coefficient in the weight matrix of the layer will add 0.001 * weight_coefficient_value**2 to the total loss of the network """ if n_outputs >= n_bins: param = n_bins if param*15 < (2*n_outputs): ## quick hack; make Proper implementation param = (n_bins + n_outputs)//2 else: # param = n_outputs ## More reasonable ??? param = n_outputs*2 ## More reasonable ??? # param = n_bins//2 model = Sequential() model.add(keras.Input(shape=(n_bins,))) ## Hidden layer 1 model.add(Dense(n_bins, kernel_regularizer=l2(kernel_coeff), bias_regularizer=l2(bias_coeff))) # model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(0.3)) ## Adding dropout as we introduce some uncertain data with noise ## Hidden layer 2 model.add(Dense(((param)*15 + n_bins)//2, kernel_regularizer=l2(kernel_coeff), bias_regularizer=l2(bias_coeff))) # model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(0.3)) ## Hidden layer 3 model.add(Dense((param)*15, kernel_regularizer=l2(kernel_coeff), bias_regularizer=l2(bias_coeff))) # model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dropout(0.3)) ## Output layer model.add(Dense(n_outputs, activation='softmax')) ## Compile model if lr != None: otp = tf.keras.optimizers.Adam(learning_rate=lr) model.compile(loss='categorical_crossentropy', optimizer=otp, metrics=[metricsNN]) else: model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=[metricsNN]) if verbose == 1: model.summary() stringlist = [] model.summary(print_fn=lambda x: stringlist.append(x)) short_model_summary = "\n".join(stringlist) if write_to_console!=None: write_to_console(short_model_summary) return model def generate_classHKL(n, rules, lattice_material, symmetry, material_, crystal=None, SG=None, general_diff_cond=False, save_directory="", write_to_console=None, progress=None, qapp=None, ang_maxx = None, step = None): temp_ = GT.threeindices_up_to(int(n)) classhkl_ = temp_ if write_to_console !=None: write_to_console("Generating HKL objects") # generate HKL object if progress !=None: progress.setMaximum(len(classhkl_)) hkl_all = {} # another_method = False for i in range(len(classhkl_)): new_hkl = classhkl_[i,:] if general_diff_cond: cond_proceed = crystal.hkl_allowed(new_hkl, returnequivalents=False) else: cond_proceed = True if not cond_proceed: continue new_rounded_hkl = _round_indices(new_hkl) mul_family = crystal.equivalent_hkls(new_rounded_hkl) family = [] for sym in mul_family: family.append(sym) hkl_all[str(new_rounded_hkl)] = {"hkl":new_rounded_hkl, "family": family} if progress !=None: progress.setValue(i+1) if qapp !=None: qapp.processEvents() ## FAST IMPLEMENTATION ## make comprehensive list of dictionary equ_hkl = np.zeros((1,3)) for j in hkl_all.keys(): equ_hkl = np.vstack((equ_hkl, hkl_all[j]["family"])) equ_hkl = np.delete(equ_hkl, 0, axis =0) index_hkl = [j for j,k in enumerate(hkl_all.keys()) for i in range(len(hkl_all[k]["family"]))] if write_to_console !=None: write_to_console("Removing harmonics and building equivalent HKL objects") if progress !=None: progress.setMaximum(len(hkl_all.keys())) ind_rmv = [] for j1, i1 in enumerate(hkl_all.keys()): hkl_1 = hkl_all[i1]["hkl"] temp1_ = np.all(hkl_1 == equ_hkl, axis=1) if len(np.where(temp1_)[0]) != 0: ind_ = np.where(temp1_)[0] for inin in ind_: if index_hkl[inin] > j1: ind_rmv.append(i1) break if progress !=None: progress.setValue(j1+1) if qapp !=None: qapp.processEvents() if len(ind_rmv) != 0: for inrmv in ind_rmv: _ = hkl_all.pop(inrmv, None) #Check same class HKL and remove them to avoid conflict #ADD the removed class as Multiplicity for the non removed class classhkl = np.zeros((len(hkl_all),3)) keys_rmv = [] for j1, i1 in enumerate(hkl_all.keys()): hkl_object = hkl_all[i1]["hkl"] classhkl[j1,:] = hkl_object keys_rmv.append(i1) if ang_maxx == None: ang_maxx= 90 if step == None: step=0.1 codebars, angbins = get_material_data(material_ = material_, ang_maxx = ang_maxx, step = step, hkl_ref=n, classhkl=classhkl) # if write_to_console !=None: # write_to_console("Verifying if two different HKL class have same angular distribution (can be very time consuming depending on the symmetry)") list_appended = [] list_remove = [] for i, j in enumerate(codebars): for k, l in enumerate(codebars): # if i in list_appended and k in list_appended: # continue if i != k and np.all(j == l): # string0 = "HKL's "+ str(classhkl[i])+" and "+str(classhkl[k])+" have exactly the same angular distribution." # if write_to_console !=None: # write_to_console(string0) if keys_rmv[i] in list_remove or keys_rmv[k] in list_remove: if write_to_console !=None: continue # write_to_console("list already added") else: list_remove.append(keys_rmv[i]) ind_rmv.append(keys_rmv[i]) for ijk in hkl_all[keys_rmv[i]]['family']: hkl_all[keys_rmv[k]]['family'].append(ijk) list_appended.append(i) list_appended.append(k) if len(list_remove) != 0: for inrmv in list_remove: _ = hkl_all.pop(inrmv, None) if write_to_console !=None: write_to_console("Finalizing the HKL objects") hkl_all_class = hkl_all hkl_millerindices = {} classhkl = np.zeros((len(hkl_all),3)) for j1, i1 in enumerate(hkl_all.keys()): hkl_object = hkl_all[i1]["hkl"] classhkl[j1,:] = hkl_object family = hkl_all_class[i1]["family"] hkl_millerindices[i1] = np.array([ii for ii in family]) tempdict = hkl_millerindices with open(save_directory + "//classhkl_data_"+material_+".pickle", "wb") as output_file: cPickle.dump([classhkl, classhkl_, ind_rmv, n, temp_, \ hkl_all_class, hkl_all, lattice_material, symmetry], output_file) with open(save_directory + "//classhkl_data_nonpickled_"+material_+".pickle", "wb") as output_file: cPickle.dump([tempdict], output_file) if write_to_console !=None: write_to_console("Saved class HKL data in : "+save_directory + "//classhkl_data_"+material_+".pickle") def write_training_testing_dataMTEX(save_directory,material_, material1_, lattice_material, lattice_material1, material0_lauegroup, material1_lauegroup): for imh in ["training_data", "testing_data"]: image_files = [] path_ = save_directory+"//"+imh for dir_entry in os.listdir(path_): if os.path.isfile(os.path.join(path_, dir_entry)) and \ os.path.splitext(dir_entry)[1] in ACCEPTABLE_FORMATS: file_name, file_extension = os.path.splitext(dir_entry) image_files.append((file_name, file_extension, os.path.join(path_, dir_entry))) return_value = [] for image_file, _, image_full_path in image_files: if image_file == "grain_classhkl_angbin" or image_file == "grain_classhkl_angbin1" or\ image_file == "grain_init" or image_file == "grain_init1": continue return_value.append((image_full_path)) ori_array1 = np.zeros((1,3,3)) if material_ != material1_: ori_array2 = np.zeros((1,3,3)) for bs in return_value: obj = np.load(bs) ori1 = obj["arr_2"] ori2 = obj["arr_3"] flag = obj["arr_4"] ## flag 0 is random data ## flag 1, 2, 3 are small angle miori data if flag == 0: if len(ori1) != 0: ori_array1 = np.vstack((ori_array1,ori1)) if material_ != material1_: if len(ori2) != 0: ori_array2 = np.vstack((ori_array2,ori2)) ori_array1 = np.delete(ori_array1, 0, axis=0) phase_ori1 = np.ones(len(ori_array1)) ori_array = ori_array1 phase_ori = phase_ori1 if material_ != material1_: ori_array2 = np.delete(ori_array2, 0, axis=0) phase_ori2 = np.ones(len(ori_array2))*2 ori_array = np.vstack((ori_array, ori_array2)) phase_ori = np.hstack((phase_ori, phase_ori2)) if material_ == material1_: lattice = lattice_material material0_LG = material0_lauegroup header = [ "Channel Text File", "Prj lauetoolsnn", "Author [Ravi raj purohit]", "JobMode Grid", "XCells "+str(len(ori_array)), "YCells "+str(1), "XStep 1.0", "YStep 1.0", "AcqE1 0", "AcqE2 0", "AcqE3 0", "Euler angles refer to Sample Coordinate system (CS0)! Mag 100 Coverage 100 Device 0 KV 15 TiltAngle 40 TiltAxis 0", "Phases 1", str(round(lattice._lengths[0]*10,5))+";"+str(round(lattice._lengths[1]*10,5))+";"+\ str(round(lattice._lengths[2]*10,5))+"\t"+str(round(lattice._angles[0],5))+";"+\ str(round(lattice._angles[1],5))+";"+str(round(lattice._angles[2],5))+"\t"+"Material1"+ "\t"+material0_LG+ "\t"+"????"+"\t"+"????", "Phase X Y Bands Error Euler1 Euler2 Euler3 MAD BC BS"] else: lattice = lattice_material lattice1 = lattice_material1 material0_LG = material0_lauegroup material1_LG = material1_lauegroup header = [ "Channel Text File", "Prj lauetoolsnn", "Author [Ravi raj purohit]", "JobMode Grid", "XCells "+str(len(ori_array)), "YCells "+str(1), "XStep 1.0", "YStep 1.0", "AcqE1 0", "AcqE2 0", "AcqE3 0", "Euler angles refer to Sample Coordinate system (CS0)! Mag 100 Coverage 100 Device 0 KV 15 TiltAngle 40 TiltAxis 0", "Phases 2", str(round(lattice._lengths[0]*10,5))+";"+str(round(lattice._lengths[1]*10,5))+";"+\ str(round(lattice._lengths[2]*10,5))+"\t"+str(round(lattice._angles[0],5))+";"+\ str(round(lattice._angles[1],5))+";"+str(round(lattice._angles[2],5))+"\t"+"Material1"+ "\t"+material0_LG+ "\t"+"????"+"\t"+"????", str(round(lattice1._lengths[0]*10,5))+";"+str(round(lattice1._lengths[1]*10,5))+";"+\ str(round(lattice1._lengths[2]*10,5))+"\t"+str(round(lattice1._angles[0],5))+";"+\ str(round(lattice1._angles[1],5))+";"+str(round(lattice1._angles[2],5))+"\t"+"Material2"+ "\t"+material1_LG+ "\t"+"????"+"\t"+"????", "Phase X Y Bands Error Euler1 Euler2 Euler3 MAD BC BS"] # =================CALCULATION OF POSITION===================================== euler_angles = np.zeros((len(ori_array),3)) phase_euler_angles = np.zeros(len(ori_array)) for i in range(len(ori_array)): # euler_angles[i,:] = rot_mat_to_euler(ori_array[i,:,:]) euler_angles[i,:] = OrientationMatrix2Euler(ori_array[i,:,:]) phase_euler_angles[i] = phase_ori[i] a = euler_angles if material_ != material1_: filename125 = save_directory+ "//"+material_+"_"+material1_+"_MTEX_UBmat_"+imh+".ctf" else: filename125 = save_directory+ "//"+material_+"_MTEX_UBmat_"+imh+".ctf" f = open(filename125, "w") for ij in range(len(header)): f.write(header[ij]+" \n") for j123 in range(euler_angles.shape[0]): y_step = 1 x_step = 1 * j123 phase_id = int(phase_euler_angles[j123]) eul = str(phase_id)+'\t' + "%0.4f" % x_step +'\t'+"%0.4f" % y_step+'\t8\t0\t'+ \ "%0.4f" % a[j123,0]+'\t'+"%0.4f" % a[j123,1]+ \ '\t'+"%0.4f" % a[j123,2]+'\t0.0001\t180\t0\n' string = eul f.write(string) f.close() def get_material_data(material_="Cu", ang_maxx = 45, step = 0.5, hkl_ref=13, classhkl = None): a, b, c, alpha, beta, gamma = dictLT.dict_Materials[material_][1] Gstar = CP.Gstar_from_directlatticeparams(a, b, c, alpha, beta, gamma) rules = dictLT.dict_Materials[material_][-1] hkl2 = GT.threeindices_up_to(int(hkl_ref)) hkl2 = CP.ApplyExtinctionrules(hkl2,rules) hkl2 = hkl2.astype(np.int16) query_angle = ang_maxx/2. angle_tol = ang_maxx/2. metrics = Gstar hkl1 = classhkl H1 = hkl1 n1 = hkl1.shape[0] H2 = hkl2 n2 = hkl2.shape[0] dstar_square_1 = np.diag(np.inner(np.inner(H1, metrics), H1)) dstar_square_2 = np.diag(np.inner(np.inner(H2, metrics), H2)) scalar_product = np.inner(np.inner(H1, metrics), H2) * 1.0 d1 = np.sqrt(dstar_square_1.reshape((n1, 1))) * 1.0 d2 = np.sqrt(dstar_square_2.reshape((n2, 1))) * 1.0 outy = np.outer(d1, d2) ratio = scalar_product / outy ratio = np.round(ratio, decimals=7) tab_angulardist = np.arccos(ratio) / (np.pi / 180.0) np.putmask(tab_angulardist, np.abs(tab_angulardist) < 0.001, 400) # self.write_to_console("Calculating Mutual angular distances") # self.progress.setMaximum(len(tab_angulardist)) closest_angles_values = [] for ang_ in range(len(tab_angulardist)): tab_angulardist_ = tab_angulardist[ang_,:] angles_set = np.ravel(tab_angulardist_) # 1D array sorted_ind = np.argsort(angles_set) sorted_angles = angles_set[sorted_ind] angle_query = angle_tol if isinstance(query_angle, (list, np.ndarray, tuple)): angle_query = query_angle[0] array_angledist = np.abs(sorted_angles - angle_query) pos_min = np.argmin(array_angledist) closest_angle = sorted_angles[pos_min] if np.abs(closest_angle - query_angle) > angle_tol: if angle_query > 0.5: pass print("TODO function get_material_data") condition = array_angledist <= angle_tol closest_index_in_sorted_angles_raw = np.where(condition)[0] closest_angles_values.append(np.take(sorted_angles, closest_index_in_sorted_angles_raw)) # self.progress.setValue(ang_+1) # QApplication.processEvents() # self.write_to_console("Constructing histograms") # self.progress.setMaximum(len(closest_angles_values)) codebars = [] angbins = np.arange(0, ang_maxx+step, step) for i in range(len(closest_angles_values)): angles = closest_angles_values[i] fingerprint = np.histogram(angles, bins=angbins)[0] # fingerprint = histogram1d(angles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## Normalize the histogram by its maximum: simple way ## Maybe better normalization is possible.. to be seen max_codebars = np.max(fingerprint) fingerprint = fingerprint/ max_codebars codebars.append(fingerprint) # self.progress.setValue(i+1) # QApplication.processEvents() # self.progress.setValue(0) return codebars, angbins def Euler2OrientationMatrix(euler): """Compute the orientation matrix :math:`\mathbf{g}` associated with the 3 Euler angles :math:`(\phi_1, \Phi, \phi_2)`. :param euler: The triplet of the Euler angles (in degrees). :return g: The 3x3 orientation matrix. """ (rphi1, rPhi, rphi2) = np.radians(euler) c1 = np.cos(rphi1) s1 = np.sin(rphi1) c = np.cos(rPhi) s = np.sin(rPhi) c2 = np.cos(rphi2) s2 = np.sin(rphi2) # rotation matrix g g11 = c1 * c2 - s1 * s2 * c g12 = s1 * c2 + c1 * s2 * c g13 = s2 * s g21 = -c1 * s2 - s1 * c2 * c g22 = -s1 * s2 + c1 * c2 * c g23 = c2 * s g31 = s1 * s g32 = -c1 * s g33 = c g = np.array([[g11, g12, g13], [g21, g22, g23], [g31, g32, g33]]) return g def getpatterns_(nb, nb1, material_=None, material1_=None, emin=5, emax=23, detectorparameters=None, pixelsize=None, sortintensity = False, ang_maxx = 45, step = 0.5, classhkl = None, classhkl1 = None, noisy_data=False, remove_peaks=False, seed = None,hkl_all=None, lattice_material=None, family_hkl=None, normal_hkl=None, index_hkl=None, hkl_all1=None, lattice_material1=None, family_hkl1=None, normal_hkl1=None, index_hkl1=None, dim1=2048, dim2=2048, removeharmonics=1, flag = 0, img_i=None, img_j=None, save_directory_=None, odf_data=None, odf_data1=None, modelp=None, misorientation_angle=None, max_millerindex=0, max_millerindex1=0, general_diff_cond=False, crystal=None, crystal1=None, phase_always_present=None): s_tth, s_chi, s_miller_ind, _, _, _, \ ori_mat, ori_mat1 = simulatemultiplepatterns(nb, nb1, seed=seed, key_material=material_, key_material1=material1_, emin=emin, emax=emax, detectorparameters=detectorparameters, pixelsize=pixelsize, sortintensity = sortintensity, dim1=dim1, dim2=dim2, removeharmonics=removeharmonics, flag=flag, odf_data=odf_data, odf_data1=odf_data1, mode=modelp, misorientation_angle=misorientation_angle, phase_always_present=phase_always_present) if noisy_data: ## apply random gaussian type noise to the data (tth and chi) ## So adding noise to the angular distances ## Instead of adding noise to all HKL's ... Add to few selected HKLs ## Adding noise to randomly 30% of the HKLs ## Realistic way of introducting strains is through Pixels and not 2theta noisy_pixel = 0.15 indices_noise = np.random.choice(len(s_tth), int(len(s_tth)*0.3), replace=False) noise_ = np.random.normal(0,noisy_pixel,len(indices_noise)) s_tth[indices_noise] = s_tth[indices_noise] + noise_ noise_ = np.random.normal(0,noisy_pixel,len(indices_noise)) s_chi[indices_noise] = s_chi[indices_noise] + noise_ if remove_peaks: len_mi = np.array([iq for iq in range(len(s_miller_ind))]) len_mi = len_mi[int(0.6*len(s_miller_ind)):] indices_remove = np.random.choice(len_mi, int(len(len_mi)*0.3), replace=False) ## delete randomly selected less intense peaks ## to simulate real peak detection, where some peaks may not be ## well detected ## Include maybe Intensity approach: Delete peaks based on their SF and position in detector if len(indices_remove) !=0: s_tth = np.delete(s_tth, indices_remove) s_chi = np.delete(s_chi, indices_remove) s_miller_ind = np.delete(s_miller_ind, indices_remove, axis=0) else: print(nb, nb1, material_, material1_, odf_data, odf_data1) # replace all hkl class with relevant hkls ## skip HKLS that dont follow the general diffraction rules location = [] skip_hkl = [] delete_spots = [] for j, i in enumerate(s_miller_ind): new_hkl = _round_indices(i[:3]) if i[3] == 0: ##material 1 if general_diff_cond: cond_proceed = crystal.hkl_allowed(i[:3], returnequivalents=False) else: cond_proceed = True if not cond_proceed: delete_spots.append(j) continue if np.any(np.abs(new_hkl)>max_millerindex): skip_hkl.append(j) continue temp_ = np.all(new_hkl == normal_hkl, axis=1) if len(np.where(temp_)[0]) == 1: ind_ = np.where(temp_)[0][0] location.append(index_hkl[ind_]) elif len(np.where(temp_)[0]) == 0: # print("Entering -100 for "+ str(i) + "\n") skip_hkl.append(j) elif len(np.where(temp_)[0]) > 1: ## first check if they both are same class or not class_output = [] for ij in range(len(np.where(temp_)[0])): indc = index_hkl[np.where(temp_)[0][ij]] class_output.append(indc) if len(set(class_output)) <= 1: location.append(class_output[0]) else: skip_hkl.append(j) print(i) print(np.where(temp_)[0]) for ij in range(len(np.where(temp_)[0])): indc = index_hkl[np.where(temp_)[0][ij]] print(classhkl[indc]) print("Entering -500: Skipping HKL as something is not proper with equivalent HKL module") elif i[3] == 1: ##material 2 if general_diff_cond: cond_proceed1 = crystal1.hkl_allowed(i[:3], returnequivalents=False) else: cond_proceed1 = True if not cond_proceed1: delete_spots.append(j) continue if np.any(np.abs(new_hkl)>max_millerindex1): skip_hkl.append(j) continue temp_ = np.all(new_hkl == normal_hkl1, axis=1) if len(np.where(temp_)[0]) == 1: ind_ = np.where(temp_)[0][0] location.append(index_hkl1[ind_]) elif len(np.where(temp_)[0]) == 0: # print("Entering -100 for "+ str(i) + "\n") skip_hkl.append(j) elif len(np.where(temp_)[0]) > 1: ## first check if they both are same class or not class_output = [] for ij in range(len(np.where(temp_)[0])): indc = index_hkl1[np.where(temp_)[0][ij]] class_output.append(indc) if len(set(class_output)) <= 1: location.append(class_output[0]) else: skip_hkl.append(j) print(i) print(np.where(temp_)[0]) for ij in range(len(np.where(temp_)[0])): indc = index_hkl1[np.where(temp_)[0][ij]] print(classhkl[indc]) print("Entering -500: Skipping HKL as something is not proper with equivalent HKL module") allspots_the_chi = np.transpose(np.array([s_tth/2., s_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(allspots_the_chi, allspots_the_chi)) codebars = [] angbins = np.arange(0,ang_maxx+step,step) for i in range(len(tabledistancerandom)): if i in skip_hkl or i in delete_spots: ## not saving skipped HKL continue angles = tabledistancerandom[i] spots_delete = [i] for del_spts in delete_spots: spots_delete.append(del_spts) angles = np.delete(angles, spots_delete) # angles = np.delete(angles, i)# removing the self distance fingerprint = np.histogram(angles, bins=angbins)[0] # fingerprint = histogram1d(angles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## same normalization as before max_codebars = np.max(fingerprint) fingerprint = fingerprint/ max_codebars codebars.append(fingerprint) if phase_always_present != None: suffix_ = "_development" else: suffix_ = "" ########################################################### if flag in [0,1,2,3] and plot_images: fig = plt.figure() plt.scatter(s_tth, s_chi, c='k') plt.ylabel(r'$\chi$ (in deg)',fontsize=8) plt.xlabel(r'2$\theta$ (in deg)', fontsize=10) plt.grid(linestyle='--', linewidth=0.5) texts1=[] for i, txt_hkl in enumerate(s_miller_ind): txt = _round_indices(txt_hkl[:3]) # print("Actual hkl: "+str(txt_hkl[:3])+" ; Rounded hkl: "+str(txt[:3])) if txt_hkl[3] == 0: if np.any(np.abs(txt) > max_millerindex): continue elif txt_hkl[3] == 1: if np.any(np.abs(txt) > max_millerindex1): continue txt = txt_hkl texts1.append(plt.text(s_tth[i], s_chi[i], str(int(txt[0]))+" "+str(int(txt[1]))+" "+str(int(txt[2])), size=8)) adjust_text(texts1, only_move={'points':'y', 'text':'y'}) ########################################################### if flag == 0: if plot_images: plt.savefig(save_directory_+'//grain_'+str(img_i)+"_"+\ str(img_j)+suffix_+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) if len(codebars) != 0: if nb == 0: np.savez_compressed(save_directory_+'//'+material1_+'_grain_'+str(img_i)+"_"+\ str(img_j)+suffix_+'.npz', codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) elif nb1 == 0: np.savez_compressed(save_directory_+'//'+material_+'_grain_'+str(img_i)+"_"+\ str(img_j)+suffix_+'.npz', codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) else: np.savez_compressed(save_directory_+'//'+material_+"_"+material1_+'_grain_'+str(img_i)+"_"+\ str(img_j)+suffix_+'.npz', codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) else: print("Skipping a simulation file: "+save_directory_+'//grain_'+\ str(img_i)+"_"+str(img_j)+suffix_+'.npz'+"; Due to no data conforming user settings") elif flag == 1: if plot_images: plt.savefig(save_directory_+'//grain_'+str(img_j)+suffix_+'_smo.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) if len(codebars) != 0: np.savez_compressed(save_directory_+'//grain_'+str(img_j)+suffix_+'_smo.npz', \ codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) else: print("Skipping a simulation file: "+save_directory_+'//grain_'+\ str(img_j)+'_smo.npz'+"; Due to no data conforming user settings") elif flag == 2: if plot_images: plt.savefig(save_directory_+'//grain_'+str(img_j)+suffix_+'_smo1.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) if len(codebars) != 0: np.savez_compressed(save_directory_+'//grain_'+str(img_j)+suffix_+'_smo1.npz', \ codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) else: print("Skipping a simulation file: "+save_directory_+'//grain_'+\ str(img_j)+'_smo1.npz'+"; Due to no data conforming user settings") elif flag == 3: if plot_images: plt.savefig(save_directory_+'//grain_'+str(img_j)+suffix_+'_smo2.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) if len(codebars) != 0: np.savez_compressed(save_directory_+'//grain_'+str(img_j)+suffix_+'_smo2.npz', \ codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) else: print("Skipping a simulation file: "+save_directory_+'//grain_'+\ str(img_j)+suffix_+'_smo2.npz'+"; Due to no data conforming user settings") def simulatemultiplepatterns(nbUBs, nbUBs1, seed=123, key_material=None, key_material1=None, emin=5, emax=23, detectorparameters=None, pixelsize=None, sortintensity = False, dim1=2048, dim2=2048, removeharmonics=1, flag = 0, odf_data=None, odf_data1=None, mode="random", misorientation_angle = 1, phase_always_present=None): detectordiameter = pixelsize * dim1 #TODO * 2.0 # UBelemagnles = np.random.random((3,nbUBs))*360-180 orientation_send = [] orientation_send1 = [] if flag == 0: g = np.zeros((nbUBs, 3, 3)) if key_material != key_material1: g1 = np.zeros((nbUBs1, 3, 3)) if mode == "random": if key_material != key_material1: for igr in range(nbUBs1): phi1 = rand1() * 360. phi = 180. * acos(2 * rand1() - 1) / np.pi phi2 = rand1() * 360. g1[igr] = Euler2OrientationMatrix((phi1, phi, phi2)) orientation_send1.append(g1[igr]) for igr in range(nbUBs): phi1 = rand1() * 360. phi = 180. * acos(2 * rand1() - 1) / np.pi phi2 = rand1() * 360. g[igr] = Euler2OrientationMatrix((phi1, phi, phi2)) orientation_send.append(g[igr]) elif mode == "uniform": if key_material != key_material1: g1 = odf_data1 for igr in range(len(g1)): orientation_send1.append(g1[igr]) g = odf_data for igr in range(len(g)): orientation_send.append(g[igr]) elif flag == 1 or flag == 2 or flag == 3: nbUBs = 2 g = np.zeros((nbUBs, 3, 3)) for igr in range(nbUBs): if igr == 0: phi1 = rand1() * 360. phi = 180. * acos(2 * rand1() - 1) / np.pi phi2 = rand1() * 360. g[igr] = Euler2OrientationMatrix((phi1, phi, phi2)) orientation_send.append(g[igr]) elif igr == 1: phi2 = phi2 + misorientation_angle ## adding user defined deg misorientation along phi2 g[igr] = Euler2OrientationMatrix((phi1, phi, phi2)) orientation_send1.append(g[igr]) l_tth, l_chi, l_miller_ind, l_posx, l_posy, l_E, l_intensity = [],[],[],[],[],[],[] if flag == 1: for grainind in range(nbUBs): UBmatrix = g[grainind] grain = CP.Prepare_Grain(key_material, UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) s_miller_ind = np.c_[s_miller_ind, np.zeros(len(s_miller_ind))] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) elif flag == 2: for grainind in range(nbUBs): UBmatrix = g[grainind] grain = CP.Prepare_Grain(key_material1, UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) s_miller_ind = np.c_[s_miller_ind, np.ones(len(s_miller_ind))] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) elif flag == 3: for grainind in range(nbUBs): UBmatrix = g[grainind] if grainind == 0: grain = CP.Prepare_Grain(key_material, UBmatrix) else: grain = CP.Prepare_Grain(key_material1, UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) s_miller_ind = np.c_[s_miller_ind, np.ones(len(s_miller_ind))] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) else: for grainind in range(nbUBs): UBmatrix = g[grainind] grain = CP.Prepare_Grain(key_material, UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) s_miller_ind = np.c_[s_miller_ind, np.zeros(len(s_miller_ind))] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) if (key_material != key_material1): for grainind in range(nbUBs1): # print(nbUBs, nbUBs1, key_material, key_material1, flag) UBmatrix = g1[grainind] grain = CP.Prepare_Grain(key_material1, UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) s_miller_ind = np.c_[s_miller_ind, np.ones(len(s_miller_ind))] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) ## add constant UB matrix to the simulated data if phase_always_present != None: UBmatrix, key_material_new = phase_always_present.split(';') UBmat = [] for kk in UBmatrix.split(","): UBmat.append(float(kk)) UBmat = np.array(UBmat) UBmatrix = UBmat.reshape((3,3)) grain = CP.Prepare_Grain(key_material_new, UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) if key_material_new == key_material: s_miller_ind = np.c_[s_miller_ind, np.zeros(len(s_miller_ind))] elif key_material_new == key_material1: s_miller_ind = np.c_[s_miller_ind, np.ones(len(s_miller_ind))] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) #flat_list = [item for sublist in l for item in sublist] s_tth = np.array([item for sublist in l_tth for item in sublist]) s_chi = np.array([item for sublist in l_chi for item in sublist]) s_miller_ind = np.array([item for sublist in l_miller_ind for item in sublist]) s_posx = np.array([item for sublist in l_posx for item in sublist]) s_posy = np.array([item for sublist in l_posy for item in sublist]) s_E = np.array([item for sublist in l_E for item in sublist]) s_intensity=np.array([item for sublist in l_intensity for item in sublist]) if sortintensity: indsort = np.argsort(s_intensity)[::-1] s_tth=np.take(s_tth, indsort) s_chi=np.take(s_chi, indsort) s_miller_ind=np.take(s_miller_ind, indsort, axis=0) s_posx=np.take(s_posx, indsort) s_posy=np.take(s_posy, indsort) s_E=np.take(s_E, indsort) s_intensity=np.take(s_intensity, indsort) return s_tth, s_chi, s_miller_ind, s_posx, s_posy, s_intensity, orientation_send, orientation_send1 def chunker_list(seq, size): return (seq[i::size] for i in range(size)) def worker_generation(inputs_queue, outputs_queue, proc_id): while True: time.sleep(0.01) if not inputs_queue.empty(): message = inputs_queue.get() num1, _, meta = message flag1 = meta['flag'] for ijk in range(len(num1)): nb, nb1, material_, material1_, emin, emax, detectorparameters, pixelsize, \ sortintensity, ang_maxx, step, classhkl, classhkl1, noisy_data, \ remove_peaks, seed,hkl_all, lattice_material, family_hkl,\ normal_hkl, index_hkl, hkl_all1, lattice_material1, family_hkl1,\ normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, flag,\ img_i, img_j, save_directory_, odf_data, odf_data1, modelp,\ misorientation_angle, max_millerindex, max_millerindex1,\ general_diff_cond, crystal, crystal1, phase_always_present = num1[ijk] getpatterns_(nb, nb1, material_, material1_, emin, emax, detectorparameters, pixelsize, \ sortintensity, ang_maxx, step, classhkl, classhkl1, noisy_data, \ remove_peaks, seed,hkl_all, lattice_material, family_hkl,\ normal_hkl, index_hkl, hkl_all1, lattice_material1, family_hkl1,\ normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, flag,\ img_i, img_j, save_directory_, odf_data, odf_data1, modelp, \ misorientation_angle, max_millerindex, max_millerindex1, general_diff_cond, crystal, \ crystal1, phase_always_present) if ijk%10 == 0 and ijk!=0: outputs_queue.put(11) if flag1 == 1: break def ComputeGnomon_singledata(tth, chi, CenterProjection=(45 * DEG, 0 * DEG)): data_theta = tth / 2.0 data_chi = chi lat = np.arcsin(np.cos(data_theta * DEG) * np.cos(data_chi * DEG)) # in rads longit = np.arctan( -np.sin(data_chi * DEG) / np.tan(data_theta * DEG)) # + ones(len(data_chi))*(np.pi) centerlat, centerlongit = CenterProjection slat0 = np.sin(centerlat) clat0 = np.cos(centerlat) longit0 = centerlongit slat = np.sin(lat) clat = np.cos(lat) cosanguldist = slat * slat0 + clat * clat0 * np.cos(longit - longit0) Xgno = clat * np.sin(longit0 - longit) / cosanguldist Ygno = (slat * clat0 - clat * slat0 * np.cos(longit - longit0)) / cosanguldist NbptsGno = 300 maxsize = max(Xgno,Ygno,-Xgno,-Ygno)+.0 xgnomin,xgnomax,ygnomin,ygnomax=(-0.8,0.8,-0.5,0.5) xgnomin,xgnomax,ygnomin,ygnomax=(-maxsize,maxsize,-maxsize,maxsize) XGNO = int((Xgno-xgnomin)/(xgnomax-xgnomin)*NbptsGno) YGNO = int((Ygno-ygnomin)/(ygnomax-ygnomin)*NbptsGno) return np.array((XGNO, YGNO)) def ComputeGnomon_2(TwiceTheta_Chi, CenterProjection=(45 * DEG, 0 * DEG)): data_theta = TwiceTheta_Chi[0] / 2.0 data_chi = TwiceTheta_Chi[1] lat = np.arcsin(np.cos(data_theta * DEG) * np.cos(data_chi * DEG)) # in rads longit = np.arctan( -np.sin(data_chi * DEG) / np.tan(data_theta * DEG)) # + ones(len(data_chi))*(np.pi) centerlat, centerlongit = CenterProjection slat0 = np.ones(len(data_chi)) * np.sin(centerlat) clat0 = np.ones(len(data_chi)) * np.cos(centerlat) longit0 = np.ones(len(data_chi)) * centerlongit slat = np.sin(lat) clat = np.cos(lat) cosanguldist = slat * slat0 + clat * clat0 * np.cos(longit - longit0) _gnomonx = clat * np.sin(longit0 - longit) / cosanguldist _gnomony = (slat * clat0 - clat * slat0 * np.cos(longit - longit0)) / cosanguldist return _gnomonx, _gnomony def computeGnomonicImage(TwiceTheta,Chi): DEG = np.pi/180. # CenterProjectionAngleTheta = 50#45 TwiceTheta_Chi = TwiceTheta,Chi Xgno,Ygno = ComputeGnomon_2(TwiceTheta_Chi, CenterProjection=(45 * DEG, 0 * DEG)) pts =(np.array([Xgno,Ygno]).T) nbpeaks=len(pts) NbptsGno = 300 maxsize = max(Xgno.max(),Ygno.max(),-Xgno.min(),-Ygno.min())+.0 xgnomin,xgnomax,ygnomin,ygnomax=(-0.8,0.8,-0.5,0.5) xgnomin,xgnomax,ygnomin,ygnomax=(-maxsize,maxsize,-maxsize,maxsize) halfdiagonal = np.sqrt(xgnomax**2+ygnomax**2)*NbptsGno XGNO = np.array((Xgno-xgnomin)/(xgnomax-xgnomin)*NbptsGno, dtype=np.int) YGNO = np.array((Ygno-ygnomin)/(ygnomax-ygnomin)*NbptsGno, dtype=np.int) imageGNO=np.zeros((NbptsGno+1,NbptsGno+1)) imageGNO[XGNO,YGNO]=100 return imageGNO, nbpeaks, halfdiagonal def read_hdf5(path): weights = {} keys = [] with h5py.File(path, 'r') as f: # open file f.visit(keys.append) # append all keys to list for key in keys: if ':' in key: # contains data if ':' in key weights[f[key].name] = f[key][:] return weights def softmax(x): return (np.exp(x).T / np.sum(np.exp(x).T, axis=0)).T def predict(x, wb, temp_key): # first layer layer0 = np.dot(x, wb[temp_key[1]]) + wb[temp_key[0]] layer0 = np.maximum(0, layer0) ## ReLU activation # Second layer layer1 = np.dot(layer0, wb[temp_key[3]]) + wb[temp_key[2]] layer1 = np.maximum(0, layer1) # Third layer layer2 = np.dot(layer1, wb[temp_key[5]]) + wb[temp_key[4]] layer2 = np.maximum(0, layer2) # Output layer layer3 = np.dot(layer2, wb[temp_key[7]]) + wb[temp_key[6]] layer3 = softmax(layer3) ## output softmax activation return layer3 def worker(inputs_queue, outputs_queue, proc_id, run_flag):#, mp_rotation_matrix): print(f'Initializing worker {proc_id}') while True: if not run_flag.value: break time.sleep(0.01) if not inputs_queue.empty(): message = inputs_queue.get() if message == 'STOP': print(f'[{proc_id}] stopping') break num1, num2, meta = message files_worked = [] while True: if len(num1) == len(files_worked) or len(num1) == 0: print("process finished") break for ijk in range(len(num1)): if ijk in files_worked: continue if not run_flag.value: num1, files_worked = [], [] print(f'[{proc_id}] stopping') break files, cnt, rotation_matrix, strain_matrix, strain_matrixs,\ col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global,\ check,detectorparameters,pixelsize,angbins,\ classhkl, hkl_all_class0, hkl_all_class1, emin, emax,\ material_, material1_, symmetry, symmetry1,lim_x,lim_y,\ strain_calculation, ind_mat, ind_mat1,\ model_direc, tolerance , tolerance1,\ matricies, ccd_label,\ filename_bkg,intensity_threshold,\ boxsize,bkg_treatment,\ filenameDirec, experimental_prefix,\ blacklist_file, text_file, \ files_treated,try_previous1,\ wb, temp_key, cor_file_directory, mode_spotCycle1,\ softmax_threshold_global123,mr_threshold_global123,\ cap_matchrate123, tolerance_strain123, tolerance_strain1231,\ NumberMaxofFits123,fit_peaks_gaussian_global123,\ FitPixelDev_global123,coeff123,coeff_overlap,\ material0_limit, material1_limit, use_previous_UBmatrix_name1,\ material_phase_always_present1, crystal, crystal1, strain_free_parameters = num1[ijk] if np.all(check[cnt,:]) == 1: #TODO continue if os.path.isfile(files): # try: strain_matrix12, strain_matrixs12, \ rotation_matrix12, col12, \ colx12, coly12,\ match_rate12, mat_global12, cnt12,\ files_treated12, spots_len12, \ iR_pix12, fR_pix12, check12, \ best_match12, pred_hkl = predict_preprocessMP(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match, mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, hkl_all_class1, emin, emax, material_, material1_, symmetry, symmetry1,lim_x,lim_y, strain_calculation, ind_mat, ind_mat1, model_direc, tolerance, tolerance1, matricies, ccd_label, filename_bkg,intensity_threshold, boxsize,bkg_treatment, filenameDirec, experimental_prefix, blacklist_file, text_file, files_treated,try_previous1, wb, temp_key, cor_file_directory, mode_spotCycle1, softmax_threshold_global123,mr_threshold_global123, cap_matchrate123, tolerance_strain123, tolerance_strain1231,NumberMaxofFits123, fit_peaks_gaussian_global123, FitPixelDev_global123, coeff123,coeff_overlap, material0_limit,material1_limit, use_previous_UBmatrix_name1, material_phase_always_present1, crystal, crystal1, strain_free_parameters) files_worked.append(ijk) meta['proc_id'] = proc_id r_message = (strain_matrix12, strain_matrixs12, rotation_matrix12, col12, \ colx12, coly12, match_rate12, mat_global12, cnt12, meta, \ files_treated12, spots_len12, iR_pix12, fR_pix12, best_match12, check12) outputs_queue.put(r_message) # except Exception as e: # print(e) # continue print("broke the worker while loop") def predict_preprocessMP_vsingle(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, hkl_all_class1, emin, emax, material_, material1_, symmetry, symmetry1,lim_x,lim_y, strain_calculation, ind_mat, ind_mat1, model_direc=None, tolerance =None, tolerance1 =None, matricies=None, ccd_label=None, filenameDirec=None, experimental_prefix=None, files_treated=None,try_previous1=False, wb=None, temp_key=None, cor_file_directory=None, mode_spotCycle1=None, softmax_threshold_global123=None,mr_threshold_global123=None, cap_matchrate123=None,tolerance_strain123=None,tolerance_strain1231=None,\ coeff123=None, coeff_overlap=None, material0_limit=None, material1_limit=None, use_previous_UBmatrix_name=None, material_phase_always_present=None, crystal=None, crystal1=None, peak_XY=None, strain_free_parameters=None): if files in files_treated: return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match print("# Predicting for "+ files) call_global() CCDLabel=ccd_label seednumber = "Experimental "+CCDLabel+" file" s_ix = np.argsort(peak_XY[:, 2])[::-1] peak_XY = peak_XY[s_ix] framedim = dictLT.dict_CCD[CCDLabel][0] twicetheta, chi = Lgeo.calc_uflab(peak_XY[:,0], peak_XY[:,1], detectorparameters, returnAngles=1, pixelsize=pixelsize, kf_direction='Z>0') data_theta, data_chi = twicetheta/2., chi framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0]#TODO*2 dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peak_XY[:,0] dict_dp['peakY']=peak_XY[:,1] dict_dp['intensity']=peak_XY[:,2] CCDcalib = {"CCDLabel":CCDLabel, "dd":detectorparameters[0], "xcen":detectorparameters[1], "ycen":detectorparameters[2], "xbet":detectorparameters[3], "xgam":detectorparameters[4], "pixelsize": pixelsize} path = os.path.normpath(files) IOLT.writefile_cor(cor_file_directory+"//"+path.split(os.sep)[-1].split(".")[0], twicetheta, chi, peak_XY[:,0], peak_XY[:,1], peak_XY[:,2], param=CCDcalib, sortedexit=0) sorted_data = np.transpose(np.array([data_theta, data_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(sorted_data, sorted_data)) codebars_all = [] spots_in_center = np.arange(0,len(data_theta)) spots_in_center = spots_in_center[:nb_spots_consider] for i in spots_in_center: spotangles = tabledistancerandom[i] spotangles = np.delete(spotangles, i)# removing the self distance codebars = np.histogram(spotangles, bins=angbins)[0] # codebars = histogram1d(spotangles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## normalize the same way as training data max_codebars = np.max(codebars) codebars = codebars/ max_codebars codebars_all.append(codebars) ## reshape for the model to predict all spots at once codebars = np.array(codebars_all) ## Do prediction of all spots at once prediction = predict(codebars, wb, temp_key) max_pred = np.max(prediction, axis = 1) class_predicted = np.argmax(prediction, axis = 1) predicted_hkl123 = classhkl[class_predicted] predicted_hkl123 = predicted_hkl123.astype(int) s_tth = data_theta * 2. s_chi = data_chi rotation_matrix1, mr_highest, mat_highest, \ strain_crystal, strain_sample, iR_pix1, \ fR_pix1, spots_len1,\ best_match1, check12 = predict_ubmatrix(seednumber, spots_in_center, classhkl, hkl_all_class0, hkl_all_class1, files, s_tth1=s_tth,s_chi1=s_chi, predicted_hkl1=predicted_hkl123, class_predicted1=class_predicted, max_pred1=max_pred, emin=emin,emax=emax, material_=material_, material1_=material1_, lim_y=lim_y, lim_x=lim_x, cnt=cnt, dict_dp=dict_dp, rotation_matrix=rotation_matrix, mat_global=mat_global, strain_calculation=strain_calculation, ind_mat=ind_mat, ind_mat1=ind_mat1, tolerance=tolerance, tolerance1 =tolerance1, matricies=matricies, tabledistancerandom=tabledistancerandom, text_file = None, try_previous1=True, mode_spotCycle=mode_spotCycle1, softmax_threshold_global123 = softmax_threshold_global123, mr_threshold_global123=mr_threshold_global123, cap_matchrate123=cap_matchrate123, tolerance_strain123=tolerance_strain123, tolerance_strain1231=tolerance_strain1231, coeff123=coeff123, coeff_overlap=coeff_overlap, material0_limit=material0_limit, material1_limit=material1_limit, model_direc=model_direc, use_previous_UBmatrix_name=use_previous_UBmatrix_name, material_phase_always_present=material_phase_always_present, match_rate=match_rate, check=check[cnt,:], crystal=crystal, crystal1=crystal1, angbins=angbins, wb=wb, temp_key=temp_key, strain_free_parameters=strain_free_parameters) for intmat in range(matricies): if len(rotation_matrix1[intmat]) == 0: col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 else: mat_global[intmat][0][cnt] = mat_highest[intmat][0] final_symm =symmetry final_crystal = crystal if mat_highest[intmat][0] == 1: final_symm = symmetry final_crystal = crystal elif mat_highest[intmat][0] == 2: final_symm = symmetry1 final_crystal = crystal1 symm_operator = final_crystal._hklsym strain_matrix[intmat][0][cnt,:,:] = strain_crystal[intmat][0] strain_matrixs[intmat][0][cnt,:,:] = strain_sample[intmat][0] rotation_matrix[intmat][0][cnt,:,:] = rotation_matrix1[intmat][0] col_temp = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 0., 1.]), final_symm, symm_operator) col[intmat][0][cnt,:] = col_temp col_tempx = get_ipf_colour(rotation_matrix1[intmat][0], np.array([1., 0., 0.]), final_symm, symm_operator) colx[intmat][0][cnt,:] = col_tempx col_tempy = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 1., 0.]), final_symm, symm_operator) coly[intmat][0][cnt,:] = col_tempy match_rate[intmat][0][cnt] = mr_highest[intmat][0] spots_len[intmat][0][cnt] = spots_len1[intmat][0] iR_pix[intmat][0][cnt] = iR_pix1[intmat][0] fR_pix[intmat][0][cnt] = fR_pix1[intmat][0] best_match[intmat][0][cnt] = best_match1[intmat][0] check[cnt,intmat] = check12[intmat] files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, match_rate, \ mat_global, cnt, files_treated, spots_len, iR_pix, fR_pix, check, best_match, predicted_hkl123 def predict_preprocessMP(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, hkl_all_class1, emin, emax, material_, material1_, symmetry, symmetry1,lim_x,lim_y, strain_calculation, ind_mat, ind_mat1, model_direc=None, tolerance =None, tolerance1 =None, matricies=None, ccd_label=None, filename_bkg=None,intensity_threshold=None, boxsize=None,bkg_treatment=None, filenameDirec=None, experimental_prefix=None, blacklist_file =None, text_file=None, files_treated=None,try_previous1=False, wb=None, temp_key=None, cor_file_directory=None, mode_spotCycle1=None, softmax_threshold_global123=None,mr_threshold_global123=None, cap_matchrate123=None,tolerance_strain123=None,tolerance_strain1231=None,\ NumberMaxofFits123=None,fit_peaks_gaussian_global123=None, FitPixelDev_global123=None,coeff123=None, coeff_overlap=None, material0_limit=None, material1_limit=None, use_previous_UBmatrix_name=None, material_phase_always_present=None, crystal=None, crystal1=None, strain_free_parameters=None): if files in files_treated: return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match, None print("# Predicting for "+ files) call_global() if files.split(".")[-1] != "cor": CCDLabel=ccd_label seednumber = "Experimental "+CCDLabel+" file" try: out_name = blacklist_file except: out_name = None if bkg_treatment == None: bkg_treatment = "A-B" try: ### Max space = space between pixles peak_XY = RMCCD.PeakSearch( files, stackimageindex = -1, CCDLabel=CCDLabel, NumberMaxofFits=NumberMaxofFits123, PixelNearRadius=10, removeedge=2, IntensityThreshold=intensity_threshold, local_maxima_search_method=0, boxsize=boxsize, position_definition=1, verbose=0, fit_peaks_gaussian=fit_peaks_gaussian_global123, xtol=0.001, FitPixelDev=FitPixelDev_global123, return_histo=0, # Saturation_value=1e10, # to be merged in CCDLabel # Saturation_value_flatpeak=1e10, MinIntensity=0, PeakSizeRange=(0.65,200), write_execution_time=1, Data_for_localMaxima = "auto_background", formulaexpression=bkg_treatment, Remove_BlackListedPeaks_fromfile=out_name, reject_negative_baseline=True, Fit_with_Data_for_localMaxima=False, maxPixelDistanceRejection=15.0, ) peak_XY = peak_XY[0]#[:,:2] ##[2] Integer peak lists except: print("Error in Peak detection for "+ files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match, None try: s_ix = np.argsort(peak_XY[:, 2])[::-1] peak_XY = peak_XY[s_ix] except: print("Error in Peak detection (argsort routine) for "+ files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match, None framedim = dictLT.dict_CCD[CCDLabel][0] twicetheta, chi = Lgeo.calc_uflab(peak_XY[:,0], peak_XY[:,1], detectorparameters, returnAngles=1, pixelsize=pixelsize, kf_direction='Z>0') data_theta, data_chi = twicetheta/2., chi framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0]#TODO*2 dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peak_XY[:,0] dict_dp['peakY']=peak_XY[:,1] dict_dp['intensity']=peak_XY[:,2] CCDcalib = {"CCDLabel":CCDLabel, "dd":detectorparameters[0], "xcen":detectorparameters[1], "ycen":detectorparameters[2], "xbet":detectorparameters[3], "xgam":detectorparameters[4], "pixelsize": pixelsize} path = os.path.normpath(files) IOLT.writefile_cor(cor_file_directory+"//"+path.split(os.sep)[-1].split(".")[0], twicetheta, chi, peak_XY[:,0], peak_XY[:,1], peak_XY[:,2], param=CCDcalib, sortedexit=0) elif files.split(".")[-1] == "cor": seednumber = "Experimental COR file" allres = IOLT.readfile_cor(files, True) data_theta, data_chi, peakx, peaky, intensity = allres[1:6] CCDcalib = allres[-1] detectorparameters = allres[-2] pixelsize = CCDcalib['pixelsize'] CCDLabel = CCDcalib['CCDLabel'] framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0]#TODO*2 dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peakx dict_dp['peakY']=peaky dict_dp['intensity']=intensity sorted_data = np.transpose(np.array([data_theta, data_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(sorted_data, sorted_data)) codebars_all = [] if len(data_theta) == 0: print("No peaks Found for : " + files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match, None if not use_om_user: spots_in_center = np.arange(0,len(data_theta)) spots_in_center = spots_in_center[:nb_spots_consider] for i in spots_in_center: spotangles = tabledistancerandom[i] spotangles = np.delete(spotangles, i)# removing the self distance codebars = np.histogram(spotangles, bins=angbins)[0] # codebars = histogram1d(spotangles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## normalize the same way as training data max_codebars = np.max(codebars) codebars = codebars/ max_codebars codebars_all.append(codebars) ## reshape for the model to predict all spots at once codebars = np.array(codebars_all) ## Do prediction of all spots at once prediction = predict(codebars, wb, temp_key) max_pred = np.max(prediction, axis = 1) class_predicted = np.argmax(prediction, axis = 1) predicted_hkl123 = classhkl[class_predicted] predicted_hkl123 = predicted_hkl123.astype(int) else: max_pred = None class_predicted = None predicted_hkl123 = None spots_in_center = None s_tth = data_theta * 2. s_chi = data_chi rotation_matrix1, mr_highest, mat_highest, \ strain_crystal, strain_sample, iR_pix1, \ fR_pix1, spots_len1,\ best_match1, check12 = predict_ubmatrix(seednumber, spots_in_center, classhkl, hkl_all_class0, hkl_all_class1, files, s_tth1=s_tth,s_chi1=s_chi, predicted_hkl1=predicted_hkl123, class_predicted1=class_predicted, max_pred1=max_pred, emin=emin,emax=emax, material_=material_, material1_=material1_, lim_y=lim_y, lim_x=lim_x, cnt=cnt, dict_dp=dict_dp, rotation_matrix=rotation_matrix, mat_global=mat_global, strain_calculation=strain_calculation, ind_mat=ind_mat, ind_mat1=ind_mat1, tolerance=tolerance, tolerance1 =tolerance1, matricies=matricies, tabledistancerandom=tabledistancerandom, text_file = text_file, try_previous1=try_previous1, mode_spotCycle=mode_spotCycle1, softmax_threshold_global123 = softmax_threshold_global123, mr_threshold_global123=mr_threshold_global123, cap_matchrate123=cap_matchrate123, tolerance_strain123=tolerance_strain123, tolerance_strain1231=tolerance_strain1231, coeff123=coeff123, coeff_overlap=coeff_overlap, material0_limit=material0_limit, material1_limit=material1_limit, model_direc=model_direc, use_previous_UBmatrix_name=use_previous_UBmatrix_name, material_phase_always_present=material_phase_always_present, match_rate=match_rate, check=check[cnt,:], crystal=crystal, crystal1=crystal1, angbins=angbins, wb=wb, temp_key=temp_key, strain_free_parameters=strain_free_parameters) for intmat in range(matricies): if len(rotation_matrix1[intmat]) == 0: col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 else: mat_global[intmat][0][cnt] = mat_highest[intmat][0] final_symm =symmetry final_crystal = crystal if mat_highest[intmat][0] == 1: final_symm = symmetry final_crystal = crystal elif mat_highest[intmat][0] == 2: final_symm = symmetry1 final_crystal = crystal1 symm_operator = final_crystal._hklsym strain_matrix[intmat][0][cnt,:,:] = strain_crystal[intmat][0] strain_matrixs[intmat][0][cnt,:,:] = strain_sample[intmat][0] rotation_matrix[intmat][0][cnt,:,:] = rotation_matrix1[intmat][0] col_temp = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 0., 1.]), final_symm, symm_operator) col[intmat][0][cnt,:] = col_temp col_tempx = get_ipf_colour(rotation_matrix1[intmat][0], np.array([1., 0., 0.]), final_symm, symm_operator) colx[intmat][0][cnt,:] = col_tempx col_tempy = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 1., 0.]), final_symm, symm_operator) coly[intmat][0][cnt,:] = col_tempy match_rate[intmat][0][cnt] = mr_highest[intmat][0] spots_len[intmat][0][cnt] = spots_len1[intmat][0] iR_pix[intmat][0][cnt] = iR_pix1[intmat][0] fR_pix[intmat][0][cnt] = fR_pix1[intmat][0] best_match[intmat][0][cnt] = best_match1[intmat][0] check[cnt,intmat] = check12[intmat] files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, match_rate, \ mat_global, cnt, files_treated, spots_len, iR_pix, fR_pix, check, best_match, predicted_hkl123 def predict_ubmatrix(seednumber, spots_in_center, classhkl, hkl_all_class0, hkl_all_class1, filename, s_tth1,s_chi1,predicted_hkl1,class_predicted1,max_pred1, emin, emax, material_, material1_, lim_y, lim_x, cnt, dict_dp,rotation_matrix,mat_global,strain_calculation, ind_mat, ind_mat1, tolerance=None, tolerance1 =None, matricies=None, tabledistancerandom=None, text_file=None,try_previous1=False, mode_spotCycle=None, softmax_threshold_global123=None,mr_threshold_global123=None, cap_matchrate123=None, tolerance_strain123=None,tolerance_strain1231=None, coeff123=None, coeff_overlap=None, material0_limit=None, material1_limit=None, model_direc=None, use_previous_UBmatrix_name=None, material_phase_always_present=None, match_rate=None, check = None, crystal=None, crystal1=None, angbins=None, wb=None, temp_key=None, strain_free_parameters=None): input_params = {"tolerance": tolerance, "tolerance1":tolerance1, "tolerancestrain": tolerance_strain123, ## For strain calculations "tolerancestrain1": tolerance_strain1231, "emin": emin, "emax": emax, "mat":0} call_global() strain_matrix = [[] for i in range(matricies)] strain_matrixs = [[] for i in range(matricies)] best_matrix = [[] for i in range(matricies)] mr_highest = [[] for i in range(matricies)] ir_pixels = [[] for i in range(matricies)] fr_pixels = [[] for i in range(matricies)] spots_len = [[] for i in range(matricies)] mat_highest = [[] for i in range(matricies)] best_match = [[] for i in range(matricies)] spots1 = [] spots1_global = [[] for i in range(matricies)] if not use_om_user: dist = tabledistancerandom ## one time calculations lattice_params0 = dictLT.dict_Materials[material_][1] B0 = CP.calc_B_RR(lattice_params0) Gstar_metric0 = CP.Gstar_from_directlatticeparams(lattice_params0[0],lattice_params0[1],\ lattice_params0[2],lattice_params0[3],\ lattice_params0[4],lattice_params0[5]) tab_distance_classhkl_data0 = get_material_dataP(Gstar_metric0, predicted_hkl1[:nb_spots_consider,:]) if material_ != material1_: lattice_params1 = dictLT.dict_Materials[material1_][1] B1 = CP.calc_B_RR(lattice_params1) Gstar_metric1 = CP.Gstar_from_directlatticeparams(lattice_params1[0],lattice_params1[1],\ lattice_params1[2],lattice_params1[3],\ lattice_params1[4],lattice_params1[5]) tab_distance_classhkl_data1 = get_material_dataP(Gstar_metric1, predicted_hkl1[:nb_spots_consider,:]) else: tab_distance_classhkl_data1 = None Gstar_metric1 = None B1 = None else: dist = tabledistancerandom tab_distance_classhkl_data0 = None tab_distance_classhkl_data1 = None ## one time calculations lattice_params0 = dictLT.dict_Materials[material_][1] B0 = CP.calc_B_RR(lattice_params0) Gstar_metric0 = CP.Gstar_from_directlatticeparams(lattice_params0[0],lattice_params0[1],\ lattice_params0[2],lattice_params0[3],\ lattice_params0[4],lattice_params0[5]) if material_ != material1_: lattice_params1 = dictLT.dict_Materials[material1_][1] B1 = CP.calc_B_RR(lattice_params1) Gstar_metric1 = CP.Gstar_from_directlatticeparams(lattice_params1[0],lattice_params1[1],\ lattice_params1[2],lattice_params1[3],\ lattice_params1[4],lattice_params1[5]) else: Gstar_metric1 = None B1 = None spots = [] first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr = 0 mat = 0 iR = 0 fR = 0 strain_crystal = np.zeros((3,3)) strain_sample = np.zeros((3,3)) material0_count = 0 material1_count = 0 calcul_done = False objective_function1 = None for igrain in range(matricies): # if check[igrain] == 1: # or len(spots1_global[igrain]) != 0: # continue try_previous = try_previous1 max_mr, min_mr = 0, 0 iR, fR= 0, 0 case = "None" if use_om_user: use_previous_UBmatrix_name = False try_previous = False temp_qsd = np.loadtxt(path_user_OM, delimiter=",") temp_qsd = temp_qsd.reshape((len(temp_qsd),3,3)) rotationmatrix_indexed = temp_qsd[igrain,:,:] mat = 1 if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey spots_prev, theo_spots_prev = remove_spots(s_tth1, s_chi1, rotationmatrix_indexed, Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp) newmatchrate = 100*len(spots_prev)/theo_spots_prev ## Filter indexation by matching rate if newmatchrate < cap_matchrate123: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] spots = [] max_mr, min_mr = 0, 0 else: if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotationmatrix_indexed, Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotationmatrix_indexed) spots = spots_prev expected = theo_spots_prev max_mr, min_mr = 100*(len(spots)/expected), 100*(len(spots)/expected) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] try_previous = False calcul_done = True elif use_previous_UBmatrix_name: try: try_previous = False ### try already indexed UB matricies # xy = np.load('xy.npz') # xy.zip.fp.close() # xy.close() with np.load(model_direc+"//rotation_matrix_indexed_1.npz") as load_objectind: # load_objectind = np.load(model_direc+"//rotation_matrix_indexed.npz") rotationmatrix_indexed = load_objectind["arr_0"] mat_global_indexed = load_objectind["arr_1"] match_rate_indexed = load_objectind["arr_2"] avg_match_rate_indexed = load_objectind["arr_3"] calcul_done = False for ind_mat_UBmat in range(len(rotationmatrix_indexed[igrain][0])): if calcul_done: continue if np.all(rotationmatrix_indexed[igrain][0][ind_mat_UBmat,:,:]) == 0: continue if match_rate_indexed[igrain][0][ind_mat_UBmat] < 0.8*avg_match_rate_indexed[igrain]: continue mat = mat_global_indexed[igrain][0][ind_mat_UBmat] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue spots_prev, theo_spots_prev = remove_spots(s_tth1, s_chi1, rotationmatrix_indexed[igrain][0][ind_mat_UBmat,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp) newmatchrate = 100*len(spots_prev)/theo_spots_prev condition_prev = newmatchrate < 0.8*(match_rate_indexed[igrain][0][ind_mat_UBmat]) current_spots = [len(list(set(spots_prev) & set(spots1_global[igr]))) > coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if condition_prev or (newmatchrate <= cap_matchrate123) or np.any(current_spots):# or overlap: try_previous = try_previous1 else: try_previous = False calcul_done = True if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotationmatrix_indexed[igrain][0][ind_mat_UBmat,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotationmatrix_indexed[igrain][0][ind_mat_UBmat,:,:]) spots = spots_prev expected = theo_spots_prev max_mr, min_mr = 100*(len(spots)/expected), 100*(len(spots)/expected) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] break except: try_previous = False calcul_done = False if try_previous and (cnt % lim_y == 0) and cnt != 0: if np.all(rotation_matrix[igrain][0][cnt-lim_y,:,:]) == 0: try_previous = False else: mat = mat_global[igrain][0][cnt-lim_y] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue spots_lr, theo_spots_lr = remove_spots(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-lim_y,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp) # last_row = len(spots_lr) <= coeff123*theo_spots_lr newmatchrate = 100*(len(spots_lr)/theo_spots_lr) condition_prev = newmatchrate < 0.9*(match_rate[igrain][0][cnt-lim_y]) last_row = condition_prev if last_row or condition_prev: ## new spots less than 8 count, not good match SKIP try_previous = False else: try_previous = True current_spots = [len(list(set(spots_lr) & set(spots1_global[igr]))) > coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): try_previous = False continue if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-lim_y,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotation_matrix[igrain][0][cnt-lim_y,:,:]) spots = spots_lr expected = theo_spots_lr max_mr, min_mr = 100*(len(spots_lr)/theo_spots_lr), 100*(len(spots_lr)/theo_spots_lr) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] elif try_previous and (cnt % lim_y != 0): last_row = True left_row = True condition_prev = True condition_prev1 = True if np.all(rotation_matrix[igrain][0][cnt-1,:,:]) == 0: left_row = True else: mat = mat_global[igrain][0][cnt-1] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue ## new row start when % == 0 ## use left index pixels matrix values spots_left, theo_spots_left = remove_spots(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-1,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp) # left_row = len(spots_left) <= coeff123*theo_spots_left newmatchrate = 100*(len(spots_left)/theo_spots_left) condition_prev = newmatchrate < 0.9*(match_rate[igrain][0][cnt-1]) left_row = condition_prev if cnt >= lim_y: if np.all(rotation_matrix[igrain][0][cnt-lim_y,:,:]) == 0: last_row = True else: mat = mat_global[igrain][0][cnt-lim_y] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue ## use bottom index pixels matrix values spots_lr, theo_spots_lr = remove_spots(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-lim_y,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp) # last_row = len(spots_lr) <= coeff123*theo_spots_lr newmatchrate1 = 100*(len(spots_lr)/theo_spots_lr) condition_prev1 = newmatchrate1 < 0.9*(match_rate[igrain][0][cnt-lim_y]) last_row = condition_prev1 if (left_row and last_row): try_previous = False elif condition_prev and condition_prev1: try_previous = False elif not left_row and not last_row: try_previous = True if len(spots_lr) > len(spots_left): current_spots = [len(list(set(spots_lr) & set(spots1_global[igr]))) > coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): try_previous = False continue mat = mat_global[igrain][0][cnt-lim_y] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-lim_y,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotation_matrix[igrain][0][cnt-lim_y,:,:]) spots = spots_lr expected = theo_spots_lr max_mr, min_mr = 100*(len(spots_lr)/theo_spots_lr), 100*(len(spots_lr)/theo_spots_lr) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] else: current_spots = [len(list(set(spots_left) & set(spots1_global[igr]))) > coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): try_previous = False continue mat = mat_global[igrain][0][cnt-1] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-1,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotation_matrix[igrain][0][cnt-1,:,:]) spots = spots_left expected = theo_spots_left max_mr, min_mr = 100*(len(spots_left)/theo_spots_left), 100*(len(spots_left)/theo_spots_left) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] elif not left_row and last_row: try_previous = True current_spots = [len(list(set(spots_left) & set(spots1_global[igr]))) > coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): try_previous = False continue mat = mat_global[igrain][0][cnt-1] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-1,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotation_matrix[igrain][0][cnt-1,:,:]) spots = spots_left expected = theo_spots_left max_mr, min_mr = 100*(len(spots_left)/theo_spots_left), 100*(len(spots_left)/theo_spots_left) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] elif left_row and not last_row: try_previous = True current_spots = [len(list(set(spots_lr) & set(spots1_global[igr]))) > coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): try_previous = False continue mat = mat_global[igrain][0][cnt-lim_y] if mat == 1: Keymaterial_ = material_ case = material_ Bkey = B0 input_params["mat"] = 1 input_params["Bmat"] = Bkey elif mat == 2: Keymaterial_ = material1_ case = material1_ Bkey = B1 input_params["mat"] = 2 input_params["Bmat"] = Bkey else: Keymaterial_ = None Bkey = None input_params["mat"] = 0 input_params["Bmat"] = None continue if strain_calculation: strain_crystal, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth1, s_chi1, rotation_matrix[igrain][0][cnt-lim_y,:,:], Keymaterial_, input_params, dict_dp['detectorparameters'], dict_dp, spots1, Bkey, strain_free_parameters) else: strain_crystal, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(rotation_matrix[igrain][0][cnt-lim_y,:,:]) spots = spots_lr expected = theo_spots_lr max_mr, min_mr = 100*(len(spots_lr)/theo_spots_lr), 100*(len(spots_lr)/theo_spots_lr) first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, len(spots), expected, max_mr, 0, rot_mat_UB] else: try_previous = False if not try_previous and not calcul_done: ### old version if mode_spotCycle == "slow": # print("Slow mode of analysis") first_match, max_mr, min_mr, spots, \ case, mat, strain_crystal, \ strain_sample, iR, fR = get_orient_mat(s_tth1, s_chi1, material_, material1_, classhkl, class_predicted1, predicted_hkl1, input_params, hkl_all_class0, hkl_all_class1, max_pred1, dict_dp, spots1, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=softmax_threshold_global123, mr_threshold=mr_threshold_global123, tab_distance_classhkl_data0=tab_distance_classhkl_data0, tab_distance_classhkl_data1=tab_distance_classhkl_data1, spots1_global = spots1_global, coeff_overlap = coeff_overlap, ind_mat=ind_mat, ind_mat1=ind_mat1, strain_calculation=strain_calculation, cap_matchrate123=cap_matchrate123, material0_count=material0_count, material1_count=material1_count, material0_limit=material0_limit, material1_limit=material1_limit, igrain=igrain, material_phase_always_present=material_phase_always_present, strain_free_parameters=strain_free_parameters) elif mode_spotCycle == "houghmode": # print("Slow mode of analysis") first_match, max_mr, min_mr, spots, \ case, mat, strain_crystal, \ strain_sample, iR, fR = get_orient_mat_HM(s_tth1, s_chi1, material_, material1_, classhkl, class_predicted1, predicted_hkl1, input_params, hkl_all_class0, hkl_all_class1, max_pred1, dict_dp, spots1, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=softmax_threshold_global123, mr_threshold=mr_threshold_global123, tab_distance_classhkl_data0=tab_distance_classhkl_data0, tab_distance_classhkl_data1=tab_distance_classhkl_data1, spots1_global = spots1_global, coeff_overlap = coeff_overlap, ind_mat=ind_mat, ind_mat1=ind_mat1, strain_calculation=strain_calculation, cap_matchrate123=cap_matchrate123, material0_count=material0_count, material1_count=material1_count, material0_limit=material0_limit, material1_limit=material1_limit, igrain=igrain, material_phase_always_present=material_phase_always_present, strain_free_parameters=strain_free_parameters) elif mode_spotCycle == "houghgraphmode": # print("Fast mode of analysis") first_match, max_mr, min_mr, spots, \ case, mat, strain_crystal, \ strain_sample, iR, fR,\ objective_function1 = get_orient_mat_graphv1HM(s_tth1, s_chi1, material_, material1_, classhkl, class_predicted1, predicted_hkl1, input_params, hkl_all_class0, hkl_all_class1, max_pred1, dict_dp, spots1, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=softmax_threshold_global123, mr_threshold=mr_threshold_global123, tab_distance_classhkl_data0=tab_distance_classhkl_data0, tab_distance_classhkl_data1=tab_distance_classhkl_data1, spots1_global = spots1_global, coeff_overlap = coeff_overlap, ind_mat=ind_mat, ind_mat1=ind_mat1, strain_calculation=strain_calculation, cap_matchrate123=cap_matchrate123, material0_count=material0_count, material1_count=material1_count, material0_limit=material0_limit, material1_limit=material1_limit, igrain=igrain, material_phase_always_present=material_phase_always_present, objective_function= objective_function1, crystal=crystal, crystal1=crystal1, strain_free_parameters=strain_free_parameters) elif mode_spotCycle == "graphmode": # print("Fast mode of analysis") first_match, max_mr, min_mr, spots, \ case, mat, strain_crystal, \ strain_sample, iR, fR,\ objective_function1 = get_orient_mat_graphv1(s_tth1, s_chi1, material_, material1_, classhkl, class_predicted1, predicted_hkl1, input_params, hkl_all_class0, hkl_all_class1, max_pred1, dict_dp, spots1, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=softmax_threshold_global123, mr_threshold=mr_threshold_global123, tab_distance_classhkl_data0=tab_distance_classhkl_data0, tab_distance_classhkl_data1=tab_distance_classhkl_data1, spots1_global = spots1_global, coeff_overlap = coeff_overlap, ind_mat=ind_mat, ind_mat1=ind_mat1, strain_calculation=strain_calculation, cap_matchrate123=cap_matchrate123, material0_count=material0_count, material1_count=material1_count, material0_limit=material0_limit, material1_limit=material1_limit, igrain=igrain, material_phase_always_present=material_phase_always_present, objective_function= objective_function1, crystal=crystal, crystal1=crystal1, strain_free_parameters=strain_free_parameters) elif mode_spotCycle == "update_reupdate": # print("Fast mode of analysis") first_match, max_mr, min_mr, spots, \ case, mat, strain_crystal, \ strain_sample, iR, fR, objective_function1,\ s_tth1, s_chi1, class_predicted1, \ predicted_hkl1, max_pred1, dist = get_orient_mat_repredict(s_tth1, s_chi1, material_, material1_, classhkl, class_predicted1, predicted_hkl1, input_params, hkl_all_class0, hkl_all_class1, max_pred1, dict_dp, spots1, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=softmax_threshold_global123, mr_threshold=mr_threshold_global123, tab_distance_classhkl_data0=tab_distance_classhkl_data0, tab_distance_classhkl_data1=tab_distance_classhkl_data1, spots1_global = spots1_global, coeff_overlap = coeff_overlap, ind_mat=ind_mat, ind_mat1=ind_mat1, strain_calculation=strain_calculation, cap_matchrate123=cap_matchrate123, material0_count=material0_count, material1_count=material1_count, material0_limit=material0_limit, material1_limit=material1_limit, igrain=igrain, material_phase_always_present=material_phase_always_present, objective_function= objective_function1, crystal=crystal, crystal1=crystal1, angbins=angbins, wb=wb, temp_key=temp_key, strain_free_parameters=strain_free_parameters) else: print("selected mode of treating spots is not ready") for ispot in spots: spots1.append(ispot) spots1_global[igrain].append(ispot) ## make copy of best rotation matrix best_match[igrain].append(np.copy(first_match)) best_matrix[igrain].append(np.copy(first_match[14])) mr_highest[igrain].append(np.copy(max_mr)) mat_highest[igrain].append(np.copy(mat)) ir_pixels[igrain].append(np.copy(iR)) fr_pixels[igrain].append(np.copy(fR)) spots_len[igrain].append(np.copy(len(spots))) strain_matrix[igrain].append(np.copy(strain_crystal)) strain_matrixs[igrain].append(np.copy(strain_sample)) if np.all(first_match[14] != 0): check[igrain] = 1 if mat == 1: material0_count += 1 if mat == 2: material1_count += 1 return best_matrix, mr_highest, mat_highest, strain_matrix, strain_matrixs, ir_pixels, fr_pixels, spots_len, best_match, check def get_material_dataP(Gstar, classhkl = None): hkl2 = np.copy(classhkl) hkl1 = np.copy(classhkl) # compute square matrix containing angles metrics = Gstar H1 = hkl1 n1 = hkl1.shape[0] H2 = hkl2 n2 = hkl2.shape[0] dstar_square_1 = np.diag(np.inner(np.inner(H1, metrics), H1)) dstar_square_2 = np.diag(np.inner(np.inner(H2, metrics), H2)) scalar_product = np.inner(np.inner(H1, metrics), H2) * 1.0 d1 = np.sqrt(dstar_square_1.reshape((n1, 1))) * 1.0 d2 = np.sqrt(dstar_square_2.reshape((n2, 1))) * 1.0 outy = np.outer(d1, d2) ratio = scalar_product / outy ratio = np.round(ratio, decimals=7) tab_angulardist = np.arccos(ratio) / (np.pi / 180.0) np.putmask(tab_angulardist, np.abs(tab_angulardist) < 0.001, 400) return tab_angulardist def get_orient_mat_repredict(s_tth, s_chi, material0_, material1_, classhkl, class_predicted, predicted_hkl, input_params, hkl_all_class0, hkl_all_class1, max_pred, dict_dp, spots, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=0.85, mr_threshold=0.85, tab_distance_classhkl_data0=None, tab_distance_classhkl_data1=None, spots1_global=None, coeff_overlap = None, ind_mat=None, ind_mat1=None, strain_calculation=None, cap_matchrate123=None, material0_count=None, material1_count=None, material0_limit=None, material1_limit=None, igrain=None, material_phase_always_present=None, objective_function=None, crystal=None, crystal1=None, angbins=None, wb=None, temp_key=None, strain_free_parameters=None): if objective_function == None: call_global() init_mr = 0 init_mat = 0 init_material = "None" init_case = "None" init_B = None final_match_rate = 0 match_rate_mma = [] final_rmv_ind = [] if material0_ == material1_: list_of_sets = [] for ii in range(0, min(nb_spots_consider, len(dist))): if max_pred[ii] < softmax_threshold: continue a1 = np.round(dist[ii],3) for i in range(0, min(nb_spots_consider, len(dist))): if ii==i: continue if (ii,i) in list_of_sets or (i,ii) in list_of_sets: continue if max_pred[i] < softmax_threshold: continue hkl1 = hkl_all_class0[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class0[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric0 tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) np.putmask(tab_angulardist_temp, np.abs(tab_angulardist_temp) < 0.001, 400) list_ = np.where(np.abs(tab_angulardist_temp-a1[i]) < input_params["tolerance"]) if len(list_[0]) != 0: list_of_sets.append((ii,i)) else: list_of_sets = [] for ii in range(0, min(nb_spots_consider, len(dist))): if max_pred[ii] < softmax_threshold: continue a1 = np.round(dist[ii],3) for i in range(0, min(nb_spots_consider, len(dist))): if ii==i: continue if (ii,i) in list_of_sets or (i,ii) in list_of_sets: continue if max_pred[i] < softmax_threshold: continue if class_predicted[ii] < ind_mat and class_predicted[i] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 tolerance_new = input_params["tolerance"] hkl1 = hkl_all_class0[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class0[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric0 elif (ind_mat <= class_predicted[ii] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 tolerance_new = input_params["tolerance1"] hkl1 = hkl_all_class1[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class1[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric1 else: continue tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) np.putmask(tab_angulardist_temp, np.abs(tab_angulardist_temp) < 0.001, 400) list_ = np.where(np.abs(tab_angulardist_temp-a1[i]) < tolerance_new) if len(list_[0]) != 0: list_of_sets.append((ii,i)) ## build a direct connection graph object graph_obj = nx.DiGraph(list_of_sets) connected_nodes_length = [] connected_nodes = [[] for i in range(len(graph_obj))] for i,line in enumerate(nx.generate_adjlist(graph_obj)): connected_nodes_length.append(len(line.split(" "))) connected_nodes[i].append([int(jj) for jj in line.split(" ")]) ## sort by maximum node occurance connected_nodes_length = np.array(connected_nodes_length) connected_nodes_length_sort_ind = np.argsort(connected_nodes_length)[::-1] mat = 0 case = "None" tried_spots = [] objective_function = [] for toplist in range(len(graph_obj)): # ## continue if less than 3 connections are found for a graph # if connected_nodes_length[connected_nodes_length_sort_ind[toplist]] < 2: # continue for j in connected_nodes[connected_nodes_length_sort_ind[toplist]][0]: init_mr = 0 final_match_rate = 0 final_rmv_ind = [] all_stats = [] for i in connected_nodes[connected_nodes_length_sort_ind[toplist]][0]: if j == i: continue if j in tried_spots and i in tried_spots: continue if material0_ == material1_: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B else: if class_predicted[i] < ind_mat and class_predicted[j] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B elif (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[j] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 hkl_all_class = hkl_all_class1 material_ = material1_ B = B1 Gstar_metric = Gstar_metric1 case = material_ mat = 2 input_params["mat"] = mat input_params["Bmat"] = B else: mat = 0 case = "None" input_params["mat"] = mat input_params["Bmat"] = None if mat == 0: continue tth_chi_spot1 = np.array([s_tth[i], s_chi[i]]) tth_chi_spot2 = np.array([s_tth[j], s_chi[j]]) hkl1 = hkl_all_class[str(predicted_hkl[i])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class[str(predicted_hkl[j])] hkl2_list = np.array(hkl2) actual_mat, flagAM, \ spot1_hkl, spot2_hkl = propose_UB_matrix(hkl1_list, hkl2_list, Gstar_metric, input_params, dist[i,j], tth_chi_spot1, tth_chi_spot2, B, method=0, crystal=crystal, crystal1=crystal1) if flagAM: continue for iind in range(len(actual_mat)): rot_mat123 = actual_mat[iind] rmv_ind, theospots = remove_spots(s_tth, s_chi, rot_mat123, material_, input_params, dict_dp['detectorparameters'], dict_dp) match_rate = np.round(100 * len(rmv_ind)/theospots, 3) match_rate_mma.append(match_rate) if match_rate > init_mr: final_rmv_ind = rmv_ind init_mat = np.copy(mat) input_params["mat"] = init_mat init_material = np.copy(material_) init_case = np.copy(case) init_B = np.copy(B) input_params["Bmat"] = init_B final_match_rate = np.copy(match_rate) init_mr = np.copy(match_rate) all_stats = [i, j, \ spot1_hkl[iind], spot2_hkl[iind], \ tth_chi_spot1, tth_chi_spot2, \ dist[i,j], tab_distance_classhkl_data[i,j], np.round(max_pred[i]*100,3), \ np.round(max_pred[j]*100,3), len(rmv_ind), theospots,\ match_rate, 0.0, rot_mat123, init_mat, init_material, init_B, init_case] tried_spots.append(i) if (final_match_rate <= cap_matchrate123): ## Nothing found!! ## Either peaks are not well defined or not found within tolerance and prediction accuracy all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" objective_function.append([0, [], []]) else: objective_function.append([final_match_rate, final_rmv_ind, all_stats]) tried_spots.append(j) sort_ind = [] for ijk in objective_function: sort_ind.append(ijk[0]) sort_ind = np.array(sort_ind) sort_ind = np.argsort(sort_ind)[::-1] for gr_count123 in range(len(sort_ind)): max_mr = objective_function[sort_ind[gr_count123]][0] rmv_ind = objective_function[sort_ind[gr_count123]][1] all_stats = objective_function[sort_ind[gr_count123]][2] if len(rmv_ind) == 0 or max_mr==0: continue mat = all_stats[15] if mat == 1: if igrain==0 and material_phase_always_present ==2: mat = 0 case="None" if material0_count >= material0_limit: mat = 0 case="None" elif mat == 2: if igrain==0 and material_phase_always_present ==1: mat = 0 case="None" if material1_count >= material1_limit: mat = 0 case="None" if mat == 0: continue current_spots = [len(list(set(rmv_ind) & set(spots1_global[igr])))> coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): continue input_params["mat"] = all_stats[15] if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(all_stats[16]), input_params, dict_dp['detectorparameters'], dict_dp, spots, all_stats[17], strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB ## delete the indexed spots and repredict the spots HKL in the absence of indexed spots ## maybe it makes it easier to detect some grains ##update list # s_tth = np.delete(s_tth, rmv_ind, axis=0) # s_chi = np.delete(s_chi, rmv_ind, axis=0) s_tth[rmv_ind] = np.nan s_chi[rmv_ind] = np.nan sorted_data = np.transpose(np.array([s_tth/2., s_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(sorted_data, sorted_data)) spots_in_center = np.arange(0,len(s_tth)) spots_in_center = spots_in_center[:nb_spots_consider] codebars_all = [] for i in spots_in_center: spotangles = tabledistancerandom[i] spotangles = np.delete(spotangles, i)# removing the self distance codebars = np.histogram(spotangles, bins=angbins)[0] # codebars = histogram1d(spotangles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## normalize the same way as training data max_codebars = np.max(codebars) codebars = codebars/ max_codebars codebars_all.append(codebars) ## reshape for the model to predict all spots at once codebars = np.array(codebars_all) ## Do prediction of all spots at once prediction = predict(codebars, wb, temp_key) max_pred = np.max(prediction, axis = 1) class_predicted = np.argmax(prediction, axis = 1) predicted_hkl123 = classhkl[class_predicted] predicted_hkl123 = predicted_hkl123.astype(int) return all_stats, np.max(max_mr), np.min(max_mr), \ rmv_ind, str(all_stats[18]), all_stats[15], dev_strain, strain_sample, iR, fR, objective_function,\ s_tth, s_chi, class_predicted, predicted_hkl123, max_pred, tabledistancerandom all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" return all_stats, max_mr, min_mr, spot_ind, case, mat, np.zeros((3,3)), np.zeros((3,3)), 0, 0, objective_function,\ s_tth, s_chi, class_predicted, predicted_hkl, max_pred, dist def get_orient_mat_graphv1HM(s_tth, s_chi, material0_, material1_, classhkl, class_predicted, predicted_hkl, input_params, hkl_all_class0, hkl_all_class1, max_pred, dict_dp, spots, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=0.85, mr_threshold=0.85, tab_distance_classhkl_data0=None, tab_distance_classhkl_data1=None, spots1_global=None, coeff_overlap = None, ind_mat=None, ind_mat1=None, strain_calculation=None, cap_matchrate123=None, material0_count=None, material1_count=None, material0_limit=None, material1_limit=None, igrain=None, material_phase_always_present=None, objective_function=None, crystal=None, crystal1=None, strain_free_parameters=None): if objective_function == None: call_global() init_mr = 0 init_mat = 0 init_material = "None" init_case = "None" init_B = None final_match_rate = 0 match_rate_mma = [] final_rmv_ind = [] #calculate the gnemonic projection space imageGNO, nbpeaks, halfdiagonal = computeGnomonicImage(s_tth, s_chi) hough, theta_h, d_h = hough_line(imageGNO) if material0_ == material1_: list_of_sets = [] for ii in range(0, min(nb_spots_consider, len(dist))): if max_pred[ii] < softmax_threshold: continue a1 = np.round(dist[ii],3) for i in range(0, min(nb_spots_consider, len(dist))): if ii==i: continue if (ii,i) in list_of_sets or (i,ii) in list_of_sets: continue if max_pred[i] < softmax_threshold: continue hkl1 = hkl_all_class0[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class0[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric0 tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) np.putmask(tab_angulardist_temp, np.abs(tab_angulardist_temp) < 0.001, 400) list_ = np.where(np.abs(tab_angulardist_temp-a1[i]) < input_params["tolerance"]) if len(list_[0]) != 0: list_of_sets.append((ii,i)) else: list_of_sets = [] for ii in range(0, min(nb_spots_consider, len(dist))): if max_pred[ii] < softmax_threshold: continue a1 = np.round(dist[ii],3) for i in range(0, min(nb_spots_consider, len(dist))): if ii==i: continue if (ii,i) in list_of_sets or (i,ii) in list_of_sets: continue if max_pred[i] < softmax_threshold: continue if class_predicted[ii] < ind_mat and class_predicted[i] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 tolerance_new = input_params["tolerance"] hkl1 = hkl_all_class0[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class0[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric0 elif (ind_mat <= class_predicted[ii] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 tolerance_new = input_params["tolerance1"] hkl1 = hkl_all_class1[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class1[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric1 else: continue tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) np.putmask(tab_angulardist_temp, np.abs(tab_angulardist_temp) < 0.001, 400) list_ = np.where(np.abs(tab_angulardist_temp-a1[i]) < tolerance_new) if len(list_[0]) != 0: list_of_sets.append((ii,i)) ## build a direct connection graph object graph_obj = nx.DiGraph(list_of_sets) connected_nodes_length = [] connected_nodes = [[] for i in range(len(graph_obj))] for i,line in enumerate(nx.generate_adjlist(graph_obj)): connected_nodes_length.append(len(line.split(" "))) connected_nodes[i].append([int(jj) for jj in line.split(" ")]) ## sort by maximum node occurance connected_nodes_length = np.array(connected_nodes_length) connected_nodes_length_sort_ind = np.argsort(connected_nodes_length)[::-1] mat = 0 case = "None" tried_spots = [] objective_function = [] for toplist in range(len(graph_obj)): # ## continue if less than 3 connections are found for a graph # if connected_nodes_length[connected_nodes_length_sort_ind[toplist]] < 2: # continue for j in connected_nodes[connected_nodes_length_sort_ind[toplist]][0]: init_mr = 0 final_match_rate = 0 final_rmv_ind = [] all_stats = [] for i in connected_nodes[connected_nodes_length_sort_ind[toplist]][0]: if j == i: continue if j in tried_spots and i in tried_spots: continue ## condition to check if spots lie on the same line in_hough_line = False for _, anglehs, disths in zip(*hough_line_peaks(hough, theta_h, d_h)): y0 = (disths - 0 * np.cos(anglehs)) / np.sin(anglehs) y1 = (disths - imageGNO.shape[1] * np.cos(anglehs)) / np.sin(anglehs) p1 = np.array((0,y0)) p2 = np.array((imageGNO.shape[1], y1)) p3_0 = ComputeGnomon_singledata(s_tth[i], s_chi[i]) p3_1 = ComputeGnomon_singledata(s_tth[j], s_chi[j]) distance_0 = np.abs(np.cross(p2-p1, p3_0-p1)) / np.linalg.norm(p2-p1) distance_1 = np.abs(np.cross(p2-p1, p3_1-p1)) / np.linalg.norm(p2-p1) if distance_0 < dist_threshold and distance_1 < dist_threshold: # print(distance_0, distance_1) in_hough_line = True if in_hough_line: break if not in_hough_line: continue if material0_ == material1_: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B else: if class_predicted[i] < ind_mat and class_predicted[j] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B elif (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[j] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 hkl_all_class = hkl_all_class1 material_ = material1_ B = B1 Gstar_metric = Gstar_metric1 case = material_ mat = 2 input_params["mat"] = mat input_params["Bmat"] = B else: mat = 0 case = "None" input_params["mat"] = mat input_params["Bmat"] = None if mat == 0: continue tth_chi_spot1 = np.array([s_tth[i], s_chi[i]]) tth_chi_spot2 = np.array([s_tth[j], s_chi[j]]) hkl1 = hkl_all_class[str(predicted_hkl[i])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class[str(predicted_hkl[j])] hkl2_list = np.array(hkl2) actual_mat, flagAM, \ spot1_hkl, spot2_hkl = propose_UB_matrix(hkl1_list, hkl2_list, Gstar_metric, input_params, dist[i,j], tth_chi_spot1, tth_chi_spot2, B, method=0, crystal=crystal, crystal1=crystal1) if flagAM: continue for iind in range(len(actual_mat)): rot_mat123 = actual_mat[iind] rmv_ind, theospots = remove_spots(s_tth, s_chi, rot_mat123, material_, input_params, dict_dp['detectorparameters'], dict_dp) match_rate = np.round(100 * len(rmv_ind)/theospots, 3) match_rate_mma.append(match_rate) if match_rate > init_mr: final_rmv_ind = rmv_ind init_mat = np.copy(mat) input_params["mat"] = init_mat init_material = np.copy(material_) init_case = np.copy(case) init_B = np.copy(B) input_params["Bmat"] = init_B final_match_rate = np.copy(match_rate) init_mr = np.copy(match_rate) all_stats = [i, j, \ spot1_hkl[iind], spot2_hkl[iind], \ tth_chi_spot1, tth_chi_spot2, \ dist[i,j], tab_distance_classhkl_data[i,j], np.round(max_pred[i]*100,3), \ np.round(max_pred[j]*100,3), len(rmv_ind), theospots,\ match_rate, 0.0, rot_mat123, init_mat, init_material, init_B, init_case] tried_spots.append(i) if (final_match_rate <= cap_matchrate123): ## Nothing found!! ## Either peaks are not well defined or not found within tolerance and prediction accuracy all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" objective_function.append([0, [], []]) else: objective_function.append([final_match_rate, final_rmv_ind, all_stats]) tried_spots.append(j) sort_ind = [] for ijk in objective_function: sort_ind.append(ijk[0]) sort_ind = np.array(sort_ind) sort_ind = np.argsort(sort_ind)[::-1] for gr_count123 in range(len(sort_ind)): max_mr = objective_function[sort_ind[gr_count123]][0] rmv_ind = objective_function[sort_ind[gr_count123]][1] all_stats = objective_function[sort_ind[gr_count123]][2] if len(rmv_ind) == 0 or max_mr==0: continue mat = all_stats[15] if mat == 1: if igrain==0 and material_phase_always_present ==2: mat = 0 case="None" if material0_count >= material0_limit: mat = 0 case="None" elif mat == 2: if igrain==0 and material_phase_always_present ==1: mat = 0 case="None" if material1_count >= material1_limit: mat = 0 case="None" if mat == 0: continue current_spots = [len(list(set(rmv_ind) & set(spots1_global[igr])))> coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): continue input_params["mat"] = all_stats[15] if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(all_stats[16]), input_params, dict_dp['detectorparameters'], dict_dp, spots, all_stats[17], strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(max_mr), np.min(max_mr), \ rmv_ind, str(all_stats[18]), all_stats[15], dev_strain, strain_sample, iR, fR, objective_function all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" return all_stats, max_mr, min_mr, spot_ind, case, mat, np.zeros((3,3)), np.zeros((3,3)), 0, 0, objective_function def get_orient_mat_graphv1(s_tth, s_chi, material0_, material1_, classhkl, class_predicted, predicted_hkl, input_params, hkl_all_class0, hkl_all_class1, max_pred, dict_dp, spots, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=0.85, mr_threshold=0.85, tab_distance_classhkl_data0=None, tab_distance_classhkl_data1=None, spots1_global=None, coeff_overlap = None, ind_mat=None, ind_mat1=None, strain_calculation=None, cap_matchrate123=None, material0_count=None, material1_count=None, material0_limit=None, material1_limit=None, igrain=None, material_phase_always_present=None, objective_function=None, crystal=None, crystal1=None, strain_free_parameters=None): if objective_function == None: call_global() init_mr = 0 init_mat = 0 init_material = "None" init_case = "None" init_B = None final_match_rate = 0 match_rate_mma = [] final_rmv_ind = [] if material0_ == material1_: list_of_sets = [] for ii in range(0, min(nb_spots_consider, len(dist))): if max_pred[ii] < softmax_threshold: continue a1 = np.round(dist[ii],3) for i in range(0, min(nb_spots_consider, len(dist))): if ii==i: continue if (ii,i) in list_of_sets or (i,ii) in list_of_sets: continue if max_pred[i] < softmax_threshold: continue hkl1 = hkl_all_class0[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class0[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric0 tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) np.putmask(tab_angulardist_temp, np.abs(tab_angulardist_temp) < 0.001, 400) list_ = np.where(np.abs(tab_angulardist_temp-a1[i]) < input_params["tolerance"]) if len(list_[0]) != 0: list_of_sets.append((ii,i)) else: list_of_sets = [] for ii in range(0, min(nb_spots_consider, len(dist))): if max_pred[ii] < softmax_threshold: continue a1 = np.round(dist[ii],3) for i in range(0, min(nb_spots_consider, len(dist))): if ii==i: continue if (ii,i) in list_of_sets or (i,ii) in list_of_sets: continue if max_pred[i] < softmax_threshold: continue if class_predicted[ii] < ind_mat and class_predicted[i] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 tolerance_new = input_params["tolerance"] hkl1 = hkl_all_class0[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class0[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric0 elif (ind_mat <= class_predicted[ii] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 tolerance_new = input_params["tolerance1"] hkl1 = hkl_all_class1[str(predicted_hkl[ii])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class1[str(predicted_hkl[i])] hkl2_list = np.array(hkl2) Gstar_metric = Gstar_metric1 else: continue tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) np.putmask(tab_angulardist_temp, np.abs(tab_angulardist_temp) < 0.001, 400) list_ = np.where(np.abs(tab_angulardist_temp-a1[i]) < tolerance_new) if len(list_[0]) != 0: list_of_sets.append((ii,i)) ## build a direct connection graph object graph_obj = nx.DiGraph(list_of_sets) connected_nodes_length = [] connected_nodes = [[] for i in range(len(graph_obj))] for i,line in enumerate(nx.generate_adjlist(graph_obj)): connected_nodes_length.append(len(line.split(" "))) connected_nodes[i].append([int(jj) for jj in line.split(" ")]) ## sort by maximum node occurance connected_nodes_length = np.array(connected_nodes_length) connected_nodes_length_sort_ind = np.argsort(connected_nodes_length)[::-1] mat = 0 case = "None" tried_spots = [] objective_function = [] for toplist in range(len(graph_obj)): # ## continue if less than 3 connections are found for a graph # if connected_nodes_length[connected_nodes_length_sort_ind[toplist]] < 2: # continue for j in connected_nodes[connected_nodes_length_sort_ind[toplist]][0]: init_mr = 0 final_match_rate = 0 final_rmv_ind = [] all_stats = [] for i in connected_nodes[connected_nodes_length_sort_ind[toplist]][0]: if j == i: continue if j in tried_spots and i in tried_spots: continue if material0_ == material1_: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B else: if class_predicted[i] < ind_mat and class_predicted[j] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B elif (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[j] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 hkl_all_class = hkl_all_class1 material_ = material1_ B = B1 Gstar_metric = Gstar_metric1 case = material_ mat = 2 input_params["mat"] = mat input_params["Bmat"] = B else: mat = 0 case = "None" input_params["mat"] = mat input_params["Bmat"] = None if mat == 0: continue tth_chi_spot1 = np.array([s_tth[i], s_chi[i]]) tth_chi_spot2 = np.array([s_tth[j], s_chi[j]]) hkl1 = hkl_all_class[str(predicted_hkl[i])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class[str(predicted_hkl[j])] hkl2_list = np.array(hkl2) actual_mat, flagAM, \ spot1_hkl, spot2_hkl = propose_UB_matrix(hkl1_list, hkl2_list, Gstar_metric, input_params, dist[i,j], tth_chi_spot1, tth_chi_spot2, B, method=0, crystal=crystal, crystal1=crystal1) if flagAM: continue for iind in range(len(actual_mat)): rot_mat123 = actual_mat[iind] rmv_ind, theospots = remove_spots(s_tth, s_chi, rot_mat123, material_, input_params, dict_dp['detectorparameters'], dict_dp) match_rate = np.round(100 * len(rmv_ind)/theospots, 3) match_rate_mma.append(match_rate) if match_rate > init_mr: final_rmv_ind = rmv_ind init_mat = np.copy(mat) input_params["mat"] = init_mat init_material = np.copy(material_) init_case = np.copy(case) init_B = np.copy(B) input_params["Bmat"] = init_B final_match_rate = np.copy(match_rate) init_mr = np.copy(match_rate) all_stats = [i, j, \ spot1_hkl[iind], spot2_hkl[iind], \ tth_chi_spot1, tth_chi_spot2, \ dist[i,j], tab_distance_classhkl_data[i,j], np.round(max_pred[i]*100,3), \ np.round(max_pred[j]*100,3), len(rmv_ind), theospots,\ match_rate, 0.0, rot_mat123, init_mat, init_material, init_B, init_case] tried_spots.append(i) if (final_match_rate <= cap_matchrate123): ## Nothing found!! ## Either peaks are not well defined or not found within tolerance and prediction accuracy all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" objective_function.append([0, [], []]) else: objective_function.append([final_match_rate, final_rmv_ind, all_stats]) tried_spots.append(j) sort_ind = [] for ijk in objective_function: sort_ind.append(ijk[0]) sort_ind = np.array(sort_ind) sort_ind = np.argsort(sort_ind)[::-1] for gr_count123 in range(len(sort_ind)): max_mr = objective_function[sort_ind[gr_count123]][0] rmv_ind = objective_function[sort_ind[gr_count123]][1] all_stats = objective_function[sort_ind[gr_count123]][2] if len(rmv_ind) == 0 or max_mr==0: continue mat = all_stats[15] if mat == 1: if igrain==0 and material_phase_always_present ==2: mat = 0 case="None" if material0_count >= material0_limit: mat = 0 case="None" elif mat == 2: if igrain==0 and material_phase_always_present ==1: mat = 0 case="None" if material1_count >= material1_limit: mat = 0 case="None" if mat == 0: continue current_spots = [len(list(set(rmv_ind) & set(spots1_global[igr])))> coeff_overlap*len(spots1_global[igr]) for igr in range(len(spots1_global))] if np.any(current_spots): continue input_params["mat"] = all_stats[15] if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(all_stats[16]), input_params, dict_dp['detectorparameters'], dict_dp, spots, all_stats[17], strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(max_mr), np.min(max_mr), \ rmv_ind, str(all_stats[18]), all_stats[15], dev_strain, strain_sample, iR, fR, objective_function all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" return all_stats, max_mr, min_mr, spot_ind, case, mat, np.zeros((3,3)), np.zeros((3,3)), 0, 0, objective_function def get_orient_mat_HM(s_tth, s_chi, material0_, material1_, classhkl, class_predicted, predicted_hkl, input_params, hkl_all_class0, hkl_all_class1, max_pred, dict_dp, spots, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=0.85, mr_threshold=0.85, tab_distance_classhkl_data0=None, tab_distance_classhkl_data1=None, spots1_global=None, coeff_overlap = None, ind_mat=None, ind_mat1=None, strain_calculation=None,cap_matchrate123=None, material0_count=None, material1_count=None, material0_limit=None, material1_limit=None, igrain=None, material_phase_always_present=None, strain_free_parameters=None): call_global() init_mr = 0 init_mat = 0 init_material = "None" init_case = "None" init_B = None final_match_rate = 0 match_rate_mma = [] final_rmv_ind = [] current_spots1 = [0 for igr in range(len(spots1_global))] mat = 0 case = "None" all_stats = [] #calculate the gnemonic projection space imageGNO, nbpeaks, halfdiagonal = computeGnomonicImage(s_tth, s_chi) hough, theta_h, d_h = hough_line(imageGNO) for i in range(0, min(nb_spots_consider, len(s_tth))): for j in range(i+1, min(nb_spots_consider, len(s_tth))): overlap = False ## condition to check if spots lie on the same line in_hough_line = False for _, anglehs, disths in zip(*hough_line_peaks(hough, theta_h, d_h)): y0 = (disths - 0 * np.cos(anglehs)) / np.sin(anglehs) y1 = (disths - imageGNO.shape[1] * np.cos(anglehs)) / np.sin(anglehs) p1 = np.array((0,y0)) p2 = np.array((imageGNO.shape[1], y1)) p3_0 = ComputeGnomon_singledata(s_tth[i], s_chi[i]) p3_1 = ComputeGnomon_singledata(s_tth[j], s_chi[j]) distance_0 = np.abs(np.cross(p2-p1, p3_0-p1)) / np.linalg.norm(p2-p1) distance_1 = np.abs(np.cross(p2-p1, p3_1-p1)) / np.linalg.norm(p2-p1) if distance_0 < dist_threshold and distance_1 < dist_threshold: # print(distance_0, distance_1) in_hough_line = True if in_hough_line: break if not in_hough_line: continue if (max_pred[j] < softmax_threshold) or (j in spots) or \ (max_pred[i] < softmax_threshold) or (i in spots): continue if material0_ == material1_: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B else: if class_predicted[i] < ind_mat and class_predicted[j] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 if igrain==0 and material_phase_always_present == 2: mat = 0 case="None" if material0_count >= material0_limit: mat = 0 case="None" input_params["mat"] = mat input_params["Bmat"] = B elif (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[j] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 hkl_all_class = hkl_all_class1 material_ = material1_ B = B1 Gstar_metric = Gstar_metric1 case = material_ mat = 2 if igrain==0 and material_phase_always_present == 1: mat = 0 case="None" if material1_count >= material1_limit: mat = 0 case="None" input_params["mat"] = mat input_params["Bmat"] = B else: mat = 0 case = "None" input_params["mat"] = mat input_params["Bmat"] = None if mat == 0: continue tth_chi_spot1 = np.array([s_tth[i], s_chi[i]]) tth_chi_spot2 = np.array([s_tth[j], s_chi[j]]) hkl1 = hkl_all_class[str(predicted_hkl[i])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class[str(predicted_hkl[j])] hkl2_list = np.array(hkl2) actual_mat, flagAM, \ spot1_hkl, spot2_hkl = propose_UB_matrix(hkl1_list, hkl2_list, Gstar_metric, input_params, dist[i,j], tth_chi_spot1, tth_chi_spot2, B, method=0) if flagAM: continue for iind in range(len(actual_mat)): rot_mat123 = actual_mat[iind] rmv_ind, theospots = remove_spots(s_tth, s_chi, rot_mat123, material_, input_params, dict_dp['detectorparameters'], dict_dp) overlap = False current_spots = [len(list(set(rmv_ind) & set(spots1_global[igr]))) for igr in range(len(spots1_global))] for igr in range(len(spots1_global)): if current_spots[igr] > coeff_overlap*len(spots1_global[igr]): overlap = True break if overlap: continue match_rate = np.round(100 * len(rmv_ind)/theospots,3) match_rate_mma.append(match_rate) if match_rate > init_mr: current_spots1 = current_spots init_mat = np.copy(mat) input_params["mat"] = init_mat init_material = np.copy(material_) init_case = np.copy(case) init_B = np.copy(B) input_params["Bmat"] = init_B final_rmv_ind = rmv_ind final_match_rate = np.copy(match_rate) init_mr = np.copy(match_rate) all_stats = [i, j, \ spot1_hkl[iind], spot2_hkl[iind], \ tth_chi_spot1, tth_chi_spot2, \ dist[i,j], tab_distance_classhkl_data[i,j], np.round(max_pred[i]*100,3), \ np.round(max_pred[j]*100,3), len(rmv_ind), theospots,\ match_rate, 0.0, rot_mat123] if (final_match_rate >= mr_threshold*100.) and not overlap: if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(init_material), input_params, dict_dp['detectorparameters'], dict_dp, spots, init_B, strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(match_rate_mma), np.min(match_rate_mma), \ final_rmv_ind, str(init_case), init_mat, dev_strain, strain_sample, iR, fR overlap = False for igr in range(len(spots1_global)): if current_spots1[igr] > coeff_overlap*len(spots1_global[igr]): overlap = True if (final_match_rate <= cap_matchrate123) or overlap: ## Nothing found!! ## Either peaks are not well defined or not found within tolerance and prediction accuracy all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" return all_stats, max_mr, min_mr, spot_ind, case, mat, np.zeros((3,3)), np.zeros((3,3)), 0, 0 input_params["mat"] = init_mat if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(init_material), input_params, dict_dp['detectorparameters'], dict_dp, spots, init_B, strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(match_rate_mma), np.min(match_rate_mma), \ final_rmv_ind, str(init_case), init_mat, dev_strain, strain_sample, iR, fR def get_orient_mat(s_tth, s_chi, material0_, material1_, classhkl, class_predicted, predicted_hkl, input_params, hkl_all_class0, hkl_all_class1, max_pred, dict_dp, spots, dist, Gstar_metric0, Gstar_metric1, B0, B1, softmax_threshold=0.85, mr_threshold=0.85, tab_distance_classhkl_data0=None, tab_distance_classhkl_data1=None, spots1_global=None, coeff_overlap = None, ind_mat=None, ind_mat1=None, strain_calculation=None,cap_matchrate123=None, material0_count=None, material1_count=None, material0_limit=None, material1_limit=None, igrain=None, material_phase_always_present=None, strain_free_parameters=None): call_global() init_mr = 0 init_mat = 0 init_material = "None" init_case = "None" init_B = None final_match_rate = 0 match_rate_mma = [] final_rmv_ind = [] current_spots1 = [0 for igr in range(len(spots1_global))] mat = 0 case = "None" all_stats = [] for i in range(0, min(nb_spots_consider, len(s_tth))): for j in range(i+1, min(nb_spots_consider, len(s_tth))): overlap = False if (max_pred[j] < softmax_threshold) or (j in spots) or \ (max_pred[i] < softmax_threshold) or (i in spots): continue if material0_ == material1_: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 input_params["mat"] = mat input_params["Bmat"] = B else: if class_predicted[i] < ind_mat and class_predicted[j] < ind_mat: tab_distance_classhkl_data = tab_distance_classhkl_data0 hkl_all_class = hkl_all_class0 material_ = material0_ B = B0 Gstar_metric = Gstar_metric0 case = material_ mat = 1 if igrain==0 and material_phase_always_present == 2: mat = 0 case="None" if material0_count >= material0_limit: mat = 0 case="None" input_params["mat"] = mat input_params["Bmat"] = B elif (ind_mat <= class_predicted[i] < (ind_mat+ind_mat1)) and \ (ind_mat <= class_predicted[j] < (ind_mat+ind_mat1)): tab_distance_classhkl_data = tab_distance_classhkl_data1 hkl_all_class = hkl_all_class1 material_ = material1_ B = B1 Gstar_metric = Gstar_metric1 case = material_ mat = 2 if igrain==0 and material_phase_always_present == 1: mat = 0 case="None" if material1_count >= material1_limit: mat = 0 case="None" input_params["mat"] = mat input_params["Bmat"] = B else: mat = 0 case = "None" input_params["mat"] = mat input_params["Bmat"] = None if mat == 0: continue tth_chi_spot1 = np.array([s_tth[i], s_chi[i]]) tth_chi_spot2 = np.array([s_tth[j], s_chi[j]]) hkl1 = hkl_all_class[str(predicted_hkl[i])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class[str(predicted_hkl[j])] hkl2_list = np.array(hkl2) actual_mat, flagAM, \ spot1_hkl, spot2_hkl = propose_UB_matrix(hkl1_list, hkl2_list, Gstar_metric, input_params, dist[i,j], tth_chi_spot1, tth_chi_spot2, B, method=0) if flagAM: continue for iind in range(len(actual_mat)): rot_mat123 = actual_mat[iind] rmv_ind, theospots = remove_spots(s_tth, s_chi, rot_mat123, material_, input_params, dict_dp['detectorparameters'], dict_dp) overlap = False current_spots = [len(list(set(rmv_ind) & set(spots1_global[igr]))) for igr in range(len(spots1_global))] for igr in range(len(spots1_global)): if current_spots[igr] > coeff_overlap*len(spots1_global[igr]): overlap = True break if overlap: continue match_rate = np.round(100 * len(rmv_ind)/theospots,3) match_rate_mma.append(match_rate) if match_rate > init_mr: current_spots1 = current_spots init_mat = np.copy(mat) input_params["mat"] = init_mat init_material = np.copy(material_) init_case = np.copy(case) init_B = np.copy(B) input_params["Bmat"] = init_B final_rmv_ind = rmv_ind final_match_rate = np.copy(match_rate) init_mr = np.copy(match_rate) all_stats = [i, j, \ spot1_hkl[iind], spot2_hkl[iind], \ tth_chi_spot1, tth_chi_spot2, \ dist[i,j], tab_distance_classhkl_data[i,j], np.round(max_pred[i]*100,3), \ np.round(max_pred[j]*100,3), len(rmv_ind), theospots,\ match_rate, 0.0, rot_mat123] if (final_match_rate >= mr_threshold*100.) and not overlap: if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(init_material), input_params, dict_dp['detectorparameters'], dict_dp, spots, init_B, strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(match_rate_mma), np.min(match_rate_mma), \ final_rmv_ind, str(init_case), init_mat, dev_strain, strain_sample, iR, fR overlap = False for igr in range(len(spots1_global)): if current_spots1[igr] > coeff_overlap*len(spots1_global[igr]): overlap = True if (final_match_rate <= cap_matchrate123) or overlap: ## Nothing found!! ## Either peaks are not well defined or not found within tolerance and prediction accuracy all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" return all_stats, max_mr, min_mr, spot_ind, case, mat, np.zeros((3,3)), np.zeros((3,3)), 0, 0 input_params["mat"] = init_mat if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUB(s_tth, s_chi, all_stats[14], str(init_material), input_params, dict_dp['detectorparameters'], dict_dp, spots, init_B, strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(match_rate_mma), np.min(match_rate_mma), \ final_rmv_ind, str(init_case), init_mat, dev_strain, strain_sample, iR, fR def propose_UB_matrix(hkl1_list, hkl2_list, Gstar_metric, input_params, dist123, tth_chi_spot1, tth_chi_spot2, B, method=0, crystal=None, crystal1=None): if method == 0: tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) if input_params["mat"] == 1: list_ = np.where(np.abs(tab_angulardist_temp-dist123) < input_params["tolerance"]) final_crystal=crystal elif input_params["mat"] == 2: list_ = np.where(np.abs(tab_angulardist_temp-dist123) < input_params["tolerance1"]) final_crystal=crystal1 if final_crystal != None: symm_operator = final_crystal._hklsym else: symm_operator = np.eye(3) if len(list_[0]) == 0: return None, True, 0, 0 rot_mat_abs = [] actual_mat = [] spot1_hkl = [] spot2_hkl = [] triedspots = [] for ii, jj in zip(list_[0], list_[1]): if ii in triedspots and jj in triedspots: continue conti_ = False try: rot_mat1 = FindO.OrientMatrix_from_2hkl(hkl1_list[ii], tth_chi_spot1, \ hkl2_list[jj], tth_chi_spot2, B) # rot_mat1 = find_uniq_u(rot_mat1, symm_operator) except: continue copy_rm = np.copy(rot_mat1) copy_rm = np.round(np.abs(copy_rm),5) copy_rm.sort(axis=1) for iji in rot_mat_abs: iji.sort(axis=1) if np.all(iji==copy_rm): conti_ = True break if conti_: continue rot_mat_abs.append(np.round(np.abs(rot_mat1),5)) actual_mat.append(rot_mat1) spot1_hkl.append(hkl1_list[ii]) spot2_hkl.append(hkl2_list[jj]) triedspots.append(ii) triedspots.append(jj) else: # method 2 hkl_all = np.vstack((hkl1_list, hkl2_list)) LUT = FindO.GenerateLookUpTable(hkl_all, Gstar_metric) if input_params["mat"] == 1: hkls = FindO.PlanePairs_2(dist123, input_params["tolerance"], LUT, onlyclosest=1) elif input_params["mat"] == 2: hkls = FindO.PlanePairs_2(dist123, input_params["tolerance1"], LUT, onlyclosest=1) if np.all(hkls == None): return None, True, 0, 0 rot_mat_abs = [] actual_mat = [] spot1_hkl = [] spot2_hkl = [] for ii in range(len(hkls)): if np.all(hkls[ii][0] == hkls[ii][1]): continue conti_ = False try: rot_mat1 = FindO.OrientMatrix_from_2hkl(hkls[ii][0], tth_chi_spot1, \ hkls[ii][1], tth_chi_spot2, B) # rot_mat1 = find_uniq_u(rot_mat1, symm_operator) except: continue copy_rm = np.copy(rot_mat1) copy_rm = np.round(np.abs(copy_rm),5) copy_rm.sort(axis=1) for iji in rot_mat_abs: iji.sort(axis=1) if np.all(iji==copy_rm): conti_ = True break if conti_: continue rot_mat_abs.append(np.round(np.abs(rot_mat1),5)) actual_mat.append(rot_mat1) spot1_hkl.append(hkls[ii][0]) spot2_hkl.append(hkls[ii][1]) #TODO ## just fixing a* to x seems ok; if not think of aligning b* to xy plane sum_sign = [] for nkl in range(len(actual_mat)): temp_mat = np.dot(actual_mat[nkl], B) ## fix could be to choose a matrix that aligns best the b* vector to Y axis or a* to X axis # if np.argmax(np.abs(temp_mat[:2,0])) == 0 and \ # np.argmax(np.abs(temp_mat[:2,1])) == 1: ##a* along x, b*along y if np.argmax(np.abs(temp_mat[:2,0])) == 0: ##a* along x sum_sign.append(2) elif np.argmax(np.abs(temp_mat[:2,0])) == np.argmax(np.abs(temp_mat[:2,1])): sum_sign.append(0) else: sum_sign.append(1) ind_sort = np.argsort(sum_sign)[::-1] ## re-arrange actual_mat1 = [] spot1_hkl1, spot2_hkl1 = [], [] for inin in ind_sort: actual_mat1.append(actual_mat[inin]) spot1_hkl1.append(spot1_hkl[inin]) spot2_hkl1.append(spot2_hkl[inin]) actual_mat, spot1_hkl, spot2_hkl = actual_mat1, spot1_hkl1, spot2_hkl1 return actual_mat, False, spot1_hkl, spot2_hkl def find_uniq_u(u, syms): """ Unique representation of rotation matrix: apply this function before strain as distorted unit cell may produce undesireable matrix """ uniq = u tmax = np.trace(uniq) for sym in syms: cand = np.dot(sym, uniq) t = np.trace(cand) if np.trace(cand) > tmax: uniq = cand tmax = t return np.array(uniq) def remove_spots(s_tth, s_chi, first_match123, material_, input_params, detectorparameters, dict_dp): try: grain = CP.Prepare_Grain(material_, first_match123, dictmaterials=dictLT.dict_Materials) ### initialize global variables to be used later call_global() except: return [], 100 #### Perhaps better than SimulateResult function kf_direction = dict_dp["kf_direction"] detectordistance = dict_dp["detectorparameters"][0] detectordiameter = dict_dp["detectordiameter"] pixelsize = dict_dp["pixelsize"] dim = dict_dp["dim"] spots2pi = LT.getLaueSpots(CST_ENERGYKEV / input_params["emax"], CST_ENERGYKEV / input_params["emin"], [grain], fastcompute=1, verbose=0, kf_direction=kf_direction, ResolutionAngstrom=False, dictmaterials=dictLT.dict_Materials) TwicethetaChi = LT.filterLaueSpots_full_np(spots2pi[0][0], None, onlyXYZ=False, HarmonicsRemoval=0, fastcompute=1, kf_direction=kf_direction, detectordistance=detectordistance, detectordiameter=detectordiameter, pixelsize=pixelsize, dim=dim) ## get proximity for exp and theo spots if input_params["mat"] == 1: angtol = input_params["tolerance"] elif input_params["mat"] == 2: angtol = input_params["tolerance1"] else: return [], 100 if option_global =="v1": # print("entering v1") List_Exp_spot_close, residues_link, _ = getProximityv1(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) elif option_global =="v2": List_Exp_spot_close, residues_link, _ = getProximityv1_ambigious(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) else: List_Exp_spot_close, residues_link, _ = getProximityv1_ambigious(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) List_Exp_spot_close, ind_uniq = np.unique(List_Exp_spot_close, return_index=True) residues_link = np.take(residues_link, ind_uniq) if np.average(residues_link) > residues_threshold: return [], 100 if len(np.unique(List_Exp_spot_close)) < nb_spots_global_threshold: return [], 100 return List_Exp_spot_close, len(TwicethetaChi[0]) def simulate_spots(rot_mat, material_, emax, emin, detectorparameters, dict_dp, angtol, s_tth, s_chi): try: grain = CP.Prepare_Grain(material_, rot_mat, dictmaterials=dictLT.dict_Materials) ### initialize global variables to be used later call_global() except: return [], [], [], [], [] #### Perhaps better than SimulateResult function kf_direction = dict_dp["kf_direction"] detectordistance = dict_dp["detectorparameters"][0] detectordiameter = dict_dp["detectordiameter"] pixelsize = dict_dp["pixelsize"] dim = dict_dp["dim"] spots2pi = LT.getLaueSpots(CST_ENERGYKEV / emax, CST_ENERGYKEV / emin, [grain], fastcompute=0, verbose=0, kf_direction=kf_direction, ResolutionAngstrom=False, dictmaterials=dictLT.dict_Materials) TwicethetaChi = LT.filterLaueSpots_full_np(spots2pi[0][0], spots2pi[1][0], onlyXYZ=False, HarmonicsRemoval=0, fastcompute=0, kf_direction=kf_direction, detectordistance=detectordistance, detectordiameter=detectordiameter, pixelsize=pixelsize, dim=dim) if option_global =="v1": List_Exp_spot_close, residues_link, theo_index = getProximityv1(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) elif option_global =="v2": List_Exp_spot_close, residues_link, theo_index = getProximityv1_ambigious(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) else: List_Exp_spot_close, residues_link, theo_index = getProximityv1_ambigious(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) List_Exp_spot_close, ind_uniq = np.unique(List_Exp_spot_close, return_index=True) residues_link = np.take(residues_link, ind_uniq) theo_index = np.take(theo_index, ind_uniq) return TwicethetaChi[0], TwicethetaChi[1], TwicethetaChi[2], TwicethetaChi[3], List_Exp_spot_close, residues_link, theo_index def getProximityv1_ambigious(TwicethetaChi, data_theta, data_chi, angtol=0.5): # theo simul data theodata = np.array([TwicethetaChi[0] / 2.0, TwicethetaChi[1]]).T # exp data sorted_data = np.array([data_theta, data_chi]).T table_dist = GT.calculdist_from_thetachi(sorted_data, theodata) prox_table = np.argmin(table_dist, axis=1) allresidues = np.amin(table_dist, axis=1) very_close_ind = np.where(allresidues < angtol)[0] List_Exp_spot_close = [] theo_index = [] if len(very_close_ind) > 0: for theospot_ind in very_close_ind: # loop over theo spots index List_Exp_spot_close.append(prox_table[theospot_ind]) theo_index.append(theospot_ind) return List_Exp_spot_close, allresidues[very_close_ind], theo_index def getProximityv1( TwicethetaChi, data_theta, data_chi, angtol=0.5): theodata = np.array([TwicethetaChi[0] / 2.0, TwicethetaChi[1]]).T # exp data sorted_data = np.array([data_theta, data_chi]).T table_dist = GT.calculdist_from_thetachi(sorted_data, theodata) prox_table = np.argmin(table_dist, axis=1) allresidues = np.amin(table_dist, axis=1) very_close_ind = np.where(allresidues < angtol)[0] List_Exp_spot_close = [] Miller_Exp_spot = [] if len(very_close_ind) > 0: for theospot_ind in very_close_ind: # loop over theo spots index List_Exp_spot_close.append(prox_table[theospot_ind]) Miller_Exp_spot.append(1) else: return [], [], [] # removing exp spot which appears many times(close to several simulated spots of one grain)-------------- arrayLESC = np.array(List_Exp_spot_close, dtype=float) sorted_LESC = np.sort(arrayLESC) diff_index = sorted_LESC - np.array(list(sorted_LESC[1:]) + [sorted_LESC[0]]) toremoveindex = np.where(diff_index == 0)[0] if len(toremoveindex) > 0: # index of exp spot in arrayLESC that are duplicated ambiguous_exp_ind = GT.find_closest(np.array(sorted_LESC[toremoveindex], dtype=float), arrayLESC, 0.1)[1] for ind in ambiguous_exp_ind: Miller_Exp_spot[ind] = None ProxTablecopy = np.copy(prox_table) for theo_ind, exp_ind in enumerate(prox_table): where_th_ind = np.where(ProxTablecopy == exp_ind)[0] if len(where_th_ind) > 1: for indy in where_th_ind: ProxTablecopy[indy] = -prox_table[indy] closest = np.argmin(allresidues[where_th_ind]) ProxTablecopy[where_th_ind[closest]] = -ProxTablecopy[where_th_ind[closest]] singleindices = [] refine_indexed_spots = {} # loop over close exp. spots for k in range(len(List_Exp_spot_close)): exp_index = List_Exp_spot_close[k] if not singleindices.count(exp_index): singleindices.append(exp_index) theo_index = np.where(ProxTablecopy == exp_index)[0] if (len(theo_index) == 1): # only one theo spot close to the current exp. spot refine_indexed_spots[exp_index] = [exp_index, theo_index, Miller_Exp_spot[k]] else: # recent PATCH: closest_theo_ind = np.argmin(allresidues[theo_index]) if allresidues[theo_index][closest_theo_ind] < angtol: refine_indexed_spots[exp_index] = [exp_index, theo_index[closest_theo_ind], Miller_Exp_spot[k]] listofpairs = [] theo_index = [] linkResidues = [] selectedAbsoluteSpotIndices = np.arange(len(data_theta)) for val in list(refine_indexed_spots.values()): if val[2] is not None: localspotindex = val[0] if not isinstance(val[1], (list, np.ndarray)): closetheoindex = val[1] else: closetheoindex = val[1][0] absolute_spot_index = selectedAbsoluteSpotIndices[localspotindex] listofpairs.append(absolute_spot_index) # Exp, Theo, where -1 for specifying that it came from automatic linking theo_index.append(closetheoindex) linkResidues.append(allresidues[closetheoindex]) return listofpairs, linkResidues, theo_index def refineonce_fromUB(s_tth, s_chi, UBmat, grain, input_params, detectorparameters, dict_dp, B_matrix): # starting B0matrix corresponding to the unit cell ----- B0matrix = np.copy(B_matrix) if input_params["mat"] == 1: AngTol = input_params["tolerance"] elif input_params["mat"] == 2: AngTol = input_params["tolerance1"] #### Spots in first match (no refining, just simple auto links to filter spots) Twicetheta, Chi, Miller_ind, posx, posy, _ = LT.SimulateLaue(grain, input_params["emin"], input_params["emax"], detectorparameters, kf_direction=dict_dp['kf_direction'], removeharmonics=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], ResolutionAngstrom=False, detectordiameter=dict_dp['detectordiameter'], dictmaterials=dictLT.dict_Materials) ## get proximity for exp and theo spots linkedspots_link, linkExpMiller_link, \ linkResidues_link = getProximityv0(np.array([Twicetheta, Chi]), # warning array(2theta, chi) s_tth/2.0, s_chi, Miller_ind, # warning theta, chi for exp angtol=float(AngTol)) if len(linkedspots_link) < 8: return UBmat linkedspots_fit = linkedspots_link linkExpMiller_fit = linkExpMiller_link arraycouples = np.array(linkedspots_fit) exp_indices = np.array(arraycouples[:, 0], dtype=np.int) sim_indices = np.array(arraycouples[:, 1], dtype=np.int) nb_pairs = len(exp_indices) Data_Q = np.array(linkExpMiller_fit)[:, 1:] sim_indices = np.arange(nb_pairs) # for fitting function this must be an arange... pixX = np.take(dict_dp['peakX'], exp_indices) pixY = np.take(dict_dp['peakY'], exp_indices) weights = None #np.take(dict_dp['intensity'], exp_indices) starting_orientmatrix = np.copy(UBmat) results = None # ---------------------------------- # refinement model # ---------------------------------- # ------------------------------------------------------- allparameters = np.array(detectorparameters + [1, 1, 0, 0, 0] + [0, 0, 0]) # strain & orient initial_values = np.array([1.0, 1.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0]) arr_indexvaryingparameters = np.arange(5, 13) results = FitO.fit_on_demand_strain(initial_values, Data_Q, allparameters, FitO.error_function_on_demand_strain, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], verbose=0, weights=weights, kf_direction=dict_dp['kf_direction']) if results is None: return UBmat residues, deltamat, newmatrix = FitO.error_function_on_demand_strain( results, Data_Q, allparameters, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pureRotation=0, verbose=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction']) UBmat = np.copy(newmatrix) return UBmat def calculate_strains_fromUB(s_tth, s_chi, UBmat, material_, input_params, detectorparameters, dict_dp, spots, B_matrix, strain_free_parameters): ## for the moment strain_free_parameters is a trial implementation #TODO to be verified if ("a" not in strain_free_parameters) and len(strain_free_parameters)>=5: if additional_expression[0] != "none": print("Note: additional_expression is not applied for the current set of strain free parameters") # starting B0matrix corresponding to the unit cell ----- B0matrix = np.copy(B_matrix) latticeparams = dictLT.dict_Materials[material_][1] ## Included simple multi level refinement of strains init_residues = -0.1 final_residues = -0.1 if input_params["mat"] == 1: straintolerance = input_params["tolerancestrain"] elif input_params["mat"] == 2: straintolerance = input_params["tolerancestrain1"] devstrain, deviatoricstrain_sampleframe = np.zeros((3,3)), np.zeros((3,3)) for ijk, AngTol in enumerate(straintolerance): #### Spots in first match (no refining, just simple auto links to filter spots) grain = CP.Prepare_Grain(material_, UBmat, dictmaterials=dictLT.dict_Materials) Twicetheta, Chi, Miller_ind, posx, posy, _ = LT.SimulateLaue(grain, input_params["emin"], input_params["emax"], detectorparameters, kf_direction=dict_dp['kf_direction'], removeharmonics=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], ResolutionAngstrom=False, detectordiameter=dict_dp['detectordiameter'], dictmaterials=dictLT.dict_Materials) ## get proximity for exp and theo spots linkedspots_link, linkExpMiller_link, \ linkResidues_link = getProximityv0(np.array([Twicetheta, Chi]), # warning array(2theta, chi) s_tth/2.0, s_chi, Miller_ind, # warning theta, chi for exp angtol=float(AngTol)) if len(linkedspots_link) < 8: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat linkedspots_fit = linkedspots_link linkExpMiller_fit = linkExpMiller_link arraycouples = np.array(linkedspots_fit) exp_indices = np.array(arraycouples[:, 0], dtype=np.int) sim_indices = np.array(arraycouples[:, 1], dtype=np.int) nb_pairs = len(exp_indices) Data_Q = np.array(linkExpMiller_fit)[:, 1:] sim_indices = np.arange(nb_pairs) # for fitting function this must be an arange... pixX = np.take(dict_dp['peakX'], exp_indices) pixY = np.take(dict_dp['peakY'], exp_indices) weights = None #np.take(dict_dp['intensity'], exp_indices) starting_orientmatrix = np.copy(UBmat) results = None # ---------------------------------- # refinement model # ---------------------------------- # ------------------------------------------------------- allparameters = np.array(detectorparameters + [1, 1, 0, 0, 0] + [0, 0, 0]) # strain & orient initial_values = np.array([1.0, 1.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0]) arr_indexvaryingparameters = np.arange(5, 13) residues, deltamat, newmatrix = FitO.error_function_on_demand_strain( initial_values, Data_Q, allparameters, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pureRotation=0, verbose=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction']) init_mean_residues = np.copy(np.mean(residues)) if ijk == 0: init_residues = np.copy(init_mean_residues) results = FitO.fit_on_demand_strain(initial_values, Data_Q, allparameters, FitO.error_function_on_demand_strain, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], verbose=0, weights=weights, kf_direction=dict_dp['kf_direction']) if results is None: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat residues, deltamat, newmatrix = FitO.error_function_on_demand_strain( results, Data_Q, allparameters, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pureRotation=0, verbose=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction']) # if np.mean(residues) > final_residues: # return devstrain, deviatoricstrain_sampleframe, init_residues, final_residues, UBmat final_mean_residues = np.copy(np.mean(residues)) final_residues = np.copy(final_mean_residues) # building B mat # param_strain_sol = results # varyingstrain = np.array([[1.0, param_strain_sol[2], param_strain_sol[3]], # [0, param_strain_sol[0], param_strain_sol[4]], # [0, 0, param_strain_sol[1]]]) # newUmat = np.dot(deltamat, starting_orientmatrix) # newUBmat = np.dot(newUmat, varyingstrain) newUBmat = np.copy(newmatrix) # Bstar_s = np.dot(newUBmat, B0matrix) # --------------------------------------------------------------- # postprocessing of unit cell orientation and strain refinement # --------------------------------------------------------------- UBmat = np.copy(newmatrix) (devstrain, lattice_parameter_direct_strain) = CP.compute_deviatoricstrain(newUBmat, B0matrix, latticeparams) # overwrite and rescale possibly lattice lengthes # constantlength = "a" # lattice_parameter_direct_strain = CP.computeLatticeParameters_from_UB(newUBmat, material_, constantlength, dictmaterials=dictLT.dict_Materials) # print(lattice_parameter_direct_strain) deviatoricstrain_sampleframe = CP.strain_from_crystal_to_sample_frame2(devstrain, newUBmat) # in % already devstrain = np.round(devstrain * 100, decimals=3) deviatoricstrain_sampleframe = np.round(deviatoricstrain_sampleframe * 100, decimals=3) else: # starting B0matrix corresponding to the unit cell ----- B0matrix = np.copy(B_matrix) latticeparams = dictLT.dict_Materials[material_][1] ## Included simple multi level refinement of strains init_residues = -0.1 final_residues = -0.1 if input_params["mat"] == 1: straintolerance = input_params["tolerancestrain"] elif input_params["mat"] == 2: straintolerance = input_params["tolerancestrain1"] devstrain, deviatoricstrain_sampleframe = np.zeros((3,3)), np.zeros((3,3)) for ijk, AngTol in enumerate(straintolerance): #### Spots in first match (no refining, just simple auto links to filter spots) grain = CP.Prepare_Grain(material_, UBmat, dictmaterials=dictLT.dict_Materials) Twicetheta, Chi, Miller_ind, posx, posy, _ = LT.SimulateLaue(grain, input_params["emin"], input_params["emax"], detectorparameters, kf_direction=dict_dp['kf_direction'], removeharmonics=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], ResolutionAngstrom=False, detectordiameter=dict_dp['detectordiameter'], dictmaterials=dictLT.dict_Materials) ## get proximity for exp and theo spots linkedspots_link, linkExpMiller_link, \ linkResidues_link = getProximityv0(np.array([Twicetheta, Chi]), # warning array(2theta, chi) s_tth/2.0, s_chi, Miller_ind, # warning theta, chi for exp angtol=float(AngTol)) if len(linkedspots_link) < 8: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat linkedspots_fit = linkedspots_link linkExpMiller_fit = linkExpMiller_link arraycouples = np.array(linkedspots_fit) exp_indices = np.array(arraycouples[:, 0], dtype=np.int) sim_indices = np.array(arraycouples[:, 1], dtype=np.int) nb_pairs = len(exp_indices) Data_Q = np.array(linkExpMiller_fit)[:, 1:] sim_indices = np.arange(nb_pairs) # for fitting function this must be an arange... pixX = np.take(dict_dp['peakX'], exp_indices) pixY = np.take(dict_dp['peakY'], exp_indices) weights = None #np.take(dict_dp['intensity'], exp_indices) starting_orientmatrix = np.copy(UBmat) results = None # ---------------------------------- # refinement model # ---------------------------------- # ------------------------------------------------------- allparameters = np.array(detectorparameters + [0, 0, 0] + latticeparams) fitting_parameters_keys = ["anglex", "angley", "anglez"] fitting_parameters_values = [0, 0, 0] constantlength = "a" if ("a" in strain_free_parameters) and ("b" in strain_free_parameters) and ("c" in strain_free_parameters): constantlength = "a" elif ("b" not in strain_free_parameters) and additional_expression[0]=="none" and\ "b" not in additional_expression[0]: constantlength = "b" elif ("c" not in strain_free_parameters): constantlength = "c" for jjkk in strain_free_parameters: if jjkk == "a" and constantlength != "a": fitting_parameters_keys.append("a") fitting_parameters_values.append(latticeparams[0]) if jjkk == "b" and constantlength != "b": fitting_parameters_keys.append("b") fitting_parameters_values.append(latticeparams[1]) if jjkk == "c" and constantlength != "c": fitting_parameters_keys.append("c") fitting_parameters_values.append(latticeparams[2]) if jjkk == "alpha": fitting_parameters_keys.append("alpha") fitting_parameters_values.append(latticeparams[3]) if jjkk == "beta": fitting_parameters_keys.append("beta") fitting_parameters_values.append(latticeparams[4]) if jjkk == "gamma": fitting_parameters_keys.append("gamma") fitting_parameters_values.append(latticeparams[5]) pureUmatrix, _ = GT.UBdecomposition_RRPP(starting_orientmatrix) absolutespotsindices = np.arange(len(pixX)) (residues, _, _, _, _, ) = FitO.error_function_latticeparameters(fitting_parameters_values, fitting_parameters_keys, Data_Q, allparameters, absolutespotsindices, pixX, pixY, initrot=pureUmatrix, pureRotation=0, verbose=0, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction'], returnalldata=True, additional_expression = additional_expression[0]) init_mean_residues = np.copy(np.mean(residues)) if ijk == 0: init_residues = np.copy(init_mean_residues) results = FitO.fit_function_latticeparameters(fitting_parameters_values, fitting_parameters_keys, Data_Q, allparameters, absolutespotsindices, pixX, pixY, UBmatrix_start=pureUmatrix, nb_grains=1, pureRotation=0, verbose=0, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction'], additional_expression = additional_expression[0]) if results is None: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat (residues, Uxyz, newUmat, newB0matrix, _, ) = FitO.error_function_latticeparameters(results, fitting_parameters_keys, Data_Q, allparameters, absolutespotsindices, pixX, pixY, initrot=pureUmatrix, pureRotation=0, verbose=0, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction'], returnalldata=True, additional_expression = additional_expression[0]) final_mean_residues = np.copy(np.mean(residues)) final_residues = np.copy(final_mean_residues) newUBmat = np.dot(np.dot(newUmat, newB0matrix), np.linalg.inv(B0matrix)) UBmat = np.copy(newUBmat) # --------------------------------------------------------------- # postprocessing of unit cell orientation and strain refinement # --------------------------------------------------------------- (devstrain, lattice_parameter_direct_strain) = CP.compute_deviatoricstrain(newUBmat, B0matrix, latticeparams) deviatoricstrain_sampleframe = CP.strain_from_crystal_to_sample_frame2(devstrain, newUBmat) # in % already devstrain = np.round(devstrain * 100, decimals=3) deviatoricstrain_sampleframe = np.round(deviatoricstrain_sampleframe * 100, decimals=3) return devstrain, deviatoricstrain_sampleframe, init_residues, final_residues, UBmat def getProximityv0(TwicethetaChi, data_theta, data_chi, data_hkl, angtol=0.5): # theo simul data theodata = np.array([TwicethetaChi[0] / 2.0, TwicethetaChi[1]]).T # exp data sorted_data = np.array([data_theta, data_chi]).T table_dist = GT.calculdist_from_thetachi(sorted_data, theodata) prox_table = np.argmin(table_dist, axis=1) allresidues = np.amin(table_dist, axis=1) very_close_ind = np.where(allresidues < angtol)[0] List_Exp_spot_close = [] Miller_Exp_spot = [] if len(very_close_ind) > 0: for theospot_ind in very_close_ind: # loop over theo spots index List_Exp_spot_close.append(prox_table[theospot_ind]) Miller_Exp_spot.append(data_hkl[theospot_ind]) else: return [],[],[] # removing exp spot which appears many times(close to several simulated spots of one grain)-------------- arrayLESC = np.array(List_Exp_spot_close, dtype=float) sorted_LESC = np.sort(arrayLESC) diff_index = sorted_LESC - np.array(list(sorted_LESC[1:]) + [sorted_LESC[0]]) toremoveindex = np.where(diff_index == 0)[0] if len(toremoveindex) > 0: # index of exp spot in arrayLESC that are duplicated ambiguous_exp_ind = GT.find_closest(np.array(sorted_LESC[toremoveindex], dtype=float), arrayLESC, 0.1)[1] for ind in ambiguous_exp_ind: Miller_Exp_spot[ind] = None ProxTablecopy = np.copy(prox_table) for theo_ind, exp_ind in enumerate(prox_table): where_th_ind = np.where(ProxTablecopy == exp_ind)[0] if len(where_th_ind) > 1: for indy in where_th_ind: ProxTablecopy[indy] = -prox_table[indy] closest = np.argmin(allresidues[where_th_ind]) ProxTablecopy[where_th_ind[closest]] = -ProxTablecopy[where_th_ind[closest]] singleindices = [] refine_indexed_spots = {} # loop over close exp. spots for k in range(len(List_Exp_spot_close)): exp_index = List_Exp_spot_close[k] if not singleindices.count(exp_index): singleindices.append(exp_index) theo_index = np.where(ProxTablecopy == exp_index)[0] if (len(theo_index) == 1): # only one theo spot close to the current exp. spot refine_indexed_spots[exp_index] = [exp_index, theo_index, Miller_Exp_spot[k]] else: # recent PATCH: closest_theo_ind = np.argmin(allresidues[theo_index]) if allresidues[theo_index][closest_theo_ind] < angtol: refine_indexed_spots[exp_index] = [exp_index, theo_index[closest_theo_ind], Miller_Exp_spot[k]] listofpairs = [] linkExpMiller = [] linkResidues = [] selectedAbsoluteSpotIndices = np.arange(len(data_theta)) for val in list(refine_indexed_spots.values()): if val[2] is not None: localspotindex = val[0] if not isinstance(val[1], (list, np.ndarray)): closetheoindex = val[1] else: closetheoindex = val[1][0] absolute_spot_index = selectedAbsoluteSpotIndices[localspotindex] listofpairs.append([absolute_spot_index, closetheoindex]) # Exp, Theo, where -1 for specifying that it came from automatic linking linkExpMiller.append([float(absolute_spot_index)] + [float(elem) for elem in val[2]]) # float(val) for further handling as floats array linkResidues.append([absolute_spot_index, closetheoindex, allresidues[closetheoindex]]) linkedspots_link = np.array(listofpairs) linkExpMiller_link = linkExpMiller linkResidues_link = linkResidues return linkedspots_link, linkExpMiller_link, linkResidues_link def get_ipf_colour(orientation_matrix1, axis=np.array([0., 0., 1.]), symmetry=None, symm_operator=None): """Compute the IPF (inverse pole figure) colour for this orientation. Given a particular axis expressed in the laboratory coordinate system, one can compute the so called IPF colour based on that direction expressed in the crystal coordinate system as :math:`[x_c,y_c,z_c]`. There is only one tuple (u,v,w) such that: .. math:: [x_c,y_c,z_c]=u.[0,0,1]+v.[0,1,1]+w.[1,1,1] and it is used to assign the RGB colour. :param ndarray axis: the direction to use to compute the IPF colour. :param Symmetry symmetry: the symmetry operator to use. :return tuple: a tuple contining the RGB values. """ if not np.all(orientation_matrix1==0): orientation_matrix = orientation_matrix1 else: return 0,0,0 # ## rotate orientation by 40degrees to bring in Sample RF omega = np.deg2rad(-40.0) # rotation de -omega autour de l'axe x (or Y?) pour repasser dans Rsample cw = np.cos(omega) sw = np.sin(omega) mat_from_lab_to_sample_frame = np.array([[cw, 0.0, sw], [0.0, 1.0, 0.0], [-sw, 0, cw]]) orientation_matrix = np.dot(mat_from_lab_to_sample_frame.T, orientation_matrix) if np.linalg.det(orientation_matrix) < 0: orientation_matrix = -orientation_matrix axis /= np.linalg.norm(axis) # rgb = get_field_color(orientation_matrix, axis, symmetry=symmetry, syms=syms) # return rgb Vc = np.dot(orientation_matrix, axis) # get the symmetry operators syms = np.array(symm_operator) #symmetry.symmetry_operators() syms = np.concatenate((syms, -syms)) syms = np.unique(syms, axis=0) if symmetry == symmetry.cubic: rgb = get_field_color(orientation_matrix, axis, symmetry, syms) return rgb # angleR = 45 - Vc_chi # red color proportional to (45 - chi) # minAngleR = 0 # maxAngleR = 45 # angleB = Vc_phi # blue color proportional to phi # minAngleB = 0 # maxAngleB = 45 elif symmetry == symmetry.hexagonal: Vc_syms = np.dot(syms, Vc) # phi: rotation around 001 axis, from 100 axis to Vc vector, projected on (100,010) plane Vc_phi = np.arctan2(Vc_syms[:, 1], Vc_syms[:, 0]) * 180 / np.pi # chi: rotation around 010 axis, from 001 axis to Vc vector, projected on (100,001) plane # Vc_chi = np.arctan2(Vc_syms[:, 0], Vc_syms[:, 2]) * 180 / np.pi # psi : angle from 001 axis to Vc vector Vc_psi = np.arccos(Vc_syms[:, 2]) * 180 / np.pi angleR = 90 - Vc_psi # red color proportional to (90 - psi) minAngleR = 0 maxAngleR = 90 angleB = Vc_phi # blue color proportional to phi minAngleB = 0 maxAngleB = 30 else: rgb = get_field_color(orientation_matrix, axis, symmetry, syms) return rgb # find the axis lying in the fundamental zone fz_list = ((angleR >= minAngleR) & (angleR < maxAngleR) & (angleB >= minAngleB) & (angleB < maxAngleB)).tolist() if not fz_list.count(True) == 1: # print("funda problem") rgb = get_field_color(orientation_matrix, axis, symmetry, syms) return rgb i_SST = fz_list.index(True) r = angleR[i_SST] / maxAngleR g = (maxAngleR - angleR[i_SST]) / maxAngleR * (maxAngleB - angleB[i_SST]) / maxAngleB b = (maxAngleR - angleR[i_SST]) / maxAngleR * angleB[i_SST] / maxAngleB rgb = np.array([r, g, b]) rgb = rgb / rgb.max() return rgb def get_field_color(orientation_matrix, axis=np.array([0., 0., 1.]), symmetry=None, syms=None): """Compute the IPF (inverse pole figure) colour for this orientation. Given a particular axis expressed in the laboratory coordinate system, one can compute the so called IPF colour based on that direction expressed in the crystal coordinate system as :math:`[x_c,y_c,z_c]`. There is only one tuple (u,v,w) such that: .. math:: [x_c,y_c,z_c]=u.[0,0,1]+v.[0,1,1]+w.[1,1,1] and it is used to assign the RGB colour. :param ndarray axis: the direction to use to compute the IPF colour. :param Symmetry symmetry: the symmetry operator to use. :return tuple: a tuple contining the RGB values. """ for sym in syms: Osym = np.dot(sym, orientation_matrix) Vc = np.dot(Osym, axis) if Vc[2] < 0: Vc *= -1. # using the upward direction uvw = np.array([Vc[2] - Vc[1], Vc[1] - Vc[0], Vc[0]]) uvw /= np.linalg.norm(uvw) uvw /= max(uvw) if (uvw[0] >= 0. and uvw[0] <= 1.0) and (uvw[1] >= 0. and uvw[1] <= 1.0) and ( uvw[2] >= 0. and uvw[2] <= 1.0): break uvw = uvw / uvw.max() return uvw class Symmetry(enum.Enum): """ Class to describe crystal symmetry defined by its Laue class symbol. # Laue Groups #group 1 -- triclinic: '-1' #group 2 -- monoclinic: '2/m' #group 3 -- orthorhombic: 'mmm' #group 4 -- tetragonal: '4/m' #group 5 -- tetragonal: '4/mmm' #group 6 -- trigonal: '-3' #group 7 -- trigonal: '-3m' #group 8 -- hexagonal: '6/m' #group 9 -- hexagonal: '6/mmm' #group 10 -- cubic: 'm3' #group 11 -- cubic: 'm3m' """ cubic = 'm3m' hexagonal = '6/mmm' orthorhombic = 'mmm' tetragonal = '4/mmm' trigonal = 'bar3m' monoclinic = '2/m' triclinic = 'bar1' # operation_rotation = None def symmetry_operators(self, use_miller_bravais=False): """Define the equivalent crystal symmetries. Those come from Randle & Engler, 2000. For instance in the cubic crystal struture, for instance there are 24 equivalent cube orientations. :returns array: A numpy array of shape (n, 3, 3) where n is the \ number of symmetries of the given crystal structure. """ if self is Symmetry.cubic: #m-3 only 24 component #m-3m 48 component sym = np.zeros((48, 3, 3), dtype=np.float) sym[0] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[1] = np.array([[0., 0., -1.], [0., -1., 0.], [-1., 0., 0.]]) sym[2] = np.array([[0., 0., -1.], [0., 1., 0.], [1., 0., 0.]]) sym[3] = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]]) sym[4] = np.array([[0., 0., 1.], [0., 1., 0.], [-1., 0., 0.]]) sym[5] = np.array([[1., 0., 0.], [0., 0., -1.], [0., 1., 0.]]) sym[6] = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) sym[7] = np.array([[1., 0., 0.], [0., 0., 1.], [0., -1., 0.]]) sym[8] = np.array([[0., -1., 0.], [1., 0., 0.], [0., 0., 1.]]) sym[9] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) sym[10] = np.array([[0., 1., 0.], [-1., 0., 0.], [0., 0., 1.]]) sym[11] = np.array([[0., 0., 1.], [1., 0., 0.], [0., 1., 0.]]) sym[12] = np.array([[0., 1., 0.], [0., 0., 1.], [1., 0., 0.]]) sym[13] = np.array([[0., 0., -1.], [-1., 0., 0.], [0., 1., 0.]]) sym[14] = np.array([[0., -1., 0.], [0., 0., 1.], [-1., 0., 0.]]) sym[15] = np.array([[0., 1., 0.], [0., 0., -1.], [-1., 0., 0.]]) sym[16] = np.array([[0., 0., -1.], [1., 0., 0.], [0., -1., 0.]]) sym[17] = np.array([[0., 0., 1.], [-1., 0., 0.], [0., -1., 0.]]) sym[18] = np.array([[0., -1., 0.], [0., 0., -1.], [1., 0., 0.]]) sym[19] = np.array([[0., 1., 0.], [1., 0., 0.], [0., 0., -1.]]) sym[20] = np.array([[-1., 0., 0.], [0., 0., 1.], [0., 1., 0.]]) sym[21] = np.array([[0., 0., 1.], [0., -1., 0.], [1., 0., 0.]]) sym[22] = np.array([[0., -1., 0.], [-1., 0., 0.], [0., 0., -1.]]) sym[23] = np.array([[-1., 0., 0.], [0., 0., -1.], [0., -1., 0.]]) elif self is Symmetry.hexagonal: # using the Miller-Bravais representation here if use_miller_bravais: sym = np.zeros((12, 4, 4), dtype=np.int) sym[0] = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) sym[1] = np.array([[0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) sym[2] = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 0, 1]]) sym[3] = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, -1]]) sym[4] = np.array([[0, 0, 1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, -1]]) sym[5] = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0], [0, 0, 0, -1]]) sym[6] = np.array([[-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]) sym[7] = np.array([[0, 0, -1, 0], [-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 0, 1]]) sym[8] = np.array([[0, -1, 0, 0], [0, 0, -1, 0], [-1, 0, 0, 0], [0, 0, 0, 1]]) sym[9] = np.array([[-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, -1]]) sym[10] = np.array([[0, 0, -1, 0], [-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 0, -1]]) sym[11] = np.array([[0, -1, 0, 0], [0, 0, -1, 0], [-1, 0, 0, 0], [0, 0, 0, -1]]) else: sym = np.zeros((12, 3, 3), dtype=np.float) s60 = np.sin(60 * np.pi / 180) sym[0] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[1] = np.array([[0.5, s60, 0.], [-s60, 0.5, 0.], [0., 0., 1.]]) sym[2] = np.array([[-0.5, s60, 0.], [-s60, -0.5, 0.], [0., 0., 1.]]) sym[3] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) sym[4] = np.array([[-0.5, -s60, 0.], [s60, -0.5, 0.], [0., 0., 1.]]) sym[5] = np.array([[0.5, -s60, 0.], [s60, 0.5, 0.], [0., 0., 1.]]) sym[6] = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) sym[7] = np.array([[0.5, s60, 0.], [s60, -0.5, 0.], [0., 0., -1.]]) sym[8] = np.array([[-0.5, s60, 0.], [s60, 0.5, 0.], [0., 0., -1.]]) sym[9] = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]]) sym[10] = np.array([[-0.5, -s60, 0.], [-s60, 0.5, 0.], [0., 0., -1.]]) sym[11] = np.array([[0.5, -s60, 0.], [-s60, -0.5, 0.], [0., 0., -1.]]) elif self is Symmetry.orthorhombic: sym = np.zeros((8, 3, 3), dtype=np.float) sym[0] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[1] = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) sym[2] = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]]) sym[3] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) sym[4] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) sym[5] = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[6] = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) sym[7] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., -1.]]) elif self is Symmetry.tetragonal: sym = np.zeros((8, 3, 3), dtype=np.float) sym[0] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[1] = np.array([[0., -1., 0.], [1., 0., 0.], [0., 0., 1.]]) sym[2] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) sym[3] = np.array([[0., 1., 0.], [-1., 0., 0.], [0., 0., 1.]]) sym[4] = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) sym[5] = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]]) sym[6] = np.array([[0., 1., 0.], [1., 0., 0.], [0., 0., -1.]]) sym[7] = np.array([[0., -1., 0.], [-1., 0., 0.], [0., 0., -1.]]) elif self is Symmetry.monoclinic: sym = np.zeros((4, 3, 3), dtype=np.float) sym[0] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[1] = np.array([[-1., 0., 0.], [0., 1., 0.], [0., 0., -1.]]) sym[2] = np.array([[1., 0., 0.], [0., -1., 0.], [0., 0., 1.]]) sym[3] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) elif self is Symmetry.triclinic: sym = np.zeros((2, 3, 3), dtype=np.float) sym[0] = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) sym[1] = np.array([[-1., 0., 0.], [0., -1., 0.], [0., 0., -1.]]) else: raise ValueError('warning, symmetry not supported: %s' % self) return sym class Lattice: ''' The Lattice class to create one of the 14 Bravais lattices. This particular class has been partly inspired from the pymatgen project at https://github.com/materialsproject/pymatgen Any of the 7 lattice systems (each corresponding to one point group) can be easily created and manipulated. The lattice centering can be specified to form any of the 14 Bravais lattices: * Primitive (P): lattice points on the cell corners only (default); * Body (I): one additional lattice point at the center of the cell; * Face (F): one additional lattice point at the center of each of the faces of the cell; * Base (A, B or C): one additional lattice point at the center of each of one pair of the cell faces. :: a = 0.352 # FCC Nickel l = Lattice.face_centered_cubic(a) print(l.volume()) Addditionnally the point-basis can be controlled to address non Bravais lattice cells. It is set to a single atoms at (0, 0, 0) by default so that each cell is a Bravais lattice but may be changed to something more complex to achieve HCP structure or Diamond structure for instance. ''' def __init__(self, matrix, centering='P', symmetry=None): '''Create a crystal lattice (unit cell). Create a lattice from a 3x3 matrix. Each row in the matrix represents one lattice vector. ''' m = np.array(matrix, dtype=np.float64).reshape((3, 3)) lengths = np.sqrt(np.sum(m ** 2, axis=1)) angles = np.zeros(3) for i in range(3): j = (i + 1) % 3 k = (i + 2) % 3 angles[i] = dot(m[j], m[k]) / (lengths[j] * lengths[k]) angles = np.arccos(angles) * 180. / pi self._angles = angles self._lengths = lengths self._matrix = m self._centering = centering self._symmetry = symmetry def __eq__(self, other): """Override the default Equals behavior. The equality of two Lattice objects is based on the equality of their angles, lengths, and centering. """ if not isinstance(other, self.__class__): return False for i in range(3): if self._angles[i] != other._angles[i]: return False elif self._lengths[i] != other._lengths[i]: return False if self._centering != other._centering: return False if self._symmetry != other._symmetry: return False return True def reciprocal_lattice(self): '''Compute the reciprocal lattice. The reciprocal lattice defines a crystal in terms of vectors that are normal to a plane and whose lengths are the inverse of the interplanar spacing. This method computes the three reciprocal lattice vectors defined by: .. math:: * a.a^* = 1 * b.b^* = 1 * c.c^* = 1 ''' [a, b, c] = self._matrix V = self.volume() astar = np.cross(b, c) / V bstar = np.cross(c, a) / V cstar = np.cross(a, b) / V return [astar, bstar, cstar] @property def matrix(self): """Returns a copy of matrix representing the Lattice.""" return np.copy(self._matrix) def get_symmetry(self): """Returns the type of `Symmetry` of the Lattice.""" return self._symmetry def symmetry(crystal_structure=Symmetry.cubic, use_miller_bravais=False): """Define the equivalent crystal symmetries. Those come from Randle & Engler, 2000. For instance in the cubic crystal struture, for instance there are 24 equivalent cube orientations. :param crystal_structure: an instance of the `Symmetry` class describing the crystal symmetry. :raise ValueError: if the given symmetry is not supported. :returns array: A numpy array of shape (n, 3, 3) where n is the \ number of symmetries of the given crystal structure. """ return crystal_structure.symmetry_operators(use_miller_bravais=use_miller_bravais) @staticmethod def cubic(a): ''' Create a cubic Lattice unit cell. *Parameters* **a**: first lattice length parameter (a = b = c here) *Returns* A `Lattice` instance corresponding to a primitice cubic lattice. ''' return Lattice([[a, 0.0, 0.0], [0.0, a, 0.0], [0.0, 0.0, a]], symmetry=Symmetry.cubic) @staticmethod def body_centered_cubic(a): ''' Create a body centered cubic Lattice unit cell. *Parameters* **a**: first lattice length parameter (a = b = c here) *Returns* A `Lattice` instance corresponding to a body centered cubic lattice. ''' return Lattice.from_parameters(a, a, a, 90, 90, 90, centering='I', symmetry=Symmetry.cubic) @staticmethod def face_centered_cubic(a): ''' Create a face centered cubic Lattice unit cell. *Parameters* **a**: first lattice length parameter (a = b = c here) *Returns* A `Lattice` instance corresponding to a face centered cubic lattice. ''' return Lattice.from_parameters(a, a, a, 90, 90, 90, centering='F', symmetry=Symmetry.cubic) @staticmethod def tetragonal(a, c): ''' Create a tetragonal Lattice unit cell. *Parameters* **a**: first lattice length parameter **c**: third lattice length parameter (b = a here) *Returns* A `Lattice` instance corresponding to a primitive tetragonal lattice. ''' return Lattice.from_parameters(a, a, c, 90, 90, 90, symmetry=Symmetry.tetragonal) @staticmethod def body_centered_tetragonal(a, c): ''' Create a body centered tetragonal Lattice unit cell. *Parameters* **a**: first lattice length parameter **c**: third lattice length parameter (b = a here) *Returns* A `Lattice` instance corresponding to a body centered tetragonal lattice. ''' return Lattice.from_parameters(a, a, c, 90, 90, 90, centering='I', symmetry=Symmetry.tetragonal) @staticmethod def orthorhombic(a, b, c): ''' Create a tetragonal Lattice unit cell with 3 different length parameters a, b and c. ''' return Lattice.from_parameters(a, b, c, 90, 90, 90, symmetry=Symmetry.orthorhombic) @staticmethod def base_centered_orthorhombic(a, b, c): ''' Create a based centered orthorombic Lattice unit cell. *Parameters* **a**: first lattice length parameter **b**: second lattice length parameter **c**: third lattice length parameter *Returns* A `Lattice` instance corresponding to a based centered orthorombic lattice. ''' return Lattice.from_parameters(a, b, c, 90, 90, 90, centering='C', symmetry=Symmetry.orthorhombic) @staticmethod def body_centered_orthorhombic(a, b, c): ''' Create a body centered orthorombic Lattice unit cell. *Parameters* **a**: first lattice length parameter **b**: second lattice length parameter **c**: third lattice length parameter *Returns* A `Lattice` instance corresponding to a body centered orthorombic lattice. ''' return Lattice.from_parameters(a, b, c, 90, 90, 90, centering='I', symmetry=Symmetry.orthorhombic) @staticmethod def face_centered_orthorhombic(a, b, c): ''' Create a face centered orthorombic Lattice unit cell. *Parameters* **a**: first lattice length parameter **b**: second lattice length parameter **c**: third lattice length parameter *Returns* A `Lattice` instance corresponding to a face centered orthorombic lattice. ''' return Lattice.from_parameters(a, b, c, 90, 90, 90, centering='F', symmetry=Symmetry.orthorhombic) @staticmethod def hexagonal(a, c): ''' Create a hexagonal Lattice unit cell with length parameters a and c. ''' return Lattice.from_parameters(a, a, c, 90, 90, 120, symmetry=Symmetry.hexagonal) @staticmethod def rhombohedral(a, alpha): ''' Create a rhombohedral Lattice unit cell with one length parameter a and the angle alpha. ''' return Lattice.from_parameters(a, a, a, alpha, alpha, alpha, symmetry=Symmetry.trigonal) @staticmethod def monoclinic(a, b, c, alpha): ''' Create a monoclinic Lattice unit cell with 3 different length parameters a, b and c. The cell angle is given by alpha. The lattice centering id primitive ie. 'P' ''' return Lattice.from_parameters(a, b, c, alpha, 90, 90, symmetry=Symmetry.monoclinic) @staticmethod def base_centered_monoclinic(a, b, c, alpha): ''' Create a based centered monoclinic Lattice unit cell. *Parameters* **a**: first lattice length parameter **b**: second lattice length parameter **c**: third lattice length parameter **alpha**: first lattice angle parameter *Returns* A `Lattice` instance corresponding to a based centered monoclinic lattice. ''' return Lattice.from_parameters(a, b, c, alpha, 90, 90, centering='C', symmetry=Symmetry.monoclinic) @staticmethod def triclinic(a, b, c, alpha, beta, gamma): ''' Create a triclinic Lattice unit cell with 3 different length parameters a, b, c and three different cell angles alpha, beta and gamma. ..note:: This method is here for the sake of completeness since one can create the triclinic cell directly using the `from_parameters` method. ''' return Lattice.from_parameters(a, b, c, alpha, beta, gamma, symmetry=Symmetry.triclinic) @staticmethod def from_parameters(a, b, c, alpha, beta, gamma, x_aligned_with_a=False, centering='P', symmetry=Symmetry.triclinic): """ Create a Lattice using unit cell lengths and angles (in degrees). The lattice centering can also be specified (among 'P', 'I', 'F', 'A', 'B' or 'C'). :param float a: first lattice length parameter. :param float b: second lattice length parameter. :param float c: third lattice length parameter. :param float alpha: first lattice angle parameter. :param float beta: second lattice angle parameter. :param float gamma: third lattice angle parameter. :param bool x_aligned_with_a: flag to control the convention used to define the Cartesian frame. :param str centering: lattice centering ('P' by default) passed to the `Lattice` class. :param symmetry: a `Symmetry` instance to be passed to the lattice. :return: A `Lattice` instance with the specified lattice parameters and centering. """ alpha_r = radians(alpha) beta_r = radians(beta) gamma_r = radians(gamma) if x_aligned_with_a: # first lattice vector (a) is aligned with X vector_a = a * np.array([1, 0, 0]) vector_b = b * np.array([np.cos(gamma_r), np.sin(gamma_r), 0]) c1 = c * np.cos(beta_r) c2 = c * (np.cos(alpha_r) - np.cos(gamma_r) * np.cos(beta_r)) / np.sin(gamma_r) vector_c = np.array([c1, c2, np.sqrt(c ** 2 - c1 ** 2 - c2 ** 2)]) else: # third lattice vector (c) is aligned with Z cos_gamma_star = (np.cos(alpha_r) * np.cos(beta_r) - np.cos(gamma_r)) / (np.sin(alpha_r) * np.sin(beta_r)) sin_gamma_star = np.sqrt(1 - cos_gamma_star ** 2) vector_a = [a * np.sin(beta_r), 0.0, a * np.cos(beta_r)] vector_b = [-b * np.sin(alpha_r) * cos_gamma_star, b * np.sin(alpha_r) * sin_gamma_star, b * np.cos(alpha_r)] vector_c = [0.0, 0.0, float(c)] return Lattice([vector_a, vector_b, vector_c], centering=centering, symmetry=symmetry) def volume(self): """Compute the volume of the unit cell.""" m = self._matrix return abs(np.dot(np.cross(m[0], m[1]), m[2])) def get_hkl_family(self, hkl): """Get a list of the hkl planes composing the given family for this crystal lattice. *Parameters* **hkl**: miller indices of the requested family *Returns* A list of the hkl planes in the given family. """ planes = HklPlane.get_family(hkl, lattice=self, crystal_structure=self._symmetry) return planes class HklObject: def __init__(self, h, k, l, lattice=None): '''Create a new hkl object with the given Miller indices and crystal lattice. ''' if lattice == None: lattice = Lattice.cubic(1.0) self._lattice = lattice self._h = h self._k = k self._l = l @property def lattice(self): return self._lattice def set_lattice(self, lattice): """Assign a new `Lattice` to this instance. :param lattice: the new crystal lattice. """ self._lattice = lattice @property def h(self): return self._h @property def k(self): return self._k @property def l(self): return self._l def miller_indices(self): ''' Returns an immutable tuple of the plane Miller indices. ''' return (self._h, self._k, self._l) class HklDirection(HklObject): def direction(self): '''Returns a normalized vector, expressed in the cartesian coordinate system, corresponding to this crystallographic direction. ''' (h, k, l) = self.miller_indices() M = self._lattice.matrix.T # the columns of M are the a, b, c vector in the cartesian coordinate system l_vect = M.dot(np.array([h, k, l])) return l_vect / np.linalg.norm(l_vect) def angle_with_direction(self, hkl): '''Computes the angle between this crystallographic direction and the given direction (in radian).''' return np.arccos(np.dot(self.direction(), hkl.direction())) @staticmethod def angle_between_directions(hkl1, hkl2, lattice=None): '''Computes the angle between two crystallographic directions (in radian). :param tuple hkl1: The triplet of the miller indices of the first direction. :param tuple hkl2: The triplet of the miller indices of the second direction. :param Lattice lattice: The crystal lattice, will default to cubic if not specified. :returns float: The angle in radian. ''' d1 = HklDirection(*hkl1, lattice=lattice) d2 = HklDirection(*hkl2, lattice=lattice) return d1.angle_with_direction(d2) @staticmethod def three_to_four_indices(u, v, w): """Convert from Miller indices to Miller-Bravais indices. this is used for hexagonal crystal lattice.""" return (2 * u - v) / 3., (2 * v - u) / 3., -(u + v) / 3., w @staticmethod def four_to_three_indices(U, V, T, W): """Convert from Miller-Bravais indices to Miller indices. this is used for hexagonal crystal lattice.""" u, v, w = U - T, V - T, W gcd = functools.reduce(math.gcd, (u, v, w)) return u / gcd, v / gcd, w / gcd @staticmethod def angle_between_4indices_directions(hkil1, hkil2, ac): """Computes the angle between two crystallographic directions in a hexagonal lattice. The solution was derived by F. Frank in: On Miller - Bravais indices and four dimensional vectors. Acta Cryst. 18, 862-866 (1965) :param tuple hkil1: The quartet of the indices of the first direction. :param tuple hkil2: The quartet of the indices of the second direction. :param tuple ac: the lattice parameters of the hexagonal structure in the form (a, c). :returns float: The angle in radian. """ h1, k1, i1, l1 = hkil1 h2, k2, i2, l20 = hkil2 a, c = ac lambda_square = 2. / 3 * (c / a) ** 2 value = (h1 * h2 + k1 * k2 + i1 * i2 + lambda_square * l1 * l20) / \ (np.sqrt(h1 ** 2 + k1 ** 2 + i1 ** 2 + lambda_square * l1 ** 2) * np.sqrt(h2 ** 2 + k2 ** 2 + i2 ** 2 + lambda_square * l20 ** 2)) return np.arccos(value) class HklPlane(HklObject): ''' This class define crystallographic planes using Miller indices. A plane can be create by speficying its Miller indices and the crystal lattice (default is cubic with lattice parameter of 1.0) :: a = 0.405 # FCC Aluminium l = Lattice.cubic(a) p = HklPlane(1, 1, 1, lattice=l) print(p) print(p.scattering_vector()) print(p.interplanar_spacing()) .. note:: Miller indices are defined in terms of the inverse of the intercept of the plane on the three crystal axes a, b, and c. ''' def __eq__(self, other): """Override the default Equals behavior. The equality of two HklObjects is based on the equality of their miller indices. """ if isinstance(other, self.__class__): return self._h == other._h and self._k == other._k and \ self._l == other._l and self._lattice == other._lattice return False def __ne__(self, other): """Define a non-equality test""" return not self.__eq__(other) def normal(self): '''Returns the unit vector normal to the plane. We use of the repiprocal lattice to compute the normal to the plane and return a normalised vector. ''' n = self.scattering_vector() return n / np.linalg.norm(n) def scattering_vector(self): '''Calculate the scattering vector of this `HklPlane`. The scattering vector (or reciprocal lattice vector) is normal to this `HklPlane` and its length is equal to the inverse of the interplanar spacing. In the cartesian coordinate system of the crystal, it is given by: ..math G_c = h.a^* + k.b^* + l.c^* :returns: a numpy vector expressed in the cartesian coordinate system of the crystal. ''' [astar, bstar, cstar] = self._lattice.reciprocal_lattice() (h, k, l) = self.miller_indices() # express (h, k, l) in the cartesian crystal CS Gc = h * astar + k * bstar + l * cstar return Gc def friedel_pair(self): """Create the Friedel pair of the HklPlane.""" (h, k, l) = self.miller_indices() pair = HklPlane(-h, -k, -l, self._lattice) return pair def interplanar_spacing(self): ''' Compute the interplanar spacing. For cubic lattice, it is: .. math:: d = a / \sqrt{h^2 + k^2 + l^2} The general formula comes from 'Introduction to Crystallography' p. 68 by Donald E. Sands. ''' (a, b, c) = self._lattice._lengths (h, k, l) = self.miller_indices() (alpha, beta, gamma) = radians(self._lattice._angles) # d = a / np.sqrt(h**2 + k**2 + l**2) # for cubic structure only d = self._lattice.volume() / np.sqrt(h ** 2 * b ** 2 * c ** 2 * np.sin(alpha) ** 2 + \ k ** 2 * a ** 2 * c ** 2 * np.sin( beta) ** 2 + l ** 2 * a ** 2 * b ** 2 * np.sin(gamma) ** 2 + \ 2 * h * l * a * b ** 2 * c * ( np.cos(alpha) * np.cos(gamma) - np.cos(beta)) + \ 2 * h * k * a * b * c ** 2 * ( np.cos(alpha) * np.cos(beta) - np.cos(gamma)) + \ 2 * k * l * a ** 2 * b * c * ( np.cos(beta) * np.cos(gamma) - np.cos(alpha))) return d @staticmethod def four_to_three_indices(U, V, T, W): """Convert four to three index representation of a slip plane (used for hexagonal crystal lattice).""" return U, V, W @staticmethod def three_to_four_indices(u, v, w): """Convert three to four index representation of a slip plane (used for hexagonal crystal lattice).""" return u, v, -(u + v), w def is_in_list(self, hkl_planes, friedel_pair=False): """Check if the hkl plane is in the given list. By default this relies on the built in in test from the list type which in turn calls in the __eq__ method. This means it will return True if a plane with the exact same miller indices (and same lattice) is in the list. Turning on the friedel_pair flag will allow to test also the Friedel pair (-h, -k, -l) and return True if it is in the list. For instance (0,0,1) and (0,0,-1) are in general considered as the same lattice plane. """ if not friedel_pair: return self in hkl_planes else: return self in hkl_planes or self.friedel_pair() in hkl_planes @staticmethod def is_same_family(hkl1, hkl2, crystal_structure=Symmetry.cubic): """Static mtd to test if both lattice planes belongs to same family. A family {hkl} is composed by all planes that are equivalent to (hkl) using the symmetry of the lattice. The lattice assoiated with `hkl2` is not taken into account here. """ return hkl1.is_in_list(HklPlane.get_family(hkl2.miller_indices(), lattice=hkl1._lattice, crystal_structure=crystal_structure)) @staticmethod def get_family(hkl, lattice=None, include_friedel_pairs=False, crystal_structure=Symmetry.cubic): """Static method to obtain a list of the different crystallographic planes in a particular family. :param str hkl: a sequence of 3 (4 for hexagonal) numbers corresponding to the miller indices. :param Lattice lattice: The reference crystal lattice (default None). :param bool include_friedel_pairs: Flag to include the Friedel pairs in the list (False by default). :param str crystal_structure: A string descibing the crystal structure (cubic by default). :raise ValueError: if the given string does not correspond to a supported family. :returns list: a list of the :py:class:`~HklPlane` in the given hkl family. .. note:: The method account for the lattice symmetry to create a list of equivalent lattice plane from the point of view of the point group symmetry. A flag can be used to include or not the Friedel pairs. If not, the family is contstructed using the miller indices limited the number of minus signs. For instance (1,0,0) will be in the list and not (-1,0,0). """ if not (len(hkl) == 3 or (len(hkl) == 4 and crystal_structure == Symmetry.hexagonal)): raise ValueError('warning, family not supported: {}'.format(hkl)) # handle hexagonal case if len(hkl) == 4: h = int(hkl[0]) k = int(hkl[1]) i = int(hkl[2]) l = int(hkl[3]) (h, k, l) = HklPlane.four_to_three_indices(h, k, i, l) # useless as it just drops i else: # 3 indices h = int(hkl[0]) k = int(hkl[1]) l = int(hkl[2]) if crystal_structure == Symmetry.hexagonal: i = -(h + k) family = [] # construct lattice plane family from the symmetry operators if crystal_structure == Symmetry.hexagonal: syms = Lattice.symmetry(crystal_structure, use_miller_bravais=True) else: syms = Lattice.symmetry(crystal_structure) for sym in syms: if crystal_structure == Symmetry.hexagonal: n_sym = np.dot(sym, np.array([h, k, i, l])) n_sym = HklPlane.four_to_three_indices(*n_sym) else: # 3 indices n_sym = np.dot(sym, np.array([h, k, l])) hkl_sym = HklPlane(*n_sym, lattice=lattice) if not hkl_sym.is_in_list(family, friedel_pair=True): family.append(hkl_sym) if include_friedel_pairs: hkl_sym = HklPlane(-n_sym[0], -n_sym[1], -n_sym[2], lattice=lattice) if not hkl_sym.is_in_list(family, friedel_pair=False): family.append(hkl_sym) if not include_friedel_pairs: # for each hkl plane chose between (h, k, l) and (-h, -k, -l) to have the less minus signs for i in range(len(family)): hkl = family[i] (h, k, l) = hkl.miller_indices() if np.where(np.array([h, k, l]) < 0)[0].size > 0 and np.where(np.array([h, k, l]) <= 0)[0].size >= 2: family[i] = hkl.friedel_pair() #print('replacing plane (%d%d%d) by its pair: (%d%d%d)' % (h, k, l, -h, -k, -l)) return family def multiplicity(self, symmetry=Symmetry.cubic): """compute the general multiplicity for this `HklPlane` and the given `Symmetry`. :param Symmetry symmetry: The crystal symmetry to take into account. :return: the number of equivalent planes in the family. """ return len(HklPlane.get_family(self.miller_indices(), include_friedel_pairs=True, crystal_structure=symmetry)) class PoleFigure: """A class to handle pole figures. A pole figure is a popular tool to plot multiple crystal orientations, either in the sample coordinate system (direct pole figure) or alternatively plotting a particular direction in the crystal coordinate system (inverse pole figure). """ def __init__(self, lattice=None, axis='Z', hkl='111', proj='stereo'): """ Create an empty PoleFigure object associated with an empty Microstructure. :param microstructure: the :py:class:`~pymicro.crystal.microstructure.Microstructure` containing the collection of orientations to plot (None by default). :param lattice: the crystal :py:class:`~pymicro.crystal.lattice.Lattice`. :param str axis: the pole figure axis ('Z' by default), vertical axis in the direct pole figure and direction plotted on the inverse pole figure. .. warning:: Any crystal structure is now supported (you have to set the proper crystal lattice) but it has only really be tested for cubic. :param str hkl: slip plane family ('111' by default) :param str proj: projection type, can be either 'stereo' (default) or 'flat' """ self.proj = proj self.axis = axis if self.axis == 'Z': self.axis_crystal = np.array([0, 0, 1]) elif self.axis == 'Y': self.axis_crystal = np.array([0, 1, 0]) else: self.axis_crystal = np.array([1, 0, 0]) if lattice: self.lattice = lattice else: self.lattice = Lattice.cubic(1.0) self.family = None self.poles = [] self.set_hkl_poles(hkl) self.mksize = 50 self.x = np.array([1., 0., 0.]) self.y = np.array([0., 1., 0.]) self.z = np.array([0., 0., 1.]) def set_hkl_poles(self, hkl='111'): """Set the pole (aka hkl planes) list to to use in the `PoleFigure`. The list of poles can be given by the family type or directly by a list of `HklPlanes` objects. :params str/list hkl: slip plane family ('111' by default) """ if type(hkl) is str: self.family = hkl # keep a record of this hkl_planes = self.lattice.get_hkl_family(self.family) elif type(hkl) is list: self.family = None hkl_planes = hkl self.poles = hkl_planes #[p.normal() for p in hkl_planes] def plot_line_between_crystal_dir(self, c1, c2, ax=None, steps=25, col='k'): '''Plot a curve between two crystal directions. The curve is actually composed of several straight lines segments to draw from direction 1 to direction 2. :param c1: vector describing crystal direction 1 :param c2: vector describing crystal direction 2 :param ax: a reference to a pyplot ax to draw the line :param int steps: number of straight lines composing the curve (11 by default) :param col: line color (black by default) ''' path = np.zeros((steps, 2), dtype=float) for j, i in enumerate(np.linspace(0., 1., steps)): ci = i * c1 + (1 - i) * c2 ci /= np.linalg.norm(ci) if self.proj == 'stereo': ci += self.z ci /= ci[2] path[j, 0] = ci[0] path[j, 1] = ci[1] ax.plot(path[:, 0], path[:, 1], color=col, markersize=self.mksize, linewidth=0.5, zorder=0) plt.axis("off") def plot_pf_background(self, ax, labels=True): '''Function to plot the background of the pole figure. :param ax: a reference to a pyplot ax to draw the backgroud. :param bool labels: add lables to axes (True by default). ''' an = np.linspace(0, 2 * np.pi, 100) ax.plot(np.cos(an), np.sin(an), 'k-', zorder=0) ax.plot([-1, 1], [0, 0], 'k-', zorder=0) ax.plot([0, 0], [-1, 1], 'k-', zorder=0) axe_labels = ['X', 'Y', 'Z'] if self.axis == 'Z': (h, v, _) = (0, 1, 2) elif self.axis == 'Y': (h, v, _) = (0, 2, 1) else: (h, v, _) = (1, 2, 0) if labels: ax.annotate(axe_labels[h], (1.01, 0.0), xycoords='data', fontsize=8, horizontalalignment='left', verticalalignment='center') ax.annotate(axe_labels[v], (0.0, 1.01), xycoords='data', fontsize=8, horizontalalignment='center', verticalalignment='bottom') def sst_symmetry(self, v, symms): """Transform a given vector according to the lattice symmetry associated with the pole figure. This function transform a vector so that it lies in the smallest symmetry equivalent zone. :param v: the vector to transform. :return: the transformed vector. """ # get the symmetry from the lattice associated with the pole figure symmetry = self.lattice._symmetry if symmetry == symmetry.cubic: return PoleFigure.sst_symmetry_cubic(v) elif symmetry == symmetry.hexagonal: #syms = symmetry.symmetry_operators() # syms = np.concatenate((symms, -symms)) syms = np.unique(symms, axis=0) for i in range(len(syms)): sym = syms[i] v_sym = np.dot(sym, v) # look at vectors pointing up if v_sym[2] < 0: v_sym *= -1 # now evaluate if projection is in the sst if v_sym[1] < 0 or v_sym[0] < 0: continue elif v_sym[1] / v_sym[0] > np.tan(np.pi / 6): continue else: break return v_sym else: print('unsupported symmetry: %s' % symmetry) return None @staticmethod def sst_symmetry_cubic(z_rot): '''Transform a given vector according to the cubic symmetry. This function transform a vector so that it lies in the unit SST triangle. :param z_rot: vector to transform. :return: the transformed vector. ''' if z_rot[0] < 0: z_rot[0] = -z_rot[0] if z_rot[1] < 0: z_rot[1] = -z_rot[1] if z_rot[2] < 0: z_rot[2] = -z_rot[2] if (z_rot[2] > z_rot[1]): z_rot[1], z_rot[2] = z_rot[2], z_rot[1] if (z_rot[1] > z_rot[0]): z_rot[0], z_rot[1] = z_rot[1], z_rot[0] if (z_rot[2] > z_rot[1]): z_rot[1], z_rot[2] = z_rot[2], z_rot[1] return np.array([z_rot[1], z_rot[2], z_rot[0]]) def plot_pf(self, col, orient_data, ax=None, mk='o', ann=False, ftsize=6): """Create the direct pole figure. :param ax: a reference to a pyplot ax to draw the poles. :param mk: marker used to plot the poles (disc by default). :param bool ann: Annotate the pole with the coordinates of the vector if True (False by default). """ self.plot_pf_background(ax) cp_0, cp_1 = [], [] colors = [] for igr, g in enumerate(orient_data): if np.isnan(g).all() or np.all(g==0): continue gt = g.transpose() for i, hkl_plane in enumerate(self.poles): c = hkl_plane.normal() c_rot = gt.dot(c) color = col[igr] if self.axis == 'Z': (h, v, u) = (0, 1, 2) elif self.axis == 'Y': (h, v, u) = (0, 2, 1) else: (h, v, u) = (1, 2, 0) axis_rot = c_rot[[h, v, u]] # the direction to plot is given by c_dir[h,v,u] if axis_rot[2] < 0: axis_rot *= -1 # make unit vector have z>0 if self.proj == 'flat': cp = axis_rot elif self.proj == 'stereo': c = axis_rot + self.z c /= c[2] # SP'/SP = r/z with r=1 cp = c # cp = np.cross(c, self.z) else: raise ValueError('Error, unsupported projection type', self.proj) cp_0.append(cp[0]) cp_1.append(cp[1]) colors.append(color) # Next 3 lines are necessary in case c_dir[2]=0, as for Euler angles [45, 45, 0] if axis_rot[2] < 0.000001: cp_0.append(-cp[0]) cp_1.append(-cp[1]) colors.append(color) # ax.scatter(-cp[0], -cp[1], linewidth=0, c=color, marker='o', s=axis_rot) ax.scatter(cp_0, cp_1, c=colors, s=self.mksize, zorder=2) ax.axis([-1.1, 1.1, -1.1, 1.1]) ax.axis('off') ax.set_title('{%s} direct %s projection' % (self.family, self.proj), fontsize = ftsize) def plot_sst_color(self, col, orient_data, ax=None, mk='s', \ ann=False, ftsize=6, phase = 0, symms=None): """ Create the inverse pole figure in the unit standard triangle. :param ax: a reference to a pyplot ax to draw the poles. :param mk: marker used to plot the poles (square by default). :param bool ann: Annotate the pole with the coordinates of the vector if True (False by default). """ system = None symmetry = self.lattice._symmetry if phase==0: sst_poles = [(0, 0, 1), (1, 0, 1), (1, 1, 1)] ax.axis([-0.05, 0.45, -0.05, 0.40]) system = 'cubic' elif phase==1: sst_poles = [(0, 0, 1), (2, -1, 0), (1, 0, 0)] ax.axis([-0.05, 1.05, -0.05, 0.6]) system = 'hexa' else: print('unssuported symmetry: %s' % symmetry) A = HklPlane(*sst_poles[0], lattice=self.lattice) B = HklPlane(*sst_poles[1], lattice=self.lattice) C = HklPlane(*sst_poles[2], lattice=self.lattice) if system == 'cubic': self.plot_line_between_crystal_dir(A.normal(), B.normal(), ax=ax, steps=int(1+(45/5)), col='k') self.plot_line_between_crystal_dir(B.normal(), C.normal(), ax=ax, steps=int(1+(35/5)), col='k') self.plot_line_between_crystal_dir(C.normal(), A.normal(), ax=ax, steps=int(1+(55/5)), col='k') elif system == 'hexa': self.plot_line_between_crystal_dir(A.normal(), B.normal(), ax=ax, steps=int(1+(90/5)), col='k') self.plot_line_between_crystal_dir(B.normal(), C.normal(), ax=ax, steps=int(1+(30/5)), col='k') self.plot_line_between_crystal_dir(C.normal(), A.normal(), ax=ax, steps=int(1+(90/5)), col='k') else: self.plot_line_between_crystal_dir(A.normal(), B.normal(), ax=ax, col='k') self.plot_line_between_crystal_dir(B.normal(), C.normal(), ax=ax, col='k') self.plot_line_between_crystal_dir(C.normal(), A.normal(), ax=ax, col='k') # display the 3 crystal axes poles = [A, B, C] v_align = ['top', 'top', 'bottom'] for i in range(3): hkl = poles[i] c_dir = hkl.normal() c = c_dir + self.z c /= c[2] # SP'/SP = r/z with r=1 pole_str = '%d%d%d' % hkl.miller_indices() if phase==1: pole_str = '%d%d%d%d' % HklPlane.three_to_four_indices(*hkl.miller_indices()) ax.annotate(pole_str, (c[0], c[1] - (2 * (i < 2) - 1) * 0.01), xycoords='data', fontsize=8, horizontalalignment='center', verticalalignment=v_align[i]) # now plot the sample axis cp_0, cp_1 = [], [] colors = [] for igr, g in enumerate(orient_data): if np.isnan(g).all() or np.all(g==0): continue # compute axis and apply SST symmetry if self.axis == 'Z': axis = self.z elif self.axis == 'Y': axis = self.y else: axis = self.x axis_rot = self.sst_symmetry(g.dot(axis), symms) color = np.round(col[igr],5) if axis_rot[2] < 0: axis_rot *= -1 # make unit vector have z>0 if self.proj == 'flat': cp = axis_rot elif self.proj == 'stereo': c = axis_rot + self.z c /= c[2] # SP'/SP = r/z with r=1 cp = c # cp = np.cross(c, self.z) else: raise ValueError('Error, unsupported projection type', self.proj) cp_0.append(cp[0]) cp_1.append(cp[1]) colors.append(color) # Next 3 lines are necessary in case c_dir[2]=0, as for Euler angles [45, 45, 0] if axis_rot[2] < 0.000001: cp_0.append(-cp[0]) cp_1.append(-cp[1]) colors.append(color) # ax.scatter(-cp[0], -cp[1], linewidth=0, c=color, marker='o', s=axis_rot) ax.scatter(cp_0, cp_1, c=colors, s=self.mksize, zorder=2) ax.set_title('%s-axis SST inverse %s projection' % (self.axis, self.proj), fontsize = ftsize) plt.axis("off") # ============================================================================= # Plot functions # ============================================================================= # def rot_mat_to_euler(rot_mat): # r = R.from_matrix(rot_mat) # return r.as_euler('zxz')* 180/np.pi def OrientationMatrix2Euler(g): """ Compute the Euler angles from the orientation matrix. This conversion follows the paper of Rowenhorst et al. :cite:`Rowenhorst2015`. In particular when :math:`g_{33} = 1` within the machine precision, there is no way to determine the values of :math:`\phi_1` and :math:`\phi_2` (only their sum is defined). The convention is to attribute the entire angle to :math:`\phi_1` and set :math:`\phi_2` to zero. :param g: The 3x3 orientation matrix :return: The 3 euler angles in degrees. """ eps = np.finfo('float').eps (phi1, Phi, phi2) = (0.0, 0.0, 0.0) # treat special case where g[2, 2] = 1 if np.abs(g[2, 2]) >= 1 - eps: if g[2, 2] > 0.0: phi1 = np.arctan2(g[0][1], g[0][0]) else: phi1 = -np.arctan2(-g[0][1], g[0][0]) Phi = np.pi else: Phi = np.arccos(g[2][2]) zeta = 1.0 / np.sqrt(1.0 - g[2][2] ** 2) phi1 = np.arctan2(g[2][0] * zeta, -g[2][1] * zeta) phi2 = np.arctan2(g[0][2] * zeta, g[1][2] * zeta) # ensure angles are in the range [0, 2*pi] if phi1 < 0.0: phi1 += 2 * np.pi if Phi < 0.0: Phi += 2 * np.pi if phi2 < 0.0: phi2 += 2 * np.pi return np.degrees([phi2, Phi, phi1]) def simple_plots(lim_x, lim_y, strain_matrix, strain_matrixs, col, colx, coly, match_rate, mat_global, spots_len, iR_pix, fR_pix, model_direc, material_, material1_, match_rate_threshold=5, bins=30): if material_ == material1_: matid = 0 for index in range(len(strain_matrix)): nan_index = np.where(match_rate[index][0] <= match_rate_threshold)[0] col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan,np.nan,np.nan col_plot = col_plot.reshape((lim_x, lim_y, 3)) mr_plot = np.copy(match_rate[index][0]) mr_plot[nan_index,:] = np.nan mr_plot = mr_plot.reshape((lim_x, lim_y)) mat_glob = np.copy(mat_global[index][0]) mat_glob[nan_index,:] = np.nan mat_glob = mat_glob.reshape((lim_x, lim_y)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"IPF Z map", loc='center', fontsize=8) axs[0].imshow(col_plot, origin='lower') axs[0].set_xticks([]) axs[0].set_yticks([]) axs[1].set_title(r"Material Index", loc='center', fontsize=8) im = axs[1].imshow(mat_glob, origin='lower', vmin=0, vmax=1) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Matching rate", loc='center', fontsize=8) im = axs[2].imshow(mr_plot, origin='lower', cmap=plt.cm.jet, vmin=0, vmax=100) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ "//figure_global_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) spots_len_plot = np.copy(spots_len[index][0]) spots_len_plot[nan_index,:] = np.nan spots_len_plot = spots_len_plot.reshape((lim_x, lim_y)) iR_pix_plot = np.copy(iR_pix[index][0]) iR_pix_plot[nan_index,:] = np.nan iR_pix_plot = iR_pix_plot.reshape((lim_x, lim_y)) fR_pix_plot = np.copy(fR_pix[index][0]) fR_pix_plot[nan_index,:] = np.nan fR_pix_plot = fR_pix_plot.reshape((lim_x, lim_y)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"Number of spots detected", loc='center', fontsize=8) im = axs[0].imshow(spots_len_plot, origin='lower', cmap=plt.cm.jet) axs[0].set_xticks([]) axs[0].set_yticks([]) divider = make_axes_locatable(axs[0]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[1].set_title(r"Initial pixel residues", loc='center', fontsize=8) im = axs[1].imshow(iR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Final pixel residues", loc='center', fontsize=8) im = axs[2].imshow(fR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+'//figure_mr_ir_fr_UB'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) else: for matid in range(2): for index in range(len(strain_matrix)): nan_index1 = np.where(match_rate[index][0] <= match_rate_threshold)[0] mat_id_index = np.where(mat_global[index][0] != matid+1)[0] nan_index = np.hstack((mat_id_index,nan_index1)) nan_index = np.unique(nan_index) try: col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan,np.nan,np.nan col_plot = col_plot.reshape((lim_x, lim_y, 3)) mr_plot = np.copy(match_rate[index][0]) mr_plot[nan_index,:] = np.nan mr_plot = mr_plot.reshape((lim_x, lim_y)) mat_glob = np.copy(mat_global[index][0]) mat_glob[nan_index,:] = np.nan mat_glob = mat_glob.reshape((lim_x, lim_y)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"IPF Z map", loc='center', fontsize=8) axs[0].imshow(col_plot, origin='lower') axs[0].set_xticks([]) axs[0].set_yticks([]) axs[1].set_title(r"Material Index", loc='center', fontsize=8) im = axs[1].imshow(mat_glob, origin='lower', vmin=0, vmax=2) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Matching rate", loc='center', fontsize=8) im = axs[2].imshow(mr_plot, origin='lower', cmap=plt.cm.jet, vmin=0, vmax=100) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ "//figure_global_mat"+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: print("Error in plots") spots_len_plot = np.copy(spots_len[index][0]) spots_len_plot[nan_index,:] = np.nan spots_len_plot = spots_len_plot.reshape((lim_x, lim_y)) iR_pix_plot = np.copy(iR_pix[index][0]) iR_pix_plot[nan_index,:] = np.nan iR_pix_plot = iR_pix_plot.reshape((lim_x, lim_y)) fR_pix_plot = np.copy(fR_pix[index][0]) fR_pix_plot[nan_index,:] = np.nan fR_pix_plot = fR_pix_plot.reshape((lim_x, lim_y)) try: fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"Number of spots detected", loc='center', fontsize=8) im = axs[0].imshow(spots_len_plot, origin='lower', cmap=plt.cm.jet) axs[0].set_xticks([]) axs[0].set_yticks([]) divider = make_axes_locatable(axs[0]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[1].set_title(r"Initial pixel residues", loc='center', fontsize=8) im = axs[1].imshow(iR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Final pixel residues", loc='center', fontsize=8) im = axs[2].imshow(fR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+'//figure_mr_ir_fr_mat'+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: print("Error in plots") def global_plots(lim_x, lim_y, rotation_matrix1, strain_matrix, strain_matrixs, col, colx, coly, match_rate, mat_global, spots_len, iR_pix, fR_pix, model_direc, material_, material1_, match_rate_threshold=5, bins=30, constantlength="a"): call_global() if material_ == material1_: mu_sd = [] mu_sdc = [] for index in range(len(spots_len)): ### index for nans nan_index = np.where(match_rate[index][0] <= match_rate_threshold)[0] if index == 0: spots_len_plot = np.copy(spots_len[index][0]) mr_plot = np.copy(match_rate[index][0]) iR_pix_plot = np.copy(iR_pix[index][0]) fR_pix_plot = np.copy(fR_pix[index][0]) strain_matrix_plot = np.copy(strain_matrix[index][0]) e11c = strain_matrix_plot[:,0,0]#.reshape((lim_x, lim_y)) e22c = strain_matrix_plot[:,1,1]#.reshape((lim_x, lim_y)) e33c = strain_matrix_plot[:,2,2]#.reshape((lim_x, lim_y)) e12c = strain_matrix_plot[:,0,1]#.reshape((lim_x, lim_y)) e13c = strain_matrix_plot[:,0,2]#.reshape((lim_x, lim_y)) e23c = strain_matrix_plot[:,1,2]#.reshape((lim_x, lim_y)) strain_matrixs_plot = np.copy(strain_matrixs[index][0]) e11s = strain_matrixs_plot[:,0,0]#.reshape((lim_x, lim_y)) e22s = strain_matrixs_plot[:,1,1]#.reshape((lim_x, lim_y)) e33s = strain_matrixs_plot[:,2,2]#.reshape((lim_x, lim_y)) e12s = strain_matrixs_plot[:,0,1]#.reshape((lim_x, lim_y)) e13s = strain_matrixs_plot[:,0,2]#.reshape((lim_x, lim_y)) e23s = strain_matrixs_plot[:,1,2]#.reshape((lim_x, lim_y)) spots_len_plot[nan_index] = np.nan mr_plot[nan_index] = np.nan iR_pix_plot[nan_index] = np.nan fR_pix_plot[nan_index] = np.nan e11c[nan_index] = np.nan e22c[nan_index] = np.nan e33c[nan_index] = np.nan e12c[nan_index] = np.nan e13c[nan_index] = np.nan e23c[nan_index] = np.nan e11s[nan_index] = np.nan e22s[nan_index] = np.nan e33s[nan_index] = np.nan e12s[nan_index] = np.nan e13s[nan_index] = np.nan e23s[nan_index] = np.nan else: temp = np.copy(spots_len[index][0]) temp[nan_index] = np.nan spots_len_plot = np.vstack((spots_len_plot,temp)) temp = np.copy(match_rate[index][0]) temp[nan_index] = np.nan mr_plot = np.vstack((mr_plot,temp)) temp = np.copy(iR_pix[index][0]) temp[nan_index] = np.nan iR_pix_plot = np.vstack((iR_pix_plot,temp)) temp = np.copy(fR_pix[index][0]) temp[nan_index] = np.nan fR_pix_plot = np.vstack((fR_pix_plot,temp)) strain_matrix_plot = np.copy(strain_matrix[index][0]) temp = np.copy(strain_matrix_plot[:,0,0]) temp[nan_index] = np.nan e11c = np.vstack((e11c,temp)) temp = np.copy(strain_matrix_plot[:,1,1]) temp[nan_index] = np.nan e22c = np.vstack((e22c,temp)) temp = np.copy(strain_matrix_plot[:,2,2]) temp[nan_index] = np.nan e33c = np.vstack((e33c,temp)) temp = np.copy(strain_matrix_plot[:,0,1]) temp[nan_index] = np.nan e12c = np.vstack((e12c,temp)) temp = np.copy(strain_matrix_plot[:,0,2]) temp[nan_index] = np.nan e13c = np.vstack((e13c,temp)) temp = np.copy(strain_matrix_plot[:,1,2]) temp[nan_index] = np.nan e23c = np.vstack((e23c,temp)) ## strain_matrixs_plot = np.copy(strain_matrixs[index][0]) temp = np.copy(strain_matrixs_plot[:,0,0]) temp[nan_index] = np.nan e11s = np.vstack((e11s,temp)) temp = np.copy(strain_matrixs_plot[:,1,1]) temp[nan_index] = np.nan e22s = np.vstack((e22s,temp)) temp = np.copy(strain_matrixs_plot[:,2,2]) temp[nan_index] = np.nan e33s = np.vstack((e33s,temp)) temp = np.copy(strain_matrixs_plot[:,0,1]) temp[nan_index] = np.nan e12s = np.vstack((e12s,temp)) temp = np.copy(strain_matrixs_plot[:,0,2]) temp[nan_index] = np.nan e13s = np.vstack((e13s,temp)) temp = np.copy(strain_matrixs_plot[:,1,2]) temp[nan_index] = np.nan e23s = np.vstack((e23s,temp)) spots_len_plot = spots_len_plot.flatten() mr_plot = mr_plot.flatten() iR_pix_plot = iR_pix_plot.flatten() fR_pix_plot = fR_pix_plot.flatten() e11c = e11c.flatten() e22c = e22c.flatten() e33c = e33c.flatten() e12c = e12c.flatten() e13c = e13c.flatten() e23c = e23c.flatten() e11s = e11s.flatten() e22s = e22s.flatten() e33s = e33s.flatten() e12s = e12s.flatten() e13s = e13s.flatten() e23s = e23s.flatten() spots_len_plot = spots_len_plot[~np.isnan(spots_len_plot)] mr_plot = mr_plot[~np.isnan(mr_plot)] iR_pix_plot = iR_pix_plot[~np.isnan(iR_pix_plot)] fR_pix_plot = fR_pix_plot[~np.isnan(fR_pix_plot)] e11c = e11c[~np.isnan(e11c)] e22c = e22c[~np.isnan(e22c)] e33c = e33c[~np.isnan(e33c)] e12c = e12c[~np.isnan(e12c)] e13c = e13c[~np.isnan(e13c)] e23c = e23c[~np.isnan(e23c)] e11s = e11s[~np.isnan(e11s)] e22s = e22s[~np.isnan(e22s)] e33s = e33s[~np.isnan(e33s)] e12s = e12s[~np.isnan(e12s)] e13s = e13s[~np.isnan(e13s)] e23s = e23s[~np.isnan(e23s)] try: title = "Number of spots and matching rate" fig = plt.figure() axs = fig.subplots(1, 2) axs[0].set_title("Number of spots", loc='center', fontsize=8) axs[0].hist(spots_len_plot, bins=bins) axs[0].set_ylabel('Frequency', fontsize=8) axs[0].tick_params(axis='both', which='major', labelsize=8) axs[0].tick_params(axis='both', which='minor', labelsize=8) axs[1].set_title("matching rate", loc='center', fontsize=8) axs[1].hist(mr_plot, bins=bins) axs[1].set_ylabel('Frequency', fontsize=8) axs[1].tick_params(axis='both', which='major', labelsize=8) axs[1].tick_params(axis='both', which='minor', labelsize=8) plt.tight_layout() plt.savefig(model_direc+ "//"+title+'.png', format='png', dpi=1000) plt.close(fig) except: pass try: title = "Initial and Final residues" fig = plt.figure() axs = fig.subplots(1, 2) axs[0].set_title("Initial residues", loc='center', fontsize=8) axs[0].hist(iR_pix_plot, bins=bins) axs[0].set_ylabel('Frequency', fontsize=8) axs[0].tick_params(axis='both', which='major', labelsize=8) axs[0].tick_params(axis='both', which='minor', labelsize=8) axs[1].set_title("Final residues", loc='center', fontsize=8) axs[1].hist(fR_pix_plot, bins=bins) axs[1].set_ylabel('Frequency', fontsize=8) axs[1].tick_params(axis='both', which='major', labelsize=8) axs[1].tick_params(axis='both', which='minor', labelsize=8) plt.tight_layout() plt.savefig(model_direc+ "//"+title+'.png',format='png', dpi=1000) plt.close(fig) except: pass try: title = "strain Crystal reference" fig = plt.figure() fig.suptitle(title, fontsize=10) axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) logdata = e11c #np.log(e11c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 0].axvline(x=estimated_mu, c="k") axs[0, 0].plot(x1, pdf, 'r') axs[0, 0].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[0, 0].set_ylabel('Frequency', fontsize=8) axs[0, 0].tick_params(axis='both', which='major', labelsize=8) axs[0, 0].tick_params(axis='both', which='minor', labelsize=8) axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) logdata = e22c #np.log(e22c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 1].axvline(x=estimated_mu, c="k") axs[0, 1].plot(x1, pdf, 'r') axs[0, 1].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[0, 1].hist(e22c, bins=bins) axs[0, 1].set_ylabel('Frequency', fontsize=8) axs[0, 1].tick_params(axis='both', which='major', labelsize=8) axs[0, 1].tick_params(axis='both', which='minor', labelsize=8) axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) logdata = e33c #np.log(e33c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 2].axvline(x=estimated_mu, c="k") axs[0, 2].plot(x1, pdf, 'r') axs[0, 2].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[0, 2].hist(e33c, bins=bins) axs[0, 2].set_ylabel('Frequency', fontsize=8) axs[0, 2].tick_params(axis='both', which='major', labelsize=8) axs[0, 2].tick_params(axis='both', which='minor', labelsize=8) axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) logdata = e12c#np.log(e12c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 0].axvline(x=estimated_mu, c="k") axs[1, 0].plot(x1, pdf, 'r') axs[1, 0].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[1, 0].hist(e12c, bins=bins) axs[1, 0].set_ylabel('Frequency', fontsize=8) axs[1, 0].tick_params(axis='both', which='major', labelsize=8) axs[1, 0].tick_params(axis='both', which='minor', labelsize=8) axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) logdata = e13c#np.log(e13c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 1].axvline(x=estimated_mu, c="k") axs[1, 1].plot(x1, pdf, 'r') axs[1, 1].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[1, 1].hist(e13c, bins=bins) axs[1, 1].set_ylabel('Frequency', fontsize=8) axs[1, 1].tick_params(axis='both', which='major', labelsize=8) axs[1, 1].tick_params(axis='both', which='minor', labelsize=8) axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) logdata = e23c#np.log(e23c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 2].axvline(x=estimated_mu, c="k") axs[1, 2].plot(x1, pdf, 'r') axs[1, 2].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[1, 2].hist(e23c, bins=bins) axs[1, 2].set_ylabel('Frequency', fontsize=8) axs[1, 2].tick_params(axis='both', which='major', labelsize=8) axs[1, 2].tick_params(axis='both', which='minor', labelsize=8) plt.tight_layout() plt.savefig(model_direc+ "//"+title+'.png', format='png', dpi=1000) plt.close(fig) except: pass try: title = "strain Sample reference" fig = plt.figure() fig.suptitle(title, fontsize=10) axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) logdata = e11s #np.log(e11c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 0].axvline(x=estimated_mu, c="k") axs[0, 0].plot(x1, pdf, 'r') axs[0, 0].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[0, 0].hist(e11s, bins=bins) axs[0, 0].set_ylabel('Frequency', fontsize=8) axs[0, 0].tick_params(axis='both', which='major', labelsize=8) axs[0, 0].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) logdata = e22s #np.log(e22c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 1].axvline(x=estimated_mu, c="k") axs[0, 1].plot(x1, pdf, 'r') axs[0, 1].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[0, 1].hist(e22s, bins=bins) axs[0, 1].set_ylabel('Frequency', fontsize=8) axs[0, 1].tick_params(axis='both', which='major', labelsize=8) axs[0, 1].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) logdata = e33s #np.log(e33c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 2].axvline(x=estimated_mu, c="k") axs[0, 2].plot(x1, pdf, 'r') axs[0, 2].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[0, 2].hist(e33s, bins=bins) axs[0, 2].set_ylabel('Frequency', fontsize=8) axs[0, 2].tick_params(axis='both', which='major', labelsize=8) axs[0, 2].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) logdata = e12s#np.log(e12c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 0].axvline(x=estimated_mu, c="k") axs[1, 0].plot(x1, pdf, 'r') axs[1, 0].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 0].hist(e12s, bins=bins) axs[1, 0].set_ylabel('Frequency', fontsize=8) axs[1, 0].tick_params(axis='both', which='major', labelsize=8) axs[1, 0].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) logdata = e13s#np.log(e13c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 1].axvline(x=estimated_mu, c="k") axs[1, 1].plot(x1, pdf, 'r') axs[1, 1].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 1].hist(e13s, bins=bins) axs[1, 1].set_ylabel('Frequency', fontsize=8) axs[1, 1].tick_params(axis='both', which='major', labelsize=8) axs[1, 1].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) logdata = e23s#np.log(e23c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 2].axvline(x=estimated_mu, c="k") axs[1, 2].plot(x1, pdf, 'r') axs[1, 2].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 2].hist(e23s, bins=bins) axs[1, 2].set_ylabel('Frequency', fontsize=8) axs[1, 2].tick_params(axis='both', which='major', labelsize=8) axs[1, 2].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) plt.tight_layout() plt.savefig(model_direc+ "//"+title+'.png', format='png', dpi=1000) plt.close(fig) except: pass else: mu_sd = [] mu_sdc = [] material_id = [material_, material1_] for matid in range(2): for index in range(len(spots_len)): ### index for nans nan_index1 = np.where(match_rate[index][0] <= match_rate_threshold)[0] mat_id_index = np.where(mat_global[index][0] != matid+1)[0] nan_index = np.hstack((mat_id_index,nan_index1)) nan_index = np.unique(nan_index) if index == 0: spots_len_plot = np.copy(spots_len[index][0]) mr_plot = np.copy(match_rate[index][0]) iR_pix_plot = np.copy(iR_pix[index][0]) fR_pix_plot = np.copy(fR_pix[index][0]) strain_matrix_plot = np.copy(strain_matrix[index][0]) e11c = strain_matrix_plot[:,0,0]#.reshape((lim_x, lim_y)) e22c = strain_matrix_plot[:,1,1]#.reshape((lim_x, lim_y)) e33c = strain_matrix_plot[:,2,2]#.reshape((lim_x, lim_y)) e12c = strain_matrix_plot[:,0,1]#.reshape((lim_x, lim_y)) e13c = strain_matrix_plot[:,0,2]#.reshape((lim_x, lim_y)) e23c = strain_matrix_plot[:,1,2]#.reshape((lim_x, lim_y)) strain_matrixs_plot = np.copy(strain_matrixs[index][0]) e11s = strain_matrixs_plot[:,0,0]#.reshape((lim_x, lim_y)) e22s = strain_matrixs_plot[:,1,1]#.reshape((lim_x, lim_y)) e33s = strain_matrixs_plot[:,2,2]#.reshape((lim_x, lim_y)) e12s = strain_matrixs_plot[:,0,1]#.reshape((lim_x, lim_y)) e13s = strain_matrixs_plot[:,0,2]#.reshape((lim_x, lim_y)) e23s = strain_matrixs_plot[:,1,2]#.reshape((lim_x, lim_y)) spots_len_plot[nan_index] = np.nan mr_plot[nan_index] = np.nan iR_pix_plot[nan_index] = np.nan fR_pix_plot[nan_index] = np.nan e11c[nan_index] = np.nan e22c[nan_index] = np.nan e33c[nan_index] = np.nan e12c[nan_index] = np.nan e13c[nan_index] = np.nan e23c[nan_index] = np.nan e11s[nan_index] = np.nan e22s[nan_index] = np.nan e33s[nan_index] = np.nan e12s[nan_index] = np.nan e13s[nan_index] = np.nan e23s[nan_index] = np.nan else: temp = np.copy(spots_len[index][0]) temp[nan_index] = np.nan spots_len_plot = np.vstack((spots_len_plot,temp)) temp = np.copy(match_rate[index][0]) temp[nan_index] = np.nan mr_plot = np.vstack((mr_plot,temp)) temp = np.copy(iR_pix[index][0]) temp[nan_index] = np.nan iR_pix_plot = np.vstack((iR_pix_plot,temp)) temp = np.copy(fR_pix[index][0]) temp[nan_index] = np.nan fR_pix_plot = np.vstack((fR_pix_plot,temp)) strain_matrix_plot = np.copy(strain_matrix[index][0]) temp = np.copy(strain_matrix_plot[:,0,0]) temp[nan_index] = np.nan e11c = np.vstack((e11c,temp)) temp = np.copy(strain_matrix_plot[:,1,1]) temp[nan_index] = np.nan e22c = np.vstack((e22c,temp)) temp = np.copy(strain_matrix_plot[:,2,2]) temp[nan_index] = np.nan e33c = np.vstack((e33c,temp)) temp = np.copy(strain_matrix_plot[:,0,1]) temp[nan_index] = np.nan e12c = np.vstack((e12c,temp)) temp = np.copy(strain_matrix_plot[:,0,2]) temp[nan_index] = np.nan e13c = np.vstack((e13c,temp)) temp = np.copy(strain_matrix_plot[:,1,2]) temp[nan_index] = np.nan e23c = np.vstack((e23c,temp)) ## strain_matrixs_plot = np.copy(strain_matrixs[index][0]) temp = np.copy(strain_matrixs_plot[:,0,0]) temp[nan_index] = np.nan e11s = np.vstack((e11s,temp)) temp = np.copy(strain_matrixs_plot[:,1,1]) temp[nan_index] = np.nan e22s = np.vstack((e22s,temp)) temp = np.copy(strain_matrixs_plot[:,2,2]) temp[nan_index] = np.nan e33s = np.vstack((e33s,temp)) temp = np.copy(strain_matrixs_plot[:,0,1]) temp[nan_index] = np.nan e12s = np.vstack((e12s,temp)) temp = np.copy(strain_matrixs_plot[:,0,2]) temp[nan_index] = np.nan e13s = np.vstack((e13s,temp)) temp = np.copy(strain_matrixs_plot[:,1,2]) temp[nan_index] = np.nan e23s = np.vstack((e23s,temp)) spots_len_plot = spots_len_plot.flatten() mr_plot = mr_plot.flatten() iR_pix_plot = iR_pix_plot.flatten() fR_pix_plot = fR_pix_plot.flatten() e11c = e11c.flatten() e22c = e22c.flatten() e33c = e33c.flatten() e12c = e12c.flatten() e13c = e13c.flatten() e23c = e23c.flatten() e11s = e11s.flatten() e22s = e22s.flatten() e33s = e33s.flatten() e12s = e12s.flatten() e13s = e13s.flatten() e23s = e23s.flatten() spots_len_plot = spots_len_plot[~np.isnan(spots_len_plot)] mr_plot = mr_plot[~np.isnan(mr_plot)] iR_pix_plot = iR_pix_plot[~np.isnan(iR_pix_plot)] fR_pix_plot = fR_pix_plot[~np.isnan(fR_pix_plot)] e11c = e11c[~np.isnan(e11c)] e22c = e22c[~np.isnan(e22c)] e33c = e33c[~np.isnan(e33c)] e12c = e12c[~np.isnan(e12c)] e13c = e13c[~np.isnan(e13c)] e23c = e23c[~np.isnan(e23c)] e11s = e11s[~np.isnan(e11s)] e22s = e22s[~np.isnan(e22s)] e33s = e33s[~np.isnan(e33s)] e12s = e12s[~np.isnan(e12s)] e13s = e13s[~np.isnan(e13s)] e23s = e23s[~np.isnan(e23s)] try: title = "Number of spots and matching rate" fig = plt.figure() axs = fig.subplots(1, 2) axs[0].set_title("Number of spots", loc='center', fontsize=8) axs[0].hist(spots_len_plot, bins=bins) axs[0].set_ylabel('Frequency', fontsize=8) axs[0].tick_params(axis='both', which='major', labelsize=8) axs[0].tick_params(axis='both', which='minor', labelsize=8) axs[1].set_title("matching rate", loc='center', fontsize=8) axs[1].hist(mr_plot, bins=bins) axs[1].set_ylabel('Frequency', fontsize=8) axs[1].tick_params(axis='both', which='major', labelsize=8) axs[1].tick_params(axis='both', which='minor', labelsize=8) plt.tight_layout() plt.savefig(model_direc+ "//"+title+"_"+material_id[matid]+'.png', format='png', dpi=1000) plt.close(fig) except: pass try: title = "Initial and Final residues" fig = plt.figure() axs = fig.subplots(1, 2) axs[0].set_title("Initial residues", loc='center', fontsize=8) axs[0].hist(iR_pix_plot, bins=bins) axs[0].set_ylabel('Frequency', fontsize=8) axs[0].tick_params(axis='both', which='major', labelsize=8) axs[0].tick_params(axis='both', which='minor', labelsize=8) axs[1].set_title("Final residues", loc='center', fontsize=8) axs[1].hist(fR_pix_plot, bins=bins) axs[1].set_ylabel('Frequency', fontsize=8) axs[1].tick_params(axis='both', which='major', labelsize=8) axs[1].tick_params(axis='both', which='minor', labelsize=8) plt.tight_layout() plt.savefig(model_direc+ "//"+title+"_"+material_id[matid]+'.png',format='png', dpi=1000) plt.close(fig) except: pass try: title = "strain Crystal reference"+" "+material_id[matid] fig = plt.figure() fig.suptitle(title, fontsize=10) axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) logdata = e11c #np.log(e11c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 0].axvline(x=estimated_mu, c="k") axs[0, 0].plot(x1, pdf, 'r') axs[0, 0].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[0, 0].set_ylabel('Frequency', fontsize=8) axs[0, 0].tick_params(axis='both', which='major', labelsize=8) axs[0, 0].tick_params(axis='both', which='minor', labelsize=8) axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) logdata = e22c #np.log(e22c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 1].axvline(x=estimated_mu, c="k") axs[0, 1].plot(x1, pdf, 'r') axs[0, 1].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[0, 1].hist(e22c, bins=bins) axs[0, 1].set_ylabel('Frequency', fontsize=8) axs[0, 1].tick_params(axis='both', which='major', labelsize=8) axs[0, 1].tick_params(axis='both', which='minor', labelsize=8) axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) logdata = e33c #np.log(e33c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 2].axvline(x=estimated_mu, c="k") axs[0, 2].plot(x1, pdf, 'r') axs[0, 2].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[0, 2].hist(e33c, bins=bins) axs[0, 2].set_ylabel('Frequency', fontsize=8) axs[0, 2].tick_params(axis='both', which='major', labelsize=8) axs[0, 2].tick_params(axis='both', which='minor', labelsize=8) axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) logdata = e12c#np.log(e12c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 0].axvline(x=estimated_mu, c="k") axs[1, 0].plot(x1, pdf, 'r') axs[1, 0].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[1, 0].hist(e12c, bins=bins) axs[1, 0].set_ylabel('Frequency', fontsize=8) axs[1, 0].tick_params(axis='both', which='major', labelsize=8) axs[1, 0].tick_params(axis='both', which='minor', labelsize=8) axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) logdata = e13c#np.log(e13c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 1].axvline(x=estimated_mu, c="k") axs[1, 1].plot(x1, pdf, 'r') axs[1, 1].hist(logdata, bins=bins, density=True, alpha=0.8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) # axs[1, 1].hist(e13c, bins=bins) axs[1, 1].set_ylabel('Frequency', fontsize=8) axs[1, 1].tick_params(axis='both', which='major', labelsize=8) axs[1, 1].tick_params(axis='both', which='minor', labelsize=8) axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) logdata = e23c#np.log(e23c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 2].axvline(x=estimated_mu, c="k") axs[1, 2].plot(x1, pdf, 'r') axs[1, 2].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 2].hist(e23c, bins=bins) axs[1, 2].set_ylabel('Frequency', fontsize=8) axs[1, 2].tick_params(axis='both', which='major', labelsize=8) axs[1, 2].tick_params(axis='both', which='minor', labelsize=8) mu_sdc.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) plt.tight_layout() plt.savefig(model_direc+ "//"+title+'.png', format='png', dpi=1000) plt.close(fig) except: pass try: title = "strain Sample reference"+" "+material_id[matid] fig = plt.figure() fig.suptitle(title, fontsize=10) axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) logdata = e11s #np.log(e11c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 0].axvline(x=estimated_mu, c="k") axs[0, 0].plot(x1, pdf, 'r') axs[0, 0].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[0, 0].hist(e11s, bins=bins) axs[0, 0].set_ylabel('Frequency', fontsize=8) axs[0, 0].tick_params(axis='both', which='major', labelsize=8) axs[0, 0].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) logdata = e22s #np.log(e22c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 1].axvline(x=estimated_mu, c="k") axs[0, 1].plot(x1, pdf, 'r') axs[0, 1].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[0, 1].hist(e22s, bins=bins) axs[0, 1].set_ylabel('Frequency', fontsize=8) axs[0, 1].tick_params(axis='both', which='major', labelsize=8) axs[0, 1].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) logdata = e33s #np.log(e33c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[0, 2].axvline(x=estimated_mu, c="k") axs[0, 2].plot(x1, pdf, 'r') axs[0, 2].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[0, 2].hist(e33s, bins=bins) axs[0, 2].set_ylabel('Frequency', fontsize=8) axs[0, 2].tick_params(axis='both', which='major', labelsize=8) axs[0, 2].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) logdata = e12s#np.log(e12c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 0].axvline(x=estimated_mu, c="k") axs[1, 0].plot(x1, pdf, 'r') axs[1, 0].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 0].hist(e12s, bins=bins) axs[1, 0].set_ylabel('Frequency', fontsize=8) axs[1, 0].tick_params(axis='both', which='major', labelsize=8) axs[1, 0].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) logdata = e13s#np.log(e13c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 1].axvline(x=estimated_mu, c="k") axs[1, 1].plot(x1, pdf, 'r') axs[1, 1].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 1].hist(e13s, bins=bins) axs[1, 1].set_ylabel('Frequency', fontsize=8) axs[1, 1].tick_params(axis='both', which='major', labelsize=8) axs[1, 1].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) logdata = e23s#np.log(e23c) xmin = logdata.min() xmax = logdata.max() x1 = np.linspace(xmin, xmax, 1000) estimated_mu, estimated_sigma = scipy.stats.norm.fit(logdata) pdf = scipy.stats.norm.pdf(x1, loc=estimated_mu, scale=estimated_sigma) axs[1, 2].axvline(x=estimated_mu, c="k") axs[1, 2].plot(x1, pdf, 'r') axs[1, 2].hist(logdata, bins=bins, density=True, alpha=0.8) # axs[1, 2].hist(e23s, bins=bins) axs[1, 2].set_ylabel('Frequency', fontsize=8) axs[1, 2].tick_params(axis='both', which='major', labelsize=8) axs[1, 2].tick_params(axis='both', which='minor', labelsize=8) mu_sd.append((estimated_mu-estimated_sigma, estimated_mu+estimated_sigma)) plt.tight_layout() plt.savefig(model_direc+ "//"+title+'.png', format='png', dpi=1000) plt.close(fig) except: pass if material_ == material1_: matid = 0 for index in range(len(strain_matrix)): nan_index = np.where(match_rate[index][0] <= match_rate_threshold)[0] strain_matrix_plot = np.copy(strain_matrixs[index][0]) strain_matrix_plot[nan_index,:,:] = np.nan fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin, vmax = mu_sd[matid*6] axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) im=axs[0, 0].imshow(strain_matrix_plot[:,0,0].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+1] axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) im=axs[0, 1].imshow(strain_matrix_plot[:,1,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+2] axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) im=axs[0, 2].imshow(strain_matrix_plot[:,2,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+3] axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) im=axs[1, 0].imshow(strain_matrix_plot[:,0,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+4] axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) im=axs[1, 1].imshow(strain_matrix_plot[:,0,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+5] axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) im = axs[1, 2].imshow(strain_matrix_plot[:,1,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ '//figure_strain_UBsample_UB'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) strain_matrix_plot = np.copy(strain_matrix[index][0]) strain_matrix_plot[nan_index,:,:] = np.nan fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin, vmax = mu_sdc[matid*6] axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) im=axs[0, 0].imshow(strain_matrix_plot[:,0,0].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+1] axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) im=axs[0, 1].imshow(strain_matrix_plot[:,1,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+2] axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) im=axs[0, 2].imshow(strain_matrix_plot[:,2,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+3] axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) im=axs[1, 0].imshow(strain_matrix_plot[:,0,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+4] axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) im=axs[1, 1].imshow(strain_matrix_plot[:,0,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+5] axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) im = axs[1, 2].imshow(strain_matrix_plot[:,1,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ '//figure_strain_UBcrystal_UB'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan,np.nan,np.nan col_plot = col_plot.reshape((lim_x, lim_y, 3)) colx_plot = np.copy(colx[index][0]) colx_plot[nan_index,:] = np.nan,np.nan,np.nan colx_plot = colx_plot.reshape((lim_x, lim_y,3)) coly_plot = np.copy(coly[index][0]) coly_plot[nan_index,:] = np.nan,np.nan,np.nan coly_plot = coly_plot.reshape((lim_x, lim_y,3)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"IPF Z map", loc='center', fontsize=8) axs[0].imshow(col_plot, origin='lower') axs[0].set_xticks([]) axs[0].set_yticks([]) axs[1].set_title(r"IPF Y map", loc='center', fontsize=8) axs[1].imshow(coly_plot, origin='lower') axs[1].set_xticks([]) axs[1].set_yticks([]) axs[2].set_title(r"IPF X map", loc='center', fontsize=8) im = axs[2].imshow(colx_plot, origin='lower') axs[2].set_xticks([]) axs[2].set_yticks([]) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ '//IPF_map_UB'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan,np.nan,np.nan col_plot = col_plot.reshape((lim_x, lim_y, 3)) mr_plot = np.copy(match_rate[index][0]) mr_plot[nan_index,:] = np.nan mr_plot = mr_plot.reshape((lim_x, lim_y)) mat_glob = np.copy(mat_global[index][0]) mat_glob[nan_index,:] = np.nan mat_glob = mat_glob.reshape((lim_x, lim_y)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"IPF Z map", loc='center', fontsize=8) axs[0].imshow(col_plot, origin='lower') axs[0].set_xticks([]) axs[0].set_yticks([]) axs[1].set_title(r"Material Index", loc='center', fontsize=8) im = axs[1].imshow(mat_glob, origin='lower', vmin=0, vmax=1) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Matching rate", loc='center', fontsize=8) im = axs[2].imshow(mr_plot, origin='lower', cmap=plt.cm.jet, vmin=0, vmax=100) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ "//figure_global_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) spots_len_plot = np.copy(spots_len[index][0]) spots_len_plot[nan_index,:] = np.nan spots_len_plot = spots_len_plot.reshape((lim_x, lim_y)) iR_pix_plot = np.copy(iR_pix[index][0]) iR_pix_plot[nan_index,:] = np.nan iR_pix_plot = iR_pix_plot.reshape((lim_x, lim_y)) fR_pix_plot = np.copy(fR_pix[index][0]) fR_pix_plot[nan_index,:] = np.nan fR_pix_plot = fR_pix_plot.reshape((lim_x, lim_y)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"Number of spots detected", loc='center', fontsize=8) im = axs[0].imshow(spots_len_plot, origin='lower', cmap=plt.cm.jet) axs[0].set_xticks([]) axs[0].set_yticks([]) divider = make_axes_locatable(axs[0]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[1].set_title(r"Initial pixel residues", loc='center', fontsize=8) im = axs[1].imshow(iR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Final pixel residues", loc='center', fontsize=8) im = axs[2].imshow(fR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+'//figure_mr_ir_fr_UB'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) try: a,b,c,alp,bet,gam = [],[],[],[],[],[] constantlength = "a" if ("a" in strain_free_parameters) and ("b" in strain_free_parameters) and ("c" in strain_free_parameters): constantlength = "a" elif ("b" not in strain_free_parameters) and additional_expression[0]=="none" and\ "b" not in additional_expression[0]: constantlength = "b" elif ("c" not in strain_free_parameters): constantlength = "c" for irot in range(len(rotation_matrix1[index][0])): lattice_parameter_direct_strain = CP.computeLatticeParameters_from_UB(rotation_matrix1[index][0][irot,:,:], material_, constantlength, dictmaterials=dictLT.dict_Materials) a.append(lattice_parameter_direct_strain[0]) b.append(lattice_parameter_direct_strain[1]) c.append(lattice_parameter_direct_strain[2]) alp.append(lattice_parameter_direct_strain[3]) bet.append(lattice_parameter_direct_strain[4]) gam.append(lattice_parameter_direct_strain[5]) logdata = np.array(a) logdata = logdata[~np.isnan(logdata)] rangemina, rangemaxa = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(b) logdata = logdata[~np.isnan(logdata)] rangeminb, rangemaxb = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(c) logdata = logdata[~np.isnan(logdata)] rangeminc, rangemaxc = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(alp) logdata = logdata[~np.isnan(logdata)] rangeminal, rangemaxal = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(bet) logdata = logdata[~np.isnan(logdata)] rangeminbe, rangemaxbe = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(gam) logdata = logdata[~np.isnan(logdata)] rangeminga, rangemaxga = np.min(logdata)-0.01, np.max(logdata)+0.01 fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin = rangemina vmax = rangemaxa axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$a$", loc='center', fontsize=8) strain_matrix_plot = np.array(a) im=axs[0, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminb vmax = rangemaxb axs[0, 1].set_title(r"$b$", loc='center', fontsize=8) strain_matrix_plot = np.array(b) im=axs[0, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminc vmax = rangemaxc axs[0, 2].set_title(r"$c$", loc='center', fontsize=8) strain_matrix_plot = np.array(c) im=axs[0, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminal vmax = rangemaxal axs[1, 0].set_title(r"$\alpha$", loc='center', fontsize=8) strain_matrix_plot = np.array(alp) im=axs[1, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminbe vmax = rangemaxbe axs[1, 1].set_title(r"$\beta$", loc='center', fontsize=8) strain_matrix_plot = np.array(bet) im=axs[1, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminga vmax = rangemaxga axs[1, 2].set_title(r"$\gamma$", loc='center', fontsize=8) strain_matrix_plot = np.array(gam) im = axs[1, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.formatter.set_useOffset(False) cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ "//"+'figure_unitcell_'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: pass try: latticeparams = dictLT.dict_Materials[material_][1] a,b,c,alp,bet,gam = [],[],[],[],[],[] constantlength = "a" if ("a" in strain_free_parameters) and ("b" in strain_free_parameters) and ("c" in strain_free_parameters): constantlength = "a" elif ("b" not in strain_free_parameters) and additional_expression[0]=="none" and \ "b" not in additional_expression[0]: constantlength = "b" elif ("c" not in strain_free_parameters): constantlength = "c" for irot in range(len(rotation_matrix1[index][0])): lattice_parameter_direct_strain = CP.computeLatticeParameters_from_UB(rotation_matrix1[index][0][irot,:,:], material_, constantlength, dictmaterials=dictLT.dict_Materials) a.append(lattice_parameter_direct_strain[0]) b.append(lattice_parameter_direct_strain[1]) c.append(lattice_parameter_direct_strain[2]) alp.append(lattice_parameter_direct_strain[3]) bet.append(lattice_parameter_direct_strain[4]) gam.append(lattice_parameter_direct_strain[5]) logdata = np.array(a) - latticeparams[0] logdata = logdata[~np.isnan(logdata)] rangemina, rangemaxa = np.min(logdata) - 0.01e-2, np.max(logdata) + 0.01e-2 logdata = np.array(b) - latticeparams[1] logdata = logdata[~np.isnan(logdata)] rangeminb, rangemaxb = np.min(logdata) - 0.01e-2, np.max(logdata) + 0.01e-2 logdata = np.array(c) - latticeparams[2] logdata = logdata[~np.isnan(logdata)] rangeminc, rangemaxc = np.min(logdata) - 0.01e-2, np.max(logdata) + 0.01e-2 logdata = np.array(alp) - latticeparams[3] logdata = logdata[~np.isnan(logdata)] rangeminal, rangemaxal = np.min(logdata) - 0.01, np.max(logdata) + 0.01 logdata = np.array(bet) - latticeparams[4] logdata = logdata[~np.isnan(logdata)] rangeminbe, rangemaxbe = np.min(logdata) - 0.01, np.max(logdata) + 0.01 logdata = np.array(gam) - latticeparams[5] logdata = logdata[~np.isnan(logdata)] rangeminga, rangemaxga = np.min(logdata) - 0.01, np.max(logdata) + 0.01 fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin = rangemina vmax = rangemaxa axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$a$", loc='center', fontsize=8) strain_matrix_plot = np.array(a) - latticeparams[0] im=axs[0, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminb vmax = rangemaxb axs[0, 1].set_title(r"$b$", loc='center', fontsize=8) strain_matrix_plot = np.array(b) - latticeparams[1] im=axs[0, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminc vmax = rangemaxc axs[0, 2].set_title(r"$c$", loc='center', fontsize=8) strain_matrix_plot = np.array(c) - latticeparams[2] im=axs[0, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminal vmax = rangemaxal axs[1, 0].set_title(r"$\alpha$", loc='center', fontsize=8) strain_matrix_plot = np.array(alp) - latticeparams[3] im=axs[1, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminbe vmax = rangemaxbe axs[1, 1].set_title(r"$\beta$", loc='center', fontsize=8) strain_matrix_plot = np.array(bet) - latticeparams[4] im=axs[1, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminga vmax = rangemaxga axs[1, 2].set_title(r"$\gamma$", loc='center', fontsize=8) strain_matrix_plot = np.array(gam) - latticeparams[5] im = axs[1, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.formatter.set_useOffset(False) cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc + "//" + 'figure_unitcell_relative_'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: pass else: for matid in range(2): for index in range(len(strain_matrix)): nan_index1 = np.where(match_rate[index][0] <= match_rate_threshold)[0] mat_id_index = np.where(mat_global[index][0] != matid+1)[0] nan_index = np.hstack((mat_id_index,nan_index1)) nan_index = np.unique(nan_index) strain_matrix_plot = np.copy(strain_matrixs[index][0]) strain_matrix_plot[nan_index,:,:] = np.nan fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) try: vmin, vmax = mu_sd[matid*6] axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) im=axs[0, 0].imshow(strain_matrix_plot[:,0,0].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+1] axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) im=axs[0, 1].imshow(strain_matrix_plot[:,1,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+2] axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) im=axs[0, 2].imshow(strain_matrix_plot[:,2,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+3] axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) im=axs[1, 0].imshow(strain_matrix_plot[:,0,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+4] axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) im=axs[1, 1].imshow(strain_matrix_plot[:,0,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sd[matid*6+5] axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) im = axs[1, 2].imshow(strain_matrix_plot[:,1,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ '//figure_strain_UBsample_mat'+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: print("Error in strain plot") strain_matrix_plot = np.copy(strain_matrix[index][0]) strain_matrix_plot[nan_index,:,:] = np.nan try: fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin, vmax = mu_sdc[matid*6] axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$\epsilon_{11}$ (%)", loc='center', fontsize=8) im=axs[0, 0].imshow(strain_matrix_plot[:,0,0].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+1] axs[0, 1].set_title(r"$\epsilon_{22}$ (%)", loc='center', fontsize=8) im=axs[0, 1].imshow(strain_matrix_plot[:,1,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+2] axs[0, 2].set_title(r"$\epsilon_{33}$ (%)", loc='center', fontsize=8) im=axs[0, 2].imshow(strain_matrix_plot[:,2,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+3] axs[1, 0].set_title(r"$\epsilon_{12}$ (%)", loc='center', fontsize=8) im=axs[1, 0].imshow(strain_matrix_plot[:,0,1].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+4] axs[1, 1].set_title(r"$\epsilon_{13}$ (%)", loc='center', fontsize=8) im=axs[1, 1].imshow(strain_matrix_plot[:,0,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin, vmax = mu_sdc[matid*6+5] axs[1, 2].set_title(r"$\epsilon_{23}$ (%)", loc='center', fontsize=8) im = axs[1, 2].imshow(strain_matrix_plot[:,1,2].reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ '//figure_strain_UBcrystal_mat'+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: print("Error in strain plots") col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan,np.nan,np.nan col_plot = col_plot.reshape((lim_x, lim_y, 3)) colx_plot = np.copy(colx[index][0]) colx_plot[nan_index,:] = np.nan,np.nan,np.nan colx_plot = colx_plot.reshape((lim_x, lim_y,3)) coly_plot = np.copy(coly[index][0]) coly_plot[nan_index,:] = np.nan,np.nan,np.nan coly_plot = coly_plot.reshape((lim_x, lim_y,3)) try: fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"IPF Z map", loc='center', fontsize=8) axs[0].imshow(col_plot, origin='lower') axs[0].set_xticks([]) axs[0].set_yticks([]) axs[1].set_title(r"IPF Y map", loc='center', fontsize=8) axs[1].imshow(coly_plot, origin='lower') axs[1].set_xticks([]) axs[1].set_yticks([]) axs[2].set_title(r"IPF X map", loc='center', fontsize=8) im = axs[2].imshow(colx_plot, origin='lower') axs[2].set_xticks([]) axs[2].set_yticks([]) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ '//IPF_map_mat'+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan,np.nan,np.nan col_plot = col_plot.reshape((lim_x, lim_y, 3)) mr_plot = np.copy(match_rate[index][0]) mr_plot[nan_index,:] = np.nan mr_plot = mr_plot.reshape((lim_x, lim_y)) mat_glob = np.copy(mat_global[index][0]) mat_glob[nan_index,:] = np.nan mat_glob = mat_glob.reshape((lim_x, lim_y)) fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"IPF Z map", loc='center', fontsize=8) axs[0].imshow(col_plot, origin='lower') axs[0].set_xticks([]) axs[0].set_yticks([]) axs[1].set_title(r"Material Index", loc='center', fontsize=8) im = axs[1].imshow(mat_glob, origin='lower', vmin=0, vmax=2) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Matching rate", loc='center', fontsize=8) im = axs[2].imshow(mr_plot, origin='lower', cmap=plt.cm.jet, vmin=0, vmax=100) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ "//figure_global_mat"+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: print("Error in plots") spots_len_plot = np.copy(spots_len[index][0]) spots_len_plot[nan_index,:] = np.nan spots_len_plot = spots_len_plot.reshape((lim_x, lim_y)) iR_pix_plot = np.copy(iR_pix[index][0]) iR_pix_plot[nan_index,:] = np.nan iR_pix_plot = iR_pix_plot.reshape((lim_x, lim_y)) fR_pix_plot = np.copy(fR_pix[index][0]) fR_pix_plot[nan_index,:] = np.nan fR_pix_plot = fR_pix_plot.reshape((lim_x, lim_y)) try: fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) axs = fig.subplots(1, 3) axs[0].set_title(r"Number of spots detected", loc='center', fontsize=8) im = axs[0].imshow(spots_len_plot, origin='lower', cmap=plt.cm.jet) axs[0].set_xticks([]) axs[0].set_yticks([]) divider = make_axes_locatable(axs[0]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[1].set_title(r"Initial pixel residues", loc='center', fontsize=8) im = axs[1].imshow(iR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[1].set_xticks([]) axs[1].set_yticks([]) divider = make_axes_locatable(axs[1]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') axs[2].set_title(r"Final pixel residues", loc='center', fontsize=8) im = axs[2].imshow(fR_pix_plot, origin='lower', cmap=plt.cm.jet) axs[2].set_xticks([]) axs[2].set_yticks([]) divider = make_axes_locatable(axs[2]) cax = divider.append_axes('right', size='5%', pad=0.05) fig.colorbar(im, cax=cax, orientation='vertical') for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+'//figure_mr_ir_fr_mat'+str(matid)+"_UB"+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: print("Error in plots") try: a,b,c,alp,bet,gam = [],[],[],[],[],[] constantlength = "a" if ("a" in strain_free_parameters) and ("b" in strain_free_parameters) and ("c" in strain_free_parameters): constantlength = "a" elif ("b" not in strain_free_parameters) and additional_expression[0]=="none" and\ "b" not in additional_expression[0]: constantlength = "b" elif ("c" not in strain_free_parameters): constantlength = "c" for irot in range(len(rotation_matrix1[index][0])): lattice_parameter_direct_strain = CP.computeLatticeParameters_from_UB(rotation_matrix1[index][0][irot,:,:], material_, constantlength, dictmaterials=dictLT.dict_Materials) a.append(lattice_parameter_direct_strain[0]) b.append(lattice_parameter_direct_strain[1]) c.append(lattice_parameter_direct_strain[2]) alp.append(lattice_parameter_direct_strain[3]) bet.append(lattice_parameter_direct_strain[4]) gam.append(lattice_parameter_direct_strain[5]) logdata = np.array(a) logdata = logdata[~np.isnan(logdata)] rangemina, rangemaxa = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(b) logdata = logdata[~np.isnan(logdata)] rangeminb, rangemaxb = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(c) logdata = logdata[~np.isnan(logdata)] rangeminc, rangemaxc = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(alp) logdata = logdata[~np.isnan(logdata)] rangeminal, rangemaxal = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(bet) logdata = logdata[~np.isnan(logdata)] rangeminbe, rangemaxbe = np.min(logdata)-0.01, np.max(logdata)+0.01 logdata = np.array(gam) logdata = logdata[~np.isnan(logdata)] rangeminga, rangemaxga = np.min(logdata)-0.01, np.max(logdata)+0.01 fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin = rangemina vmax = rangemaxa axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$a$", loc='center', fontsize=8) strain_matrix_plot = np.array(a) im=axs[0, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminb vmax = rangemaxb axs[0, 1].set_title(r"$b$", loc='center', fontsize=8) strain_matrix_plot = np.array(b) im=axs[0, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminc vmax = rangemaxc axs[0, 2].set_title(r"$c$", loc='center', fontsize=8) strain_matrix_plot = np.array(c) im=axs[0, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminal vmax = rangemaxal axs[1, 0].set_title(r"$\alpha$", loc='center', fontsize=8) strain_matrix_plot = np.array(alp) im=axs[1, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminbe vmax = rangemaxbe axs[1, 1].set_title(r"$\beta$", loc='center', fontsize=8) strain_matrix_plot = np.array(bet) im=axs[1, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminga vmax = rangemaxga axs[1, 2].set_title(r"$\gamma$", loc='center', fontsize=8) strain_matrix_plot = np.array(gam) im = axs[1, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.formatter.set_useOffset(False) cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc+ "//"+'figure_unitcell_'+str(matid)+'_'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: pass try: latticeparams = dictLT.dict_Materials[material_][1] a,b,c,alp,bet,gam = [],[],[],[],[],[] constantlength = "a" if ("a" in strain_free_parameters) and ("b" in strain_free_parameters) and ("c" in strain_free_parameters): constantlength = "a" elif ("b" not in strain_free_parameters) and additional_expression[0]=="none" and \ "b" not in additional_expression[0]: constantlength = "b" elif ("c" not in strain_free_parameters): constantlength = "c" for irot in range(len(rotation_matrix1[index][0])): lattice_parameter_direct_strain = CP.computeLatticeParameters_from_UB(rotation_matrix1[index][0][irot,:,:], material_, constantlength, dictmaterials=dictLT.dict_Materials) a.append(lattice_parameter_direct_strain[0]) b.append(lattice_parameter_direct_strain[1]) c.append(lattice_parameter_direct_strain[2]) alp.append(lattice_parameter_direct_strain[3]) bet.append(lattice_parameter_direct_strain[4]) gam.append(lattice_parameter_direct_strain[5]) logdata = np.array(a) - latticeparams[0] logdata = logdata[~np.isnan(logdata)] rangemina, rangemaxa = np.min(logdata) - 0.01e-2, np.max(logdata) + 0.01e-2 logdata = np.array(b) - latticeparams[1] logdata = logdata[~np.isnan(logdata)] rangeminb, rangemaxb = np.min(logdata) - 0.01e-2, np.max(logdata) + 0.01e-2 logdata = np.array(c) - latticeparams[2] logdata = logdata[~np.isnan(logdata)] rangeminc, rangemaxc = np.min(logdata) - 0.01e-2, np.max(logdata) + 0.01e-2 logdata = np.array(alp) - latticeparams[3] logdata = logdata[~np.isnan(logdata)] rangeminal, rangemaxal = np.min(logdata) - 0.01, np.max(logdata) + 0.01 logdata = np.array(bet) - latticeparams[4] logdata = logdata[~np.isnan(logdata)] rangeminbe, rangemaxbe = np.min(logdata) - 0.01, np.max(logdata) + 0.01 logdata = np.array(gam) - latticeparams[5] logdata = logdata[~np.isnan(logdata)] rangeminga, rangemaxga = np.min(logdata) - 0.01, np.max(logdata) + 0.01 fig = plt.figure(figsize=(11.69,8.27), dpi=100) bottom, top = 0.1, 0.9 left, right = 0.1, 0.8 fig.subplots_adjust(top=top, bottom=bottom, left=left, right=right, hspace=0.15, wspace=0.25) vmin = rangemina vmax = rangemaxa axs = fig.subplots(2, 3) axs[0, 0].set_title(r"$a$", loc='center', fontsize=8) strain_matrix_plot = np.array(a) - latticeparams[0] im=axs[0, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[0, 0].set_xticks([]) axs[0, 0].set_yticks([]) divider = make_axes_locatable(axs[0,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminb vmax = rangemaxb axs[0, 1].set_title(r"$b$", loc='center', fontsize=8) strain_matrix_plot = np.array(b) - latticeparams[1] im=axs[0, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminc vmax = rangemaxc axs[0, 2].set_title(r"$c$", loc='center', fontsize=8) strain_matrix_plot = np.array(c) - latticeparams[2] im=axs[0, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) divider = make_axes_locatable(axs[0,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminal vmax = rangemaxal axs[1, 0].set_title(r"$\alpha$", loc='center', fontsize=8) strain_matrix_plot = np.array(alp) - latticeparams[3] im=axs[1, 0].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 0].set_xticks([]) axs[1, 0].set_yticks([]) divider = make_axes_locatable(axs[1,0]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminbe vmax = rangemaxbe axs[1, 1].set_title(r"$\beta$", loc='center', fontsize=8) strain_matrix_plot = np.array(bet) - latticeparams[4] im=axs[1, 1].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 1].set_xticks([]) divider = make_axes_locatable(axs[1,1]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.ax.tick_params(labelsize=8) vmin = rangeminga vmax = rangemaxga axs[1, 2].set_title(r"$\gamma$", loc='center', fontsize=8) strain_matrix_plot = np.array(gam) - latticeparams[5] im = axs[1, 2].imshow(strain_matrix_plot.reshape((lim_x, lim_y)), origin='lower', cmap=plt.cm.jet, vmin=vmin, vmax=vmax) axs[1, 2].set_xticks([]) divider = make_axes_locatable(axs[1,2]) cax = divider.append_axes('right', size='5%', pad=0.05) cbar = fig.colorbar(im, cax=cax, orientation='vertical') cbar.formatter.set_useOffset(False) cbar.ax.tick_params(labelsize=8) for ax in axs.flat: ax.label_outer() plt.savefig(model_direc + "//" + 'figure_unitcell_relative_'+str(matid)+'_'+str(index)+'.png', bbox_inches='tight',format='png', dpi=1000) plt.close(fig) except: pass def sst_texture(orient_data=None, col_array=None, direc="", symmetry=None, symmetry_name=None, lattice=None, axis="Z", fn="", symms=None): print("symmetry of the current phase is : "+symmetry_name) if np.max(col_array) > 1: col_array[np.where(col_array>1)]=1 fig = plt.figure(1) if symmetry_name == "cubic": pole_hkls = ['111','110','100'] ax1 = fig.add_subplot(221, aspect='equal') ax2 = fig.add_subplot(222, aspect='equal') ax3 = fig.add_subplot(223, aspect='equal') ax4 = fig.add_subplot(224, aspect='equal') elif symmetry_name == "hexagonal": pole_hkls = ['001','100','101','102','110'] ax1 = fig.add_subplot(231, aspect='equal') ax2 = fig.add_subplot(232, aspect='equal') ax3 = fig.add_subplot(233, aspect='equal') ax4 = fig.add_subplot(234, aspect='equal') ax5 = fig.add_subplot(235, aspect='equal') ax6 = fig.add_subplot(236, aspect='equal') else: print("PF and IPF plots are only supported for Cubic and Hexagonal systems for now") return for pfs in range(len(pole_hkls)): pf1 = PoleFigure(hkl=pole_hkls[pfs], proj='stereo', lattice=lattice, axis=axis) pf1.mksize = 1. if pfs == 0: pf1.plot_pf(col_array, orient_data, ax=ax1, ftsize=6) elif pfs == 1: pf1.plot_pf(col_array, orient_data, ax=ax2, ftsize=6) elif pfs == 2: pf1.plot_pf(col_array, orient_data, ax=ax3, ftsize=6) elif pfs == 3: pf1.plot_pf(col_array, orient_data, ax=ax4, ftsize=6) elif pfs == 4: pf1.plot_pf(col_array, orient_data, ax=ax5, ftsize=6) if symmetry_name == "cubic": pf1.plot_sst_color(col_array, orient_data, ax=ax4, ftsize=6, phase=0, symms=symms) elif symmetry_name == "hexagonal": pf1.plot_sst_color(col_array, orient_data, ax=ax6, ftsize=6, phase=1, symms=symms) plt.savefig(direc+"//PF_IPF_"+fn+".png", bbox_inches='tight',format='png', dpi=1000) plt.close() def save_sst(lim_x, lim_y, strain_matrix, strain_matrixs, col, colx, coly, match_rate, mat_global, spots_len, iR_pix, fR_pix, model_direc, material_, material1_, lattice_, lattice1_, symmetry_, symmetry1_, crystal, crystal1, rotation_matrix1, symmetry_name, symmetry1_name, mac_axis = [0., 0., 1.],axis_text="Z",match_rate_threshold = 5): rotation_matrix_sst = [[] for i in range(len(rotation_matrix1))] for i in range(len(rotation_matrix1)): rotation_matrix_sst[i].append(np.zeros((lim_x*lim_y,3,3))) for i in range(len(rotation_matrix1)): temp_mat = rotation_matrix1[i][0] for j in range(len(temp_mat)): orientation_matrix123 = temp_mat[j,:,:] # ## rotate orientation by 40degrees to bring in Sample RF omega = np.deg2rad(-40.0) # rotation de -omega autour de l'axe x (or Y?) pour repasser dans Rsample cw = np.cos(omega) sw = np.sin(omega) mat_from_lab_to_sample_frame = np.array([[cw, 0.0, sw], [0.0, 1.0, 0.0], [-sw, 0, cw]]) orientation_matrix123 = np.dot(mat_from_lab_to_sample_frame.T, orientation_matrix123) if np.linalg.det(orientation_matrix123) < 0: orientation_matrix123 = -orientation_matrix123 rotation_matrix_sst[i][0][j,:,:] = orientation_matrix123 rangeval = len(match_rate) if material_ == material1_: for index in range(rangeval): ### index for nans nan_index = np.where(match_rate[index][0] <= match_rate_threshold)[0] if index == 0: rotation_matrix_plot = np.copy(rotation_matrix_sst[index][0]) col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan rotation_matrix_plot[nan_index,:,:] = np.nan sst_texture(orient_data=rotation_matrix_plot, col_array=col_plot, direc=model_direc, symmetry=symmetry_, symmetry_name = symmetry_name, lattice=lattice_, axis=axis_text, fn="UB_"+str(index), symms=crystal._hklsym) else: tempori = np.copy(rotation_matrix_sst[index][0]) tempori[nan_index,:,:] = np.nan rotation_matrix_plot = np.vstack((rotation_matrix_plot,tempori)) tempcol = np.copy(col[index][0]) tempcol[nan_index,:] = np.nan col_plot = np.vstack((col_plot,tempcol)) sst_texture(orient_data=tempori, col_array=tempcol, direc=model_direc, symmetry=symmetry_, symmetry_name = symmetry_name, lattice=lattice_, axis=axis_text, fn="UB_"+str(index), symms=crystal._hklsym) ### Plot pole figures and IPF (cubic and hexagonal are supported for now) sst_texture(orient_data=rotation_matrix_plot, col_array=col_plot, direc=model_direc, symmetry=symmetry_, symmetry_name = symmetry_name, lattice=lattice_, axis=axis_text, fn="all_UBs", symms=crystal._hklsym) else: for matid in range(2): if matid == 0: symmetry_name_plot = symmetry_name symmetry_plot = symmetry_ lattice_plot = lattice_ symms = crystal._hklsym else: symmetry_name_plot = symmetry1_name symmetry_plot = symmetry1_ lattice_plot = lattice1_ symms = crystal1._hklsym for index in range(rangeval): ### index for nans nan_index1 = np.where(match_rate[index][0] <= match_rate_threshold)[0] mat_id_index = np.where(mat_global[index][0] != matid+1)[0] nan_index = np.hstack((mat_id_index,nan_index1)) nan_index = np.unique(nan_index) if index == 0: rotation_matrix_plot = np.copy(rotation_matrix_sst[index][0]) rotation_matrix_plot[nan_index,:,:] = np.nan col_plot = np.copy(col[index][0]) col_plot[nan_index,:] = np.nan sst_texture(orient_data=rotation_matrix_plot, col_array=col_plot, direc=model_direc, symmetry=symmetry_plot, symmetry_name = symmetry_name_plot, lattice=lattice_plot, axis=axis_text, fn="mat_"+str(matid)+"_UB_"+str(index), symms=symms) else: tempori = np.copy(rotation_matrix_sst[index][0]) tempori[nan_index,:,:] = np.nan rotation_matrix_plot = np.vstack((rotation_matrix_plot,tempori)) tempcol = np.copy(col[index][0]) tempcol[nan_index,:] = np.nan col_plot = np.vstack((col_plot,tempcol)) sst_texture(orient_data=tempori, col_array=tempcol, direc=model_direc, symmetry=symmetry_plot, symmetry_name = symmetry_name_plot, lattice=lattice_plot, axis=axis_text, fn="mat_"+str(matid)+"_UB_"+str(index), symms=symms) sst_texture(orient_data=rotation_matrix_plot, col_array=col_plot, direc=model_direc, symmetry=symmetry_plot, symmetry_name = symmetry_name_plot, lattice=lattice_plot, axis=axis_text, fn="mat_"+str(matid)+"_all_UBs", symms=symms) texttstr = "\n\ ### config file for LaueNeuralNetwork \n\ [CPU]\n\ n_cpu = 8\n\ \n\ [GLOBAL_DIRECTORY]\n\ prefix = \n\ ## directory where all training related data and results will be saved \n\ main_directory = C:\\Users\\purushot\\Desktop\\pattern_matching\\experimental\\GUIv0\\latest_version\n\ \n\ [MATERIAL]\n\ ## same material key as lauetools (see dictlauetools.py for complete key)\n\ ## as of now symmetry can be cubic, hexagonal, orthorhombic, tetragonal, trigonal, monoclinic, triclinic\n\ \n\ material = In2Bi\n\ symmetry = hexagonal\n\ space_group = between 1 and 230\n\ general_diffraction_rules = true\n\ \n\ ## if second phase is present, else none\n\ material1 = In_epsilon\n\ symmetry1 = tetragonal\n\ space_group1 = between 1 and 230\n\ general_diffraction_rules1 = true\n\ \n\ [DETECTOR]\n\ ## path to detector calibration file (.det)\n\ detectorfile = C:\\Users\\purushot\\Desktop\\In_JSM\\calib.det\n\ ## Max and Min energy to be used for generating training dataset, as well as for calcualting matching rate\n\ emax = 21\n\ emin = 5\n\ \n\ [TRAINING]\n\ ## classes_with_frequency_to_remove: HKL class with less appearance than specified will be ignored in output\n\ ## desired_classes_output : can be all or an integer: to limit the number of output classes\n\ ## max_HKL_index : can be auto or integer: Maximum index of HKL to build output classes\n\ ## max_nb_grains : Maximum number of grains to simulate per lauepattern\n\ ####### Material 0\n\ classes_with_frequency_to_remove = 500\n\ desired_classes_output = all\n\ max_HKL_index = 5\n\ max_nb_grains = 1\n\ ####### Material 1\n\ ## HKL class with less appearance than specified will be ignored in output\n\ classes_with_frequency_to_remove1 = 500\n\ desired_classes_output1 = all\n\ max_HKL_index1 = 5\n\ max_nb_grains1 = 1\n\ \n\ ## Max number of simulations per number of grains\n\ ## Include single crystal misorientation (1 deg) data in training\n\ ## Maximum angular distance to probe (in deg)\n\ ## step size in angular distribution to discretize (in deg)\n\ ## batch size and epochs for training\n\ max_simulations = 1000\n\ include_small_misorientation = false\n\ misorientation_angle = 30\n\ angular_distance = 90\n\ step_size = 0.1\n\ batch_size = 50\n\ epochs = 5\n\ \n\ [PREDICTION]\n\ # model_weight_file: if none, it will select by default the latest H5 weight file, else provide a specific model\n\ # softmax_threshold_global: thresholding to limit the predicted spots search zone\n\ # mr_threshold_global: thresholding to ignore all matricies less than the MR threshold\n\ # cap_matchrate: any UB matrix providing MR less than this will be ignored\n\ # coeff: should be same as cap_matchrate or no? (this is for try previous UB matrix)\n\ # coeff_overlap: coefficient to limit the overlapping between spots; if more than this, new solution will be computed\n\ # mode_spotCycle: How to cycle through predicted spots (slow or graphmode )\n\ UB_matrix_to_detect = 1\n\ \n\ matrix_tolerance = 0.9\n\ matrix_tolerance1 = 0.9\n\ \n\ material0_limit = 1\n\ material1_limit = 1\n\ \n\ model_weight_file = none\n\ softmax_threshold_global = 0.85\n\ mr_threshold_global = 0.80\n\ cap_matchrate = 0.01\n\ coeff = 0.3\n\ coeff_overlap = 0.3\n\ mode_spotCycle = slow\n\ ##true for few crystal and prefered texture case, otherwise time consuming; advised for single phase alone\n\ use_previous = true\n\ \n\ [EXPERIMENT]\n\ experiment_directory = C:\\Users\\purushot\\Desktop\\In_JSM\\ech875_ROI01\n\ experiment_file_prefix = ech875_ROI01_\n\ image_grid_x = 51\n\ image_grid_y = 51\n\ \n\ [PEAKSEARCH]\n\ intensity_threshold = 90\n\ boxsize = 15\n\ fit_peaks_gaussian = 1\n\ FitPixelDev = 15\n\ NumberMaxofFits = 3000\n\ \n\ [STRAINCALCULATION]\n\ strain_compute = true\n\ tolerance_strain_refinement = 0.7,0.6,0.5,0.4,0.3,0.2\n\ tolerance_strain_refinement1 = 0.7,0.6,0.5,0.4,0.3,0.2\n\ free_parameters = b,c,alpha,beta,gamma\n\ \n\ [POSTPROCESS]\n\ hkls_subsets = [1,1,0],[1,0,0],[1,1,1]\n\ \n\ \n\ [CALLER]\n\ residues_threshold=0.15\n\ nb_spots_global_threshold=10\n\ option_global = v1\n\ use_om_user = true\n\ nb_spots_consider = 100\n\ # User defined orientation matrix supplied in a file\n\ use_om_user = false\n\ path_user_OM = ""\n\ [DEVELOPMENT]\n\ # could be 1 or 2 / none in case of single phase\n\ material_phase_always_present = 1\n\ matrix_phase_always_present = 0.5673,0.5334,-0.6264,-0.6814,0.7330,0.00604,0.4625,0.4245,0.7805;Si\n\ generate_additional_data=false\n\ write_MTEX_file = true\n\ \n\ # Laue Groups\n\ # space group 1 -- triclinic: '-1'\n\ # space group 2 -- monoclinic: '2/m'\n\ # space group 3 -- orthorhombic: 'mmm'\n\ # space group 4 -- tetragonal: '4/m'\n\ # space group 5 -- tetragonal: '4/mmm'\n\ # space group 6 -- trigonal: '-3'\n\ # space group 7 -- trigonal: '-3m'\n\ # space group 8 -- hexagonal: '6/m'\n\ # space group 9 -- hexagonal: '6/mmm'\n\ # space group 10 -- cubic: 'm3'\n\ # space group 11 -- cubic: 'm3m'" class Transform(object): def __init__(self, matrix): self.matrix = matrix self._imatrix = None @property def imatrix(self): if self._imatrix is None: try: self._imatrix = np.linalg.inv(self.matrix) except np.linalg.LinAlgError: raise Exception("XU.math.Transform: matrix cannot be inverted" " - seems to be singular") return self._imatrix def inverse(self, args, rank=1): """ performs inverse transformation a vector, matrix or tensor of rank 4 Parameters ---------- args : list or array-like object to transform, list or np array of shape (..., n) (..., n, n), (..., n, n, n, n) where n is the size of the transformation matrix. rank : int rank of the supplied object. allowed values are 1, 2, and 4 """ it = Transform(self.imatrix) return it(args, rank) def __call__(self, args, rank=1): """ transforms a vector, matrix or tensor of rank 4 (e.g. elasticity tensor) Parameters ---------- args : list or array-like object to transform, list or np array of shape (..., n) (..., n, n), (..., n, n, n, n) where n is the size of the transformation matrix. rank : int rank of the supplied object. allowed values are 1, 2, and 4 """ m = self.matrix if rank == 1: # argument is a vector # out_i = m_ij * args_j out = np.einsum('ij,...j', m, args) elif rank == 2: # argument is a matrix # out_ij = m_ik * m_jl * args_kl out = np.einsum('ik, jl,...kl', m, m, args) elif rank == 4: # cp_ijkl = m_in * m_jo * m_kp * m_lq * args_nopq out = np.einsum('in, jo, kp, lq,...nopq', m, m, m, m, args) return out def __str__(self): ostr = "Transformation matrix:\n" ostr += str(self.matrix) return ostr def VecCross(v1, v2, out=None): """ Calculate the vector cross product. Parameters ---------- v1, v2 : list or array-like input vector(s), either one vector or an array of vectors with shape (n, 3) out : list or array-like, optional output vector Returns ------- ndarray cross product either of shape (3, ) or (n, 3) """ if isinstance(v1, np.ndarray): if len(v1.shape) >= 2 or len(v2.shape) >= 2: return np.cross(v1, v2) if len(v1) != 3 or len(v2) != 3: raise ValueError("Vectors must be of size 3! (len(v1)=%d len(v2)=%d)" % (len(v1), len(v2))) if out is None: out = np.empty(3) out[0] = v1[1] * v2[2] - v1[2] * v2[1] out[1] = v1[2] * v2[0] - v1[0] * v2[2] out[2] = v1[0] * v2[1] - v1[1] * v2[0] return out def get_possible_sgrp_suf(sgrp_nr): """ determine possible space group suffix. Multiple suffixes might be possible for one space group due to different origin choice, unique axis, or choice of the unit cell shape. Parameters ---------- sgrp_nr : int space group number Returns ------- str or list either an empty string or a list of possible valid suffix strings """ sgrp_suf = '' if sgrp_nr in [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]: sgrp_suf = [':b', ':c'] elif sgrp_nr in [48, 50, 59, 68, 70, 85, 86, 88, 125, 126, 129, 130, 133, 134, 137, 138, 141, 142, 201, 203, 222, 224, 227, 228]: sgrp_suf = [':1', ':2'] elif sgrp_nr in [146, 148, 155, 160, 161, 166, 167]: sgrp_suf = [':H', ':R'] return sgrp_suf def get_default_sgrp_suf(sgrp_nr): """ determine default space group suffix """ possibilities = get_possible_sgrp_suf(sgrp_nr) if possibilities: return possibilities[0] else: return '' class SGLattice(object): """ lattice object created from the space group number and corresponding unit cell parameters. """ def __init__(self, sgrp, *args): """ initialize class with space group number and atom list Parameters ---------- sgrp : int or str Space group number *args : float space group parameters. depending on the space group number this are 1 (cubic) to 6 (triclinic) parameters. cubic : a (lattice parameter). hexagonal : a, c. trigonal : a, c. tetragonal : a, c. orthorhombic : a, b, c. monoclinic : a, b, c, beta (in degree). triclinic : a, b, c, alpha, beta, gamma (in degree). """ self.space_group = str(sgrp) self.space_group_nr = int(self.space_group.split(':')[0]) try: self.space_group_suf = ':' + self.space_group.split(':')[1] except IndexError: self.space_group_suf = get_default_sgrp_suf(self.space_group_nr) if self.space_group_suf != '': self.space_group = str(self.space_group_nr) + self.space_group_suf self.name = sgrp_name[str(self.space_group_nr)] + self.space_group_suf self.crystal_system, nargs = sgrp_sym[self.space_group_nr] self.crystal_system += self.space_group_suf if len(args) != nargs: raise ValueError('XU: number of parameters (%d) does not match the' ' crystal symmetry (%s:%d)' % (len(args), self.crystal_system, nargs)) self.free_parameters = OrderedDict() for a, par in zip(args, sgrp_params[self.crystal_system][0]): self.free_parameters[par] = a self._parameters = OrderedDict() for i, p in enumerate(('a', 'b', 'c', 'alpha', 'beta', 'gamma')): key = sgrp_params[self.crystal_system][1][i] if isinstance(key, str): self._parameters[p] = self.free_parameters[key] else: self._parameters[p] = key # define lattice vectors self._ai = np.zeros((3, 3)) self._bi = np.empty((3, 3)) a, b, c, alpha, beta, gamma = self._parameters.values() ra = radians(alpha) self._paramhelp = [cos(ra), cos(radians(beta)), cos(radians(gamma)), sin(ra), 0] self._setlat() # save general Wyckoff position self._gplabel = sorted(wp[self.space_group], key=lambda s: int(s[:-1]))[-1] self._gp = wp[self.space_group][self._gplabel] # symmetry operations and reflection conditions placeholder self._hklmat = [] self._symops = [] self._hklcond = [] self._hklcond_wp = [] self._iscentrosymmetric = None @property def symops(self): """ return the set of symmetry operations from the general Wyckoff position of the space group. """ if self._symops == []: for p in self._gp[1]: self._symops.append(SymOp.from_xyz(p)) return self._symops @property def _hklsym(self): if self._hklmat == []: for s in self.symops: self._hklmat.append(np.round(self._qtransform.imatrix @ self._transform.matrix @ s.D @ self._transform.imatrix @ self._qtransform.matrix, DIGITS)) return self._hklmat def _setlat(self): a, b, c, alpha, beta, gamma = self._parameters.values() ca, cb, cg, sa, vh = self._paramhelp vh = sqrt(1 - ca**2-cb**2-cg**2 + 2*ca*cb*cg) self._paramhelp[4] = vh self._ai[0, 0] = a * vh / sa self._ai[0, 1] = a * (cg-cb*ca) / sa self._ai[0, 2] = a * cb self._ai[1, 1] = b * sa self._ai[1, 2] = b * ca self._ai[2, 2] = c self._transform = Transform(self._ai.T) self._setb() def _setb(self): V = self.UnitCellVolume() p = 2. * np.pi / V VecCross(p*self._ai[1, :], self._ai[2, :], out=self._bi[0, :]) VecCross(p*self._ai[2, :], self._ai[0, :], out=self._bi[1, :]) VecCross(p*self._ai[0, :], self._ai[1, :], out=self._bi[2, :]) self._qtransform = Transform(self._bi.T) def _set_params_from_sym(self): for i, p in enumerate(('a', 'b', 'c', 'alpha', 'beta', 'gamma')): key = sgrp_params[self.crystal_system][1][i] if isinstance(key, str): if p not in self.free_parameters: self._parameters[p] = self.free_parameters[key] @property def a(self): return self._parameters['a'] @a.setter def a(self, value): if 'a' not in self.free_parameters: raise RuntimeError("a can not be set, its not a free parameter!") self._parameters['a'] = value self.free_parameters['a'] = value self._set_params_from_sym() self._setlat() @property def b(self): return self._parameters['b'] @b.setter def b(self, value): if 'b' not in self.free_parameters: raise RuntimeError("b can not be set, its not a free parameter!") self._parameters['b'] = value self.free_parameters['b'] = value self._set_params_from_sym() self._setlat() @property def c(self): return self._parameters['c'] @c.setter def c(self, value): if 'c' not in self.free_parameters: raise RuntimeError("c can not be set, its not a free parameter!") self._parameters['c'] = value self.free_parameters['c'] = value self._set_params_from_sym() self._setlat() @property def alpha(self): return self._parameters['alpha'] @alpha.setter def alpha(self, value): if 'alpha' not in self.free_parameters: raise RuntimeError("alpha can not be set for this space group!") self._parameters['alpha'] = value self.free_parameters['alpha'] = value self._set_params_from_sym() ra = radians(value) self._paramhelp[0] = cos(ra) self._paramhelp[3] = sin(ra) self._setlat() @property def beta(self): return self._parameters['beta'] @beta.setter def beta(self, value): if 'beta' not in self.free_parameters: raise RuntimeError("beta can not be set for this space group!") self._parameters['beta'] = value self.free_parameters['beta'] = value self._set_params_from_sym() self._paramhelp[1] = cos(radians(value)) self._setlat() @property def gamma(self): return self._parameters['gamma'] @gamma.setter def gamma(self, value): if 'gamma' not in self.free_parameters: raise RuntimeError("gamma can not be set for this space group!") self._parameters['gamma'] = value self.free_parameters['gamma'] = value self._set_params_from_sym() self._paramhelp[2] = cos(radians(value)) self._setlat() def UnitCellVolume(self): """ function to calculate the unit cell volume of a lattice (angstrom^3) """ a, b, c, alpha, beta, gamma = self._parameters.values() return a * b * c * self._paramhelp[4] @property def iscentrosymmetric(self): """ returns a boolean to determine if the lattice has centrosymmetry. """ if self._iscentrosymmetric is None: self._iscentrosymmetric = False for s in self.symops: if np.all(-np.identity(3) == s.D): self._iscentrosymmetric = True break return self._iscentrosymmetric def isequivalent(self, hkl1, hkl2): """ determining if hkl1 and hkl2 are two crystallographical equivalent pairs of Miller indices. Note that this function considers the effect of non-centrosymmetry! Parameters ---------- hkl1, hkl2 : list Miller indices to be checked for equivalence Returns ------- bool """ return tuple(hkl2) in self.equivalent_hkls(hkl1) def equivalent_hkls(self, hkl): """ returns a list of equivalent hkl peaks depending on the crystal system """ suf = self.space_group_suf nr = self.space_group_nr if suf == get_default_sgrp_suf(nr): ehkl = set(eqhkl_default[nr](hkl[0], hkl[1], hkl[2])) elif suf in get_possible_sgrp_suf(nr): ehkl = set(eqhkl_custom[nr](hkl[0], hkl[1], hkl[2])) else: # fallback calculation with symmetry operations ehkl = np.unique(np.einsum('...ij,j', self._hklsym, hkl), axis=0) ehkl = set(tuple(e) for e in ehkl) return ehkl def hkl_allowed(self, hkl, returnequivalents=False): """ check if Bragg reflection with Miller indices hkl can exist according to the reflection conditions. If no reflection conditions are available this function returns True for all hkl values! Parameters ---------- hkl : tuple or list Miller indices of the reflection to check returnequivalents : bool, optional If True all the equivalent Miller indices of hkl are returned in a set as second return argument. Returns ------- allowed : bool True if reflection can have non-zero structure factor, false otherwise equivalents : set, optional set of equivalent Miller indices if returnequivalents is True """ # generate all equivalent hkl values which also need to be checked: hkls = self.equivalent_hkls(hkl) def build_return(allowed, requi=returnequivalents): if requi: return allowed, hkls else: return allowed # load reflection conditions if needed if self._gp[2] == 'n/a': return build_return(True) if self._hklcond == [] and self._gp[2] is not None: self._hklcond = hklcond_group.findall(self._gp[2]) ret = testhklcond(hkls, self._hklcond) return build_return(ret) def check2n(h): if (h % 2 == 0): return 1 else: return 0 def check2np1(h): if ((h-1) % 2 == 0): return 1 else: return 0 def check3n(h): if (h % 3 == 0): return 1 else: return 0 def check3np1(h): if ((h-1) % 3 == 0): return 1 else: return 0 def check3np2(h): if ((h-2) % 3 == 0): return 1 else: return 0 def check4n(h): if (h % 4 == 0): return 1 else: return 0 def check4np2(h): if ((h-2) % 4 == 0): return 1 else: return 0 def check6n(h): if (h % 6 == 0): return 1 else: return 0 def check8n(h): if (h % 8 == 0): return 1 else: return 0 def check8np1(h): if ((h-1) % 8 == 0): return 1 else: return 0 def check8nm1(h): if ((h+1) % 8 == 0): return 1 else: return 0 def check8np3(h): if ((h-3) % 8 == 0): return 1 else: return 0 def check8nm3(h): if ((h+3) % 8 == 0): return 1 else: return 0 def check8np4(h): if ((h-4) % 8 == 0): return 1 else: return 0 def check8np5(h): if ((h-5) % 8 == 0): return 1 else: return 0 def check8np7(h): if ((h-7) % 8 == 0): return 1 else: return 0 def testhklcond(hkls, condition, verbose=False): """ * test if a Bragg peak is allowed according to reflection conditions * * Parameters * ---------- * hkl : Miller indices of the peak to test (integer array) * condgeneral : General reflection conditions (list of tuples) * condwp : Reflection conditions for Wyckoff positions * (list of list of tuples) * * Returns * ------- * bool : True if peak is allowed, False otherwise """ # /* test general reflection conditions # * if they are violated the peak is forbidden # */ pattern_applied = 0 condition_met = 2 for hkl in hkls: for i in condition: hklpattern = i[0] cond = i[1] if hklpattern_applies(hkl, hklpattern): pattern_applied = 1 if verbose: print(hkl, hklpattern, cond) r = reflection_condition_met(hkl, cond) if r == 1: condition_met = 1 else: condition_met = 0 if verbose: print(condition_met, pattern_applied) if condition_met == 0: break if condition_met == 0: break if (condition_met == 1 or pattern_applied == 0): return True else: if pattern_applied == 1: return False else: return True def hklpattern_applies(hkl, condhkl): """/* * helper function to determine if Miller indices fit a certain pattern * * Parameters * ---------- * hkl : array of three integers Miller indices * condhkl : condition string similar to 'hkl', 'hh0', or '0k0' * * Returns * ------- * 1 if hkl fulfills the pattern, 0 otherwise */""" n=0 if (condhkl[n] == '0' and hkl[0] != 0): return 0 n = n + 1 if (condhkl[n] == '-'): n = n + 1 if (condhkl[n] == 'h' and hkl[1] != -hkl[0]): return 0 elif (condhkl[n] == '0' and hkl[1] != 0): return 0 elif (condhkl[n] == 'h' and hkl[1] != hkl[0]): return 0 if (condhkl[len(condhkl)-1] == '0' and hkl[2] != 0): return 0 return 1 def strcmp(expa, expb): if expa == expb: return 1 else: return 0 def reflection_condition_met(hkl, cond): """/* * helper function to determine allowed Miller indices * * Parameters * ---------- * hkl: list or tuple * Miller indices of the reflection * cond: str * condition string similar to 'h+k=2n, h+l,k+l=2n' * * Returns * ------- * 1 if condition is met, 0 otherwise */""" fulfilled = 1 condi = cond.split("=") if len(condi) > 2: condi = cond.split(", ") if len(condi) >2: fulfilled = 0 print("right hand expression error") for kun in condi: condi1 = kun.split("=") rexpr = condi1[1] lexpr_global = condi1[0] if strcmp(rexpr, "2n"): checkfunc = check2n elif strcmp(rexpr, "2n+1"): checkfunc = check2np1 elif strcmp(rexpr, "3n"): checkfunc = check3n elif strcmp(rexpr, "3n+1"): checkfunc = check3np1 elif strcmp(rexpr, "3n+2"): checkfunc = check3np2 elif strcmp(rexpr, "4n"): checkfunc = check4n elif strcmp(rexpr, "4n+2"): checkfunc = check4np2 elif strcmp(rexpr, "6n"): checkfunc = check6n elif strcmp(rexpr, "8n"): checkfunc = check8n elif strcmp(rexpr, "8n+1"): checkfunc = check8np1 elif strcmp(rexpr, "8n-1"): checkfunc = check8nm1 elif strcmp(rexpr, "8n+3"): checkfunc = check8np3 elif strcmp(rexpr, "8n-3"): checkfunc = check8nm3 elif strcmp(rexpr, "8n+4"): checkfunc = check8np4 elif strcmp(rexpr, "8n+5"): checkfunc = check8np5 elif strcmp(rexpr, "8n+7"): checkfunc = check8np7 else: print("Right hand side of reflection condition (%s) not implemented" %(rexpr)) return -1 for lexpr in lexpr_global.split(','): if strcmp(lexpr, "h"): if (checkfunc(hkl[0]) == 0): fulfilled = 0 elif strcmp(lexpr, "k"): if (checkfunc(hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "l"): if (checkfunc(hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "h+k"): if (checkfunc(hkl[0] + hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "h-k"): if (checkfunc(hkl[0] - hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "-h+k"): if (checkfunc(-hkl[0] + hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "h+l"): if (checkfunc(hkl[0] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "k+l"): if (checkfunc(hkl[1] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "h+k+l"): if (checkfunc(hkl[0] + hkl[1] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "-h+k+l"): if (checkfunc(-hkl[0] + hkl[1] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "2h+l"): if (checkfunc(2*hkl[0] + hkl[2]) == 0): fulfilled = 0; elif strcmp(lexpr, "2k+l"): if (checkfunc(2*hkl[1] + hkl[2]) == 0): fulfilled = 0 else: rexpr = condi[1] lexpr_global = condi[0] if strcmp(rexpr, "2n"): checkfunc = check2n elif strcmp(rexpr, "2n+1"): checkfunc = check2np1 elif strcmp(rexpr, "3n"): checkfunc = check3n elif strcmp(rexpr, "3n+1"): checkfunc = check3np1 elif strcmp(rexpr, "3n+2"): checkfunc = check3np2 elif strcmp(rexpr, "4n"): checkfunc = check4n elif strcmp(rexpr, "4n+2"): checkfunc = check4np2 elif strcmp(rexpr, "6n"): checkfunc = check6n elif strcmp(rexpr, "8n"): checkfunc = check8n elif strcmp(rexpr, "8n+1"): checkfunc = check8np1 elif strcmp(rexpr, "8n-1"): checkfunc = check8nm1 elif strcmp(rexpr, "8n+3"): checkfunc = check8np3 elif strcmp(rexpr, "8n-3"): checkfunc = check8nm3 elif strcmp(rexpr, "8n+4"): checkfunc = check8np4 elif strcmp(rexpr, "8n+5"): checkfunc = check8np5 elif strcmp(rexpr, "8n+7"): checkfunc = check8np7 else: print("Right hand side of reflection condition (%s) not implemented" %(rexpr)) return -1 for lexpr in lexpr_global.split(','): if strcmp(lexpr, "h"): if (checkfunc(hkl[0]) == 0): fulfilled = 0 elif strcmp(lexpr, "k"): if (checkfunc(hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "l"): if (checkfunc(hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "h+k"): if (checkfunc(hkl[0] + hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "h-k"): if (checkfunc(hkl[0] - hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "-h+k"): if (checkfunc(-hkl[0] + hkl[1]) == 0): fulfilled = 0 elif strcmp(lexpr, "h+l"): if (checkfunc(hkl[0] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "k+l"): if (checkfunc(hkl[1] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "h+k+l"): if (checkfunc(hkl[0] + hkl[1] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "-h+k+l"): if (checkfunc(-hkl[0] + hkl[1] + hkl[2]) == 0): fulfilled = 0 elif strcmp(lexpr, "2h+l"): if (checkfunc(2*hkl[0] + hkl[2]) == 0): fulfilled = 0; elif strcmp(lexpr, "2k+l"): if (checkfunc(2*hkl[1] + hkl[2]) == 0): fulfilled = 0 if (fulfilled == 1): return 1 else: return 0 class SymOp(object): """ Class descriping a symmetry operation in a crystal. The symmetry operation is characterized by a 3x3 transformation matrix as well as a 3-vector describing a translation. For magnetic symmetry operations also the time reversal symmetry can be specified (not used in xrayutilities) """ def __init__(self, D, t, m=1): """ Initialize the symmetry operation Parameters ---------- D : array-like transformation matrix (3x3) t : array-like translation vector (3) m : int, optional indicates time reversal in magnetic groups. +1 (default, no time reveral) or -1 """ self._W = np.zeros((4, 4)) self._W[:3, :3] = np.asarray(D) self._W[:3, 3] = np.asarray(t) self._W[3, 3] = 1 self._m = m @classmethod def from_xyz(cls, xyz): """ create a SymOp from the xyz notation typically used in CIF files. Parameters ---------- xyz : str string describing the symmetry operation (e.g. '-y, -x, z') """ D = np.zeros((3, 3)) t = np.array(eval(xyz, {'x': 0, 'y': 0, 'z': 0})[:3]) m = 1 for i, expr in enumerate(xyz.strip('()').split(',')): if i == 3: # time reversal property m = int(expr) continue if 'x' in expr: D[i, 0] = -1 if '-x' in expr else 1 if 'y' in expr: D[i, 1] = -1 if '-y' in expr else 1 if 'z' in expr: D[i, 2] = -1 if '-z' in expr else 1 return SymOp(D, t, m) def xyz(self, showtimerev=False): """ return the symmetry operation in xyz notation """ ret = '' t = self.t for i in range(3): expr = '' if abs(self._W[i, 0]) == 1: expr += '+x' if self._W[i, 0] == 1 else '-x' if abs(self._W[i, 1]) == 1: expr += '+y' if self._W[i, 1] == 1 else '-y' if abs(self._W[i, 2]) == 1: expr += '+z' if self._W[i, 2] == 1 else '-z' if t[i] != 0: expr += '+' if t[i] > 0 else '' expr += str(fractions.Fraction(t[i]).limit_denominator(100)) expr = expr.strip('+') ret += expr + ', ' if showtimerev: ret += '{:+d}'.format(self._m) return ret.strip(', ') @property def D(self): """transformation matrix of the symmetry operation""" return self._W[:3, :3] @property def t(self): """translation vector of the symmetry operation""" return self._W[:3, 3] def __eq__(self, other): if not isinstance(other, SymOp): return NotImplemented return self._m == other._m and np.all(self._W == other._W) @staticmethod def foldback(v): return v - np.round(v, DIGITS) // 1 def apply_rotation(self, vec): return self.D @ vec def apply(self, vec, foldback=True): lv = np.asarray(list(vec) + [1, ]) result = (self._W @ lv)[:3] if foldback: return self.foldback(result) return result def apply_axial(self, vec): return self._m * np.linalg.det(self.D) * self.D @ vec def combine(self, other): if not isinstance(other, SymOp): return NotImplemented W = self._W @ other._W return SymOp(W[:3, :3], self.foldback(W[:3, 3]), self._m*other._m) def __str__(self): return '({})'.format(self.xyz(showtimerev=True)) def __repr__(self): return self.__str__() def _round_indices(indices, max_index=12): """Round a set of index triplet (Miller) or quartet (Miller-Bravais) to the *closest* smallest integers. Adopted from MTEX's Miller.round function. Parameters ---------- indices : list, tuple, or np.ndarray Set of index triplet(s) or quartet(s) to round. max_index : int, optional Maximum integer index to round to, by default 12. Return ------ new_indices : np.ndarray Integer array of rounded set of index triplet(s) or quartet(s). """ # Allow list and tuple input (and don't overwrite `indices`) idx = np.asarray(indices) # Flatten and remove redundant third index if Miller-Bravais n_idx = idx.shape[-1] # 3 or 4 idx_flat = np.reshape(idx, (-1, n_idx)) if n_idx == 4: idx_flat = idx_flat[..., [0, 1, 3]] # Get number of sets, max. index per set, and all possible integer # multipliers between 1 and `max_index` n_sets = idx_flat.size // 3 max_per_set = np.max(np.abs(idx_flat), axis=-1) multipliers = np.arange(1, max_index + 1) # Divide by highest index, repeat array `max_index` number of times, # and multiply with all multipliers idx_scaled = ( np.broadcast_to(idx_flat / max_per_set[..., np.newaxis], (max_index, n_sets, 3)) * multipliers[..., np.newaxis, np.newaxis] ) # Find the most suitable multiplier per set, which gives the # smallest error between the initial set and the scaled and rounded # set error = 1e-7 * np.round( 1e7 * np.sum((idx_scaled - np.round(idx_scaled)) ** 2, axis=-1) / np.sum(idx_scaled ** 2, axis=-1) ) idx_min_error = np.argmin(error, axis=0) multiplier = (idx_min_error + 1) / max_per_set # Reshape `multiplier` to match indices shape multiplier = multiplier.reshape(idx.shape[:-1])[..., np.newaxis] # Finally, multiply each set with their most suitable multiplier, # and round new_indices = np.round(multiplier * idx).astype(int) return new_indices # ============================================================================= # PYMICRO FUNCTION IMPORTS # ============================================================================= def move_rotation_to_FZ(g, symmetry_operators = None): """Compute the rotation matrix in the Fundamental Zone of a given `Symmetry` instance. :param g: a 3x3 matrix representing the rotation. :param verbose: flag for verbose mode. :return: a new 3x3 matrix for the rotation in the fundamental zone. """ omegas = [] # list to store all the rotation angles syms = symmetry_operators for sym in syms: # apply the symmetry operator om = np.dot(sym, g) cw = 0.5 * (om.trace() - 1) omega = np.arccos(cw) omegas.append(omega) index = np.argmin(omegas) return np.dot(syms[index], g) def misorientation_axis_from_delta(delta): """Compute the misorientation axis from the misorientation matrix. :param delta: The 3x3 misorientation matrix. :returns: the misorientation axis (normalised vector). """ n = np.array([delta[1, 2] - delta[2, 1], delta[2, 0] - delta[0, 2], delta[0, 1] - delta[1, 0]]) n /= np.sqrt((delta[1, 2] - delta[2, 1]) ** 2 + (delta[2, 0] - delta[0, 2]) ** 2 + (delta[0, 1] - delta[1, 0]) ** 2) return n def misorientation_angle_from_delta(delta): """Compute the misorientation angle from the misorientation matrix. Compute the angle associated with this misorientation matrix :math:`\\Delta g`. It is defined as :math:`\\omega = \\arccos(\\text{trace}(\\Delta g)/2-1)`. To avoid float rounding error, the argument is rounded to 1.0 if it is within 1 and 1 plus 32 bits floating point precison. .. note:: This does not account for the crystal symmetries. If you want to find the disorientation between two orientations, use the :py:meth:`~pymicro.crystal.microstructure.Orientation.disorientation` method. :param delta: The 3x3 misorientation matrix. :returns float: the misorientation angle in radians. """ cw = 0.5 * (delta.trace() - 1) if cw > 1. and cw - 1. < 10 * np.finfo('float32').eps: cw = 1. omega = np.arccos(cw) return omega def disorientation(orientation_matrix, orientation_matrix1, crystal_structure=None): """Compute the disorientation another crystal orientation. Considering all the possible crystal symmetries, the disorientation is defined as the combination of the minimum misorientation angle and the misorientation axis lying in the fundamental zone, which can be used to bring the two lattices into coincidence. .. note:: Both orientations are supposed to have the same symmetry. This is not necessarily the case in multi-phase materials. :param orientation: an instance of :py:class:`~pymicro.crystal.microstructure.Orientation` class describing the other crystal orientation from which to compute the angle. :param crystal_structure: an instance of the `Symmetry` class describing the crystal symmetry, triclinic (no symmetry) by default. :returns tuple: the misorientation angle in radians, the axis as a numpy vector (crystal coordinates), the axis as a numpy vector (sample coordinates). """ the_angle = np.pi symmetries = crystal_structure.symmetry_operators() (gA, gB) = (orientation_matrix, orientation_matrix1) # nicknames for (g1, g2) in [(gA, gB), (gB, gA)]: for j in range(symmetries.shape[0]): sym_j = symmetries[j] oj = np.dot(sym_j, g1) # the crystal symmetry operator is left applied for i in range(symmetries.shape[0]): sym_i = symmetries[i] oi = np.dot(sym_i, g2) delta = np.dot(oi, oj.T) mis_angle = misorientation_angle_from_delta(delta) if mis_angle < the_angle: # now compute the misorientation axis, should check if it lies in the fundamental zone mis_axis = misorientation_axis_from_delta(delta) the_angle = mis_angle the_axis = mis_axis the_axis_xyz = np.dot(oi.T, the_axis) return the_angle, the_axis, the_axis_xyz # ============================================================================= # Notebook functions # ============================================================================= def generate_dataset(material_="Cu", material1_="Cu", ang_maxx=18.,step=0.1, mode=0, nb_grains=1, nb_grains1=1, grains_nb_simulate=100, data_realism = False, detectorparameters=None, pixelsize=None, type_="training", var0 = 0, dim1=2048, dim2=2048, removeharmonics=1, save_directory="", write_to_console=None, emin=5, emax=22, modelp = "random", misorientation_angle = None, general_diff_rules = False, crystal = None, crystal1 = None, include_scm=False, matrix_phase_always_present=None): """ works for all symmetries now. """ from multiprocessing import Process, Queue, cpu_count ncpu = cpu_count() ## make sure directory exists save_directory_ = save_directory+"//"+type_ if not os.path.exists(save_directory_): os.makedirs(save_directory_) try: with open(save_directory+"//classhkl_data_"+material_+".pickle", "rb") as input_file: classhkl, _, _, n, _, \ hkl_all_class, _, lattice_material, symmetry = cPickle.load(input_file) max_millerindex = int(n) max_millerindex1 = int(n) if material_ != material1_: with open(save_directory+"//classhkl_data_"+material1_+".pickle", "rb") as input_file: classhkl1, _, _, n1, _, \ hkl_all_class1, _, lattice_material1, symmetry1 = cPickle.load(input_file) max_millerindex1 = int(n1) except: write_to_console("Class HKL library data not found, please run it first") return None if var0==1: codebars, angbins = get_material_data(material_ = material_, ang_maxx = ang_maxx, step = step, hkl_ref=n, classhkl=classhkl) loc = np.array([ij for ij in range(len(classhkl))]) write_to_console("Verifying if two different HKL class have same angular distribution (can be very time consuming depending on the symmetry)") index = [] list_appended = [] count_cbs = 0 for i, j in enumerate(codebars): for k, l in enumerate(codebars): # if i in list_appended and k in list_appended: # continue if i != k and np.all(j == l): index.append((i,k)) string0 = "HKL's "+ str(classhkl[i])+" and "+str(classhkl[k])+" have exactly the same angular distribution." write_to_console(string0) list_appended.append(i) list_appended.append(k) count_cbs += 1 if len(index) == 0: write_to_console("Great! No two HKL class have same angular distribution") #np.savez_compressed(save_directory_+'//grain_init.npz', codebars, loc) else: write_to_console("Some HKL's have similar angular distribution; this will likely reduce the accuracy of the neural network; verify if symmetry matrix and other parameters are properly configured; this is just for the dictionary; keep eye on the dataset being generated for training") write_to_console("This is likely the result of the symmetry operation available in a user_defined space group; this shouldn't affect the general accuracy of the model") np.savez_compressed(save_directory+'//conflict_angular_distribution_debug.npz', codebars, index) np.savez_compressed(save_directory+'//grain_classhkl_angbin.npz', classhkl, angbins) if material_ != material1_: codebars, angbins = get_material_data(material_ = material1_, ang_maxx = ang_maxx, step = step, hkl_ref=n1, classhkl=classhkl1) ind_offset = loc[-1] + 1 loc = np.array([ind_offset + ij for ij in range(len(classhkl1))]) write_to_console("Verifying if two different HKL class have same angular distribution (can be very time consuming depending on the symmetry)") index = [] list_appended = [] count_cbs = 0 for i, j in enumerate(codebars): for k, l in enumerate(codebars): # if i in list_appended and k in list_appended: # continue if i != k and np.all(j == l): index.append((i,k)) string0 = "HKL's "+ str(classhkl1[i])+" and "+str(classhkl1[k])+" have exactly the same angular distribution." write_to_console(string0) list_appended.append(i) list_appended.append(k) count_cbs += 1 if len(index) == 0: write_to_console("Great! No two HKL class have same angular distribution") #np.savez_compressed(save_directory_+'//grain_init1.npz', codebars, loc) else: write_to_console("Some HKL's have similar angular distribution; this will likely reduce the accuracy of the neural network; verify if symmetry matrix and other parameters are properly configured; this is just for the dictionary; keep eye on the dataset being generated for training") write_to_console("This is likely the result of the symmetry operation available in a user_defined space group; this shouldn't affect the general accuracy of the model") np.savez_compressed(save_directory+'//conflict_angular_distribution1_debug.npz', codebars, index) np.savez_compressed(save_directory+'//grain_classhkl_angbin1.npz', classhkl1, angbins) ## make comprehensive list of dictionary normal_hkl_ = np.zeros((1,3)) for j in hkl_all_class.keys(): normal_hkl_ = np.vstack((normal_hkl_, hkl_all_class[j]["family"])) normal_hkl = np.delete(normal_hkl_, 0, axis =0) if material_ != material1_: normal_hkl1_ = np.zeros((1,3)) for j in hkl_all_class1.keys(): normal_hkl1_ = np.vstack((normal_hkl1_, hkl_all_class1[j]["family"])) normal_hkl1 = np.delete(normal_hkl1_, 0, axis =0) index_hkl = [j for j,k in enumerate(hkl_all_class.keys()) for i in range(len(hkl_all_class[k]["family"]))] if material_ != material1_: ind_offset = index_hkl[-1] + 1 index_hkl1 = [ind_offset+j for j,k in enumerate(hkl_all_class1.keys()) for i in range(len(hkl_all_class1[k]["family"]))] if material_ == material1_: index_hkl1 = None normal_hkl1 = None classhkl1 = None hkl_all_class1 = None lattice_material1 = None symmetry1 = None write_to_console("Generating "+type_+" and saving them") if material_ != material1_: nb_grains_list = list(range(nb_grains+1)) nb_grains1_list = list(range(nb_grains1+1)) list_permute = list(itertools.product(nb_grains_list, nb_grains1_list)) list_permute.pop(0) max_progress = len(list_permute)*grains_nb_simulate if matrix_phase_always_present != None and type_ != "testing_data": dummy_, key_material_new = matrix_phase_always_present.split(';') if key_material_new == material_: max_progress = len(list_permute)*grains_nb_simulate + (len(nb_grains1_list)-1)*grains_nb_simulate else: max_progress = len(list_permute)*grains_nb_simulate + (len(nb_grains_list)-1)*grains_nb_simulate else: max_progress = nb_grains*grains_nb_simulate if matrix_phase_always_present != None and type_ != "testing_data": max_progress = nb_grains*grains_nb_simulate*2 if include_scm: max_progress = max_progress + grains_nb_simulate if material_ != material1_: max_progress = max_progress + 2*grains_nb_simulate _inputs_queue = Queue() _outputs_queue = Queue() _worker_process = {} for i in range(ncpu): _worker_process[i]= Process(target=worker_generation, args=(_inputs_queue, _outputs_queue, i+1),) for i in range(ncpu): _worker_process[i].start() time.sleep(0.1) if material_ != material1_: if modelp == "uniform": if type_ =="training_data": xlim, ylim = 0, int(0.8*2000) else: xlim, ylim = int(0.8*2000), 2000-1 path_array = resource_path("uniform_orientations_2000.npz") arr = np.load(path_array) if symmetry == symmetry.cubic: odf_data = arr["arr_6"][xlim:ylim] # print("Laue group 11") elif symmetry == symmetry.hexagonal: odf_data = arr["arr_5"][xlim:ylim] # print("Laue group 9") elif symmetry == symmetry.trigonal: odf_data = arr["arr_4"][xlim:ylim] # print("Laue group 7") elif symmetry == symmetry.tetragonal: odf_data = arr["arr_3"][xlim:ylim] # print("Laue group 5") elif symmetry == symmetry.orthorhombic: odf_data = arr["arr_2"][xlim:ylim] # print("Laue group 3") elif symmetry == symmetry.monoclinic: odf_data = arr["arr_1"][xlim:ylim] # print("Laue group 2") elif symmetry == symmetry.triclinic: odf_data = arr["arr_0"][xlim:ylim] # print("Laue group 1") if symmetry1 == symmetry.cubic: odf_data1 = arr["arr_6"][xlim:ylim] # print("Laue group 11") elif symmetry1 == symmetry.hexagonal: odf_data1 = arr["arr_5"][xlim:ylim] # print("Laue group 9") elif symmetry1 == symmetry.trigonal: odf_data1 = arr["arr_4"][xlim:ylim] # print("Laue group 7") elif symmetry1 == symmetry.tetragonal: odf_data1 = arr["arr_3"][xlim:ylim] # print("Laue group 5") elif symmetry1 == symmetry.orthorhombic: odf_data1 = arr["arr_2"][xlim:ylim] # print("Laue group 3") elif symmetry1 == symmetry.monoclinic: odf_data1 = arr["arr_1"][xlim:ylim] # print("Laue group 2") elif symmetry1 == symmetry.triclinic: odf_data1 = arr["arr_0"][xlim:ylim] # print("Laue group 1") ## list of combination of training dataset ## to be seen if this improves the prediction quality ## increases time significantly to generate the data nb_grains_list = list(range(nb_grains+1)) nb_grains1_list = list(range(nb_grains1+1)) list_permute = list(itertools.product(nb_grains_list, nb_grains1_list)) list_permute.pop(0) ## removing the 0,0 index # Idea 2 Or generate a database upto n grain LP values = [] for i in range(len(list_permute)): ii, jj = list_permute[i] for j in range(grains_nb_simulate): if data_realism: ## three types of data augmentation to mimic reality ? if j < grains_nb_simulate*0.25: noisy_data = False remove_peaks = False elif (j >= grains_nb_simulate*0.25) and (j < grains_nb_simulate*0.5): noisy_data = True remove_peaks = False elif (j >= grains_nb_simulate*0.5) and (j < grains_nb_simulate*0.75): noisy_data = False remove_peaks = True elif (j >= grains_nb_simulate*0.75): noisy_data = True remove_peaks = True else: noisy_data = False remove_peaks = False if modelp == "uniform": rand_choice = np.random.choice(len(odf_data), ii, replace=False) rand_choice1 = np.random.choice(len(odf_data1), jj, replace=False) data_odf_data = odf_data[rand_choice,:,:] data_odf_data1 = odf_data1[rand_choice1,:,:] else: data_odf_data = None data_odf_data1 = None seednumber = np.random.randint(1e6) values.append([ii, jj, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 0, i, j, save_directory_, data_odf_data, data_odf_data1, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, None]) if matrix_phase_always_present != None and \ type_ != "testing_data": dummy_, key_material_new = matrix_phase_always_present.split(';') if key_material_new == material_ and ii == 0: values.append([0, jj, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 0, i, j, save_directory_, data_odf_data, data_odf_data1, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, matrix_phase_always_present]) elif key_material_new == material1_ and jj == 0: values.append([ii, 0, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 0, i, j, save_directory_, data_odf_data, data_odf_data1, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, matrix_phase_always_present]) chunks = chunker_list(values, ncpu) chunks_mp = list(chunks) if include_scm: meta = {'t1':time.time(), 'flag':0} else: meta = {'t1':time.time(), 'flag':1} for ijk in range(int(ncpu)): _inputs_queue.put((chunks_mp[ijk], ncpu, meta)) else: # Idea 2 Or generate a database upto n grain LP if modelp == "uniform": ## training split if type_ =="training_data": xlim, ylim = 0, int(0.8*2000) else: xlim, ylim = int(0.8*2000), 2000-1 path_array = resource_path("uniform_orientations_2000.npz") arr = np.load(path_array) if symmetry == symmetry.cubic: odf_data = arr["arr_6"][xlim:ylim] print("Laue group 11") elif symmetry == symmetry.hexagonal: odf_data = arr["arr_5"][xlim:ylim] print("Laue group 9") elif symmetry == symmetry.trigonal: odf_data = arr["arr_4"][xlim:ylim] print("Laue group 7") elif symmetry == symmetry.tetragonal: odf_data = arr["arr_3"][xlim:ylim] print("Laue group 5") elif symmetry == symmetry.orthorhombic: odf_data = arr["arr_2"][xlim:ylim] print("Laue group 3") elif symmetry == symmetry.monoclinic: odf_data = arr["arr_1"][xlim:ylim] print("Laue group 2") elif symmetry == symmetry.triclinic: odf_data = arr["arr_0"][xlim:ylim] print("Laue group 1") values = [] for i in range(nb_grains): for j in range(grains_nb_simulate): if data_realism: ## three types of data augmentation to mimic reality ? if j < grains_nb_simulate*0.25: noisy_data = False remove_peaks = False elif (j >= grains_nb_simulate*0.25) and (j < grains_nb_simulate*0.5): noisy_data = True remove_peaks = False elif (j >= grains_nb_simulate*0.5) and (j < grains_nb_simulate*0.75): noisy_data = False remove_peaks = True elif (j >= grains_nb_simulate*0.75): noisy_data = True remove_peaks = True else: noisy_data = False remove_peaks = False if modelp == "uniform": rand_choice = np.random.choice(len(odf_data), i+1, replace=False) data_odf_data = odf_data[rand_choice,:,:] data_odf_data1 = None else: data_odf_data = None data_odf_data1 = None seednumber = np.random.randint(1e6) values.append([i+1, 0, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 0, i, j, save_directory_, data_odf_data, data_odf_data1, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, None]) if matrix_phase_always_present != None and \ type_ != "testing_data": values.append([i+1, 0, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 0, i, j, save_directory_, data_odf_data, data_odf_data1, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, matrix_phase_always_present]) chunks = chunker_list(values, ncpu) chunks_mp = list(chunks) if include_scm: meta = {'t1':time.time(), 'flag':0} else: meta = {'t1':time.time(), 'flag':1} for ijk in range(int(ncpu)): _inputs_queue.put((chunks_mp[ijk], ncpu, meta)) if include_scm: write_to_console("Generating small angle misorientation single crystals") values = [] for i in range(grains_nb_simulate): if data_realism: ## three types of data augmentation to mimic reality ? if i < grains_nb_simulate*0.25: noisy_data = False remove_peaks = False elif (i >= grains_nb_simulate*0.25) and (i < grains_nb_simulate*0.5): noisy_data = True remove_peaks = False elif (i >= grains_nb_simulate*0.5) and (i < grains_nb_simulate*0.75): noisy_data = False remove_peaks = True elif (i >= grains_nb_simulate*0.75): noisy_data = True remove_peaks = True else: noisy_data = False remove_peaks = False seednumber = np.random.randint(1e6) values.append([1, 0, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 1, i, i, save_directory_, None, None, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, None]) if material_ != material1_: seednumber = np.random.randint(1e6) values.append([0, 1, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 2, i, i, save_directory_, None, None, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, None]) ### include slightly misoriented two crystals of different materails seednumber = np.random.randint(1e6) values.append([1, 1, material_,material1_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, classhkl1, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl, index_hkl, hkl_all_class1, lattice_material1, None, normal_hkl1, index_hkl1, dim1, dim2, removeharmonics, 3, i, i, save_directory_, None, None, modelp, misorientation_angle, max_millerindex,max_millerindex1, general_diff_rules, crystal, crystal1, None]) chunks = chunker_list(values, ncpu) chunks_mp = list(chunks) meta = {'t1':time.time(), 'flag':1} for ijk in range(int(ncpu)): _inputs_queue.put((chunks_mp[ijk], ncpu, meta)) max_progress = max_progress while True: count = 0 for i in range(ncpu): if not _worker_process[i].is_alive(): _worker_process[i].join() count += 1 else: time.sleep(0.1) if count == ncpu: return def get_material_detail(material_=None, SG=None, symm_=None, material1_=None, SG1=None, symm1_=None): """ Returns material details """ a, b, c, alpha, beta, gamma = dictLT.dict_Materials[material_][1] # Gstar = CP.Gstar_from_directlatticeparams(a, b, c, alpha, beta, gamma) rules = dictLT.dict_Materials[material_][-1] if symm_ =="cubic": symmetry = Symmetry.cubic lattice_material = Lattice.cubic(a) if SG == None: SG = 230 crystal = SGLattice(int(SG), a) elif symm_ =="monoclinic": symmetry = Symmetry.monoclinic lattice_material = Lattice.monoclinic(a, b, c, beta) if SG == None: SG = 10 crystal = SGLattice(int(SG),a, b, c, beta) elif symm_ == "hexagonal": symmetry = Symmetry.hexagonal lattice_material = Lattice.hexagonal(a, c) if SG == None: SG = 191 crystal = SGLattice(int(SG),a, c) elif symm_ == "orthorhombic": symmetry = Symmetry.orthorhombic lattice_material = Lattice.orthorhombic(a, b, c) if SG == None: SG = 47 crystal = SGLattice(int(SG),a, b, c) elif symm_ == "tetragonal": symmetry = Symmetry.tetragonal lattice_material = Lattice.tetragonal(a, c) if SG == None: SG = 123 crystal = SGLattice(int(SG),a, c) elif symm_ == "trigonal": symmetry = Symmetry.trigonal lattice_material = Lattice.rhombohedral(a, alpha) if SG == None: SG = 162 crystal = SGLattice(int(SG),a, alpha) elif symm_ == "triclinic": symmetry = Symmetry.triclinic lattice_material = Lattice.triclinic(a, b, c, alpha, beta, gamma) if SG == None: SG = 2 crystal = SGLattice(int(SG),a, b, c, alpha, beta, gamma) if material_ != material1_: a1, b1, c1, alpha1, beta1, gamma1 = dictLT.dict_Materials[material1_][1] # Gstar1 = CP.Gstar_from_directlatticeparams(a1, b1, c1, alpha1, beta1, gamma1) rules1 = dictLT.dict_Materials[material1_][-1] # ============================================================================= # Symmetry input # ============================================================================= if symm1_ =="cubic": symmetry1 = Symmetry.cubic lattice_material1 = Lattice.cubic(a1) if SG1 == None: SG1 = 230 crystal1 = SGLattice(int(SG1), a1) elif symm1_ =="monoclinic": symmetry1 = Symmetry.monoclinic lattice_material1 = Lattice.monoclinic(a1, b1, c1, beta1) if SG1 == None: SG1 = 10 crystal1 = SGLattice(int(SG1),a1, b1, c1, beta1) elif symm1_ == "hexagonal": symmetry1 = Symmetry.hexagonal lattice_material1 = Lattice.hexagonal(a1, c1) if SG1 == None: SG1 = 191 crystal1 = SGLattice(int(SG1),a1, c1) elif symm1_ == "orthorhombic": symmetry1 = Symmetry.orthorhombic lattice_material1 = Lattice.orthorhombic(a1, b1, c1) if SG1 == None: SG1 = 47 crystal1 = SGLattice(int(SG1),a1, b1, c1) elif symm1_ == "tetragonal": symmetry1 = Symmetry.tetragonal lattice_material1 = Lattice.tetragonal(a1, c1) if SG1 == None: SG1 = 123 crystal1 = SGLattice(int(SG1),a1, c1) elif symm1_ == "trigonal": symmetry1 = Symmetry.trigonal lattice_material1 = Lattice.rhombohedral(a1, alpha1) if SG1 == None: SG1 = 162 crystal1 = SGLattice(int(SG1),a1, alpha1) elif symm1_ == "triclinic": symmetry1 = Symmetry.triclinic lattice_material1 = Lattice.triclinic(a1, b1, c1, alpha1, beta1, gamma1) if SG1 == None: SG1 = 2 crystal1 = SGLattice(int(SG1),a1, b1, c1, alpha1, beta1, gamma1) else: rules1 = None symmetry1 = None lattice_material1 = None crystal1 = None return rules, symmetry, lattice_material, crystal, SG, rules1, symmetry1, lattice_material1, crystal1, SG1 def predict_preprocessMultiProcess(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, hkl_all_class1, emin, emax, material_, material1_, symmetry, symmetry1,lim_x,lim_y, strain_calculation, ind_mat, ind_mat1, model_direc=None, tolerance =None, tolerance1 =None, matricies=None, ccd_label=None, filename_bkg=None,intensity_threshold=None, boxsize=None,bkg_treatment=None, filenameDirec=None, experimental_prefix=None, blacklist_file =None, text_file=None, files_treated=None,try_previous1=False, wb=None, temp_key=None, cor_file_directory=None, mode_spotCycle1=None, softmax_threshold_global123=None,mr_threshold_global123=None, cap_matchrate123=None,tolerance_strain123=None,tolerance_strain1231=None,\ NumberMaxofFits123=None,fit_peaks_gaussian_global123=None, FitPixelDev_global123=None,coeff123=None, coeff_overlap=None, material0_limit=None, material1_limit=None, use_previous_UBmatrix_name=None, material_phase_always_present=None, crystal=None, crystal1=None, strain_free_parameters=None): if files in files_treated: return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match call_global() # print("Predicting for "+files) if files.split(".")[-1] != "cor": CCDLabel=ccd_label seednumber = "Experimental "+CCDLabel+" file" try: out_name = blacklist_file except: out_name = None if bkg_treatment == None: bkg_treatment = "A-B" try: ### Max space = space betzeen pixles peak_XY = RMCCD.PeakSearch( files, stackimageindex = -1, CCDLabel=CCDLabel, NumberMaxofFits=NumberMaxofFits123, PixelNearRadius=10, removeedge=2, IntensityThreshold=intensity_threshold, local_maxima_search_method=0, boxsize=boxsize, position_definition=1, verbose=0, fit_peaks_gaussian=fit_peaks_gaussian_global123, xtol=0.001, FitPixelDev=FitPixelDev_global123, return_histo=0, # Saturation_value=1e10, # to be merged in CCDLabel # Saturation_value_flatpeak=1e10, MinIntensity=0, PeakSizeRange=(0.65,200), write_execution_time=1, Data_for_localMaxima = "auto_background", formulaexpression=bkg_treatment, Remove_BlackListedPeaks_fromfile=out_name, reject_negative_baseline=True, Fit_with_Data_for_localMaxima=False, maxPixelDistanceRejection=15.0, ) peak_XY = peak_XY[0]#[:,:2] ##[2] Integer peak lists except: print("Error in Peak detection for "+ files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 # files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match try: s_ix = np.argsort(peak_XY[:, 2])[::-1] peak_XY = peak_XY[s_ix] except: print("Error in Peak detection for "+ files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 # files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match framedim = dictLT.dict_CCD[CCDLabel][0] twicetheta, chi = Lgeo.calc_uflab(peak_XY[:,0], peak_XY[:,1], detectorparameters, returnAngles=1, pixelsize=pixelsize, kf_direction='Z>0') data_theta, data_chi = twicetheta/2., chi framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0] dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peak_XY[:,0] dict_dp['peakY']=peak_XY[:,1] dict_dp['intensity']=peak_XY[:,2] CCDcalib = {"CCDLabel":CCDLabel, "dd":detectorparameters[0], "xcen":detectorparameters[1], "ycen":detectorparameters[2], "xbet":detectorparameters[3], "xgam":detectorparameters[4], "pixelsize": pixelsize} path = os.path.normpath(files) IOLT.writefile_cor(cor_file_directory+"//"+path.split(os.sep)[-1].split(".")[0], twicetheta, chi, peak_XY[:,0], peak_XY[:,1], peak_XY[:,2], param=CCDcalib, sortedexit=0) elif files.split(".")[-1] == "cor": # print("Entering Cor file read section") seednumber = "Experimental COR file" allres = IOLT.readfile_cor(files, True) data_theta, data_chi, peakx, peaky, intensity = allres[1:6] CCDcalib = allres[-1] detectorparameters = allres[-2] # print('detectorparameters from file are: '+ str(detectorparameters)) pixelsize = CCDcalib['pixelsize'] CCDLabel = CCDcalib['CCDLabel'] framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0] dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peakx dict_dp['peakY']=peaky dict_dp['intensity']=intensity sorted_data = np.transpose(np.array([data_theta, data_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(sorted_data, sorted_data)) codebars_all = [] if len(data_theta) == 0: print("No peaks Found for : " + files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match if not use_om_user: # print("Entering GOOD section") spots_in_center = np.arange(0,len(data_theta)) spots_in_center = spots_in_center[:nb_spots_consider] for i in spots_in_center: spotangles = tabledistancerandom[i] spotangles = np.delete(spotangles, i)# removing the self distance codebars = np.histogram(spotangles, bins=angbins)[0] # codebars = histogram1d(spotangles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## normalize the same way as training data max_codebars = np.max(codebars) codebars = codebars/ max_codebars codebars_all.append(codebars) ## reshape for the model to predict all spots at once codebars = np.array(codebars_all) ## Do prediction of all spots at once prediction = predict(codebars, wb, temp_key) # prediction = model.predict(codebars) max_pred = np.max(prediction, axis = 1) class_predicted = np.argmax(prediction, axis = 1) predicted_hkl123 = classhkl[class_predicted] predicted_hkl123 = predicted_hkl123.astype(int) else: max_pred = None class_predicted = None predicted_hkl123 = None spots_in_center = None s_tth = data_theta * 2. s_chi = data_chi # print("Computing UB") rotation_matrix1, mr_highest, mat_highest, \ strain_crystal, strain_sample, iR_pix1, \ fR_pix1, spots_len1,\ best_match1, check12 = predict_ubmatrix(seednumber, spots_in_center, classhkl, hkl_all_class0, hkl_all_class1, files, s_tth1=s_tth,s_chi1=s_chi, predicted_hkl1=predicted_hkl123, class_predicted1=class_predicted, max_pred1=max_pred, emin=emin,emax=emax, material_=material_, material1_=material1_, lim_y=lim_y, lim_x=lim_x, cnt=cnt, dict_dp=dict_dp, rotation_matrix=rotation_matrix, mat_global=mat_global, strain_calculation=strain_calculation, ind_mat=ind_mat, ind_mat1=ind_mat1, tolerance=tolerance, tolerance1 =tolerance1, matricies=matricies, tabledistancerandom=tabledistancerandom, text_file = text_file, try_previous1=try_previous1, mode_spotCycle=mode_spotCycle1, softmax_threshold_global123 = softmax_threshold_global123, mr_threshold_global123=mr_threshold_global123, cap_matchrate123=cap_matchrate123, tolerance_strain123=tolerance_strain123, tolerance_strain1231=tolerance_strain1231, coeff123=coeff123, coeff_overlap=coeff_overlap, material0_limit=material0_limit, material1_limit=material1_limit, model_direc=model_direc, use_previous_UBmatrix_name=use_previous_UBmatrix_name, material_phase_always_present=material_phase_always_present, match_rate=match_rate, check=check[cnt,:], crystal=crystal, crystal1=crystal1, angbins=angbins, wb=wb, temp_key=temp_key, strain_free_parameters=strain_free_parameters) for intmat in range(matricies): if len(rotation_matrix1[intmat]) == 0: col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 else: mat_global[intmat][0][cnt] = mat_highest[intmat][0] final_symm =symmetry final_crystal = crystal if mat_highest[intmat][0] == 1: final_symm = symmetry final_crystal = crystal elif mat_highest[intmat][0] == 2: final_symm = symmetry1 final_crystal = crystal1 symm_operator = final_crystal._hklsym strain_matrix[intmat][0][cnt,:,:] = strain_crystal[intmat][0] strain_matrixs[intmat][0][cnt,:,:] = strain_sample[intmat][0] rotation_matrix[intmat][0][cnt,:,:] = rotation_matrix1[intmat][0] col_temp = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 0., 1.]), final_symm, symm_operator) col[intmat][0][cnt,:] = col_temp col_tempx = get_ipf_colour(rotation_matrix1[intmat][0], np.array([1., 0., 0.]), final_symm, symm_operator) colx[intmat][0][cnt,:] = col_tempx col_tempy = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 1., 0.]), final_symm, symm_operator) coly[intmat][0][cnt,:] = col_tempy match_rate[intmat][0][cnt] = mr_highest[intmat][0] spots_len[intmat][0][cnt] = spots_len1[intmat][0] iR_pix[intmat][0][cnt] = iR_pix1[intmat][0] fR_pix[intmat][0][cnt] = fR_pix1[intmat][0] best_match[intmat][0][cnt] = best_match1[intmat][0] check[cnt,intmat] = check12[intmat] files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, match_rate, \ mat_global, cnt, files_treated, spots_len, iR_pix, fR_pix, check, best_match def new_MP_function(argu): files, cnt, rotation_matrix, strain_matrix, strain_matrixs,\ col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global,\ check,detectorparameters,pixelsize,angbins,\ classhkl, hkl_all_class0, hkl_all_class1, emin, emax,\ material_, material1_, symmetry, symmetry1,lim_x,lim_y,\ strain_calculation, ind_mat, ind_mat1,\ model_direc, tolerance , tolerance1,\ matricies, ccd_label,\ filename_bkg,intensity_threshold,\ boxsize,bkg_treatment,\ filenameDirec, experimental_prefix,\ blacklist_file, text_file, \ files_treated,try_previous1,\ wb, temp_key, cor_file_directory, mode_spotCycle1,\ softmax_threshold_global123,mr_threshold_global123,\ cap_matchrate123, tolerance_strain123, tolerance_strain1231,\ NumberMaxofFits123,fit_peaks_gaussian_global123,\ FitPixelDev_global123,coeff123,coeff_overlap,\ material0_limit, material1_limit, use_previous_UBmatrix_name1,\ material_phase_always_present1, crystal, crystal1, strain_free_parameters = argu strain_matrix12, strain_matrixs12, \ rotation_matrix12, col12, \ colx12, coly12,\ match_rate12, mat_global12, cnt12,\ files_treated12, spots_len12, \ iR_pix12, fR_pix12, check12, best_match12 = predict_preprocessMultiProcess(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match, mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, hkl_all_class1, emin, emax, material_, material1_, symmetry, symmetry1,lim_x,lim_y, strain_calculation, ind_mat, ind_mat1, model_direc, tolerance, tolerance1, matricies, ccd_label, filename_bkg,intensity_threshold, boxsize,bkg_treatment, filenameDirec, experimental_prefix, blacklist_file, text_file, files_treated,try_previous1, wb, temp_key, cor_file_directory, mode_spotCycle1, softmax_threshold_global123,mr_threshold_global123, cap_matchrate123, tolerance_strain123, tolerance_strain1231,NumberMaxofFits123, fit_peaks_gaussian_global123, FitPixelDev_global123, coeff123,coeff_overlap, material0_limit,material1_limit, use_previous_UBmatrix_name1, material_phase_always_present1, crystal, crystal1, strain_free_parameters) meta = {} return strain_matrix12, strain_matrixs12, rotation_matrix12, col12, \ colx12, coly12, match_rate12, mat_global12, cnt12, meta, \ files_treated12, spots_len12, iR_pix12, fR_pix12, best_match12, check12 def prepare_LP_NB(nbgrains, nbgrains1, material_, verbose, material1_=None, seed=None, sortintensity=False, detectorparameters=None, pixelsize=None, dim1=2048,dim2=2048, emin=5, emax=23, flag=0, noisy_data=False, remove_peaks = False): if flag == 10: s_tth, s_chi, s_miller_ind, s_posx, s_posy, \ s_intensity, g, g1 = simulatemultiplepatterns_NB(nbgrains, nbgrains1, seed=seed, key_material=material_, key_material1=material1_, emin=emin, emax=emax, detectorparameters=detectorparameters, pixelsize=pixelsize, sortintensity=sortintensity, dim1=dim1,dim2=dim2, flag=flag) else: s_tth, s_chi, s_miller_ind, s_posx, s_posy, \ s_intensity = simulatemultiplepatterns_NB(nbgrains, nbgrains1, seed=seed, key_material=material_, key_material1=material1_, emin=emin, emax=emax, detectorparameters=detectorparameters, pixelsize=pixelsize, sortintensity=sortintensity, dim1=dim1,dim2=dim2, flag=flag) if noisy_data: ## apply random gaussian type noise to the data (tth and chi) ## So adding noise to the angular distances ## Instead of adding noise to all HKL's ... Add to few selected HKLs ## Adding noise to randomly 30% of the HKLs indices_noise = np.random.choice(len(s_tth), int(len(s_tth)*0.2), replace=False) noise_ = np.random.normal(0,0.1,len(indices_noise)) s_tth[indices_noise] = s_tth[indices_noise] + noise_ s_chi[indices_noise] = s_chi[indices_noise] + noise_ if remove_peaks: len_mi = np.array([iq for iq in range(len(s_miller_ind))]) len_mi = len_mi[int(0.5*len(s_miller_ind)):] indices_remove = np.random.choice(len_mi, int(len(len_mi)*0.2), replace=False) ## delete randomly selected less intense peaks ## to simulate real peak detection, where some peaks may not be ## well detected s_tth = np.delete(s_tth, indices_remove) s_chi = np.delete(s_chi, indices_remove) s_posx = np.delete(s_posx, indices_remove) s_posy = np.delete(s_posy, indices_remove) s_intensity = np.delete(s_intensity, indices_remove) s_miller_ind = np.delete(s_miller_ind, indices_remove, axis=0) # considering all spots allspots_the_chi = np.transpose(np.array([s_tth/2., s_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(allspots_the_chi, allspots_the_chi)) # ground truth hkl_sol = s_miller_ind if flag == 10: return tabledistancerandom, hkl_sol, s_posx, s_posy, s_intensity, s_tth, s_chi, g, g1 return tabledistancerandom, hkl_sol, s_posx, s_posy, s_intensity, s_tth, s_chi def simulatemultiplepatterns_NB(nbUBs, nbUBs1, seed=123, key_material=None, key_material1=None, emin=5, emax=23, detectorparameters=None, pixelsize=None, sortintensity = False, dim1=2048, dim2=2048, flag=0): detectordiameter = pixelsize * dim1 #TODO g = np.zeros((nbUBs, 3, 3)) g1 = np.zeros((nbUBs1, 3, 3)) for igr in range(nbUBs): phi1 = np.random.rand() * 360. phi = 180. * acos(2 * np.random.rand() - 1) / np.pi phi2 = np.random.rand() * 360. g[igr] = Euler2OrientationMatrix((phi1, phi, phi2)) if key_material != key_material1: for igr in range(nbUBs1): phi1 = np.random.rand() * 360. phi = 180. * acos(2 * np.random.rand() - 1) / np.pi phi2 = np.random.rand() * 360. g1[igr] = Euler2OrientationMatrix((phi1, phi, phi2)) l_tth, l_chi, l_miller_ind, l_posx, l_posy, l_E, l_intensity = [],[],[],[],[],[],[] for grainind in range(nbUBs): UBmatrix = g[grainind] grain = CP.Prepare_Grain(key_material, UBmatrix) s_tth, s_chi, s_miller_ind, s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=1) s_miller_ind = np.c_[ s_miller_ind, np.zeros(len(s_miller_ind)) ] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) if key_material != key_material1: for grainind in range(nbUBs1): if key_material1 != None: UBmatrix = g1[grainind] grain = CP.Prepare_Grain(key_material1, UBmatrix) s_tth, s_chi, s_miller_ind, s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=1) s_miller_ind = np.c_[ s_miller_ind, np.ones(len(s_miller_ind)) ] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) #flat_list = [item for sublist in l for item in sublist] s_tth = np.array([item for sublist in l_tth for item in sublist]) s_chi = np.array([item for sublist in l_chi for item in sublist]) s_miller_ind = np.array([item for sublist in l_miller_ind for item in sublist]) s_posx = np.array([item for sublist in l_posx for item in sublist]) s_posy = np.array([item for sublist in l_posy for item in sublist]) s_E = np.array([item for sublist in l_E for item in sublist]) s_intensity=np.array([item for sublist in l_intensity for item in sublist]) if sortintensity: indsort = np.argsort(s_intensity)[::-1] s_tth=np.take(s_tth, indsort) s_chi=np.take(s_chi, indsort) s_miller_ind=np.take(s_miller_ind, indsort, axis=0) s_posx=np.take(s_posx, indsort) s_posy=np.take(s_posy, indsort) s_E=np.take(s_E, indsort) s_intensity=np.take(s_intensity, indsort) if flag == 10: return s_tth, s_chi, s_miller_ind, s_posx, s_posy, s_intensity, g, g1 return s_tth, s_chi, s_miller_ind, s_posx, s_posy, s_intensity # ============================================================================= # Multi material functions # ============================================================================= def generate_multimat_dataset( material_=["Cu"], ang_maxx=18., step=0.1, nb_grains=[1], grains_nb_simulate=100, data_realism = False, detectorparameters=None, pixelsize=None, type_="training", var0 = 0, dim1=2048, dim2=2048, removeharmonics=1, save_directory="", write_to_console=None, emin=5, emax=22, modelp = "random", general_diff_rules = False, crystal = [None]): """ works for n phases now. """ from multiprocessing import Process, Queue, cpu_count ncpu = cpu_count() ## make sure directory exists save_directory_ = save_directory+"//"+type_ if not os.path.exists(save_directory_): os.makedirs(save_directory_) classhkl, n, hkl_all_class, lattice_material, symmetry = [], [], [], [],[] max_millerindex = [] try: for imat in material_: with open(save_directory+"//classhkl_data_"+imat+".pickle", "rb") as input_file: classhkl_mat, _, _, n_mat, _, \ hkl_all_class_mat, _, \ lattice_material_mat, symmetry_mat = cPickle.load(input_file) classhkl.append(classhkl_mat) n.append(n_mat) hkl_all_class.append(hkl_all_class_mat) lattice_material.append(lattice_material_mat) symmetry.append(symmetry_mat) max_millerindex.append(int(n_mat)) if var0==1: codebars, angbins = get_material_data(material_ = imat, ang_maxx = ang_maxx, step = step, hkl_ref=n_mat, classhkl=classhkl_mat) np.savez_compressed(save_directory+'//grain_classhkl_angbin_'+imat+'.npz',\ classhkl_mat, angbins) except: write_to_console("Class HKL library data not found, please run it first") return None ## make comprehensive list of dictionary normal_hkl_multimat = [] index_hkl_mutimat = [] for ino, imat in enumerate(material_): normal_hkl_ = np.zeros((1,3)) for j in hkl_all_class[ino].keys(): normal_hkl_ = np.vstack((normal_hkl_, hkl_all_class[ino][j]["family"])) normal_hkl = np.delete(normal_hkl_, 0, axis =0) normal_hkl_multimat.append(normal_hkl) if ino > 0: ind_offset = index_hkl_mutimat[ino-1][-1] + 1 index_hkl = [ind_offset+j for j,k in enumerate(hkl_all_class[ino].keys()) for i in range(len(hkl_all_class[ino][k]["family"]))] else: index_hkl = [j for j,k in enumerate(hkl_all_class[ino].keys()) for i in range(len(hkl_all_class[ino][k]["family"]))] index_hkl_mutimat.append(index_hkl) write_to_console("Generating "+type_+" and saving them") _inputs_queue = Queue() _outputs_queue = Queue() _worker_process = {} for i in range(ncpu): _worker_process[i]= Process(target=worker_generation_multimat, args=(_inputs_queue, _outputs_queue, i+1),) for i in range(ncpu): _worker_process[i].start() time.sleep(0.1) ## list of combination of training dataset ## to be seen if this improves the prediction quality ## increases time significantly to generate the data nb_grains_list = [] for ino, imat in enumerate(material_): nb_grains_list.append(list(range(nb_grains[ino]+1))) list_permute = list(itertools.product(*nb_grains_list)) list_permute.pop(0) max_progress = len(list_permute)*(grains_nb_simulate) # generate a database upto n grain LP values = [] for i in range(len(list_permute)): for j in range(grains_nb_simulate): if data_realism: ## three types of data augmentation to mimic reality ? if j < grains_nb_simulate*0.25: noisy_data = False remove_peaks = False elif (j >= grains_nb_simulate*0.25) and (j < grains_nb_simulate*0.5): noisy_data = True remove_peaks = False elif (j >= grains_nb_simulate*0.5) and (j < grains_nb_simulate*0.75): noisy_data = False remove_peaks = True elif (j >= grains_nb_simulate*0.75): noisy_data = True remove_peaks = True else: noisy_data = False remove_peaks = False seednumber = np.random.randint(1e6) values.append([ list_permute[i], material_, emin, emax, detectorparameters, pixelsize,True, ang_maxx, step, classhkl, noisy_data, remove_peaks, seednumber, hkl_all_class, lattice_material, None, normal_hkl_multimat, index_hkl_mutimat, dim1, dim2, removeharmonics, 0, i, j, save_directory_, modelp, max_millerindex, general_diff_rules, crystal,]) chunks = chunker_list(values, ncpu) chunks_mp = list(chunks) meta = {'t1':time.time(), 'flag':1} for ijk in range(int(ncpu)): _inputs_queue.put((chunks_mp[ijk], ncpu, meta)) max_progress = max_progress while True: count = 0 for i in range(ncpu): if not _worker_process[i].is_alive(): _worker_process[i].join() count += 1 else: time.sleep(0.1) if count == ncpu: return def getMMpatterns_(nb, material_=None, emin=5, emax=23, detectorparameters=None, pixelsize=None, sortintensity = False, ang_maxx = 45, step = 0.5, classhkl = None, noisy_data=False, remove_peaks=False, seed = None,hkl_all=None, lattice_material=None, family_hkl=None, normal_hkl=None, index_hkl=None, dim1=2048, dim2=2048, removeharmonics=1, flag = 0, img_i=None, img_j=None, save_directory_=None, modelp=None, max_millerindex=0, general_diff_cond=False, crystal=None, ): if np.all(np.array(nb)==0): print("Skipping a simulation file: "+save_directory_+'//grain_'+\ str(img_i)+"_"+str(img_j)+'.npz'+"; Due to zero UBmatrix") return ori_mat, ori_mat1 = [], [] s_tth, s_chi, s_miller_ind, _, _, _ = simulatemultimatpatterns(nb, seed=seed, key_material=material_, emin=emin, emax=emax, detectorparameters=detectorparameters, pixelsize=pixelsize, sortintensity = sortintensity, dim1=dim1, dim2=dim2, removeharmonics=removeharmonics, flag=flag, mode=modelp, ) if noisy_data: ## apply random gaussian type noise to the data (tth and chi) ## So adding noise to the angular distances ## Instead of adding noise to all HKL's ... Add to few selected HKLs ## Adding noise to randomly 30% of the HKLs ## Realistic way of introducting strains is through Pixels and not 2theta noisy_pixel = 0.15 indices_noise = np.random.choice(len(s_tth), int(len(s_tth)*0.3), replace=False) noise_ = np.random.normal(0,noisy_pixel,len(indices_noise)) s_tth[indices_noise] = s_tth[indices_noise] + noise_ noise_ = np.random.normal(0,noisy_pixel,len(indices_noise)) s_chi[indices_noise] = s_chi[indices_noise] + noise_ if remove_peaks: len_mi = np.array([iq for iq in range(len(s_miller_ind))]) len_mi = len_mi[int(0.6*len(s_miller_ind)):] indices_remove = np.random.choice(len_mi, int(len(len_mi)*0.3), replace=False) ## delete randomly selected less intense peaks ## to simulate real peak detection, where some peaks may not be ## well detected ## Include maybe Intensity approach: Delete peaks based on their SF and position in detector if len(indices_remove) !=0: s_tth = np.delete(s_tth, indices_remove) s_chi = np.delete(s_chi, indices_remove) s_miller_ind = np.delete(s_miller_ind, indices_remove, axis=0) # replace all hkl class with relevant hkls ## skip HKLS that dont follow the general diffraction rules location = [] skip_hkl = [] delete_spots = [] for j, i in enumerate(s_miller_ind): new_hkl = _round_indices(i[:3]) mat_index = int(i[3]) if general_diff_cond: cond_proceed = crystal[mat_index].hkl_allowed(i[:3], returnequivalents=False) else: cond_proceed = True if not cond_proceed: delete_spots.append(j) continue if np.any(np.abs(new_hkl)>max_millerindex[mat_index]): skip_hkl.append(j) continue temp_ = np.all(new_hkl == normal_hkl[mat_index], axis=1) if len(np.where(temp_)[0]) == 1: ind_ = np.where(temp_)[0][0] location.append(index_hkl[mat_index][ind_]) elif len(np.where(temp_)[0]) == 0: # print("Entering -100 for "+ str(i) + "\n") skip_hkl.append(j) elif len(np.where(temp_)[0]) > 1: ## first check if they both are same class or not class_output = [] for ij in range(len(np.where(temp_)[0])): indc = index_hkl[mat_index][np.where(temp_)[0][ij]] class_output.append(indc) if len(set(class_output)) <= 1: location.append(class_output[0]) else: skip_hkl.append(j) print(i) print(np.where(temp_)[0]) for ij in range(len(np.where(temp_)[0])): indc = index_hkl[mat_index][np.where(temp_)[0][ij]] print(classhkl[mat_index][indc]) print("Entering -500: Skipping HKL as something is not proper with equivalent HKL module") allspots_the_chi = np.transpose(np.array([s_tth/2., s_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(allspots_the_chi, allspots_the_chi)) codebars = [] angbins = np.arange(0,ang_maxx+step,step) for i in range(len(tabledistancerandom)): if i in skip_hkl or i in delete_spots: ## not saving skipped HKL continue angles = tabledistancerandom[i] spots_delete = [i] for del_spts in delete_spots: spots_delete.append(del_spts) angles = np.delete(angles, spots_delete) # angles = np.delete(angles, i)# removing the self distance fingerprint = np.histogram(angles, bins=angbins)[0] # fingerprint = histogram1d(angles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## same normalization as before max_codebars = np.max(fingerprint) fingerprint = fingerprint/ max_codebars codebars.append(fingerprint) suffix_ = "" if flag == 0: if len(codebars) != 0: mat_prefix = "" for no, i in enumerate(nb): if i != 0: mat_prefix = mat_prefix + material_[no] np.savez_compressed(save_directory_+'//'+mat_prefix+'_grain_'+str(img_i)+"_"+\ str(img_j)+suffix_+'.npz', codebars, location, ori_mat, ori_mat1, flag,\ s_tth, s_chi, s_miller_ind) else: print("Skipping a simulation file: "+save_directory_+'//grain_'+\ str(img_i)+"_"+str(img_j)+suffix_+'.npz'+"; Due to no data conforming user settings") def simulatemultimatpatterns(nbUBs, seed=123, key_material=None, emin=5, emax=23, detectorparameters=None, pixelsize=None, sortintensity = False, dim1=2048, dim2=2048, removeharmonics=1, flag = 0, mode="random"): l_tth, l_chi, l_miller_ind, l_posx, l_posy, l_E, l_intensity = [],[],[],[],[],[],[] detectordiameter = pixelsize * dim1 #TODO * 2.0 if flag == 0: if mode == "random": for no, i in enumerate(nbUBs): if i != 0: for igr in range(i): phi1 = rand1() * 360. phi = 180. * acos(2 * rand1() - 1) / np.pi phi2 = rand1() * 360. UBmatrix = Euler2OrientationMatrix((phi1, phi, phi2)) grain = CP.Prepare_Grain(key_material[no], UBmatrix) s_tth, s_chi, s_miller_ind, \ s_posx, s_posy, s_E= LT.SimulateLaue_full_np(grain, emin, emax, detectorparameters, pixelsize=pixelsize, dim=(dim1, dim2), detectordiameter=detectordiameter, removeharmonics=removeharmonics) s_miller_ind = np.c_[s_miller_ind, np.ones(len(s_miller_ind))*no] s_intensity = 1./s_E l_tth.append(s_tth) l_chi.append(s_chi) l_miller_ind.append(s_miller_ind) l_posx.append(s_posx) l_posy.append(s_posy) l_E.append(s_E) l_intensity.append(s_intensity) #flat_list = [item for sublist in l for item in sublist] s_tth = np.array([item for sublist in l_tth for item in sublist]) s_chi = np.array([item for sublist in l_chi for item in sublist]) s_miller_ind = np.array([item for sublist in l_miller_ind for item in sublist]) s_posx = np.array([item for sublist in l_posx for item in sublist]) s_posy = np.array([item for sublist in l_posy for item in sublist]) s_E = np.array([item for sublist in l_E for item in sublist]) s_intensity=np.array([item for sublist in l_intensity for item in sublist]) if sortintensity: indsort = np.argsort(s_intensity)[::-1] s_tth=np.take(s_tth, indsort) s_chi=np.take(s_chi, indsort) s_miller_ind=np.take(s_miller_ind, indsort, axis=0) s_posx=np.take(s_posx, indsort) s_posy=np.take(s_posy, indsort) s_E=np.take(s_E, indsort) s_intensity=np.take(s_intensity, indsort) return s_tth, s_chi, s_miller_ind, s_posx, s_posy, s_intensity def worker_generation_multimat(inputs_queue, outputs_queue, proc_id): while True: time.sleep(0.01) if not inputs_queue.empty(): message = inputs_queue.get() num1, _, meta = message flag1 = meta['flag'] for ijk in range(len(num1)): nb, material_, emin, emax, detectorparameters, pixelsize, \ sortintensity, ang_maxx, step, classhkl, noisy_data, \ remove_peaks, seed,hkl_all, lattice_material, family_hkl,\ normal_hkl, index_hkl, dim1, dim2, removeharmonics, flag,\ img_i, img_j, save_directory_, modelp, max_millerindex,\ general_diff_cond, crystal = num1[ijk] getMMpatterns_(nb, material_, emin, emax, detectorparameters, pixelsize, \ sortintensity, ang_maxx, step, classhkl, noisy_data, \ remove_peaks, seed,hkl_all, lattice_material, family_hkl,\ normal_hkl, index_hkl, dim1, dim2, removeharmonics, flag,\ img_i, img_j, save_directory_, modelp, \ max_millerindex, general_diff_cond, crystal) # if ijk%10 == 0 and ijk!=0: # outputs_queue.put(11) if flag1 == 1: break def get_multimaterial_detail(material_=None, SG_mat=None, symm_mat=None): """ Returns material details """ rules, symmetry, lattice_material, crystal, SG = [],[],[],[],[] for ino, imat in enumerate(material_): a, b, c, alpha, beta, gamma = dictLT.dict_Materials[imat][1] rules.append(dictLT.dict_Materials[imat][-1]) symm_ = symm_mat[ino] if symm_ =="cubic": symmetry.append(Symmetry.cubic) lattice_material.append(Lattice.cubic(a)) if SG_mat[ino] == None: SG.append(230) SG_mat[ino] = 230 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]), a)) elif symm_ =="monoclinic": symmetry.append(Symmetry.monoclinic) lattice_material.append(Lattice.monoclinic(a, b, c, beta)) if SG_mat[ino] == None: SG.append(10) SG_mat[ino] = 10 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]),a, b, c, beta)) elif symm_ == "hexagonal": symmetry.append(Symmetry.hexagonal) lattice_material.append(Lattice.hexagonal(a, c)) if SG_mat[ino] == None: SG.append(191) SG_mat[ino] = 191 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]),a, c)) elif symm_ == "orthorhombic": symmetry.append(Symmetry.orthorhombic) lattice_material.append(Lattice.orthorhombic(a, b, c)) if SG_mat[ino] == None: SG.append(47) SG_mat[ino] = 47 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]),a, b, c)) elif symm_ == "tetragonal": symmetry.append(Symmetry.tetragonal) lattice_material.append(Lattice.tetragonal(a, c)) if SG_mat[ino] == None: SG.append(123) SG_mat[ino] = 123 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]),a, c)) elif symm_ == "trigonal": symmetry.append(Symmetry.trigonal) lattice_material.append(Lattice.rhombohedral(a, alpha)) if SG_mat[ino] == None: SG.append(162) SG_mat[ino] = 162 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]),a, alpha)) elif symm_ == "triclinic": symmetry.append(Symmetry.triclinic) lattice_material.append(Lattice.triclinic(a, b, c, alpha, beta, gamma)) if SG_mat[ino] == None: SG.append(2) SG_mat[ino] = 2 else: SG.append(SG_mat[ino]) crystal.append(SGLattice(int(SG_mat[ino]),a, b, c, alpha, beta, gamma)) return rules, symmetry, lattice_material, crystal, SG def rmv_freq_class_MM(freq_rmv = [0], elements=["all"], save_directory="", material_=None, write_to_console=None, progress=None, qapp=None): classhkl_mm = [] ind_mat_mm = [] for ino, imat in enumerate(material_): if ino == 0: classhkl0 = np.load(save_directory+"//grain_classhkl_angbin_"+imat+".npz")["arr_0"] angbins = np.load(save_directory+"//grain_classhkl_angbin_"+imat+".npz")["arr_1"] if write_to_console != None: write_to_console(imat +" material index length: " + str(len(classhkl0))) ind_mat = np.array([ij for ij in range(len(classhkl0))]) classhkl_mm.append(classhkl0) ind_mat_mm.append(ind_mat) else: classhkl0 = np.load(save_directory+"//grain_classhkl_angbin_"+imat+".npz")["arr_0"] if write_to_console != None: write_to_console(imat +" material index length: " + str(len(classhkl0))) pre_ind = ind_mat_mm[ino-1][-1] + 1 ind_mat = np.array([pre_ind+ij for ij in range(len(classhkl0))]) classhkl_mm.append(classhkl0) ind_mat_mm.append(ind_mat) for ino, classhkl0 in enumerate(classhkl_mm): if ino == 0: classhkl = classhkl0 else: classhkl = np.vstack((classhkl, classhkl0)) loc = np.array([ij for ij in range(len(classhkl))]) trainy_ = array_generatorV2(save_directory+"//training_data", 0, progress, qapp) ## split trainy_ for two materials index trainy_mat_MM = [[] for _ in range(len(material_))] for ino, imat in enumerate(material_): for ijnode in trainy_: if ijnode in ind_mat_mm[ino]: trainy_mat_MM[ino].append(ijnode) if write_to_console != None: write_to_console("Class ID and frequency; check for data imbalance and select "+\ "appropriate LOSS function for training the model") ## lets extract the least common occuring classes to simplify the training dataset for ino, imat in enumerate(material_): if elements[ino] == "all": most_common0 = collections.Counter(np.array(trainy_mat_MM[ino])).most_common() else: most_common0 = collections.Counter(np.array(trainy_mat_MM[ino])).most_common()[:elements[ino]] print("Most common classhkl elements in "+imat+" are:") print(most_common0) if ino == 0: most_common = most_common0 else: most_common = most_common + most_common0 class_present = [most_common[i][0] for i in range(len(most_common))] rmv_indices = [] for i in loc: if i not in class_present: rmv_indices.append(i) elif i in class_present: ind_ = np.where(np.array(class_present)==i)[0] ij = most_common[ind_[0]] for ino, imat in enumerate(material_): if (ij[0] in ind_mat_mm[ino]) and (ij[1] <= freq_rmv[ino]): rmv_indices.append(int(ij[0])) else: if write_to_console != None: write_to_console("Something Fishy in Remove Freq Class module") for ino, imat in enumerate(material_): for i in rmv_indices: if i in ind_mat_mm[ino]: indd = np.where(ind_mat_mm[ino] == i)[0] ind_mat_mm[ino] = np.delete(ind_mat_mm[ino], indd, axis=0) loc_new = np.delete(loc, rmv_indices) occurances = [most_common[i][1] for i in range(len(most_common)) if int(most_common[i][0]) in loc_new] occurances = np.array(occurances) class_weight = {} class_weight_temp = {} count = 0 for i in loc_new: for ij in most_common: if int(ij[0]) == i: class_weight[count] = int(np.max(occurances)/ij[1]) class_weight_temp[int(ij[0])] = int(np.max(occurances)/ij[1]) count += 1 for occ in range(len(most_common)): if int(most_common[occ][0]) in loc_new: if write_to_console != None: suffix_string = "" for ino, imat in enumerate(material_): if int(most_common[occ][0]) in ind_mat_mm[ino]: suffix_string = "; material: "+imat if int(most_common[occ][0]) == -100: write_to_console("Unclassified HKL (-100); occurance : "+str(most_common[occ][1])+\ "; NN_weights : 0.0 "+suffix_string) else: write_to_console("HKL : " +str(classhkl[int(most_common[occ][0])])+"; occurance : "+\ str(most_common[occ][1])+\ "; NN_weights : "+ \ str(class_weight_temp[int(most_common[occ][0])])+suffix_string) if write_to_console != None: write_to_console(str(len(rmv_indices))+ " classes removed from the classHKL object [removal frequency: "+\ str(freq_rmv)+"] (before:"+str(len(classhkl))+", now:"+str(len(classhkl)-len(rmv_indices))+")") print(str(len(rmv_indices))+ " classes removed from the classHKL object [removal frequency: "+\ str(freq_rmv)+"] (before:"+str(len(classhkl))+", now:"+str(len(classhkl)-len(rmv_indices))+")") if len(rmv_indices) == len(classhkl): if write_to_console != None: write_to_console("Error; no classes left in the classhkl array; please reduce frequency to remove some classes") else: print("Error; no classes left in the classhkl array; please reduce frequency to remove some classes") return None classhkl = np.delete(classhkl, rmv_indices, axis=0) ## save the altered classHKL object np.savez_compressed(save_directory+'//MOD_grain_classhkl_angbin.npz', classhkl, angbins, loc_new, rmv_indices, freq_rmv, ind_mat_mm) with open(save_directory + "//class_weights.pickle", "wb") as output_file: cPickle.dump([class_weight], output_file) if write_to_console != None: write_to_console("Saved class weights data") def predict_preprocessMultiMatProcess(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, emin, emax, material_, symmetry, lim_x, lim_y, strain_calculation, ind_mat, model_direc=None, tolerance =None, matricies=None, ccd_label=None, filename_bkg=None,intensity_threshold=None, boxsize=None,bkg_treatment=None, filenameDirec=None, experimental_prefix=None, blacklist_file =None, text_file=None, files_treated=None,try_previous1=False, wb=None, temp_key=None, cor_file_directory=None, mode_spotCycle1=None, softmax_threshold_global123=None,mr_threshold_global123=None, cap_matchrate123=None,tolerance_strain123=None,\ NumberMaxofFits123=None,fit_peaks_gaussian_global123=None, FitPixelDev_global123=None,coeff123=None, coeff_overlap=None, material0_limit=None, use_previous_UBmatrix_name=None, material_phase_always_present=None, crystal=None, strain_free_parameters=None): if files in files_treated: return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match call_global() # print("Predicting for "+files) if files.split(".")[-1] != "cor": CCDLabel=ccd_label seednumber = "Experimental "+CCDLabel+" file" try: out_name = blacklist_file except: out_name = None if bkg_treatment == None: bkg_treatment = "A-B" try: ### Max space = space betzeen pixles peak_XY = RMCCD.PeakSearch( files, stackimageindex = -1, CCDLabel=CCDLabel, NumberMaxofFits=NumberMaxofFits123, PixelNearRadius=10, removeedge=2, IntensityThreshold=intensity_threshold, local_maxima_search_method=0, boxsize=boxsize, position_definition=1, verbose=0, fit_peaks_gaussian=fit_peaks_gaussian_global123, xtol=0.001, FitPixelDev=FitPixelDev_global123, return_histo=0, # Saturation_value=1e10, # to be merged in CCDLabel # Saturation_value_flatpeak=1e10, MinIntensity=0, PeakSizeRange=(0.65,200), write_execution_time=1, Data_for_localMaxima = "auto_background", formulaexpression=bkg_treatment, Remove_BlackListedPeaks_fromfile=out_name, reject_negative_baseline=True, Fit_with_Data_for_localMaxima=False, maxPixelDistanceRejection=15.0, ) peak_XY = peak_XY[0]#[:,:2] ##[2] Integer peak lists except: print("Error in Peak detection for "+ files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 # files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match try: s_ix = np.argsort(peak_XY[:, 2])[::-1] peak_XY = peak_XY[s_ix] except: print("Error in Peak detection for "+ files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 # files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match framedim = dictLT.dict_CCD[CCDLabel][0] twicetheta, chi = Lgeo.calc_uflab(peak_XY[:,0], peak_XY[:,1], detectorparameters, returnAngles=1, pixelsize=pixelsize, kf_direction='Z>0') data_theta, data_chi = twicetheta/2., chi framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0] dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peak_XY[:,0] dict_dp['peakY']=peak_XY[:,1] dict_dp['intensity']=peak_XY[:,2] CCDcalib = {"CCDLabel":CCDLabel, "dd":detectorparameters[0], "xcen":detectorparameters[1], "ycen":detectorparameters[2], "xbet":detectorparameters[3], "xgam":detectorparameters[4], "pixelsize": pixelsize} path = os.path.normpath(files) IOLT.writefile_cor(cor_file_directory+"//"+path.split(os.sep)[-1].split(".")[0], twicetheta, chi, peak_XY[:,0], peak_XY[:,1], peak_XY[:,2], param=CCDcalib, sortedexit=0) elif files.split(".")[-1] == "cor": # print("Entering Cor file read section") seednumber = "Experimental COR file" allres = IOLT.readfile_cor(files, True) data_theta, data_chi, peakx, peaky, intensity = allres[1:6] CCDcalib = allres[-1] detectorparameters = allres[-2] # print('detectorparameters from file are: '+ str(detectorparameters)) pixelsize = CCDcalib['pixelsize'] CCDLabel = CCDcalib['CCDLabel'] framedim = dictLT.dict_CCD[CCDLabel][0] dict_dp={} dict_dp['kf_direction']='Z>0' dict_dp['detectorparameters']=detectorparameters dict_dp['detectordistance']=detectorparameters[0] dict_dp['detectordiameter']=pixelsize*framedim[0] dict_dp['pixelsize']=pixelsize dict_dp['dim']=framedim dict_dp['peakX']=peakx dict_dp['peakY']=peaky dict_dp['intensity']=intensity sorted_data = np.transpose(np.array([data_theta, data_chi])) tabledistancerandom = np.transpose(GT.calculdist_from_thetachi(sorted_data, sorted_data)) codebars_all = [] if len(data_theta) == 0: print("No peaks Found for : " + files) for intmat in range(matricies): rotation_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrix[intmat][0][cnt,:,:] = np.zeros((3,3)) strain_matrixs[intmat][0][cnt,:,:] = np.zeros((3,3)) col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 match_rate[intmat][0][cnt] = 0 mat_global[intmat][0][cnt] = 0 spots_len[intmat][0][cnt] = 0 iR_pix[intmat][0][cnt] = 0 fR_pix[intmat][0][cnt] = 0 check[cnt,intmat] = 0 return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, \ match_rate, mat_global, cnt, files_treated,spots_len,iR_pix,fR_pix, check, best_match # print("Entering GOOD section") spots_in_center = np.arange(0,len(data_theta)) spots_in_center = spots_in_center[:nb_spots_consider] for i in spots_in_center: spotangles = tabledistancerandom[i] spotangles = np.delete(spotangles, i)# removing the self distance codebars = np.histogram(spotangles, bins=angbins)[0] # codebars = histogram1d(spotangles, range=[min(angbins),max(angbins)], bins=len(angbins)-1) ## normalize the same way as training data max_codebars = np.max(codebars) codebars = codebars/ max_codebars codebars_all.append(codebars) codebars = np.array(codebars_all) ## Do prediction of all spots at once prediction = predict(codebars, wb, temp_key) # prediction = model.predict(codebars) max_pred = np.max(prediction, axis = 1) class_predicted = np.argmax(prediction, axis = 1) predicted_hkl123 = classhkl[class_predicted] predicted_hkl123 = predicted_hkl123.astype(int) s_tth = data_theta * 2. s_chi = data_chi # print("Computing UB") rotation_matrix1, mr_highest, mat_highest, \ strain_crystal, strain_sample, iR_pix1, \ fR_pix1, spots_len1,\ best_match1, check12 = predict_ub_MM(seednumber, spots_in_center, classhkl, hkl_all_class0, files, s_tth1=s_tth,s_chi1=s_chi, predicted_hkl1=predicted_hkl123, class_predicted1=class_predicted, max_pred1=max_pred, emin=emin,emax=emax, material_=material_, lim_y=lim_y, lim_x=lim_x, cnt=cnt, dict_dp=dict_dp, rotation_matrix=rotation_matrix, mat_global=mat_global, strain_calculation=strain_calculation, ind_mat=ind_mat, tolerance=tolerance, matricies=matricies, tabledistancerandom=tabledistancerandom, text_file = text_file, try_previous1=try_previous1, mode_spotCycle=mode_spotCycle1, softmax_threshold_global123 = softmax_threshold_global123, mr_threshold_global123=mr_threshold_global123, cap_matchrate123=cap_matchrate123, tolerance_strain123=tolerance_strain123, coeff123=coeff123, coeff_overlap=coeff_overlap, material0_limit=material0_limit, model_direc=model_direc, use_previous_UBmatrix_name=use_previous_UBmatrix_name, material_phase_always_present=material_phase_always_present, match_rate=match_rate, check=check[cnt,:], crystal=crystal, angbins=angbins, wb=wb, temp_key=temp_key, strain_free_parameters=strain_free_parameters) for intmat in range(matricies): if len(rotation_matrix1[intmat]) == 0: col[intmat][0][cnt,:] = 0,0,0 colx[intmat][0][cnt,:] = 0,0,0 coly[intmat][0][cnt,:] = 0,0,0 else: mat_global[intmat][0][cnt] = mat_highest[intmat][0] final_symm = symmetry[mat_highest[intmat][0]-1] final_crystal = crystal[mat_highest[intmat][0]-1] symm_operator = final_crystal._hklsym strain_matrix[intmat][0][cnt,:,:] = strain_crystal[intmat][0] strain_matrixs[intmat][0][cnt,:,:] = strain_sample[intmat][0] rotation_matrix[intmat][0][cnt,:,:] = rotation_matrix1[intmat][0] col_temp = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 0., 1.]), final_symm, symm_operator) col[intmat][0][cnt,:] = col_temp col_tempx = get_ipf_colour(rotation_matrix1[intmat][0], np.array([1., 0., 0.]), final_symm, symm_operator) colx[intmat][0][cnt,:] = col_tempx col_tempy = get_ipf_colour(rotation_matrix1[intmat][0], np.array([0., 1., 0.]), final_symm, symm_operator) coly[intmat][0][cnt,:] = col_tempy match_rate[intmat][0][cnt] = mr_highest[intmat][0] spots_len[intmat][0][cnt] = spots_len1[intmat][0] iR_pix[intmat][0][cnt] = iR_pix1[intmat][0] fR_pix[intmat][0][cnt] = fR_pix1[intmat][0] best_match[intmat][0][cnt] = best_match1[intmat][0] check[cnt,intmat] = check12[intmat] files_treated.append(files) return strain_matrix, strain_matrixs, rotation_matrix, col, colx, coly, match_rate, \ mat_global, cnt, files_treated, spots_len, iR_pix, fR_pix, check, best_match def new_MP_multimat_function(argu): files, cnt, rotation_matrix, strain_matrix, strain_matrixs,\ col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match,mat_global,\ check,detectorparameters,pixelsize,angbins,\ classhkl, hkl_all_class0, emin, emax,\ material_, symmetry, lim_x, lim_y,\ strain_calculation, ind_mat,\ model_direc, tolerance,\ matricies, ccd_label,\ filename_bkg,intensity_threshold,\ boxsize,bkg_treatment,\ filenameDirec, experimental_prefix,\ blacklist_file, text_file, \ files_treated,try_previous1,\ wb, temp_key, cor_file_directory, mode_spotCycle1,\ softmax_threshold_global123,mr_threshold_global123,\ cap_matchrate123, tolerance_strain123,\ NumberMaxofFits123,fit_peaks_gaussian_global123,\ FitPixelDev_global123,coeff123,coeff_overlap,\ material0_limit, use_previous_UBmatrix_name1,\ material_phase_always_present1, crystal, strain_free_parameters = argu strain_matrix12, strain_matrixs12, \ rotation_matrix12, col12, \ colx12, coly12,\ match_rate12, mat_global12, cnt12,\ files_treated12, spots_len12, \ iR_pix12, fR_pix12, check12, best_match12 = predict_preprocessMultiMatProcess(files, cnt, rotation_matrix,strain_matrix,strain_matrixs, col,colx,coly,match_rate,spots_len,iR_pix,fR_pix,best_match, mat_global, check,detectorparameters,pixelsize,angbins, classhkl, hkl_all_class0, emin, emax, material_, symmetry,lim_x, lim_y, strain_calculation, ind_mat, model_direc, tolerance, matricies, ccd_label, filename_bkg,intensity_threshold, boxsize,bkg_treatment, filenameDirec, experimental_prefix, blacklist_file, text_file, files_treated,try_previous1, wb, temp_key, cor_file_directory, mode_spotCycle1, softmax_threshold_global123,mr_threshold_global123, cap_matchrate123, tolerance_strain123, NumberMaxofFits123, fit_peaks_gaussian_global123, FitPixelDev_global123, coeff123,coeff_overlap, material0_limit, use_previous_UBmatrix_name1, material_phase_always_present1, crystal, strain_free_parameters) meta = {} return strain_matrix12, strain_matrixs12, rotation_matrix12, col12, \ colx12, coly12, match_rate12, mat_global12, cnt12, meta, \ files_treated12, spots_len12, iR_pix12, fR_pix12, best_match12, check12 def predict_ub_MM(seednumber, spots_in_center, classhkl, hkl_all_class0, filename, s_tth1,s_chi1,predicted_hkl1,class_predicted1,max_pred1, emin, emax, material_, lim_y, lim_x, cnt, dict_dp,rotation_matrix,mat_global,strain_calculation, ind_mat, tolerance=None, matricies=None, tabledistancerandom=None, text_file=None, try_previous1=False, mode_spotCycle=None, softmax_threshold_global123=None,mr_threshold_global123=None, cap_matchrate123=None, tolerance_strain123=None, coeff123=None, coeff_overlap=None, material0_limit=None, model_direc=None, use_previous_UBmatrix_name=None, material_phase_always_present=None, match_rate=None, check = None, crystal=None, angbins=None, wb=None, temp_key=None, strain_free_parameters=None): input_params = {"tolerance": tolerance, "tolerancestrain": tolerance_strain123, ## For strain calculations "emin": emin, "emax": emax, "mat":0} call_global() strain_matrix = [[] for i in range(matricies)] strain_matrixs = [[] for i in range(matricies)] best_matrix = [[] for i in range(matricies)] mr_highest = [[] for i in range(matricies)] ir_pixels = [[] for i in range(matricies)] fr_pixels = [[] for i in range(matricies)] spots_len = [[] for i in range(matricies)] mat_highest = [[] for i in range(matricies)] best_match = [[] for i in range(matricies)] spots1 = [] spots1_global = [[] for i in range(matricies)] dist = tabledistancerandom ## one time calculations B0mat, Gstar_metric0mat, tab_distance_classhkl_data0mat = [], [], [] for ino, imat in enumerate(material_): lattice_params00 = dictLT.dict_Materials[imat][1] B00 = CP.calc_B_RR(lattice_params00) Gstar_metric00 = CP.Gstar_from_directlatticeparams(lattice_params00[0],lattice_params00[1],\ lattice_params00[2],lattice_params00[3],\ lattice_params00[4],lattice_params00[5]) tab_distance_classhkl_data00 = get_material_dataP(Gstar_metric00, predicted_hkl1[:nb_spots_consider,:]) B0mat.append(B00) Gstar_metric0mat.append(Gstar_metric00) tab_distance_classhkl_data0mat.append(tab_distance_classhkl_data00) spots = [] first_match = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr = 0 mat = 0 iR = 0 fR = 0 strain_crystal = np.zeros((3,3)) strain_sample = np.zeros((3,3)) material0_count = [0 for _ in range(len(material_))] objective_function1 = None for igrain in range(matricies): ### old version if mode_spotCycle == "slow": # print("Slow mode of analysis") first_match, max_mr, min_mr, spots, \ case, mat, strain_crystal, \ strain_sample, iR, fR = get_orient_matMM(s_tth1, s_chi1, material_, classhkl, class_predicted1, predicted_hkl1, input_params, hkl_all_class0, max_pred1, dict_dp, spots1, dist, Gstar_metric0mat, B0mat, softmax_threshold=softmax_threshold_global123, mr_threshold=mr_threshold_global123, tab_distance_classhkl_data0=tab_distance_classhkl_data0mat, spots1_global = spots1_global, coeff_overlap = coeff_overlap, ind_mat=ind_mat, strain_calculation=strain_calculation, cap_matchrate123=cap_matchrate123, material0_count=material0_count, material0_limit=material0_limit, igrain=igrain, material_phase_always_present=material_phase_always_present, strain_free_parameters=strain_free_parameters) else: print("selected mode of treating spots is not ready") for ispot in spots: spots1.append(ispot) spots1_global[igrain].append(ispot) ## make copy of best rotation matrix best_match[igrain].append(np.copy(first_match)) best_matrix[igrain].append(np.copy(first_match[14])) mr_highest[igrain].append(np.copy(max_mr)) mat_highest[igrain].append(np.copy(mat)) ir_pixels[igrain].append(np.copy(iR)) fr_pixels[igrain].append(np.copy(fR)) spots_len[igrain].append(np.copy(len(spots))) strain_matrix[igrain].append(np.copy(strain_crystal)) strain_matrixs[igrain].append(np.copy(strain_sample)) if np.all(first_match[14] != 0): check[igrain] = 1 material0_count[mat-1] = material0_count[mat-1]+1 return best_matrix, mr_highest, mat_highest, strain_matrix, strain_matrixs, ir_pixels, fr_pixels, spots_len, best_match, check def get_orient_matMM(s_tth, s_chi, material0_, classhkl, class_predicted, predicted_hkl, input_params, hkl_all_class0, max_pred, dict_dp, spots, dist, Gstar_metric0, B0, softmax_threshold=0.85, mr_threshold=0.85, tab_distance_classhkl_data0=None, spots1_global=None, coeff_overlap = None, ind_mat=None, strain_calculation=None,cap_matchrate123=None, material0_count=None, material0_limit=None, igrain=None, material_phase_always_present=None, strain_free_parameters=None): call_global() init_mr = 0 init_mat = 0 init_material = "None" init_case = "None" init_B = None final_match_rate = 0 match_rate_mma = [] final_rmv_ind = [] current_spots1 = [0 for igr in range(len(spots1_global))] mat = 0 case = "None" all_stats = [] for i in range(0, min(nb_spots_consider, len(s_tth))): for j in range(i+1, min(nb_spots_consider, len(s_tth))): overlap = False if (max_pred[j] < softmax_threshold) or (j in spots) or \ (max_pred[i] < softmax_threshold) or (i in spots): continue mat = 0 case = "None" input_params["mat"] = mat input_params["Bmat"] = None for ino, imat in enumerate(material0_): if ino == 0: if class_predicted[i] < ind_mat[ino] and class_predicted[j] < ind_mat[ino] : tab_distance_classhkl_data = tab_distance_classhkl_data0[ino] hkl_all_class = hkl_all_class0[ino] material_ = imat B = B0[ino] Gstar_metric = Gstar_metric0[ino] case = imat mat = ino + 1 if material0_count[ino] >= material0_limit[ino]: mat = 0 case="None" input_params["mat"] = mat input_params["Bmat"] = B else: if (ind_mat[ino-1] <= class_predicted[i] < ind_mat[ino]) and \ (ind_mat[ino-1] <= class_predicted[j] < ind_mat[ino]): tab_distance_classhkl_data = tab_distance_classhkl_data0[ino] hkl_all_class = hkl_all_class0[ino] material_ = imat B = B0[ino] Gstar_metric = Gstar_metric0[ino] case = imat mat = ino + 1 if material0_count[ino] >= material0_limit[ino]: mat = 0 case="None" input_params["mat"] = mat input_params["Bmat"] = B if mat == 0: continue tth_chi_spot1 = np.array([s_tth[i], s_chi[i]]) tth_chi_spot2 = np.array([s_tth[j], s_chi[j]]) hkl1 = hkl_all_class[str(predicted_hkl[i])] hkl1_list = np.array(hkl1) hkl2 = hkl_all_class[str(predicted_hkl[j])] hkl2_list = np.array(hkl2) actual_mat, flagAM, \ spot1_hkl, spot2_hkl = propose_UB_matrixMM(hkl1_list, hkl2_list, Gstar_metric, input_params, dist[i,j], tth_chi_spot1, tth_chi_spot2, B, method=0) if flagAM: continue for iind in range(len(actual_mat)): rot_mat123 = actual_mat[iind] rmv_ind, theospots = remove_spotsMM(s_tth, s_chi, rot_mat123, material_, input_params, dict_dp['detectorparameters'], dict_dp) overlap = False current_spots = [len(list(set(rmv_ind) & set(spots1_global[igr]))) for igr in range(len(spots1_global))] for igr in range(len(spots1_global)): if current_spots[igr] > coeff_overlap*len(spots1_global[igr]): overlap = True break if overlap: continue match_rate = np.round(100 * len(rmv_ind)/theospots,3) match_rate_mma.append(match_rate) if match_rate > init_mr: current_spots1 = current_spots init_mat = np.copy(mat) input_params["mat"] = init_mat init_material = np.copy(material_) init_case = np.copy(case) init_B = np.copy(B) input_params["Bmat"] = init_B final_rmv_ind = rmv_ind final_match_rate = np.copy(match_rate) init_mr = np.copy(match_rate) all_stats = [i, j, \ spot1_hkl[iind], spot2_hkl[iind], \ tth_chi_spot1, tth_chi_spot2, \ dist[i,j], tab_distance_classhkl_data[i,j], np.round(max_pred[i]*100,3), \ np.round(max_pred[j]*100,3), len(rmv_ind), theospots,\ match_rate, 0.0, rot_mat123] if (final_match_rate >= mr_threshold*100.) and not overlap: if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUBMM(s_tth, s_chi, all_stats[14], str(init_material), input_params, dict_dp['detectorparameters'], dict_dp, spots, init_B, strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(match_rate_mma), np.min(match_rate_mma), \ final_rmv_ind, str(init_case), init_mat, dev_strain, strain_sample, iR, fR overlap = False for igr in range(len(spots1_global)): if current_spots1[igr] > coeff_overlap*len(spots1_global[igr]): overlap = True if (final_match_rate <= cap_matchrate123) or overlap: ## Nothing found!! ## Either peaks are not well defined or not found within tolerance and prediction accuracy all_stats = [0, 0, 0, 0, 0, 0, 0, 0, 0, \ 0, 0, 0, 0, 0, np.zeros((3,3))] max_mr, min_mr = 0, 0 spot_ind = [] mat = 0 input_params["mat"] = 0 case = "None" return all_stats, max_mr, min_mr, spot_ind, case, mat, np.zeros((3,3)), np.zeros((3,3)), 0, 0 input_params["mat"] = init_mat if strain_calculation: dev_strain, strain_sample, iR, fR, rot_mat_UB = calculate_strains_fromUBMM(s_tth, s_chi, all_stats[14], str(init_material), input_params, dict_dp['detectorparameters'], dict_dp, spots, init_B, strain_free_parameters) else: dev_strain, strain_sample, iR, fR = np.zeros((3,3)), np.zeros((3,3)), 0, 0 rot_mat_UB = np.copy(all_stats[14]) all_stats[14] = rot_mat_UB return all_stats, np.max(match_rate_mma), np.min(match_rate_mma), \ final_rmv_ind, str(init_case), init_mat, dev_strain, strain_sample, iR, fR def propose_UB_matrixMM(hkl1_list, hkl2_list, Gstar_metric, input_params, dist123, tth_chi_spot1, tth_chi_spot2, B, method=0, crystal=None): if method == 0: tab_angulardist_temp = CP.AngleBetweenNormals(hkl1_list, hkl2_list, Gstar_metric) list_ = np.where(np.abs(tab_angulardist_temp-dist123) < input_params["tolerance"][input_params["mat"]-1]) if crystal != None: final_crystal=crystal[input_params["mat"]-1] symm_operator = final_crystal._hklsym else: symm_operator = np.eye(3) if len(list_[0]) == 0: return None, True, 0, 0 rot_mat_abs = [] actual_mat = [] spot1_hkl = [] spot2_hkl = [] triedspots = [] for ii, jj in zip(list_[0], list_[1]): if ii in triedspots and jj in triedspots: continue conti_ = False try: rot_mat1 = FindO.OrientMatrix_from_2hkl(hkl1_list[ii], tth_chi_spot1, \ hkl2_list[jj], tth_chi_spot2, B) # rot_mat1 = find_uniq_u(rot_mat1, symm_operator) except: continue copy_rm = np.copy(rot_mat1) copy_rm = np.round(np.abs(copy_rm),5) copy_rm.sort(axis=1) for iji in rot_mat_abs: iji.sort(axis=1) if np.all(iji==copy_rm): conti_ = True break if conti_: continue rot_mat_abs.append(np.round(np.abs(rot_mat1),5)) actual_mat.append(rot_mat1) spot1_hkl.append(hkl1_list[ii]) spot2_hkl.append(hkl2_list[jj]) triedspots.append(ii) triedspots.append(jj) else: # method 2 hkl_all = np.vstack((hkl1_list, hkl2_list)) LUT = FindO.GenerateLookUpTable(hkl_all, Gstar_metric) hkls = FindO.PlanePairs_2(dist123, input_params["tolerance"][input_params["mat"]-1], LUT, onlyclosest=1) if np.all(hkls == None): return None, True, 0, 0 rot_mat_abs = [] actual_mat = [] spot1_hkl = [] spot2_hkl = [] for ii in range(len(hkls)): if np.all(hkls[ii][0] == hkls[ii][1]): continue conti_ = False try: rot_mat1 = FindO.OrientMatrix_from_2hkl(hkls[ii][0], tth_chi_spot1, \ hkls[ii][1], tth_chi_spot2, B) # rot_mat1 = find_uniq_u(rot_mat1, symm_operator) except: continue copy_rm = np.copy(rot_mat1) copy_rm = np.round(np.abs(copy_rm),5) copy_rm.sort(axis=1) for iji in rot_mat_abs: iji.sort(axis=1) if np.all(iji==copy_rm): conti_ = True break if conti_: continue rot_mat_abs.append(np.round(np.abs(rot_mat1),5)) actual_mat.append(rot_mat1) spot1_hkl.append(hkls[ii][0]) spot2_hkl.append(hkls[ii][1]) #TODO ## just fixing a* to x seems ok; if not think of aligning b* to xy plane sum_sign = [] for nkl in range(len(actual_mat)): temp_mat = np.dot(actual_mat[nkl], B) ## fix could be to choose a matrix that aligns best the b* vector to Y axis or a* to X axis # if np.argmax(np.abs(temp_mat[:2,0])) == 0 and \ # np.argmax(np.abs(temp_mat[:2,1])) == 1: ##a* along x, b*along y if np.argmax(np.abs(temp_mat[:2,0])) == 0: ##a* along x sum_sign.append(2) elif np.argmax(np.abs(temp_mat[:2,0])) == np.argmax(np.abs(temp_mat[:2,1])): sum_sign.append(0) else: sum_sign.append(1) ind_sort = np.argsort(sum_sign)[::-1] ## re-arrange actual_mat1 = [] spot1_hkl1, spot2_hkl1 = [], [] for inin in ind_sort: actual_mat1.append(actual_mat[inin]) spot1_hkl1.append(spot1_hkl[inin]) spot2_hkl1.append(spot2_hkl[inin]) actual_mat, spot1_hkl, spot2_hkl = actual_mat1, spot1_hkl1, spot2_hkl1 return actual_mat, False, spot1_hkl, spot2_hkl def remove_spotsMM(s_tth, s_chi, first_match123, material_, input_params, detectorparameters, dict_dp): try: grain = CP.Prepare_Grain(material_, first_match123, dictmaterials=dictLT.dict_Materials) ### initialize global variables to be used later call_global() except: return [], 100 #### Perhaps better than SimulateResult function kf_direction = dict_dp["kf_direction"] detectordistance = dict_dp["detectorparameters"][0] detectordiameter = dict_dp["detectordiameter"] pixelsize = dict_dp["pixelsize"] dim = dict_dp["dim"] spots2pi = LT.getLaueSpots(CST_ENERGYKEV / input_params["emax"], CST_ENERGYKEV / input_params["emin"], [grain], fastcompute=1, verbose=0, kf_direction=kf_direction, ResolutionAngstrom=False, dictmaterials=dictLT.dict_Materials) TwicethetaChi = LT.filterLaueSpots_full_np(spots2pi[0][0], None, onlyXYZ=False, HarmonicsRemoval=0, fastcompute=1, kf_direction=kf_direction, detectordistance=detectordistance, detectordiameter=detectordiameter, pixelsize=pixelsize, dim=dim) ## get proximity for exp and theo spots if input_params["mat"] == 0: return [], 100 angtol = input_params["tolerance"][input_params["mat"] -1] if option_global =="v1": # print("entering v1") List_Exp_spot_close, residues_link, _ = getProximityv1(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) elif option_global =="v2": List_Exp_spot_close, residues_link, _ = getProximityv1_ambigious(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) else: List_Exp_spot_close, residues_link, _ = getProximityv1_ambigious(np.array([TwicethetaChi[0], TwicethetaChi[1]]), # warning array(2theta, chi) s_tth/2.0, s_chi, # warning theta, chi for exp angtol=angtol) List_Exp_spot_close, ind_uniq = np.unique(List_Exp_spot_close, return_index=True) residues_link = np.take(residues_link, ind_uniq) if np.average(residues_link) > residues_threshold: return [], 100 if len(np.unique(List_Exp_spot_close)) < nb_spots_global_threshold: return [], 100 return List_Exp_spot_close, len(TwicethetaChi[0]) def calculate_strains_fromUBMM(s_tth, s_chi, UBmat, material_, input_params, detectorparameters, dict_dp, spots, B_matrix, strain_free_parameters): ## for the moment strain_free_parameters is a trial implementation #TODO to be verified if ("a" not in strain_free_parameters) and len(strain_free_parameters)>=5: if additional_expression[0] != "none": print("Note: additional_expression is not applied for the current set of strain free parameters") # starting B0matrix corresponding to the unit cell ----- B0matrix = np.copy(B_matrix) latticeparams = dictLT.dict_Materials[material_][1] ## Included simple multi level refinement of strains init_residues = -0.1 final_residues = -0.1 straintolerance = input_params["tolerancestrain"][input_params["mat"]-1] devstrain, deviatoricstrain_sampleframe = np.zeros((3,3)), np.zeros((3,3)) for ijk, AngTol in enumerate(straintolerance): #### Spots in first match (no refining, just simple auto links to filter spots) grain = CP.Prepare_Grain(material_, UBmat, dictmaterials=dictLT.dict_Materials) Twicetheta, Chi, Miller_ind, posx, posy, _ = LT.SimulateLaue(grain, input_params["emin"], input_params["emax"], detectorparameters, kf_direction=dict_dp['kf_direction'], removeharmonics=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], ResolutionAngstrom=False, detectordiameter=dict_dp['detectordiameter'], dictmaterials=dictLT.dict_Materials) ## get proximity for exp and theo spots linkedspots_link, linkExpMiller_link, \ linkResidues_link = getProximityv0(np.array([Twicetheta, Chi]), # warning array(2theta, chi) s_tth/2.0, s_chi, Miller_ind, # warning theta, chi for exp angtol=float(AngTol)) if len(linkedspots_link) < 8: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat linkedspots_fit = linkedspots_link linkExpMiller_fit = linkExpMiller_link arraycouples = np.array(linkedspots_fit) exp_indices = np.array(arraycouples[:, 0], dtype=np.int) sim_indices = np.array(arraycouples[:, 1], dtype=np.int) nb_pairs = len(exp_indices) Data_Q = np.array(linkExpMiller_fit)[:, 1:] sim_indices = np.arange(nb_pairs) # for fitting function this must be an arange... pixX = np.take(dict_dp['peakX'], exp_indices) pixY = np.take(dict_dp['peakY'], exp_indices) weights = None #np.take(dict_dp['intensity'], exp_indices) starting_orientmatrix = np.copy(UBmat) results = None # ---------------------------------- # refinement model # ---------------------------------- # ------------------------------------------------------- allparameters = np.array(detectorparameters + [1, 1, 0, 0, 0] + [0, 0, 0]) # strain & orient initial_values = np.array([1.0, 1.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0]) arr_indexvaryingparameters = np.arange(5, 13) residues, deltamat, newmatrix = FitO.error_function_on_demand_strain( initial_values, Data_Q, allparameters, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pureRotation=0, verbose=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction']) init_mean_residues = np.copy(np.mean(residues)) if ijk == 0: init_residues = np.copy(init_mean_residues) results = FitO.fit_on_demand_strain(initial_values, Data_Q, allparameters, FitO.error_function_on_demand_strain, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], verbose=0, weights=weights, kf_direction=dict_dp['kf_direction']) if results is None: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat residues, deltamat, newmatrix = FitO.error_function_on_demand_strain( results, Data_Q, allparameters, arr_indexvaryingparameters, sim_indices, pixX, pixY, initrot=starting_orientmatrix, Bmat=B0matrix, pureRotation=0, verbose=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction']) # if np.mean(residues) > final_residues: # return devstrain, deviatoricstrain_sampleframe, init_residues, final_residues, UBmat final_mean_residues = np.copy(np.mean(residues)) final_residues = np.copy(final_mean_residues) # building B mat # param_strain_sol = results # varyingstrain = np.array([[1.0, param_strain_sol[2], param_strain_sol[3]], # [0, param_strain_sol[0], param_strain_sol[4]], # [0, 0, param_strain_sol[1]]]) # newUmat = np.dot(deltamat, starting_orientmatrix) # newUBmat = np.dot(newUmat, varyingstrain) newUBmat = np.copy(newmatrix) # Bstar_s = np.dot(newUBmat, B0matrix) # --------------------------------------------------------------- # postprocessing of unit cell orientation and strain refinement # --------------------------------------------------------------- UBmat = np.copy(newmatrix) (devstrain, lattice_parameter_direct_strain) = CP.compute_deviatoricstrain(newUBmat, B0matrix, latticeparams) # overwrite and rescale possibly lattice lengthes # constantlength = "a" # lattice_parameter_direct_strain = CP.computeLatticeParameters_from_UB(newUBmat, material_, constantlength, dictmaterials=dictLT.dict_Materials) # print(lattice_parameter_direct_strain) deviatoricstrain_sampleframe = CP.strain_from_crystal_to_sample_frame2(devstrain, newUBmat) # in % already devstrain = np.round(devstrain * 100, decimals=3) deviatoricstrain_sampleframe = np.round(deviatoricstrain_sampleframe * 100, decimals=3) else: # starting B0matrix corresponding to the unit cell ----- B0matrix = np.copy(B_matrix) latticeparams = dictLT.dict_Materials[material_][1] ## Included simple multi level refinement of strains init_residues = -0.1 final_residues = -0.1 straintolerance = input_params["tolerancestrain"][input_params["mat"]-1] devstrain, deviatoricstrain_sampleframe = np.zeros((3,3)), np.zeros((3,3)) for ijk, AngTol in enumerate(straintolerance): #### Spots in first match (no refining, just simple auto links to filter spots) grain = CP.Prepare_Grain(material_, UBmat, dictmaterials=dictLT.dict_Materials) Twicetheta, Chi, Miller_ind, posx, posy, _ = LT.SimulateLaue(grain, input_params["emin"], input_params["emax"], detectorparameters, kf_direction=dict_dp['kf_direction'], removeharmonics=1, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], ResolutionAngstrom=False, detectordiameter=dict_dp['detectordiameter'], dictmaterials=dictLT.dict_Materials) ## get proximity for exp and theo spots linkedspots_link, linkExpMiller_link, \ linkResidues_link = getProximityv0(np.array([Twicetheta, Chi]), # warning array(2theta, chi) s_tth/2.0, s_chi, Miller_ind, # warning theta, chi for exp angtol=float(AngTol)) if len(linkedspots_link) < 8: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat linkedspots_fit = linkedspots_link linkExpMiller_fit = linkExpMiller_link arraycouples = np.array(linkedspots_fit) exp_indices = np.array(arraycouples[:, 0], dtype=np.int) sim_indices = np.array(arraycouples[:, 1], dtype=np.int) nb_pairs = len(exp_indices) Data_Q = np.array(linkExpMiller_fit)[:, 1:] sim_indices = np.arange(nb_pairs) # for fitting function this must be an arange... pixX = np.take(dict_dp['peakX'], exp_indices) pixY = np.take(dict_dp['peakY'], exp_indices) weights = None #np.take(dict_dp['intensity'], exp_indices) starting_orientmatrix = np.copy(UBmat) results = None # ---------------------------------- # refinement model # ---------------------------------- # ------------------------------------------------------- allparameters = np.array(detectorparameters + [0, 0, 0] + latticeparams) fitting_parameters_keys = ["anglex", "angley", "anglez"] fitting_parameters_values = [0, 0, 0] constantlength = "a" if ("a" in strain_free_parameters) and ("b" in strain_free_parameters) and ("c" in strain_free_parameters): constantlength = "a" elif ("b" not in strain_free_parameters) and additional_expression[0]=="none" and\ "b" not in additional_expression[0]: constantlength = "b" elif ("c" not in strain_free_parameters): constantlength = "c" for jjkk in strain_free_parameters: if jjkk == "a" and constantlength != "a": fitting_parameters_keys.append("a") fitting_parameters_values.append(latticeparams[0]) if jjkk == "b" and constantlength != "b": fitting_parameters_keys.append("b") fitting_parameters_values.append(latticeparams[1]) if jjkk == "c" and constantlength != "c": fitting_parameters_keys.append("c") fitting_parameters_values.append(latticeparams[2]) if jjkk == "alpha": fitting_parameters_keys.append("alpha") fitting_parameters_values.append(latticeparams[3]) if jjkk == "beta": fitting_parameters_keys.append("beta") fitting_parameters_values.append(latticeparams[4]) if jjkk == "gamma": fitting_parameters_keys.append("gamma") fitting_parameters_values.append(latticeparams[5]) pureUmatrix, _ = GT.UBdecomposition_RRPP(starting_orientmatrix) absolutespotsindices = np.arange(len(pixX)) (residues, _, _, _, _, ) = FitO.error_function_latticeparameters(fitting_parameters_values, fitting_parameters_keys, Data_Q, allparameters, absolutespotsindices, pixX, pixY, initrot=pureUmatrix, pureRotation=0, verbose=0, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction'], returnalldata=True, additional_expression = additional_expression[0]) init_mean_residues = np.copy(np.mean(residues)) if ijk == 0: init_residues = np.copy(init_mean_residues) results = FitO.fit_function_latticeparameters(fitting_parameters_values, fitting_parameters_keys, Data_Q, allparameters, absolutespotsindices, pixX, pixY, UBmatrix_start=pureUmatrix, nb_grains=1, pureRotation=0, verbose=0, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction'], additional_expression = additional_expression[0]) if results is None: return np.zeros((3,3)), np.zeros((3,3)), init_residues, final_residues, UBmat (residues, Uxyz, newUmat, newB0matrix, _, ) = FitO.error_function_latticeparameters(results, fitting_parameters_keys, Data_Q, allparameters, absolutespotsindices, pixX, pixY, initrot=pureUmatrix, pureRotation=0, verbose=0, pixelsize=dict_dp['pixelsize'], dim=dict_dp['dim'], weights=weights, kf_direction=dict_dp['kf_direction'], returnalldata=True, additional_expression = additional_expression[0]) final_mean_residues = np.copy(np.mean(residues)) final_residues = np.copy(final_mean_residues) newUBmat = np.dot(np.dot(newUmat, newB0matrix), np.linalg.inv(B0matrix)) UBmat = np.copy(newUBmat) # --------------------------------------------------------------- # postprocessing of unit cell orientation and strain refinement # --------------------------------------------------------------- (devstrain, lattice_parameter_direct_strain) = CP.compute_deviatoricstrain(newUBmat, B0matrix, latticeparams) deviatoricstrain_sampleframe = CP.strain_from_crystal_to_sample_frame2(devstrain, newUBmat) # in % already devstrain = np.round(devstrain * 100, decimals=3) deviatoricstrain_sampleframe = np.round(deviatoricstrain_sampleframe * 100, decimals=3) return devstrain, deviatoricstrain_sampleframe, init_residues, final_residues, UBmat

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a96eb5eb68c6ef17f0357b34dc617192c8dd62bc

169

py

Python

Coding-Challenges/removeParens/remove_parens.py

FergusDevelopmentLLC/Coders-Workshop

3513bd5f79eaa85b4d2a648c5f343a224842325d

[ "MIT" ]

33

2019-12-02T23:29:47.000Z

2022-03-24T02:40:36.000Z

Coding-Challenges/removeParens/remove_parens.py

FergusDevelopmentLLC/Coders-Workshop

3513bd5f79eaa85b4d2a648c5f343a224842325d

[ "MIT" ]

39

2020-01-15T19:28:12.000Z

2021-11-26T05:13:29.000Z

Coding-Challenges/removeParens/remove_parens.py

FergusDevelopmentLLC/Coders-Workshop

3513bd5f79eaa85b4d2a648c5f343a224842325d

[ "MIT" ]

49

2019-12-02T23:29:53.000Z

2022-03-03T01:11:37.000Z

#!/usr/bin/env python3 def remove_parens(string): pass print(remove_parens("()())()")) # 1 print(remove_parens("()((()()")) # 2 print(remove_parens(")(")) # 2

16.9

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0

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169

9

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0.627586

0.159763

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0.123188

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1

0.2

false

0.2

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0.2

0.6

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1

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null

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1

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1

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7

a9970a2835f87639a4994842e4f985a29c90b127

155,923

py

Python

angr/procedures/definitions/win32_shell32.py

r4b3rt/angr

c133cfd4f83ffea2a1d9e064241e9459eaabc55f

[ "BSD-2-Clause" ]

null

angr/procedures/definitions/win32_shell32.py

r4b3rt/angr

c133cfd4f83ffea2a1d9e064241e9459eaabc55f

[ "BSD-2-Clause" ]

null

angr/procedures/definitions/win32_shell32.py

r4b3rt/angr

c133cfd4f83ffea2a1d9e064241e9459eaabc55f

[ "BSD-2-Clause" ]

null

# pylint:disable=line-too-long import logging from ...sim_type import SimTypeFunction, SimTypeShort, SimTypeInt, SimTypeLong, SimTypeLongLong, SimTypeDouble, SimTypeFloat, SimTypePointer, SimTypeChar, SimStruct, SimTypeFixedSizeArray, SimTypeBottom, SimUnion, SimTypeBool from ...calling_conventions import SimCCStdcall, SimCCMicrosoftAMD64 from .. import SIM_PROCEDURES as P from . import SimLibrary _l = logging.getLogger(name=__name__) lib = SimLibrary() lib.set_default_cc('X86', SimCCStdcall) lib.set_default_cc('AMD64', SimCCMicrosoftAMD64) lib.set_library_names("shell32.dll") prototypes = \ { # 'SHGetPropertyStoreFromIDList': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="GETPROPERTYSTOREFLAGS"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "flags", "riid", "ppv"]), # 'SHGetPropertyStoreFromParsingName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeBottom(label="IBindCtx"), SimTypeInt(signed=False, label="GETPROPERTYSTOREFLAGS"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath", "pbc", "flags", "riid", "ppv"]), # 'SHAddDefaultPropertiesByExt': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeBottom(label="IPropertyStore")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszExt", "pPropStore"]), # 'PifMgr_OpenProperties': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["pszApp", "pszPIF", "hInf", "flOpt"]), # 'PifMgr_GetProperties': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hProps", "pszGroup", "lpProps", "cbProps", "flOpt"]), # 'PifMgr_SetProperties': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hProps", "pszGroup", "lpProps", "cbProps", "flOpt"]), # 'PifMgr_CloseProperties': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hProps", "flOpt"]), # 'SHPropStgCreate': SimTypeFunction([SimTypeBottom(label="IPropertySetStorage"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="IPropertyStorage"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psstg", "fmtid", "pclsid", "grfFlags", "grfMode", "dwDisposition", "ppstg", "puCodePage"]), # 'SHPropStgReadMultiple': SimTypeFunction([SimTypeBottom(label="IPropertyStorage"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"ulKind": SimTypeInt(signed=False, label="PROPSPEC_KIND"), "Anonymous": SimUnion({"propid": SimTypeInt(signed=False, label="UInt32"), "lpwstr": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="<anon>", label="None")}, name="PROPSPEC", pack=False, align=None), label="LPArray", offset=0), SimTypePointer(SimStruct({"Anonymous": SimUnion({"Anonymous": SimStruct({"vt": SimTypeShort(signed=False, label="UInt16"), "wReserved1": SimTypeShort(signed=False, label="UInt16"), "wReserved2": SimTypeShort(signed=False, label="UInt16"), "wReserved3": SimTypeShort(signed=False, label="UInt16"), "Anonymous": SimUnion({"cVal": SimTypeBottom(label="CHAR"), "bVal": SimTypeChar(label="Byte"), "iVal": SimTypeShort(signed=True, label="Int16"), "uiVal": SimTypeShort(signed=False, label="UInt16"), "lVal": SimTypeInt(signed=True, label="Int32"), "ulVal": SimTypeInt(signed=False, label="UInt32"), "intVal": SimTypeInt(signed=True, label="Int32"), "uintVal": SimTypeInt(signed=False, label="UInt32"), "hVal": SimTypeBottom(label="LARGE_INTEGER"), "uhVal": SimTypeBottom(label="ULARGE_INTEGER"), "fltVal": SimTypeFloat(size=32), "dblVal": SimTypeFloat(size=64), "boolVal": SimTypeShort(signed=True, label="Int16"), "__OBSOLETE__VARIANT_BOOL": SimTypeShort(signed=True, label="Int16"), "scode": SimTypeInt(signed=True, label="Int32"), "cyVal": SimTypeBottom(label="CY"), "date": SimTypeFloat(size=64), "filetime": SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), "puuid": SimTypePointer(SimTypeBottom(label="Guid"), offset=0), "pclipdata": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0), "bstrVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "bstrblobVal": SimTypeBottom(label="BSTRBLOB"), "blob": SimTypeBottom(label="BLOB"), "pszVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pwszVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "punkVal": SimTypeBottom(label="IUnknown"), "pdispVal": SimTypeBottom(label="IDispatch"), "pStream": SimTypeBottom(label="IStream"), "pStorage": SimTypeBottom(label="IStorage"), "pVersionedStream": SimTypePointer(SimStruct({"guidVersion": SimTypeBottom(label="Guid"), "pStream": SimTypeBottom(label="IStream")}, name="VERSIONEDSTREAM", pack=False, align=None), offset=0), "parray": SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), "cac": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAC", pack=False, align=None), "caub": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAUB", pack=False, align=None), "cai": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CAI", pack=False, align=None), "caui": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)}, name="CAUI", pack=False, align=None), "cal": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CAL", pack=False, align=None), "caul": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)}, name="CAUL", pack=False, align=None), "cah": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="LARGE_INTEGER"), offset=0)}, name="CAH", pack=False, align=None), "cauh": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="ULARGE_INTEGER"), offset=0)}, name="CAUH", pack=False, align=None), "caflt": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=32), offset=0)}, name="CAFLT", pack=False, align=None), "cadbl": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADBL", pack=False, align=None), "cabool": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CABOOL", pack=False, align=None), "cascode": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CASCODE", pack=False, align=None), "cacy": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CY"), offset=0)}, name="CACY", pack=False, align=None), "cadate": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADATE", pack=False, align=None), "cafiletime": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0)}, name="CAFILETIME", pack=False, align=None), "cauuid": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="Guid"), offset=0)}, name="CACLSID", pack=False, align=None), "caclipdata": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0)}, name="CACLIPDATA", pack=False, align=None), "cabstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CABSTR", pack=False, align=None), "cabstrblob": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="BSTRBLOB"), offset=0)}, name="CABSTRBLOB", pack=False, align=None), "calpstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Byte"), offset=0), offset=0)}, name="CALPSTR", pack=False, align=None), "calpwstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CALPWSTR", pack=False, align=None), "capropvar": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="CAPROPVARIANT", pack=False, align=None), "pcVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pbVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "piVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "puiVal": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), "plVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pulVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pintVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "puintVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pfltVal": SimTypePointer(SimTypeFloat(size=32), offset=0), "pdblVal": SimTypePointer(SimTypeFloat(size=64), offset=0), "pboolVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "pdecVal": SimTypePointer(SimTypeBottom(label="DECIMAL"), offset=0), "pscode": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pcyVal": SimTypePointer(SimTypeBottom(label="CY"), offset=0), "pdate": SimTypePointer(SimTypeFloat(size=64), offset=0), "pbstrVal": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), "ppunkVal": SimTypePointer(SimTypeBottom(label="IUnknown"), offset=0), "ppdispVal": SimTypePointer(SimTypeBottom(label="IDispatch"), offset=0), "pparray": SimTypePointer(SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), offset=0), "pvarVal": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="<anon>", label="None")}, name="_Anonymous_e__Struct", pack=False, align=None), "decVal": SimTypeBottom(label="DECIMAL")}, name="<anon>", label="None")}, name="PROPVARIANT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pps", "uCodePage", "cpspec", "rgpspec", "rgvar"]), # 'SHPropStgWriteMultiple': SimTypeFunction([SimTypeBottom(label="IPropertyStorage"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"ulKind": SimTypeInt(signed=False, label="PROPSPEC_KIND"), "Anonymous": SimUnion({"propid": SimTypeInt(signed=False, label="UInt32"), "lpwstr": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="<anon>", label="None")}, name="PROPSPEC", pack=False, align=None), label="LPArray", offset=0), SimTypePointer(SimStruct({"Anonymous": SimUnion({"Anonymous": SimStruct({"vt": SimTypeShort(signed=False, label="UInt16"), "wReserved1": SimTypeShort(signed=False, label="UInt16"), "wReserved2": SimTypeShort(signed=False, label="UInt16"), "wReserved3": SimTypeShort(signed=False, label="UInt16"), "Anonymous": SimUnion({"cVal": SimTypeBottom(label="CHAR"), "bVal": SimTypeChar(label="Byte"), "iVal": SimTypeShort(signed=True, label="Int16"), "uiVal": SimTypeShort(signed=False, label="UInt16"), "lVal": SimTypeInt(signed=True, label="Int32"), "ulVal": SimTypeInt(signed=False, label="UInt32"), "intVal": SimTypeInt(signed=True, label="Int32"), "uintVal": SimTypeInt(signed=False, label="UInt32"), "hVal": SimTypeBottom(label="LARGE_INTEGER"), "uhVal": SimTypeBottom(label="ULARGE_INTEGER"), "fltVal": SimTypeFloat(size=32), "dblVal": SimTypeFloat(size=64), "boolVal": SimTypeShort(signed=True, label="Int16"), "__OBSOLETE__VARIANT_BOOL": SimTypeShort(signed=True, label="Int16"), "scode": SimTypeInt(signed=True, label="Int32"), "cyVal": SimTypeBottom(label="CY"), "date": SimTypeFloat(size=64), "filetime": SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), "puuid": SimTypePointer(SimTypeBottom(label="Guid"), offset=0), "pclipdata": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0), "bstrVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "bstrblobVal": SimTypeBottom(label="BSTRBLOB"), "blob": SimTypeBottom(label="BLOB"), "pszVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pwszVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "punkVal": SimTypeBottom(label="IUnknown"), "pdispVal": SimTypeBottom(label="IDispatch"), "pStream": SimTypeBottom(label="IStream"), "pStorage": SimTypeBottom(label="IStorage"), "pVersionedStream": SimTypePointer(SimStruct({"guidVersion": SimTypeBottom(label="Guid"), "pStream": SimTypeBottom(label="IStream")}, name="VERSIONEDSTREAM", pack=False, align=None), offset=0), "parray": SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), "cac": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAC", pack=False, align=None), "caub": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAUB", pack=False, align=None), "cai": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CAI", pack=False, align=None), "caui": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)}, name="CAUI", pack=False, align=None), "cal": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CAL", pack=False, align=None), "caul": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)}, name="CAUL", pack=False, align=None), "cah": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="LARGE_INTEGER"), offset=0)}, name="CAH", pack=False, align=None), "cauh": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="ULARGE_INTEGER"), offset=0)}, name="CAUH", pack=False, align=None), "caflt": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=32), offset=0)}, name="CAFLT", pack=False, align=None), "cadbl": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADBL", pack=False, align=None), "cabool": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CABOOL", pack=False, align=None), "cascode": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CASCODE", pack=False, align=None), "cacy": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CY"), offset=0)}, name="CACY", pack=False, align=None), "cadate": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADATE", pack=False, align=None), "cafiletime": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0)}, name="CAFILETIME", pack=False, align=None), "cauuid": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="Guid"), offset=0)}, name="CACLSID", pack=False, align=None), "caclipdata": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0)}, name="CACLIPDATA", pack=False, align=None), "cabstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CABSTR", pack=False, align=None), "cabstrblob": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="BSTRBLOB"), offset=0)}, name="CABSTRBLOB", pack=False, align=None), "calpstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Byte"), offset=0), offset=0)}, name="CALPSTR", pack=False, align=None), "calpwstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CALPWSTR", pack=False, align=None), "capropvar": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="CAPROPVARIANT", pack=False, align=None), "pcVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pbVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "piVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "puiVal": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), "plVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pulVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pintVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "puintVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pfltVal": SimTypePointer(SimTypeFloat(size=32), offset=0), "pdblVal": SimTypePointer(SimTypeFloat(size=64), offset=0), "pboolVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "pdecVal": SimTypePointer(SimTypeBottom(label="DECIMAL"), offset=0), "pscode": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pcyVal": SimTypePointer(SimTypeBottom(label="CY"), offset=0), "pdate": SimTypePointer(SimTypeFloat(size=64), offset=0), "pbstrVal": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), "ppunkVal": SimTypePointer(SimTypeBottom(label="IUnknown"), offset=0), "ppdispVal": SimTypePointer(SimTypeBottom(label="IDispatch"), offset=0), "pparray": SimTypePointer(SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), offset=0), "pvarVal": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="<anon>", label="None")}, name="_Anonymous_e__Struct", pack=False, align=None), "decVal": SimTypeBottom(label="DECIMAL")}, name="<anon>", label="None")}, name="PROPVARIANT", pack=False, align=None), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pps", "puCodePage", "cpspec", "rgpspec", "rgvar", "propidNameFirst"]), # 'SHGetPropertyStoreForWindow': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "riid", "ppv"]), # 'SHSimpleIDListFromPath': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pszPath"]), # 'SHCreateItemFromIDList': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "riid", "ppv"]), # 'SHCreateItemFromParsingName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeBottom(label="IBindCtx"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath", "pbc", "riid", "ppv"]), # 'SHCreateItemWithParent': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlParent", "psfParent", "pidl", "riid", "ppvItem"]), # 'SHCreateItemFromRelativeName': SimTypeFunction([SimTypeBottom(label="IShellItem"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeBottom(label="IBindCtx"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psiParent", "pszName", "pbc", "riid", "ppv"]), # 'SHCreateItemInKnownFolder': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["kfid", "dwKFFlags", "pszItem", "riid", "ppv"]), # 'SHGetIDListFromObject': SimTypeFunction([SimTypeBottom(label="IUnknown"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["punk", "ppidl"]), # 'SHGetItemFromObject': SimTypeFunction([SimTypeBottom(label="IUnknown"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["punk", "riid", "ppv"]), # 'SHGetNameFromIDList': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="SIGDN"), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "sigdnName", "ppszName"]), # 'SHGetItemFromDataObject': SimTypeFunction([SimTypeBottom(label="IDataObject"), SimTypeInt(signed=False, label="DATAOBJ_GET_ITEM_FLAGS"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pdtobj", "dwFlags", "riid", "ppv"]), # 'SHCreateShellItemArray': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeBottom(label="IShellFolder"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), label="LPArray", offset=0), SimTypePointer(SimTypeBottom(label="IShellItemArray"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlParent", "psf", "cidl", "ppidl", "ppsiItemArray"]), # 'SHCreateShellItemArrayFromDataObject': SimTypeFunction([SimTypeBottom(label="IDataObject"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pdo", "riid", "ppv"]), # 'SHCreateShellItemArrayFromIDLists': SimTypeFunction([SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), label="LPArray", offset=0), SimTypePointer(SimTypeBottom(label="IShellItemArray"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["cidl", "rgpidl", "ppsiItemArray"]), # 'SHCreateShellItemArrayFromShellItem': SimTypeFunction([SimTypeBottom(label="IShellItem"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psi", "riid", "ppv"]), # 'SHCreateAssociationRegistration': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["riid", "ppv"]), # 'SHCreateDefaultExtractIcon': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["riid", "ppv"]), # 'SetCurrentProcessExplicitAppUserModelID': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["AppID"]), # 'GetCurrentProcessExplicitAppUserModelID': SimTypeFunction([SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["AppID"]), # 'SHGetTemporaryPropertyForItem': SimTypeFunction([SimTypeBottom(label="IShellItem"), SimTypePointer(SimStruct({"fmtid": SimTypeBottom(label="Guid"), "pid": SimTypeInt(signed=False, label="UInt32")}, name="PROPERTYKEY", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"Anonymous": SimUnion({"Anonymous": SimStruct({"vt": SimTypeShort(signed=False, label="UInt16"), "wReserved1": SimTypeShort(signed=False, label="UInt16"), "wReserved2": SimTypeShort(signed=False, label="UInt16"), "wReserved3": SimTypeShort(signed=False, label="UInt16"), "Anonymous": SimUnion({"cVal": SimTypeBottom(label="CHAR"), "bVal": SimTypeChar(label="Byte"), "iVal": SimTypeShort(signed=True, label="Int16"), "uiVal": SimTypeShort(signed=False, label="UInt16"), "lVal": SimTypeInt(signed=True, label="Int32"), "ulVal": SimTypeInt(signed=False, label="UInt32"), "intVal": SimTypeInt(signed=True, label="Int32"), "uintVal": SimTypeInt(signed=False, label="UInt32"), "hVal": SimTypeBottom(label="LARGE_INTEGER"), "uhVal": SimTypeBottom(label="ULARGE_INTEGER"), "fltVal": SimTypeFloat(size=32), "dblVal": SimTypeFloat(size=64), "boolVal": SimTypeShort(signed=True, label="Int16"), "__OBSOLETE__VARIANT_BOOL": SimTypeShort(signed=True, label="Int16"), "scode": SimTypeInt(signed=True, label="Int32"), "cyVal": SimTypeBottom(label="CY"), "date": SimTypeFloat(size=64), "filetime": SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), "puuid": SimTypePointer(SimTypeBottom(label="Guid"), offset=0), "pclipdata": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0), "bstrVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "bstrblobVal": SimTypeBottom(label="BSTRBLOB"), "blob": SimTypeBottom(label="BLOB"), "pszVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pwszVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "punkVal": SimTypeBottom(label="IUnknown"), "pdispVal": SimTypeBottom(label="IDispatch"), "pStream": SimTypeBottom(label="IStream"), "pStorage": SimTypeBottom(label="IStorage"), "pVersionedStream": SimTypePointer(SimStruct({"guidVersion": SimTypeBottom(label="Guid"), "pStream": SimTypeBottom(label="IStream")}, name="VERSIONEDSTREAM", pack=False, align=None), offset=0), "parray": SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), "cac": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAC", pack=False, align=None), "caub": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAUB", pack=False, align=None), "cai": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CAI", pack=False, align=None), "caui": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)}, name="CAUI", pack=False, align=None), "cal": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CAL", pack=False, align=None), "caul": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)}, name="CAUL", pack=False, align=None), "cah": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="LARGE_INTEGER"), offset=0)}, name="CAH", pack=False, align=None), "cauh": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="ULARGE_INTEGER"), offset=0)}, name="CAUH", pack=False, align=None), "caflt": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=32), offset=0)}, name="CAFLT", pack=False, align=None), "cadbl": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADBL", pack=False, align=None), "cabool": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CABOOL", pack=False, align=None), "cascode": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CASCODE", pack=False, align=None), "cacy": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CY"), offset=0)}, name="CACY", pack=False, align=None), "cadate": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADATE", pack=False, align=None), "cafiletime": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0)}, name="CAFILETIME", pack=False, align=None), "cauuid": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="Guid"), offset=0)}, name="CACLSID", pack=False, align=None), "caclipdata": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0)}, name="CACLIPDATA", pack=False, align=None), "cabstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CABSTR", pack=False, align=None), "cabstrblob": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="BSTRBLOB"), offset=0)}, name="CABSTRBLOB", pack=False, align=None), "calpstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Byte"), offset=0), offset=0)}, name="CALPSTR", pack=False, align=None), "calpwstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CALPWSTR", pack=False, align=None), "capropvar": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="CAPROPVARIANT", pack=False, align=None), "pcVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pbVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "piVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "puiVal": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), "plVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pulVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pintVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "puintVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pfltVal": SimTypePointer(SimTypeFloat(size=32), offset=0), "pdblVal": SimTypePointer(SimTypeFloat(size=64), offset=0), "pboolVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "pdecVal": SimTypePointer(SimTypeBottom(label="DECIMAL"), offset=0), "pscode": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pcyVal": SimTypePointer(SimTypeBottom(label="CY"), offset=0), "pdate": SimTypePointer(SimTypeFloat(size=64), offset=0), "pbstrVal": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), "ppunkVal": SimTypePointer(SimTypeBottom(label="IUnknown"), offset=0), "ppdispVal": SimTypePointer(SimTypeBottom(label="IDispatch"), offset=0), "pparray": SimTypePointer(SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), offset=0), "pvarVal": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="<anon>", label="None")}, name="_Anonymous_e__Struct", pack=False, align=None), "decVal": SimTypeBottom(label="DECIMAL")}, name="<anon>", label="None")}, name="PROPVARIANT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psi", "propkey", "ppropvar"]), # 'SHSetTemporaryPropertyForItem': SimTypeFunction([SimTypeBottom(label="IShellItem"), SimTypePointer(SimStruct({"fmtid": SimTypeBottom(label="Guid"), "pid": SimTypeInt(signed=False, label="UInt32")}, name="PROPERTYKEY", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"Anonymous": SimUnion({"Anonymous": SimStruct({"vt": SimTypeShort(signed=False, label="UInt16"), "wReserved1": SimTypeShort(signed=False, label="UInt16"), "wReserved2": SimTypeShort(signed=False, label="UInt16"), "wReserved3": SimTypeShort(signed=False, label="UInt16"), "Anonymous": SimUnion({"cVal": SimTypeBottom(label="CHAR"), "bVal": SimTypeChar(label="Byte"), "iVal": SimTypeShort(signed=True, label="Int16"), "uiVal": SimTypeShort(signed=False, label="UInt16"), "lVal": SimTypeInt(signed=True, label="Int32"), "ulVal": SimTypeInt(signed=False, label="UInt32"), "intVal": SimTypeInt(signed=True, label="Int32"), "uintVal": SimTypeInt(signed=False, label="UInt32"), "hVal": SimTypeBottom(label="LARGE_INTEGER"), "uhVal": SimTypeBottom(label="ULARGE_INTEGER"), "fltVal": SimTypeFloat(size=32), "dblVal": SimTypeFloat(size=64), "boolVal": SimTypeShort(signed=True, label="Int16"), "__OBSOLETE__VARIANT_BOOL": SimTypeShort(signed=True, label="Int16"), "scode": SimTypeInt(signed=True, label="Int32"), "cyVal": SimTypeBottom(label="CY"), "date": SimTypeFloat(size=64), "filetime": SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), "puuid": SimTypePointer(SimTypeBottom(label="Guid"), offset=0), "pclipdata": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0), "bstrVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "bstrblobVal": SimTypeBottom(label="BSTRBLOB"), "blob": SimTypeBottom(label="BLOB"), "pszVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pwszVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "punkVal": SimTypeBottom(label="IUnknown"), "pdispVal": SimTypeBottom(label="IDispatch"), "pStream": SimTypeBottom(label="IStream"), "pStorage": SimTypeBottom(label="IStorage"), "pVersionedStream": SimTypePointer(SimStruct({"guidVersion": SimTypeBottom(label="Guid"), "pStream": SimTypeBottom(label="IStream")}, name="VERSIONEDSTREAM", pack=False, align=None), offset=0), "parray": SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), "cac": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAC", pack=False, align=None), "caub": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="CAUB", pack=False, align=None), "cai": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CAI", pack=False, align=None), "caui": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)}, name="CAUI", pack=False, align=None), "cal": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CAL", pack=False, align=None), "caul": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)}, name="CAUL", pack=False, align=None), "cah": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="LARGE_INTEGER"), offset=0)}, name="CAH", pack=False, align=None), "cauh": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="ULARGE_INTEGER"), offset=0)}, name="CAUH", pack=False, align=None), "caflt": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=32), offset=0)}, name="CAFLT", pack=False, align=None), "cadbl": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADBL", pack=False, align=None), "cabool": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0)}, name="CABOOL", pack=False, align=None), "cascode": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)}, name="CASCODE", pack=False, align=None), "cacy": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CY"), offset=0)}, name="CACY", pack=False, align=None), "cadate": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeFloat(size=64), offset=0)}, name="CADATE", pack=False, align=None), "cafiletime": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0)}, name="CAFILETIME", pack=False, align=None), "cauuid": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="Guid"), offset=0)}, name="CACLSID", pack=False, align=None), "caclipdata": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="CLIPDATA"), offset=0)}, name="CACLIPDATA", pack=False, align=None), "cabstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CABSTR", pack=False, align=None), "cabstrblob": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="BSTRBLOB"), offset=0)}, name="CABSTRBLOB", pack=False, align=None), "calpstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Byte"), offset=0), offset=0)}, name="CALPSTR", pack=False, align=None), "calpwstr": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)}, name="CALPWSTR", pack=False, align=None), "capropvar": SimStruct({"cElems": SimTypeInt(signed=False, label="UInt32"), "pElems": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="CAPROPVARIANT", pack=False, align=None), "pcVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "pbVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "piVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "puiVal": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), "plVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pulVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pintVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "puintVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pfltVal": SimTypePointer(SimTypeFloat(size=32), offset=0), "pdblVal": SimTypePointer(SimTypeFloat(size=64), offset=0), "pboolVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "pdecVal": SimTypePointer(SimTypeBottom(label="DECIMAL"), offset=0), "pscode": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pcyVal": SimTypePointer(SimTypeBottom(label="CY"), offset=0), "pdate": SimTypePointer(SimTypeFloat(size=64), offset=0), "pbstrVal": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), "ppunkVal": SimTypePointer(SimTypeBottom(label="IUnknown"), offset=0), "ppdispVal": SimTypePointer(SimTypeBottom(label="IDispatch"), offset=0), "pparray": SimTypePointer(SimTypePointer(SimTypeBottom(label="SAFEARRAY"), offset=0), offset=0), "pvarVal": SimTypePointer(SimTypeBottom(label="PROPVARIANT"), offset=0)}, name="<anon>", label="None")}, name="_Anonymous_e__Struct", pack=False, align=None), "decVal": SimTypeBottom(label="DECIMAL")}, name="<anon>", label="None")}, name="PROPVARIANT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psi", "propkey", "propvar"]), # 'SHShowManageLibraryUI': SimTypeFunction([SimTypeBottom(label="IShellItem"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="LIBRARYMANAGEDIALOGOPTIONS")], SimTypeInt(signed=True, label="Int32"), arg_names=["psiLibrary", "hwndOwner", "pszTitle", "pszInstruction", "lmdOptions"]), # 'SHResolveLibrary': SimTypeFunction([SimTypeBottom(label="IShellItem")], SimTypeInt(signed=True, label="Int32"), arg_names=["psiLibrary"]), # 'SHAssocEnumHandlers': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="ASSOC_FILTER"), SimTypePointer(SimTypeBottom(label="IEnumAssocHandlers"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszExtra", "afFilter", "ppEnumHandler"]), # 'SHAssocEnumHandlersForProtocolByApplication': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["protocol", "riid", "enumHandlers"]), # 'SHCreateDefaultPropertiesOp': SimTypeFunction([SimTypeBottom(label="IShellItem"), SimTypePointer(SimTypeBottom(label="IFileOperation"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psi", "ppFileOp"]), # 'SHSetDefaultProperties': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeBottom(label="IShellItem"), SimTypeInt(signed=False, label="UInt32"), SimTypeBottom(label="IFileOperationProgressSink")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "psi", "dwFileOpFlags", "pfops"]), # 'SHGetMalloc': SimTypeFunction([SimTypePointer(SimTypeBottom(label="IMalloc"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["ppMalloc"]), # 'SHAlloc': SimTypeFunction([SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0)], SimTypePointer(SimTypeBottom(label="Void"), offset=0), arg_names=["cb"]), # 'SHFree': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["pv"]), # 'SHGetIconOverlayIndexA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszIconPath", "iIconIndex"]), # 'SHGetIconOverlayIndexW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszIconPath", "iIconIndex"]), # 'ILClone': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidl"]), # 'ILCloneFirst': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidl"]), # 'ILCombine': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidl1", "pidl2"]), # 'ILFree': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeBottom(label="Void"), arg_names=["pidl"]), # 'ILGetNext': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidl"]), # 'ILGetSize': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["pidl"]), # 'ILFindChild': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidlParent", "pidlChild"]), # 'ILFindLastID': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidl"]), # 'ILRemoveLastID': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl"]), # 'ILIsEqual': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl1", "pidl2"]), # 'ILIsParent': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl1", "pidl2", "fImmediate"]), # 'ILSaveToStream': SimTypeFunction([SimTypeBottom(label="IStream"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pstm", "pidl"]), # 'ILLoadFromStreamEx': SimTypeFunction([SimTypeBottom(label="IStream"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pstm", "pidl"]), # 'ILCreateFromPathA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pszPath"]), # 'ILCreateFromPathW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pszPath"]), # 'SHILCreateFromPath': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath", "ppidl", "rgfInOut"]), # 'ILAppendID': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["pidl", "pmkid", "fAppend"]), # 'SHGetPathFromIDListEx': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "pszPath", "cchPath", "uOpts"]), # 'SHGetPathFromIDListA': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "pszPath"]), # 'SHGetPathFromIDListW': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "pszPath"]), # 'SHCreateDirectory': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPath"]), # 'SHCreateDirectoryExA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimStruct({"nLength": SimTypeInt(signed=False, label="UInt32"), "lpSecurityDescriptor": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "bInheritHandle": SimTypeInt(signed=True, label="Int32")}, name="SECURITY_ATTRIBUTES", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPath", "psa"]), # 'SHCreateDirectoryExW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({"nLength": SimTypeInt(signed=False, label="UInt32"), "lpSecurityDescriptor": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "bInheritHandle": SimTypeInt(signed=True, label="Int32")}, name="SECURITY_ATTRIBUTES", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPath", "psa"]), # 'SHOpenFolderAndSelectItems': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlFolder", "cidl", "apidl", "dwFlags"]), # 'SHCreateShellItem': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="IShellItem"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlParent", "psfParent", "pidl", "ppsi"]), # 'SHGetSpecialFolderLocation': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "csidl", "ppidl"]), # 'SHCloneSpecialIDList': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=True, label="Int32")], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["hwnd", "csidl", "fCreate"]), # 'SHGetSpecialFolderPathA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPath", "csidl", "fCreate"]), # 'SHGetSpecialFolderPathW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPath", "csidl", "fCreate"]), # 'SHFlushSFCache': SimTypeFunction([], SimTypeBottom(label="Void")), # 'SHGetFolderPathA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "csidl", "hToken", "dwFlags", "pszPath"]), # 'SHGetFolderPathW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "csidl", "hToken", "dwFlags", "pszPath"]), # 'SHGetFolderLocation': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "csidl", "hToken", "dwFlags", "ppidl"]), # 'SHSetFolderPathA': SimTypeFunction([SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["csidl", "hToken", "dwFlags", "pszPath"]), # 'SHSetFolderPathW': SimTypeFunction([SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["csidl", "hToken", "dwFlags", "pszPath"]), # 'SHGetFolderPathAndSubDirA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "csidl", "hToken", "dwFlags", "pszSubDir", "pszPath"]), # 'SHGetFolderPathAndSubDirW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "csidl", "hToken", "dwFlags", "pszSubDir", "pszPath"]), # 'SHGetKnownFolderIDList': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["rfid", "dwFlags", "hToken", "ppidl"]), # 'SHSetKnownFolderPath': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["rfid", "dwFlags", "hToken", "pszPath"]), # 'SHGetKnownFolderPath': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["rfid", "dwFlags", "hToken", "ppszPath"]), # 'SHGetKnownFolderItem': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeInt(signed=False, label="KNOWN_FOLDER_FLAG"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["rfid", "flags", "hToken", "riid", "ppv"]), # 'SHGetSetFolderCustomSettings': SimTypeFunction([SimTypePointer(SimStruct({"dwSize": SimTypeInt(signed=False, label="UInt32"), "dwMask": SimTypeInt(signed=False, label="UInt32"), "pvid": SimTypePointer(SimTypeBottom(label="Guid"), offset=0), "pszWebViewTemplate": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cchWebViewTemplate": SimTypeInt(signed=False, label="UInt32"), "pszWebViewTemplateVersion": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pszInfoTip": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cchInfoTip": SimTypeInt(signed=False, label="UInt32"), "pclsid": SimTypePointer(SimTypeBottom(label="Guid"), offset=0), "dwFlags": SimTypeInt(signed=False, label="UInt32"), "pszIconFile": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cchIconFile": SimTypeInt(signed=False, label="UInt32"), "iIconIndex": SimTypeInt(signed=True, label="Int32"), "pszLogo": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cchLogo": SimTypeInt(signed=False, label="UInt32")}, name="SHFOLDERCUSTOMSETTINGS", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pfcs", "pszPath", "dwReadWrite"]), # 'SHBrowseForFolderA': SimTypeFunction([SimTypePointer(SimStruct({"hwndOwner": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "pidlRoot": SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), "pszDisplayName": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "lpszTitle": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "ulFlags": SimTypeInt(signed=False, label="UInt32"), "lpfn": SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "uMsg", "lParam", "lpData"]), offset=0), "lParam": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "iImage": SimTypeInt(signed=True, label="Int32")}, name="BROWSEINFOA", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["lpbi"]), # 'SHBrowseForFolderW': SimTypeFunction([SimTypePointer(SimStruct({"hwndOwner": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "pidlRoot": SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), "pszDisplayName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpszTitle": SimTypePointer(SimTypeChar(label="Char"), offset=0), "ulFlags": SimTypeInt(signed=False, label="UInt32"), "lpfn": SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "uMsg", "lParam", "lpData"]), offset=0), "lParam": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "iImage": SimTypeInt(signed=True, label="Int32")}, name="BROWSEINFOW", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), arg_names=["lpbi"]), # 'SHLoadInProc': SimTypeFunction([SimTypePointer(SimTypeBottom(label="Guid"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["rclsid"]), # 'SHGetDesktopFolder': SimTypeFunction([SimTypePointer(SimTypeBottom(label="IShellFolder"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["ppshf"]), # 'SHChangeNotify': SimTypeFunction([SimTypeInt(signed=False, label="SHCNE_ID"), SimTypeInt(signed=False, label="SHCNF_FLAGS"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["wEventId", "uFlags", "dwItem1", "dwItem2"]), # 'SHAddToRecentDocs': SimTypeFunction([SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeBottom(label="Void"), arg_names=["uFlags", "pv"]), # 'SHHandleUpdateImage': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlExtra"]), # 'SHUpdateImageA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=True, label="Int32")], SimTypeBottom(label="Void"), arg_names=["pszHashItem", "iIndex", "uFlags", "iImageIndex"]), # 'SHUpdateImageW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=True, label="Int32")], SimTypeBottom(label="Void"), arg_names=["pszHashItem", "iIndex", "uFlags", "iImageIndex"]), # 'SHChangeNotifyRegister': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="SHCNRF_SOURCE"), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimStruct({"pidl": SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), "fRecursive": SimTypeInt(signed=True, label="Int32")}, name="SHChangeNotifyEntry", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hwnd", "fSources", "fEvents", "wMsg", "cEntries", "pshcne"]), # 'SHChangeNotifyDeregister': SimTypeFunction([SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["ulID"]), # 'SHChangeNotification_Lock': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hChange", "dwProcId", "pppidl", "plEvent"]), # 'SHChangeNotification_Unlock': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hLock"]), # 'SHGetRealIDL': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psf", "pidlSimple", "ppidlReal"]), # 'SHGetInstanceExplorer': SimTypeFunction([SimTypePointer(SimTypeBottom(label="IUnknown"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["ppunk"]), # 'SHGetDataFromIDListA': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="SHGDFIL_FORMAT"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["psf", "pidl", "nFormat", "pv", "cb"]), # 'SHGetDataFromIDListW': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="SHGDFIL_FORMAT"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["psf", "pidl", "nFormat", "pv", "cb"]), # 'RestartDialog': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPrompt", "dwReturn"]), # 'RestartDialogEx': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszPrompt", "dwReturn", "dwReasonCode"]), # 'SHCoCreateInstance': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeBottom(label="IUnknown"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszCLSID", "pclsid", "pUnkOuter", "riid", "ppv"]), # 'SHCreateDataObject': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), label="LPArray", offset=0), SimTypeBottom(label="IDataObject"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlFolder", "cidl", "apidl", "pdtInner", "riid", "ppv"]), # 'CIDLData_CreateFromIDArray': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), label="LPArray", offset=0), SimTypePointer(SimTypeBottom(label="IDataObject"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlFolder", "cidl", "apidl", "ppdtobj"]), # 'SHCreateStdEnumFmtEtc': SimTypeFunction([SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"cfFormat": SimTypeShort(signed=False, label="UInt16"), "ptd": SimTypePointer(SimStruct({"tdSize": SimTypeInt(signed=False, label="UInt32"), "tdDriverNameOffset": SimTypeShort(signed=False, label="UInt16"), "tdDeviceNameOffset": SimTypeShort(signed=False, label="UInt16"), "tdPortNameOffset": SimTypeShort(signed=False, label="UInt16"), "tdExtDevmodeOffset": SimTypeShort(signed=False, label="UInt16"), "tdData": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="DVTARGETDEVICE", pack=False, align=None), offset=0), "dwAspect": SimTypeInt(signed=False, label="UInt32"), "lindex": SimTypeInt(signed=True, label="Int32"), "tymed": SimTypeInt(signed=False, label="UInt32")}, name="FORMATETC", pack=False, align=None), label="LPArray", offset=0), SimTypePointer(SimTypeBottom(label="IEnumFORMATETC"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["cfmt", "afmt", "ppenumFormatEtc"]), # 'SHDoDragDrop': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeBottom(label="IDataObject"), SimTypeBottom(label="IDropSource"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pdata", "pdsrc", "dwEffect", "pdwEffect"]), # 'DAD_SetDragImage': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["him", "pptOffset"]), # 'DAD_DragEnterEx': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwndTarget", "ptStart"]), # 'DAD_DragEnterEx2': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None), SimTypeBottom(label="IDataObject")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwndTarget", "ptStart", "pdtObject"]), # 'DAD_ShowDragImage': SimTypeFunction([SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["fShow"]), # 'DAD_DragMove': SimTypeFunction([SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None)], SimTypeInt(signed=True, label="Int32"), arg_names=["pt"]), # 'DAD_DragLeave': SimTypeFunction([], SimTypeInt(signed=True, label="Int32")), # 'DAD_AutoScroll': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimStruct({"iNextSample": SimTypeInt(signed=True, label="Int32"), "dwLastScroll": SimTypeInt(signed=False, label="UInt32"), "bFull": SimTypeInt(signed=True, label="Int32"), "pts": SimTypeFixedSizeArray(SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None), 3), "dwTimes": SimTypeFixedSizeArray(SimTypeInt(signed=False, label="UInt32"), 3)}, name="AUTO_SCROLL_DATA", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pad", "pptNow"]), # 'ReadCabinetState': SimTypeFunction([SimTypePointer(SimStruct({"cLength": SimTypeShort(signed=False, label="UInt16"), "nVersion": SimTypeShort(signed=False, label="UInt16"), "_bitfield": SimTypeInt(signed=True, label="Int32"), "fMenuEnumFilter": SimTypeInt(signed=False, label="UInt32")}, name="CABINETSTATE", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pcs", "cLength"]), # 'WriteCabinetState': SimTypeFunction([SimTypePointer(SimStruct({"cLength": SimTypeShort(signed=False, label="UInt16"), "nVersion": SimTypeShort(signed=False, label="UInt16"), "_bitfield": SimTypeInt(signed=True, label="Int32"), "fMenuEnumFilter": SimTypeInt(signed=False, label="UInt32")}, name="CABINETSTATE", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pcs"]), # 'PathMakeUniqueName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszUniqueName", "cchMax", "pszTemplate", "pszLongPlate", "pszDir"]), # 'PathIsExe': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath"]), # 'PathCleanupSpec': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="PCS_RET"), arg_names=["pszDir", "pszSpec"]), # 'PathResolve': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), offset=0), SimTypeInt(signed=False, label="PRF_FLAGS")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath", "dirs", "fFlags"]), # 'GetFileNameFromBrowse': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszFilePath", "cchFilePath", "pszWorkingDir", "pszDefExt", "pszFilters", "pszTitle"]), # 'DriveType': SimTypeFunction([SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["iDrive"]), # 'RealDriveType': SimTypeFunction([SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["iDrive", "fOKToHitNet"]), # 'IsNetDrive': SimTypeFunction([SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["iDrive"]), # 'Shell_MergeMenus': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="MM_FLAGS")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hmDst", "hmSrc", "uInsert", "uIDAdjust", "uIDAdjustMax", "uFlags"]), # 'SHObjectProperties': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="SHOP_TYPE"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "shopObjectType", "pszObjectName", "pszPropertyPage"]), # 'SHFormatDrive': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="SHFMT_ID"), SimTypeInt(signed=False, label="SHFMT_OPT")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hwnd", "drive", "fmtID", "options"]), # 'SHDestroyPropSheetExtArray': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeBottom(label="Void"), arg_names=["hpsxa"]), # 'SHAddFromPropSheetExtArray': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["param0", "param1"]), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hpsxa", "lpfnAddPage", "lParam"]), # 'SHReplaceFromPropSheetExtArray': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["param0", "param1"]), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hpsxa", "uPageID", "lpfnReplaceWith", "lParam"]), # 'OpenRegStream': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeBottom(label="IStream"), arg_names=["hkey", "pszSubkey", "pszValue", "grfMode"]), # 'SHFindFiles': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlFolder", "pidlSaveFile"]), # 'PathGetShortPath': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0)], SimTypeBottom(label="Void"), arg_names=["pszLongPath"]), # 'PathYetAnotherMakeUniqueName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszUniqueName", "pszPath", "pszShort", "pszFileSpec"]), # 'Win32DeleteFile': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath"]), # 'SHRestricted': SimTypeFunction([SimTypeInt(signed=False, label="RESTRICTIONS")], SimTypeInt(signed=False, label="UInt32"), arg_names=["rest"]), # 'SignalFileOpen': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl"]), # 'AssocGetDetailsOfPropKey': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"fmtid": SimTypeBottom(label="Guid"), "pid": SimTypeInt(signed=False, label="UInt32")}, name="PROPERTYKEY", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"Anonymous": SimUnion({"Anonymous": SimStruct({"vt": SimTypeShort(signed=False, label="UInt16"), "wReserved1": SimTypeShort(signed=False, label="UInt16"), "wReserved2": SimTypeShort(signed=False, label="UInt16"), "wReserved3": SimTypeShort(signed=False, label="UInt16"), "Anonymous": SimUnion({"llVal": SimTypeLongLong(signed=True, label="Int64"), "lVal": SimTypeInt(signed=True, label="Int32"), "bVal": SimTypeChar(label="Byte"), "iVal": SimTypeShort(signed=True, label="Int16"), "fltVal": SimTypeFloat(size=32), "dblVal": SimTypeFloat(size=64), "boolVal": SimTypeShort(signed=True, label="Int16"), "__OBSOLETE__VARIANT_BOOL": SimTypeShort(signed=True, label="Int16"), "scode": SimTypeInt(signed=True, label="Int32"), "cyVal": SimTypeBottom(label="CY"), "date": SimTypeFloat(size=64), "bstrVal": SimTypePointer(SimTypeChar(label="Char"), offset=0), "punkVal": SimTypeBottom(label="IUnknown"), "pdispVal": SimTypeBottom(label="IDispatch"), "parray": SimTypePointer(SimStruct({"cDims": SimTypeShort(signed=False, label="UInt16"), "fFeatures": SimTypeShort(signed=False, label="UInt16"), "cbElements": SimTypeInt(signed=False, label="UInt32"), "cLocks": SimTypeInt(signed=False, label="UInt32"), "pvData": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "rgsabound": SimTypePointer(SimStruct({"cElements": SimTypeInt(signed=False, label="UInt32"), "lLbound": SimTypeInt(signed=True, label="Int32")}, name="SAFEARRAYBOUND", pack=False, align=None), offset=0)}, name="SAFEARRAY", pack=False, align=None), offset=0), "pbVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "piVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "plVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pllVal": SimTypePointer(SimTypeLongLong(signed=True, label="Int64"), offset=0), "pfltVal": SimTypePointer(SimTypeFloat(size=32), offset=0), "pdblVal": SimTypePointer(SimTypeFloat(size=64), offset=0), "pboolVal": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "__OBSOLETE__VARIANT_PBOOL": SimTypePointer(SimTypeShort(signed=True, label="Int16"), offset=0), "pscode": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "pcyVal": SimTypePointer(SimTypeBottom(label="CY"), offset=0), "pdate": SimTypePointer(SimTypeFloat(size=64), offset=0), "pbstrVal": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), "ppunkVal": SimTypePointer(SimTypeBottom(label="IUnknown"), offset=0), "ppdispVal": SimTypePointer(SimTypeBottom(label="IDispatch"), offset=0), "pparray": SimTypePointer(SimTypePointer(SimStruct({"cDims": SimTypeShort(signed=False, label="UInt16"), "fFeatures": SimTypeShort(signed=False, label="UInt16"), "cbElements": SimTypeInt(signed=False, label="UInt32"), "cLocks": SimTypeInt(signed=False, label="UInt32"), "pvData": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "rgsabound": SimTypePointer(SimStruct({"cElements": SimTypeInt(signed=False, label="UInt32"), "lLbound": SimTypeInt(signed=True, label="Int32")}, name="SAFEARRAYBOUND", pack=False, align=None), offset=0)}, name="SAFEARRAY", pack=False, align=None), offset=0), offset=0), "pvarVal": SimTypePointer(SimTypeBottom(label="VARIANT"), offset=0), "byref": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "cVal": SimTypeBottom(label="CHAR"), "uiVal": SimTypeShort(signed=False, label="UInt16"), "ulVal": SimTypeInt(signed=False, label="UInt32"), "ullVal": SimTypeLongLong(signed=False, label="UInt64"), "intVal": SimTypeInt(signed=True, label="Int32"), "uintVal": SimTypeInt(signed=False, label="UInt32"), "pdecVal": SimTypePointer(SimTypeBottom(label="DECIMAL"), offset=0), "pcVal": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "puiVal": SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), "pulVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "pullVal": SimTypePointer(SimTypeLongLong(signed=False, label="UInt64"), offset=0), "pintVal": SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), "puintVal": SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), "Anonymous": SimStruct({"pvRecord": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "pRecInfo": SimTypeBottom(label="IRecordInfo")}, name="_Anonymous_e__Struct", pack=False, align=None)}, name="<anon>", label="None")}, name="_Anonymous_e__Struct", pack=False, align=None), "decVal": SimTypeBottom(label="DECIMAL")}, name="<anon>", label="None")}, name="VARIANT", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psf", "pidl", "pkey", "pv", "pfFoundPropKey"]), # 'SHStartNetConnectionDialogW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszRemoteName", "dwType"]), # 'SHDefExtractIconA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszIconFile", "iIndex", "uFlags", "phiconLarge", "phiconSmall", "nIconSize"]), # 'SHDefExtractIconW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszIconFile", "iIndex", "uFlags", "phiconLarge", "phiconSmall", "nIconSize"]), # 'SHOpenWithDialog': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimStruct({"pcszFile": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pcszClass": SimTypePointer(SimTypeChar(label="Char"), offset=0), "oaifInFlags": SimTypeInt(signed=False, label="OPEN_AS_INFO_FLAGS")}, name="OPENASINFO", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwndParent", "poainfo"]), # 'Shell_GetImageLists': SimTypeFunction([SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["phiml", "phimlSmall"]), # 'Shell_GetCachedImageIndex': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszIconPath", "iIconIndex", "uIconFlags"]), # 'Shell_GetCachedImageIndexA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszIconPath", "iIconIndex", "uIconFlags"]), # 'Shell_GetCachedImageIndexW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszIconPath", "iIconIndex", "uIconFlags"]), # 'SHValidateUNC': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="VALIDATEUNC_OPTION")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwndOwner", "pszFile", "fConnect"]), # 'SHSetInstanceExplorer': SimTypeFunction([SimTypeBottom(label="IUnknown")], SimTypeBottom(label="Void"), arg_names=["punk"]), # 'IsUserAnAdmin': SimTypeFunction([], SimTypeInt(signed=True, label="Int32")), # 'SHShellFolderView_Message': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hwndMain", "uMsg", "lParam"]), # 'SHCreateShellFolderView': SimTypeFunction([SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "pshf": SimTypeBottom(label="IShellFolder"), "psvOuter": SimTypeBottom(label="IShellView"), "psfvcb": SimTypeBottom(label="IShellFolderViewCB")}, name="SFV_CREATE", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="IShellView"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pcsfv", "ppsv"]), # 'CDefFolderMenu_Create2': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), label="LPArray", offset=0), SimTypeBottom(label="IShellFolder"), SimTypePointer(SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeBottom(label="IDataObject"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psf", "hwnd", "pdtobj", "uMsg", "wParam", "lParam"]), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), label="LPArray", offset=0), SimTypePointer(SimTypeBottom(label="IContextMenu"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidlFolder", "hwnd", "cidl", "apidl", "psf", "pfn", "nKeys", "ahkeys", "ppcm"]), # 'SHCreateDefaultContextMenu': SimTypeFunction([SimTypePointer(SimStruct({"hwnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "pcmcb": SimTypeBottom(label="IContextMenuCB"), "pidlFolder": SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), "psf": SimTypeBottom(label="IShellFolder"), "cidl": SimTypeInt(signed=False, label="UInt32"), "apidl": SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0), "punkAssociationInfo": SimTypeBottom(label="IUnknown"), "cKeys": SimTypeInt(signed=False, label="UInt32"), "aKeys": SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)}, name="DEFCONTEXTMENU", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pdcm", "riid", "ppv"]), # 'SHFind_InitMenuPopup': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeBottom(label="IContextMenu"), arg_names=["hmenu", "hwndOwner", "idCmdFirst", "idCmdLast"]), # 'SHCreateShellFolderViewEx': SimTypeFunction([SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "pshf": SimTypeBottom(label="IShellFolder"), "psvOuter": SimTypeBottom(label="IShellView"), "pidl": SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), "lEvents": SimTypeInt(signed=True, label="Int32"), "pfnCallback": SimTypePointer(SimTypeFunction([SimTypeBottom(label="IShellView"), SimTypeBottom(label="IShellFolder"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psvOuter", "psf", "hwndMain", "uMsg", "wParam", "lParam"]), offset=0), "fvm": SimTypeInt(signed=False, label="FOLDERVIEWMODE")}, name="CSFV", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="IShellView"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pcsfv", "ppsv"]), # 'SHGetSetSettings': SimTypeFunction([SimTypePointer(SimStruct({"_bitfield1": SimTypeInt(signed=True, label="Int32"), "dwWin95Unused": SimTypeInt(signed=False, label="UInt32"), "uWin95Unused": SimTypeInt(signed=False, label="UInt32"), "lParamSort": SimTypeInt(signed=True, label="Int32"), "iSortDirection": SimTypeInt(signed=True, label="Int32"), "version": SimTypeInt(signed=False, label="UInt32"), "uNotUsed": SimTypeInt(signed=False, label="UInt32"), "_bitfield2": SimTypeInt(signed=True, label="Int32")}, name="SHELLSTATEA", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="SSF_MASK"), SimTypeInt(signed=True, label="Int32")], SimTypeBottom(label="Void"), arg_names=["lpss", "dwMask", "bSet"]), # 'SHGetSettings': SimTypeFunction([SimTypePointer(SimStruct({"_bitfield": SimTypeInt(signed=True, label="Int32")}, name="SHELLFLAGSTATE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeBottom(label="Void"), arg_names=["psfs", "dwMask"]), # 'SHBindToParent': SimTypeFunction([SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pidl", "riid", "ppv", "ppidlLast"]), # 'SHBindToFolderIDListParent': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psfRoot", "pidl", "riid", "ppv", "ppidlLast"]), # 'SHBindToFolderIDListParentEx': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeBottom(label="IBindCtx"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psfRoot", "pidl", "ppbc", "riid", "ppv", "ppidlLast"]), # 'SHBindToObject': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypeBottom(label="IBindCtx"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psf", "pidl", "pbc", "riid", "ppv"]), # 'SHParseDisplayName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeBottom(label="IBindCtx"), SimTypePointer(SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszName", "pbc", "ppidl", "sfgaoIn", "psfgaoOut"]), # 'SHPathPrepareForWriteA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeBottom(label="IUnknown"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "punkEnableModless", "pszPath", "dwFlags"]), # 'SHPathPrepareForWriteW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeBottom(label="IUnknown"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "punkEnableModless", "pszPath", "dwFlags"]), # 'SHCreateFileExtractIconW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszFile", "dwFileAttributes", "riid", "ppv"]), # 'SHLimitInputEdit': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeBottom(label="IShellFolder")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwndEdit", "psf"]), # 'SHGetAttributesFromDataObject': SimTypeFunction([SimTypeBottom(label="IDataObject"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pdo", "dwAttributeMask", "pdwAttributes", "pcItems"]), # 'SHMapPIDLToSystemImageListIndex': SimTypeFunction([SimTypeBottom(label="IShellFolder"), SimTypePointer(SimStruct({"mkid": SimStruct({"cb": SimTypeShort(signed=False, label="UInt16"), "abID": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHITEMID", pack=False, align=None)}, name="ITEMIDLIST", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pshf", "pidl", "piIndexSel"]), # 'SHCLSIDFromString': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeBottom(label="Guid"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["psz", "pclsid"]), # 'PickIconDlg': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszIconPath", "cchIconPath", "piIconIndex"]), # 'StgMakeUniqueName': SimTypeFunction([SimTypeBottom(label="IStorage"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pstgParent", "pszFileSpec", "grfMode", "riid", "ppv"]), # 'SHChangeNotifyRegisterThread': SimTypeFunction([SimTypeInt(signed=False, label="SCNRT_STATUS")], SimTypeBottom(label="Void"), arg_names=["status"]), # 'PathQualify': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeBottom(label="Void"), arg_names=["psz"]), # 'PathIsSlowA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszFile", "dwAttr"]), # 'PathIsSlowW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszFile", "dwAttr"]), # 'SHCreatePropSheetExtArray': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hKey", "pszSubKey", "max_iface"]), # 'SHOpenPropSheetW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypeBottom(label="IDataObject"), SimTypeBottom(label="IShellBrowser"), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszCaption", "ahkeys", "ckeys", "pclsidDefault", "pdtobj", "psb", "pStartPage"]), # 'SHMultiFileProperties': SimTypeFunction([SimTypeBottom(label="IDataObject"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pdtobj", "dwFlags"]), # 'SHCreateQueryCancelAutoPlayMoniker': SimTypeFunction([SimTypePointer(SimTypeBottom(label="IMoniker"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["ppmoniker"]), # 'CommandLineToArgvW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), arg_names=["lpCmdLine", "pNumArgs"]), # 'DragQueryFileA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDrop", "iFile", "lpszFile", "cch"]), # 'DragQueryFileW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDrop", "iFile", "lpszFile", "cch"]), # 'DragQueryPoint': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimStruct({"x": SimTypeInt(signed=True, label="Int32"), "y": SimTypeInt(signed=True, label="Int32")}, name="POINT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hDrop", "ppt"]), # 'DragFinish': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeBottom(label="Void"), arg_names=["hDrop"]), # 'DragAcceptFiles': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeBottom(label="Void"), arg_names=["hWnd", "fAccept"]), # 'ShellExecuteA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hwnd", "lpOperation", "lpFile", "lpParameters", "lpDirectory", "nShowCmd"]), # 'ShellExecuteW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hwnd", "lpOperation", "lpFile", "lpParameters", "lpDirectory", "nShowCmd"]), # 'FindExecutableA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["lpFile", "lpDirectory", "lpResult"]), # 'FindExecutableW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["lpFile", "lpDirectory", "lpResult"]), # 'ShellAboutA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hWnd", "szApp", "szOtherStuff", "hIcon"]), # 'ShellAboutW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hWnd", "szApp", "szOtherStuff", "hIcon"]), # 'DuplicateIcon': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "hIcon"]), # 'ExtractAssociatedIconA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0), SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "pszIconPath", "piIcon"]), # 'ExtractAssociatedIconW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "pszIconPath", "piIcon"]), # 'ExtractAssociatedIconExA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0), SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "pszIconPath", "piIconIndex", "piIconId"]), # 'ExtractAssociatedIconExW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0), SimTypePointer(SimTypeShort(signed=False, label="UInt16"), offset=0)], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "pszIconPath", "piIconIndex", "piIconId"]), # 'ExtractIconA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "pszExeFileName", "nIconIndex"]), # 'ExtractIconW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hInst", "pszExeFileName", "nIconIndex"]), # 'SHAppBarMessage': SimTypeFunction([SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "hWnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "uCallbackMessage": SimTypeInt(signed=False, label="UInt32"), "uEdge": SimTypeInt(signed=False, label="UInt32"), "rc": SimStruct({"left": SimTypeInt(signed=True, label="Int32"), "top": SimTypeInt(signed=True, label="Int32"), "right": SimTypeInt(signed=True, label="Int32"), "bottom": SimTypeInt(signed=True, label="Int32")}, name="RECT", pack=False, align=None), "lParam": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)}, name="APPBARDATA", pack=False, align=None), offset=0)], SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), arg_names=["dwMessage", "pData"]), # 'DoEnvironmentSubstA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["pszSrc", "cchSrc"]), # 'DoEnvironmentSubstW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["pszSrc", "cchSrc"]), # 'ExtractIconExA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), label="LPArray", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["lpszFile", "nIconIndex", "phiconLarge", "phiconSmall", "nIcons"]), # 'ExtractIconExW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), label="LPArray", offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["lpszFile", "nIconIndex", "phiconLarge", "phiconSmall", "nIcons"]), # 'SHFileOperationA': SimTypeFunction([SimTypePointer(SimStruct({"hwnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "wFunc": SimTypeInt(signed=False, label="UInt32"), "pFrom": SimTypePointer(SimTypeChar(label="SByte"), offset=0), "pTo": SimTypePointer(SimTypeChar(label="SByte"), offset=0), "fFlags": SimTypeShort(signed=False, label="UInt16"), "fAnyOperationsAborted": SimTypeInt(signed=True, label="Int32"), "hNameMappings": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "lpszProgressTitle": SimTypePointer(SimTypeChar(label="Byte"), offset=0)}, name="SHFILEOPSTRUCTA", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["lpFileOp"]), # 'SHFileOperationW': SimTypeFunction([SimTypePointer(SimStruct({"hwnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "wFunc": SimTypeInt(signed=False, label="UInt32"), "pFrom": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pTo": SimTypePointer(SimTypeChar(label="Char"), offset=0), "fFlags": SimTypeShort(signed=False, label="UInt16"), "fAnyOperationsAborted": SimTypeInt(signed=True, label="Int32"), "hNameMappings": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "lpszProgressTitle": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="SHFILEOPSTRUCTW", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["lpFileOp"]), # 'SHFreeNameMappings': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeBottom(label="Void"), arg_names=["hNameMappings"]), # 'ShellExecuteExA': SimTypeFunction([SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "fMask": SimTypeInt(signed=False, label="UInt32"), "hwnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "lpVerb": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "lpFile": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "lpParameters": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "lpDirectory": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "nShow": SimTypeInt(signed=True, label="Int32"), "hInstApp": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "lpIDList": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "lpClass": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "hkeyClass": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "dwHotKey": SimTypeInt(signed=False, label="UInt32"), "Anonymous": SimUnion({"hIcon": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "hMonitor": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)}, name="<anon>", label="None"), "hProcess": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)}, name="SHELLEXECUTEINFOA", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pExecInfo"]), # 'ShellExecuteExW': SimTypeFunction([SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "fMask": SimTypeInt(signed=False, label="UInt32"), "hwnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "lpVerb": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpFile": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpParameters": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpDirectory": SimTypePointer(SimTypeChar(label="Char"), offset=0), "nShow": SimTypeInt(signed=True, label="Int32"), "hInstApp": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "lpIDList": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "lpClass": SimTypePointer(SimTypeChar(label="Char"), offset=0), "hkeyClass": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "dwHotKey": SimTypeInt(signed=False, label="UInt32"), "Anonymous": SimUnion({"hIcon": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "hMonitor": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)}, name="<anon>", label="None"), "hProcess": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)}, name="SHELLEXECUTEINFOW", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pExecInfo"]), # 'SHCreateProcessAsUserW': SimTypeFunction([SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "fMask": SimTypeInt(signed=False, label="UInt32"), "hwnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "pszFile": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pszParameters": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pszCurrentDirectory": SimTypePointer(SimTypeChar(label="Char"), offset=0), "hUserToken": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "lpProcessAttributes": SimTypePointer(SimStruct({"nLength": SimTypeInt(signed=False, label="UInt32"), "lpSecurityDescriptor": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "bInheritHandle": SimTypeInt(signed=True, label="Int32")}, name="SECURITY_ATTRIBUTES", pack=False, align=None), offset=0), "lpThreadAttributes": SimTypePointer(SimStruct({"nLength": SimTypeInt(signed=False, label="UInt32"), "lpSecurityDescriptor": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "bInheritHandle": SimTypeInt(signed=True, label="Int32")}, name="SECURITY_ATTRIBUTES", pack=False, align=None), offset=0), "bInheritHandles": SimTypeInt(signed=True, label="Int32"), "dwCreationFlags": SimTypeInt(signed=False, label="UInt32"), "lpStartupInfo": SimTypePointer(SimStruct({"cb": SimTypeInt(signed=False, label="UInt32"), "lpReserved": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpDesktop": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpTitle": SimTypePointer(SimTypeChar(label="Char"), offset=0), "dwX": SimTypeInt(signed=False, label="UInt32"), "dwY": SimTypeInt(signed=False, label="UInt32"), "dwXSize": SimTypeInt(signed=False, label="UInt32"), "dwYSize": SimTypeInt(signed=False, label="UInt32"), "dwXCountChars": SimTypeInt(signed=False, label="UInt32"), "dwYCountChars": SimTypeInt(signed=False, label="UInt32"), "dwFillAttribute": SimTypeInt(signed=False, label="UInt32"), "dwFlags": SimTypeInt(signed=False, label="STARTUPINFOW_FLAGS"), "wShowWindow": SimTypeShort(signed=False, label="UInt16"), "cbReserved2": SimTypeShort(signed=False, label="UInt16"), "lpReserved2": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "hStdInput": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "hStdOutput": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "hStdError": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)}, name="STARTUPINFOW", pack=False, align=None), offset=0), "lpProcessInformation": SimTypePointer(SimStruct({"hProcess": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "hThread": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "dwProcessId": SimTypeInt(signed=False, label="UInt32"), "dwThreadId": SimTypeInt(signed=False, label="UInt32")}, name="PROCESS_INFORMATION", pack=False, align=None), offset=0)}, name="SHCREATEPROCESSINFOW", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pscpi"]), # 'SHEvaluateSystemCommandTemplate': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszCmdTemplate", "ppszApplication", "ppszCommandLine", "ppszParameters"]), # 'AssocCreateForClasses': SimTypeFunction([SimTypePointer(SimStruct({"ac": SimTypeInt(signed=False, label="ASSOCCLASS"), "hkClass": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "pszClass": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="ASSOCIATIONELEMENT", pack=False, align=None), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["rgClasses", "cClasses", "riid", "ppv"]), # 'SHQueryRecycleBinA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "i64Size": SimTypeLongLong(signed=True, label="Int64"), "i64NumItems": SimTypeLongLong(signed=True, label="Int64")}, name="SHQUERYRBINFO", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszRootPath", "pSHQueryRBInfo"]), # 'SHQueryRecycleBinW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "i64Size": SimTypeLongLong(signed=True, label="Int64"), "i64NumItems": SimTypeLongLong(signed=True, label="Int64")}, name="SHQUERYRBINFO", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszRootPath", "pSHQueryRBInfo"]), # 'SHEmptyRecycleBinA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszRootPath", "dwFlags"]), # 'SHEmptyRecycleBinW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "pszRootPath", "dwFlags"]), # 'SHQueryUserNotificationState': SimTypeFunction([SimTypePointer(SimTypeInt(signed=False, label="QUERY_USER_NOTIFICATION_STATE"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pquns"]), # 'Shell_NotifyIconA': SimTypeFunction([SimTypeInt(signed=False, label="NOTIFY_ICON_MESSAGE"), SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "hWnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "uID": SimTypeInt(signed=False, label="UInt32"), "uFlags": SimTypeInt(signed=False, label="NOTIFY_ICON_DATA_FLAGS"), "uCallbackMessage": SimTypeInt(signed=False, label="UInt32"), "hIcon": SimTypeBottom(label="HICON"), "szTip": SimTypeFixedSizeArray(SimTypeChar(label="Byte"), 128), "dwState": SimTypeInt(signed=False, label="UInt32"), "dwStateMask": SimTypeInt(signed=False, label="UInt32"), "szInfo": SimTypeFixedSizeArray(SimTypeChar(label="Byte"), 256), "Anonymous": SimUnion({"uTimeout": SimTypeInt(signed=False, label="UInt32"), "uVersion": SimTypeInt(signed=False, label="UInt32")}, name="<anon>", label="None"), "szInfoTitle": SimTypeFixedSizeArray(SimTypeChar(label="Byte"), 64), "dwInfoFlags": SimTypeInt(signed=False, label="UInt32"), "guidItem": SimTypeBottom(label="Guid"), "hBalloonIcon": SimTypeBottom(label="HICON")}, name="NOTIFYICONDATAA", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["dwMessage", "lpData"]), # 'Shell_NotifyIconW': SimTypeFunction([SimTypeInt(signed=False, label="NOTIFY_ICON_MESSAGE"), SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "hWnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "uID": SimTypeInt(signed=False, label="UInt32"), "uFlags": SimTypeInt(signed=False, label="NOTIFY_ICON_DATA_FLAGS"), "uCallbackMessage": SimTypeInt(signed=False, label="UInt32"), "hIcon": SimTypeBottom(label="HICON"), "szTip": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 128), "dwState": SimTypeInt(signed=False, label="UInt32"), "dwStateMask": SimTypeInt(signed=False, label="UInt32"), "szInfo": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 256), "Anonymous": SimUnion({"uTimeout": SimTypeInt(signed=False, label="UInt32"), "uVersion": SimTypeInt(signed=False, label="UInt32")}, name="<anon>", label="None"), "szInfoTitle": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 64), "dwInfoFlags": SimTypeInt(signed=False, label="UInt32"), "guidItem": SimTypeBottom(label="Guid"), "hBalloonIcon": SimTypeBottom(label="HICON")}, name="NOTIFYICONDATAW", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["dwMessage", "lpData"]), # 'Shell_NotifyIconGetRect': SimTypeFunction([SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "hWnd": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "uID": SimTypeInt(signed=False, label="UInt32"), "guidItem": SimTypeBottom(label="Guid")}, name="NOTIFYICONIDENTIFIER", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"left": SimTypeInt(signed=True, label="Int32"), "top": SimTypeInt(signed=True, label="Int32"), "right": SimTypeInt(signed=True, label="Int32"), "bottom": SimTypeInt(signed=True, label="Int32")}, name="RECT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["identifier", "iconLocation"]), # 'SHGetFileInfoA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="FILE_FLAGS_AND_ATTRIBUTES"), SimTypePointer(SimStruct({"hIcon": SimTypeBottom(label="HICON"), "iIcon": SimTypeInt(signed=True, label="Int32"), "dwAttributes": SimTypeInt(signed=False, label="UInt32"), "szDisplayName": SimTypeFixedSizeArray(SimTypeChar(label="Byte"), 260), "szTypeName": SimTypeFixedSizeArray(SimTypeChar(label="Byte"), 80)}, name="SHFILEINFOA", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="SHGFI_FLAGS")], SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), arg_names=["pszPath", "dwFileAttributes", "psfi", "cbFileInfo", "uFlags"]), # 'SHGetFileInfoW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="FILE_FLAGS_AND_ATTRIBUTES"), SimTypePointer(SimStruct({"hIcon": SimTypeBottom(label="HICON"), "iIcon": SimTypeInt(signed=True, label="Int32"), "dwAttributes": SimTypeInt(signed=False, label="UInt32"), "szDisplayName": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 260), "szTypeName": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 80)}, name="SHFILEINFOW", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="SHGFI_FLAGS")], SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), arg_names=["pszPath", "dwFileAttributes", "psfi", "cbFileInfo", "uFlags"]), # 'SHGetStockIconInfo': SimTypeFunction([SimTypeInt(signed=False, label="SHSTOCKICONID"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"cbSize": SimTypeInt(signed=False, label="UInt32"), "hIcon": SimTypeBottom(label="HICON"), "iSysImageIndex": SimTypeInt(signed=True, label="Int32"), "iIcon": SimTypeInt(signed=True, label="Int32"), "szPath": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 260)}, name="SHSTOCKICONINFO", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["siid", "uFlags", "psii"]), # 'SHGetDiskFreeSpaceExA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimUnion({"Anonymous": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_Anonymous_e__Struct", pack=False, align=None), "u": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_u_e__Struct", pack=False, align=None), "QuadPart": SimTypeLongLong(signed=False, label="UInt64")}, name="<anon>", label="None"), offset=0), SimTypePointer(SimUnion({"Anonymous": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_Anonymous_e__Struct", pack=False, align=None), "u": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_u_e__Struct", pack=False, align=None), "QuadPart": SimTypeLongLong(signed=False, label="UInt64")}, name="<anon>", label="None"), offset=0), SimTypePointer(SimUnion({"Anonymous": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_Anonymous_e__Struct", pack=False, align=None), "u": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_u_e__Struct", pack=False, align=None), "QuadPart": SimTypeLongLong(signed=False, label="UInt64")}, name="<anon>", label="None"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszDirectoryName", "pulFreeBytesAvailableToCaller", "pulTotalNumberOfBytes", "pulTotalNumberOfFreeBytes"]), # 'SHGetDiskFreeSpaceExW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimUnion({"Anonymous": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_Anonymous_e__Struct", pack=False, align=None), "u": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_u_e__Struct", pack=False, align=None), "QuadPart": SimTypeLongLong(signed=False, label="UInt64")}, name="<anon>", label="None"), offset=0), SimTypePointer(SimUnion({"Anonymous": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_Anonymous_e__Struct", pack=False, align=None), "u": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_u_e__Struct", pack=False, align=None), "QuadPart": SimTypeLongLong(signed=False, label="UInt64")}, name="<anon>", label="None"), offset=0), SimTypePointer(SimUnion({"Anonymous": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_Anonymous_e__Struct", pack=False, align=None), "u": SimStruct({"LowPart": SimTypeInt(signed=False, label="UInt32"), "HighPart": SimTypeInt(signed=False, label="UInt32")}, name="_u_e__Struct", pack=False, align=None), "QuadPart": SimTypeLongLong(signed=False, label="UInt64")}, name="<anon>", label="None"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszDirectoryName", "pulFreeBytesAvailableToCaller", "pulTotalNumberOfBytes", "pulTotalNumberOfFreeBytes"]), # 'SHGetNewLinkInfoA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszLinkTo", "pszDir", "pszName", "pfMustCopy", "uFlags"]), # 'SHGetNewLinkInfoW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszLinkTo", "pszDir", "pszName", "pfMustCopy", "uFlags"]), # 'SHInvokePrinterCommandA': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "uAction", "lpBuf1", "lpBuf2", "fModal"]), # 'SHInvokePrinterCommandW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hwnd", "uAction", "lpBuf1", "lpBuf2", "fModal"]), # 'SHLoadNonloadedIconOverlayIdentifiers': SimTypeFunction([], SimTypeInt(signed=True, label="Int32")), # 'SHIsFileAvailableOffline': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pwszPath", "pdwStatus"]), # 'SHSetLocalizedName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath", "pszResModule", "idsRes"]), # 'SHRemoveLocalizedName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath"]), # 'SHGetLocalizedName': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath", "pszResModule", "cch", "pidsRes"]), # 'IsLFNDriveA': SimTypeFunction([SimTypePointer(SimTypeChar(label="Byte"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath"]), # 'IsLFNDriveW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszPath"]), # 'SHEnumerateUnreadMailAccountsW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hKeyUser", "dwIndex", "pszMailAddress", "cchMailAddress"]), # 'SHGetUnreadMailCountW': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hKeyUser", "pszMailAddress", "pdwCount", "pFileTime", "pszShellExecuteCommand", "cchShellExecuteCommand"]), # 'SHSetUnreadMailCountW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszMailAddress", "dwCount", "pszShellExecuteCommand"]), # 'SHTestTokenMembership': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hToken", "ulRID"]), # 'SHGetImageList': SimTypeFunction([SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeBottom(label="Guid"), offset=0), SimTypePointer(SimTypePointer(SimTypeBottom(label="Void"), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["iImageList", "riid", "ppvObj"]), # 'InitNetworkAddressControl': SimTypeFunction([], SimTypeInt(signed=True, label="Int32")), # 'SHGetDriveMedia': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["pszDrive", "pdwMediaContent"]), } lib.set_prototypes(prototypes)

306.935039

8,757

0.724864

16,831

155,923

6.68766

0.0609

0.06449

0.079886

0.110386

0.905224

0.883884

0.865227

0.857294

0.849458

0.838184

0

0.020781

0.076313

155,923

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307.540434

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null

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8

a99cab549123ed8980e3f0bbc7343dbdb0b793e7

1,564

py

Python

seg_word_no_stopwords.py

cugdeeplearn/Visual-analytics

ecbeea8c609a9ab5600339a88973c75eefe748ae

[ "Apache-2.0" ]

1

2021-01-21T07:44:01.000Z

seg_word_no_stopwords.py

cugdeeplearn/Visual-analytics

ecbeea8c609a9ab5600339a88973c75eefe748ae

[ "Apache-2.0" ]

null

seg_word_no_stopwords.py

cugdeeplearn/Visual-analytics

ecbeea8c609a9ab5600339a88973c75eefe748ae

[ "Apache-2.0" ]

null

''' #!/usr/bin/python # -*- coding: UTF-8 -*- import jieba #给出文档路径 filename = r"D:\code\bp\visulization\files\new_outtext_zhengze_out28.txt" outfilename = r"D:\code\bp\visulization\files\new_outtext_zhengze_out28_seg.txt" #inputs = open(filename, 'r', encoding = 'UTF-8') outputs = open(outfilename, 'w', encoding = 'UTF-8') jieba.load_userdict(r"D:\code\bp\visulization\dict_stopwords\out_dict.txt") #将输出结果写入seg_outtext.txt中 with open (filename,'r', encoding = 'UTF-8') as f: outstr = '' line_content = f.read() line_seg = jieba.cut(line_content) #去停用词 for word in line_seg: outstr += word if word != '\n' outstr += " " outputs.write(outstr) outputs.close() f.close() print ("删除停用词和分词成功！") ''' #!/usr/bin/python # -*- coding: UTF-8 -*- import jieba #给出文档路径 filename = r"D:\code\bp\visulization\files\new_outtext_zhengze_out28-1.txt" outfilename = r"D:\code\bp\visulization\files\new_outtext_zhengze_out28-1_seg1.txt" #inputs = open(filename, 'r', encoding = 'UTF-8') outputs = open(outfilename, 'w', encoding = 'UTF-8') jieba.load_userdict(r"D:\code\bp\visulization\dict_stopwords\out_dict.txt") #将输出结果写入seg_outtext.txt中 with open (filename,'r', encoding = 'UTF-8') as f: outstr = '' line_content = f.read() line_seg = jieba.cut(line_content) #去停用词 for word in line_seg: outstr += word if word != '\n': outstr += " " outputs.write(outstr) outputs.close() f.close() print ("删除停用词和分词成功！")

28.436364

84

0.637468

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4.544601

0.262911

0.033058

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0.049587

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0

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1,564

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null

0

1

0

null

0

1

0

7

8d2072616c1ce537a518b97ed5feac5c7c36f592

19,761

py

Python

tests/p4transfer/test_historical.py

nrs-cerickson/p4transfer

a2e2bfd0a9f870e2e8fb9087e1f4ca73d8d4b3f2

[ "BSD-2-Clause" ]

null

tests/p4transfer/test_historical.py

nrs-cerickson/p4transfer

a2e2bfd0a9f870e2e8fb9087e1f4ca73d8d4b3f2

[ "BSD-2-Clause" ]

null

tests/p4transfer/test_historical.py

nrs-cerickson/p4transfer

a2e2bfd0a9f870e2e8fb9087e1f4ca73d8d4b3f2

[ "BSD-2-Clause" ]

null

from __future__ import annotations import logging import pytest import p4transfer @pytest.fixture def historical_transfer_config(request, default_transfer_config): historical_start_change = request.node.get_closest_marker("historical_start_change") if historical_start_change is not None: historical_start_change = int(historical_start_change.args[0]) # Adjust the start change settings for historical tests. transfer_config = default_transfer_config.copy() if historical_start_change: transfer_config["historical_start_change"] = historical_start_change return transfer_config @pytest.fixture def historical_add_delete_config(request, default_transfer_config): """Parameterizable historical start config.""" historical_start_change = int(request.param) # Adjust the start change settings for historical tests. transfer_config = default_transfer_config.copy() transfer_config["historical_start_change"] = historical_start_change return transfer_config @pytest.mark.parametrize( "historical_add_delete_config", [2, 3], indirect=True, ) def test_historical_add_delete(source, target, historical_add_delete_config): """Old style rename for historical start mode transfer.""" file1 = source.local_path("inside/file1") file2 = source.local_path("inside/file2") file3 = source.local_path("inside/file3") contents = [b"0"] * 10 file1.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") source.p4("integ", file1, file2) source.p4("submit", "-d", "2: file1 -> file2") chg = source.fetch("change") chg._description = "Test desc" source.save("change", chg) source.p4("integ", file2, file3) source.p4("delete", file2) source.p4("submit", "-d", "4: file2 delete/copy") p4transfer.test_transfer(historical_add_delete_config) assert target.counter == 4 assert len(target.p4("changes")) == 2 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 0 assert filelog[0].revisions[0].action == "delete" filelog = target.filelog("//depot/import/file3") assert len(filelog[0].revisions) == 1 assert len(filelog[0].revisions[0].integrations) == 1 assert filelog[0].revisions[0].action == "branch" @pytest.mark.xfail(reason="First historical change does not fix up file types yet.") @pytest.mark.historical_start_change(3) def test_historical_types(source, target, historical_transfer_config): """Test that file types in the historical change are transferred correctly.""" file1 = source.local_path("inside/file1") file2 = source.local_path("inside/file2") file3 = source.local_path("inside/file3") file1.write_bytes(b"I am file 1") file2.write_bytes(b"I am file 2") file3.write_bytes(b"I am file 3") source.p4("add", "-t", "binary", file1) source.p4("submit", "-d", "1: file1 added") source.p4("add", "-t", "binary+w", file2) source.p4("submit", "-d", "2: file2 added") source.p4("add", "-t", "text+D", file3) source.p4("submit", "-d", "3: file3 added") p4transfer.test_transfer(historical_transfer_config) files = target.p4("files", "//depot/import/...") assert len(files) == 3 assert files[0]["depotFile"] == "//depot/import/file1" assert files[0]["type"] == "binary" assert files[1]["depotFile"] == "//depot/import/file2" assert files[1]["type"] == "binary+w" assert files[2]["depotFile"] == "//depot/import/file3" assert files[2]["type"] in "text+D" @pytest.mark.skip("Targets with purged revisions need to be revisited.") def test_historical_existing_purged_target_revs(): """Historical integration where target has extra revs. Additionally, some target revisions have been purged. """ pass @pytest.mark.historical_start_change(2) def test_historical_existing_target_revs(source, target, historical_transfer_config): """Historical integration where target has extra revs.""" file1 = source.local_path("inside/file1") file1.write_bytes(b"Test content\n") file2 = source.local_path("inside/file2") tfile1 = target.local_path("import/file1") tfile1.write_bytes(b"Test content\n") tfile2 = target.local_path("import/file2") tfile2.write_bytes(b"Test content\n") # Add pre-existing target files. target.p4("add", tfile1) target.p4("submit", "-d", "1: file1 added") target.p4("add", tfile2) target.p4("submit", "-d", "2: file2 added") target.p4("delete", tfile2) target.p4("submit", "-d", "3: file2 deleted") tfile2.write_bytes(b"Test content\n") target.p4("add", tfile2) target.p4("submit", "-d", "4: file2 re-added to match source @2") # Now source files we want to replicate. source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") source.p4("integrate", file1, file2) source.p4("submit", "-d", "2: file1 -> file2") # This transfer should create no new changes because everything is up-to-date. # It should only set the transfer counters appropriately. p4transfer.test_transfer(historical_transfer_config) assert target.counter == 2 assert target.historical_commit == 2 assert len(target.p4("changes")) == 4 source.p4("edit", file1) file1.write_bytes(b"Test content\nMore again\n") source.p4("submit", "-d", "3: file1 edited") source.p4("integrate", file1, file2) source.p4("resolve", "-as") source.p4("submit", "-d", "4: file1 -> file2") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 4 assert len(target.p4("changes")) == 6 source_filelog = source.filelog("//depot/inside/file1@>2") target_filelog = target.filelog("//depot/import/file1@>4") assert len(target_filelog[0].revisions) == 1 assert len(source_filelog[0].revisions) == len(target_filelog[0].revisions) source_how = source_filelog[0].revisions[0].integrations[0].how target_how = target_filelog[0].revisions[0].integrations[0].how assert source_how == target_how source_filelog = source.filelog("//depot/inside/file2@>2") target_filelog = target.filelog("//depot/import/file2@>4") assert len(target_filelog[0].revisions) == 1 assert len(source_filelog[0].revisions) == len(target_filelog[0].revisions) source_how = source_filelog[0].revisions[0].integrations[0].how target_how = target_filelog[0].revisions[0].integrations[0].how assert source_how == target_how @pytest.mark.historical_start_change(3) def test_historical_rename(source, target, historical_transfer_config): """Rename for historical start mode transfer.""" file1 = source.local_path("inside/file1") file2 = source.local_path("inside/file2") contents = [b"0"] * 10 file1.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") source.p4("edit", file1) file1.write_bytes(b"file1\n") source.p4("submit", "-d", "2: file1 edit") chg = source.fetch("change") chg._description = "Test desc" source.save("change", chg) source.p4("edit", file1) source.p4("move", file1, file2) source.p4("submit", "-d", "4: file1 renamed to file2") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 4 assert len(target.p4("changes")) == 2 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 1 assert len(filelog[0].revisions[0].integrations) == 1 # assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "move/add" source.p4("edit", file2) source.p4("move", file2, file1) source.p4("submit", "-d", "5: renamed back") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 5 assert len(target.p4("changes")) == 3 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 0 assert len(filelog[0].revisions[1].integrations) == 2 # assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "move/delete" assert filelog[0].revisions[1].action == "move/add" filelog = target.filelog("//depot/import/file1") assert len(filelog[0].revisions) == 3 assert len(filelog[0].revisions[0].integrations) == 1 assert len(filelog[0].revisions[1].integrations) == 0 assert len(filelog[0].revisions[2].integrations) == 1 # assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "move/add" @pytest.mark.historical_start_change(3) def test_historical_rename_add(source, target, historical_transfer_config): """Rename with subsequent add for historical start mode transfer.""" file1 = source.local_path("inside/file1") file2 = source.local_path("inside/file2") file3 = source.local_path("inside/file3") contents = [b"0"] * 10 file1.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") source.p4("edit", file1) file1.write_bytes(b"file1\n") source.p4("submit", "-d", "2: file1 edit") chg = source.fetch("change") chg._description = "Test desc" source.save("change", chg) source.p4("edit", file1) source.p4("move", file1, file2) source.p4("submit", "-d", "4: file1 renamed to file2") contents[0] = b"file1" file3.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file3) source.p4("submit", "-d", "5: add file3") source.p4("edit", file3) source.p4("move", file3, file1) source.p4("submit", "-d", "6: rename 3->1") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 6 assert len(target.p4("changes")) == 4 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 1 assert len(filelog[0].revisions[0].integrations) == 1 # assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "move/add" filelog = target.filelog("//depot/import/file3") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 0 # assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "move/delete" filelog = target.filelog("//depot/import/file1") assert len(filelog[0].revisions) == 3 assert len(filelog[0].revisions[0].integrations) == 1 assert len(filelog[0].revisions[1].integrations) == 0 assert len(filelog[0].revisions[2].integrations) == 1 # assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "move/add" @pytest.mark.historical_start_change(4) def test_historical_start_merge(source, target, historical_transfer_config): """Merge for historical start mode transfer.""" file1 = source.local_path("inside/file1") contents = [b"0"] * 10 contents2 = contents[:] file1.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") file2 = source.local_path("inside/file2") source.p4("integrate", file1, file2) source.p4("submit", "-d", "2: file1 -> file2") source.p4("edit", file1) source.p4("edit", file2) contents[0] = b"file1" file1.write_bytes(b"\n".join(contents) + b"\n") contents2[5] = b"file2" file2.write_bytes(b"\n".join(contents2) + b"\n") source.p4("submit", "-d", "3: file1&2 edited") source.p4("integrate", file1, file2) source.p4("resolve", "-am") source.p4("submit", "-d", "4: file1 -> file2 (merge)") filelog = source.filelog("//depot/inside/file2") assert filelog[0].revisions[0].integrations[0].how == "merge from" p4transfer.test_transfer(historical_transfer_config) assert target.counter == 4 assert len(target.p4("changes")) == 1 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 1 assert len(filelog[0].revisions[0].integrations) == 0 assert filelog[0].revisions[0].action == "add" source.p4("integrate", file2, file1) source.p4("resolve", "-am") source.p4("submit", "-d", "5: file2 -> file1 (merge)") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 5 assert len(target.p4("changes")) == 2 filelog = target.filelog("//depot/import/file1") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 1 assert filelog[0].revisions[0].action == "integrate" @pytest.mark.historical_start_change(4) def test_historical_start_simple(source, target, historical_transfer_config): """Simple integration options for historical start mode transfer.""" file1 = source.local_path("inside/file1") contents = [b"0"] * 10 contents2 = contents[:] file1.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") file2 = source.local_path("inside/file2") source.p4("integrate", file1, file2) source.p4("submit", "-d", "2: file1 -> file2") source.p4("edit", file1) source.p4("edit", file2) contents[0] = b"file1" file1.write_bytes(b"\n".join(contents) + b"\n") contents2[5] = b"file2" file2.write_bytes(b"\n".join(contents2) + b"\n") source.p4("submit", "-d", "3: file1&2 edited") source.p4("integrate", file1, file2) source.p4("resolve", "-am") source.p4("submit", "-d", "4: file1 -> file2 (merge)") filelog = source.filelog("//depot/inside/file2") assert filelog[0].revisions[0].integrations[0].how == "merge from" p4transfer.test_transfer(historical_transfer_config) assert target.counter == 4 assert len(target.p4("changes")) == 1 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 1 assert len(filelog[0].revisions[0].integrations) == 0 assert filelog[0].revisions[0].action == "add" @pytest.mark.historical_start_change(3) def test_historical_start_simple2(source, target, historical_transfer_config): """Simple integration options for historical start mode transfer.""" file1 = source.local_path("inside/file1") contents = [b"0"] * 10 contents2 = contents[:] file1.write_bytes(b"\n".join(contents) + b"\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") file2 = source.local_path("inside/file2") source.p4("integrate", file1, file2) source.p4("submit", "-d", "2: file1 -> file2") source.p4("edit", file1) source.p4("edit", file2) contents[0] = b"file1" file1.write_bytes(b"\n".join(contents) + b"\n") contents2[5] = b"file2" file2.write_bytes(b"\n".join(contents2) + b"\n") source.p4("submit", "-d", "3: file1&2 edited") source.p4("integrate", file1, file2) source.p4("resolve", "-am") source.p4("submit", "-d", "file1 -> file2 (merge)") filelog = source.filelog("//depot/inside/file2") assert filelog[0].revisions[0].integrations[0].how == "merge from" p4transfer.test_transfer(historical_transfer_config) assert target.counter == 4 assert len(target.p4("changes")) == 2 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 1 assert len(filelog[0].revisions[1].integrations) == 0 assert filelog[0].revisions[0].action == "integrate" assert filelog[0].revisions[1].action == "add" @pytest.mark.historical_start_change(3) def test_historical_start_simple3(source, target, historical_transfer_config): """Simple integration options for historical start mode transfer.""" file1 = source.local_path("inside/file1") file1.write_bytes(b"Test content\n") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") file2 = source.local_path("inside/file2") source.p4("integrate", file1, file2) source.p4("submit", "-d", "2: file1 -> file2") source.p4("edit", file1) file1.write_bytes(file1.read_bytes() + b"Rev2 chg3\n") source.p4("submit", "-d", "3: file1 edited") source.p4("integrate", file1, file2) source.p4("resolve", "-at") source.p4("submit", "-d", "4: file1 -> file2 (copy rev2)") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 4 assert len(target.p4("changes")) == 2 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 1 assert filelog[0].revisions[0].action == "integrate" assert filelog[0].revisions[1].action == "add" logging.debug("========================================== Incremental change") # Now make 2 changes and integrate them one at a time. source.p4("edit", file1) file1.write_bytes(file1.read_bytes() + b"Rev3 chg5\n") source.p4("submit", "-d", "5: file1 edited") source.p4("edit", file1) file1.write_bytes(file1.read_bytes() + b"Rev4 chg6\n") source.p4("submit", "-d", "6: file1 edited") source.p4("integrate", f"{file1}#3", file2) source.p4("resolve", "-at") source.p4("submit", "-d", "7: file1 -> file2 (copy rev3)") source.p4("integrate", file1, file2) source.p4("resolve", "-at") source.p4("submit", "-d", "8: file1 -> file2 (copy rev4)") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 8 assert len(target.p4("changes")) == 6 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 4 assert len(filelog[0].revisions[1].integrations) == 1 assert filelog[0].revisions[0].integrations[0].how == "copy from" assert filelog[0].revisions[1].integrations[0].how == "copy from" assert filelog[0].revisions[2].integrations[0].how == "copy from" assert filelog[0].revisions[3].action == "add" @pytest.mark.historical_start_change(2) def test_historical_subsequent_merge(source, target, historical_transfer_config): """Integration after historical start mode transfer.""" file1 = source.local_path("inside/file1") file1.write_bytes(b"Test content\n") file2 = source.local_path("inside/file2") file2.write_bytes(b"Test content2\n") file3 = source.local_path("inside/file3") source.p4("add", file1) source.p4("submit", "-d", "1: file1 added") source.p4("add", file2) source.p4("submit", "-d", "2: file2 added") source.p4("integrate", file2, file3) source.p4("submit", "-d", "3: file2 -> file3") source.p4("edit", file2) file2.write_bytes(file2.read_bytes() + b"More\n") source.p4("submit", "-d", "4: file2 edited") source.p4("integrate", file2, file3) source.p4("resolve", "-as") source.p4("submit", "-d", "5: file2 -> file3") p4transfer.test_transfer(historical_transfer_config) assert target.counter == 5 assert len(target.p4("changes")) == 4 filelog = target.filelog("//depot/import/file2") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 1 assert filelog[0].revisions[0].action == "edit" assert filelog[0].revisions[1].action == "add" filelog = target.filelog("//depot/import/file3") assert len(filelog[0].revisions) == 2 assert len(filelog[0].revisions[0].integrations) == 1 assert len(filelog[0].revisions[1].integrations) == 1 assert filelog[0].revisions[0].integrations[0].how == "copy from" assert filelog[0].revisions[1].integrations[0].how == "branch from"

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0

1

0

1

0

null

0

7

8d4f9bcd6fa437fed5edbc3da34f6c0b0ee2289f

5,485

py

Python

wildlifecompliance/migrations/0381_auto_20200115_1056.py

preranaandure/wildlifecompliance

bc19575f7bccf7e19adadbbaf5d3eda1d1aee4b5

[ "Apache-2.0" ]

1

2020-12-07T17:12:40.000Z

wildlifecompliance/migrations/0381_auto_20200115_1056.py

preranaandure/wildlifecompliance

bc19575f7bccf7e19adadbbaf5d3eda1d1aee4b5

[ "Apache-2.0" ]

14

2020-01-08T08:08:26.000Z

2021-03-19T22:59:46.000Z

wildlifecompliance/migrations/0381_auto_20200115_1056.py

preranaandure/wildlifecompliance

bc19575f7bccf7e19adadbbaf5d3eda1d1aee4b5

[ "Apache-2.0" ]

15

2020-01-08T08:02:28.000Z

2021-11-03T06:48:32.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.10.8 on 2020-01-15 02:56 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('wildlifecompliance', '0380_physicalartifact_custodian_email'), ] operations = [ migrations.AddField( model_name='legalcase', name='accused_bad_character', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='accused_bad_character_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='applications_orders_requests', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='applications_orders_requests_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='applications_orders_required', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='applications_orders_required_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='further_persons_interviews_pending', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='further_persons_interviews_pending_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='local_public_interest', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='local_public_interest_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='other_interviews', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='other_interviews_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='other_legal_matters', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='other_legal_matters_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='other_persons_receiving_sanction_outcome', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='other_persons_receiving_sanction_outcome_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='problems_needs_prosecution_witnesses', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='problems_needs_prosecution_witnesses_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='relevant_persons_pending_charges', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='relevant_persons_pending_charges_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='statements_pending', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='statements_pending_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='victim_impact_statement_taken', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='victim_impact_statement_taken_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='vulnerable_hostile_witnesses', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='vulnerable_hostile_witnesses_details', field=models.TextField(blank=True, null=True), ), migrations.AddField( model_name='legalcase', name='witness_refusing_statement', field=models.BooleanField(default=False), ), migrations.AddField( model_name='legalcase', name='witness_refusing_statement_details', field=models.TextField(blank=True, null=True), ), ]

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6.536534

0.164927

0.160971

0.205685

0.241456

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0

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null

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10

8d750b3b1d270ea31d55fd4c96c9474202eeab46

5,175

py

Python

webstore/carts/views.py

dmusial98/WebStorePython

ed98764a40dd82db2b57e030ff9bf0bc777075a7

[ "Unlicense" ]

null

webstore/carts/views.py

dmusial98/WebStorePython

ed98764a40dd82db2b57e030ff9bf0bc777075a7

[ "Unlicense" ]

null

webstore/carts/views.py

dmusial98/WebStorePython

ed98764a40dd82db2b57e030ff9bf0bc777075a7

[ "Unlicense" ]

null

from urllib import request from .models import Cart, CartProduct from . import serializers from rest_framework.permissions import AllowAny # from . import permissions from rest_framework import generics, status from rest_framework.response import Response from django_filters.rest_framework import DjangoFilterBackend class CartListView(generics.ListAPIView): queryset = Cart.objects.all() serializer_class = serializers.CartSerializer # permission_classes = [] filter_backends = [DjangoFilterBackend] # filterset_fields = ['productId', 'productName', 'categoryType'] # ordering = ['id'] def get_permissions(self): permission_classes = [AllowAny] return [permission() for permission in permission_classes] class CartCreateView(generics.CreateAPIView): queryset = Cart.objects.all() serializer_class = serializers.CartSerializer def get_permissions(self): permission_classes = [AllowAny] return [permission() for permission in permission_classes] def create(self, request, *args, **kwargs): super(CartCreateView, self).create(request, args, kwargs) response = {"status_code": status.HTTP_200_OK, "message": "Successfully created", "result": request.data} return Response(response) class CartDetailView(generics.RetrieveUpdateDestroyAPIView): queryset = Cart.objects.all() serializer_class = serializers.CartSerializer def get_permissions(self): permission_classes = [AllowAny] return [permission() for permission in permission_classes] def retrieve(self, request, *args, **kwargs): super(CartDetailView, self).retrieve(request, args, kwargs) instance = self.get_object() serializer = self.get_serializer(instance) data = serializer.data response = {"status_code": status.HTTP_200_OK, "message": "Successfully retrieved", "result": data} return Response(response) def patch(self, request, *args, **kwargs): super(CartDetailView, self).patch(request, args, kwargs) instance = self.get_object() serializer = self.get_serializer(instance) data = serializer.data response = {"status_code": status.HTTP_200_OK, "message": "Successfully updated", "result": data} return Response(response) def delete(self, request, *args, **kwargs): super(CartDetailView, self).delete(request, args, kwargs) response = {"status_code": status.HTTP_200_OK, "message": "Successfully deleted"} return Response(response) class ProductCartListView(generics.ListAPIView): queryset = CartProduct.objects.all() serializer_class = serializers.CartProductSerializer # permission_classes = [] filter_backends = [DjangoFilterBackend] # filterset_fields = ['productId', 'productName', 'categoryType'] # ordering = ['id'] def get_permissions(self): permission_classes = [AllowAny] return [permission() for permission in permission_classes] class ProductCartCreateView(generics.CreateAPIView): queryset = CartProduct.objects.all() serializer_class = serializers.CartProductSerializer def get_permissions(self): permission_classes = [AllowAny] return [permission() for permission in permission_classes] def create(self, request, *args, **kwargs): super(ProductCartCreateView, self).create(request, args, kwargs) response = {"status_code": status.HTTP_200_OK, "message": "Successfully created", "result": request.data} return Response(response) class ProductCartDetailView(generics.RetrieveUpdateDestroyAPIView): queryset = CartProduct.objects.all() serializer_class = serializers.CartProductSerializer def get_permissions(self): permission_classes = [AllowAny] return [permission() for permission in permission_classes] def retrieve(self, request, *args, **kwargs): super(ProductCartDetailView, self).retrieve(request, args, kwargs) instance = self.get_object() serializer = self.get_serializer(instance) data = serializer.data response = {"status_code": status.HTTP_200_OK, "message": "Successfully retrieved", "result": data} return Response(response) def patch(self, request, *args, **kwargs): super(ProductCartDetailView, self).patch(request, args, kwargs) instance = self.get_object() serializer = self.get_serializer(instance) data = serializer.data response = {"status_code": status.HTTP_200_OK, "message": "Successfully updated", "result": data} return Response(response) def delete(self, request, *args, **kwargs): super(ProductCartDetailView, self).delete(request, args, kwargs) response = {"status_code": status.HTTP_200_OK, "message": "Successfully deleted"} return Response(response)

39.503817

74

0.671111

490

5,175

6.95102

0.146939

0.051674

0.079859

0.049325

0.833529

0.802701

0.762184

0

0.00603

0.230918

5,175

131

75

39.503817

0.849749

0.045797

0

0.796117

0

0.069763

0

1

0.135922

false

0

0.067961

0

0.533981

0

null

0

1

0

1

0

null

0

1

0

8

a5f61165d6f7b3352cc301538df06f12606c1d03

199

py

Python

evennia/contrib/game_systems/clothing/__init__.py

davidrideout/evennia

879eea55acdf4fe5cdc96ba8fd0ab5ccca4ae84b

[ "BSD-3-Clause" ]

null

evennia/contrib/game_systems/clothing/__init__.py

davidrideout/evennia

879eea55acdf4fe5cdc96ba8fd0ab5ccca4ae84b

[ "BSD-3-Clause" ]

null

evennia/contrib/game_systems/clothing/__init__.py

davidrideout/evennia

879eea55acdf4fe5cdc96ba8fd0ab5ccca4ae84b

[ "BSD-3-Clause" ]

null

""" Clothing contrib - Tim Ashley Jenkins 2017 """ from .clothing import ClothedCharacter # noqa from .clothing import ClothedCharacterCmdSet # noqa from .clothing import ContribClothing # noqa

22.111111

52

0.773869

21

199

7.333333

0.571429

0.233766

0.350649

0.285714

0

0.023952

0.160804

199

8

53

24.875

0.898204

0.291457

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true

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1

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null

0

1

0

1

0

7

f580fa7aabf31b408f37c1d558a8408adbce265f

49,394

py

Python

src/fidalgo/azext_fidalgo/generated/custom.py

tbyfield/azure-cli-extensions

e7e5f37fdcea3afb5c4aecb61fa72eac72c2128e

[ "MIT" ]

null

src/fidalgo/azext_fidalgo/generated/custom.py

tbyfield/azure-cli-extensions

e7e5f37fdcea3afb5c4aecb61fa72eac72c2128e

[ "MIT" ]

null

src/fidalgo/azext_fidalgo/generated/custom.py

tbyfield/azure-cli-extensions

e7e5f37fdcea3afb5c4aecb61fa72eac72c2128e

[ "MIT" ]

1

2022-02-14T21:43:29.000Z

# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- # pylint: disable=too-many-lines # pylint: disable=unused-argument from azure.cli.core.util import sdk_no_wait def fidalgo_dev_center_list(client, resource_group_name=None, top=None): if resource_group_name: return client.list_by_resource_group(resource_group_name=resource_group_name, top=top) return client.list_by_subscription(top=top) def fidalgo_dev_center_show(client, resource_group_name, dev_center_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name) def fidalgo_dev_center_create(client, resource_group_name, dev_center_name, location, tags=None, type_=None, user_assigned_identities=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location body['identity'] = {} if type_ is not None: body['identity']['type'] = type_ if user_assigned_identities is not None: body['identity']['user_assigned_identities'] = user_assigned_identities if len(body['identity']) == 0: del body['identity'] return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, body=body) def fidalgo_dev_center_update(client, resource_group_name, dev_center_name, tags=None, location=None, type_=None, user_assigned_identities=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location body['identity'] = {} if type_ is not None: body['identity']['type'] = type_ if user_assigned_identities is not None: body['identity']['user_assigned_identities'] = user_assigned_identities if len(body['identity']) == 0: del body['identity'] return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, body=body) def fidalgo_dev_center_delete(client, resource_group_name, dev_center_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, dev_center_name=dev_center_name) def fidalgo_project_list(client, resource_group_name=None, top=None): if resource_group_name: return client.list_by_resource_group(resource_group_name=resource_group_name, top=top) return client.list_by_subscription(top=top) def fidalgo_project_show(client, resource_group_name, project_name): return client.get(resource_group_name=resource_group_name, project_name=project_name) def fidalgo_project_create(client, resource_group_name, project_name, location, tags=None, dev_center_id=None, description=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location if dev_center_id is not None: body['dev_center_id'] = dev_center_id if description is not None: body['description'] = description return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, project_name=project_name, body=body) def fidalgo_project_update(client, resource_group_name, project_name, tags=None, location=None, dev_center_id=None, description=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location if dev_center_id is not None: body['dev_center_id'] = dev_center_id if description is not None: body['description'] = description return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, project_name=project_name, body=body) def fidalgo_project_delete(client, resource_group_name, project_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, project_name=project_name) def fidalgo_attached_network_list(client, resource_group_name, project_name=None, top=None, dev_center_name=None): if resource_group_name and project_name is not None: return client.list_by_project(resource_group_name=resource_group_name, project_name=project_name, top=top) return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) def fidalgo_attached_network_show(client, resource_group_name, attached_network_connection_name, project_name=None, dev_center_name=None): if resource_group_name and project_name is not None and attached_network_connection_name is not None: return client.get_by_project(resource_group_name=resource_group_name, project_name=project_name, attached_network_connection_name=attached_network_connection_name) return client.get_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, attached_network_connection_name=attached_network_connection_name) def fidalgo_attached_network_create(client, resource_group_name, dev_center_name, attached_network_connection_name, network_connection_resource_id=None, no_wait=False): body = {} if network_connection_resource_id is not None: body['network_connection_resource_id'] = network_connection_resource_id return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, attached_network_connection_name=attached_network_connection_name, body=body) def fidalgo_attached_network_update(client, resource_group_name, dev_center_name, attached_network_connection_name, network_connection_resource_id=None, no_wait=False): body = {} if network_connection_resource_id is not None: body['network_connection_resource_id'] = network_connection_resource_id return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, attached_network_connection_name=attached_network_connection_name, body=body) def fidalgo_attached_network_delete(client, resource_group_name, dev_center_name, attached_network_connection_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, dev_center_name=dev_center_name, attached_network_connection_name=attached_network_connection_name) def fidalgo_environment_list(client, resource_group_name, project_name, top=None): return client.list_by_project(resource_group_name=resource_group_name, project_name=project_name, top=top) def fidalgo_environment_show(client, resource_group_name, project_name, environment_name): return client.get(resource_group_name=resource_group_name, project_name=project_name, environment_name=environment_name) def fidalgo_environment_create(client, resource_group_name, project_name, environment_name, location, tags=None, description=None, catalog_item_name=None, template_uri=None, deployment_parameters=None, environment_type=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location if description is not None: body['description'] = description if catalog_item_name is not None: body['catalog_item_name'] = catalog_item_name if template_uri is not None: body['template_uri'] = template_uri if deployment_parameters is not None: body['deployment_parameters'] = deployment_parameters if environment_type is not None: body['environment_type'] = environment_type return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, project_name=project_name, environment_name=environment_name, body=body) def fidalgo_environment_update(client, resource_group_name, project_name, environment_name, tags=None, location=None, description=None, catalog_item_name=None, template_uri=None, deployment_parameters=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location if description is not None: body['description'] = description if catalog_item_name is not None: body['catalog_item_name'] = catalog_item_name if template_uri is not None: body['template_uri'] = template_uri if deployment_parameters is not None: body['deployment_parameters'] = deployment_parameters return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, project_name=project_name, environment_name=environment_name, body=body) def fidalgo_environment_delete(client, resource_group_name, project_name, environment_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, project_name=project_name, environment_name=environment_name) def fidalgo_environment_deploy(client, resource_group_name, project_name, environment_name, parameters=None, no_wait=False): deployment = {} if parameters is not None: deployment['parameters'] = parameters return sdk_no_wait(no_wait, client.begin_deploy, resource_group_name=resource_group_name, project_name=project_name, environment_name=environment_name, deployment=deployment) def fidalgo_deployment_list(client, resource_group_name, project_name, environment_name, top=None): return client.list_by_environment(resource_group_name=resource_group_name, project_name=project_name, environment_name=environment_name, top=top) def fidalgo_environment_type_list(client, resource_group_name, project_name=None, top=None, dev_center_name=None): if resource_group_name and project_name is not None: return client.list_by_project(resource_group_name=resource_group_name, project_name=project_name, top=top) return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) def fidalgo_environment_type_show(client, resource_group_name, dev_center_name, environment_type_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, environment_type_name=environment_type_name) def fidalgo_environment_type_create(client, resource_group_name, dev_center_name, environment_type_name, tags=None, description=None): body = {} if tags is not None: body['tags'] = tags if description is not None: body['description'] = description return client.create_or_update(resource_group_name=resource_group_name, dev_center_name=dev_center_name, environment_type_name=environment_type_name, body=body) def fidalgo_environment_type_update(client, resource_group_name, dev_center_name, environment_type_name, tags=None, description=None): body = {} if tags is not None: body['tags'] = tags if description is not None: body['description'] = description return client.update(resource_group_name=resource_group_name, dev_center_name=dev_center_name, environment_type_name=environment_type_name, body=body) def fidalgo_environment_type_delete(client, resource_group_name, dev_center_name, environment_type_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, dev_center_name=dev_center_name, environment_type_name=environment_type_name) def fidalgo_catalog_item_list(client, resource_group_name, dev_center_name=None, catalog_name=None, top=None, project_name=None): if resource_group_name and dev_center_name is not None and catalog_name is not None: return client.list_by_catalog(resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, top=top) return client.list_by_project(resource_group_name=resource_group_name, project_name=project_name, top=top) def fidalgo_catalog_item_show(client, resource_group_name, dev_center_name, catalog_name, catalog_item_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, catalog_item_name=catalog_item_name) def fidalgo_catalog_item_create(client, resource_group_name, dev_center_name, catalog_name, catalog_item_name, description=None, template_path=None, parameters=None): body = {} if description is not None: body['description'] = description body['engine'] = {} body['engine']['type'] = "ARM" if template_path is not None: body['engine']['template_path'] = template_path if parameters is not None: body['engine']['parameters'] = parameters if len(body['engine']) == 0: del body['engine'] return client.create_or_update(resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, catalog_item_name=catalog_item_name, body=body) def fidalgo_catalog_item_update(client, resource_group_name, dev_center_name, catalog_name, catalog_item_name, tags=None, description=None): body = {} if tags is not None: body['tags'] = tags if description is not None: body['description'] = description return client.update(resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, catalog_item_name=catalog_item_name, body=body) def fidalgo_catalog_item_delete(client, resource_group_name, dev_center_name, catalog_name, catalog_item_name): return client.delete(resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, catalog_item_name=catalog_item_name) def fidalgo_gallery_list(client, resource_group_name, dev_center_name, top=None): return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) def fidalgo_gallery_show(client, resource_group_name, dev_center_name, gallery_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name) def fidalgo_gallery_create(client, resource_group_name, dev_center_name, gallery_name, gallery_resource_id=None, no_wait=False): body = {} if gallery_resource_id is not None: body['gallery_resource_id'] = gallery_resource_id return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name, body=body) def fidalgo_gallery_update(instance, resource_group_name, dev_center_name, gallery_name, gallery_resource_id=None, no_wait=False): if gallery_resource_id is not None: instance.gallery_resource_id = gallery_resource_id return instance def fidalgo_gallery_delete(client, resource_group_name, dev_center_name, gallery_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name) def fidalgo_image_list(client, resource_group_name, dev_center_name, gallery_name=None, top=None): if resource_group_name and dev_center_name is not None and gallery_name is not None: return client.list_by_gallery(resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name, top=top) return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) def fidalgo_image_show(client, resource_group_name, dev_center_name, gallery_name, image_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name, image_name=image_name) def fidalgo_image_version_list(client, resource_group_name, dev_center_name, gallery_name, image_name): return client.list_by_image(resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name, image_name=image_name) def fidalgo_image_version_show(client, resource_group_name, dev_center_name, gallery_name, image_name, version_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, gallery_name=gallery_name, image_name=image_name, version_name=version_name) def fidalgo_catalog_list(client, resource_group_name, dev_center_name, top=None): return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) def fidalgo_catalog_show(client, resource_group_name, dev_center_name, catalog_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name) def fidalgo_catalog_create(client, resource_group_name, dev_center_name, catalog_name, git_hub=None, ado_git=None, no_wait=False): body = {} if git_hub is not None: body['git_hub'] = git_hub if ado_git is not None: body['ado_git'] = ado_git return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, body=body) def fidalgo_catalog_update(client, resource_group_name, dev_center_name, catalog_name, tags=None, git_hub=None, ado_git=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if git_hub is not None: body['git_hub'] = git_hub if ado_git is not None: body['ado_git'] = ado_git return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name, body=body) def fidalgo_catalog_delete(client, resource_group_name, dev_center_name, catalog_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name) def fidalgo_catalog_sync(client, resource_group_name, dev_center_name, catalog_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_sync, resource_group_name=resource_group_name, dev_center_name=dev_center_name, catalog_name=catalog_name) def fidalgo_mapping_list(client, resource_group_name, dev_center_name, top=None): return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) def fidalgo_mapping_show(client, resource_group_name, dev_center_name, mapping_name): return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, mapping_name=mapping_name) def fidalgo_mapping_create(client, resource_group_name, dev_center_name, mapping_name, mapped_subscription_id=None, environment_type=None, project_id=None): body = {} if mapped_subscription_id is not None: body['mapped_subscription_id'] = mapped_subscription_id if environment_type is not None: body['environment_type'] = environment_type if project_id is not None: body['project_id'] = project_id return client.create_or_update(resource_group_name=resource_group_name, dev_center_name=dev_center_name, mapping_name=mapping_name, body=body) def fidalgo_mapping_update(client, resource_group_name, dev_center_name, mapping_name, mapped_subscription_id=None): body = {} if mapped_subscription_id is not None: body['mapped_subscription_id'] = mapped_subscription_id return client.update(resource_group_name=resource_group_name, dev_center_name=dev_center_name, mapping_name=mapping_name, body=body) def fidalgo_mapping_delete(client, resource_group_name, dev_center_name, mapping_name): return client.delete(resource_group_name=resource_group_name, dev_center_name=dev_center_name, mapping_name=mapping_name) def fidalgo_dev_box_definition_list(client, resource_group_name, dev_center_name=None, top=None, project_name=None): if resource_group_name and dev_center_name is not None: return client.list_by_dev_center(resource_group_name=resource_group_name, dev_center_name=dev_center_name, top=top) return client.list_by_project(resource_group_name=resource_group_name, project_name=project_name, top=top) def fidalgo_dev_box_definition_show(client, resource_group_name, dev_box_definition_name, dev_center_name=None, project_name=None): if resource_group_name and dev_center_name is not None and dev_box_definition_name is not None: return client.get(resource_group_name=resource_group_name, dev_center_name=dev_center_name, dev_box_definition_name=dev_box_definition_name) return client.get_by_project(resource_group_name=resource_group_name, project_name=project_name, dev_box_definition_name=dev_box_definition_name) def fidalgo_dev_box_definition_create(client, resource_group_name, dev_center_name, dev_box_definition_name, location, tags=None, image_reference=None, name=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location if image_reference is not None: body['image_reference'] = image_reference body['sku'] = {} if name is not None: body['sku']['name'] = name if len(body['sku']) == 0: del body['sku'] return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, dev_box_definition_name=dev_box_definition_name, body=body) def fidalgo_dev_box_definition_update(client, resource_group_name, dev_center_name, dev_box_definition_name, tags=None, location=None, image_reference=None, name=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location if image_reference is not None: body['image_reference'] = image_reference body['sku'] = {} if name is not None: body['sku']['name'] = name if len(body['sku']) == 0: del body['sku'] return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, dev_center_name=dev_center_name, dev_box_definition_name=dev_box_definition_name, body=body) def fidalgo_dev_box_definition_delete(client, resource_group_name, dev_center_name, dev_box_definition_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, dev_center_name=dev_center_name, dev_box_definition_name=dev_box_definition_name) def fidalgo_operation_statuses_show(client, location, operation_id): return client.get(location=location, operation_id=operation_id) def fidalgo_sku_list(client, top=None): return client.list_by_subscription(top=top) def fidalgo_pool_list(client, resource_group_name, project_name, top=None): return client.list_by_project(resource_group_name=resource_group_name, project_name=project_name, top=top) def fidalgo_pool_show(client, resource_group_name, project_name, pool_name): return client.get(resource_group_name=resource_group_name, project_name=project_name, pool_name=pool_name) def fidalgo_pool_create(client, resource_group_name, project_name, pool_name, location, tags=None, machine_definition_id=None, dev_box_definition_name=None, network_settings_id=None, network_connection_name=None, name=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location if machine_definition_id is not None: body['machine_definition_id'] = machine_definition_id if dev_box_definition_name is not None: body['dev_box_definition_name'] = dev_box_definition_name if network_settings_id is not None: body['network_settings_id'] = network_settings_id if network_connection_name is not None: body['network_connection_name'] = network_connection_name body['sku'] = {} if name is not None: body['sku']['name'] = name if len(body['sku']) == 0: del body['sku'] return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, project_name=project_name, pool_name=pool_name, body=body) def fidalgo_pool_update(client, resource_group_name, project_name, pool_name, tags=None, location=None, machine_definition_id=None, dev_box_definition_name=None, network_settings_id=None, network_connection_name=None, name=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location if machine_definition_id is not None: body['machine_definition_id'] = machine_definition_id if dev_box_definition_name is not None: body['dev_box_definition_name'] = dev_box_definition_name if network_settings_id is not None: body['network_settings_id'] = network_settings_id if network_connection_name is not None: body['network_connection_name'] = network_connection_name body['sku'] = {} if name is not None: body['sku']['name'] = name if len(body['sku']) == 0: del body['sku'] return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, project_name=project_name, pool_name=pool_name, body=body) def fidalgo_pool_delete(client, resource_group_name, project_name, pool_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, project_name=project_name, pool_name=pool_name) def fidalgo_machine_definition_list(client, resource_group_name=None, top=None): if resource_group_name: return client.list_by_resource_group(resource_group_name=resource_group_name, top=top) return client.list_by_subscription(top=top) def fidalgo_machine_definition_show(client, resource_group_name, machine_definition_name): return client.get(resource_group_name=resource_group_name, machine_definition_name=machine_definition_name) def fidalgo_machine_definition_create(client, resource_group_name, machine_definition_name, location, tags=None, image_reference=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location if image_reference is not None: body['image_reference'] = image_reference return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, machine_definition_name=machine_definition_name, body=body) def fidalgo_machine_definition_update(client, resource_group_name, machine_definition_name, tags=None, location=None, image_reference=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location if image_reference is not None: body['image_reference'] = image_reference return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, machine_definition_name=machine_definition_name, body=body) def fidalgo_machine_definition_delete(client, resource_group_name, machine_definition_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, machine_definition_name=machine_definition_name) def fidalgo_network_setting_list(client, resource_group_name=None, top=None): if resource_group_name: return client.list_by_resource_group(resource_group_name=resource_group_name, top=top) return client.list_by_subscription(top=top) def fidalgo_network_setting_show(client, resource_group_name, network_setting_name): return client.get(resource_group_name=resource_group_name, network_setting_name=network_setting_name) def fidalgo_network_setting_create(client, resource_group_name, network_setting_name, location, tags=None, subnet_id=None, networking_resource_group_id=None, domain_name=None, organization_unit=None, domain_username=None, domain_password=None, networking_resource_group_name=None, domain_join_type=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags body['location'] = location if subnet_id is not None: body['subnet_id'] = subnet_id if networking_resource_group_id is not None: body['networking_resource_group_id'] = networking_resource_group_id if domain_name is not None: body['domain_name'] = domain_name if organization_unit is not None: body['organization_unit'] = organization_unit if domain_username is not None: body['domain_username'] = domain_username if domain_password is not None: body['domain_password'] = domain_password if networking_resource_group_name is not None: body['networking_resource_group_name'] = networking_resource_group_name if domain_join_type is not None: body['domain_join_type'] = domain_join_type return sdk_no_wait(no_wait, client.begin_create_or_update, resource_group_name=resource_group_name, network_setting_name=network_setting_name, body=body) def fidalgo_network_setting_update(client, resource_group_name, network_setting_name, tags=None, location=None, subnet_id=None, networking_resource_group_id=None, domain_name=None, organization_unit=None, domain_username=None, domain_password=None, no_wait=False): body = {} if tags is not None: body['tags'] = tags if location is not None: body['location'] = location if subnet_id is not None: body['subnet_id'] = subnet_id if networking_resource_group_id is not None: body['networking_resource_group_id'] = networking_resource_group_id if domain_name is not None: body['domain_name'] = domain_name if organization_unit is not None: body['organization_unit'] = organization_unit if domain_username is not None: body['domain_username'] = domain_username if domain_password is not None: body['domain_password'] = domain_password return sdk_no_wait(no_wait, client.begin_update, resource_group_name=resource_group_name, network_setting_name=network_setting_name, body=body) def fidalgo_network_setting_delete(client, resource_group_name, network_setting_name, no_wait=False): return sdk_no_wait(no_wait, client.begin_delete, resource_group_name=resource_group_name, network_setting_name=network_setting_name) def fidalgo_network_setting_list_health_detail(client, resource_group_name, network_setting_name, top=None): return client.list_health_details(resource_group_name=resource_group_name, top=top, network_setting_name=network_setting_name) def fidalgo_network_setting_show_health_detail(client, resource_group_name, network_setting_name): return client.get_health_details(resource_group_name=resource_group_name, network_setting_name=network_setting_name)

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49,394

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0

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0

1

0

1

0

null

0

8

194e08d962c78fd13939a25aac9ccaa1feff1d35

210

py

Python

categories/tests/__init__.py

s1n4/django-categories

6af6d815e214bddbaac572c19e9c738ef1f752d6

[ "Apache-2.0" ]

1

2019-02-06T14:23:55.000Z

categories/tests/__init__.py

s1n4/django-categories

6af6d815e214bddbaac572c19e9c738ef1f752d6

[ "Apache-2.0" ]

null

categories/tests/__init__.py

s1n4/django-categories

6af6d815e214bddbaac572c19e9c738ef1f752d6

[ "Apache-2.0" ]

null

from categories.tests.category_import import * from categories.tests.templatetags import * from categories.tests.manager import * from categories.tests.registration import * __fixtures__ = ['categories.json']

30

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210

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0.095238

210

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null

0

1

0

1

0

7

197d981f745c1929a6f3bc12909392f984e64785

3,653

py

Python

test/test_interruptions_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

test/test_interruptions_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

test/test_interruptions_api.py

cvent/octopus-deploy-api-client

0e03e842e1beb29b132776aee077df570b88366a

[ "Apache-2.0" ]

null

# coding: utf-8 """ Octopus Server API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: 2019.6.7+Branch.tags-2019.6.7.Sha.aa18dc6809953218c66f57eff7d26481d9b23d6a Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import octopus_deploy_swagger_client from octopus_deploy_client.interruptions_api import InterruptionsApi # noqa: E501 from octopus_deploy_swagger_client.rest import ApiException class TestInterruptionsApi(unittest.TestCase): """InterruptionsApi unit test stubs""" def setUp(self): self.api = octopus_deploy_client.interruptions_api.InterruptionsApi() # noqa: E501 def tearDown(self): pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder """ pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder_0(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder_0 """ pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder_spaces(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder_spaces """ pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder_spaces_0(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_interruption_responsibility_responder_spaces_0 """ pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_submit_interruption_responder(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_submit_interruption_responder """ pass def test_custom_action_response_descriptor_octopus_server_web_api_actions_submit_interruption_responder_spaces(self): """Test case for custom_action_response_descriptor_octopus_server_web_api_actions_submit_interruption_responder_spaces """ pass def test_custom_query_response_descriptor_octopus_server_web_api_actions_list_interruptions_responder(self): """Test case for custom_query_response_descriptor_octopus_server_web_api_actions_list_interruptions_responder """ pass def test_custom_query_response_descriptor_octopus_server_web_api_actions_list_interruptions_responder_spaces(self): """Test case for custom_query_response_descriptor_octopus_server_web_api_actions_list_interruptions_responder_spaces """ pass def test_load_response_descriptor_server_tasks_interruption_interruption_resource(self): """Test case for load_response_descriptor_server_tasks_interruption_interruption_resource Get a InterruptionResource by ID # noqa: E501 """ pass def test_load_response_descriptor_server_tasks_interruption_interruption_resource_spaces(self): """Test case for load_response_descriptor_server_tasks_interruption_interruption_resource_spaces Get a InterruptionResource by ID # noqa: E501 """ pass if __name__ == '__main__': unittest.main()

37.659794

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null

0

1

0

1

0

1

0

11

27a4313d707a2de40650a2db4c11d5cab5084c9b

186

py

Python

src/python/providers/movement/__init__.py

daboross/dxnr

8f73e9d5f4473b97dcfe05804a40c9a0826e51b6

[ "MIT" ]

null

src/python/providers/movement/__init__.py

daboross/dxnr

8f73e9d5f4473b97dcfe05804a40c9a0826e51b6

[ "MIT" ]

null

src/python/providers/movement/__init__.py

daboross/dxnr

8f73e9d5f4473b97dcfe05804a40c9a0826e51b6

[ "MIT" ]

null

from providers.movement import passing_movement def apply_prototypes() -> None: passing_movement.apply_prototypes() def instantiate() -> None: passing_movement.instantiate()

18.6

47

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186

6.9

0.5

0.326087

0.275362

0

0.139785

186

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0

1

0.4

true

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0.6

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null

1

0

1

0

null

0

1

0

7

27e603a406e4174d1a0ca5baad275ac169e4d22d

6,269

py

Python

loldib/getratings/models/NA/na_kayn/na_kayn_sup.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_kayn/na_kayn_sup.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_kayn/na_kayn_sup.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Kayn_Sup_Aatrox(Ratings): pass class NA_Kayn_Sup_Ahri(Ratings): pass class NA_Kayn_Sup_Akali(Ratings): pass class NA_Kayn_Sup_Alistar(Ratings): pass class NA_Kayn_Sup_Amumu(Ratings): pass class NA_Kayn_Sup_Anivia(Ratings): pass class NA_Kayn_Sup_Annie(Ratings): pass class NA_Kayn_Sup_Ashe(Ratings): pass class NA_Kayn_Sup_AurelionSol(Ratings): pass class NA_Kayn_Sup_Azir(Ratings): pass class NA_Kayn_Sup_Bard(Ratings): pass class NA_Kayn_Sup_Blitzcrank(Ratings): pass class NA_Kayn_Sup_Brand(Ratings): pass class NA_Kayn_Sup_Braum(Ratings): pass class NA_Kayn_Sup_Caitlyn(Ratings): pass class NA_Kayn_Sup_Camille(Ratings): pass class NA_Kayn_Sup_Cassiopeia(Ratings): pass class NA_Kayn_Sup_Chogath(Ratings): pass class NA_Kayn_Sup_Corki(Ratings): pass class NA_Kayn_Sup_Darius(Ratings): pass class NA_Kayn_Sup_Diana(Ratings): pass class NA_Kayn_Sup_Draven(Ratings): pass class NA_Kayn_Sup_DrMundo(Ratings): pass class NA_Kayn_Sup_Ekko(Ratings): pass class NA_Kayn_Sup_Elise(Ratings): pass class NA_Kayn_Sup_Evelynn(Ratings): pass class NA_Kayn_Sup_Ezreal(Ratings): pass class NA_Kayn_Sup_Fiddlesticks(Ratings): pass class NA_Kayn_Sup_Fiora(Ratings): pass class NA_Kayn_Sup_Fizz(Ratings): pass class NA_Kayn_Sup_Galio(Ratings): pass class NA_Kayn_Sup_Gangplank(Ratings): pass class NA_Kayn_Sup_Garen(Ratings): pass class NA_Kayn_Sup_Gnar(Ratings): pass class NA_Kayn_Sup_Gragas(Ratings): pass class NA_Kayn_Sup_Graves(Ratings): pass class NA_Kayn_Sup_Hecarim(Ratings): pass class NA_Kayn_Sup_Heimerdinger(Ratings): pass class NA_Kayn_Sup_Illaoi(Ratings): pass class NA_Kayn_Sup_Irelia(Ratings): pass class NA_Kayn_Sup_Ivern(Ratings): pass class NA_Kayn_Sup_Janna(Ratings): pass class NA_Kayn_Sup_JarvanIV(Ratings): pass class NA_Kayn_Sup_Jax(Ratings): pass class NA_Kayn_Sup_Jayce(Ratings): pass class NA_Kayn_Sup_Jhin(Ratings): pass class NA_Kayn_Sup_Jinx(Ratings): pass class NA_Kayn_Sup_Kalista(Ratings): pass class NA_Kayn_Sup_Karma(Ratings): pass class NA_Kayn_Sup_Karthus(Ratings): pass class NA_Kayn_Sup_Kassadin(Ratings): pass class NA_Kayn_Sup_Katarina(Ratings): pass class NA_Kayn_Sup_Kayle(Ratings): pass class NA_Kayn_Sup_Kayn(Ratings): pass class NA_Kayn_Sup_Kennen(Ratings): pass class NA_Kayn_Sup_Khazix(Ratings): pass class NA_Kayn_Sup_Kindred(Ratings): pass class NA_Kayn_Sup_Kled(Ratings): pass class NA_Kayn_Sup_KogMaw(Ratings): pass class NA_Kayn_Sup_Leblanc(Ratings): pass class NA_Kayn_Sup_LeeSin(Ratings): pass class NA_Kayn_Sup_Leona(Ratings): pass class NA_Kayn_Sup_Lissandra(Ratings): pass class NA_Kayn_Sup_Lucian(Ratings): pass class NA_Kayn_Sup_Lulu(Ratings): pass class NA_Kayn_Sup_Lux(Ratings): pass class NA_Kayn_Sup_Malphite(Ratings): pass class NA_Kayn_Sup_Malzahar(Ratings): pass class NA_Kayn_Sup_Maokai(Ratings): pass class NA_Kayn_Sup_MasterYi(Ratings): pass class NA_Kayn_Sup_MissFortune(Ratings): pass class NA_Kayn_Sup_MonkeyKing(Ratings): pass class NA_Kayn_Sup_Mordekaiser(Ratings): pass class NA_Kayn_Sup_Morgana(Ratings): pass class NA_Kayn_Sup_Nami(Ratings): pass class NA_Kayn_Sup_Nasus(Ratings): pass class NA_Kayn_Sup_Nautilus(Ratings): pass class NA_Kayn_Sup_Nidalee(Ratings): pass class NA_Kayn_Sup_Nocturne(Ratings): pass class NA_Kayn_Sup_Nunu(Ratings): pass class NA_Kayn_Sup_Olaf(Ratings): pass class NA_Kayn_Sup_Orianna(Ratings): pass class NA_Kayn_Sup_Ornn(Ratings): pass class NA_Kayn_Sup_Pantheon(Ratings): pass class NA_Kayn_Sup_Poppy(Ratings): pass class NA_Kayn_Sup_Quinn(Ratings): pass class NA_Kayn_Sup_Rakan(Ratings): pass class NA_Kayn_Sup_Rammus(Ratings): pass class NA_Kayn_Sup_RekSai(Ratings): pass class NA_Kayn_Sup_Renekton(Ratings): pass class NA_Kayn_Sup_Rengar(Ratings): pass class NA_Kayn_Sup_Riven(Ratings): pass class NA_Kayn_Sup_Rumble(Ratings): pass class NA_Kayn_Sup_Ryze(Ratings): pass class NA_Kayn_Sup_Sejuani(Ratings): pass class NA_Kayn_Sup_Shaco(Ratings): pass class NA_Kayn_Sup_Shen(Ratings): pass class NA_Kayn_Sup_Shyvana(Ratings): pass class NA_Kayn_Sup_Singed(Ratings): pass class NA_Kayn_Sup_Sion(Ratings): pass class NA_Kayn_Sup_Sivir(Ratings): pass class NA_Kayn_Sup_Skarner(Ratings): pass class NA_Kayn_Sup_Sona(Ratings): pass class NA_Kayn_Sup_Soraka(Ratings): pass class NA_Kayn_Sup_Swain(Ratings): pass class NA_Kayn_Sup_Syndra(Ratings): pass class NA_Kayn_Sup_TahmKench(Ratings): pass class NA_Kayn_Sup_Taliyah(Ratings): pass class NA_Kayn_Sup_Talon(Ratings): pass class NA_Kayn_Sup_Taric(Ratings): pass class NA_Kayn_Sup_Teemo(Ratings): pass class NA_Kayn_Sup_Thresh(Ratings): pass class NA_Kayn_Sup_Tristana(Ratings): pass class NA_Kayn_Sup_Trundle(Ratings): pass class NA_Kayn_Sup_Tryndamere(Ratings): pass class NA_Kayn_Sup_TwistedFate(Ratings): pass class NA_Kayn_Sup_Twitch(Ratings): pass class NA_Kayn_Sup_Udyr(Ratings): pass class NA_Kayn_Sup_Urgot(Ratings): pass class NA_Kayn_Sup_Varus(Ratings): pass class NA_Kayn_Sup_Vayne(Ratings): pass class NA_Kayn_Sup_Veigar(Ratings): pass class NA_Kayn_Sup_Velkoz(Ratings): pass class NA_Kayn_Sup_Vi(Ratings): pass class NA_Kayn_Sup_Viktor(Ratings): pass class NA_Kayn_Sup_Vladimir(Ratings): pass class NA_Kayn_Sup_Volibear(Ratings): pass class NA_Kayn_Sup_Warwick(Ratings): pass class NA_Kayn_Sup_Xayah(Ratings): pass class NA_Kayn_Sup_Xerath(Ratings): pass class NA_Kayn_Sup_XinZhao(Ratings): pass class NA_Kayn_Sup_Yasuo(Ratings): pass class NA_Kayn_Sup_Yorick(Ratings): pass class NA_Kayn_Sup_Zac(Ratings): pass class NA_Kayn_Sup_Zed(Ratings): pass class NA_Kayn_Sup_Ziggs(Ratings): pass class NA_Kayn_Sup_Zilean(Ratings): pass class NA_Kayn_Sup_Zyra(Ratings): pass

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1

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7

27e97720b7f7607b81cff91683a91c7ab63ddf6f

6,268

py

Python

tests/test_add.py

noqqe/rvo

423e1ea1aea0a2dc849ceae838e18896a13e7771

[ "MIT" ]

14

2016-05-04T13:56:10.000Z

2019-08-01T14:31:33.000Z

tests/test_add.py

noqqe/rvo

423e1ea1aea0a2dc849ceae838e18896a13e7771

[ "MIT" ]

12

2016-08-01T12:42:53.000Z

2022-02-16T09:37:47.000Z

tests/test_add.py

noqqe/rvo

423e1ea1aea0a2dc849ceae838e18896a13e7771

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import sys from conftest import rvo_output, rvo_err from click.testing import CliRunner from rvo import cli def test_add_all_parameters(isatty_true): options = ['add', '-t', 'test', '-c', 'test', '--content', 'test'] output = ['Document "test" created.'] rvo_output(options,output) def test_add_tags(isatty_true): options = ['add', '-t', 'test', '--content', 'test'] output = ['Document "test" created.'] rvo_output(options,output) def test_add_title_test(isatty_true): options = ['add', '-t', 'test', '--content', 'THIS IS A TITLE'] output = ['Document "THIS IS A TITLE" created.'] rvo_output(options,output) def test_add_title_test_gnarf(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-c', 'töstcät', '-x', 'gnarf']) assert not result.exception assert result.output.strip().endswith('Document "gnarf" created.') def test_add_title_test_gnarf(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-c', 'töstcät', '-x', 'gnarf\nfoo']) assert not result.exception assert result.output.strip().endswith('Document "gnarf" created.') def test_add_title_test_hashtag(isatty_true): options = ['add', '-t', 'test', '--content', '# THIS IS A TITLE'] output = ['Document "THIS IS A TITLE" created.'] rvo_output(options,output) def test_add_title_test_hashtag(isatty_true): options = ['add', '-t', 'test', '--content', '# THIS IS A TITLE\nmutliline'] output = ['Document "THIS IS A TITLE" created.'] rvo_output(options,output) def test_add_very_long_title(isatty_true): options = ['add', '-t', 'test', '--content', '# THIS IS A VERY VERY LONG NEVER ENDING TITLE THAT EXCEEDS LIMITS'] output = ['Document "THIS IS A VERY VERY LONG NEVER ENDING TITLE THAT E" created.'] rvo_output(options,output) def test_add_no_parameters(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_one_parameters_tag(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-t', 'testtag']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_utf8_cat(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-c', 'töstcät']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_utf8_cat_multi(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-c', 'tüütüü', '-c', 'töstcät']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_utf8_tag(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-t', 'töstcät']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_utf8_tag_multi(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-t', 'tüütüü', '-t', 'töstcät']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_encrypt_by_parameter_wrong_pw(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-e', '-p', 'thispasswordistotallywrong', '-t', 'encryption', '-c', 'test']) assert result.output.strip().endswith('Invalid Password') assert result.exception def test_add_encrypt_by_parameter(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-e', '-p', 'test123', '-t', 'encryption', '-c', 'test']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_encrypt_by_input(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-e', '-t', 'encryption', '-c', 'test'], input="test123\n") assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_encrypt_by_input_with_content(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-e', '-t', 'encryption', '-x', 'TEST', '-c', 'test'], input="test123\n") assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_encrypt_by_input_wrong_pw(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-e', '-t', 'encryption', '-c', 'test'], input="test2123\n") assert result.output.strip().endswith('Invalid Password') assert result.exception def test_add_read_from_stdin(isatty_false): runner = CliRunner() result = runner.invoke(cli.cli, ['add'], input="Schwifty\nSchwifty..lol\nMorty\n\n") assert result.output.strip().endswith('Document "Schwifty" created.') assert not result.exception def test_add_read_from_stdin_with_cat(isatty_false): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-c', 'test'], input="Schwifty\nSchwifty..lol\nMorty\n\n") assert result.output.strip().endswith('Document "Schwifty" created.') assert not result.exception def test_add_read_from_stdin_with_tag(isatty_false): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-t', 'tag'], input="Schwifty\nSchwifty..lol\nMorty\n\n") assert not result.exception assert result.output.strip().endswith('Document "Schwifty" created.') def test_add_conflicting_stdin_reading(isatty_false): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-e'], input="Schwifty\nSchwifty..lol\nMorty\n\n") assert result.exception assert result.output.strip().endswith('Invalid Password') def test_add_location_germany(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-l', 'Nuremberg', '-c', 'test']) assert result.output.strip().endswith('Document "TEST" created.') assert not result.exception def test_add_location_invalid(isatty_true): runner = CliRunner() result = runner.invoke(cli.cli, ['add', '-l', 'DOESNOTEXISTTOWNATLEASTIHOPE', '-c', 'test']) assert result.exception

41.509934

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0

1

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null

0

1

0

7

7e0c7360cb4fc3b74d3575fe05bf3a83e6c59a89

5,932

py

Python

mltrain-nips-2017/ben_athiwaratkun/pytorch-bayesgan/models/discriminators.py

gopala-kr/ds-notebooks

bc35430ecdd851f2ceab8f2437eec4d77cb59423

[ "MIT" ]

1

2019-05-10T09:16:23.000Z

mltrain-nips-2017/ben_athiwaratkun/pytorch-bayesgan/models/discriminators.py

gopala-kr/ds-notebooks

bc35430ecdd851f2ceab8f2437eec4d77cb59423

[ "MIT" ]

null

mltrain-nips-2017/ben_athiwaratkun/pytorch-bayesgan/models/discriminators.py

gopala-kr/ds-notebooks

bc35430ecdd851f2ceab8f2437eec4d77cb59423

[ "MIT" ]

1

2019-10-14T07:30:18.000Z

import torch import torch.nn as nn class _netD64(nn.Module): def __init__(self, ngpu, num_classes=1, nc=3, ndf=64): super(_netD64, self).__init__() self.ngpu = ngpu self.num_classes = num_classes self.main = nn.Sequential( # input is (nc) x 64 x 64 nn.Conv2d(nc, ndf, 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf) x 32 x 32 nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 2), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*2) x 16 x 16 nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 4), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*4) x 8 x 8 nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 8), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*8) x 4 x 4 #nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False), nn.Conv2d(ndf * 8, num_classes, 4, 1, 0, bias=False), # out size = batch x num_classes x 1 x 1 #nn.Sigmoid() ) if self.num_classes == 1: self.main.add_module('prob', nn.Sigmoid()) # output = probability else: pass # output = scores def forward(self, input): if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: output = nn.parallel.data_parallel(self.main, input, range(self.ngpu)) else: output = self.main(input) return output.view(input.size(0), self.num_classes).squeeze(1) class _netD(nn.Module): def __init__(self, ngpu, num_classes=1, nc=3, ndf=64): super(_netD, self).__init__() self.ngpu = ngpu self.num_classes = num_classes self.main = nn.Sequential( # input is (nc) x 32 x 32 # conv2D(in_channels, out_channels, kernelsize, stride, padding) nn.Conv2d(nc, ndf , 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf) x 16 x 16 nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 2), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*2) x 8 x 8 nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 4), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*4) x 4 x 4 nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False), nn.BatchNorm2d(ndf * 8), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*8) x 2 x 2 nn.Conv2d(ndf * 8, num_classes, 2, 1, 0, bias=False), # out size = batch x num_classes x 1 x 1 ) if self.num_classes == 1: self.main.add_module('prob', nn.Sigmoid()) # output = probability else: pass # output = scores def forward(self, input): if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: output = nn.parallel.data_parallel(self.main, input, range(self.ngpu)) else: output = self.main(input) return output.view(input.size(0), self.num_classes).squeeze(1) class Reshape(nn.Module): def __init__(self, *args): super(Reshape, self).__init__() self.shape = args def forward(self, x): return x.view(self.shape) class _netD_v2(nn.Module): def __init__(self, ngpu, num_classes=1, nc=3, ndf=64): super(_netD_v2, self).__init__() self.ngpu = ngpu self.num_classes = num_classes self.main = nn.Sequential( # input is (nc) x 32 x 32 # conv2D(in_channels, out_channels, kernelsize, stride, padding) nn.Conv2d(nc, ndf , 4, 2, 1), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf) x 16 x 16 nn.Conv2d(ndf, ndf * 2, 4, 2, 1), nn.BatchNorm2d(ndf * 2), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*2) x 8 x 8 nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1), nn.BatchNorm2d(ndf * 4), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*4) x 4 x 4 nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1), nn.BatchNorm2d(ndf * 8), nn.LeakyReLU(0.2, inplace=True), # state size. (ndf*8) x 2 x 2 Reshape(-1, ndf*8*2*2), nn.Linear(ndf*8*2*2, num_classes), # Note: the difference from v1 is: using linear at the last layer # and use bias=True ) if self.num_classes == 1: self.main.add_module('prob', nn.Sigmoid()) # output = probability else: pass # output = scores def forward(self, input): if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: output = nn.parallel.data_parallel(self.main, input, range(self.ngpu)) else: output = self.main(input) return output.view(input.size(0), self.num_classes).squeeze(1) class _netD_synth(nn.Module): def __init__(self, ngpu, dimx=100, leaky_inplace=False): super(_netD_synth, self).__init__() self.ngpu = ngpu self.main = nn.Sequential( nn.Linear(dimx, 1000, bias=True), nn.LeakyReLU(0.2, inplace=leaky_inplace), nn.Linear(1000, 1, bias=True) # This has two classes (one logit) ) def forward(self, input): if isinstance(input.data, torch.cuda.FloatTensor) and self.ngpu > 1: output = nn.parallel.data_parallel(self.main, input, range(self.ngpu)) else: output = self.main(input) return output.view(input.size(0), self.num_classes).squeeze(1)

37.308176

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null

0

1

0

1

0

null

0

7

fdbcfaf4be5b5c59a34735ee19bf93ebb2c0a6af

2,044

py

Python

jpntextgen/date_provider.py

nerophung/japanese-fake-text

96576bdba3482923da94eeee99554c46c2ef5a54

[ "MIT" ]

null

jpntextgen/date_provider.py

nerophung/japanese-fake-text

96576bdba3482923da94eeee99554c46c2ef5a54

[ "MIT" ]

null

jpntextgen/date_provider.py

nerophung/japanese-fake-text

96576bdba3482923da94eeee99554c46c2ef5a54

[ "MIT" ]

1

2022-01-18T04:29:50.000Z

# -*- coding: utf-8 -*- import random from .utils import constants class DateProvider(object): date_format = { 0: '{}{}/{:02d}/{:02d}'.format(random.choice(constants.YEAR_LIST), random.randint(0, 70), random.randint(1, 31), random.randint(1, 12)), 1: '{}{}/{:02d}'.format(random.choice(constants.YEAR_LIST),random.randint(0, 70), random.randint(1, 12)), 2: '{}{}.{:02d}'.format(random.choice(constants.YEAR_LIST),random.randint(0, 70), random.randint(1, 12)), 3: '{}{}.{:02d}.{:02d}'.format(random.choice(constants.YEAR_LIST),random.randint(0, 70), random.randint(1, 31), random.randint(1, 12)), 4: '{}{}年{:02d}月{:02d}日'.format(random.choice(constants.YEAR_LIST),random.randint(0, 70), random.randint(0, 31), random.randint(0, 12)), 5: 'R{}年{:02d}月{:02d}日'.format(random.randint(0, 70), random.randint(1, 12), random.randint(1, 31)), 6: 'R{}年{:02d}月'.format(random.randint(0, 70), random.randint(1, 12)), 7: 'R{}/{:02d}/{:02d}'.format(random.randint(0, 70), random.randint(1, 12), random.randint(1, 31)), 8: 'R{}/{:02d}'.format(random.randint(0, 70), random.randint(1, 12)), 9: '{}年{:02d}月{:02d}日'.format(random.randint(1800, 2100), random.randint(1, 31), random.randint(1, 12)), 10: '{}{}.{:02d}.{:02d}'.format(random.choice(constants.YEAR_LIST),random.randint(0, 70), random.randint(1, 31), random.randint(1, 12)), 11: '{}{}/{:02d}'.format(random.choice(constants.YEAR_LIST),random.randint(0, 70), random.randint(1, 12)), 12: '{}.{:02d}.{:02d}'.format(random.randint(1800, 2100), random.randint(1, 12), random.randint(1, 31)), 13: '{}/{:02d}/{:02d}'.format(random.randint(1800, 2100), random.randint(1, 12), random.randint(1, 31)), 14: '{}/{:02d}'.format(random.randint(1800, 2100), random.randint(1, 12)), 15: '{}.{:02d}'.format(random.randint(1800, 2100), random.randint(1, 12)), } def __init__(self): pass def get_date(self): return self.date_format[random.randint(0, 15)]

65.935484

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2,044

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null

1

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null

0

9

fdbf8325010225d45378db8bdca140cb5bc3fe0f

2,570

py

Python

utils/builder/shared/basic_test_template.py

jeremybennett/force-riscv

a5222a3b3fa8a0b9464204056ddca148f16b7e49

[ "Apache-2.0" ]

null

utils/builder/shared/basic_test_template.py

jeremybennett/force-riscv

a5222a3b3fa8a0b9464204056ddca148f16b7e49

[ "Apache-2.0" ]

null

utils/builder/shared/basic_test_template.py

jeremybennett/force-riscv

a5222a3b3fa8a0b9464204056ddca148f16b7e49

[ "Apache-2.0" ]

1

2020-06-17T09:37:45.000Z

# # Copyright (C) [2020] Futurewei Technologies, Inc. # # FORCE-RISCV is licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR # FIT FOR A PARTICULAR PURPOSE. # See the License for the specific language governing permissions and # limitations under the License. # basic_template_str = """from riscv.EnvRISCV import EnvRISCV from riscv.GenThreadRISCV import GenThreadRISCV from riscv.ModifierUtils import PageMemoryAttributeModifier from base.Sequence import Sequence class MainSequence(Sequence): def generate(self, **kargs): for instr in [%s]: self.genInstruction(instr, {"NoSkip":1}) def gen_thread_initialization(gen_thread): gen_thread.applyChoiceModifier(PageMemoryAttributeModifier) ## Points to the generator thread initialization function defined in this file, optional GenThreadInitialization = gen_thread_initialization ## Points to the MainSequence defined in this file MainSequenceClass = MainSequence ## Using GenThreadRISCV by default, can be overriden with extended classes GenThreadClass = GenThreadRISCV ## Using EnvRISCV by default, can be overriden with extended classes EnvClass = EnvRISCV """ basic_non_standard_template_str = """from riscv.EnvRISCV import EnvRISCV from riscv.GenThreadRISCV import GenThreadRISCV from riscv.ModifierUtils import PageMemoryAttributeModifier from base.Sequence import Sequence class MainSequence(Sequence): def generate(self, **kargs): for instr in [%s]: if (self.isRegisterReserved("X17", "Write") or self.isRegisterReserved("X16", "Read")): self.genInstruction(instr, {"NoSkip":0}) else: self.genInstruction(instr, {"NoSkip":1}) def gen_thread_initialization(gen_thread): gen_thread.applyChoiceModifier(PageMemoryAttributeModifier) ## Points to the generator thread initialization function defined in this file, optional GenThreadInitialization = gen_thread_initialization ## Points to the MainSequence defined in this file MainSequenceClass = MainSequence ## Using GenThreadRISCV by default, can be overriden with extended classes GenThreadClass = GenThreadRISCV ## Using EnvRISCV by default, can be overriden with extended classes EnvClass = EnvRISCV """

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fde5162ca16843c5abd88a750ab85142dc8aca19

105

py

Python

5 kyu/Not very secure/Not very secure.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

5 kyu/Not very secure/Not very secure.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

5 kyu/Not very secure/Not very secure.py

anthonyjatoba/codewars

76b0d66dd1ba76a4d136b658920cdf85fd5c4b06

[ "MIT" ]

null

import re def alphanumeric(password): return True if re.match('^[a-zA-Z0-9]+$', password) else False

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fdfb4ce4e3c3d33ec652d33f1c240fe8e53ba6cb

22,657

py

Python

sdk/communication/azure-communication-callingserver/tests/test_callingserver_client_async.py

zihzhan-msft/azure-sdk-for-python

f4b3484dbf75ec9db1f0ade2ca568c9bd538d62e

[ "MIT" ]

null

sdk/communication/azure-communication-callingserver/tests/test_callingserver_client_async.py

zihzhan-msft/azure-sdk-for-python

f4b3484dbf75ec9db1f0ade2ca568c9bd538d62e

[ "MIT" ]

null

sdk/communication/azure-communication-callingserver/tests/test_callingserver_client_async.py

zihzhan-msft/azure-sdk-for-python

f4b3484dbf75ec9db1f0ade2ca568c9bd538d62e

[ "MIT" ]

null

# ------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- import pytest import utils._test_mock_utils_async as _mock_utils_async import utils._test_constants as _test_constants from typing import List from parameterized import parameterized from azure.communication.callingserver import ( CommunicationIdentifier, CallLocator, CallMediaType, CallingEventSubscriptionType, CallRejectReason ) from utils._unit_test_utils import CallingServerUnitTestUtils @parameterized.expand(CallingServerUnitTestUtils.data_source_test_create_connection()) @pytest.mark.asyncio async def test_create_connection_succeed( test_name, # type: str source_user, # type: CommunicationIdentifier target_users, # type: List[CommunicationIdentifier] callback_uri, # type: str requested_media_types, # type: List[CallMediaType] requested_call_events, # type: List[CallingEventSubscriptionType] use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=201, payload=_test_constants.CreateOrJoinCallPayload, use_managed_identity = use_managed_identity ) call_connection = await calling_server_client.create_call_connection( source_user, target_users, callback_uri, requested_media_types, requested_call_events ) assert call_connection.call_connection_id == _test_constants.CALL_ID @parameterized.expand(CallingServerUnitTestUtils.data_source_test_create_connection()) @pytest.mark.asyncio async def test_create_connection_failed( test_name, # type: str source_user, # type: CommunicationIdentifier target_users, # type: List[CommunicationIdentifier] callback_uri, # type: str requested_media_types, # type: List[CallMediaType] requested_call_events, # type: List[CallingEventSubscriptionType] use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.create_call_connection( source_user, target_users, callback_uri, requested_media_types, requested_call_events ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_join_call()) @pytest.mark.asyncio async def test_join_call_succeed( test_name, # type: str call_locator, # type: CallLocator source_user, # type: CommunicationIdentifier callback_uri, # type: str requested_media_types, # type: List[CallMediaType] requested_call_events, # type: List[CallingEventSubscriptionType] use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=_test_constants.CreateOrJoinCallPayload, use_managed_identity = use_managed_identity ) call_connection = await calling_server_client.join_call( call_locator, source_user, callback_uri, requested_media_types, requested_call_events ) assert call_connection.call_connection_id == _test_constants.CALL_ID @parameterized.expand(CallingServerUnitTestUtils.data_source_test_join_call()) @pytest.mark.asyncio async def test_join_call_failed( test_name, # type: str call_locator, # type: CallLocator source_user, # type: CommunicationIdentifier callback_uri, # type: str requested_media_types, # type: List[CallMediaType] requested_call_events, # type: List[CallingEventSubscriptionType] use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.join_call( call_locator, source_user, callback_uri, requested_media_types, requested_call_events ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_answer_call()) @pytest.mark.asyncio async def test_answer_call_succeed( test_name, # type: str incoming_call_context, # type: str callback_uri, # type: str requested_media_types, # type: List[CallMediaType] requested_call_events, # type: List[CallingEventSubscriptionType] use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=_test_constants.AnswerCallResponsePayload, use_managed_identity = use_managed_identity ) result = await calling_server_client.answer_call( incoming_call_context, callback_uri=callback_uri, requested_media_types=requested_media_types, requested_call_events=requested_call_events ) CallingServerUnitTestUtils.verify_answer_call_result(result) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_answer_call()) @pytest.mark.asyncio async def test_answer_call_failed( test_name, # type: str incoming_call_context, # type: str callback_uri, # type: str requested_media_types, # type: List[CallMediaType] requested_call_events, # type: List[CallingEventSubscriptionType] use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.answer_call( incoming_call_context=incoming_call_context, callback_uri=callback_uri, requested_media_types=requested_media_types, requested_call_events=requested_call_events ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_reject_call()) @pytest.mark.asyncio async def test_reject_call_succeed( test_name, # type: str incoming_call_context, # type: str call_reject_reason, # type: CallRejectReason use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=None, use_managed_identity = use_managed_identity ) await calling_server_client.reject_call( incoming_call_context=incoming_call_context, call_reject_reason=call_reject_reason ) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_reject_call()) @pytest.mark.asyncio async def test_reject_call_failed( test_name, # type: str incoming_call_context, # type: str call_reject_reason, # type: CallRejectReason use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.reject_call( incoming_call_context=incoming_call_context, call_reject_reason=call_reject_reason ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_redirect_call()) @pytest.mark.asyncio async def test_redirect_call_succeed( test_name, # type: str incoming_call_context, # type: str target, # type: CommunicationIdentifier use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=None, use_managed_identity = use_managed_identity ) await calling_server_client.redirect_call( incoming_call_context=incoming_call_context, target=target ) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_redirect_call()) @pytest.mark.asyncio async def test_redirect_call_failed( test_name, # type: str incoming_call_context, # type: str target, # type: CommunicationIdentifier use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.redirect_call( incoming_call_context=incoming_call_context, target=target ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_play_audio()) @pytest.mark.asyncio async def test_play_audio_succeed( test_name, # type: str call_locator, # type: CallLocator audio_url, # type: str is_looped, # type: bool audio_file_id, # type: str callback_uri, # type: str operation_context, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=_test_constants.PlayAudioResponsePayload, use_managed_identity=use_managed_identity ) result = await calling_server_client.play_audio( call_locator, audio_url, is_looped, audio_file_id = audio_file_id, callback_uri = callback_uri, operation_context = operation_context ) CallingServerUnitTestUtils.verify_play_audio_result(result) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_play_audio()) @pytest.mark.asyncio async def test_play_audio_failed( test_name, # type: str call_locator, # type: CallLocator audio_url, # type: str is_looped, # type: bool audio_file_id, # type: str callback_uri, # type: str operation_context, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.play_audio( call_locator, audio_url, is_looped, audio_file_id = audio_file_id, callback_uri = callback_uri, operation_context = operation_context ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_play_audio_to_participant()) @pytest.mark.asyncio async def test_play_audio_to_participant_succeed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier audio_url, # type: str is_looped, # type: bool audio_file_id, # type: str callback_uri, # type: str operation_context, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=_test_constants.PlayAudioResponsePayload, use_managed_identity=use_managed_identity ) result = await calling_server_client.play_audio_to_participant( call_locator, participant, audio_url, is_looped, audio_file_id = audio_file_id, callback_uri = callback_uri, operation_context = operation_context ) CallingServerUnitTestUtils.verify_play_audio_result(result) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_play_audio_to_participant()) @pytest.mark.asyncio async def test_play_audio_to_participant_failed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier audio_url, # type: str is_looped, # type: bool audio_file_id, # type: str callback_uri, # type: str operation_context, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.play_audio_to_participant( call_locator, participant, audio_url, is_looped, audio_file_id = audio_file_id, callback_uri = callback_uri, operation_context = operation_context ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_add_participant()) @pytest.mark.asyncio async def test_add_participant_succeed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier callback_uri, # type: str alternate_caller_id, # type: str operation_context, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=_test_constants.AddParticipantResultPayload, use_managed_identity=use_managed_identity ) result = await calling_server_client.add_participant( call_locator, participant, callback_uri, alternate_caller_id=alternate_caller_id, operation_context=operation_context ) CallingServerUnitTestUtils.verify_add_participant_result(result) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_add_participant()) @pytest.mark.asyncio async def test_add_participant_failed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier callback_uri, # type: str alternate_caller_id, # type: str operation_context, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.add_participant( call_locator, participant, callback_uri, alternate_caller_id=alternate_caller_id, operation_context=operation_context ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_remove_participant_with_call_locator()) @pytest.mark.asyncio async def test_remove_participant_succeed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=202, payload=None, use_managed_identity=use_managed_identity ) await calling_server_client.remove_participant( call_locator, participant ) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_remove_participant_with_call_locator()) @pytest.mark.asyncio async def test_remove_participant_failed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.remove_participant( call_locator, participant ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_list_participants_with_call_locator()) @pytest.mark.asyncio async def test_list_participants_succeed( test_name, # type: str call_locator, # type: CallLocator use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=200, payload=_test_constants.GetParticipantsResponsePayload, use_managed_identity=use_managed_identity ) result = await calling_server_client.list_participants( call_locator ) CallingServerUnitTestUtils.verify_list_participants_result(result) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_list_participants_with_call_locator()) @pytest.mark.asyncio async def test_list_participants_failed( test_name, # type: str call_locator, # type: CallLocator use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.list_participants( call_locator ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_get_participant_with_call_locator()) @pytest.mark.asyncio async def test_get_participant_succeed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=200, payload=_test_constants.GetParticipantResponsePayload, use_managed_identity=use_managed_identity ) result = await calling_server_client.get_participant( call_locator, participant=participant ) CallingServerUnitTestUtils.verify_get_participant_result(result) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_get_participant_with_call_locator()) @pytest.mark.asyncio async def test_get_participant_failed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.get_participant( call_locator, participant=participant ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_cancel_media_operation()) @pytest.mark.asyncio async def test_cancel_media_operation_succeed( test_name, # type: str call_locator, # type: CallLocator media_operation_id, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=200, payload=None, use_managed_identity=use_managed_identity ) await calling_server_client.cancel_media_operation( call_locator, media_operation_id ) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_cancel_media_operation()) @pytest.mark.asyncio async def test_cancel_media_operation_failed( test_name, # type: str call_locator, # type: CallLocator media_operation_id, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.cancel_media_operation( call_locator, media_operation_id ) except: raised = True assert raised == True @parameterized.expand(CallingServerUnitTestUtils.data_source_test_cancel_participant_media_operation_with_callLocator()) @pytest.mark.asyncio async def test_cancel_participant_media_operation( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier media_operation_id, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=200, payload=None, use_managed_identity=use_managed_identity ) await calling_server_client.cancel_participant_media_operation( call_locator, participant, media_operation_id ) @parameterized.expand(CallingServerUnitTestUtils.data_source_test_cancel_participant_media_operation_with_callLocator()) @pytest.mark.asyncio async def test_cancel_participant_media_operation_failed( test_name, # type: str call_locator, # type: CallLocator participant, # type: CommunicationIdentifier media_operation_id, # type: str use_managed_identity = False # type: bool ): calling_server_client = _mock_utils_async.create_mock_calling_server_client( status_code=404, payload=_test_constants.ErrorPayload, use_managed_identity = use_managed_identity ) raised = False try: await calling_server_client.cancel_participant_media_operation( call_locator, participant, media_operation_id ) except: raised = True assert raised == True

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7

8bfb4573ac4f556160bc85ccf5baf960bf3d9278

161

py

Python

mmdetect/configs/cascade_rcnn_r50_fpn_1x_coco.py

aydindemircioglu/knee.lat

555725222f860d4ad8fea7452685803d9e323d43

[ "MIT" ]

null

mmdetect/configs/cascade_rcnn_r50_fpn_1x_coco.py

aydindemircioglu/knee.lat

555725222f860d4ad8fea7452685803d9e323d43

[ "MIT" ]

null

mmdetect/configs/cascade_rcnn_r50_fpn_1x_coco.py

aydindemircioglu/knee.lat

555725222f860d4ad8fea7452685803d9e323d43

[ "MIT" ]

null

_base_ = [ './cascade_rcnn_r50_fpn.py', '/mmdetection/configs/_base_/datasets/coco_detection.py', '/mmdetection/configs/_base_/default_runtime.py' ]

26.833333

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7

e37ef428803efe0f80a87e7678a1c535d9d7d89d

2,151

py

Python

smart/wavelength_calibration/cal_param.py

Lingfeng-Wei/smart

2316e50bfb6f050d5dcdd0ee1e5eab6831e8a669

[ "MIT" ]

10

2020-01-21T09:09:54.000Z

2022-02-12T18:24:02.000Z

smart/wavelength_calibration/cal_param.py

Lingfeng-Wei/smart

2316e50bfb6f050d5dcdd0ee1e5eab6831e8a669

[ "MIT" ]

9

2020-02-07T19:03:11.000Z

2022-02-07T01:21:56.000Z

smart/wavelength_calibration/cal_param.py

Lingfeng-Wei/smart

2316e50bfb6f050d5dcdd0ee1e5eab6831e8a669

[ "MIT" ]

2

2021-07-22T21:54:39.000Z

2021-10-11T05:16:53.000Z

## define the telluric wavelength calibration parameters for each orders import numpy as np cal_param_nirspec = { '30':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '31':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '32':{'xcorr_range':40, 'outlier_rej':3., 'pixel_range_start':10, 'pixel_range_end':-60 }, '33':{'xcorr_range':25, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-50 }, '34':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-50 }, '35':{'xcorr_range':5, 'outlier_rej':2., 'pixel_range_start':10, 'pixel_range_end':-10}, '36':{'xcorr_range':10, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-50 }, '37':{'xcorr_range':10, 'outlier_rej':2.5, 'pixel_range_start':50, 'pixel_range_end':-20 }, '38':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':50, 'pixel_range_end':-20 }, '39':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':50, 'pixel_range_end':-20 }, '55':{'xcorr_range':5, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-90 }, '56':{'xcorr_range':5, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-30 }, '57':{'xcorr_range':20, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '58':{'xcorr_range':15, 'outlier_rej':2., 'pixel_range_start':0, 'pixel_range_end':-30 }, '59':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '60':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':5, 'pixel_range_end':-5 }, '61':{'xcorr_range':10, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '62':{'xcorr_range':10, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '63':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '64':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '65':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':0, 'pixel_range_end':-1 }, '66':{'xcorr_range':15, 'outlier_rej':3., 'pixel_range_start':10, 'pixel_range_end':-1 }, }

79.666667

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0.25076

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0.851824

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0.800912

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0.728723

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null

0

9

8b4d6700b13d3e3a327cbb4bcbb40f50b6b525f6

2,588

py

Python

test/pyaz/acr/token/__init__.py

bigdatamoore/py-az-cli

54383a4ee7cc77556f6183e74e992eec95b28e01

[ "MIT" ]

null

test/pyaz/acr/token/__init__.py

bigdatamoore/py-az-cli

54383a4ee7cc77556f6183e74e992eec95b28e01

[ "MIT" ]

9

2021-09-24T16:37:24.000Z

2021-12-24T00:39:19.000Z

test/pyaz/acr/token/__init__.py

bigdatamoore/py-az-cli

54383a4ee7cc77556f6183e74e992eec95b28e01

[ "MIT" ]

null

import json, subprocess from ... pyaz_utils import get_cli_name, get_params def create(registry, name, scope_map=None, repository=None, gateway=None, status=None, resource_group=None, no_passwords=None, expiration=None, expiration_in_days=None): params = get_params(locals()) command = "az acr token create " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def delete(registry, name, yes=None, resource_group=None): params = get_params(locals()) command = "az acr token delete " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def update(registry, name, scope_map=None, status=None, resource_group=None): params = get_params(locals()) command = "az acr token update " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def show(registry, name, resource_group=None): params = get_params(locals()) command = "az acr token show " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr) def list(registry, resource_group=None): params = get_params(locals()) command = "az acr token list " + params print(command) output = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout = output.stdout.decode("utf-8") stderr = output.stderr.decode("utf-8") if stdout: return json.loads(stdout) print(stdout) else: raise Exception(stderr) print(stderr)

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169

0.664606

321

2,588

5.299065

0.165109

0.082305

0.058789

0.05585

0.874192

0.848325

0.824221

0.79953

0

0.004941

0.217929

2,588

73

170

35.452055

0.835474

0

0.820896

0

0.056414

0

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false

0.014925

0.029851

0

0.179104

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0

null

0

1

0

1

0

null

0

7

8b8b94ca714d1629960c4b4fd6767d39ce6ccf8c

14,452

py

Python

tests/services/test_risk_profile_service.py

thiagosalvatore/origin-takehome

5a348099d03dd518f495a9f3a9217120e8cc195d

[ "Apache-2.0" ]

null

tests/services/test_risk_profile_service.py

thiagosalvatore/origin-takehome

5a348099d03dd518f495a9f3a9217120e8cc195d

[ "Apache-2.0" ]

null

tests/services/test_risk_profile_service.py

thiagosalvatore/origin-takehome

5a348099d03dd518f495a9f3a9217120e8cc195d

[ "Apache-2.0" ]

null

from datetime import datetime from origin_takehome.services.risk_profile import RiskProfileService def test_calculate_base_score_should_return_3(): profile = { "age": 35, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 1500, "marital_status": "married", "risk_questions": [1, 1, 1], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_base_score() assert score == { "auto": 3, "disability": 3, "home": 3, "life": 3 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_base_score_should_return_1(): profile = { "age": 35, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 1500, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_base_score() assert score == { "auto": 1, "disability": 1, "home": 1, "life": 1 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_age_score_should_deduct_one(): profile = { "age": 35, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 1500, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_age_score() assert score == { "auto": -1, "disability": -1, "home": -1, "life": -1 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_age_score_should_deduct_two(): profile = { "age": 25, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 1500, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_age_score() assert score == { "auto": -2, "disability": -2, "home": -2, "life": -2 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_age_score_should_be_ineligible_due_to_age(): profile = { "age": 61, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 1500, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_age_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "ineligible", "home": "economic", "life": "ineligible" } def test_calculate_income_score_high_should_deduct_one_from_everything(): profile = { "age": 35, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 201000, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_income_score() assert score == { "auto": -1, "disability": -1, "home": -1, "life": -1 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_income_score_low_should_do_nothing(): profile = { "age": 35, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 20, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_income_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_income_score_no_income_should_be_ineligible(): profile = { "age": 35, "dependents": 1, "house": {"ownership_status": "wrong"}, "income": 0, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_income_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "ineligible", "home": "economic", "life": "economic" } def test_calculate_house_score_without_house_should_be_ineligible(): profile = { "age": 35, "dependents": 1, "income": 201000, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_house_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "ineligible", "life": "economic" } def test_calculate_house_score_mortgaged_house_should_add_points(): profile = { "age": 35, "dependents": 1, "income": 201000, "house": {"ownership_status": "mortgaged"}, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_house_score() assert score == { "auto": 0, "disability": 1, "home": 1, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_house_score_owned_house_should_do_nothing(): profile = { "age": 35, "dependents": 1, "income": 201000, "marital_status": "married", "house": {"ownership_status": "owned"}, "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_house_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_dependent_score_zero_dependents_should_do_nothing(): profile = { "age": 35, "dependents": 0, "income": 201000, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_dependent_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_dependent_score_two_dependents_should_add_one(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_dependent_score() assert score == { "auto": 0, "disability": 1, "home": 0, "life": 1 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_marital_status_score_married_should_change_score(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_relationship_score() assert score == { "auto": 0, "disability": -1, "home": 0, "life": 1 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_marital_status_score_single_should_not_change_score(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "single", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) score = rp_service.calculate_relationship_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_vehicle_status_score_without_vehicle_should_be_ineligible(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "single", "risk_questions": [0, 1, 0], } rp_service = RiskProfileService(profile) score = rp_service.calculate_vehicle_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "ineligible", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_vehicle_status_score_old_vehicle_should_not_change_score(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "single", "risk_questions": [0, 1, 0], "vehicle": {"year": 1990} } rp_service = RiskProfileService(profile) score = rp_service.calculate_vehicle_score() assert score == { "auto": 0, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_calculate_vehicle_status_score_new_vehicle_should_add_one_auto(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "single", "risk_questions": [0, 1, 0], "vehicle": {"year": datetime.now().year - 1} } rp_service = RiskProfileService(profile) score = rp_service.calculate_vehicle_score() assert score == { "auto": 1, "disability": 0, "home": 0, "life": 0 } assert rp_service.risk_profile == { "auto": "economic", "disability": "economic", "home": "economic", "life": "economic" } def test_set_risk_from_score_should_work_without_ineligible(): rp_service = RiskProfileService(None) rp_service.score = { "auto": 3, "disability": 2, "home": 1, "life": 0 } profile = rp_service.set_risk_from_score() assert profile == { "auto": "responsible", "disability": "regular", "home": "regular", "life": "economic" } def test_set_risk_from_score_should_work_with_ineligible(): rp_service = RiskProfileService(None) rp_service.score = { "auto": 3, "disability": 2, "home": 1, "life": 0 } rp_service.risk_profile["life"] = "ineligible" profile = rp_service.set_risk_from_score() assert profile == { "auto": "responsible", "disability": "regular", "home": "regular", "life": "ineligible" } def test_calculate_risk_profile_should_work_without_income(): profile = { "age": 35, "dependents": 2, "house": {"ownership_status": "owned"}, "income": 0, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) profile = rp_service.calculate_risk_profile() assert profile == { "auto": "regular", "disability": "ineligible", "home": "economic", "life": "regular" } def test_calculate_risk_profile_should_work_without_house(): profile = { "age": 35, "dependents": 2, "income": 1000, "marital_status": "married", "risk_questions": [0, 1, 0], "vehicle": {"year": 2018} } rp_service = RiskProfileService(profile) profile = rp_service.calculate_risk_profile() assert profile == { "auto": "regular", "disability": "economic", "home": "ineligible", "life": "regular" } def test_calculate_risk_profile_should_work_without_vehicle(): profile = { "age": 35, "dependents": 2, "income": 201000, "marital_status": "married", "house": {"ownership_status": "owned"}, "risk_questions": [0, 1, 0] } rp_service = RiskProfileService(profile) profile = rp_service.calculate_risk_profile() assert profile == { "auto": "ineligible", "disability": "economic", "home": "economic", "life": "regular" }

24.167224

79

0.54297

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0.083055

0.095493

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0.911061

0.907048

0.882172

0.859569

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0.306947

14,452

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false

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null

0

1

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1

0

null

0

7

8b8d7fc9ab3fa3b20c9a4e1fbfe709640a333cf6

43,801

py

Python

src/vigorish/data/json_storage.py

a-luna/vigorish

6cede5ced76c7d2c9ad0aacdbd2b18c2f1ee4ee6

[ "MIT" ]

2

2021-07-15T13:53:33.000Z

2021-07-25T17:03:29.000Z

src/vigorish/data/json_storage.py

a-luna/vigorish

6cede5ced76c7d2c9ad0aacdbd2b18c2f1ee4ee6

[ "MIT" ]

650

2019-05-18T07:00:12.000Z

2022-01-21T19:38:55.000Z

src/vigorish/data/json_storage.py

a-luna/vigorish

6cede5ced76c7d2c9ad0aacdbd2b18c2f1ee4ee6

[ "MIT" ]

2

2020-03-28T21:01:31.000Z

2022-01-06T05:16:11.000Z

"""Functions for reading and writing files.""" import json from vigorish.enums import DataSet, LocalFileTask, S3FileTask, VigFile from vigorish.util.dt_format_strings import HTTP_TIME from vigorish.util.result import Result from vigorish.util.string_helpers import ( validate_bbref_game_id, validate_brooks_game_id, validate_pitch_app_id, ) from vigorish.util.sys_helpers import get_last_mod_time_utc class JsonStorage: """Perform CRUD operations on JSON files stored locally and/or in S3.""" def __init__(self, config, file_helper): self.config = config self.file_helper = file_helper def save_json(self, data_set, parsed_data): local_filepath = None s3_object_key = None result_local = Result.Ok() result_s3 = Result.Ok() if self.json_stored_local_folder(VigFile.PARSED_JSON, data_set): result_local = self.save_json_local(data_set, parsed_data) if result_local.success: local_filepath = result_local.value if self.json_stored_s3(VigFile.PARSED_JSON, data_set): # pragma: no cover result_s3 = self.save_json_s3(data_set, parsed_data) if result_s3.success: s3_object_key = result_s3.value result = Result.Combine([result_local, result_s3]) if result.failure: return result return Result.Ok({"local_filepath": local_filepath, "s3_object_key": s3_object_key}) def json_stored_local_folder(self, file_type, data_set): return self.file_helper.check_file_stored_local(file_type, data_set) def save_json_local(self, data_set, parsed_data): save_json_local_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.save_json_brooks_games_for_date_local_file, DataSet.BROOKS_PITCH_LOGS: self.save_json_brooks_pitch_logs_for_game_local_file, DataSet.BROOKS_PITCHFX: self.save_json_brooks_pitchfx_log_local_file, DataSet.BBREF_GAMES_FOR_DATE: self.save_json_bbref_games_for_date_local_file, DataSet.BBREF_BOXSCORES: self.save_json_bbref_boxscore_local_file, } return save_json_local_dict[data_set](parsed_data) def save_json_brooks_games_for_date_local_file(self, games_for_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=games_for_date.game_date, scraped_data=games_for_date, ) def save_json_brooks_pitch_logs_for_game_local_file(self, pitch_logs_for_game): result = validate_brooks_game_id(pitch_logs_for_game.bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], scraped_data=pitch_logs_for_game, bb_game_id=pitch_logs_for_game.bb_game_id, ) def save_json_brooks_pitchfx_log_local_file(self, pitchfx_log): result = validate_pitch_app_id(pitchfx_log.pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], scraped_data=pitchfx_log, pitch_app_id=pitchfx_log.pitch_app_id, ) def save_json_bbref_games_for_date_local_file(self, games_for_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=games_for_date.game_date, scraped_data=games_for_date, ) def save_json_bbref_boxscore_local_file(self, boxscore): return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=boxscore.game_date, scraped_data=boxscore, bbref_game_id=boxscore.bbref_game_id, ) def json_stored_s3(self, file_type, data_set): # pragma: no cover return self.file_helper.check_file_stored_s3(file_type, data_set) def save_json_s3(self, data_set, parsed_data): # pragma: no cover save_json_s3_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.save_json_brooks_games_for_date_s3, DataSet.BROOKS_PITCH_LOGS: self.save_json_brooks_pitch_logs_for_game_s3, DataSet.BROOKS_PITCHFX: self.save_json_brooks_pitchfx_log_s3, DataSet.BBREF_GAMES_FOR_DATE: self.save_json_bbref_games_for_date_s3, DataSet.BBREF_BOXSCORES: self.save_json_bbref_boxscore_s3, } return save_json_s3_dict[data_set](parsed_data) def save_json_brooks_games_for_date_s3(self, games_for_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=games_for_date.game_date, scraped_data=games_for_date, ) def save_json_brooks_pitch_logs_for_game_s3(self, pitch_logs_for_game): # pragma: no cover result = validate_brooks_game_id(pitch_logs_for_game.bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], scraped_data=pitch_logs_for_game, bb_game_id=pitch_logs_for_game.bb_game_id, ) def save_json_brooks_pitchfx_log_s3(self, pitchfx_log): # pragma: no cover result = validate_pitch_app_id(pitchfx_log.pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], scraped_data=pitchfx_log, pitch_app_id=pitchfx_log.pitch_app_id, ) def save_json_bbref_games_for_date_s3(self, games_for_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=games_for_date.game_date, scraped_data=games_for_date, ) def save_json_bbref_boxscore_s3(self, boxscore): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=boxscore.game_date, scraped_data=boxscore, bbref_game_id=boxscore.bbref_game_id, ) def save_patch_list(self, data_set, patch_list): local_filepath = None s3_object_key = None result_local = Result.Ok() result_s3 = Result.Ok() if self.json_stored_local_folder(VigFile.PATCH_LIST, data_set): result_local = self.save_patch_list_local(data_set, patch_list) if result_local.success: local_filepath = result_local.value if self.json_stored_s3(VigFile.PATCH_LIST, data_set): # pragma: no cover result_s3 = self.save_patch_list_s3(data_set, patch_list) if result_s3.success: s3_object_key = result_s3.value result = Result.Combine([result_local, result_s3]) if result.failure: return result return Result.Ok({"local_filepath": local_filepath, "s3_object_key": s3_object_key}) def save_patch_list_local(self, data_set, patch_list): save_patch_list_local_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.save_brooks_games_for_date_patch_list_local_file, DataSet.BROOKS_PITCHFX: self.save_brooks_pitchfx_patch_list_local_file, DataSet.BBREF_GAMES_FOR_DATE: self.save_bbref_games_for_date_patch_list_local_file, DataSet.BBREF_BOXSCORES: self.save_bbref_boxscore_patch_list_local_file, } save_patch_list_for_data_set = save_patch_list_local_dict.get(data_set) return save_patch_list_for_data_set(patch_list) if save_patch_list_for_data_set else None def save_brooks_games_for_date_patch_list_local_file(self, patch_list): return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=patch_list.game_date, scraped_data=patch_list, ) def save_brooks_pitchfx_patch_list_local_file(self, patch_list): result = validate_bbref_game_id(patch_list.url_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], scraped_data=patch_list, bbref_game_id=patch_list.url_id, ) def save_bbref_games_for_date_patch_list_local_file(self, patch_list): return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=patch_list.game_date, scraped_data=patch_list, ) def save_bbref_boxscore_patch_list_local_file(self, patch_list): return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PATCH_LIST, game_date=patch_list.game_date, scraped_data=patch_list, pitch_app_id=patch_list.url_id, ) def save_patch_list_s3(self, data_set, patch_list): # pragma: no cover save_patch_list_s3_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.save_brooks_games_for_date_patch_list_s3, DataSet.BROOKS_PITCHFX: self.save_brooks_pitchfx_patch_list_s3, DataSet.BBREF_GAMES_FOR_DATE: self.save_bbref_games_for_date_patch_list_s3, DataSet.BBREF_BOXSCORES: self.save_bbref_boxscore_patch_list_s3, } save_patch_list_for_data_set = save_patch_list_s3_dict.get(data_set) return save_patch_list_for_data_set(patch_list) if save_patch_list_for_data_set else None def save_brooks_games_for_date_patch_list_s3(self, patch_list): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=patch_list.game_date, scraped_data=patch_list, ) def save_brooks_pitchfx_patch_list_s3(self, patch_list): # pragma: no cover result = validate_bbref_game_id(patch_list.url_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], scraped_data=patch_list, bbref_game_id=patch_list.url_id, ) def save_bbref_games_for_date_patch_list_s3(self, patch_list): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=patch_list.game_date, scraped_data=patch_list, ) def save_bbref_boxscore_patch_list_s3(self, patch_list): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PATCH_LIST, game_date=patch_list.game_date, scraped_data=patch_list, pitch_app_id=patch_list.url_id, ) def save_combined_game_data(self, combined_data): local_filepath = None s3_object_key = None result_local = Result.Ok() result_s3 = Result.Ok() if self.json_stored_local_folder(VigFile.COMBINED_GAME_DATA, DataSet.ALL): result_local = self.save_combined_game_data_local_file(combined_data) if result_local.success: local_filepath = result_local.value if self.json_stored_s3(VigFile.COMBINED_GAME_DATA, DataSet.ALL): # pragma: no cover result_s3 = self.save_combined_game_data_s3(combined_data) if result_s3.success: s3_object_key = result_s3.value result = Result.Combine([result_local, result_s3]) if result.failure: return result return Result.Ok({"local_filepath": local_filepath, "s3_object_key": s3_object_key}) def save_combined_game_data_local_file(self, combined_data): result = validate_bbref_game_id(combined_data["bbref_game_id"]) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.WRITE_FILE, data_set=DataSet.ALL, file_type=VigFile.COMBINED_GAME_DATA, game_date=game_dict["game_date"], scraped_data=combined_data, bbref_game_id=combined_data["bbref_game_id"], ) def save_combined_game_data_s3(self, combined_data): # pragma: no cover result = validate_bbref_game_id(combined_data["bbref_game_id"]) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.UPLOAD, data_set=DataSet.ALL, file_type=VigFile.COMBINED_GAME_DATA, game_date=game_dict["game_date"], scraped_data=combined_data, bbref_game_id=combined_data["bbref_game_id"], ) def get_combined_game_data(self, bbref_game_id): if self.json_stored_local_folder(VigFile.COMBINED_GAME_DATA, DataSet.ALL): result = self.decode_combined_game_data_local_file(bbref_game_id) if result.success: return result.value if self.json_stored_s3(VigFile.COMBINED_GAME_DATA, DataSet.ALL): # pragma: no cover result = self.decode_combined_game_data_s3(bbref_game_id) if result.success: return result.value return None def decode_combined_game_data_local_file(self, bbref_game_id): result = self.get_combined_game_data_local_file(bbref_game_id) if result.failure: return result filepath = result.value try: json_dict = json.loads(filepath.read_text()) json_dict["last_modified"] = get_last_mod_time_utc(filepath).strftime(HTTP_TIME) return Result.Ok(json_dict) except Exception as e: error = f"Error: {repr(e)}" return Result.Fail(error) def get_combined_game_data_local_file(self, bbref_game_id): result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.ALL, file_type=VigFile.COMBINED_GAME_DATA, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def decode_combined_game_data_s3(self, bbref_game_id): # pragma: no cover result = self.get_combined_game_data_s3(bbref_game_id) if result.failure: return result filepath = result.value try: json_dict = json.loads(filepath.read_text()) json_dict["last_modified"] = get_last_mod_time_utc(filepath).strftime(HTTP_TIME) return Result.Ok(json_dict) except Exception as e: error = f"Error: {repr(e)}" return Result.Fail(error) def get_combined_game_data_s3(self, bbref_game_id): # pragma: no cover result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.ALL, file_type=VigFile.COMBINED_GAME_DATA, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def get_scraped_data(self, data_set, url_id): if self.json_stored_local_folder(VigFile.PARSED_JSON, data_set): result = self.get_scraped_data_local(data_set, url_id) if result.success: return result.value if self.json_stored_s3(VigFile.PARSED_JSON, data_set): # pragma: no cover result = self.get_scraped_data_s3(data_set, url_id) if result.success: return result.value return None def get_scraped_data_local(self, data_set, url_id): get_scraped_data_local_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.decode_json_brooks_games_for_date_local_file, DataSet.BROOKS_PITCH_LOGS: self.decode_json_brooks_pitch_logs_for_game_local_file, DataSet.BROOKS_PITCHFX: self.decode_json_brooks_pitchfx_log_local_file, DataSet.BBREF_GAMES_FOR_DATE: self.decode_json_bbref_games_for_date_local_file, DataSet.BBREF_BOXSCORES: self.decode_json_bbref_boxscore_local_file, } return get_scraped_data_local_dict[data_set](url_id) def decode_json_brooks_games_for_date_local_file(self, game_date): result = self.get_json_brooks_games_for_date_local_file(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, delete_file=False, ) def get_json_brooks_games_for_date_local_file(self, game_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def decode_json_brooks_pitch_logs_for_game_local_file(self, bb_game_id): result = self.get_json_brooks_pitch_logs_for_game_local_file(bb_game_id) if result.failure: return result result = validate_brooks_game_id(bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bb_game_id=bb_game_id, delete_file=False, ) def get_json_brooks_pitch_logs_for_game_local_file(self, bb_game_id): result = validate_brooks_game_id(bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bb_game_id=bb_game_id, ) def decode_json_brooks_pitchfx_log_local_file(self, pitch_app_id): result = self.get_json_brooks_pitchfx_local_file(pitch_app_id) if result.failure: return result result = validate_pitch_app_id(pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], pitch_app_id=pitch_app_id, delete_file=False, ) def get_json_brooks_pitchfx_local_file(self, pitch_app_id): result = validate_pitch_app_id(pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], pitch_app_id=pitch_app_id, ) def decode_json_bbref_games_for_date_local_file(self, game_date): result = self.get_json_bbref_games_for_date_local_file(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, delete_file=False, ) def get_json_bbref_games_for_date_local_file(self, game_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def decode_json_bbref_boxscore_local_file(self, bbref_game_id): result = self.get_json_bbref_boxscore_local_file(bbref_game_id) if result.failure: return result result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, delete_file=False, ) def get_json_bbref_boxscore_local_file(self, bbref_game_id): result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def get_scraped_data_s3(self, data_set, url_id): # pragma: no cover get_scraped_data_s3_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.decode_json_brooks_games_for_date_s3, DataSet.BROOKS_PITCH_LOGS: self.decode_json_brooks_pitch_logs_for_game_s3, DataSet.BROOKS_PITCHFX: self.decode_json_brooks_pitchfx_log_s3, DataSet.BBREF_GAMES_FOR_DATE: self.decode_json_bbref_games_for_date_s3, DataSet.BBREF_BOXSCORES: self.decode_json_bbref_boxscore_s3, } return get_scraped_data_s3_dict[data_set](url_id) def decode_json_brooks_games_for_date_s3(self, game_date): # pragma: no cover result = self.get_json_brooks_games_for_date_s3(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, delete_file=True, ) def get_json_brooks_games_for_date_s3(self, game_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def decode_json_brooks_pitchfx_log_s3(self, pitch_app_id): # pragma: no cover result = self.get_json_brooks_pitchfx_s3(pitch_app_id) if result.failure: return result result = validate_pitch_app_id(pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], pitch_app_id=pitch_app_id, delete_file=True, ) def decode_json_brooks_pitch_logs_for_game_s3(self, bb_game_id): # pragma: no cover result = self.get_json_brooks_pitch_logs_for_game_s3(bb_game_id) if result.failure: return result result = validate_brooks_game_id(bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bb_game_id=bb_game_id, delete_file=True, ) def get_json_brooks_pitch_logs_for_game_s3(self, bb_game_id): # pragma: no cover result = validate_brooks_game_id(bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bb_game_id=bb_game_id, ) def get_json_brooks_pitchfx_s3(self, pitch_app_id): # pragma: no cover result = validate_pitch_app_id(pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], pitch_app_id=pitch_app_id, ) def decode_json_bbref_games_for_date_s3(self, game_date): # pragma: no cover result = self.get_json_bbref_games_for_date_s3(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, delete_file=True, ) def get_json_bbref_games_for_date_s3(self, game_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def decode_json_bbref_boxscore_s3(self, bbref_game_id): # pragma: no cover result = self.get_json_bbref_boxscore_s3(bbref_game_id) if result.failure: return result result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, delete_file=True, ) def get_json_bbref_boxscore_s3(self, bbref_game_id): # pragma: no cover result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def get_patch_list(self, data_set, url_id): if self.json_stored_local_folder(VigFile.PATCH_LIST, data_set): result = self.get_patch_list_local(data_set, url_id) if result.success: return result.value if self.json_stored_s3(VigFile.PATCH_LIST, data_set): # pragma: no cover result = self.get_patch_list_s3(data_set, url_id) if result.success: return result.value return None def get_patch_list_local(self, data_set, url_id): get_patch_list_local_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.decode_brooks_games_for_date_patch_list_local_file, DataSet.BROOKS_PITCHFX: self.decode_brooks_pitchfx_patch_list_local_file, DataSet.BBREF_GAMES_FOR_DATE: self.decode_bbref_games_for_date_patch_list_local_file, DataSet.BBREF_BOXSCORES: self.decode_bbref_boxscore_patch_list_local_file, } get_patch_list_for_data_set = get_patch_list_local_dict.get(data_set) return get_patch_list_for_data_set(url_id) if get_patch_list_for_data_set else Result.Ok({}) def decode_brooks_games_for_date_patch_list_local_file(self, game_date): result = self.get_brooks_games_for_date_patch_list_local_file(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, delete_file=False, ) def get_brooks_games_for_date_patch_list_local_file(self, game_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, ) def decode_brooks_pitchfx_patch_list_local_file(self, bbref_game_id): result = self.get_brooks_pitchfx_patch_list_local_file(bbref_game_id) if result.failure: return result result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, delete_file=False, ) def get_brooks_pitchfx_patch_list_local_file(self, bbref_game_id): result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def decode_bbref_games_for_date_patch_list_local_file(self, game_date): result = self.get_bbref_games_for_date_patch_list_local_file(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, delete_file=False, ) def get_bbref_games_for_date_patch_list_local_file(self, game_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, ) def decode_bbref_boxscore_patch_list_local_file(self, bbref_game_id): result = self.get_bbref_boxscore_patch_list_local_file(bbref_game_id) if result.failure: return result result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, delete_file=False, ) def get_bbref_boxscore_patch_list_local_file(self, bbref_game_id): result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.READ_FILE, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def get_patch_list_s3(self, data_set, url_id): # pragma: no cover get_patch_list_s3_dict = { DataSet.BROOKS_GAMES_FOR_DATE: self.decode_brooks_games_for_date_patch_list_s3, DataSet.BROOKS_PITCHFX: self.decode_brooks_pitchfx_patch_list_s3, DataSet.BBREF_GAMES_FOR_DATE: self.decode_bbref_games_for_date_patch_list_s3, DataSet.BBREF_BOXSCORES: self.decode_bbref_boxscore_patch_list_s3, } get_patch_list_for_data_set = get_patch_list_s3_dict.get(data_set) return get_patch_list_for_data_set(url_id) if get_patch_list_for_data_set else None def decode_brooks_games_for_date_patch_list_s3(self, game_date): # pragma: no cover result = self.get_brooks_games_for_date_patch_list_s3(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, delete_file=True, ) def get_brooks_games_for_date_patch_list_s3(self, game_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, ) def decode_brooks_pitchfx_patch_list_s3(self, bbref_game_id): # pragma: no cover result = self.get_brooks_pitchfx_patch_list_local_file(bbref_game_id) if result.failure: return result result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, delete_file=True, ) def get_brooks_pitchfx_patch_list_s3(self, bbref_game_id): # pragma: no cover result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def decode_bbref_games_for_date_patch_list_s3(self, game_date): # pragma: no cover result = self.get_bbref_games_for_date_patch_list_s3(game_date) if result.failure: return result return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, delete_file=True, ) def get_bbref_games_for_date_patch_list_s3(self, game_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PATCH_LIST, game_date=game_date, ) def decode_bbref_boxscore_patch_list_s3(self, bbref_game_id): # pragma: no cover result = self.get_bbref_boxscore_patch_list_s3(bbref_game_id) if result.failure: return result result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DECODE_JSON, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, delete_file=True, ) def get_bbref_boxscore_patch_list_s3(self, bbref_game_id): # pragma: no cover result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DOWNLOAD, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PATCH_LIST, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def delete_brooks_games_for_date_local_file(self, game_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.DELETE_FILE, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def delete_brooks_pitch_logs_for_game_local_file(self, bb_game_id): result = validate_brooks_game_id(bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DELETE_FILE, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bb_game_id=bb_game_id, ) def delete_brooks_pitchfx_log_local_file(self, pitch_app_id): result = validate_pitch_app_id(pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DELETE_FILE, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], pitch_app_id=pitch_app_id, ) def delete_bbref_games_for_date_local_file(self, game_date): return self.file_helper.perform_local_file_task( task=LocalFileTask.DELETE_FILE, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def delete_bbref_boxscore_local_file(self, bbref_game_id): result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_local_file_task( task=LocalFileTask.DELETE_FILE, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def delete_brooks_games_for_date_s3(self, game_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.DELETE, data_set=DataSet.BROOKS_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def delete_brooks_pitch_logs_for_game_s3(self, bb_game_id): # pragma: no cover result = validate_brooks_game_id(bb_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DELETE, data_set=DataSet.BROOKS_PITCH_LOGS, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bb_game_id=bb_game_id, ) def delete_brooks_pitchfx_log_s3(self, pitch_app_id): # pragma: no cover result = validate_pitch_app_id(pitch_app_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DELETE, data_set=DataSet.BROOKS_PITCHFX, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], pitch_app_id=pitch_app_id, ) def delete_bbref_games_for_date_s3(self, game_date): # pragma: no cover return self.file_helper.perform_s3_task( task=S3FileTask.DELETE, data_set=DataSet.BBREF_GAMES_FOR_DATE, file_type=VigFile.PARSED_JSON, game_date=game_date, ) def delete_bbref_boxscore_s3(self, bbref_game_id): # pragma: no cover result = validate_bbref_game_id(bbref_game_id) if result.failure: return result game_dict = result.value return self.file_helper.perform_s3_task( task=S3FileTask.DELETE, data_set=DataSet.BBREF_BOXSCORES, file_type=VigFile.PARSED_JSON, game_date=game_dict["game_date"], bbref_game_id=bbref_game_id, ) def rename_brooks_pitchfx_log(self, old_pitch_app_id, new_pitch_app_id, year): # pragma: no cover result = validate_pitch_app_id(old_pitch_app_id) if result.failure: return result game_dict = result.value old_key = self.file_helper.get_object_key( file_type=VigFile.PARSED_JSON, data_set=DataSet.BROOKS_PITCHFX, game_date=game_dict["game_date"], pitch_app_id=old_pitch_app_id, ) result = validate_pitch_app_id(new_pitch_app_id) if result.failure: return result game_dict = result.value new_key = self.file_helper.get_object_key( file_type=VigFile.PARSED_JSON, data_set=DataSet.BROOKS_PITCHFX, game_date=game_dict["game_date"], pitch_app_id=new_pitch_app_id, ) return self.file_helper.rename_s3_object(old_key, new_key)

41.20508

102

0.67124

5,776

43,801

4.614093

0.020256

0.05043

0.045402

0.053281

0.97077

0.953923

0.939477

0.92687

0.9146

0.894

0

0.005209

0.263624

43,801

1,062

103

41.243879

0.821076

0.020707

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false

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0.026832

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0

null

0

1

0

1

0

null

0

7

8b90fe744d4ba9389235837569f03bb377402e47

2,113

py

Python

mitre_attack/cli/command_groups/relationships.py

check-spelling/mitre-attack

f3be1ccff235593c4277f3b9ec2696757924894b

[ "MIT" ]

1

2022-01-13T06:32:10.000Z

mitre_attack/cli/command_groups/relationships.py

check-spelling/mitre-attack

f3be1ccff235593c4277f3b9ec2696757924894b

[ "MIT" ]

null

mitre_attack/cli/command_groups/relationships.py

check-spelling/mitre-attack

f3be1ccff235593c4277f3b9ec2696757924894b

[ "MIT" ]

1

2022-01-14T00:00:27.000Z

from mitre_attack.api.client import MitreAttack import mitre_attack.cli.click as click @click.group() @click.pass_context def relationships(_): """ Query or count relationships. """ pass @relationships.command() @click.option('--relationship-ids') @click.option('--relationship-types') @click.option('--source-refs') @click.option('--source-ref-types') @click.option('--target-refs') @click.option('--target-ref-types') @click.pass_context def get_relationships( _: click.Context, relationship_ids: str, relationship_types: str, source_refs: str, source_ref_types: str, target_refs: str, target_ref_types: str): api = MitreAttack() for relationship in api.enterprise.iter_relationships( relationship_ids=click.str_to_strs(relationship_ids), relationship_types=click.str_to_strs(relationship_types), source_refs=click.str_to_strs(source_refs), source_ref_types=click.str_to_strs(source_ref_types), target_refs=click.str_to_strs(target_refs), target_ref_types=click.str_to_strs(target_ref_types), ): click.echo(relationship.to_json()) @relationships.command() @click.option('--relationship-ids') @click.option('--relationship-types') @click.option('--source-refs') @click.option('--source-ref-types') @click.option('--target-refs') @click.option('--target-ref-types') @click.pass_context def count_relationships( _: click.Context, relationship_ids: str, relationship_types: str, source_refs: str, source_ref_types: str, target_refs: str, target_ref_types: str): api = MitreAttack() n = api.enterprise.count_relationships( relationship_ids=click.str_to_strs(relationship_ids), relationship_types=click.str_to_strs(relationship_types), source_refs=click.str_to_strs(source_refs), source_ref_types=click.str_to_strs(source_ref_types), target_refs=click.str_to_strs(target_refs), target_ref_types=click.str_to_strs(target_ref_types), ) click.echo(n)

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65

0.702792

266

2,113

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0.142857

0.091429

0.085714

0.12

0.84

0

0.17274

2,113

70

66

30.185714

0.800915

0.013725

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0

0.096712

0

1

0.051724

false

0.068966

0.034483

0

0.086207

0

null

0

1

0

1

0

null

0

1

0

8

8bb37a45f8ee91bc8a6d89438c0021585522e82c

1,850

py

Python

docs/papers/wpmvp14/experiments/rosen_fs.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

1,647

2015-01-13T01:45:38.000Z

2022-03-28T01:23:41.000Z

docs/papers/wpmvp14/experiments/rosen_fs.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

1,116

2015-01-01T09:52:05.000Z

2022-03-18T21:06:40.000Z

docs/papers/wpmvp14/experiments/rosen_fs.py

davidbrochart/pythran

24b6c8650fe99791a4091cbdc2c24686e86aa67c

[ "BSD-3-Clause" ]

180

2015-02-12T02:47:28.000Z

2022-03-14T10:28:18.000Z

import numpy as np #pythran export rosen(float[]) def rosen(x): t0 = 100 * (x[1:] - x[:-1] ** 2) ** 2 t1 = (1 - x[:-1]) ** 2 return np.sum(t0 + t1) #pythran export rosen2(float[]) def rosen2(x): return np.sum(100 * (x[1:] - x[:-1] ** 2) ** 2 + (1 - x[:-1]) ** 2) # a = numpy.arange(100000) # CPython # In [5]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 1.64 ms per loop # # In [6]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 1.38 ms per loop # # In [7]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 1.37 ms per loop # # In [8]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 1.62 ms per loop # # Pythran # In [7]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 494 us per loop # # In [8]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 495 us per loop # # In [9]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 201 us per loop # # In [10]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 201 us per loop # # Pytran SIMD # In [4]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 414 us per loop # # In [6]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 415 us per loop # # In [7]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 244 us per loop # In [5]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 245 us per loop # # # # # Pythran with lazy # In [4]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 287 us per loop # # In [5]: %timeit -n100 rosen.rosen(a) # 100 loops, best of 3: 288 us per loop # # In [6]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 201 us per loop # # In [7]: %timeit -n100 rosen.rosen2(a) # 100 loops, best of 3: 201 us per loop #

25.694444

71

0.550811

321

1,850

3.174455

0.17757

0.157017

0.235525

0.204122

0.756624

0.75368

0.736016

0.701668

0

0.164894

0.288649

1,850

71

72

26.056338

0.609422

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0

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0

null

0

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1

0

1

0

null

0

1

0

1

0

11

4782c161717a5653ea2032866f0d63082400b27f

135

py

Python

mamp_cli/api/mampapi.py

Honda-a/mamp-cli

562b5a3dbac1a2d05b600be7e860c0ec1fa21c1f

[ "Apache-2.0" ]

null

mamp_cli/api/mampapi.py

Honda-a/mamp-cli

562b5a3dbac1a2d05b600be7e860c0ec1fa21c1f

[ "Apache-2.0" ]

3

2019-12-02T02:03:32.000Z

2021-06-02T00:58:01.000Z

mamp_cli/api/mampapi.py

Honda-a/mamp-cli

562b5a3dbac1a2d05b600be7e860c0ec1fa21c1f

[ "Apache-2.0" ]

null

""" api wraper for mamp tools """ from mamp_cli.base import ApiBase from mamp_cli.tools import mamp class MampApi(ApiBase): pass

13.5

33

0.740741

21

135

4.666667

0.619048

0.163265

0.22449

0

0.177778

135

9

34

15

0.882883

0.185185

0

1

0

true

0.25

0.5

0

0.75

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

7

47a95f09fa9707c2dd51520b73118f661597cd68

14,136

py

Python

restApp/migrations/0001_initial.py

ibamacsr/painelmma_api

a11a6cd63e312f09f445b139fcff8c11ab383764

[ "MIT" ]

null

restApp/migrations/0001_initial.py

ibamacsr/painelmma_api

a11a6cd63e312f09f445b139fcff8c11ab383764

[ "MIT" ]

null

restApp/migrations/0001_initial.py

ibamacsr/painelmma_api

a11a6cd63e312f09f445b139fcff8c11ab383764

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models import django.contrib.gis.db.models.fields class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='DailyAlertaAwifs', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('area_km2', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=38)), ('dominio', models.CharField(blank=True, null=True, max_length=200)), ('tipo', models.CharField(blank=True, null=True, max_length=15)), ('uf', models.SmallIntegerField(blank=True, null=True)), ('estado', models.CharField(blank=True, null=True, max_length=2)), ('data_imagem', models.DateTimeField(blank=True, null=True)), ('shape', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('centroide', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('mesid', models.TextField(blank=True, null=True)), ('estagio', models.CharField(blank=True, null=True, max_length=50)), ('periodo_prodes', models.CharField(blank=True, null=True, max_length=10)), ], options={ 'db_table': 'ibama"."vw_alerta_awifs', 'managed': False, }, ), migrations.CreateModel( name='DailyAlertaDeter', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('area_km2', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=38)), ('dominio', models.CharField(blank=True, null=True, max_length=200)), ('tipo', models.CharField(blank=True, null=True, max_length=15)), ('uf', models.SmallIntegerField(blank=True, null=True)), ('estado', models.CharField(blank=True, null=True, max_length=2)), ('data_imagem', models.DateTimeField(blank=True, null=True)), ('shape', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('centroide', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('mesid', models.TextField(blank=True, null=True)), ('estagio', models.CharField(blank=True, null=True, max_length=50)), ('periodo_prodes', models.CharField(blank=True, null=True, max_length=10)), ], options={ 'db_table': 'ibama"."vw_alerta_deter', 'managed': False, }, ), migrations.CreateModel( name='DailyAlertaDeterQualif', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('periodo_prodes', models.CharField(blank=True, null=True, max_length=10)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('mes_ano', models.CharField(blank=True, null=True, max_length=6)), ('cicatriz_fogo', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('corte_raso_deter', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('degradacao_deter', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('alta', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('leve', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('moderada', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('falso_positivo', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('nao_avaliado', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('deter_total', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('total_avaliado', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('porc_area_avaliada', models.SmallIntegerField(blank=True, null=True)), ('mesid', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'ibama"."vw_deter_qualificado', 'managed': False, }, ), migrations.CreateModel( name='DailyAlertaLandsat', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('area_km2', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=38)), ('dominio', models.CharField(blank=True, null=True, max_length=200)), ('tipo', models.CharField(blank=True, null=True, max_length=15)), ('uf', models.SmallIntegerField(blank=True, null=True)), ('estado', models.CharField(blank=True, null=True, max_length=2)), ('data_imagem', models.DateTimeField(blank=True, null=True)), ('shape', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('centroide', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('mesid', models.TextField(blank=True, null=True)), ('estagio', models.CharField(blank=True, null=True, max_length=50)), ('periodo_prodes', models.CharField(blank=True, null=True, max_length=10)), ], options={ 'db_table': 'ibama"."vw_alerta_indicar', 'managed': False, }, ), migrations.CreateModel( name='PublicAlertaDeter', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('area_km2', models.DecimalField(blank=True, null=True, decimal_places=8, max_digits=38)), ('area_ha', models.DecimalField(blank=True, null=True, decimal_places=8, max_digits=38)), ('municipio', models.CharField(blank=True, null=True, max_length=200)), ('dominio', models.CharField(blank=True, null=True, max_length=200)), ('tipo', models.CharField(blank=True, null=True, max_length=15)), ('quinzena', models.CharField(blank=True, null=True, max_length=5)), ('id_des', models.CharField(blank=True, null=True, unique=True, max_length=16)), ('ai', models.IntegerField(blank=True, null=True)), ('tei', models.IntegerField(blank=True, null=True)), ('processo', models.CharField(blank=True, null=True, max_length=20)), ('url', models.CharField(blank=True, null=True, max_length=200)), ('vistoria', models.CharField(blank=True, null=True, max_length=100)), ('resp_vistoria', models.CharField(blank=True, null=True, max_length=150)), ('longitude', models.CharField(blank=True, null=True, max_length=17)), ('latitude', models.CharField(blank=True, null=True, max_length=17)), ('uf', models.SmallIntegerField(blank=True, null=True)), ('estado', models.CharField(blank=True, null=True, max_length=2)), ('obs', models.CharField(blank=True, null=True, max_length=250)), ('id_tablet', models.CharField(blank=True, null=True, max_length=10)), ('data_vist', models.CharField(blank=True, null=True, max_length=50)), ('globalid', models.CharField(blank=True, null=True, max_length=50)), ('dado_final', models.CharField(blank=True, null=True, max_length=1)), ('estagio', models.CharField(blank=True, null=True, max_length=50)), ('data_imagem', models.DateTimeField(blank=True, null=True)), ('shape', django.contrib.gis.db.models.fields.GeometryField(blank=True, null=True, srid=4326)), ('veg_sec', models.CharField(blank=True, null=True, max_length=100)), ('periodo_prodes', models.CharField(blank=True, null=True, max_length=10)), ('mesid', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'ibama"."vw_publica_alerta_deter_por_periodo', 'managed': False, }, ), migrations.CreateModel( name='PublicAlertaDeterQualif', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('periodo_prodes', models.CharField(blank=True, null=True, max_length=10)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('mes_ano', models.CharField(blank=True, null=True, max_length=6)), ('cicatriz_fogo', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('corte_raso_deter', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('degradacao_deter', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('alta', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('leve', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('moderada', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('falso_positivo', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('nao_avaliado', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('deter_total', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('total_avaliado', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=6)), ('porc_area_avaliada', models.SmallIntegerField(blank=True, null=True)), ('mesid', models.TextField(blank=True, null=True)), ], options={ 'db_table': 'ibama"."vw_publica_deter_qualificado', 'managed': False, }, ), migrations.CreateModel( name='TaxaNuvens', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('mes', models.CharField(blank=True, null=True, max_length=10)), ('ano', models.SmallIntegerField(blank=True, null=True)), ('uf', models.CharField(blank=True, null=True, max_length=2)), ('area_km2', models.DecimalField(blank=True, null=True, decimal_places=2, max_digits=10)), ('porc_area_km2', models.DecimalField(blank=True, null=True, decimal_places=0, max_digits=2)), ('dat_cadastro', models.DateTimeField(blank=True, null=True)), ], options={ 'db_table': 'ibama"."taxa_nuvem', 'managed': False, }, ), migrations.CreateModel( name='TaxaNuvensAml', fields=[ ('objectid', models.AutoField(primary_key=True, serialize=False)), ('dat_src', models.DateTimeField(blank=True, null=True)), ('f_area', models.DecimalField(blank=True, null=True, decimal_places=8, max_digits=38)), ('percent', models.DecimalField(blank=True, null=True, decimal_places=8, max_digits=38)), ('mes_maiusc', models.TextField(blank=True, null=True)), ('ano', models.SmallIntegerField(blank=True, null=True)), ], options={ 'db_table': 'ibama"."vw_taxa_nuvem_aml', 'managed': False, }, ), migrations.CreateModel( name='TaxaProdes', fields=[ ('ano_prodes', models.CharField(blank=True, max_length=9, primary_key=True, serialize=False)), ('ac', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('am', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('ap', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('ma', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('mt', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('pa', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('ro', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('rr', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ('to', models.DecimalField(blank=True, decimal_places=2, max_digits=7)), ], options={ 'db_table': 'public"."taxa_prodes', 'managed': False, }, ), ]

62.548673

115

0.586729

1,533

14,136

5.267449

0.101109

0.135975

0.18031

0.235789

0.920867

0.886192

0.859071

0.842724

0.817957

0.745511

0

0.020625

0.262592

14,136

225

116

62.826667

0.754029

0.001486

0

0.671233

0

0.104018

0.017572

0

1

0

false

0

0.013699

0

0.027397

0

null

0

1

0

null

0

8

47e0f3b2f925e0a70aa1a62776cf6313d1a78234

157,366

py

Python

dark2/dark.py

Alpha-Demon404/RE-14

b5b46a9f0eee218f2a642b615c77135c33c6f4ad

[ "MIT" ]

39

2020-02-26T09:44:36.000Z

2022-03-23T00:18:25.000Z

dark2/dark.py

B4BY-DG/reverse-enginnering

b5b46a9f0eee218f2a642b615c77135c33c6f4ad

[ "MIT" ]

15

2020-05-14T10:07:26.000Z

2022-01-06T02:55:32.000Z

dark2/dark.py

B4BY-DG/reverse-enginnering

b5b46a9f0eee218f2a642b615c77135c33c6f4ad

[ "MIT" ]

41

2020-03-16T22:36:38.000Z

2022-03-17T14:47:19.000Z

#Compile By Ariya Saputra #GitHub : https://github.com/Ariya-Coder import marshal 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9a29ea8e2e85a75edd2dae672a10c1d23292c302

192

py

Python

powerline/segments/ipython.py

zhaocai/powerline

8aae1145835b4b2a71f1ed71b81d490e2907bd39

[ "MIT" ]

19

2015-09-01T20:49:16.000Z

2022-01-08T22:13:23.000Z

powerline/segments/ipython.py

zhaocai/powerline

8aae1145835b4b2a71f1ed71b81d490e2907bd39

[ "MIT" ]

null

powerline/segments/ipython.py

zhaocai/powerline

8aae1145835b4b2a71f1ed71b81d490e2907bd39

[ "MIT" ]

6

2019-04-25T03:42:35.000Z

2020-06-05T15:25:23.000Z

# vim:fileencoding=utf-8:noet from powerline.theme import requires_segment_info @requires_segment_info def prompt_count(pl, segment_info): return str(segment_info['ipython'].prompt_count)

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9a4e4cd6f2835fedf68c154e4800c0ef72a66c87

3,230

py

Python

hgapp/profiles/migrations/0014_auto_20210909_1452.py

shadytradesman/The-Contract-Website

d8b353064f91c53ebab951dec784a0a36caba260

[ "Apache-2.0" ]

6

2020-10-03T12:15:05.000Z

2021-10-15T04:43:36.000Z

hgapp/profiles/migrations/0014_auto_20210909_1452.py

shadytradesman/The-Contract-Website

d8b353064f91c53ebab951dec784a0a36caba260

[ "Apache-2.0" ]

99

2020-06-04T17:43:56.000Z

2022-03-12T01:07:20.000Z

hgapp/profiles/migrations/0014_auto_20210909_1452.py

shadytradesman/The-Contract-Website

d8b353064f91c53ebab951dec784a0a36caba260

[ "Apache-2.0" ]

9

2020-06-06T16:39:09.000Z

2020-10-02T16:24:17.000Z

# Generated by Django 2.2.13 on 2021-09-09 14:52 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('profiles', '0013_auto_20210906_2005'), ] operations = [ migrations.AlterField( model_name='profile', name='num_contractors_played', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_deadly_player_games', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_games_gmed', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_gm_kills', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_gm_losses', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_gm_victories', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_gmed_cells', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_gmed_contractors', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_gmed_players', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_golden_ratios', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_played_ringers', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_player_deaths', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_player_games', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_player_losses', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_player_survivals', field=models.IntegerField(blank=True, default=0, null=True), ), migrations.AlterField( model_name='profile', name='num_player_victories', field=models.IntegerField(blank=True, default=0, null=True), ), ]

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d0409900f938da4f18bf6fda521fceb414abb053

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py

Python

ex108/ex107.py

arthurfas123/Curso-De-Python

c4a15d92811bd101a8562d2c3a90fe2d5a3c360d

[ "MIT" ]

null

ex108/ex107.py

arthurfas123/Curso-De-Python

c4a15d92811bd101a8562d2c3a90fe2d5a3c360d

[ "MIT" ]

null

ex108/ex107.py

arthurfas123/Curso-De-Python

c4a15d92811bd101a8562d2c3a90fe2d5a3c360d

[ "MIT" ]

null

# Formatando moedas em python from ex108 import moedas valor = int(input('Valor: ')) taxa = int(input('Taxa % : ')) moedas.linha() print(f'Aumento de {taxa}%: {moedas.formatando(moedas.aumentar(valor, taxa))}') print(f'Menos {taxa}%: {moedas.formatando(moedas.diminuir(valor, taxa))}') print(f'Dobro: {moedas.formatando(moedas.dobro(valor))}') print(f'Metade: {moedas.formatando(moedas.metade(valor))}')

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163

py

Python

wsgi.py

jsugg/locate-apple-devices

69de53064e1f33bb2f890c026eedd3d2654feb2b

[ "MIT" ]

1

2018-05-22T05:34:13.000Z

wsgi.py

jsugg/locate-apple-devices

69de53064e1f33bb2f890c026eedd3d2654feb2b

[ "MIT" ]

null

wsgi.py

jsugg/locate-apple-devices

69de53064e1f33bb2f890c026eedd3d2654feb2b

[ "MIT" ]

null

import sys sys.path.append('api') from handy_tools_apple_devices import apple_devices_handy_tools if __name__ == "__main__": apple_devices_handy_tools.run()

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19035c39832eedd65fa43becfe40113374812209

51,202

py

Python

jamf/api/app_request_preview_api.py

jensenbox/python-jamf

85213085b1064a00375a7aa7df5e33c19f5178eb

[ "RSA-MD" ]

1

2021-04-20T15:28:57.000Z

jamf/api/app_request_preview_api.py

jensenbox/python-jamf

85213085b1064a00375a7aa7df5e33c19f5178eb

[ "RSA-MD" ]

null

jamf/api/app_request_preview_api.py

jensenbox/python-jamf

85213085b1064a00375a7aa7df5e33c19f5178eb

[ "RSA-MD" ]

null

# coding: utf-8 """ Jamf Pro API ## Overview This is a sample Jamf Pro server which allows for usage without any authentication. The Jamf Pro environment which supports the Try it Out functionality does not run the current beta version of Jamf Pro, thus any newly added endpoints will result in an error and should be used soley for documentation purposes. # noqa: E501 The version of the OpenAPI document: 10.25.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from jamf.api_client import ApiClient from jamf.exceptions import ( # noqa: F401 ApiTypeError, ApiValueError ) class AppRequestPreviewApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def v1_app_request_form_input_fields_get(self, **kwargs): # noqa: E501 """Search for Form Input Fields # noqa: E501 Search for form input fields # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_get(async_req=True) >>> result = thread.get() :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: AppRequestFormInputFieldSearchResults """ kwargs['_return_http_data_only'] = True return self.v1_app_request_form_input_fields_get_with_http_info(**kwargs) # noqa: E501 def v1_app_request_form_input_fields_get_with_http_info(self, **kwargs): # noqa: E501 """Search for Form Input Fields # noqa: E501 Search for form input fields # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(AppRequestFormInputFieldSearchResults, status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_form_input_fields_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 200: "AppRequestFormInputFieldSearchResults", } return self.api_client.call_api( '/v1/app-request/form-input-fields', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_form_input_fields_id_delete(self, id, **kwargs): # noqa: E501 """Remove specified Form Input Field record # noqa: E501 Removes specified form input field record # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_id_delete(id, async_req=True) >>> result = thread.get() :param id: Instance id of form input field record (required) :type id: int :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: None """ kwargs['_return_http_data_only'] = True return self.v1_app_request_form_input_fields_id_delete_with_http_info(id, **kwargs) # noqa: E501 def v1_app_request_form_input_fields_id_delete_with_http_info(self, id, **kwargs): # noqa: E501 """Remove specified Form Input Field record # noqa: E501 Removes specified form input field record # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_id_delete_with_http_info(id, async_req=True) >>> result = thread.get() :param id: Instance id of form input field record (required) :type id: int :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: None """ local_var_params = locals() all_params = [ 'id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_form_input_fields_id_delete" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `v1_app_request_form_input_fields_id_delete`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = {} return self.api_client.call_api( '/v1/app-request/form-input-fields/{id}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_form_input_fields_id_get(self, id, **kwargs): # noqa: E501 """Get specified Form Input Field object # noqa: E501 Gets specified form input field object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_id_get(id, async_req=True) >>> result = thread.get() :param id: Instance id of form input field record (required) :type id: int :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: AppRequestFormInputField """ kwargs['_return_http_data_only'] = True return self.v1_app_request_form_input_fields_id_get_with_http_info(id, **kwargs) # noqa: E501 def v1_app_request_form_input_fields_id_get_with_http_info(self, id, **kwargs): # noqa: E501 """Get specified Form Input Field object # noqa: E501 Gets specified form input field object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_id_get_with_http_info(id, async_req=True) >>> result = thread.get() :param id: Instance id of form input field record (required) :type id: int :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(AppRequestFormInputField, status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ 'id' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_form_input_fields_id_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `v1_app_request_form_input_fields_id_get`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 200: "AppRequestFormInputField", 404: "ApiError", } return self.api_client.call_api( '/v1/app-request/form-input-fields/{id}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_form_input_fields_id_put(self, id, app_request_form_input_field, **kwargs): # noqa: E501 """Update specified Form Input Field object # noqa: E501 Update specified form input field object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_id_put(id, app_request_form_input_field, async_req=True) >>> result = thread.get() :param id: Instance id of form input field record (required) :type id: int :param app_request_form_input_field: form input field object to create. ids defined in this body will be ignored (required) :type app_request_form_input_field: AppRequestFormInputField :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: AppRequestFormInputField """ kwargs['_return_http_data_only'] = True return self.v1_app_request_form_input_fields_id_put_with_http_info(id, app_request_form_input_field, **kwargs) # noqa: E501 def v1_app_request_form_input_fields_id_put_with_http_info(self, id, app_request_form_input_field, **kwargs): # noqa: E501 """Update specified Form Input Field object # noqa: E501 Update specified form input field object # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_id_put_with_http_info(id, app_request_form_input_field, async_req=True) >>> result = thread.get() :param id: Instance id of form input field record (required) :type id: int :param app_request_form_input_field: form input field object to create. ids defined in this body will be ignored (required) :type app_request_form_input_field: AppRequestFormInputField :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(AppRequestFormInputField, status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ 'id', 'app_request_form_input_field' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_form_input_fields_id_put" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'id' is set if self.api_client.client_side_validation and ('id' not in local_var_params or # noqa: E501 local_var_params['id'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `id` when calling `v1_app_request_form_input_fields_id_put`") # noqa: E501 # verify the required parameter 'app_request_form_input_field' is set if self.api_client.client_side_validation and ('app_request_form_input_field' not in local_var_params or # noqa: E501 local_var_params['app_request_form_input_field'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `app_request_form_input_field` when calling `v1_app_request_form_input_fields_id_put`") # noqa: E501 collection_formats = {} path_params = {} if 'id' in local_var_params: path_params['id'] = local_var_params['id'] # noqa: E501 query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'app_request_form_input_field' in local_var_params: body_params = local_var_params['app_request_form_input_field'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 200: "AppRequestFormInputField", 400: "ApiError", 404: "ApiError", } return self.api_client.call_api( '/v1/app-request/form-input-fields/{id}', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_form_input_fields_post(self, app_request_form_input_field, **kwargs): # noqa: E501 """Create Form Input Field record # noqa: E501 Create form input field record # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_post(app_request_form_input_field, async_req=True) >>> result = thread.get() :param app_request_form_input_field: form input field object to create. ids defined in this body will be ignored (required) :type app_request_form_input_field: AppRequestFormInputField :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: AppRequestFormInputField """ kwargs['_return_http_data_only'] = True return self.v1_app_request_form_input_fields_post_with_http_info(app_request_form_input_field, **kwargs) # noqa: E501 def v1_app_request_form_input_fields_post_with_http_info(self, app_request_form_input_field, **kwargs): # noqa: E501 """Create Form Input Field record # noqa: E501 Create form input field record # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_post_with_http_info(app_request_form_input_field, async_req=True) >>> result = thread.get() :param app_request_form_input_field: form input field object to create. ids defined in this body will be ignored (required) :type app_request_form_input_field: AppRequestFormInputField :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(AppRequestFormInputField, status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ 'app_request_form_input_field' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_form_input_fields_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'app_request_form_input_field' is set if self.api_client.client_side_validation and ('app_request_form_input_field' not in local_var_params or # noqa: E501 local_var_params['app_request_form_input_field'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `app_request_form_input_field` when calling `v1_app_request_form_input_fields_post`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'app_request_form_input_field' in local_var_params: body_params = local_var_params['app_request_form_input_field'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 201: "AppRequestFormInputField", 400: "ApiError", } return self.api_client.call_api( '/v1/app-request/form-input-fields', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_form_input_fields_put(self, app_request_form_input_field, **kwargs): # noqa: E501 """Replace all Form Input Fields # noqa: E501 Replace all form input fields. Will delete, update, and create all input fields accordingly. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_put(app_request_form_input_field, async_req=True) >>> result = thread.get() :param app_request_form_input_field: list of form input fields to replace all existing fields. Will delete, update, and create all input fields accordingly. (required) :type app_request_form_input_field: list[AppRequestFormInputField] :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: list[AppRequestFormInputField] """ kwargs['_return_http_data_only'] = True return self.v1_app_request_form_input_fields_put_with_http_info(app_request_form_input_field, **kwargs) # noqa: E501 def v1_app_request_form_input_fields_put_with_http_info(self, app_request_form_input_field, **kwargs): # noqa: E501 """Replace all Form Input Fields # noqa: E501 Replace all form input fields. Will delete, update, and create all input fields accordingly. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_form_input_fields_put_with_http_info(app_request_form_input_field, async_req=True) >>> result = thread.get() :param app_request_form_input_field: list of form input fields to replace all existing fields. Will delete, update, and create all input fields accordingly. (required) :type app_request_form_input_field: list[AppRequestFormInputField] :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(list[AppRequestFormInputField], status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ 'app_request_form_input_field' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_form_input_fields_put" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'app_request_form_input_field' is set if self.api_client.client_side_validation and ('app_request_form_input_field' not in local_var_params or # noqa: E501 local_var_params['app_request_form_input_field'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `app_request_form_input_field` when calling `v1_app_request_form_input_fields_put`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'app_request_form_input_field' in local_var_params: body_params = local_var_params['app_request_form_input_field'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 200: "list[AppRequestFormInputField]", 400: "ApiError", 404: "ApiError", } return self.api_client.call_api( '/v1/app-request/form-input-fields', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_settings_get(self, **kwargs): # noqa: E501 """Get Applicastion Request Settings # noqa: E501 Get app request settings # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_settings_get(async_req=True) >>> result = thread.get() :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: AppRequestSettings """ kwargs['_return_http_data_only'] = True return self.v1_app_request_settings_get_with_http_info(**kwargs) # noqa: E501 def v1_app_request_settings_get_with_http_info(self, **kwargs): # noqa: E501 """Get Applicastion Request Settings # noqa: E501 Get app request settings # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_settings_get_with_http_info(async_req=True) >>> result = thread.get() :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(AppRequestSettings, status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_settings_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 200: "AppRequestSettings", } return self.api_client.call_api( '/v1/app-request/settings', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth')) def v1_app_request_settings_put(self, app_request_settings, **kwargs): # noqa: E501 """Update Application Request Settings # noqa: E501 Update app request settings # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_settings_put(app_request_settings, async_req=True) >>> result = thread.get() :param app_request_settings: App request settings object (required) :type app_request_settings: AppRequestSettings :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: AppRequestSettings """ kwargs['_return_http_data_only'] = True return self.v1_app_request_settings_put_with_http_info(app_request_settings, **kwargs) # noqa: E501 def v1_app_request_settings_put_with_http_info(self, app_request_settings, **kwargs): # noqa: E501 """Update Application Request Settings # noqa: E501 Update app request settings # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v1_app_request_settings_put_with_http_info(app_request_settings, async_req=True) >>> result = thread.get() :param app_request_settings: App request settings object (required) :type app_request_settings: AppRequestSettings :param async_req: Whether to execute the request asynchronously. :type async_req: bool, optional :param _return_http_data_only: response data without head status code and headers :type _return_http_data_only: bool, optional :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :type _preload_content: bool, optional :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :param _request_auth: set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. :type _request_auth: dict, optional :return: Returns the result object. If the method is called asynchronously, returns the request thread. :rtype: tuple(AppRequestSettings, status_code(int), headers(HTTPHeaderDict)) """ local_var_params = locals() all_params = [ 'app_request_settings' ] all_params.extend( [ 'async_req', '_return_http_data_only', '_preload_content', '_request_timeout', '_request_auth' ] ) for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v1_app_request_settings_put" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'app_request_settings' is set if self.api_client.client_side_validation and ('app_request_settings' not in local_var_params or # noqa: E501 local_var_params['app_request_settings'] is None): # noqa: E501 raise ApiValueError("Missing the required parameter `app_request_settings` when calling `v1_app_request_settings_put`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'app_request_settings' in local_var_params: body_params = local_var_params['app_request_settings'] # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = [] # noqa: E501 response_types_map = { 200: "AppRequestSettings", 400: "ApiError", } return self.api_client.call_api( '/v1/app-request/settings', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_types_map=response_types_map, auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats, _request_auth=local_var_params.get('_request_auth'))

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1

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null

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7

efaa6a2b32a911c246d012e0b8967c5b2a3d1871

79

py

Python

elephant/utils/__init__.py

bear-in-white-house/elephant

a30ecacb6ed3bba397e06a89e1cd28377d5f54f0

[ "Apache-2.0" ]

null

elephant/utils/__init__.py

bear-in-white-house/elephant

a30ecacb6ed3bba397e06a89e1cd28377d5f54f0

[ "Apache-2.0" ]

null

elephant/utils/__init__.py

bear-in-white-house/elephant

a30ecacb6ed3bba397e06a89e1cd28377d5f54f0

[ "Apache-2.0" ]

null

from elephant.utils.phone_code import * from elephant.utils.renderers import *

26.333333

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0.822785

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1

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1

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1

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7

efd313cc4ba802a078bb86784f2e247ae7ffbc69

150

py

Python

beobench/data/agents/rllib.py

rdnfn/beobench

c51e9a6d320e3e1db035cb298936ac4dacaca64b

[ "MIT" ]

9

2022-01-10T13:51:38.000Z

2022-03-31T15:08:23.000Z

beobench/data/agents/rllib.py

rdnfn/beobench

c51e9a6d320e3e1db035cb298936ac4dacaca64b

[ "MIT" ]

25

2022-01-09T16:35:43.000Z

2022-03-31T15:39:15.000Z

beobench/data/agents/rllib.py

rdnfn/beobench

c51e9a6d320e3e1db035cb298936ac4dacaca64b

[ "MIT" ]

1

2022-03-30T13:24:07.000Z

"""RLlib agent.""" import beobench.integration.rllib from beobench.experiment.provider import config beobench.integration.rllib.run_in_tune(config)

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0

7

4bf9cbfea19f9e5d4e2ccfd5ee76707d1540aa3d

4,354

py

Python

healthcareai/tests/test_deploy_supervised_model_class.py

Dokotta/healthcareai-py

700b1f7a14f2087481aa98c01dba00dfe3efc2cb

[ "MIT" ]

null

healthcareai/tests/test_deploy_supervised_model_class.py

Dokotta/healthcareai-py

700b1f7a14f2087481aa98c01dba00dfe3efc2cb

[ "MIT" ]

null

healthcareai/tests/test_deploy_supervised_model_class.py

Dokotta/healthcareai-py

700b1f7a14f2087481aa98c01dba00dfe3efc2cb

[ "MIT" ]

1

2019-10-11T10:40:44.000Z

import unittest import numpy as np import pandas as pd from healthcareai import DeploySupervisedModel from healthcareai.tests.helpers import fixture class TestRFDeployNoTreesNoMtry(unittest.TestCase): def setUp(self): df = pd.read_csv(fixture('DiabetesClinicalSampleData.csv'), na_values=['None']) df.drop('PatientID', axis=1, inplace=True) # drop uninformative column print(df.head()) np.random.seed(42) self.o = DeploySupervisedModel(modeltype='classification', df=df, graincol='PatientEncounterID', windowcol='InTestWindowFLG', predictedcol='ThirtyDayReadmitFLG', impute=True) self.o.deploy( method='rf', cores=1, server='localhost', dest_db_schema_table='[SAM].[dbo].[HCPyDeployClassificationBASE]', use_saved_model=False) def runTest(self): self.assertAlmostEqual(np.round(self.o.y_pred[5], 6), 0.060000) def tearDown(self): del self.o class TestRFDeployNoTreesWithMtry(unittest.TestCase): def setUp(self): df = pd.read_csv(fixture('DiabetesClinicalSampleData.csv'), na_values=['None']) df.drop('PatientID', axis=1, inplace=True) # drop uninformative column np.random.seed(42) self.o = DeploySupervisedModel(modeltype='classification', df=df, graincol='PatientEncounterID', windowcol='InTestWindowFLG', predictedcol='ThirtyDayReadmitFLG', impute=True) self.o.deploy( method='rf', cores=1, mtry=3, server='localhost', dest_db_schema_table='[SAM].[dbo].[HCPyDeployClassificationBASE]', use_saved_model=False) def runTest(self): self.assertAlmostEqual(np.round(self.o.y_pred[5], 6), 0.1) def tearDown(self): del self.o class TestRFDeployWithTreesNoMtry(unittest.TestCase): def setUp(self): df = pd.read_csv(fixture('DiabetesClinicalSampleData.csv'), na_values=['None']) df.drop('PatientID', axis=1, inplace=True) # drop uninformative column np.random.seed(42) self.o = DeploySupervisedModel(modeltype='classification', df=df, graincol='PatientEncounterID', windowcol='InTestWindowFLG', predictedcol='ThirtyDayReadmitFLG', impute=True) self.o.deploy( method='rf', cores=1, trees=100, server='localhost', dest_db_schema_table='[SAM].[dbo].[HCPyDeployClassificationBASE]', use_saved_model=False) def runTest(self): self.assertAlmostEqual(np.round(self.o.y_pred[5], 6), 0.060000) def tearDown(self): del self.o class TestLinearDeploy(unittest.TestCase): def setUp(self): df = pd.read_csv(fixture('DiabetesClinicalSampleData.csv'), na_values=['None']) df.drop('PatientID', axis=1, inplace=True) # drop uninformative column np.random.seed(42) self.o = DeploySupervisedModel(modeltype='classification', df=df, graincol='PatientEncounterID', windowcol='InTestWindowFLG', predictedcol='ThirtyDayReadmitFLG', impute=True) self.o.deploy( method='linear', cores=1, server='localhost', dest_db_schema_table='[SAM].[dbo].[HCPyDeployClassificationBASE]', use_saved_model=False) def runTest(self): self.assertAlmostEqual(np.round(self.o.y_pred[5], 5), 0.18087) def tearDown(self): del self.o

35.398374

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null

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1

0

1

0

null

0

7

ef19c038134d3e8a35aaaee63945a5f7dadda605

4,682

py

Python

tests/image_jaco_test.py

danielkychen/rasl

75cea33a615e6ebe0e7f6d43d93bf81c8077f7e4

[ "MIT" ]

49

2016-04-18T17:22:26.000Z

2021-10-31T01:31:35.000Z

tests/image_jaco_test.py

danielkychen/rasl

75cea33a615e6ebe0e7f6d43d93bf81c8077f7e4

[ "MIT" ]

3

2018-05-11T11:59:25.000Z

2018-05-23T14:21:56.000Z

tests/image_jaco_test.py

danielkychen/rasl

75cea33a615e6ebe0e7f6d43d93bf81c8077f7e4

[ "MIT" ]

12

2016-11-08T08:31:02.000Z

2021-04-23T18:12:09.000Z

# basic liveness tests for image_jaco # from __future__ import division import pytest import numpy as np from rasl.toolbox import image_jaco # pylint:disable=import-error image = np.zeros((4, 3)) image[1,:] = 1.0 zeros = np.zeros((4, 3)) def test_translate(): paramv = [10, -100] J = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'translate', paramv) J = J.reshape((4,3,2)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], 1) # translation preserves Iu assert np.allclose(J[1, :, 1], 0) # translation preserves Iv J = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'translate', paramv) J = J.reshape((4,3,2)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], 0) # translation preserves Iu assert np.allclose(J[1, :, 1], 1) # translation preserves Iv def test_scale(): paramv = [1] J = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'scale', paramv) J = J.reshape((4,3,1)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], [0, 1, 2]) # scale increases with u J = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'scale', paramv) J = J.reshape((4,3,1)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], 1) # scale fixed with fixed v def test_rotate(): paramv = [0] J = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'rotate', paramv) J = J.reshape((4,3,1)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], -1) # rotation from 0 fixed with v J = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'rotate', paramv) J = J.reshape((4,3,1)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], [0, 1, 2]) # rotation from 0 increases with u def test_similarity(): paramv = [1, 0, 10, -100] J = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'similarity', paramv) J = J.reshape((4,3,4)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], [0, 1, 2]) # scale increases with u assert np.allclose(J[1, :, 1], -1) # rotation from 0 fixed with v assert np.allclose(J[1, :, 2], 1) # translation preserves Iu assert np.allclose(J[1, :, 3], 0) # translation preserves Iv J = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'similarity', paramv) J = J.reshape((4,3,4)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], 1) # scale fixed with fixed v assert np.allclose(J[1, :, 1], [0, 1, 2]) # rotation from 0 increases with u assert np.allclose(J[1, :, 2], 0) # translation preserves Iu assert np.allclose(J[1, :, 3], 1) # translation preserves Iv def test_affine(): J = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'affine', None) J = J.reshape((4,3,6)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 0], [0, 1, 2]) # increases with u assert np.allclose(J[1, :, 1], 1) # fixed with fixed v assert np.allclose(J[1, :, 2], 1) # fixed assert np.allclose(J[1:, :, 3:], 0) J = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'affine', None) J = J.reshape((4,3,6)) assert np.allclose(J[0, :, :], 0) assert np.allclose(J[2:, :, :], 0) assert np.allclose(J[1, :, 3], [0, 1, 2]) # increases with u assert np.allclose(J[1, :, 4], 1) # fixed with fixed v assert np.allclose(J[1, :, 5], 1) # fixed assert np.allclose(J[1:, :, :3], 0) def test_projective(): paramv = np.zeros(8) J = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'projective', paramv) J = J.reshape((4,3,8)) # with paramv[6:]==0, reduces to affine, a simpler (though incomplete) test Jaff = image_jaco(image.flatten(), zeros.flatten(), image.shape, 'affine', None) Jaff = Jaff.reshape((4,3,6)) assert np.allclose(J[:, :, 0:6], Jaff) J = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'projective', paramv) J = J.reshape((4,3,8)) Jaff = image_jaco(zeros.flatten(), image.flatten(), image.shape, 'affine', None) Jaff = Jaff.reshape((4,3,6)) assert np.allclose(J[:, :, 0:6], Jaff) def test_BOGUS(): with pytest.raises(ValueError) as info: image_jaco(None, None, (4, 3), 'BOGUS', None) assert str(info.value).endswith('BOGUS')

41.803571

87

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743

4,682

3.756393

0.094213

0.131852

0.263705

0.280186

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0

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0

null

0

1

0

null

0

1

0

9

329382ef63339522426ab5907c8820cdc9d068ac

2,518

py

Python

tachyon/propiedades/forms.py

Tachyon-BR/TachyonBR

9afc99bbf0f6dea19d34dc1487165174733a0e66

[ "MIT" ]

1

2020-01-29T18:35:22.000Z

tachyon/propiedades/forms.py

Tachyon-BR/TachyonBR

9afc99bbf0f6dea19d34dc1487165174733a0e66

[ "MIT" ]

12

2020-03-04T20:37:33.000Z

2022-03-12T00:17:24.000Z

tachyon/propiedades/forms.py

Tachyon-BR/TachyonBR

9afc99bbf0f6dea19d34dc1487165174733a0e66

[ "MIT" ]

null

from django import forms from .models import * class IbanForm(forms.Form): phone = forms.CharField(max_length = 100) name = forms.CharField(max_length = 100) class CrearPropiedadForma(forms.Form): oferta = forms.CharField(max_length = 30) tipo = forms.CharField(max_length = 30) titulo = forms.CharField(max_length = 200) desc = forms.CharField() habs = forms.IntegerField(required=False) banos = forms.FloatField(required=False) garaje = forms.IntegerField(required=False) pais = forms.CharField(max_length = 100) estado = forms.CharField(max_length = 100) codigo_postal = forms.CharField(max_length = 5) colonia = forms.CharField(max_length = 200) direccion = forms.CharField(max_length = 300) precio = forms.CharField(max_length = 100) negociable = forms.BooleanField(widget=forms.CheckboxInput(), required=False) dif = forms.CharField(max_length = 100, required=False) m_terr = forms.CharField(max_length = 30) m_cons = forms.CharField(max_length = 30) pisos = forms.IntegerField(required=False) portada = forms.ImageField(widget=forms.ClearableFileInput()) # extra = forms.ImageField(widget=forms.ClearableFileInput(attrs={'multiple': True})) video = forms.CharField(max_length = 150, required=False) class EditarPropiedadForma(forms.Form): oferta = forms.CharField(max_length = 30) tipo = forms.CharField(max_length = 30) titulo = forms.CharField(max_length = 200) desc = forms.CharField() habs = forms.IntegerField(required=False) banos = forms.FloatField(required=False) garaje = forms.IntegerField(required=False) pais = forms.CharField(max_length = 100) estado = forms.CharField(max_length = 100) codigo_postal = forms.CharField(max_length = 5) colonia = forms.CharField(max_length = 200) direccion = forms.CharField(max_length = 300) precio = forms.CharField(max_length = 100) negociable = forms.BooleanField(widget=forms.CheckboxInput(), required=False) dif = forms.CharField(max_length = 100, required=False) m_terr = forms.CharField(max_length = 30) m_cons = forms.CharField(max_length = 30) pisos = forms.IntegerField(required=False) portada = forms.ImageField(widget=forms.ClearableFileInput(), required=False) extra = forms.ImageField(widget=forms.ClearableFileInput(attrs={'multiple': True}), required=False) video = forms.CharField(max_length = 150, required=False)

46.62963

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1

0

1

0

null

0

1

0

11

32b19e9447f20e616b89c8e5275797ed3fafb77c

12,432

py

Python

morse-stf/stensorflow/basic/operator/sigmoid.py

alipay/Antchain-MPC

f6916465e1da5722ca7efadc4eeaca13ec229707

[ "Apache-2.0" ]

33

2021-11-23T09:04:03.000Z

2022-03-14T07:56:31.000Z

morse-stf/stensorflow/basic/operator/sigmoid.py

qizhi-zhang/Antchain-MPC

f551170f68b0baff328e6594484e9832230fe719

[ "Apache-2.0" ]

null

morse-stf/stensorflow/basic/operator/sigmoid.py

qizhi-zhang/Antchain-MPC

f551170f68b0baff328e6594484e9832230fe719

[ "Apache-2.0" ]

6

2021-11-25T12:38:41.000Z

2022-02-23T03:29:51.000Z

#!/usr/bin/env python # coding=utf-8 """ Ant Group Copyright (c) 2004-2020 All Rights Reserved. ------------------------------------------------------ File Name : sigmoid Author : Qizhi Zhang Email: qizhi.zqz@antgroup.com Create Time : 2020-05-14 11:42 Description : description what the main function of this file """ from stensorflow.basic.basic_class.private import PrivateTensor from stensorflow.basic.basic_class.pair import SharedPair from stensorflow.basic.basic_class.share import sin2pi as sin2pi_share, cos2pi as cos2pi_share, SharedTensor import tensorflow as tf from stensorflow.global_var import StfConfig from stensorflow.random.random import get_seed from typing import Union import numpy as np def sigmoid_poly(x: SharedPair): """A Chebyshev polynomial approximation of the sigmoid function.""" w0 = 0.5 w1 = 0.2159198015 w3 = -0.0082176259 w5 = 0.0001825597 w7 = -0.0000018848 w9 = 0.0000000072 x1 = x x2 = (x1 * x).dup_with_precision(x.fixedpoint) x3 = (x2 * x).dup_with_precision(x.fixedpoint) x5 = (x2 * x3).dup_with_precision(x.fixedpoint) x7 = (x2 * x5).dup_with_precision(x.fixedpoint) x9 = (x2 * x7).dup_with_precision(x.fixedpoint) y1 = w1 * x1 y3 = w3 * x3 y5 = w5 * x5 y7 = w7 * x7 y9 = w9 * x9 z = y9 + y7 + y5 + y3 + y1 + tf.constant(w0) return z def sigmoid_poly_minmax(x: SharedPair): """A minmax polynomial approximation of the sigmoid function. """ w0 = 0.5 w1 = 0.197 w3 = -0.004 x1 = x x2 = x1 * x x3 = x2 * x y1 = w1 * x1 y3 = w3 * x3 z = y3 + y1 + tf.constant(w0) # z = y7 + y5 + y3 + y1 + w0 return z def sin2pi_bak(x: SharedPair, T: int = 1, k: Union[int, tf.Tensor] = None) -> SharedPair: # sin(2kpix/T) # print("x.xL.shape=", x.xL.shape) # print("x.xR.shape=", x.xR.shape) n = int(np.log2(T)) if 1 << n != T: raise Exception("T must be a power of 2") if k is None: k = 1 if isinstance(k, int): pass elif isinstance(k, tf.Tensor): if k.dtype in [tf.dtypes.int8, tf.dtypes.int16, tf.dtypes.int32, tf.dtypes.int64]: pass else: raise Exception("the type of k is error") with tf.device(x.ownerL): yL = tf.stack([sin2pi_share(x.xL, x.fixedpoint + n, k), cos2pi_share(x.xL, x.fixedpoint + n, k)], axis=-1) with tf.device(x.ownerR): yR = tf.stack([cos2pi_share(x.xR, x.fixedpoint + n, k), sin2pi_share(x.xR, x.fixedpoint + n, k)], axis=-1) if StfConfig.parties == 3: with tf.device(x.ownerL): yL = tf.expand_dims(yL, axis=-2) zL = PrivateTensor(owner=x.ownerL) zL.load_from_tf_tensor(yL) with tf.device(x.ownerR): yR = tf.expand_dims(yR, axis=-1) zR = PrivateTensor(owner=x.ownerR) zR.load_from_tf_tensor(yR) result = (zL @ zR).dup_with_precision(new_fixedpoint=StfConfig.default_fixed_point) result = result.squeeze(axis=[-1, -2]) else: with tf.device(x.ownerL): zL = PrivateTensor(owner=x.ownerL) zL.load_from_tf_tensor(yL) with tf.device(x.ownerR): zR = PrivateTensor(owner=x.ownerR) zR.load_from_tf_tensor(yR) result = (zL * zR).reduce_sum(axis=[-1]).dup_with_precision(new_fixedpoint=StfConfig.default_fixed_point) return result def sin2pi(x: SharedPair, T: int = 1, k: Union[int, tf.Tensor] = None) -> SharedPair: # sin(2kpix/T) # print("x.xL.shape=", x.xL.shape) # print("x.xR.shape=", x.xR.shape) n = int(np.log2(T)) if 1 << n != T: raise Exception("T must be a power of 2, but is {}".format(T)) if k is None: k = 1 if isinstance(k, int): pass elif isinstance(k, tf.Tensor): if k.dtype in [tf.dtypes.int8, tf.dtypes.int16, tf.dtypes.int32, tf.dtypes.int64]: pass else: raise Exception("the type of k is error") if StfConfig.parties == 3: with tf.device(StfConfig.RS[0]): prf_flag = StfConfig.prf_flag if prf_flag: seed_xL = get_seed() seed_xR = get_seed() seed_sin = get_seed() seed_cos = get_seed() else: seed_xL = None seed_xR = None seed_sin = None seed_cos = None xL_adjoint = x.xL.random_uniform_adjoint(seed_xL) xR_adjoint = x.xR.random_uniform_adjoint(seed_xR) _sin2pi_adjoint = sin2pi_share(xL_adjoint+xR_adjoint, x.fixedpoint + n, k) _sin2pi_adjoint = tf.cast(_sin2pi_adjoint * (1 << x.fixedpoint), 'int64') _cos2pi_adjoint = cos2pi_share(xL_adjoint+xR_adjoint, x.fixedpoint + n, k) _cos2pi_adjoint = tf.cast(_cos2pi_adjoint * (1 << x.fixedpoint), 'int64') sin2pi_adjointL = SharedTensor(shape=_sin2pi_adjoint.shape.as_list()).random_uniform_adjoint(seed_sin) sin2pi_adjointR = SharedTensor(inner_value=_sin2pi_adjoint) - sin2pi_adjointL cos2pi_adjointR = SharedTensor(shape=_cos2pi_adjoint.shape.as_list()).random_uniform_adjoint(seed_cos) cos2pi_adjointL = SharedTensor(inner_value=_cos2pi_adjoint) - cos2pi_adjointR with tf.device(x.ownerL): if prf_flag: xL_adjoint = x.xL.random_uniform_adjoint(seed_xL) delta_xL = (x.xL - xL_adjoint) % (1 << n+x.fixedpoint) #print("delta_xL=", delta_xL) with tf.device(x.ownerR): if prf_flag: xR_adjoint = x.xR.random_uniform_adjoint(seed_xR) delta_xR = (x.xR - xR_adjoint) % (1 << n+x.fixedpoint) with tf.device(x.ownerL): if prf_flag: sin2pi_adjointL = sin2pi_adjointL.random_uniform_adjoint(seed_sin) yL = sin2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * cos2pi_adjointL + \ cos2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * sin2pi_adjointL with tf.device(x.ownerR): if prf_flag: cos2pi_adjointR = cos2pi_adjointR.random_uniform_adjoint(seed_cos) yR = cos2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * sin2pi_adjointR + \ sin2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * cos2pi_adjointR result = SharedPair(ownerL=x.ownerL, ownerR=x.ownerR, xL=yL, xR=yR, fixedpoint=2*x.fixedpoint) result = result.dup_with_precision(x.fixedpoint) else: with tf.device(x.ownerL): yL = tf.stack([sin2pi_share(x.xL, x.fixedpoint + n, k), cos2pi_share(x.xL, x.fixedpoint + n, k)], axis=-1) zL = PrivateTensor(owner=x.ownerL) zL.load_from_tf_tensor(yL) with tf.device(x.ownerR): yR = tf.stack([cos2pi_share(x.xR, x.fixedpoint + n, k), sin2pi_share(x.xR, x.fixedpoint + n, k)], axis=-1) zR = PrivateTensor(owner=x.ownerR) zR.load_from_tf_tensor(yR) result = (zL * zR).reduce_sum(axis=[-1]).dup_with_precision(new_fixedpoint=StfConfig.default_fixed_point) return result def sin2pi(x: SharedPair, T: int = 1, k: Union[int, tf.Tensor] = None) -> SharedPair: # sin(2kpix/T) # print("x.xL.shape=", x.xL.shape) # print("x.xR.shape=", x.xR.shape) n = int(np.log2(T)) if 1 << n != T: raise Exception("T must be a power of 2") if k is None: k = 1 if isinstance(k, int): pass elif isinstance(k, tf.Tensor): if k.dtype in [tf.dtypes.int8, tf.dtypes.int16, tf.dtypes.int32, tf.dtypes.int64]: pass else: raise Exception("the type of k is error") if StfConfig.parties == 3: with tf.device(StfConfig.RS[0]): prf_flag = StfConfig.prf_flag if prf_flag: seed_xL = get_seed() seed_xR = get_seed() seed_sin = get_seed() seed_cos = get_seed() else: seed_xL = None seed_xR = None seed_sin = None seed_cos = None xL_adjoint = x.xL.random_uniform_adjoint(seed_xL) xR_adjoint = x.xR.random_uniform_adjoint(seed_xR) _sin2pi_adjoint = sin2pi_share(xL_adjoint+xR_adjoint, x.fixedpoint + n, k) _sin2pi_adjoint = tf.cast(_sin2pi_adjoint * (1 << x.fixedpoint), 'int64') _cos2pi_adjoint = cos2pi_share(xL_adjoint+xR_adjoint, x.fixedpoint + n, k) _cos2pi_adjoint = tf.cast(_cos2pi_adjoint * (1 << x.fixedpoint), 'int64') sin2pi_adjointL = SharedTensor(shape=_sin2pi_adjoint.shape.as_list()).random_uniform_adjoint(seed_sin) sin2pi_adjointR = SharedTensor(inner_value=_sin2pi_adjoint) - sin2pi_adjointL cos2pi_adjointR = SharedTensor(shape=_cos2pi_adjoint.shape.as_list()).random_uniform_adjoint(seed_cos) cos2pi_adjointL = SharedTensor(inner_value=_cos2pi_adjoint) - cos2pi_adjointR with tf.device(x.ownerL): if prf_flag: xL_adjoint = x.xL.random_uniform_adjoint(seed_xL) delta_xL = (x.xL - xL_adjoint) % (1 << n+x.fixedpoint) print("dela_xL=", delta_xL) with tf.device(x.ownerR): if prf_flag: xR_adjoint = x.xR.random_uniform_adjoint(seed_xR) delta_xR = (x.xR - xR_adjoint) % (1 << n+x.fixedpoint) with tf.device(x.ownerL): if prf_flag: sin2pi_adjointL = sin2pi_adjointL.random_uniform_adjoint(seed_sin) yL = sin2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * cos2pi_adjointL + \ cos2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * sin2pi_adjointL with tf.device(x.ownerR): if prf_flag: cos2pi_adjointR = cos2pi_adjointR.random_uniform_adjoint(seed_cos) yR = cos2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * sin2pi_adjointR + \ sin2pi_share(delta_xL + delta_xR, x.fixedpoint+n, k) * (1<<x.fixedpoint) * cos2pi_adjointR result = SharedPair(ownerL=x.ownerL, ownerR=x.ownerR, xL=yL, xR=yR, fixedpoint=2*x.fixedpoint) result = result.dup_with_precision(x.fixedpoint) else: with tf.device(x.ownerL): yL = tf.stack([sin2pi_share(x.xL, x.fixedpoint + n, k), cos2pi_share(x.xL, x.fixedpoint + n, k)], axis=-1) zL = PrivateTensor(owner=x.ownerL) zL.load_from_tf_tensor(yL) with tf.device(x.ownerR): yR = tf.stack([cos2pi_share(x.xR, x.fixedpoint + n, k), sin2pi_share(x.xR, x.fixedpoint + n, k)], axis=-1) zR = PrivateTensor(owner=x.ownerR) zR.load_from_tf_tensor(yR) result = (zL * zR).reduce_sum(axis=[-1]).dup_with_precision(new_fixedpoint=StfConfig.default_fixed_point) return result def sigmoid_sin(x: SharedPair, M=16): """Fourier series approximation of the sigmoid function. https://arxiv.org/pdf/2109.11726.pdf """ if 1 << int(np.log2(M)) != M: raise Exception("M must be a power of 2") term = 6 sample_num = 256 X = np.linspace(-M, M, sample_num, endpoint=False) # -M to+M的256个值 sigmoid = 1 / (1 + np.exp(-X)) sm5 = sigmoid - 0.5 sm5_odd = sm5 * 1.0 sm5_odd[0] = 0 F = np.fft.fft(sm5_odd) a = F[0:term].imag # a = tf.constant(a, dtype='float32', shape=[term]+[1]*len(x.shape)) a = np.reshape(a, newshape=[term] + [1] * len(x.shape)) a = tf.constant(a, dtype='float32') integers = np.reshape(range(term), newshape=[term] + [1] * len(x.shape)) integers = tf.constant(integers, dtype='int64') x = x.expend_dims(axis=[0]) s = sin2pi(x - M, T=2 * M, k=integers) y = -a / 128 * s y = y.reduce_sum(axis=[0]) y = y + 0.5 return y def sigmoid_idea(x: SharedPair, M=16): """The idea sigmoid""" y=tf.sigmoid(x.to_tf_tensor("R")) z = SharedPair(ownerL="L", ownerR="R", shape=y.shape) z.load_from_tf_tensor(y) return z def sigmoid_local(x: PrivateTensor): z = PrivateTensor(owner=x.owner) with tf.device(x.owner): y = tf.sigmoid(x.to_tf_tensor()) z.load_from_tf_tensor(y) return z

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Python

pysit/solvers/variable_density_acoustic/frequency/__init__.py

zfang-slim/pysit

8fca42b9749841abc302d1f8195a1437fad7ae4d

[ "BSD-3-Clause" ]

64

2015-09-08T06:23:27.000Z

2022-03-09T23:35:24.000Z

pysit/solvers/variable_density_acoustic/frequency/__init__.py

zfang-slim/pysit

8fca42b9749841abc302d1f8195a1437fad7ae4d

[ "BSD-3-Clause" ]

23

2015-10-08T01:14:24.000Z

2021-07-15T11:37:05.000Z

pysit/solvers/variable_density_acoustic/frequency/__init__.py

zfang-slim/pysit

8fca42b9749841abc302d1f8195a1437fad7ae4d

[ "BSD-3-Clause" ]

48

2015-06-25T14:48:22.000Z

2021-12-06T19:50:25.000Z

from .variable_density_acoustic_frequency_scalar_1D import * from .variable_density_acoustic_frequency_scalar_2D import * from .variable_density_acoustic_frequency_scalar_3D import *

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