xszheng2020/the_stack_dedup_python_hits_7 · Datasets at Hugging Face

e5cebb394da49e8ed887f3594adea7b782eab267

210

py

Python

cmf_rateslib/products/base_product.py

lantalex/cmf_rateslib

efefe45bfc349a18a4b318f0d524744e0140e155

[ "MIT" ]

3

2021-11-12T16:14:29.000Z

2021-12-08T17:44:35.000Z

cmf_rateslib/products/base_product.py

lantalex/cmf_rateslib

efefe45bfc349a18a4b318f0d524744e0140e155

[ "MIT" ]

null

cmf_rateslib/products/base_product.py

lantalex/cmf_rateslib

efefe45bfc349a18a4b318f0d524744e0140e155

[ "MIT" ]

13

2021-11-09T17:53:51.000Z

2021-12-13T11:19:12.000Z

class BaseProduct(object): params: dict = {} def __init__(self): pass def get_cashflows(self, *args, **kwargs): return None def pv(self, *args, **kwargs): return 0

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7

e5fd1c3e2a9590f8542aa241a24dca9e82772723

2,621

py

Python

tests/clpy_tests/opencl_tests/ultima_tests/test_ultima_carray.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

142

2018-06-07T07:43:10.000Z

2021-10-30T21:06:32.000Z

tests/clpy_tests/opencl_tests/ultima_tests/test_ultima_carray.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

282

2018-06-07T08:35:03.000Z

2021-03-31T03:14:32.000Z

tests/clpy_tests/opencl_tests/ultima_tests/test_ultima_carray.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

19

2018-06-19T11:07:53.000Z

2021-05-13T20:57:04.000Z

# flake8: noqa # TODO(vorj): When we will meet flake8 3.7.0+, # we should ignore only W291 for whole file # using --per-file-ignores . import clpy import unittest class TestUltimaCArray(unittest.TestCase): def test_carray_argument_mutation(self): x = clpy.backend.ultima.exec_ultima('', '#include <cupy/carray.hpp>') + ''' void f(__global int* const __restrict__ arr, const CArray_2 arr_info) { } '''[1:] y = clpy.backend.ultima.exec_ultima( ''' void f(CArray<int, 2> arr){} ''', '#include <cupy/carray.hpp>') self.maxDiff = None self.assertEqual(x, y) def test_carray_member_function(self): x = clpy.backend.ultima.exec_ultima('', '#include <cupy/carray.hpp>') + ''' void f(__global int* const __restrict__ arr, const CArray_2 arr_info) { ((const size_t)arr_info.size_); ((const size_t*)arr_info.shape_); ((const size_t*)arr_info.strides_); } '''[1:] y = clpy.backend.ultima.exec_ultima( ''' void f(CArray<int, 2> arr){ arr.size(); arr.shape(); arr.strides(); } ''', '#include <cupy/carray.hpp>') self.maxDiff = None self.assertEqual(x, y) def test_carray_0_member_function(self): x = clpy.backend.ultima.exec_ultima('', '#include <cupy/carray.hpp>') + ''' void f(__global int* const __restrict__ arr, const CArray_0 arr_info) { ((const size_t)arr_info.size_); ((const size_t*)NULL); ((const size_t*)NULL); } '''[1:] y = clpy.backend.ultima.exec_ultima( ''' void f(CArray<int, 0> arr){ arr.size(); arr.shape(); arr.strides(); } ''', '#include <cupy/carray.hpp>') self.maxDiff = None self.assertEqual(x, y) def test_carray_1_member_function(self): x = clpy.backend.ultima.exec_ultima('', '#include <cupy/carray.hpp>') + ''' void f(__global int* const __restrict__ arr, const CArray_1 arr_info) { ((const size_t)arr_info.size_); ((const size_t*)&arr_info.shape_); ((const size_t*)&arr_info.strides_); } '''[1:] y = clpy.backend.ultima.exec_ultima( ''' void f(CArray<int, 1> arr){ arr.size(); arr.shape(); arr.strides(); } ''', '#include <cupy/carray.hpp>') self.maxDiff = None self.assertEqual(x, y) if __name__ == "__main__": unittest.main()

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0.066815

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null

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7

00556e553743d1430d37baf919cb2e6c5d00dd18

23,470

py

Python

tests/e2e/basic/performance_tests/wifi_capacity_test/wpa2_personal/test_bridge_mode.py

DYeag/wlan-testing

81e879d04ea3c6a55d14a330d461d8914507e3b2

[ "BSD-3-Clause" ]

7

2020-08-19T16:45:46.000Z

2022-02-10T09:55:22.000Z

tests/e2e/basic/performance_tests/wifi_capacity_test/wpa2_personal/test_bridge_mode.py

DYeag/wlan-testing

81e879d04ea3c6a55d14a330d461d8914507e3b2

[ "BSD-3-Clause" ]

47

2020-12-20T16:06:03.000Z

2022-03-23T03:01:22.000Z

tests/e2e/basic/performance_tests/wifi_capacity_test/wpa2_personal/test_bridge_mode.py

DYeag/wlan-testing

81e879d04ea3c6a55d14a330d461d8914507e3b2

[ "BSD-3-Clause" ]

9

2021-02-04T22:32:06.000Z

2021-12-14T17:45:51.000Z

""" Performance Test: Wifi Capacity Test : BRIDGE Mode pytest -m "wifi_capacity_test and BRIDGE" """ import os import pytest import allure pytestmark = [pytest.mark.performance, pytest.mark.bridge] # """pytest.mark.usefixtures("setup_test_run")"""] setup_params_general_dual_band = { "mode": "BRIDGE", "ssid_modes": { "wpa2_personal": [ {"ssid_name": "ssid_wpa2_dual_band", "appliedRadios": ["2G", "5G"], "security_key": "something"} ] }, "rf": {}, "radius": False } @allure.feature("BRIDGE MODE CLIENT CONNECTIVITY") @pytest.mark.parametrize( 'setup_profiles', [setup_params_general_dual_band], indirect=True, scope="class" ) @pytest.mark.usefixtures("setup_profiles") @pytest.mark.bridge @pytest.mark.twog @pytest.mark.fiveg @pytest.mark.dual_band @pytest.mark.wpa2_personal @pytest.mark.wifi_capacity_test class TestWifiCapacityBRIDGEModeDualBand(object): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE" """ @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3926", name="WIFI-3926") @pytest.mark.tcp_download def test_client_wpa2_BRIDGE_tcp_dl(self, get_vif_state, lf_tools, setup_profiles, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ lf_tools.reset_scenario() profile_data = setup_params_general_dual_band["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 get_vif_state.append(ssid_name) if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) # lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() influx_tags = ["tcp", "download", "2.4G-5G Combined"] wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_tcp_dl", mode=mode, vlan_id=vlan, download_rate="1Gbps", batch_size="1,5,10,20,40,64,128,256", influx_tags=influx_tags, upload_rate="0", protocol="TCP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3927", name="WIFI-3927") @pytest.mark.udp_download def test_client_wpa2_BRIDGE_udp_dl(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ lf_tools.reset_scenario() profile_data = setup_params_general_dual_band["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 get_vif_state.append(ssid_name) if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) # lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() influx_tags = ["udp", "download", "2.4G-5G Combined"] wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_udp_dl", mode=mode, vlan_id=vlan, download_rate="1Gbps", batch_size="1,5,10,20,40,64,128,256", influx_tags=influx_tags, upload_rate="0", protocol="UDP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3932", name="WIFI-3932") @pytest.mark.tcp_bidirectional def test_client_wpa2_BRIDGE_tcp_bidirectional(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ lf_tools.reset_scenario() profile_data = setup_params_general_dual_band["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) # lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() influx_tags = ["tcp", "bidirectional", "2.4G-5G Combined"] wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_tcp_bi", mode=mode, vlan_id=vlan, download_rate="1Gbps", batch_size="1,5,10,20,40,64,128,256", influx_tags=influx_tags, upload_rate="1Gbps", protocol="TCP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3933", name="WIFI-3933") @pytest.mark.udp_bidirectional def test_client_wpa2_BRIDGE_udp_bidirectional(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ lf_tools.reset_scenario() profile_data = setup_params_general_dual_band["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) # lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() influx_tags = ["udp", "bidirectional", "2.4G-5G Combined"] wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_udp_bi", mode=mode, vlan_id=vlan, download_rate="1Gbps", batch_size="1,5,10,20,40,64,128,256", influx_tags=influx_tags, upload_rate="1Gbps", protocol="UDP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True setup_params_general_2G = { "mode": "BRIDGE", "ssid_modes": { "wpa2_personal": [ {"ssid_name": "ssid_wpa2_2g", "appliedRadios": ["2G"], "security_key": "something"} ] }, "rf": {}, "radius": False } @allure.feature("BRIDGE MODE CLIENT CONNECTIVITY") @pytest.mark.parametrize( 'setup_profiles', [setup_params_general_2G], indirect=True, scope="class" ) @pytest.mark.usefixtures("setup_profiles") @pytest.mark.wpa2_personal @pytest.mark.twog @pytest.mark.twog_band class TestWifiCapacityBRIDGEMode2G(object): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE" """ @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3928", name="WIFI-3928") @pytest.mark.tcp_download def test_client_wpa2_BRIDGE_tcp_dl(self, get_vif_state, lf_tools, setup_profiles, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_2G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_tcp_dl", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="0", protocol="TCP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3930", name="WIFI-3930") @pytest.mark.udp_download def test_client_wpa2_BRIDGE_udp_dl(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_2G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_udp_dl", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="0", protocol="UDP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3934", name="WIFI-3934") @pytest.mark.tcp_bidirectional def test_client_wpa2_BRIDGE_tcp_bidirectional(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_2G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_tcp_bi", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="1Gbps", protocol="TCP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3935", name="WIFI-3935") @pytest.mark.udp_bidirectional def test_client_wpa2_BRIDGE_udp_bidirectional(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_2G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="2G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_udp_bi", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="1Gbps", protocol="UDP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True setup_params_general_5G = { "mode": "BRIDGE", "ssid_modes": { "wpa2_personal": [ {"ssid_name": "ssid_wpa2_5g", "appliedRadios": ["5G"], "security_key": "something"} ] }, "rf": {}, "radius": False } @allure.feature("BRIDGE MODE CLIENT CONNECTIVITY") @pytest.mark.parametrize( 'setup_profiles', [setup_params_general_5G], indirect=True, scope="class" ) @pytest.mark.usefixtures("setup_profiles") @pytest.mark.wpa2_personal @pytest.mark.fiveg @pytest.mark.fiveg_band class TestWifiCapacityBRIDGEMode5G(object): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE" """ @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3929", name="WIFI-3929") @pytest.mark.tcp_download def test_client_wpa2_BRIDGE_tcp_dl(self, get_vif_state, lf_tools, setup_profiles, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_5G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_tcp_dl", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="0", protocol="TCP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3931", name="WIFI-3931") @pytest.mark.udp_download def test_client_wpa2_BRIDGE_udp_dl(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_5G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_udp_dl", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="0", protocol="UDP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3936", name="WIFI-3936") @pytest.mark.tcp_bidirectional def test_client_wpa2_BRIDGE_tcp_bidirectional(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_5G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_tcp_bi", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="1Gbps", protocol="TCP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True @allure.testcase(url="https://telecominfraproject.atlassian.net/browse/WIFI-3937", name="WIFI-3937") @pytest.mark.udp_bidirectional def test_client_wpa2_BRIDGE_udp_bidirectional(self, get_vif_state, lf_tools, lf_test, station_names_twog, create_lanforge_chamberview_dut, get_configuration): """ Wifi Capacity Test BRIDGE mode pytest -m "wifi_capacity_test and BRIDGE and wpa2_personal and twog" """ profile_data = setup_params_general_5G["ssid_modes"]["wpa2_personal"][0] ssid_name = profile_data["ssid_name"] mode = "BRIDGE" vlan = 1 if ssid_name not in get_vif_state: allure.attach(name="retest,vif state ssid not available:", body=str(get_vif_state)) pytest.xfail("SSID NOT AVAILABLE IN VIF STATE") lf_tools.add_stations(band="5G", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.add_stations(band="ax", num_stations="max", dut=lf_tools.dut_name, ssid_name=ssid_name) lf_tools.Chamber_View() wct_obj = lf_test.wifi_capacity(instance_name="test_client_wpa2_BRIDGE_udp_bi", mode=mode, vlan_id=vlan, download_rate="1Gbps", upload_rate="1Gbps", protocol="UDP-IPv4", duration="60000") report_name = wct_obj.report_name[0]['LAST']["response"].split(":::")[1].split("/")[-1] lf_tools.attach_report_graphs(report_name=report_name) print("Test Completed... Cleaning up Stations") assert True

51.356674

112

0.633404

2,973

23,470

4.678776

0.049781

0.055787

0.048311

0.036233

0.963911

0.954278

0.951977

0

0.023942

0.250788

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456

113

51.469298

0.767118

0.083042

0

0.860399

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0.196551

0.021413

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false

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0.034188

0

null

0

1

0

1

0

null

0

7

00857be160580391324499add35f7d9c03e38fb6

604

py

Python

spine_json_lib/data/spine_exceptions.py

ivan-ah/spine-json-lib

1ea8460127f005d57af56090d2a48e6039437306

[ "MIT" ]

6

2019-12-02T15:25:57.000Z

2021-11-02T04:14:19.000Z

spine_json_lib/data/spine_exceptions.py

ivan-ah/spine-json-lib

1ea8460127f005d57af56090d2a48e6039437306

[ "MIT" ]

3

2020-03-20T11:09:22.000Z

2022-02-18T10:07:26.000Z

spine_json_lib/data/spine_exceptions.py

ivan-ah/spine-json-lib

1ea8460127f005d57af56090d2a48e6039437306

[ "MIT" ]

2

2019-12-02T14:56:50.000Z

2020-02-24T07:53:20.000Z

class SpineParsingException(Exception): def __init__(self, message, code_error=None, *args, **kwargs): self.message = message self.code_error = code_error super(SpineParsingException, self).__init__(*args, **kwargs) def __str__(self): return str(self.message) class SpineJsonEditorError(Exception): def __init__(self, message, code_error=None, *args, **kwargs): self.message = message self.code_error = code_error super(SpineJsonEditorError, self).__init__(*args, **kwargs) def __str__(self): return str(self.message)

28.761905

68

0.675497

66

604

5.727273

0.242424

0.174603

0.084656

0.10582

0.756614

0

0.211921

604

20

69

30.2

0.794118

0

0.714286

0

1

0.285714

false

0

0.142857

0.571429

0

null

0

1

0

1

0

null

0

1

0

1

0

9

00a195019fe8d6cd83c8bc33244c586ebbbc306b

2,635

py

Python

exp_configs/__init__.py

dongzhuoyao/embedding-propagation

1f14947bbd8be8a9950e7c4093fbfed0536809b9

[ "Apache-2.0" ]

null

exp_configs/__init__.py

dongzhuoyao/embedding-propagation

1f14947bbd8be8a9950e7c4093fbfed0536809b9

[ "Apache-2.0" ]

null

exp_configs/__init__.py

dongzhuoyao/embedding-propagation

1f14947bbd8be8a9950e7c4093fbfed0536809b9

[ "Apache-2.0" ]

null

from . import pretrain_exps, ssl_exps from . import pretrain_exps from . import pretrain_miniin_wrn_exps from . import pretrain_miniin_wrn50_2_exps from . import pretrain_miniin_resnet12_exps from . import pretrain_miniin_resnet50_exps from . import pretrain_miniin_densenet121_exps from . import pretrain_tieredin_wrn_exps from . import finetune_exps from . import finetune_miniin_wrn_exps from . import finetune_miniin_resnet12_exps from . import finetune_tieredin_wrn_exps from . import finetune_miniin_wrn50_2_exps from . import ssl_large_miniin_wrn_exps from . import ssl_large_inductive_miniin_wrn_exps from . import ssl_large_inductive_tieredin_wrn_exps from . import ssl_large_inductive_miniin_cars_wrn_exps from . import ssl_large_inductive_miniin_cub_wrn_exps from . import ssl_large_inductive_miniin_places_wrn_exps from . import ssl_large_inductive_miniin_plantae_wrn_exps from . import ssl_large_inductive_tieredin_cars_wrn_exps from . import ssl_large_inductive_tieredin_cub_wrn_exps from . import ssl_large_inductive_tieredin_places_wrn_exps from . import ssl_large_inductive_tieredin_plantae_wrn_exps EXP_GROUPS = {} EXP_GROUPS = pretrain_exps.EXP_GROUPS EXP_GROUPS.update(ssl_exps.EXP_GROUPS) EXP_GROUPS.update(pretrain_miniin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(pretrain_miniin_wrn50_2_exps.EXP_GROUPS) EXP_GROUPS.update(pretrain_miniin_resnet12_exps.EXP_GROUPS) EXP_GROUPS.update(pretrain_miniin_resnet50_exps.EXP_GROUPS) EXP_GROUPS.update(pretrain_miniin_densenet121_exps.EXP_GROUPS) EXP_GROUPS.update(pretrain_tieredin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(finetune_exps.EXP_GROUPS) EXP_GROUPS.update(finetune_miniin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(finetune_miniin_resnet12_exps.EXP_GROUPS) EXP_GROUPS.update(finetune_miniin_wrn50_2_exps.EXP_GROUPS) EXP_GROUPS.update(finetune_tieredin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_miniin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_miniin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_tieredin_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_miniin_cars_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_miniin_cub_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_miniin_places_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_miniin_plantae_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_tieredin_cars_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_tieredin_cub_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_tieredin_places_wrn_exps.EXP_GROUPS) EXP_GROUPS.update(ssl_large_inductive_tieredin_plantae_wrn_exps.EXP_GROUPS)

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2,635

5.135593

0.05569

0.207921

0.15323

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0.78265

0.736445

0.367751

0.296558

0

0.012063

0.056167

2,635

71

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0.840772

0

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false

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1

0

1

0

null

0

1

0

7

00ae9c03e4d6d1585e5b80aafb9680daa464fc7f

20,925

py

Python

spytest/spytest/prompts.py

mykolaf/sonic-mgmt

de77268526173c5e3a345f3f3703b56eb40c5eed

[ "Apache-2.0" ]

1

2021-09-15T17:09:13.000Z

spytest/spytest/prompts.py

mykolaf/sonic-mgmt

de77268526173c5e3a345f3f3703b56eb40c5eed

[ "Apache-2.0" ]

1

2020-02-05T16:51:53.000Z

spytest/spytest/prompts.py

mykolaf/sonic-mgmt

de77268526173c5e3a345f3f3703b56eb40c5eed

[ "Apache-2.0" ]

null

import os import re import logging from spytest.dicts import SpyTestDict from spytest.ordyaml import OrderedYaml prompts_root = os.path.join(os.path.dirname(__file__), '..', "datastore", "prompts") class Prompts(object): """ todo: Update Documentation """ def __init__(self, model=None, logger=None): """ Construction of Prompts object :param logger: :type logger: """ self.logger = logger or logging.getLogger() self.oyaml = None model = "sonic" if not model else re.sub("_(ssh|terminal)$", "", model) filename = "{}_prompts.yaml".format(model) filename = os.path.join(os.path.abspath(prompts_root), filename) self.oyaml = OrderedYaml(filename,[]) prompts_file_data = self.oyaml.get_data() or dict() self.patterns = prompts_file_data.patterns if "patterns" in prompts_file_data else SpyTestDict() self.modes = prompts_file_data.modes if "modes" in prompts_file_data else SpyTestDict() self.required_args = prompts_file_data.required_args if "required_args" in prompts_file_data else SpyTestDict() self.sudo_include_prompts = prompts_file_data.sudo_include_prompts if "sudo_include_prompts" in prompts_file_data else [] self.do_exclude_prompts = prompts_file_data.do_exclude_prompts if "do_exclude_prompts" in prompts_file_data else [] self.stored_values = SpyTestDict() def __del__(self): pass def update_with_hostname(self, hostname): for pattern in self.patterns: if re.search(r"{}", self.patterns[pattern]): #print("Matched Pattern: '{}' : '{}' : '{}'".format(pattern, self.patterns[pattern], self.patterns[pattern].format(hostname))) self.patterns[pattern] = re.sub(r"{}", hostname, self.patterns[pattern]) def get_mode_for_prompt(self, prompt): prompt2 = prompt.replace("\\", "") for mode in self.patterns: lpattern = self.patterns[mode] if re.search(lpattern, prompt2): return mode return "unknown-prompt" def get_prompt_for_mode(self, mode): if mode in self.patterns: return self.patterns[mode] return "unknown-mode" def check_args_for_req_mode(self, mode, **kwargs): missing_args_flag = 0 args_str = "" if mode in self.required_args: if mode == "vtysh-router-config": if "router" not in kwargs.keys(): missing_args_flag = 1 args_str = ", ".join(self.required_args[mode]) elif kwargs["router"] in ["bgp", "eigrp", "isis", "openfabric", "ospf"]: if "instance" not in kwargs.keys(): missing_args_flag = 1 args_str = ", ".join(self.required_args[mode]) elif mode == "vtysh-router-af-config" and "addr_family" not in kwargs.keys(): missing_args_flag = 1 args_str = ", ".join(self.required_args[mode]) else: for arg in self.required_args[mode]: if arg not in kwargs.keys(): missing_args_flag = 1 args_str = ", ".join(self.required_args[mode]) break if missing_args_flag: msg = "{} option(s) must be provided for {}.".format(args_str, mode) raise ValueError(msg) return def check_move_for_parent_of_frommode(self, prompt, mode, **kwargs): if mode == "vtysh-intf-config": return True if mode == "vtysh-router-config": if "router" not in self.stored_values: self.stored_values["router"] = kwargs["router"] return False else: if self.stored_values["router"] != kwargs["router"]: self.stored_values["router"] = kwargs["router"] return True if mode == "mgmt-ipv4-acl-config": if "aclname" not in self.stored_values: self.stored_values["aclname"] = kwargs["aclname"] return False else: if self.stored_values["aclname"] != kwargs["aclname"]: self.stored_values["aclname"] = kwargs["aclname"] return True if mode == "mgmt-evpn-view": if "evpnname" not in self.stored_values: self.stored_values["evpnname"] = kwargs["evpnname"] return False else: if self.stored_values["evpnname"] != kwargs["evpnname"]: self.stored_values["evpnname"] = kwargs["evpnname"] return True if mode == "mgmt-bfd-peer-view": if "peer_ip" not in self.stored_values: self.stored_values["peer_ip"] = kwargs["peer_ip"] return False else: if self.stored_values["peer_ip"] != kwargs["peer_ip"]: self.stored_values["peer_ip"] = kwargs["peer_ip"] return True if mode == "mgmt-route-map-view": if "map_name" not in self.stored_values: self.stored_values["map_name"] = kwargs["map_name"] self.stored_values["action"] = kwargs["action"] self.stored_values["seq_num"] = kwargs["seq_num"] return False else: if self.stored_values["map_name"] != kwargs["map_name"] or \ self.stored_values["action"] != kwargs["action"] or \ self.stored_values["seq_num"] != kwargs["seq_num"]: self.stored_values["map_name"] = kwargs["map_name"] self.stored_values["action"] = kwargs["action"] self.stored_values["seq_num"] = kwargs["seq_num"] return True if mode == "mgmt-link-state-track-view": if "track_name" not in self.stored_values: self.stored_values["track_name"] = kwargs["track_name"] return False else: if self.stored_values["track_name"] != kwargs["track_name"]: self.stored_values["track_name"] = kwargs["track_name"] return True if mode == "mgmt-router-bgp-view": if "bgp_instance" not in self.stored_values: self.stored_values["bgp_instance"] = kwargs["bgp_instance"] self.stored_values["bgp_vrf_name"] = kwargs["bgp_vrf_name"] return False else: if self.stored_values["bgp_instance"] != kwargs["bgp_instance"] or \ self.stored_values["bgp_vrf_name"] != kwargs["bgp_vrf_name"]: self.stored_values["bgp_instance"] = kwargs["bgp_instance"] self.stored_values["bgp_vrf_name"] = kwargs["bgp_vrf_name"] return True if mode == "mgmt-router-bgp-af-view": if "af_type" not in self.stored_values: self.stored_values["af_type"] = kwargs["af_type"] self.stored_values["af_family"] = kwargs["af_family"] return False else: if self.stored_values["af_type"] != kwargs["af_type"] or \ self.stored_values["af_family"] != kwargs["af_family"]: self.stored_values["af_type"] = kwargs["af_type"] self.stored_values["af_family"] = kwargs["af_family"] return True if mode == "mgmt-router-bgp-nbr-view": if "ip_address" not in self.stored_values: self.stored_values["ip_address"] = kwargs["ip_address"] return False else: if self.stored_values["ip_address"] != kwargs["ip_address"]: self.stored_values["ip_address"] = kwargs["ip_address"] return True if mode == "mgmt-router-bgp-nbr-af-view": if "nbr_af_type" not in self.stored_values: self.stored_values["nbr_af_type"] = kwargs["nbr_af_type"] self.stored_values["nbr_af_family"] = kwargs["nbr_af_family"] return False else: if self.stored_values["nbr_af_type"] != kwargs["nbr_af_type"] or \ self.stored_values["nbr_af_family"] != kwargs["nbr_af_family"]: self.stored_values["nbr_af_type"] = kwargs["nbr_af_type"] self.stored_values["nbr_af_family"] = kwargs["nbr_af_family"] return True if mode == "mgmt-router-bgp-template-view": if "group_name" not in self.stored_values: self.stored_values["group_name"] = kwargs["group_name"] return False else: if self.stored_values["group_name"] != kwargs["group_name"]: self.stored_values["group_name"] = kwargs["group_name"] return True if mode == "mgmt-router-bgp-template-af-view": if "tpl_af_type" not in self.stored_values: self.stored_values["tpl_af_type"] = kwargs["tpl_af_type"] self.stored_values["tpl_af_family"] = kwargs["tpl_af_family"] return False else: if self.stored_values["tpl_af_type"] != kwargs["tpl_af_type"] or \ self.stored_values["tpl_af_family"] != kwargs["tpl_af_family"]: self.stored_values["tpl_af_type"] = kwargs["tpl_af_type"] self.stored_values["tpl_af_family"] = kwargs["tpl_af_family"] return True if mode == "mgmt-router-bgp-l2vpn-vni-view": if "vxlan_id" not in self.stored_values: self.stored_values["vxlan_id"] = kwargs["vxlan_id"] return False else: if self.stored_values["vxlan_id"] != kwargs["vxlan_id"]: self.stored_values["vxlan_id"] = kwargs["vxlan_id"] return True if mode == "mgmt-intf-config": prompt2 = prompt.replace("\\", "") intfNum = "-{})".format(kwargs["interface"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-vlan-config": prompt2 = prompt.replace("\\", "") intfNum = "-Vlan{})".format(kwargs["vlan"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-lag-config": prompt2 = prompt.replace("\\", "") intfNum = "-po{})".format(kwargs["portchannel"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-management-config": prompt2 = prompt.replace("\\", "") intfNum = "-eth{})".format(kwargs["management"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-vxlan-view": prompt2 = prompt.replace("\\", "") intfNum = "-Vxlan-{})".format(kwargs["vxlan"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-mirror-session-config": prompt2 = prompt.replace("\\", "") intfNum = "-mirror-{})".format(kwargs["session_name"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-mclag-view": prompt2 = prompt.replace("\\", "") intfNum = "mclag-domain-{})".format(kwargs["domain_id"]) if intfNum in prompt2: return False else: return True if mode == "mgmt-lo-view": prompt2 = prompt.replace("\\", "") intfNum = "-lo{})".format(kwargs["loopback_id"]) if intfNum in prompt2: return False else: return True return False def check_move_for_parent_of_tomode(self, prompt, mode, **kwargs): check_for_parents = False if mode == "vtysh-router-config": if "router" not in self.stored_values: self.stored_values["router"] = kwargs["router"] return False else: if self.stored_values["router"] != kwargs["router"]: self.stored_values["router"] = kwargs["router"] check_for_parents = True if mode == "vtysh-router-af-config": if "router" in kwargs: if "router" not in self.stored_values: self.stored_values["router"] = kwargs["router"] return False else: if self.stored_values["router"] != kwargs["router"]: self.stored_values["router"] = kwargs["router"] check_for_parents = True if mode == "mgmt-ipv4-acl-config": if "aclname" not in self.stored_values: self.stored_values["aclname"] = kwargs["aclname"] return False else: if self.stored_values["aclname"] != kwargs["aclname"]: self.stored_values["aclname"] = kwargs["aclname"] if mode == "mgmt-evpn-view": if "evpnname" not in self.stored_values: self.stored_values["evpnname"] = kwargs["evpnname"] return False else: if self.stored_values["evpnname"] != kwargs["evpnname"]: self.stored_values["evpnname"] = kwargs["evpnname"] return True if mode == "mgmt-bfd-peer-view": if "peer_ip" not in self.stored_values: self.stored_values["peer_ip"] = kwargs["peer_ip"] return False else: if self.stored_values["peer_ip"] != kwargs["peer_ip"]: self.stored_values["peer_ip"] = kwargs["peer_ip"] return True if mode == "mgmt-route-map-view": if "map_name" not in self.stored_values: self.stored_values["map_name"] = kwargs["map_name"] self.stored_values["action"] = kwargs["action"] self.stored_values["seq_num"] = kwargs["seq_num"] return False else: if self.stored_values["map_name"] != kwargs["map_name"] or \ self.stored_values["action"] != kwargs["action"] or \ self.stored_values["seq_num"] != kwargs["seq_num"]: self.stored_values["map_name"] = kwargs["map_name"] self.stored_values["action"] = kwargs["action"] self.stored_values["seq_num"] = kwargs["seq_num"] return True if mode == "mgmt-link-state-track-view": if "track_name" not in self.stored_values: self.stored_values["track_name"] = kwargs["track_name"] return False else: if self.stored_values["track_name"] != kwargs["track_name"]: self.stored_values["track_name"] = kwargs["track_name"] return True if mode == "mgmt-router-bgp-view": if "bgp_instance" not in self.stored_values: self.stored_values["bgp_instance"] = kwargs["bgp_instance"] self.stored_values["bgp_vrf_name"] = kwargs["bgp_vrf_name"] return False else: if self.stored_values["bgp_instance"] != kwargs["bgp_instance"] or \ self.stored_values["bgp_vrf_name"] != kwargs["bgp_vrf_name"]: self.stored_values["bgp_instance"] = kwargs["bgp_instance"] self.stored_values["bgp_vrf_name"] = kwargs["bgp_vrf_name"] return True if mode == "mgmt-router-bgp-af-view": if "af_type" not in self.stored_values: self.stored_values["af_type"] = kwargs["af_type"] self.stored_values["af_family"] = kwargs["af_family"] return False else: if self.stored_values["af_type"] != kwargs["af_type"] or \ self.stored_values["af_family"] != kwargs["af_family"]: self.stored_values["af_type"] = kwargs["af_type"] self.stored_values["af_family"] = kwargs["af_family"] return True if mode == "mgmt-router-bgp-nbr-view": if "ip_address" not in self.stored_values: self.stored_values["ip_address"] = kwargs["ip_address"] return False else: if self.stored_values["ip_address"] != kwargs["ip_address"]: self.stored_values["ip_address"] = kwargs["ip_address"] return True if mode == "mgmt-router-bgp-nbr-af-view": if "nbr_af_type" not in self.stored_values: self.stored_values["nbr_af_type"] = kwargs["nbr_af_type"] self.stored_values["nbr_af_family"] = kwargs["nbr_af_family"] return False else: if self.stored_values["nbr_af_type"] != kwargs["nbr_af_type"] or \ self.stored_values["nbr_af_family"] != kwargs["nbr_af_family"]: self.stored_values["nbr_af_type"] = kwargs["nbr_af_type"] self.stored_values["nbr_af_family"] = kwargs["nbr_af_family"] return True if mode == "mgmt-router-bgp-template-view": if "group_name" not in self.stored_values: self.stored_values["group_name"] = kwargs["group_name"] return False else: if self.stored_values["group_name"] != kwargs["group_name"]: self.stored_values["group_name"] = kwargs["group_name"] return True if mode == "mgmt-router-bgp-template-af-view": if "tpl_af_type" not in self.stored_values: self.stored_values["tpl_af_type"] = kwargs["tpl_af_type"] self.stored_values["tpl_af_family"] = kwargs["tpl_af_family"] return False else: if self.stored_values["tpl_af_type"] != kwargs["tpl_af_type"] or \ self.stored_values["tpl_af_family"] != kwargs["tpl_af_family"]: self.stored_values["tpl_af_type"] = kwargs["tpl_af_type"] self.stored_values["tpl_af_family"] = kwargs["tpl_af_family"] return True if mode == "mgmt-router-bgp-l2vpn-vni-view": if "vxlan_id" not in self.stored_values: self.stored_values["vxlan_id"] = kwargs["vxlan_id"] return False else: if self.stored_values["vxlan_id"] != kwargs["vxlan_id"]: self.stored_values["vxlan_id"] = kwargs["vxlan_id"] return True if check_for_parents: parent_modes_list = [] curr_mode = self.get_mode_for_prompt(prompt) while True: parent_modes_list.append(self.modes[curr_mode][0]) curr_mode = self.modes[curr_mode][0] if curr_mode == "": break if mode in parent_modes_list: return True return False def get_backward_command_and_prompt(self, mode): if mode not in self.modes: return ["", ""] cmd = self.modes[mode][2] expected_prompt = self.get_prompt_for_mode(self.modes[mode][0]) return [cmd, expected_prompt] def get_forward_command_and_prompt_with_values(self, mode, **kwargs): if mode not in self.modes: return ["", ""] cmd = self.modes[mode][1] expected_prompt = self.get_prompt_for_mode(mode) if mode in self.required_args: values = [] for arg in self.required_args[mode]: if arg in kwargs.keys(): if mode == "mgmt-intf-config" and arg == "interface": intf_value = re.sub("Ethernet", "Ethernet ", kwargs[arg]) values.append(intf_value) else: values.append(kwargs[arg]) else: values.append("") cmd = cmd.format(*values) return [cmd, expected_prompt]

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Python

tests/test_modis.py

up42/modis

05df85d606abe60cf101efa522a31a6bd6479bda

[ "MIT" ]

4

2020-03-17T22:10:29.000Z

2021-08-05T11:34:45.000Z

tests/test_modis.py

up42/modis

05df85d606abe60cf101efa522a31a6bd6479bda

[ "MIT" ]

11

2019-08-16T08:30:42.000Z

2022-03-12T00:11:12.000Z

tests/test_modis.py

up42/modis

05df85d606abe60cf101efa522a31a6bd6479bda

[ "MIT" ]

1

2021-05-06T12:24:42.000Z

""" Integration tests for the higher-level fetch methods """ # pylint: disable=unused-import, redefined-outer-name # requests_mock used as fixture in tests import os import re import rasterio as rio import numpy as np import pytest from rio_cogeo.cogeo import cog_validate from context import STACQuery, Modis from blockutils.exceptions import UP42Error @pytest.fixture() def modis_instance(): return Modis(default_zoom_level=9) @pytest.mark.live def test_aoiclipped_fetcher_fetch_in_dry_run_mode(modis_instance): """ Test for dry-run mode i.e. only metadata is returned """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [ 123.59349578619005, -10.188159969024264, 123.70257586240771, -10.113232998848046, ], "imagery_layers": ["MODIS_Terra_CorrectedReflectance_TrueColor"], } ) result = modis_instance.fetch(query, dry_run=True) assert len(result.features) == 1 assert "up42.data_path" not in result.features[0]["properties"].keys() assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") @pytest.mark.live def test_aoiclipped_fetcher_multiple_fetch_in_dry_run_mode(modis_instance): """ Test for dry-run mode i.e. only metadata is returned, multiple imagery_layers """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [ 123.59349578619005, -10.188159969024264, 123.70257586240771, -10.113232998848046, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "MODIS_Aqua_CorrectedReflectance_TrueColor", ], } ) result = modis_instance.fetch(query, dry_run=True) assert len(result.features) == 1 assert "up42.data_path" not in result.features[0]["properties"].keys() assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") @pytest.mark.live def test_aoiclipped_fetcher_layer_error_fetch_in_dry_run_mode(modis_instance): """ Test for dry-run mode i.e. only metadata is returned, error in name of layer """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [ 123.59349578619005, -10.188159969024264, 123.70257586240771, -10.113232998848046, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "AN_ERROR_FOR_SURE", ], } ) with pytest.raises(UP42Error, match=r".*['AN_ERROR_FOR_SURE'].*"): modis_instance.fetch(query, dry_run=True) @pytest.mark.live def test_aoiclipped_fetcher_geom_error_fetch_in_dry_run_mode(modis_instance): """ Test for dry-run mode i.e. only metadata is returned, error in geometry """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [200, 200, 210, 210], "imagery_layers": ["MODIS_Terra_CorrectedReflectance_TrueColor"], } ) with pytest.raises(UP42Error): modis_instance.fetch(query, dry_run=True) def test_aoiclipped_dry_run_only_bbox(requests_mock, modis_instance): """ Mocked test for fetching data with only bbox param """ _location_ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) with open( os.path.join(_location_, "mock_data/available_imagery_layers.xml"), "rb" ) as xml_file: mock_xml: object = xml_file.read() with open(os.path.join(_location_, "mock_data/tile.jpg"), "rb") as tile_file: mock_image: object = tile_file.read() matcher_get_capabilities = re.compile("WMTSCapabilities.xml") matcher_wms = re.compile( "https://gibs.earthdata.nasa.gov/wms/epsg4326/best/wms.cgi?" ) matcher_wmts = re.compile( "https://gibs.earthdata.nasa.gov/wmts/epsg3857/" "best/MODIS_Terra_CorrectedReflectance_TrueColor/" ) matcher_get_capabilities = re.compile("WMTSCapabilities.xml") requests_mock.get(matcher_get_capabilities, content=mock_xml) requests_mock.get(matcher_wms, content=mock_image) requests_mock.get(matcher_wmts, content=mock_image) query = STACQuery.from_dict({"bbox": [76.231358, 9.909276, 76.300637, 9.971047]}) res = modis_instance.fetch(query, dry_run=True) assert len(res.features) == 1 def test_aoiclipped_fetcher_fetch(requests_mock, modis_instance): """ Mocked test for fetching data - quicker than the live one and therefore valuable for testing purposes """ _location_ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) with open(os.path.join(_location_, "mock_data/tile.jpg"), "rb") as tile_file: mock_image: object = tile_file.read() with open( os.path.join(_location_, "mock_data/available_imagery_layers.xml"), "rb" ) as xml_file: mock_xml: object = xml_file.read() matcher_wms = re.compile( "https://gibs.earthdata.nasa.gov/wms/epsg4326/best/wms.cgi?" ) matcher_wmts = re.compile( "https://gibs.earthdata.nasa.gov/wmts/epsg3857/" "best/MODIS_Terra_CorrectedReflectance_TrueColor/" ) matcher_get_capabilities = re.compile("WMTSCapabilities.xml") requests_mock.get(matcher_get_capabilities, content=mock_xml) requests_mock.get(matcher_wms, content=mock_image) requests_mock.get(matcher_wmts, content=mock_image) query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [ 123.59349578619005, -10.188159969024264, 123.70257586240771, -10.113232998848046, ], "imagery_layers": ["MODIS_Terra_CorrectedReflectance_TrueColor"], } ) result = modis_instance.fetch(query, dry_run=False) assert len(result.features) == 1 img_filename = f"/tmp/output/{result.features[0]['properties']['up42.data_path']}" assert cog_validate(img_filename)[0] with rio.open(img_filename) as dataset: band2 = dataset.read(2) assert np.sum(band2) == 7954025 assert dataset.tags(1)["layer"] == "MODIS_Terra_CorrectedReflectance_TrueColor" assert dataset.tags(1)["band"] == str(1) assert dataset.tags(2)["band"] == str(2) assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") def test_aoiclipped_dry_run_error_name_fetcher_fetch(requests_mock, modis_instance): """ Mocked test for fetching data with error in name """ _location_ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) with open( os.path.join(_location_, "mock_data/available_imagery_layers.xml"), "rb" ) as xml_file: mock_xml: object = xml_file.read() matcher_get_capabilities = re.compile("WMTSCapabilities.xml") requests_mock.get(matcher_get_capabilities, content=mock_xml) query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [ 123.59349578619005, -10.188159969024264, 123.70257586240771, -10.113232998848046, ], "imagery_layers": ["AN_ERROR_FOR_SURE"], } ) with pytest.raises(UP42Error, match=r".*['AN_ERROR_FOR_SURE'].*"): modis_instance.fetch(query, dry_run=True) def test_aoiclipped_dry_run_multiple_error_name_fetcher_fetch( requests_mock, modis_instance ): """ Mocked test for fetching data with error in name """ _location_ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) with open( os.path.join(_location_, "mock_data/available_imagery_layers.xml"), "rb" ) as xml_file: mock_xml: object = xml_file.read() matcher_get_capabilities = re.compile("WMTSCapabilities.xml") requests_mock.get(matcher_get_capabilities, content=mock_xml) query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [ 123.59349578619005, -10.188159969024264, 123.70257586240771, -10.113232998848046, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "MODIS_Aqua_CorrectedReflectance_TrueColor", "12345", "AN_ERROR_FOR_SURE", ], } ) with pytest.raises(UP42Error, match=r".*['12345','AN_ERROR_FOR_SURE'].*"): modis_instance.fetch(query, dry_run=True) def test_aoiclipped_dry_run_error_geom_fetcher_fetch(requests_mock, modis_instance): """ Mocked test for fetching data with error in geom """ _location_ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__))) with open( os.path.join(_location_, "mock_data/available_imagery_layers.xml"), "rb" ) as xml_file: mock_xml: object = xml_file.read() matcher_get_capabilities = re.compile("WMTSCapabilities.xml") requests_mock.get(matcher_get_capabilities, content=mock_xml) query = STACQuery.from_dict( { "zoom_level": 9, "time": "2018-11-01T16:40:49+00:00/2018-11-20T16:41:49+00:00", "limit": 1, "bbox": [179, 89, 180, 90], "imagery_layers": ["MODIS_Terra_CorrectedReflectance_TrueColor"], } ) with pytest.raises(UP42Error): modis_instance.fetch(query, dry_run=True) @pytest.mark.live def test_aoiclipped_fetcher_fetch_live(modis_instance): """ Unmocked ("live") test for fetching data """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2019-01-25T16:41:49+00:00", "limit": 2, "bbox": [ 38.941807150840766, 21.288749561718983, 39.686130881309516, 21.808610762909364, ], "imagery_layers": ["MODIS_Terra_CorrectedReflectance_TrueColor"], } ) result = modis_instance.fetch(query, dry_run=False) assert len(result.features) == 2 img_filename = f"/tmp/output/{result.features[0]['properties']['up42.data_path']}" with rio.open(img_filename) as dataset: band2 = dataset.read(2) assert np.sum(band2) == 28351388 assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") @pytest.mark.live def test_aoiclipped_fetcher_virs_fetch_live(modis_instance): """ Unmocked ("live") test for fetching VIIRS data in png """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2019-01-25T16:41:49+00:00", "limit": 2, "bbox": [ 38.941807150840766, 21.288749561718983, 39.686130881309516, 21.808610762909364, ], "imagery_layers": ["VIIRS_SNPP_Brightness_Temp_BandI5_Night"], } ) result = modis_instance.fetch(query, dry_run=False) assert len(result.features) == 2 img_filename = f"/tmp/output/{result.features[0]['properties']['up42.data_path']}" with rio.open(img_filename) as dataset: band1 = dataset.read(1) assert np.sum(band1) == 45232508 assert dataset.count == 1 assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") assert cog_validate(img_filename)[0] @pytest.mark.live def test_aoiclipped_fetcher_rio_tags_fetch_live(modis_instance): """ Unmocked ("live") test for fetching MODIS and VIRS data with tags """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2019-01-25T16:41:49+00:00", "limit": 2, "bbox": [ 38.941807150840766, 21.288749561718983, 39.686130881309516, 21.808610762909364, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "VIIRS_SNPP_Brightness_Temp_BandI5_Night", ], } ) result = modis_instance.fetch(query, dry_run=False) assert len(result.features) == 2 img_filename = f"/tmp/output/{result.features[0]['properties']['up42.data_path']}" with rio.open(img_filename) as dataset: assert dataset.count == 4 band1 = dataset.read(1) assert np.sum(band1) == 29570538 assert dataset.tags(1)["layer"] == "MODIS_Terra_CorrectedReflectance_TrueColor" assert dataset.tags(1)["band"] == str(1) assert dataset.tags(4)["layer"] == "VIIRS_SNPP_Brightness_Temp_BandI5_Night" assert dataset.tags(4)["band"] == str(1) assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") @pytest.mark.live def test_aoiclipped_fetcher_multiple_fetch_live(modis_instance): """ Unmocked ("live") test for fetching data, multiple imagery_layers """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2019-01-25T16:41:49+00:00", "limit": 2, "bbox": [ 38.941807150840766, 21.288749561718983, 39.686130881309516, 21.808610762909364, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "MODIS_Aqua_CorrectedReflectance_TrueColor", ], } ) result = modis_instance.fetch(query, dry_run=False) assert len(result.features) == 2 img_filename = f"/tmp/output/{result.features[0]['properties']['up42.data_path']}" with rio.open(img_filename) as dataset: band2 = dataset.read(2) assert np.sum(band2) == 28351388 assert dataset.count == 6 assert os.path.isfile(f"/tmp/quicklooks/{result.features[0]['id']}.jpg") assert cog_validate(img_filename)[0] @pytest.mark.live def test_aoiclipped_fetcher_layer_error_fetch_live(modis_instance): """ Unmocked ("live") test for fetching data, error in name of layer """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2019-01-25T16:41:49+00:00", "limit": 2, "bbox": [ 38.941807150840766, 21.288749561718983, 39.686130881309516, 21.808610762909364, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "AN_ERROR_FOR_SURE", ], } ) with pytest.raises(UP42Error, match=r".*['AN_ERROR_FOR_SURE'].*"): modis_instance.fetch(query, dry_run=False) @pytest.mark.live def test_aoiclipped_fetcher_geom_error_fetch_live(modis_instance): """ Unmocked ("live") test for fetching data, error in geometry of layer """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2019-01-25T16:41:49+00:00", "limit": 2, "bbox": [200, 200, 210, 210], "imagery_layers": ["MODIS_Terra_CorrectedReflectance_TrueColor"], } ) with pytest.raises(UP42Error): modis_instance.fetch(query, dry_run=False) @pytest.mark.live def test_aoiclipped_fetcher_layers_cog(modis_instance): """ Unmocked ("live") test for fetching data. Tests cog conversion with image, with 7 bands. """ query = STACQuery.from_dict( { "zoom_level": 9, "time": "2019-01-01T16:40:49+00:00/2021-02-15T23:59:59+00:00", "limit": 1, "bbox": [ 38.941807150840766, 21.288749561718983, 39.686130881309516, 21.808610762909364, ], "imagery_layers": [ "MODIS_Terra_CorrectedReflectance_TrueColor", "MODIS_Terra_EVI_8Day", "MODIS_Terra_CorrectedReflectance_Bands721", ], } ) result = modis_instance.fetch(query, dry_run=False) assert len(result.features) == 1 img_filename = f"/tmp/output/{result.features[0]['properties']['up42.data_path']}" with rio.open(img_filename) as dataset: band2 = dataset.read(2) assert np.sum(band2) == 28202042 assert dataset.count == 7 assert cog_validate(img_filename)[0]

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Python

app/pages/__init__.py

Anioko/CMS

b6465faf2a5d7333f494526bcddf8083d6807aee

[ "MIT" ]

null

app/pages/__init__.py

Anioko/CMS

b6465faf2a5d7333f494526bcddf8083d6807aee

[ "MIT" ]

1

2021-06-02T01:40:15.000Z

app/pages/__init__.py

Anioko/CMS

b6465faf2a5d7333f494526bcddf8083d6807aee

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Python

Define_Model/ResNet.py

Wenhao-Yang/DeepSpeaker-pytorch

99eb8de3357c85e2b7576da2a742be2ffd773ead

[ "MIT" ]

8

2020-08-26T13:32:56.000Z

2022-01-18T21:05:46.000Z

Define_Model/ResNet.py

Wenhao-Yang/DeepSpeaker-pytorch

99eb8de3357c85e2b7576da2a742be2ffd773ead

[ "MIT" ]

1

2020-07-24T17:06:16.000Z

Define_Model/ResNet.py

Wenhao-Yang/DeepSpeaker-pytorch

99eb8de3357c85e2b7576da2a742be2ffd773ead

[ "MIT" ]

5

2020-12-11T03:31:15.000Z

2021-11-23T15:57:55.000Z

#!/usr/bin/env python # encoding: utf-8 """ @Author: yangwenhao @Contact: 874681044@qq.com @Software: PyCharm @File: ResNet.py @Time: 2019/10/10 下午5:09 @Overview: Deep Speaker using Resnet with CNN, which is not ordinary Resnet. This file define resnet in 'Deep Residual Learning for Image Recognition' For all model, the pre_forward function is for extract vectors and forward for classification. """ import torch import torch.nn.functional as F from torch import nn from torchvision.models.resnet import BasicBlock from torchvision.models.resnet import Bottleneck from torchvision.models.densenet import _DenseBlock from torchvision.models.shufflenetv2 import InvertedResidual from Define_Model.FilterLayer import TimeMaskLayer, FreqMaskLayer, SqueezeExcitation, GAIN, fBLayer, fBPLayer, fLLayer from Define_Model.FilterLayer import fDLR, GRL, L2_Norm, Mean_Norm, Inst_Norm, MeanStd_Norm, CBAM from Define_Model.Pooling import SelfAttentionPooling, AttentionStatisticPooling, StatisticPooling, AdaptiveStdPool2d, \ SelfVadPooling, GhostVLAD_v2 from Define_Model.FilterLayer import fDLR, GRL from Define_Model.Pooling import SelfAttentionPooling, AttentionStatisticPooling, StatisticPooling, AdaptiveStdPool2d, \ SelfVadPooling def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) def conv3x3(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, padding=1, stride=stride, bias=False) class SEBasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction_ratio=4): super(SEBasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride self.reduction_ratio = reduction_ratio # Squeeze-and-Excitation self.se_layer = SqueezeExcitation(inplanes=planes, reduction_ratio=reduction_ratio) def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out = self.se_layer(out) out += identity out = self.relu(out) return out class CBAMBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None, reduction_ratio=16): super(CBAMBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride self.reduction_ratio = reduction_ratio # Squeeze-and-Excitation self.CBAM_layer = CBAM(planes, planes) def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out = self.CBAM_layer(out) out += identity out = self.relu(out) return out class Res2Conv2dReluBn(nn.Module): ''' in_channels == out_channels == channels ''' def __init__(self, channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False, scale=4): super().__init__() assert channels % scale == 0, "{} % {} != 0".format(channels, scale) self.scale = scale self.width = channels // scale self.nums = scale if scale == 1 else scale - 1 self.convs = [] self.bns = [] for i in range(self.nums): self.convs.append(nn.Conv2d(self.width, self.width, kernel_size, stride, padding, dilation, bias=bias)) self.bns.append(nn.BatchNorm2d(self.width)) self.convs = nn.ModuleList(self.convs) self.bns = nn.ModuleList(self.bns) def forward(self, x): out = [] spx = torch.split(x, self.width, 1) for i in range(self.nums): if i == 0: sp = spx[i] else: sp = sp + spx[i] # Order: conv -> relu -> bn sp = self.convs[i](sp) sp = self.bns[i](F.relu(sp)) out.append(sp) if self.scale != 1: out.append(spx[self.nums]) out = torch.cat(out, dim=1) return out class Conv2dReluBn(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False): super().__init__() self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias) self.bn = nn.BatchNorm2d(out_channels) def forward(self, x): return self.bn(F.relu(self.conv(x))) ''' SE-Res2Block. Note: residual connection is implemented in the ECAPA_TDNN model, not here. ''' class SE_Res2Block(nn.Module): def __init__(self, inplanes, planes, kernel_size, padding, stride=1, dilation=1, scale=8, reduction_ratio=2): super(SE_Res2Block, self).__init__() self.scale = scale self.stride = stride # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = Conv2dReluBn(inplanes, planes, kernel_size=1, stride=1, padding=0), self.conv2 = Res2Conv2dReluBn(planes, kernel_size, stride, padding, dilation, scale=scale), self.conv3 = Conv2dReluBn(planes, planes, kernel_size=1, stride=1, padding=0), # Squeeze-and-Excitation self.se_layer = SqueezeExcitation(inplanes=planes, reduction_ratio=reduction_ratio) def forward(self, x): identity = x out = self.conv1(x) out = self.conv2(out) out = self.conv3(out) out = self.se_layer(out) out += identity out = self.relu(out) return out class Block3x3(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(Block3x3, self).__init__() self.conv1 = conv3x3(inplanes, planes) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = conv3x3(planes, planes, stride) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = conv3x3(planes, planes) self.bn3 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class InstBlock3x3(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(InstBlock3x3, self).__init__() self.conv1 = conv3x3(inplanes, planes) self.bn1 = nn.InstanceNorm2d(planes) self.conv2 = conv3x3(planes, planes, stride) self.bn2 = nn.InstanceNorm2d(planes) self.conv3 = conv3x3(planes, planes) self.bn3 = nn.InstanceNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class VarSizeConv(nn.Module): def __init__(self, inplanes, planes, stride=1, kernel_size=[3, 5, 9]): super(VarSizeConv, self).__init__() self.stide = stride self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size[0], stride=stride, padding=1) self.bn1 = nn.InstanceNorm2d(planes) self.conv2 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size[1], stride=stride, padding=2) self.bn2 = nn.InstanceNorm2d(planes) self.conv3 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size[2], stride=stride, padding=4) self.bn3 = nn.InstanceNorm2d(planes) self.avg = nn.AvgPool2d(kernel_size=int(stride * 2 + 1), stride=stride, padding=stride) def forward(self, x): x1 = self.conv1(x) x1 = self.bn1(x1) x2 = self.conv2(x) x2 = self.bn2(x2) x3 = self.conv3(x) x3 = self.bn3(x3) if self.stide != 1: x = self.avg(x) return torch.cat([x, x1, x2, x3], dim=1) # return torch.cat([x, x1, x2, x3], dim=1) class SimpleResNet(nn.Module): def __init__(self, block=BasicBlock, num_classes=1000, embedding_size=128, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, **kwargs): super(SimpleResNet, self).__init__() layers = [3, 4, 6, 3] if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.embedding_size=embedding_size self.inplanes = 16 self.dilation = 1 num_filter = [16, 32, 64, 128] if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(1, num_filter[0], kernel_size=3, stride=1, padding=1, bias=False) self.bn1 = norm_layer(num_filter[0]) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) # num_filter = [16, 32, 64, 128] self.layer1 = self._make_layer(block, num_filter[0], layers[0]) self.layer2 = self._make_layer(block, num_filter[1], layers[1], stride=2) self.layer3 = self._make_layer(block, num_filter[2], layers[2], stride=2) self.layer4 = self._make_layer(block, num_filter[3], layers[3], stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc1 = nn.Linear(128 * block.expansion, embedding_size) # self.norm = self.l2_norm(num_filter[3]) self.alpha = 12 self.fc2 = nn.Linear(embedding_size, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') nn.init.normal(m.weight, mean=0., std=1.) elif isinstance(m, nn.BatchNorm2d): nn.init.constant(m.weight, 1) nn.init.constant(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant(m.bn2.weight, 0) def l2_norm(self, input): input_size = input.size() buffer = torch.pow(input, 2) normp = torch.sum(buffer, 1).add_(1e-10) norm = torch.sqrt(normp) _output = torch.div(input, norm.view(-1, 1).expand_as(input)) output = _output.view(input_size) return output def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _forward(self, x): # pdb.set_trace() x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) # print(x.shape) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) # pdb.set_trace() x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc1(x) x = self.l2_norm(x) embeddings = x * self.alpha x = self.fc2(embeddings) return x, embeddings # Allow for accessing forward method in a inherited class forward = _forward # Analysis of Length Normalization in End-to-End Speaker Verification System # https://arxiv.org/abs/1806.03209 <<<<<<< HEAD class ExporingResNet(nn.Module): def __init__(self, resnet_size=34, block=BasicBlock, inst_norm=True, kernel_size=5, stride=1, padding=2, feat_dim=64, num_classes=1000, embedding_size=128, fast=False, time_dim=2, avg_size=4, alpha=12, encoder_type='SAP', zero_init_residual=False, groups=1, width_per_group=64, input_dim=257, sr=16000, filter=True, replace_stride_with_dilation=None, norm_layer=None, **kwargs): super(ExporingResNet, self).__init__() ======= class ThinResNet(nn.Module): def __init__(self, resnet_size=34, block_type='None', expansion=1, channels=[16, 32, 64, 128], input_len=300, inst_norm=True, input_dim=257, sr=16000, gain_axis='both', kernel_size=5, stride=1, padding=2, dropout_p=0.0, exp=False, filter_fix=False, feat_dim=64, num_classes=1000, embedding_size=128, fast='None', time_dim=1, avg_size=4, alpha=12, encoder_type='STAP', zero_init_residual=False, groups=1, width_per_group=64, filter=None, replace_stride_with_dilation=None, norm_layer=None, mask='None', mask_len=10, input_norm='', gain_layer=False, **kwargs): super(ThinResNet, self).__init__() >>>>>>> Server/Server resnet_type = {8: [1, 1, 1, 0], 10: [1, 1, 1, 1], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_type[resnet_size] <<<<<<< HEAD if norm_layer is None: norm_layer = nn.BatchNorm2d self.inst_norm = inst_norm self.filter = filter self._norm_layer = norm_layer ======= freq_dim = avg_size # default 1 time_dim = time_dim # default 4 self.input_len = input_len self.input_dim = input_dim self.inst_norm = inst_norm self.filter = filter self._norm_layer = nn.BatchNorm2d >>>>>>> Server/Server self.embedding_size = embedding_size self.dropout_p = dropout_p self.gain_layer = gain_layer self.gain_axis = gain_axis self.mask = mask self.dilation = 1 <<<<<<< HEAD self.fast = fast num_filter = [16, 32, 64, 128] ======= self.fast = str(fast) self.num_filter = channels # [16, 32, 64, 128] self.inplanes = self.num_filter[0] if block_type == "seblock": block = SEBasicBlock elif block_type == 'cbam': block = CBAMBlock else: block = BasicBlock if resnet_size < 50 else Bottleneck block.expansion = expansion # num_filter = [32, 64, 128, 256] >>>>>>> Server/Server if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group <<<<<<< HEAD self.filter_layer = fDLR(input_dim=input_dim, sr=sr, num_filter=feat_dim) self.conv1 = nn.Conv2d(1, num_filter[0], kernel_size=kernel_size, stride=stride, padding=padding, bias=False) self.bn1 = norm_layer(num_filter[0]) self.relu = nn.ReLU(inplace=True) if self.fast: self.maxpool = nn.Sequential(nn.MaxPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0)), nn.AvgPool2d(kernel_size=(1, 3), stride=(1, 2), padding=(0, 1)) ) self.layer1 = self._make_layer(block, num_filter[0], layers[0]) self.layer2 = self._make_layer(block, num_filter[1], layers[1], stride=2) self.layer3 = self._make_layer(block, num_filter[2], layers[2], stride=2) if self.fast: self.layer4 = self._make_layer(block, num_filter[3], layers[3], stride=1) else: self.layer4 = self._make_layer(block, num_filter[3], layers[3], stride=2) # [64, 128, 37, 8] freq_dim = avg_size # default 1 time_dim = time_dim # default 4 ======= if self.filter == 'fDLR': self.filter_layer = fDLR(input_dim=input_dim, sr=sr, num_filter=feat_dim, exp=exp, filter_fix=filter_fix) elif self.filter == 'fBLayer': self.filter_layer = fBLayer(input_dim=input_dim, sr=sr, num_filter=feat_dim, exp=exp, filter_fix=filter_fix) elif self.filter == 'fBPLayer': self.filter_layer = fBPLayer(input_dim=input_dim, sr=sr, num_filter=feat_dim, exp=exp, filter_fix=filter_fix) elif self.filter == 'fLLayer': self.filter_layer = fLLayer(input_dim=input_dim, num_filter=feat_dim, exp=exp) elif self.filter == 'Avg': self.filter_layer = nn.AvgPool2d(kernel_size=(1, 7), stride=(1, 3)) else: self.filter_layer = None self.input_norm = input_norm if input_norm == 'Instance': self.inst_layer = Inst_Norm(input_dim) elif input_norm == 'Mean': self.inst_layer = Mean_Norm() else: self.inst_layer = None if self.mask == "time": self.maks_layer = TimeMaskLayer(mask_len=mask_len) elif self.mask == "freq": self.mask = FreqMaskLayer(mask_len=mask_len) elif self.mask == "time_freq": self.mask_layer = nn.Sequential( TimeMaskLayer(), FreqMaskLayer() ) else: self.mask_layer = None self.conv1 = nn.Conv2d(1, self.num_filter[0], kernel_size=kernel_size, stride=stride, padding=padding) self.bn1 = self._norm_layer(self.num_filter[0]) self.relu = nn.ReLU(inplace=True) if self.fast.startswith('avp'): # self.maxpool = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) self.maxpool = nn.AvgPool2d(kernel_size=(3, 3), stride=(1, 2), padding=(1, 1)) elif self.fast.startswith('mxp'): self.maxpool = nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 2), padding=(1, 1)) else: self.maxpool = None self.layer1 = self._make_layer(block, self.num_filter[0], layers[0]) self.layer2 = self._make_layer(block, self.num_filter[1], layers[1], stride=2) self.layer3 = self._make_layer(block, self.num_filter[2], layers[2], stride=2) if self.fast in ['avp1', 'mxp1']: self.layer4 = self._make_layer(block, self.num_filter[3], layers[3], stride=1) else: self.layer4 = self._make_layer(block, self.num_filter[3], layers[3], stride=2) self.gain = GAIN(time=self.input_len, freq=self.input_dim) if self.gain_layer else None self.dropout = nn.Dropout(self.dropout_p) # [64, 128, 37, 8] >>>>>>> Server/Server # self.avgpool = nn.AvgPool2d(kernel_size=(3, 4), stride=(2, 1)) # 300 is the length of features if encoder_type == 'SAP': <<<<<<< HEAD self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = SelfAttentionPooling(input_dim=num_filter[3], hidden_dim=num_filter[3]) self.fc1 = nn.Sequential( nn.Linear(num_filter[3], embedding_size), nn.BatchNorm1d(embedding_size) ) elif encoder_type == 'SASP': self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = AttentionStatisticPooling(input_dim=num_filter[3], hidden_dim=num_filter[3]) self.fc1 = nn.Sequential( nn.Linear(num_filter[3] * 2, embedding_size), nn.BatchNorm1d(embedding_size) ) elif encoder_type == 'STAP': self.avgpool = nn.AdaptiveAvgPool2d((None, freq_dim)) self.encoder = StatisticPooling(input_dim=num_filter[3]) self.fc1 = nn.Sequential( nn.Linear(num_filter[3] * 2, embedding_size), nn.BatchNorm1d(embedding_size) ) elif encoder_type == 'ASTP': self.avgpool = AdaptiveStdPool2d((time_dim, freq_dim)) self.encoder = None self.fc1 = nn.Sequential( nn.Linear(num_filter[3] * freq_dim * time_dim, embedding_size), nn.BatchNorm1d(embedding_size) ) else: self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = None self.fc1 = nn.Sequential( nn.Linear(num_filter[3] * freq_dim * time_dim, embedding_size), nn.BatchNorm1d(embedding_size) ) self.alpha = alpha ======= self.avgpool = nn.AdaptiveAvgPool2d((None, freq_dim)) self.encoder = SelfAttentionPooling(input_dim=self.num_filter[3] * block.expansion, hidden_dim=self.num_filter[3] * block.expansion) self.encoder_output = self.num_filter[3] * block.expansion elif encoder_type == 'SASP': self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = AttentionStatisticPooling(input_dim=self.num_filter[3] * block.expansion, hidden_dim=self.num_filter[3]) self.encoder_output = self.num_filter[3] * 2 * block.expansion elif encoder_type == 'STAP': self.avgpool = nn.AdaptiveAvgPool2d((None, freq_dim)) self.encoder = StatisticPooling(input_dim=self.num_filter[3] * freq_dim * block.expansion) self.encoder_output = self.num_filter[3] * freq_dim * 2 * block.expansion elif encoder_type == 'ASTP': self.avgpool = AdaptiveStdPool2d((time_dim, freq_dim)) self.encoder = None self.encoder_output = self.num_filter[3] * freq_dim * time_dim * block.expansion else: self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = None self.encoder_output = self.num_filter[3] * freq_dim * time_dim * block.expansion self.fc1 = nn.Sequential( nn.Linear(self.encoder_output, embedding_size), nn.BatchNorm1d(embedding_size) ) self.alpha = alpha if self.alpha: self.l2_norm = L2_Norm(self.alpha) >>>>>>> Server/Server self.classifier = nn.Linear(embedding_size, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') nn.init.normal_(m.weight, mean=0., std=1.) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, so that the residual branch # starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): <<<<<<< HEAD nn.init.constant(m.bn2.weight, 0) def l2_norm(self, input): if self.alpha > 0: input_size = input.size() buffer = torch.pow(input, 2) normp = torch.sum(buffer, 1).add_(1e-12) norm = torch.sqrt(normp) _output = torch.div(input, norm.view(-1, 1).expand_as(input)) output = _output.view(input_size) return output * self.alpha else: return input ======= nn.init.constant_(m.bn2.weight, 0) >>>>>>> Server/Server def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def _forward(self, x): # pdb.set_trace() # print(x.shape) <<<<<<< HEAD if self.filter: x = self.filter_layer(x) x = torch.log(x) if self.inst_norm: x = x - torch.mean(x, dim=-2, keepdim=True) ======= if self.filter_layer != None: x = self.filter_layer(x) if self.inst_layer != None: x = self.inst_layer(x) if self.mask_layer != None: x = self.mask_layer(x) >>>>>>> Server/Server x = self.conv1(x) x = self.bn1(x) x = self.relu(x) <<<<<<< HEAD if self.fast: ======= if self.maxpool != None: >>>>>>> Server/Server x = self.maxpool(x) # print(x.shape) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) # print(x.shape) x = self.avgpool(x) if self.encoder != None: x = self.encoder(x) x = x.view(x.size(0), -1) x = self.fc1(x) <<<<<<< HEAD feat = self.l2_norm(x) x = self.classifier(feat) ======= if self.alpha: x = self.l2_norm(x) logits = self.classifier(x) >>>>>>> Server/Server return logits, x # Allow for accessing forward method in a inherited class forward = _forward class ResNet(nn.Module): def __init__(self, resnet_size=18, embedding_size=512, block=BasicBlock, channels=[64, 128, 256, 512], num_classes=1000, avg_size=4, zero_init_residual=False, **kwargs): super(ResNet, self).__init__() resnet_layer = {10: [1, 1, 1, 1], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_layer[resnet_size] self.layers = layers self.avg_size = avg_size self.channels = channels self.inplanes = self.channels[0] self.conv1 = nn.Conv2d(1, self.channels[0], kernel_size=5, stride=2, padding=2, bias=False) self.bn1 = nn.BatchNorm2d(self.channels[0]) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, self.channels[0], layers[0]) self.layer2 = self._make_layer(block, self.channels[1], layers[1], stride=2) self.layer3 = self._make_layer(block, self.channels[2], layers[2], stride=2) self.layer4 = self._make_layer(block, self.channels[3], layers[3], stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, avg_size)) if self.layers[3] == 0: self.fc1 = nn.Sequential( nn.Linear(self.channels[2] * avg_size, embedding_size), nn.BatchNorm1d(embedding_size) ) else: self.fc1 = nn.Sequential( nn.Linear(self.channels[3] * avg_size, embedding_size), nn.BatchNorm1d(embedding_size) ) self.classifier = nn.Linear(embedding_size, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, so that the residual # branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) if self.layers[3] != 0: x = self.layer4(x) x = self.avgpool(x) x = x.view(x.size(0), -1) feat = self.fc1(x) logits = self.classifier(feat) return logits, feat # model = SimpleResNet(block=BasicBlock, layers=[3, 4, 6, 3]) # input = torch.torch.randn(128,1,400,64) # x_vectors = model.pre_forward(input) # outputs = model(x_vectors) # print('hello') # M. Hajibabaei and D. Dai, “Unified hypersphere embedding for speaker recognition,” # arXiv preprint arXiv:1807.08312, 2018. class ResNet20(nn.Module): def __init__(self, num_classes=1000, embedding_size=128, dropout_p=0.0, block=BasicBlock, input_frames=300, **kwargs): super(ResNet20, self).__init__() self.dropout_p = dropout_p self.inplanes = 1 self.layer1 = self._make_layer(Block3x3, planes=64, blocks=1, stride=2) self.inplanes = 64 self.layer2 = self._make_layer(Block3x3, planes=128, blocks=1, stride=2) self.inplanes = 128 self.layer3 = self._make_layer(BasicBlock, 128, 1) self.inplanes = 128 self.layer4 = self._make_layer(Block3x3, planes=256, blocks=1, stride=2) self.inplanes = 256 self.layer5 = self._make_layer(BasicBlock, 256, 3) self.inplanes = 256 self.layer6 = self._make_layer(Block3x3, planes=512, blocks=1, stride=2) self.inplanes = 512 self.avgpool = nn.AdaptiveAvgPool2d((1, None)) self.dropout = nn.Dropout(p=dropout_p) self.fc1 = nn.Sequential( nn.Linear(17 * self.inplanes, embedding_size), nn.BatchNorm1d(embedding_size) ) self.classifier = nn.Linear(embedding_size, num_classes) def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.layer5(x) x = self.layer6(x) x = self.avgpool(x) x = x.view(x.size(0), -1) if self.dropout_p != 0: x = self.dropout(x) feat = self.fc1(x) logits = self.classifier(feat) return logits, feat class LocalResNet(nn.Module): """ Define the ResNet model with A-softmax and AM-softmax loss. Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. """ def __init__(self, embedding_size, num_classes, block_type='basic', input_dim=161, input_len=300, gain_layer=False, relu_type='relu', resnet_size=8, channels=[64, 128, 256], dropout_p=0., encoder_type='None', input_norm=None, alpha=12, stride=2, transform=False, time_dim=1, fast=False, avg_size=4, kernal_size=5, padding=2, filter=None, mask='None', mask_len=25, **kwargs): super(LocalResNet, self).__init__() resnet_type = {8: [1, 1, 1, 0], 10: [1, 1, 1, 1], 14: [2, 2, 2, 0], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_type[resnet_size] if block_type == "seblock": block = SEBasicBlock elif block_type == 'cbam': block = CBAMBlock else: block = BasicBlock self.input_len = input_len self.input_dim = input_dim self.alpha = alpha self.layers = layers self.dropout_p = dropout_p self.transform = transform self.fast = fast self.mask = mask self.relu_type = relu_type self.embedding_size = embedding_size self.gain_layer = gain_layer # if self.relu_type == 'relu6': self.relu = nn.ReLU6(inplace=True) elif self.relu_type == 'leakyrelu': self.relu = nn.LeakyReLU() elif self.relu_type == 'relu': self.relu = nn.ReLU(inplace=True) self.input_norm = input_norm self.input_len = input_len self.filter = filter if self.filter == 'Avg': self.filter_layer = nn.AvgPool2d(kernel_size=(1, 5), stride=(1, 2)) else: self.filter_layer = None if input_norm == 'Inst': self.inst_layer = Inst_Norm(self.input_len) elif input_norm == 'Mean': self.inst_layer = Mean_Norm() elif input_norm == 'Mstd': self.inst_layer = MeanStd_Norm() else: self.inst_layer = None if self.mask == "time": self.maks_layer = TimeMaskLayer(mask_len=mask_len) elif self.mask == "freq": self.mask = FreqMaskLayer(mask_len=mask_len) elif self.mask == "time_freq": self.mask_layer = nn.Sequential( TimeMaskLayer(), FreqMaskLayer() ) else: self.mask_layer = None self.inplanes = channels[0] self.conv1 = nn.Conv2d(1, channels[0], kernel_size=kernal_size, stride=stride, padding=padding) self.bn1 = nn.BatchNorm2d(channels[0]) if self.fast.startswith('avp'): # self.maxpool = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) # self.maxpool = nn.AvgPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) self.maxpool = nn.Sequential( nn.Conv2d(channels[0], channels[0], kernel_size=1, stride=1), nn.ReLU(), nn.BatchNorm2d(channels[0]), nn.AvgPool2d(kernel_size=3, stride=2) ) else: self.maxpool = None # self.maxpool = nn.MaxPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0)) self.layer1 = self._make_layer(block, channels[0], layers[0]) self.inplanes = channels[1] self.conv2 = nn.Conv2d(channels[0], channels[1], kernel_size=(5, 5), stride=2, padding=padding, bias=False) self.bn2 = nn.BatchNorm2d(channels[1]) self.layer2 = self._make_layer(block, channels[1], layers[1]) self.inplanes = channels[2] self.conv3 = nn.Conv2d(channels[1], channels[2], kernel_size=(5, 5), stride=2, padding=padding, bias=False) self.bn3 = nn.BatchNorm2d(channels[2]) self.layer3 = self._make_layer(block, channels[2], layers[2]) if layers[3] != 0: assert len(channels) == 4 self.inplanes = channels[3] stride = 1 if self.fast else 2 self.conv4 = nn.Conv2d(channels[2], channels[3], kernel_size=(5, 5), stride=stride, padding=padding, bias=False) self.bn4 = nn.BatchNorm2d(channels[3]) self.layer4 = self._make_layer(block=block, planes=channels[3], blocks=layers[3]) self.gain = GAIN(time=self.input_len, freq=self.input_dim) if self.gain_layer else None self.dropout = nn.Dropout(self.dropout_p) last_conv_chn = channels[-1] freq_dim = avg_size if encoder_type == 'SAP': self.avgpool = nn.AdaptiveAvgPool2d((None, freq_dim)) self.encoder = SelfAttentionPooling(input_dim=last_conv_chn*freq_dim, hidden_dim=int(last_conv_chn/2)) self.encoder_output = last_conv_chn*freq_dim elif encoder_type == 'SASP': self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = AttentionStatisticPooling(input_dim=last_conv_chn, hidden_dim=last_conv_chn) self.encoder_output = last_conv_chn * 2 elif encoder_type == 'STAP': self.avgpool = nn.AdaptiveAvgPool2d((None, freq_dim)) self.encoder = StatisticPooling(input_dim=last_conv_chn * freq_dim) self.encoder_output = last_conv_chn * freq_dim * 2 elif encoder_type == 'ASTP': self.avgpool = AdaptiveStdPool2d((time_dim, freq_dim)) self.encoder = None self.encoder_output = last_conv_chn * freq_dim * time_dim else: self.avgpool = nn.AdaptiveAvgPool2d((time_dim, freq_dim)) self.encoder = None self.encoder_output = last_conv_chn * freq_dim * time_dim # self.fc1 = nn.Sequential( # nn.Linear(self.encoder_output, embedding_size), # nn.ReLU(), # nn.BatchNorm1d(embedding_size) # ) # self.fc1 = nn.Sequential( # nn.Linear(self.encoder_output, embedding_size), # nn.BatchNorm1d(embedding_size) # ) self.fc = nn.Sequential( nn.Linear(self.encoder_output, embedding_size), nn.BatchNorm1d(embedding_size) ) if self.transform == 'Linear': self.trans_layer = nn.Sequential( nn.Linear(embedding_size, embedding_size), nn.ReLU(), nn.BatchNorm1d(embedding_size) ) elif self.transform == 'GhostVLAD': self.trans_layer = GhostVLAD_v2(num_clusters=8, gost=1, dim=embedding_size, normalize_input=True) else: self.trans_layer = None if self.alpha: self.l2_norm = L2_Norm(self.alpha) # self.fc = nn.Linear(self.inplanes * avg_size, embedding_size) self.classifier = nn.Linear(self.embedding_size, num_classes) for m in self.modules(): # 对于各层参数的初始化 if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): if self.filter_layer != None: x = self.filter_layer(x) if self.inst_layer != None: x = self.inst_layer(x) if self.mask_layer != None: x = self.mask_layer(x) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if self.maxpool != None: x = self.maxpool(x) x = self.layer1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu(x) x = self.layer3(x) if self.layers[3] != 0: x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = self.layer4(x) if self.dropout_p > 0: x = self.dropout(x) x = self.avgpool(x) if self.encoder != None: x = self.encoder(x) x = x.view(x.size(0), -1) # x = self.fc1(x) x = self.fc(x) if self.trans_layer != None: x = self.trans_layer(x) # x = t_x + x if self.alpha: x = self.l2_norm(x) logits = self.classifier(x) return logits, x def xvector(self, x): if self.filter_layer != None: x = self.filter_layer(x) if self.inst_layer != None: x = self.inst_layer(x) if self.mask_layer != None: x = self.mask_layer(x) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) if self.fast: x = self.maxpool(x) x = self.layer1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu(x) x = self.layer3(x) if self.layers[3] != 0: x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = self.layer4(x) if self.dropout_p > 0: x = self.dropout(x) x = self.avgpool(x) if self.encoder != None: x = self.encoder(x) x = x.view(x.size(0), -1) # x = self.fc1(x) embeddings = self.fc[0](x) return "", embeddings # previoud version for test # class LocalResNet(nn.Module): # """ # Define the ResNet model with A-softmax and AM-softmax loss. # Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. # """ # # def __init__(self, embedding_size, num_classes, # input_dim=161, block=BasicBlock, # resnet_size=8, channels=[64, 128, 256], dropout_p=0., # inst_norm=False, alpha=12, stride=2, transform=False, # avg_size=4, kernal_size=5, padding=2, **kwargs): # # super(LocalResNet, self).__init__() # resnet_type = {8: [1, 1, 1, 0], # 10: [1, 1, 1, 1], # 18: [2, 2, 2, 2], # 34: [3, 4, 6, 3], # 50: [3, 4, 6, 3], # 101: [3, 4, 23, 3]} # # layers = resnet_type[resnet_size] # self.alpha = alpha # self.layers = layers # self.dropout_p = dropout_p # self.transform = transform # # self.embedding_size = embedding_size # # self.relu = nn.LeakyReLU() # self.relu = nn.ReLU(inplace=True) # self.inst_norm = inst_norm # self.inst_layer = nn.InstanceNorm1d(input_dim) # # self.inplanes = channels[0] # self.conv1 = nn.Conv2d(1, channels[0], kernel_size=(5, 5), stride=stride, padding=(3, 2)) # self.bn1 = nn.BatchNorm2d(channels[0]) # self.maxpool = nn.MaxPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0)) # # self.layer1 = self._make_layer(block, channels[0], layers[0]) # # self.inplanes = channels[1] # self.conv2 = nn.Conv2d(channels[0], channels[1], kernel_size=kernal_size, stride=2, # padding=padding, bias=False) # self.bn2 = nn.BatchNorm2d(channels[1]) # self.layer2 = self._make_layer(block, channels[1], layers[1]) # # self.inplanes = channels[2] # self.conv3 = nn.Conv2d(channels[1], channels[2], kernel_size=kernal_size, stride=2, # padding=padding, bias=False) # self.bn3 = nn.BatchNorm2d(channels[2]) # self.layer3 = self._make_layer(block, channels[2], layers[2]) # # if layers[3] != 0: # assert len(channels) == 4 # self.inplanes = channels[3] # self.conv4 = nn.Conv2d(channels[2], channels[3], kernel_size=kernal_size, stride=2, # padding=padding, bias=False) # self.bn4 = nn.BatchNorm2d(channels[3]) # self.layer4 = self._make_layer(block=block, planes=channels[3], blocks=layers[3]) # # self.dropout = nn.Dropout(self.dropout_p) # self.avg_pool = nn.AdaptiveAvgPool2d((1, avg_size)) # # self.fc = nn.Sequential( # nn.Linear(self.inplanes * avg_size, embedding_size), # nn.BatchNorm1d(embedding_size) # ) # # if self.transform: # self.trans_layer = nn.Sequential( # nn.Linear(embedding_size, embedding_size, bias=False), # nn.BatchNorm1d(embedding_size), # nn.ReLU() # ) # # # self.fc = nn.Linear(self.inplanes * avg_size, embedding_size) # self.classifier = nn.Linear(self.embedding_size, num_classes) # # for m in self.modules(): # 对于各层参数的初始化 # if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # # m.weight.data.normal_(0, math.sqrt(2. / n)) # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 # m.weight.data.fill_(1) # m.bias.data.zero_() # # def l2_norm(self, input, alpha=1.0): # # alpha = log(p * ( class -2) / (1-p)) # input_size = input.size() # buffer = torch.pow(input, 2) # # normp = torch.sum(buffer, 1).add_(1e-12) # norm = torch.sqrt(normp) # # _output = torch.div(input, norm.view(-1, 1).expand_as(input)) # output = _output.view(input_size) # # # # input = input.renorm(p=2, dim=1, maxnorm=1.0) # # norm = input.norm(p=2, dim=1, keepdim=True).add(1e-14) # # output = input / norm # # return output * alpha # # def _make_layer(self, block, planes, blocks, stride=1): # downsample = None # if stride != 1 or self.inplanes != planes * block.expansion: # downsample = nn.Sequential( # conv1x1(self.inplanes, planes * block.expansion, stride), # nn.BatchNorm2d(planes * block.expansion), # ) # # layers = [] # layers.append(block(self.inplanes, planes, stride, downsample)) # self.inplanes = planes * block.expansion # for _ in range(1, blocks): # layers.append(block(self.inplanes, planes)) # # return nn.Sequential(*layers) # # def forward(self, x): # if self.inst_norm: # x = x.squeeze(1).transpose(1, 2) # x = self.inst_layer(x) # x = x.transpose(1, 2).unsqueeze(1) # # # x = x - torch.mean(x, dim=-2, keepdim=True) # # x = self.conv1(x) # x = self.bn1(x) # x = self.relu(x) # x = self.maxpool(x) # # x = self.layer1(x) # # x = self.conv2(x) # x = self.bn2(x) # x = self.relu(x) # x = self.layer2(x) # # x = self.conv3(x) # x = self.bn3(x) # x = self.relu(x) # x = self.layer3(x) # # if self.layers[3] != 0: # x = self.conv4(x) # x = self.bn4(x) # x = self.relu(x) # x = self.layer4(x) # # if self.dropout_p > 0: # x = self.dropout(x) # # # if self.statis_pooling: # # mean_x = self.avg_pool(x) # # mean_x = mean_x.view(mean_x.size(0), -1) # # # # std_x = self.std_pool(x) # # std_x = std_x.view(std_x.size(0), -1) # # # # x = torch.cat((mean_x, std_x), dim=1) # # # # else: # # print(x.shape) # x = self.avg_pool(x) # x = x.view(x.size(0), -1) # # x = self.fc(x) # if self.transform == True: # x += self.trans_layer(x) # t_x = self.trans_layer(x) # x = t_x + x # # if self.alpha: # x = self.l2_norm(x, alpha=self.alpha) # # logits = self.classifier(x) # # return logits, x class DomainNet(nn.Module): """ Define the ResNet model with A-softmax and AM-softmax loss. Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. """ def __init__(self, model, embedding_size, num_classes_a, num_classes_b, **kwargs): super(DomainNet, self).__init__() self.xvectors = model self.embedding_size = embedding_size self.grl = GRL(lambda_=0.) self.classifier_dom = nn.Sequential( nn.Linear(self.embedding_size, int(self.embedding_size / 2)), nn.ReLU(inplace=True), nn.BatchNorm1d(int(self.embedding_size / 2)), nn.Linear(int(self.embedding_size / 2), num_classes_b), ) self.fc2 = nn.Sequential( nn.Linear(int(num_classes_b + self.embedding_size), self.embedding_size), nn.ReLU(inplace=True), nn.BatchNorm1d(self.embedding_size) ) self.classifier_spk = nn.Linear(self.embedding_size, num_classes_a) for m in self.modules(): # 对于各层参数的初始化 if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 m.weight.data.fill_(1) m.bias.data.zero_() def forward(self, x): logits, embeddings = self.xvectors(x) # dom_x = self.grl(embeddings) dom_logits = self.classifier_dom(embeddings) spk_embeddings_new = torch.cat((embeddings, dom_logits), dim=1) spk_embeddings_new = self.fc2(spk_embeddings_new) spk_logits_new = self.classifier_spk(spk_embeddings_new) dom_logits_new = self.classifier_dom(spk_embeddings_new) all_logits = (logits, spk_logits_new, dom_logits, dom_logits_new) return all_logits, spk_embeddings_new class GradResNet(nn.Module): """ Define the ResNet model with A-softmax and AM-softmax loss. Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. """ def __init__(self, embedding_size, num_classes, block=BasicBlock, input_dim=161, resnet_size=8, channels=[64, 128, 256], dropout_p=0., ince=False, transform=False, inst_norm=False, alpha=12, vad=False, avg_size=4, kernal_size=5, padding=2, **kwargs): super(GradResNet, self).__init__() resnet_type = {8: [1, 1, 1, 0], 10: [1, 1, 1, 1], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_type[resnet_size] self.ince = ince self.alpha = alpha self.layers = layers self.dropout_p = dropout_p self.transform = transform self.embedding_size = embedding_size # self.relu = nn.LeakyReLU() self.relu = nn.ReLU(inplace=True) self.vad = vad if self.vad: self.vad_layer = SelfVadPooling(input_dim) self.inst_norm = inst_norm # self.inst_layer = nn.InstanceNorm1d(input_dim) if self.ince: self.pre_conv = VarSizeConv(1, 1) self.conv1 = nn.Conv2d(4, channels[0], kernel_size=5, stride=2, padding=2) else: self.conv1 = nn.Conv2d(1, channels[0], kernel_size=5, stride=2, padding=2) self.bn1 = nn.BatchNorm2d(channels[0]) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inplanes = channels[0] self.layer1 = self._make_layer(block, channels[0], layers[0]) self.inplanes = channels[1] self.conv2 = nn.Conv2d(channels[0], channels[1], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn2 = nn.BatchNorm2d(channels[1]) self.layer2 = self._make_layer(block, channels[1], layers[1]) self.inplanes = channels[2] self.conv3 = nn.Conv2d(channels[1], channels[2], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn3 = nn.BatchNorm2d(channels[2]) self.layer3 = self._make_layer(block, channels[2], layers[2]) if layers[3] != 0: assert len(channels) == 4 self.inplanes = channels[3] self.conv4 = nn.Conv2d(channels[2], channels[3], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn4 = nn.BatchNorm2d(channels[3]) self.layer4 = self._make_layer(block=block, planes=channels[3], blocks=layers[3]) self.dropout = nn.Dropout(self.dropout_p) self.avg_pool = nn.AdaptiveAvgPool2d((1, avg_size)) <<<<<<< HEAD class InstBlock3x3(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None): super(InstBlock3x3, self).__init__() self.conv1 = conv3x3(inplanes, planes) self.bn1 = nn.InstanceNorm2d(planes) self.conv2 = conv3x3(planes, planes, stride) self.bn2 = nn.InstanceNorm2d(planes) self.conv3 = conv3x3(planes, planes) self.bn3 = nn.InstanceNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class VarSizeConv(nn.Module): def __init__(self, inplanes, planes, stride=1, kernel_size=[3, 5, 7]): super(VarSizeConv, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size[0], stride=stride, padding=1) self.bn1 = nn.InstanceNorm2d(planes) self.conv2 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size[1], stride=stride, padding=2) self.bn2 = nn.InstanceNorm2d(planes) self.conv3 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size[2], stride=stride, padding=3) self.bn3 = nn.InstanceNorm2d(planes) def forward(self, x): x1 = self.conv1(x) x1 = self.bn1(x1) x2 = self.conv2(x) x2 = self.bn2(x2) x3 = self.conv3(x) x3 = self.bn3(x3) return torch.cat([x1, x2, x3], dim=1) class ResNet20(nn.Module): def __init__(self, num_classes=1000, embedding_size=128, dropout_p=0.0, block=BasicBlock, input_frames=300, **kwargs): super(ResNet20, self).__init__() self.dropout_p = dropout_p self.inplanes = 1 self.layer1 = self._make_layer(Block3x3, planes=64, blocks=1, stride=2) ======= if self.transform: self.trans_layer = nn.Sequential( nn.Linear(embedding_size, embedding_size, bias=False), nn.BatchNorm1d(embedding_size), nn.ReLU() ) >>>>>>> Server/Server self.fc = nn.Sequential( nn.Linear(self.inplanes * avg_size, embedding_size), nn.BatchNorm1d(embedding_size) ) # self.fc = nn.Linear(self.inplanes * avg_size, embedding_size) self.classifier = nn.Linear(self.embedding_size, num_classes) for m in self.modules(): # 对于各层参数的初始化 if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 m.weight.data.fill_(1) m.bias.data.zero_() def l2_norm(self, input, alpha=1.0): # alpha = log(p * (class -2) / (1-p)) input_size = input.size() buffer = torch.pow(input, 2) normp = torch.sum(buffer, 1).add_(1e-12) norm = torch.sqrt(normp) _output = torch.div(input, norm.view(-1, 1).expand_as(input)) output = _output.view(input_size) # # # input = input.renorm(p=2, dim=1, maxnorm=1.0) # norm = input.norm(p=2, dim=1, keepdim=True).add(1e-14) # output = input / norm return output * alpha def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): if self.vad: x = self.vad_layer(x) x = torch.log(x) if self.inst_norm: # x = self.inst_layer(x) x = x - torch.mean(x, dim=-2, keepdim=True) if self.ince: x = self.pre_conv(x) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) # x = self.maxpool(x) x = self.layer1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu(x) x = self.layer3(x) if self.layers[3] != 0: x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = self.layer4(x) if self.dropout_p > 0: x = self.dropout(x) # if self.statis_pooling: # mean_x = self.avg_pool(x) # mean_x = mean_x.view(mean_x.size(0), -1) # # std_x = self.std_pool(x) # std_x = std_x.view(std_x.size(0), -1) # # x = torch.cat((mean_x, std_x), dim=1) # # else: # print(x.shape) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self.transform: t_x = self.trans_layer(x) x = t_x + x if self.alpha: x = self.l2_norm(x, alpha=self.alpha) logits = self.classifier(x) return logits, x class TimeFreqResNet(nn.Module): """ Define the ResNet model with A-softmax and AM-softmax loss. Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. """ <<<<<<< HEAD def __init__(self, embedding_size, num_classes, input_dim=161, block=BasicBlock, resnet_size=8, channels=[64, 128, 256], dropout_p=0., inst_norm=False, alpha=12, avg_size=4, kernal_size=5, padding=2, **kwargs): ======= def __init__(self, embedding_size, num_classes, block=BasicBlock, input_dim=161, resnet_size=8, channels=[64, 128, 256], dropout_p=0., ince=False, inst_norm=False, alpha=12, vad=False, avg_size=4, kernal_size=5, padding=2, **kwargs): >>>>>>> Server/Server super(TimeFreqResNet, self).__init__() resnet_type = {8: [1, 1, 1, 0], 10: [1, 1, 1, 1], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_type[resnet_size] self.ince = ince self.alpha = alpha self.layers = layers self.dropout_p = dropout_p self.embedding_size = embedding_size # self.relu = nn.LeakyReLU() self.relu = nn.ReLU(inplace=True) <<<<<<< HEAD self.inst_norm = inst_norm self.inst_layer = nn.InstanceNorm2d(input_dim) self.inplanes = channels[0] self.conv1 = nn.Conv2d(1, channels[0], kernel_size=(5, 5), stride=2, padding=(3, 2)) self.bn1 = nn.BatchNorm2d(channels[0]) self.maxpool = nn.MaxPool2d(kernel_size=(3, 1), stride=(2, 1), padding=(1, 0)) ======= self.vad = vad if self.vad: self.vad_layer = SelfVadPooling(input_dim) self.inst_norm = inst_norm # self.inst_layer = nn.InstanceNorm1d(input_dim) self.conv1 = nn.Sequential(nn.Conv2d(1, channels[0], kernel_size=(5, 1), stride=(2, 1), padding=(2, 0)), nn.BatchNorm2d(channels[0]), nn.Conv2d(channels[0], channels[0], kernel_size=(1, 5), stride=(1, 2), padding=(0, 2)), ) self.bn1 = nn.BatchNorm2d(channels[0]) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inplanes = channels[0] >>>>>>> Server/Server self.layer1 = self._make_layer(block, channels[0], layers[0]) self.inplanes = channels[1] # self.conv2 = nn.Conv2d(channels[0], channels[1], kernel_size=kernal_size, # stride=2, padding=padding, bias=False) self.conv2 = nn.Sequential( nn.Conv2d(channels[0], channels[1], kernel_size=(5, 1), stride=(2, 1), padding=(2, 0)), nn.BatchNorm2d(channels[1]), nn.Conv2d(channels[1], channels[1], kernel_size=(1, 5), stride=(1, 2), padding=(0, 2)), ) self.bn2 = nn.BatchNorm2d(channels[1]) self.layer2 = self._make_layer(block, channels[1], layers[1]) self.inplanes = channels[2] # self.conv3 = nn.Conv2d(channels[1], channels[2], kernel_size=kernal_size, # stride=2, padding=padding, bias=False) self.conv3 = nn.Sequential( nn.Conv2d(channels[1], channels[2], kernel_size=(5, 1), stride=(2, 1), padding=(2, 0)), nn.BatchNorm2d(channels[2]), nn.Conv2d(channels[2], channels[2], kernel_size=(1, 5), stride=(1, 2), padding=(0, 2)), ) self.bn3 = nn.BatchNorm2d(channels[2]) self.layer3 = self._make_layer(block, channels[2], layers[2]) if layers[3] != 0: assert len(channels) == 4 self.inplanes = channels[3] self.conv4 = nn.Conv2d(channels[2], channels[3], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn4 = nn.BatchNorm2d(channels[3]) self.layer4 = self._make_layer(block=block, planes=channels[3], blocks=layers[3]) self.dropout = nn.Dropout(self.dropout_p) self.avg_pool = nn.AdaptiveAvgPool2d((1, avg_size)) self.fc = nn.Sequential( nn.Linear(self.inplanes * avg_size, embedding_size), nn.BatchNorm1d(embedding_size) ) # self.fc = nn.Linear(self.inplanes * avg_size, embedding_size) self.classifier = nn.Linear(self.embedding_size, num_classes) for m in self.modules(): # 对于各层参数的初始化 if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 m.weight.data.fill_(1) m.bias.data.zero_() def l2_norm(self, input, alpha=1.0): <<<<<<< HEAD # alpha = log(p * ( class -2) / (1-p)) ======= # alpha = log(p * (class -2) / (1-p)) >>>>>>> Server/Server input_size = input.size() buffer = torch.pow(input, 2) normp = torch.sum(buffer, 1).add_(1e-12) norm = torch.sqrt(normp) _output = torch.div(input, norm.view(-1, 1).expand_as(input)) output = _output.view(input_size) <<<<<<< HEAD # # # input = input.renorm(p=2, dim=1, maxnorm=1.0) # norm = input.norm(p=2, dim=1, keepdim=True).add(1e-14) # output = input / norm ======= >>>>>>> Server/Server return output * alpha def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): <<<<<<< HEAD if self.inst_norm: x = x.squeeze(1) x = self.inst_layer(x) x = x.unsqueeze(1) ======= if self.vad: x = self.vad_layer(x) x = torch.log(x) if self.inst_norm: # x = self.inst_layer(x) x = x - torch.mean(x, dim=-2, keepdim=True) if self.ince: x = self.pre_conv(x) >>>>>>> Server/Server x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu(x) x = self.layer3(x) if self.layers[3] != 0: x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = self.layer4(x) if self.dropout_p > 0: x = self.dropout(x) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self.alpha: x = F.self.l2_norm(x, alpha=self.alpha) logits = self.classifier(x) return logits, x <<<<<<< HEAD class DomainResNet(nn.Module): ======= class MultiResNet(nn.Module): >>>>>>> Server/Server """ Define the ResNet model with A-softmax and AM-softmax loss. Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. """ <<<<<<< HEAD def __init__(self, embedding_size_a, embedding_size_b, embedding_size_o, num_classes_a, num_classes_b, block=BasicBlock, input_dim=161, resnet_size=8, channels=[64, 128, 256], dropout_p=0., inst_norm=False, alpha=12, avg_size=4, kernal_size=5, padding=2, **kwargs): ======= def __init__(self, embedding_size, num_classes_a, num_classes_b, block=BasicBlock, input_dim=161, resnet_size=8, channels=[64, 128, 256], dropout_p=0., stride=2, fast=False, inst_norm=False, alpha=12, input_norm='None', transform=False, avg_size=4, kernal_size=5, padding=2, mask='None', mask_len=25, **kwargs): >>>>>>> Server/Server super(MultiResNet, self).__init__() resnet_type = {8: [1, 1, 1, 0], 10: [1, 1, 1, 1], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_type[resnet_size] self.alpha = alpha self.layers = layers self.dropout_p = dropout_p self.embedding_size = embedding_size self.relu = nn.ReLU(inplace=True) <<<<<<< HEAD self.inst_norm = inst_norm # self.inst_layer = nn.InstanceNorm1d(input_dim) ======= self.transform = transform self.fast = fast self.input_norm = input_norm self.mask = mask if input_norm == 'Instance': self.inst_layer = nn.InstanceNorm1d(input_dim) elif input_norm == 'Mean': self.inst_layer = Mean_Norm() elif input_norm == 'MeanStd': self.inst_layer = MeanStd_Norm() else: self.inst_layer = None if self.mask == "time": self.maks_layer = TimeMaskLayer(mask_len=mask_len) elif self.mask == "freq": self.mask_layer = FreqMaskLayer(mask_len=mask_len) elif self.mask == "time_freq": self.mask_layer = nn.Sequential( TimeMaskLayer(mask_len=mask_len), FreqMaskLayer(mask_len=mask_len) ) else: self.mask_layer = None >>>>>>> Server/Server self.inplanes = channels[0] self.conv1 = nn.Conv2d(1, channels[0], kernel_size=5, stride=stride, padding=2, bias=False) self.bn1 = nn.BatchNorm2d(channels[0]) <<<<<<< HEAD self.maxpool = nn.MaxPool2d(kernel_size=(3, 1), stride=(2, 1), padding=1) ======= # fast v3 if self.fast: self.maxpool = nn.Sequential( nn.Conv2d(channels[0], channels[0], kernel_size=1, stride=1), nn.ReLU(), nn.BatchNorm2d(channels[0]), nn.AvgPool2d(kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) ) else: self.maxpool = None # self.maxpool = nn.MaxPool2d(kernel_size=(3, 1), stride=(2, 1), padding=1) >>>>>>> Server/Server self.layer1 = self._make_layer(block, channels[0], layers[0]) self.inplanes = channels[1] self.conv2 = nn.Conv2d(channels[0], channels[1], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn2 = nn.BatchNorm2d(channels[1]) self.layer2 = self._make_layer(block, channels[1], layers[1]) self.inplanes = channels[2] self.conv3 = nn.Conv2d(channels[1], channels[2], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn3 = nn.BatchNorm2d(channels[2]) self.layer3 = self._make_layer(block, channels[2], layers[2]) if layers[3] != 0: assert len(channels) == 4 self.inplanes = channels[3] self.conv4 = nn.Conv2d(channels[2], channels[3], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn4 = nn.BatchNorm2d(channels[3]) self.layer4 = self._make_layer(block=block, planes=channels[3], blocks=layers[3]) self.dropout = nn.Dropout(self.dropout_p) self.avg_pool = nn.AdaptiveAvgPool2d((avg_size, 1)) # self.encoder = nn.LSTM(input_size=channels[2], # hidden_size=channels[2], # num_layers=1, # batch_first=True, # dropout=self.dropout_p) self.fc = nn.Sequential( nn.Linear(self.inplanes * avg_size, self.embedding_size), nn.BatchNorm1d(self.embedding_size) ) if self.transform == 'Linear': self.trans_layer = nn.Sequential( nn.Linear(embedding_size, embedding_size), nn.ReLU(), nn.BatchNorm1d(embedding_size)) elif self.transform == 'GhostVLAD': self.trans_layer = GhostVLAD_v2(num_clusters=8, gost=1, dim=embedding_size, normalize_input=True) else: self.trans_layer = None if self.alpha: self.l2_norm = L2_Norm(self.alpha) <<<<<<< HEAD self.classifier_spk = nn.Linear(self.embedding_size_a, num_classes_a) self.grl = GRL(lambda_=0.) self.classifier_dom = nn.Sequential(nn.Linear(self.embedding_size_b, int(self.embedding_size_b / 4)), nn.ReLU(inplace=True), nn.Linear(int(self.embedding_size_b / 4), num_classes_b), ) ======= self.classifier_a = nn.Linear(self.embedding_size, num_classes_a) self.classifier_b = nn.Linear(self.embedding_size, num_classes_b) >>>>>>> Server/Server for m in self.modules(): # 对于各层参数的初始化 if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 m.weight.data.fill_(1) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): <<<<<<< HEAD if self.inst_norm: # x = x.squeeze(1) # x = self.inst_layer(x) # x = x.unsqueeze(1) x = x - torch.mean(x, dim=-2, keepdim=True) ======= tuple_input = False if isinstance(x, tuple): tuple_input = True size_a = len(x[0]) x = torch.cat(x, dim=0) if self.inst_layer != None: x = self.inst_layer(x) if self.mask_layer != None: x = self.mask_layer(x) >>>>>>> Server/Server x = self.conv1(x) x = self.bn1(x) x = self.relu(x) <<<<<<< HEAD x = self.maxpool(x) ======= if self.maxpool != None: x = self.maxpool(x) >>>>>>> Server/Server x = self.layer1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu(x) x = self.layer3(x) if self.layers[3] != 0: x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = self.layer4(x) if self.dropout_p > 0: x = self.dropout(x) # x = self.avg_pool(x).transpose(1, 2) # x, (_, _) = self.encoder(x.squeeze(1)) # x = x[:, -1] x = self.avg_pool(x) x = x.view(x.size(0), -1) embeddings = self.fc(x) if self.trans_layer != None: embeddings = self.trans_layer(embeddings) if self.alpha: embeddings = self.l2_norm(embeddings) # embeddings = self.l2_norm(embeddings, alpha=self.alpha) <<<<<<< HEAD spk_logits = self.classifier_spk(spk_x) dom_x = self.grl(dom_x) dom_logits = self.classifier_dom(dom_x) return spk_logits, spk_x, dom_logits, dom_x class GradResNet(nn.Module): """ Define the ResNet model with A-softmax and AM-softmax loss. Added dropout as https://github.com/nagadomi/kaggle-cifar10-torch7 after average pooling and fc layer. """ def __init__(self, embedding_size, num_classes, block=BasicBlock, input_dim=161, resnet_size=8, channels=[64, 128, 256], dropout_p=0., ince=False, inst_norm=False, alpha=12, vad=False, avg_size=4, kernal_size=5, padding=2, **kwargs): super(GradResNet, self).__init__() resnet_type = {8: [1, 1, 1, 0], 10: [1, 1, 1, 1], 18: [2, 2, 2, 2], 34: [3, 4, 6, 3], 50: [3, 4, 6, 3], 101: [3, 4, 23, 3]} layers = resnet_type[resnet_size] self.ince = ince self.alpha = alpha self.layers = layers self.dropout_p = dropout_p self.embedding_size = embedding_size # self.relu = nn.LeakyReLU() self.relu = nn.ReLU(inplace=True) self.vad = vad if self.vad: self.vad_layer = SelfVadPooling(input_dim) self.inst_norm = inst_norm # self.inst_layer = nn.InstanceNorm1d(input_dim) if self.ince: self.pre_conv = VarSizeConv(1, 1) self.conv1 = nn.Conv2d(3, channels[0], kernel_size=5, stride=2, padding=2) else: self.conv1 = nn.Conv2d(1, channels[0], kernel_size=5, stride=2, padding=2) self.bn1 = nn.BatchNorm2d(channels[0]) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inplanes = channels[0] self.layer1 = self._make_layer(block, channels[0], layers[0]) self.inplanes = channels[1] self.conv2 = nn.Conv2d(channels[0], channels[1], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn2 = nn.BatchNorm2d(channels[1]) self.layer2 = self._make_layer(block, channels[1], layers[1]) self.inplanes = channels[2] self.conv3 = nn.Conv2d(channels[1], channels[2], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn3 = nn.BatchNorm2d(channels[2]) self.layer3 = self._make_layer(block, channels[2], layers[2]) if layers[3] != 0: assert len(channels) == 4 self.inplanes = channels[3] self.conv4 = nn.Conv2d(channels[2], channels[3], kernel_size=kernal_size, stride=2, padding=padding, bias=False) self.bn4 = nn.BatchNorm2d(channels[3]) self.layer4 = self._make_layer(block=block, planes=channels[3], blocks=layers[3]) self.dropout = nn.Dropout(self.dropout_p) self.avg_pool = nn.AdaptiveAvgPool2d((1, avg_size)) self.fc = nn.Sequential( nn.Linear(self.inplanes * avg_size, embedding_size), nn.BatchNorm1d(embedding_size) ) # self.fc = nn.Linear(self.inplanes * avg_size, embedding_size) self.classifier = nn.Linear(self.embedding_size, num_classes) for m in self.modules(): # 对于各层参数的初始化 if isinstance(m, nn.Conv2d): # 以2/n的开方为标准差，做均值为0的正态分布 # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.GroupNorm)): # weight设置为1，bias为0 m.weight.data.fill_(1) m.bias.data.zero_() def l2_norm(self, input, alpha=1.0): # alpha = log(p * (class -2) / (1-p)) input_size = input.size() buffer = torch.pow(input, 2) normp = torch.sum(buffer, 1).add_(1e-12) norm = torch.sqrt(normp) _output = torch.div(input, norm.view(-1, 1).expand_as(input)) output = _output.view(input_size) # # # input = input.renorm(p=2, dim=1, maxnorm=1.0) # norm = input.norm(p=2, dim=1, keepdim=True).add(1e-14) # output = input / norm return output * alpha def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): if self.vad: x = self.vad_layer(x) x = torch.log(x) if self.inst_norm: # x = self.inst_layer(x) x = x - torch.mean(x, dim=-2, keepdim=True) if self.ince: x = self.pre_conv(x) x = self.conv1(x) x = self.bn1(x) x = self.relu(x) # x = self.maxpool(x) x = self.layer1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu(x) x = self.layer2(x) x = self.conv3(x) x = self.bn3(x) x = self.relu(x) x = self.layer3(x) if self.layers[3] != 0: x = self.conv4(x) x = self.bn4(x) x = self.relu(x) x = self.layer4(x) if self.dropout_p > 0: x = self.dropout(x) # if self.statis_pooling: # mean_x = self.avg_pool(x) # mean_x = mean_x.view(mean_x.size(0), -1) # # std_x = self.std_pool(x) # std_x = std_x.view(std_x.size(0), -1) # # x = torch.cat((mean_x, std_x), dim=1) # # else: # print(x.shape) x = self.avg_pool(x) x = x.view(x.size(0), -1) x = self.fc(x) if self.alpha: x = self.l2_norm(x, alpha=self.alpha) logits = self.classifier(x) return logits, x ======= if tuple_input: embeddings_a = embeddings[:size_a] embeddings_b = embeddings[size_a:] logits_a = self.classifier_a(embeddings_a) logits_b = self.classifier_b(embeddings_b) return (logits_a, logits_b), (embeddings_a, embeddings_b) else: return '', embeddings # def cls_forward(self, a, b): # # logits_a = self.classifier_a(a) # logits_b = self.classifier_b(b) # # return logits_a, logits_b >>>>>>> Server/Server

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8

daaab5fe1c32b327e94ac561104c3ed2d8752efa

12,337

py

Python

jams/srrasa.py

MuellerSeb/jams_python

1bca04557da79d8f8a4c447f5ccc517c40ab7dfc

[ "MIT" ]

9

2019-06-03T03:24:16.000Z

2021-12-03T07:14:00.000Z

jams/srrasa.py

MuellerSeb/jams_python

1bca04557da79d8f8a4c447f5ccc517c40ab7dfc

[ "MIT" ]

6

2020-03-25T21:56:59.000Z

2021-11-08T14:58:27.000Z

jams/srrasa.py

MuellerSeb/jams_python

1bca04557da79d8f8a4c447f5ccc517c40ab7dfc

[ "MIT" ]

5

2019-10-17T12:04:33.000Z

2021-09-28T07:45:07.000Z

#!/usr/bin/env python from __future__ import division, absolute_import, print_function import numpy as np def srrasa(xy, strata=5, n=3, plot=False): """ Generates stratified random 2D points within a given rectangular area. Definition ---------- def srrasa(xy, strata=5, n=3, plot=False): Input ----- xy list of floats (4), list with the x and y coordinates enclosing the designated rectangle in the form [x1,x2,y1,y2] Optional Input -------------- strata int, number of strata per axis n int, number of random points in each strata plot bool, if True, stratas and points are plotted, otherwise not Output ------ rand_xy ndarray (n,2), x and y coordinates of the stratified random points in the given rectangular. Examples -------- >>> # seed for reproducible results in doctest >>> np.random.seed(1) >>> # gives within the rectangle of the given coordinates >>> # 16 (4**2) stratas with 3 random points in each one. >>> rand_xy = srrasa([652219.,652290.,5772970.,5773040.], strata=4, n=3, plot=False) >>> from autostring import astr >>> print(astr(rand_xy[0:4,0:2],6,pp=True)) [['6.522264e+05' '5.772975e+06'] ['6.522318e+05' '5.772973e+06'] ['6.522190e+05' '5.772972e+06'] ['6.522401e+05' '5.772979e+06']] License ------- This file is part of the JAMS Python package, distributed under the MIT License. The JAMS Python package originates from the former UFZ Python library, Department of Computational Hydrosystems, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany. Copyright (c) 2012-2013 Arndt Piayda, Matthias Cuntz - mc (at) macu (dot) de Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. History ------- Written, AP, Nov 2012 Modified, MC, Nov 2012 - default plot=False AP, Dec 2012 - documentation change MC, Feb 2013 - docstring MC, Feb 2013 - ported to Python 3 """ # calculate strata steps sw = (xy[1]-xy[0])/strata sh = (xy[3]-xy[2])/strata xsteps = np.arange(xy[0],xy[1]+sw,sw) ysteps = np.arange(xy[2],xy[3]+sh,sh) # make output array rand_xy = np.empty((strata**2*n,2)) # throw random points in each strata for j in range(strata): for i in range(strata): rand_xy[i*n+strata*n*j:(i+1)*n+strata*n*j,0] = (xsteps[i+1] - xsteps[i])*np.random.random(n) + xsteps[i] rand_xy[i*n+strata*n*j:(i+1)*n+strata*n*j,1] = (ysteps[j+1] - ysteps[j])*np.random.random(n) + ysteps[j] # plot stratas and random points within if plot: import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('font', size=20) mpl.rc('lines', linewidth=2) mpl.rc('axes', linewidth=1.5) mpl.rc('xtick.major', width=1.5) mpl.rc('ytick.major', width=1.5) mpl.rcParams['lines.markersize']=6 fig = plt.figure('stratified random sampling') sub = fig.add_subplot(111, aspect='equal') sub.set_xlim(xy[0],xy[1]) sub.set_ylim(xy[2],xy[3]) for i in range(strata): sub.axhline(y=ysteps[i], color=(166/256., 206/256., 227/256.)) sub.axvline(x=xsteps[i], color=(166/256., 206/256., 227/256.)) sub.scatter(rand_xy[:,0],rand_xy[:,1],marker='+', s=60, color=( 51/256., 160/256., 44/256.)) sub.set_xlabel('X') sub.set_ylabel('Y') sub.set_title('strata = %i, n = %i' %(strata,n)) sub.xaxis.set_major_formatter(mpl.ticker. ScalarFormatter(useOffset=False)) sub.yaxis.set_major_formatter(mpl.ticker. ScalarFormatter(useOffset=False)) fig.autofmt_xdate(rotation=45) plt.tight_layout(pad=1, h_pad=0, w_pad=0) plt.show() return rand_xy def srrasa_trans(xy,strata=5,n=3,num=3,rl=0.5,silent=True,plot=False): """ Generates stratified random 2D transects within a given rectangular area. Definition ---------- def srrasa(xy,strata=5,n=3,num=3,rl=0.5,silent=True,plot=False): Input ----- xy list of floats (4), list with the x and y coordinates enclosing the designated rectangle in the form [x1,x2,y1,y2] Optional Input -------------- strata int, number of strata per axis n int, number of random transects in each strata num int, number of points in each transect rl float [0. to 1.], relative length of transect with respect to width of stratum silent bool, if False, runtime diagnostics are printed to the console, otherwise not plot bool, if True, stratas and points are plotted, otherwise not Output ------ rand_xy ndarray (n,2), x and y coordinates of the stratified random transect points in the given rectangular. Examples -------- >>> # seed for reproducible results in doctest >>> np.random.seed(1) >>> # gives within the rectangle of the given coordinates >>> # 16 (4**2) stratas with 3 random transects in each one. >>> # Each transect is 0.5*width_of_strata long and contains 5 points logarithmical distributed. >>> rand_xy = srrasa_trans([652219.,652290.,5772970.,5773040.], strata=4, ... n=3, num=5, rl=0.5, silent=True, plot=False) >>> from autostring import astr >>> print(astr(rand_xy[0:4,0:2],6,pp=True)) [['6.522264e+05' '5.772983e+06'] ['6.522276e+05' '5.772983e+06'] ['6.522292e+05' '5.772983e+06'] ['6.522315e+05' '5.772983e+06']] License ------- This file is part of the JAMS Python package, distributed under the MIT License. Copyright (c) 2012-2013 Arndt Piayda, Matthias Cuntz - mc (at) macu (dot) de Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. History ------- Written, AP, Nov 2012 Modified, AP, Dec 2012 - documentation change MC, Feb 2013 - ported to Python 3 """ # calculate strata steps sw = (xy[1]-xy[0])/strata sh = (xy[3]-xy[2])/strata xsteps = np.arange(xy[0],xy[1]+sw,sw) ysteps = np.arange(xy[2],xy[3]+sh,sh) tl = sw*rl # make output array rand_xy = np.empty((strata**2*n*num,2)) o = 0 for j in range(strata): for i in range(strata): for k in range(n): goon = True it = 0 while goon: # random seed in strata seedx=(xsteps[i+1]-xsteps[i])*np.random.random(1)+xsteps[i] seedy=(ysteps[j+1]-ysteps[j])*np.random.random(1)+ysteps[j] # make logarithmic transect tx =np.arange(1,num+1) dis =np.sort(tl-np.log(tx)/np.max(np.log(tx))*tl) seedx=np.repeat(seedx,num)+dis seedy=np.repeat(seedy,num) # random angle in strata [deg] angle = 360 * np.random.random(1) # rotate transect to random angle seedx_trans = (-(seedy-seedy[0])*np.sin(np.deg2rad(angle))+ (seedx-seedx[0])*np.cos(np.deg2rad(angle))+ seedx[0]) seedy_trans = ((seedy-seedy[0])*np.cos(np.deg2rad(angle))+ (seedx-seedx[0])*np.sin(np.deg2rad(angle))+ seedy[0]) # test if transect is in strata if (((seedx_trans>xsteps[i]).all()) & ((seedx_trans<xsteps[i+1]).all()) & ((seedy_trans>ysteps[j]).all()) & ((seedy_trans<ysteps[j+1]).all())): goon = False if not silent: print('strata= (', i, ',', j, ')', ' it= ', it) it += 1 rand_xy[o:o+num,0] = seedx_trans rand_xy[o:o+num,1] = seedy_trans o += num # plot stratas and random transect points within if plot: import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('font', size=20) mpl.rc('lines', linewidth=2) mpl.rc('axes', linewidth=1.5) mpl.rc('xtick.major', width=1.5) mpl.rc('ytick.major', width=1.5) mpl.rcParams['lines.markersize']=6 fig = plt.figure('stratified random transect sampling') sub = fig.add_subplot(111, aspect='equal') sub.set_xlim(xy[0],xy[1]) sub.set_ylim(xy[2],xy[3]) for i in range(strata): sub.axhline(y=ysteps[i], color=(166/256., 206/256., 227/256.)) sub.axvline(x=xsteps[i], color=(166/256., 206/256., 227/256.)) sub.scatter(rand_xy[:,0],rand_xy[:,1],marker='+', s=60, color=( 51/256., 160/256., 44/256.)) sub.set_xlabel('X') sub.set_ylabel('Y') sub.set_title('strata = %i, n = %i, num = %i' %(strata,n,num)) sub.xaxis.set_major_formatter(mpl.ticker. ScalarFormatter(useOffset=False)) sub.yaxis.set_major_formatter(mpl.ticker. ScalarFormatter(useOffset=False)) fig.autofmt_xdate(rotation=45) plt.tight_layout(pad=1, h_pad=0, w_pad=0) plt.show() return rand_xy if __name__ == '__main__': import doctest doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)

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null

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dae75e638dadae084b5ab4a559caf1fdf3809036

107

py

Python

Chapter04/filesdirs_03.py

vabyte/Modern-Python-Standard-Library-Cookbook

4f53e3ab7b61aca1cca9343e7421e170280cd5b5

[ "MIT" ]

84

2018-08-09T09:30:03.000Z

2022-01-04T23:20:38.000Z

Chapter04/filesdirs_03.py

jiro74/Modern-Python-Standard-Library-Cookbook

4f53e3ab7b61aca1cca9343e7421e170280cd5b5

[ "MIT" ]

1

2019-11-04T18:57:40.000Z

2020-09-07T08:52:25.000Z

Chapter04/filesdirs_03.py

jiro74/Modern-Python-Standard-Library-Cookbook

4f53e3ab7b61aca1cca9343e7421e170280cd5b5

[ "MIT" ]

33

2018-09-26T11:05:55.000Z

2022-03-15T10:31:10.000Z

import pathlib print(list(pathlib.Path('.').glob('*.py'))) print(list(pathlib.Path('.').glob('**/*.py')))

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9

971422f6e39bd9efc9719611ea8dc5fd60e1120b

3,078

py

Python

application/service/wallet_pair_response.py

singnet/snet-converter-services

346b26f8281944a9f47d4bdd1eba54c8fb43e799

[ "MIT" ]

null

application/service/wallet_pair_response.py

singnet/snet-converter-services

346b26f8281944a9f47d4bdd1eba54c8fb43e799

[ "MIT" ]

1

2022-03-21T04:43:48.000Z

application/service/wallet_pair_response.py

singnet/snet-converter-services

346b26f8281944a9f47d4bdd1eba54c8fb43e799

[ "MIT" ]

4

2021-11-30T04:32:59.000Z

2022-03-23T07:20:53.000Z

from constants.entity import WalletPairEntities, WalletPairResponseEntities def get_wallet_pair_by_addresses_response(wallet_pair): return { WalletPairEntities.ROW_ID.value: wallet_pair[WalletPairEntities.ROW_ID.value], WalletPairEntities.ID.value: wallet_pair[WalletPairEntities.ROW_ID.value], WalletPairEntities.TOKEN_PAIR_ID.value: wallet_pair[WalletPairEntities.TOKEN_PAIR_ID.value], WalletPairEntities.FROM_ADDRESS.value: wallet_pair[WalletPairEntities.FROM_ADDRESS.value], WalletPairEntities.TO_ADDRESS.value: wallet_pair[WalletPairEntities.TO_ADDRESS.value], WalletPairEntities.DEPOSIT_ADDRESS.value: wallet_pair[WalletPairEntities.DEPOSIT_ADDRESS.value], WalletPairEntities.DEPOSIT_ADDRESS_DETAIL.value: wallet_pair[WalletPairEntities.DEPOSIT_ADDRESS_DETAIL.value], WalletPairEntities.SIGNATURE.value: wallet_pair[WalletPairEntities.SIGNATURE.value], WalletPairEntities.SIGNATURE_EXPIRY.value: wallet_pair[WalletPairEntities.SIGNATURE_EXPIRY.value], WalletPairEntities.UPDATED_AT.value: wallet_pair[WalletPairEntities.UPDATED_AT.value] } def create_wallet_pair_response(wallet_pair): return { WalletPairEntities.ROW_ID.value: wallet_pair[WalletPairEntities.ROW_ID.value], WalletPairEntities.ID.value: wallet_pair[WalletPairEntities.ID.value], WalletPairEntities.TOKEN_PAIR_ID.value: wallet_pair[WalletPairEntities.TOKEN_PAIR_ID.value], WalletPairEntities.FROM_ADDRESS.value: wallet_pair[WalletPairEntities.FROM_ADDRESS.value], WalletPairEntities.TO_ADDRESS.value: wallet_pair[WalletPairEntities.TO_ADDRESS.value], WalletPairEntities.DEPOSIT_ADDRESS.value: wallet_pair[WalletPairEntities.DEPOSIT_ADDRESS.value], WalletPairEntities.DEPOSIT_ADDRESS_DETAIL.value: wallet_pair[WalletPairEntities.DEPOSIT_ADDRESS_DETAIL.value], WalletPairEntities.SIGNATURE.value: wallet_pair[WalletPairEntities.SIGNATURE.value], WalletPairEntities.SIGNATURE_EXPIRY.value: wallet_pair[WalletPairEntities.SIGNATURE_EXPIRY.value], WalletPairEntities.UPDATED_AT.value: wallet_pair[WalletPairEntities.UPDATED_AT.value] } def get_wallet_pair_detail_by_deposit_address_response(wallet_pair): return { WalletPairEntities.ROW_ID.value: wallet_pair[WalletPairEntities.ROW_ID.value], WalletPairEntities.ID.value: wallet_pair[WalletPairEntities.ID.value], WalletPairEntities.TOKEN_PAIR_ID.value: wallet_pair[WalletPairEntities.TOKEN_PAIR_ID.value] } def get_wallet_pair_by_conversion_id_response(wallet_pair): return get_wallet_pair_by_addresses_response(wallet_pair) def get_all_deposit_address_response(wallet_pairs): return { WalletPairResponseEntities.ADDRESSES.value: [wallet_pair[WalletPairEntities.DEPOSIT_ADDRESS.value] for wallet_pair in wallet_pairs] } def get_wallets_address_by_ethereum_address_response(address): return { WalletPairResponseEntities.CARDANO_ADDRESS.value: address }

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py

Python

internos/etools/migrations/0043_auto_20190515_1341.py

UNICEFLebanonInnovation/Staging-Neuro

aac1e4f335ff4ec32041f989a9c22f8581a4961a

[ "MIT" ]

1

2020-12-12T07:41:11.000Z

internos/etools/migrations/0043_auto_20190515_1341.py

UNICEFLebanonInnovation/Staging-Neuro

aac1e4f335ff4ec32041f989a9c22f8581a4961a

[ "MIT" ]

9

2019-12-31T09:30:23.000Z

2022-01-13T00:49:47.000Z

internos/etools/migrations/0043_auto_20190515_1341.py

UNICEFLebanonInnovation/Staging-Neuro

aac1e4f335ff4ec32041f989a9c22f8581a4961a

[ "MIT" ]

1

2020-02-03T13:12:55.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11.20 on 2019-05-15 13:41 from __future__ import unicode_literals import django.contrib.postgres.fields from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('etools', '0042_actionpoint_category_name'), ] operations = [ migrations.AddField( model_name='partnerorganization', name='assessments', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='core_values_assessment_date', field=models.DateTimeField(blank=True, null=True), ), migrations.AddField( model_name='partnerorganization', name='core_values_assessments', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='flags', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='hact_min_requirements', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='hact_values', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='last_assessment_date', field=models.DateTimeField(blank=True, null=True), ), migrations.AddField( model_name='partnerorganization', name='net_ct_cy', field=models.CharField(blank=True, max_length=250, null=True), ), migrations.AddField( model_name='partnerorganization', name='planned_engagement', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='planned_visits', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='reported_cy', field=models.CharField(blank=True, max_length=250, null=True), ), migrations.AddField( model_name='partnerorganization', name='staff_members', field=django.contrib.postgres.fields.ArrayField(base_field=models.CharField(max_length=5000), blank=True, null=True, size=None), ), migrations.AddField( model_name='partnerorganization', name='total_ct_cp', field=models.CharField(blank=True, max_length=250, null=True), ), migrations.AddField( model_name='partnerorganization', name='total_ct_cy', field=models.CharField(blank=True, max_length=250, null=True), ), migrations.AddField( model_name='partnerorganization', name='total_ct_ytd', field=models.CharField(blank=True, max_length=250, null=True), ), migrations.AddField( model_name='partnerorganization', name='type_of_assessment', field=models.CharField(blank=True, max_length=250, null=True), ), ]

41.175258

140

0.631197

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3,994

6.066832

0.188119

0.117503

0.150143

0.176255

0.868625

0.848225

0.842513

0.831497

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0.024169

0.254131

3,994

96

141

41.604167

0.79859

0.017276

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0.719101

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0.146609

0.025752

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0.067416

0

null

0

1

0

1

0

null

0

9

97c27789ef8dc80300f339d494117bce96bfb361

246

py

Python

Module 3/Chapter 5/ch5_4.py

PacktPublishing/Natural-Language-Processing-Python-and-NLTK

bb7fd9a3071b4247d13accfbf0a48eefec76e925

[ "MIT" ]

50

2016-12-11T13:49:01.000Z

2022-03-20T19:47:55.000Z

Module 3/Chapter 5/ch5_4.py

PacktPublishing/Natural-Language-Processing-Python-and-NLTK

bb7fd9a3071b4247d13accfbf0a48eefec76e925

[ "MIT" ]

null

Module 3/Chapter 5/ch5_4.py

PacktPublishing/Natural-Language-Processing-Python-and-NLTK

bb7fd9a3071b4247d13accfbf0a48eefec76e925

[ "MIT" ]

40

2017-06-14T14:02:48.000Z

2021-10-14T06:25:00.000Z

import nltk from nltk.corpus import treebank_chunk print(treebank_chunk.chunked_sents()[1].leaves()) print(treebank_chunk.chunked_sents()[1].pos()) print(treebank_chunk.chunked_sents()[1].productions()) print(nltk.corpus.treebank.tagged_words())

35.142857

54

0.804878

35

246

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0

1

0

7

8ae82a1dbf777d3c05d4f79cff0dcf50c9f1f384

130

py

Python

ad2web/api/utils.py

billfor/alarmdecoder-webapp

43c3ebb2b44c7291cd89a2a7a31bbdfdb3ec06dc

[ "BSD-3-Clause", "MIT" ]

46

2015-06-14T02:19:16.000Z

2022-03-24T03:11:19.000Z

ad2web/api/utils.py

billfor/alarmdecoder-webapp

43c3ebb2b44c7291cd89a2a7a31bbdfdb3ec06dc

[ "BSD-3-Clause", "MIT" ]

66

2015-03-14T16:30:43.000Z

2021-08-28T22:20:01.000Z

ad2web/api/utils.py

billfor/alarmdecoder-webapp

43c3ebb2b44c7291cd89a2a7a31bbdfdb3ec06dc

[ "BSD-3-Clause", "MIT" ]

44

2015-02-13T19:23:37.000Z

2021-12-30T04:17:21.000Z

# -*- coding: utf-8 -*- import os import base64 def generate_api_key(): return base64.b32encode(os.urandom(7)).rstrip('==')

16.25

55

0.661538

18

130

4.666667

0.833333

0

0.072072

0.146154

130

7

56

18.571429

0.684685

0.161538

0

1

0

0.018692

0

1

0.25

true

0

0.5

0.25

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0

1

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null

0

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0

1

0

null

0

1

0

1

0

8

8aea8a1bfd66e757e3e0bd89fd033db7736fa123

1,718

py

Python

tests/test_1889.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

tests/test_1889.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

tests/test_1889.py

sungho-joo/leetcode2github

ce7730ef40f6051df23681dd3c0e1e657abba620

[ "MIT" ]

null

#!/usr/bin/env python import pytest """ Test 1889. Minimum Space Wasted From Packaging """ @pytest.fixture(scope="session") def init_variables_1889(): from src.leetcode_1889_minimum_space_wasted_from_packaging import Solution solution = Solution() def _init_variables_1889(): return solution yield _init_variables_1889 class TestClass1889: def test_solution_0(self, init_variables_1889): assert init_variables_1889().minWastedSpace([2, 3, 5], [[4, 8], [2, 8]]) == 6 def test_solution_1(self, init_variables_1889): assert init_variables_1889().minWastedSpace([2, 3, 5], [[1, 4], [2, 3], [3, 4]]) == -1 def test_solution_2(self, init_variables_1889): assert ( init_variables_1889().minWastedSpace([3, 5, 8, 10, 11, 12], [[12], [11, 9], [10, 5, 14]]) == 9 ) #!/usr/bin/env python import pytest """ Test 1889. Minimum Space Wasted From Packaging """ @pytest.fixture(scope="session") def init_variables_1889(): from src.leetcode_1889_minimum_space_wasted_from_packaging import Solution solution = Solution() def _init_variables_1889(): return solution yield _init_variables_1889 class TestClass1889: def test_solution_0(self, init_variables_1889): assert init_variables_1889().minWastedSpace([2, 3, 5], [[4, 8], [2, 8]]) == 6 def test_solution_1(self, init_variables_1889): assert init_variables_1889().minWastedSpace([2, 3, 5], [[1, 4], [2, 3], [3, 4]]) == -1 def test_solution_2(self, init_variables_1889): assert ( init_variables_1889().minWastedSpace([3, 5, 8, 10, 11, 12], [[12], [11, 9], [10, 5, 14]]) == 9 )

24.898551

101

0.648428

232

1,718

4.525862

0.181034

0.222857

0.291429

0.12

1

0

0.131657

0.213038

1,718

68

102

25.264706

0.64497

0.023283

0

0.944444

0

0.008929

0

0.166667

1

0.277778

false

0

0.111111

0.055556

0.5

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null

1

0

1

0

1

0

null

0

1

0

10

8af0cc8eab2612c0e200adfd1bbd585565b5fc71

5,130

py

Python

misc/python_sealog/cruises.py

WHOIGit/ndsf-sealog-server

e57843e3e23a924ccf6fc1ef1e40d92f36a3b612

[ "MIT" ]

4

2019-10-29T21:53:13.000Z

2021-12-02T00:38:42.000Z

misc/python_sealog/cruises.py

WHOIGit/ndsf-sealog-server

e57843e3e23a924ccf6fc1ef1e40d92f36a3b612

[ "MIT" ]

14

2020-05-28T16:39:30.000Z

2021-05-22T06:01:40.000Z

misc/python_sealog/cruises.py

WHOIGit/ndsf-sealog-server

e57843e3e23a924ccf6fc1ef1e40d92f36a3b612

[ "MIT" ]

1

2020-01-31T00:00:42.000Z

#!/usr/bin/env python3 ''' FILE: cruises.py DESCRIPTION: This script contains the wrapper functions for the sealog- server cruise routes. BUGS: NOTES: AUTHOR: Webb Pinner COMPANY: OceanDataTools.org VERSION: 0.1 CREATED: 2021-01-01 REVISION: LICENSE INFO: This code is licensed under MIT license (see LICENSE.txt for details) Copyright (C) OceanDataTools.org 2021 ''' import json import logging import requests from .settings import API_SERVER_URL, HEADERS, CRUISES_API_PATH def get_cruise(cruise_uid, export_format='json', api_server_url=API_SERVER_URL, headers=HEADERS): ''' Return a cruise record based on the cruise_id. Returns the record as a json object by default. Set export_format to 'csv' to return the record in csv format. ''' try: url = api_server_url + CRUISES_API_PATH + '/' + cruise_uid + '?format=' + export_format req = requests.get(url, headers=headers) if req.status_code == 200: if export_format == 'json': return json.loads(req.text) if export_format == 'csv': return req.text else: return None except Exception as error: logging.error(str(error)) raise error return None def get_cruises(export_format='json', api_server_url=API_SERVER_URL, headers=HEADERS): ''' Return all cruise records. Returns the records as json objects by default Set export_format to 'csv' to return the records in csv format. ''' try: url = api_server_url + CRUISES_API_PATH + '?format=' + export_format req = requests.get(url, headers=headers) if req.status_code == 200: if export_format == 'json': return json.loads(req.text) if export_format == 'csv': return req.text if req.status_code == 404: if export_format == 'json': return [] if export_format == 'csv': return "" except Exception as error: logging.error(str(error)) raise error return None def get_cruise_uid_by_id(cruise_id, api_server_url=API_SERVER_URL, headers=HEADERS): ''' Return the UID for a cruise record based on the cruise_id. ''' try: url = api_server_url + CRUISES_API_PATH + '?cruise_id=' + cruise_id req = requests.get(url, headers=headers) if req.status_code == 200: cruise = json.loads(req.text)[0] return cruise['id'] except Exception as error: logging.error(str(error)) raise error return None def get_cruise_by_id(cruise_id, export_format='json', api_server_url=API_SERVER_URL, headers=HEADERS): ''' Return the cruise record based on the cruise_id. Returns the records as json object by default. Set export_format to 'csv' to return the record in csv format. ''' try: url = api_server_url + CRUISES_API_PATH + '?cruise_id=' + cruise_id + '&format=' + export_format req = requests.get(url, headers=headers) if req.status_code == 200: if export_format == 'json': return json.loads(req.text)[0] if export_format == 'csv': return req.text else: return None except Exception as error: logging.error(str(error)) raise error return None def get_cruise_by_lowering(lowering_uid, export_format='json', api_server_url=API_SERVER_URL, headers=HEADERS): ''' Return the cruise record that contains the lowering whose uid is lowering_uid. Returns the record as a json object by default. Set export_format to 'csv' to return the record in csv format. ''' try: url = api_server_url + CRUISES_API_PATH + '/bylowering/' + lowering_uid + '?format=' + export_format req = requests.get(url, headers=headers) if req.status_code == 200: if export_format == 'json': return json.loads(req.text) if export_format == 'csv': return req.text else: return None except Exception as error: logging.error(str(error)) raise error return None def get_cruise_by_event(event_uid, export_format='json', api_server_url=API_SERVER_URL, headers=HEADERS): ''' Return the cruise record that contains the event whose uid is event_uid. Returns the record as a json object by default. Set export_format to 'csv' to return the record in csv format. ''' try: url = api_server_url + CRUISES_API_PATH + '/byevent/' + event_uid + '?format=' + export_format req = requests.get(url, headers=headers) if req.status_code == 200: if export_format == 'json': return json.loads(req.text) if export_format == 'csv': return req.text else: return None except Exception as error: logging.error(str(error)) raise error return None

27.880435

111

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668

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0.149701

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false

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0.354839

0

null

0

1

0

1

0

null

0

7

c118da10af6c651595c0014c195ea3d26010654b

25,942

py

Python

Tic-Tac-Toe/Tic-Tac-Toe.py

alexeevivan/pythonic

7957421f043205dd7d5f8f5f8579aaa48411bfc8

[ "Unlicense" ]

1

2022-03-27T12:59:12.000Z

Tic-Tac-Toe/Tic-Tac-Toe.py

alexeevivan/pythonic

7957421f043205dd7d5f8f5f8579aaa48411bfc8

[ "Unlicense" ]

null

Tic-Tac-Toe/Tic-Tac-Toe.py

alexeevivan/pythonic

7957421f043205dd7d5f8f5f8579aaa48411bfc8

[ "Unlicense" ]

null

import os from colorama import init from colorama import Fore, Back, Style # добавляем возможность выбора языка lang_selection = int(input("Для выбора русского языка введите «1»\nTo choose english language press «2»:")) while lang_selection!=1 and lang_selection!=2: print(Fore.RED) print("\nВведён некорректный символ. Попробуйте еще раз.\nAn invalid response was entered. Try again.") print(Fore.WHITE) lang_selection = int(input("Для выбора русского языка введите «1»\nTo choose english language press «2»:")) if lang_selection==1: # задаём значения всем переменным, которые будут встречаться в коде, при этом не требующие ввода данных от игроков j = ("\nВас приветствует игра «-Tic-Tac-Toe-» !!! \nКаждая ячейка выделена цифровым индикатором для удобства планирования ходов.") b2 = """ | | | | ■ | ■ | ■ 1 | 2 | 3 | | | | — — — — — — — — — — — — — — — — — — | | | | ■ | ■ | ■ 4 | 5 | 6 | | | | — — — — — — — — — — — — — — — — — — | | | | ■ | ■ | ■ 7 | 8 | 9 | | | | — — — — — — — — — — — — — — — — — — """ x = ("X") o = ("O") c = ("Отлично! Игрок") d = ("использует символ X. Осталось совсем чуть-чуть.") e = ("использует символ O. Пора начинать.") pl = ("+") pl_1 = ("/'+'/") pl_2 = ('/"+"/') pl_3 = ("«+»") pl_4 = pl or pl_1 or pl_2 or pl_3 m = ("-") m_1 = ("/'-'/") m_2 = ('/"-"/') m_3 = ("«-»") m_4 = m or m_1 or m_2 or m_3 print(j) print(Fore.MAGENTA, b2) print(Fore.WHITE) player_1 = str.title(input("Введите имя первого игрока:")) player_2 = str.title(input("\nВведите имя второго игрока:")) p_1, p_2 = player_1, player_2 print(Fore.LIGHTCYAN_EX) print("Приветствую,", player_1, "и", player_2, "!") # добавление вопроса о предпочтении использования того или иного сивола, поскольку ввод первого имени не будет являтся причиной получения права первого хода print(Fore.WHITE) f = int(input("Введите «1», если игрок №1 желает использовать символ Х, или «2» - если будет использовать символ О:")) print(Fore.LIGHTCYAN_EX) while f!=1 and f>2: print("Введён некорректный ответ. Попробуйте ещё раз.") f = int(input("Введите «1», если игрок №1 желает использовать символ Х, или «2» - если будет использовать символ О:")) if f==1: print(c, p_1, d) if f==2: print(c, p_1, e) print(Fore.WHITE) g = int(input("Введите «1», если игрок №2 использует символ Х, или «2» - если использует символ О:")) while g==f: print(Fore.RED) print("К сожалению, данный символ зарезервирован за игроком", player_1, ".") print(Fore.WHITE) g = int(input("Введите «1», если игрок №2 использует символ Х, или «2» - если использует символ О:")) while g!=1 and g>2: print(Fore.RED) print("Введён некорректный ответ. Попробуйте ещё раз.") print(Fore.WHITE) g = int(input("Введите «1», если игрок №2 использует символ Х, или «2» - если использует символ О:")) print(Fore.LIGHTCYAN_EX) if g==1: print(c, p_2, d) if g==2: print(c, p_2, e) print(Fore.WHITE) # создание дополнительной вариативности соврешения ходов u = input("Как правило, первый ход совершает игрок, чьим символом является «Х». \nВ зависимости от выбранного Вами ответа («+» или «-») я пойму, желаете ли Вы следовать этому правилу:") # отображение результатов в зависимости от внесённых условий от пользователя while u!=pl_4 and u!=m_4: print("Введён некорректный символ. Попробуйте ещё раз:") u = input("Как правило, первый ход совершает игрок, чьим символом является «Х». \nВ зависимости от выбранного Вами ответа («+» или «-») я пойму, желаете ли Вы следовать этому правилу:") print(Fore.LIGHTCYAN_EX) if (u==pl_4 and f==1) or (u!=pl_4 and g==2) or (u!=m_4 and f==1) or (u==m_4 and g==2): print("Отлично. Игрок", p_1, "использует символ", x,"и начнёт игру первым!") print("Отлично. Игрок", p_2, "использует символ", o,"и начнёт игру вторым!") elif (u!=pl_4 and f==1) or (u==pl_4 and g==2) or (u==m_4 and f==1) or (u!=m_4 and g==2): print("Отлично. Игрок", p_2, "использует символ", o,"и начнёт игру первым!") print("Отлично. Игрок", p_1, "использует символ", x,"и начнёт игру вторым!") elif (u==pl_4 and g==1) or (u!=pl_4 and f==2) or (u!=m_4 and g==1) or (u==m_4 and f==2): print("Отлично. Игрок", p_2, "использует символ", x,"и начнёт игру первым!") print("Отлично. Игрок", p_1, "использует символ", o,"и начнёт игру вторым!") elif (u!=pl_4 and g==1) or (u==pl_4 and f==2) or (u==m_4 and g==1) or (u!=m_4 and f==2): print("Отлично. Игрок", p_1, "использует символ", o,"и начнёт игру первым!") print("Отлично. Игрок", p_2, "использует символ", x,"и начнёт игру вторым!") print(Fore.MAGENTA, b2) print(Fore.WHITE) # сокращение до переменных выбора игрока # player_1 == X, player_2 == 0 xx_1 = int((u==pl_4 and f==1) or (u!=pl_4 and g==2) or (u!=m_4 and f==1) or (u==m_4 and g==2)) # player_1==O, player_2==X xx_2 = int((u!=pl_4 and g==1) or (u==pl_4 and f==2) or (u==m_4 and g==1) or (u!=m_4 and f==2)) # player_2==X, player_1==O xx_3 = int((u==pl_4 and g==1) or (u!=pl_4 and f==2) or (u!=m_4 and g==1) or (u==m_4 and f==2)) # player_2==O, player_1==X xx_4 = int((u!=pl_4 and f==1) or (u==pl_4 and g==2) or (u==m_4 and f==1) or (u!=m_4 and g==2)) win_combo = [(1, 2, 3), (4, 5, 6), (7, 8, 9), (1, 4, 7), (2, 5, 8), (3, 6, 9), (1, 5, 9), (3, 5, 7)] board = list(range(1, 10)) # функция отображения строки, которая будет использована каждый раз после совершения хода def draw_board(): for i in range(3): print(" | | ") print(" ", board[0 + i * 3], " | ", board[1 + i * 3], " | ", board[2 + i * 3]) print(" | | ") print("— — — — — — — — —") # функция, отслеживающая ситуацию на игровом поле в зависимости от выбранной ячейки для хода def take_input(player_token): while True: value = input("Введите номер ячейки для совершения хода:") if not (value in '123456789'): print("Введён некорректный символ. Попробуйте ещё раз:") continue value = int(value) if str(board[value - 1]) in "XO": print(Fore.RED) print("К сожалению, данная клетка уже занята Вашим оппонентом. Выберите пустую ячейку:") print(Fore.WHITE) continue board[value - 1] = player_token break # функция проверки условий для победы def check_win(): for each in win_combo: if (board[each[0]-1]) == (board[each[1]-1]) == (board[each[2]-1]): return board[each[1]-1] else: return False def main(): # установка счётчика ходов counter = 0 while True: # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_1 начнёт игру первым с использованием символа «Х» if xx_1==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(x) else: take_input(o) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==x: print(Fore.BLUE) print(player_1, "победил(-a)!") print(Fore.WHITE) break if victory==check_win() and victory==o: print(Fore.BLUE) print(player_2, "победил(-a)!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Ничья!") print(Fore.WHITE) break # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_1 начнёт игру первым с использованием символа «О» elif xx_2==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(o) else: take_input(x) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==o: print(Fore.BLUE) print(player_1, "победил(-a)!") print(Fore.WHITE) break if victory==check_win() and victory==x: print(Fore.BLUE) print(player_2, "победил(-a)!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Ничья!") print(Fore.WHITE) break # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_2 начнёт игру первым с использованием символа «Х» elif xx_3==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(x) else: take_input(o) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==x: print(Fore.BLUE) print(player_2, "победил(-a)!") print(Fore.WHITE) break if victory==check_win() and victory==o: print(Fore.BLUE) print(player_1, "победил(-a)!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Ничья!") print(Fore.WHITE) break # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_2 начнёт игру первым с использованием символа «О» elif xx_4==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(o) else: take_input(x) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==o: print(Fore.BLUE) print(player_2, "победил(-а)!") print(Fore.WHITE) break if victory==check_win() and victory==x: print(Fore.BLUE) print(player_1, "победил(-а)!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Ничья!") print(Fore.WHITE) break main() elif lang_selection==2: # задаём значения всем переменным, которые будут встречаться в коде, при этом не требующие ввода данных от игроков j = ("\nWelcome to the «Tic-Tac-Toe» game!!! \nEach cell is highlighted with a digital indicator for easy planning of moves.") b2 = """ | | | | ■ | ■ | ■ 1 | 2 | 3 | | | | — — — — — — — — — — — — — — — — — — | | | | ■ | ■ | ■ 4 | 5 | 6 | | | | — — — — — — — — — — — — — — — — — — | | | | ■ | ■ | ■ 7 | 8 | 9 | | | | — — — — — — — — — — — — — — — — — — """ x = ("X") o = ("O") c = ("Greeat! The player") d = ("uses X symbol. Wait for a moment.") e = ("uses O symbol. Let's start.") pl = ("+") pl_1 = ("/'+'/") pl_2 = ('/"+"/') pl_3 = ("«+»") pl_4 = pl or pl_1 or pl_2 or pl_3 m = ("-") m_1 = ("/'-'/") m_2 = ('/"-"/') m_3 = ("«-»") m_4 = m or m_1 or m_2 or m_3 print(j) print(Fore.MAGENTA, b2) print(Fore.WHITE) player_1 = str.title(input("Enter the name of the first player:")) player_2 = str.title(input("\nEnter the name of the second player:")) p_1, p_2 = player_1, player_2 print(Fore.LIGHTCYAN_EX) print("Welcome,", player_1, "and", player_2, "!") # добавление вопроса о предпочтении использования того или иного сивола, поскольку ввод первого имени не будет являтся причиной получения права первого хода print(Fore.WHITE) f = int(input("Enter «1» if player # 1 wants to use the X symbol, or «2» - if wants to use the O symbol:")) print(Fore.LIGHTCYAN_EX) while f!=1 and f>2: print("An invalid response was entered. Try again:") f = int(input("Enter «1» if player # 1 wants to use the X symbol, or «2» - if wants to use the O symbol:")) if f==1: print(c, p_1, d) if f==2: print(c, p_1, e) print(Fore.WHITE) g = int(input("Enter «1» if player # 1 wants to use the X symbol, or «2» - if he/she want to use the O symbol:")) while g==f: print(Fore.RED) print("Unfortunately, this symbol is reserved for the player", player_1, ".") print(Fore.WHITE) g = int(input("Enter «1» if player # 1 wants to use the X symbol, or «2» - if he/she want to use the O symbol:")) while g!=1 and g>2: print(Fore.RED) print("An invalid response was entered. Try again:.") print(Fore.WHITE) g = int(input("Enter «1» if player # 2 uses the X symbol, or «2» - if he/she uses the O symbol:")) print(Fore.LIGHTCYAN_EX) if g==1: print(c, p_2, d) if g==2: print(c, p_2, e) print(Fore.WHITE) # создание дополнительной вариативности соврешения ходов u = input("As a rule, the first move is made by the player whose symbol is «X». \ n depending on the answer you choose («+» or «-»), I will understand if You want to follow this rule:") # отображение результатов в зависимости от внесённых условий от пользователя while u!=pl_4 and u!=m_4: print("An invalid response was entered. Try again:") u = input("As a rule, the first move is made by the player whose symbol is «X». \ n depending on the answer you choose («+» or «-»), I will understand if You want to follow this rule:") print(Fore.LIGHTCYAN_EX) if (u==pl_4 and f==1) or (u!=pl_4 and g==2) or (u!=m_4 and f==1) or (u==m_4 and g==2): print("Great. The player", p_1, "uses sybmol", x,"and will start the game first!") print("Great. The player", p_2, "uses sybmol", o,"and will start the game first!") elif (u!=pl_4 and f==1) or (u==pl_4 and g==2) or (u==m_4 and f==1) or (u!=m_4 and g==2): print("Great. The player", p_2, "uses sybmol", o,"and will start the game first!") print("Great. The player", p_1, "uses sybmol", x,"and will start the game first!") elif (u==pl_4 and g==1) or (u!=pl_4 and f==2) or (u!=m_4 and g==1) or (u==m_4 and f==2): print("Great. The player", p_2, "uses sybmol", x,"and will start the game first!") print("Great. The player", p_1, "uses sybmol", o,"and will start the game first!") elif (u!=pl_4 and g==1) or (u==pl_4 and f==2) or (u==m_4 and g==1) or (u!=m_4 and f==2): print("Great. The player", p_1, "uses sybmol", o,"and will start the game first!") print("Great. The player", p_2, "uses sybmol", x,"and will start the game first!") print(Fore.MAGENTA, b2) print(Fore.WHITE) # сокращение до переменных выбора игрока # player_1 == X, player_2 == 0 xx_1 = int((u==pl_4 and f==1) or (u!=pl_4 and g==2) or (u!=m_4 and f==1) or (u==m_4 and g==2)) # player_1==O, player_2==X xx_2 = int((u!=pl_4 and g==1) or (u==pl_4 and f==2) or (u==m_4 and g==1) or (u!=m_4 and f==2)) # player_2==X, player_1==O xx_3 = int((u==pl_4 and g==1) or (u!=pl_4 and f==2) or (u!=m_4 and g==1) or (u==m_4 and f==2)) # player_2==O, player_1==X xx_4 = int((u!=pl_4 and f==1) or (u==pl_4 and g==2) or (u==m_4 and f==1) or (u!=m_4 and g==2)) win_combo = [(1, 2, 3), (4, 5, 6), (7, 8, 9), (1, 4, 7), (2, 5, 8), (3, 6, 9), (1, 5, 9), (3, 5, 7)] board = list(range(1, 10)) # функция отображения строки, которая будет использована каждый раз после совершения хода def draw_board(): for i in range(3): print(" | | ") print(" ", board[0 + i * 3], " | ", board[1 + i * 3], " | ", board[2 + i * 3]) print(" | | ") print("— — — — — — — — —") # функция, отслеживающая ситуацию на игровом поле в зависимости от выбранной ячейки для хода def take_input(player_token): while True: value = input("Pleaese enter the cell number to make the move:") if not (value in '123456789'): print("An invalid response was entered. Try again:") continue value = int(value) if str(board[value - 1]) in "XO": print(Fore.RED) print("Unfortunately, this cell is already occupied by your opponent. Select an empty cell:") print(Fore.WHITE) continue board[value - 1] = player_token break # функция проверки условий для победы def check_win(): for each in win_combo: if (board[each[0]-1]) == (board[each[1]-1]) == (board[each[2]-1]): return board[each[1]-1] else: return False def main(): # установка счётчика ходов counter = 0 while True: # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_1 начнёт игру первым с использованием символа «Х» if xx_1==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(x) else: take_input(o) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==x: print(Fore.BLUE) print(player_1, "won!") print(Fore.WHITE) break if victory==check_win() and victory==o: print(Fore.BLUE) print(player_2, "won!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Draw!") print(Fore.WHITE) break # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_1 начнёт игру первым с использованием символа «О» elif xx_2==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(o) else: take_input(x) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==o: print(Fore.BLUE) print(player_1, "won!") print(Fore.WHITE) break if victory==check_win() and victory==x: print(Fore.BLUE) print(player_2, "won!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Draw!") print(Fore.WHITE) break # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_2 начнёт игру первым с использованием символа «Х» elif xx_3==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(x) else: take_input(o) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==x: print(Fore.BLUE) print(player_2, "won!") print(Fore.WHITE) break if victory==check_win() and victory==o: print(Fore.BLUE) print(player_1, "won!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Draw!") print(Fore.WHITE) break # если предыдущие введённые данные пользователем привели к следующему результату:\ # игрок_2 начнёт игру первым с использованием символа «О» elif xx_4==True: os.system('cls' if os.name=='nt' else 'clear') print(Fore.MAGENTA) draw_board() print(Fore.WHITE) if counter % 2 == 0: take_input(o) else: take_input(x) if counter > 3: victory = check_win() if victory: print(Fore.GREEN) draw_board() if victory==check_win() and victory==o: print(Fore.BLUE) print(player_2, "won!") print(Fore.WHITE) break if victory==check_win() and victory==x: print(Fore.BLUE) print(player_1, "won!") print(Fore.WHITE) break counter += 1 if counter > 8: draw_board() print(Fore.YELLOW) print("Draw!") print(Fore.WHITE) break main()

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c1233065e68be493898d589af31c528a3cfbe9d7

1,155

py

Python

myPuzzleSolvers/puzzle_boards/hashi_puzzle_boards.py

giraycoskun/myPuzzleSolvers

225286c9a404012953f79094c1769f1392950b8a

[ "MIT" ]

null

myPuzzleSolvers/puzzle_boards/hashi_puzzle_boards.py

giraycoskun/myPuzzleSolvers

225286c9a404012953f79094c1769f1392950b8a

[ "MIT" ]

null

myPuzzleSolvers/puzzle_boards/hashi_puzzle_boards.py

giraycoskun/myPuzzleSolvers

225286c9a404012953f79094c1769f1392950b8a

[ "MIT" ]

null

"""Test Maps of Islands for Hashi Puzzle """ test1 = [ [ [1, 0], [0, 1] ], [ [0, 0, 1], [0, 0, 0], [1, 0, 2] ], [ [0, 1, 0, 2, 0], [0, 0, 0, 0, 0], [0, 1, 0, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0] ], [ [0, 3, 0, 2, 0, 0, 3], [1, 0, 1, 0, 0, 3, 0], [0, 3, 0, 2, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [4, 0, 2, 0, 0, 0, 2], [0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 3, 0, 3, 0] ] ] test2= [ [ [3, 0, 0, 0, 4, 0, 5, 0, 0, 4], [0, 0, 2, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 5], [2, 0, 3, 0, 5, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [3, 0, 0, 2, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 2, 0, 0], [5, 0, 0, 0, 0, 0, 0, 0, 0, 5], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 1, 0, 0, 0, 0, 0, 0, 3] ] ] test3 = [ [ [0, 2, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 2, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0] ] ]

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1

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null

0

7

c141f739a33d986f1b2b35a3ae6fdb2d1a8094c0

3,960

py

Python

baselines/lstm_classifier.py

sorokine/NeuralTripleTranslation

9a58a8981ac6ca196668a88e46515951f1a7e5de

[ "Apache-2.0" ]

47

2018-07-06T01:00:37.000Z

2021-12-05T08:05:35.000Z

baselines/lstm_classifier.py

sorokine/NeuralTripleTranslation

9a58a8981ac6ca196668a88e46515951f1a7e5de

[ "Apache-2.0" ]

6

2018-10-29T09:35:58.000Z

2022-01-02T14:06:59.000Z

baselines/lstm_classifier.py

sorokine/NeuralTripleTranslation

9a58a8981ac6ca196668a88e46515951f1a7e5de

[ "Apache-2.0" ]

14

2018-07-08T06:13:08.000Z

2021-06-18T06:21:56.000Z

import torch import torch.nn as nn from torch.autograd import Variable import torch.nn.functional as F class BiLSTM(nn.Module): def __init__(self, embedding_dim, hidden_size, num_layers, vocab_size, num_classes, dropout): super(BiLSTM, self).__init__() self.word_embeds = nn.Embedding(vocab_size, embedding_dim) self.hidden_size = hidden_size self.num_layers = num_layers self.num_directions = 1 self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=False) self.fc1 = nn.Linear(hidden_size * self.num_directions, 64) self.fc2 = nn.Linear(64, num_classes) self.dropout = nn.Dropout(p=dropout) def forward(self, inputs, seq_lengths): batch_size = inputs.size(0) inputs = self.word_embeds(inputs) # Set initial states h0 = Variable(torch.zeros(self.num_layers * self.num_directions, batch_size, self.hidden_size)) c0 = Variable(torch.zeros(self.num_layers * self.num_directions, batch_size, self.hidden_size)) # Forward propagate RNN outputs, _ = self.lstm(inputs, (h0, c0)) # Decode hidden state of last time step outputs = F.relu(self.fc1(outputs[:, -1, :])) outputs = self.dropout(outputs) outputs = self.fc2(outputs) return outputs class BasicRNN(nn.Module): def __init__(self, embedding_dim, hidden_size, num_layers, vocab_size, num_classes, dropout): super(BasicRNN, self).__init__() self.word_embeds = nn.Embedding(vocab_size, embedding_dim) self.hidden_size = hidden_size self.num_layers = num_layers self.num_directions = 1 self.rnn = nn.RNN(input_size=embedding_dim, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=False) self.fc1 = nn.Linear(hidden_size * self.num_directions, 64) self.fc2 = nn.Linear(64, num_classes) self.dropout = nn.Dropout(p=dropout) def forward(self, inputs, seq_lengths): batch_size = inputs.size(0) inputs = self.word_embeds(inputs) # Set initial states h0 = Variable(torch.zeros(self.num_layers * self.num_directions, batch_size, self.hidden_size)) # Forward propagate RNN outputs, _ = self.rnn(inputs, h0) # Decode hidden state of last time step outputs = F.relu(self.fc1(outputs[:, -1, :])) outputs = self.dropout(outputs) outputs = self.fc2(outputs) return outputs class GRURNN(nn.Module): def __init__(self, embedding_dim, hidden_size, num_layers, vocab_size, num_classes, dropout): super(GRURNN, self).__init__() self.word_embeds = nn.Embedding(vocab_size, embedding_dim) self.hidden_size = hidden_size self.num_layers = num_layers self.num_directions = 1 self.rnn = nn.GRU(input_size=embedding_dim, hidden_size=hidden_size, num_layers=num_layers, batch_first=True, bidirectional=False) self.fc1 = nn.Linear(hidden_size * self.num_directions, 64) self.fc2 = nn.Linear(64, num_classes) self.dropout = nn.Dropout(p=dropout) def forward(self, inputs, seq_lengths): batch_size = inputs.size(0) inputs = self.word_embeds(inputs) # Set initial states h0 = Variable(torch.zeros(self.num_layers * self.num_directions, batch_size, self.hidden_size)) # Forward propagate RNN outputs, _ = self.rnn(inputs, h0) # Decode hidden state of last time step outputs = F.relu(self.fc1(outputs[:, -1, :])) outputs = self.dropout(outputs) outputs = self.fc2(outputs) return outputs

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0.634091

499

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0

7

c1818cc08308030103bff4e6be825fe392fc36d4

5,618

py

Python

src/cpp/AutoWIG.py

StatisKit/Eigen

535af80557c472f735fcd0523959a54b7baeb719

[ "Apache-2.0" ]

1

2017-07-17T18:50:28.000Z

src/cpp/AutoWIG.py

StatisKit/Eigen

535af80557c472f735fcd0523959a54b7baeb719

[ "Apache-2.0" ]

null

src/cpp/AutoWIG.py

StatisKit/Eigen

535af80557c472f735fcd0523959a54b7baeb719

[ "Apache-2.0" ]

7

2017-02-10T10:31:33.000Z

2021-03-15T18:30:10.000Z

def controller(asg): from scons_tools.site_autowig.controller.statiskit_stl import controller as stl_controller asg = stl_controller(asg, library=False) # for dcl in asg['::Eigen::internal'].declarations(nested=True): # dcl.pybind11_export = False for cls in ['class ::Eigen::DenseBase< class ::Eigen::Matrix< double, 1, -1, 1, 1, -1 > >', 'class ::Eigen::DenseBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >', 'class ::Eigen::DenseBase< class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > >']: for mtd in asg[cls].methods(access='public'): if mtd.localname == 'trace': mtd.pybind11_export = False for fct in asg.functions(): if fct.localname in ['_check_template_params', 'operator()', 'operator[]']: fct.pybind11_export = False for cls in ['Vectors', 'RowVectors', 'Matrices']: cls = asg['::statiskit::linalg::' + cls].qualified_type.unqualified_type for ctr in cls.constructors(): if ctr.nb_parameters > 0: ctr.pybind11_export = False for method in asg['class ::Eigen::DenseBase< class ::Eigen::Matrix< double, 1, -1, 1, 1, -1 > >'].methods(access='public') + asg['class ::Eigen::DenseBase< class ::Eigen::Matrix< double, 1, -1, 1, 1, -1 > >'].functions(): if method.prototype(desugared=False) == 'void transposeInPlace()': method.pybind11_export = False break for method in asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].methods(access='public') + asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].functions(): if method.prototype(desugared=False) == 'class ::Eigen::TriangularView< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 >, 10 > triangularView()': method.pybind11_export = False break for method in asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].methods(access='public') + asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].functions(): if method.prototype(desugared=False) == 'class ::Eigen::TriangularView< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > const, 5 > const triangularView() const': method.pybind11_export = False break for method in asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].methods(access='public') + asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].functions(): if method.prototype(desugared=False) == 'class ::Eigen::TriangularView< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > const, 2 > const triangularView() const': method.pybind11_export = False break for method in asg['class ::Eigen::PlainObjectBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].methods(access='public') + asg['class ::Eigen::PlainObjectBase< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].functions(): if method.prototype(desugared=False) == 'void conservativeResize(::Eigen::Index )': method.pybind11_export = False break for method in asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > >'].methods(access='public') + asg['class ::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > >'].functions(): if method.prototype(desugared=False) == '::Eigen::MatrixBase< class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > >::RealScalar lpNorm() const': method.pybind11_export = False break for method in asg['class ::Eigen::FullPivLU< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].methods(access='public') + asg['class ::Eigen::FullPivLU< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].functions(): if method.prototype(desugared=False) == 'void _solve_impl_transposed(class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > const &, class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > &) const': method.pybind11_export = False break for method in asg['class ::Eigen::DenseBase< class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > >'].methods(access='public') + asg['class ::Eigen::DenseBase< class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > >'].functions(): if method.prototype(desugared=False) == 'void transposeInPlace()': method.pybind11_export = False break for method in asg['class ::Eigen::PartialPivLU< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].methods(access='public') + asg['class ::Eigen::PartialPivLU< class ::Eigen::Matrix< double, -1, -1, 0, -1, -1 > >'].functions(): if method.prototype(desugared=False) == 'void _solve_impl_transposed(class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > const &, class ::Eigen::Matrix< double, -1, 1, 0, -1, 1 > &) const': method.pybind11_export = False break return asg def generator(asg, module, decorator): import autowig import itertools autowig.generator.plugin = 'pybind11' nodes = [typedef.qualified_type.unqualified_type for typedef in asg['::statiskit::linalg'].typedefs()] nodes = list(itertools.chain(*[node.bases(inherited=True) for node in nodes])) + nodes + asg['::statiskit::linalg'].declarations() wrappers = autowig.generator(asg, nodes, module=module, decorator=decorator, closure=False) return wrappers

83.850746

241

0.60324

700

5,618

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0.132857

0.038129

0.138219

0.190051

0.756032

0.737563

0.721478

0

0.040217

0.212175

5,618

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83.850746

0.718256

0.016732

0

0.390625

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0.053966

0

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0.03125

false

0

0.046875

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0.109375

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null

0

1

0

1

0

1

0

null

0

7

c1dc4afc8b036c449196060524e6492682ac24dd

24,802

py

Python

psono/restapi/tests/emergencycode.py

dirigeant/psono-server

a18c5b3c4d8bbbe4ecf1615b210d99fb77752205

[ "Apache-2.0", "CC0-1.0" ]

48

2018-04-19T15:50:58.000Z

2022-01-23T15:58:11.000Z

psono/restapi/tests/emergencycode.py

dirigeant/psono-server

a18c5b3c4d8bbbe4ecf1615b210d99fb77752205

[ "Apache-2.0", "CC0-1.0" ]

9

2018-09-13T14:56:18.000Z

2020-01-17T16:44:33.000Z

psono/restapi/tests/emergencycode.py

dirigeant/psono-server

a18c5b3c4d8bbbe4ecf1615b210d99fb77752205

[ "Apache-2.0", "CC0-1.0" ]

11

2019-09-20T11:53:47.000Z

2021-07-18T22:41:31.000Z

from django.urls import reverse from django.contrib.auth.hashers import make_password from django.conf import settings from rest_framework import status from .base import APITestCaseExtended from restapi import models import random import string import binascii import os class CreateEmergencyCodeTest(APITestCaseExtended): """ Test to create a emergency code (PUT) """ def setUp(self): self.test_email = "test@example.com" self.test_email_bcrypt = "a" self.test_password = "myPassword" self.test_authkey = "c55066421a559f76d8ed5227622e9f95a0c67df15220e40d7bc98a8a598124fa15373ac553ef3ee27c7" \ "123d6be058e6d43cc71c1b666bdecaf33b734c8583a93" self.test_public_key = "5706a5648debec63e86714c8c489f08aee39477487d1b3f39b0bbb05dbd2c649" self.test_secret_key = "a7d028388e9d80f2679c236ebb2d0fedc5b7b0a28b393f6a20cc8f6be636aa71" self.test_secret_key_enc = "77cde8ff6a5bbead93588fdcd0d6346bb57224b55a49c0f8a22a807bf6414e4d82ff60711422" \ "996e4a26de599982d531eef3098c9a531a05f75878ac0739571d6a242e6bf68c2c28eadf1011" \ "571a48eb" self.test_secret_key_nonce = "f580cc9900ce7ae8b6f7d2bab4627e9e689dca0f13a53e3c" self.test_private_key = "d636f7cc20384475bdc30c3ede98f719ee09d1fd4709276103772dd9479f353c" self.test_private_key_enc = "abddebec9d20cecf7d1cab95ad6c6394db3826856bf21c2c6af9954e9816c2239f5df697e52" \ "d60785eb1136803407b69729c38bb50eefdd2d24f2fa0f104990eee001866ba83704cf4f576" \ "a74b9b2452" self.test_private_key_nonce = "4298a9ab3d9d5d8643dfd4445adc30301b565ab650497fb9" self.test_user_obj = models.User.objects.create( email=self.test_email, email_bcrypt=self.test_email_bcrypt, authkey=make_password(self.test_authkey), public_key=self.test_public_key, private_key=self.test_private_key_enc, private_key_nonce=self.test_private_key_nonce, secret_key=self.test_secret_key_enc, secret_key_nonce=self.test_secret_key_nonce, user_sauce='90272aaf01a2d525223f192aca069e7f5661b3a0f1b1a91f9b16d493fdf15295', is_email_active=True ) def test_create_success(self): """ Tests to create an emergency code """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': 3600, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertEqual(models.Emergency_Code.objects.count(), 1) def test_create_failure_no_description(self): """ Tests to create an emergency code without description """ url = reverse('emergencycode') data = { # 'description': 'Some Description', 'activation_delay': 3600, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_no_activation_delay(self): """ Tests to create an emergency code without activation delay """ url = reverse('emergencycode') data = { 'description': 'Some Description', # 'activation_delay': 3600, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_no_emergency_authkey(self): """ Tests to create an emergency code without emergency_authkey """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': 3600, # 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_no_emergency_data(self): """ Tests to create an emergency code without emergency_data """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': 3600, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', # 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_no_emergency_data_nonce(self): """ Tests to create an emergency code without emergency_data_nonce """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': 3600, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', # 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_no_emergency_sauce(self): """ Tests to create an emergency code without emergency_sauce """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': 3600, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', # 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_emergency_data_no_hex(self): """ Tests to create an emergency code with a emergency_data that is not hex encoded """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': -1, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123X', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_emergency_data_nonce_no_hex(self): """ Tests to create an emergency code with a emergency_data_nonce that is not hex encoded """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': -1, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA88X', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_create_failure_negative_activation_delay(self): """ Tests to create an emergency code with a negative activation delay """ url = reverse('emergencycode') data = { 'description': 'Some Description', 'activation_delay': -1, 'emergency_authkey': 'B52032040066AE04BECBBB03286469223731B0E8A2298F26DC5F01222E63D0F5', 'emergency_data': 'a123', 'emergency_data_nonce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', 'emergency_sauce': 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884' } self.client.force_authenticate(user=self.test_user_obj) response = self.client.post(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) class DeleteEmergencyCodeTest(APITestCaseExtended): """ Test to delete an emergency code (DELETE) """ def setUp(self): self.test_email = "test@example.com" self.test_email_bcrypt = "a" self.test_password = "myPassword" self.test_authkey = "c55066421a559f76d8ed5227622e9f95a0c67df15220e40d7bc98a8a598124fa15373ac553ef3ee27c7" \ "123d6be058e6d43cc71c1b666bdecaf33b734c8583a93" self.test_public_key = "5706a5648debec63e86714c8c489f08aee39477487d1b3f39b0bbb05dbd2c649" self.test_secret_key = "a7d028388e9d80f2679c236ebb2d0fedc5b7b0a28b393f6a20cc8f6be636aa71" self.test_secret_key_enc = "77cde8ff6a5bbead93588fdcd0d6346bb57224b55a49c0f8a22a807bf6414e4d82ff60711422" \ "996e4a26de599982d531eef3098c9a531a05f75878ac0739571d6a242e6bf68c2c28eadf1011" \ "571a48eb" self.test_secret_key_nonce = "f580cc9900ce7ae8b6f7d2bab4627e9e689dca0f13a53e3c" self.test_private_key = "d636f7cc20384475bdc30c3ede98f719ee09d1fd4709276103772dd9479f353c" self.test_private_key_enc = "abddebec9d20cecf7d1cab95ad6c6394db3826856bf21c2c6af9954e9816c2239f5df697e52" \ "d60785eb1136803407b69729c38bb50eefdd2d24f2fa0f104990eee001866ba83704cf4f576" \ "a74b9b2452" self.test_private_key_nonce = "4298a9ab3d9d5d8643dfd4445adc30301b565ab650497fb9" self.test_user_obj = models.User.objects.create( email=self.test_email, email_bcrypt=self.test_email_bcrypt, authkey=make_password(self.test_authkey), public_key=self.test_public_key, private_key=self.test_private_key_enc, private_key_nonce=self.test_private_key_nonce, secret_key=self.test_secret_key_enc, secret_key_nonce=self.test_secret_key_nonce, user_sauce='90272aaf01a2d525223f192aca069e7f5661b3a0f1b1a91f9b16d493fdf15295', is_email_active=True ) self.test_email2 = ''.join(random.choice(string.ascii_lowercase) for _ in range(10)) + 'test@example.com' self.test_email_bcrypt2 = 'a' self.test_username2 = ''.join(random.choice(string.ascii_lowercase) for _ in range(10)) + 'test@psono.pw' self.test_authkey2 = binascii.hexlify(os.urandom(settings.AUTH_KEY_LENGTH_BYTES)).decode() self.test_public_key2 = binascii.hexlify(os.urandom(settings.USER_PUBLIC_KEY_LENGTH_BYTES)).decode() self.test_private_key2 = binascii.hexlify(os.urandom(settings.USER_PRIVATE_KEY_LENGTH_BYTES)).decode() self.test_private_key_nonce2 = binascii.hexlify(os.urandom(settings.NONCE_LENGTH_BYTES)).decode() self.test_secret_key2 = binascii.hexlify(os.urandom(settings.USER_SECRET_KEY_LENGTH_BYTES)).decode() self.test_secret_key_nonce2 = binascii.hexlify(os.urandom(settings.NONCE_LENGTH_BYTES)).decode() self.test_user_sauce2 = 'a67fef1ff29eb8f866feaccad336fc6311fa4c71bc183b14c8fceff7416add99' self.test_user_obj2 = models.User.objects.create( username=self.test_username2, email=self.test_email2, email_bcrypt=self.test_email_bcrypt2, authkey=make_password(self.test_authkey2), public_key=self.test_public_key2, private_key=self.test_private_key2, private_key_nonce=self.test_private_key_nonce2, secret_key=self.test_secret_key2, secret_key_nonce=self.test_secret_key_nonce2, user_sauce=self.test_user_sauce2, is_email_active=True ) self.test_emergency_code_obj = models.Emergency_Code.objects.create( user = self.test_user_obj, description = 'Some description', activation_delay = 3600, emergency_authkey = make_password('abcd'), emergency_data = 'a123', emergency_data_nonce = 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', emergency_sauce = 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', ) def test_delete_success(self): """ Tests to delete an emergency code """ url = reverse('emergencycode') data = { 'emergency_code_id': self.test_emergency_code_obj.id, } self.client.force_authenticate(user=self.test_user_obj) response = self.client.delete(url, data) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_delete_failure_missing_emergency_code_id(self): """ Tests to delete an emergency code """ url = reverse('emergencycode') data = { # 'emergency_code_id': self.test_emergency_code_obj.id, } self.client.force_authenticate(user=self.test_user_obj) response = self.client.delete(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_delete_failure_belongs_to_other_user(self): """ Tests to delete an emergency code that belongs to another user """ url = reverse('emergencycode') data = { 'emergency_code_id': self.test_emergency_code_obj.id, } self.client.force_authenticate(user=self.test_user_obj2) response = self.client.delete(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_delete_failure_not_exist(self): """ Tests to delete an emergency code that does not exist """ url = reverse('emergencycode') data = { 'emergency_code_id': '494d2d69-d4f9-4ab6-8f84-583928add37d', } self.client.force_authenticate(user=self.test_user_obj) response = self.client.delete(url, data) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) class ReadEmergencyCodeTest(APITestCaseExtended): """ Test to read an emergency code (GET) """ def setUp(self): self.test_email = "test@example.com" self.test_email_bcrypt = "a" self.test_password = "myPassword" self.test_authkey = "c55066421a559f76d8ed5227622e9f95a0c67df15220e40d7bc98a8a598124fa15373ac553ef3ee27c7" \ "123d6be058e6d43cc71c1b666bdecaf33b734c8583a93" self.test_public_key = "5706a5648debec63e86714c8c489f08aee39477487d1b3f39b0bbb05dbd2c649" self.test_secret_key = "a7d028388e9d80f2679c236ebb2d0fedc5b7b0a28b393f6a20cc8f6be636aa71" self.test_secret_key_enc = "77cde8ff6a5bbead93588fdcd0d6346bb57224b55a49c0f8a22a807bf6414e4d82ff60711422" \ "996e4a26de599982d531eef3098c9a531a05f75878ac0739571d6a242e6bf68c2c28eadf1011" \ "571a48eb" self.test_secret_key_nonce = "f580cc9900ce7ae8b6f7d2bab4627e9e689dca0f13a53e3c" self.test_private_key = "d636f7cc20384475bdc30c3ede98f719ee09d1fd4709276103772dd9479f353c" self.test_private_key_enc = "abddebec9d20cecf7d1cab95ad6c6394db3826856bf21c2c6af9954e9816c2239f5df697e52" \ "d60785eb1136803407b69729c38bb50eefdd2d24f2fa0f104990eee001866ba83704cf4f576" \ "a74b9b2452" self.test_private_key_nonce = "4298a9ab3d9d5d8643dfd4445adc30301b565ab650497fb9" self.test_user_obj = models.User.objects.create( email=self.test_email, email_bcrypt=self.test_email_bcrypt, authkey=make_password(self.test_authkey), public_key=self.test_public_key, private_key=self.test_private_key_enc, private_key_nonce=self.test_private_key_nonce, secret_key=self.test_secret_key_enc, secret_key_nonce=self.test_secret_key_nonce, user_sauce='90272aaf01a2d525223f192aca069e7f5661b3a0f1b1a91f9b16d493fdf15295', is_email_active=True ) self.test_email2 = ''.join(random.choice(string.ascii_lowercase) for _ in range(10)) + 'test@example.com' self.test_email_bcrypt2 = "b" self.test_username2 = ''.join(random.choice(string.ascii_lowercase) for _ in range(10)) + 'test@psono.pw' self.test_authkey2 = binascii.hexlify(os.urandom(settings.AUTH_KEY_LENGTH_BYTES)).decode() self.test_public_key2 = binascii.hexlify(os.urandom(settings.USER_PUBLIC_KEY_LENGTH_BYTES)).decode() self.test_private_key2 = binascii.hexlify(os.urandom(settings.USER_PRIVATE_KEY_LENGTH_BYTES)).decode() self.test_private_key_nonce2 = binascii.hexlify(os.urandom(settings.NONCE_LENGTH_BYTES)).decode() self.test_secret_key2 = binascii.hexlify(os.urandom(settings.USER_SECRET_KEY_LENGTH_BYTES)).decode() self.test_secret_key_nonce2 = binascii.hexlify(os.urandom(settings.NONCE_LENGTH_BYTES)).decode() self.test_user_sauce2 = 'a67fef1ff29eb8f866feaccad336fc6311fa4c71bc183b14c8fceff7416add99' self.test_user_obj2 = models.User.objects.create( username=self.test_username2, email=self.test_email2, email_bcrypt=self.test_email_bcrypt2, authkey=make_password(self.test_authkey2), public_key=self.test_public_key2, private_key=self.test_private_key2, private_key_nonce=self.test_private_key_nonce2, secret_key=self.test_secret_key2, secret_key_nonce=self.test_secret_key_nonce2, user_sauce=self.test_user_sauce2, is_email_active=True ) self.test_emergency_code_obj = models.Emergency_Code.objects.create( user = self.test_user_obj, description = 'Some description', activation_delay = 3600, emergency_authkey = make_password('abcd'), emergency_data = 'a123', emergency_data_nonce = 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', emergency_sauce = 'D5BD6D7FCC2E086CFC28B2B2648ECA591D9F8201608A2D173E167D5B27ECA884', ) def test_read_emergency_codes_success(self): """ Tests to read all emergency_codes """ url = reverse('emergencycode') data = {} self.client.force_authenticate(user=self.test_user_obj) response = self.client.get(url, data) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertTrue(response.data.get('emegency_codes', False)) self.assertEqual(len(response.data.get('emegency_codes')), 1) emergency_codes = response.data.get('emegency_codes') emergency_code = emergency_codes[0] self.assertEqual(emergency_code.get('id'), self.test_emergency_code_obj.id) self.assertEqual(emergency_code.get('description'), self.test_emergency_code_obj.description) self.assertEqual(emergency_code.get('activation_delay'), self.test_emergency_code_obj.activation_delay) def test_read_emergency_codes_success_without_permission(self): """ Tests to read all emergency_codes with a user that has no permissions """ url = reverse('emergencycode') data = {} self.client.force_authenticate(user=self.test_user_obj2) response = self.client.get(url, data) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertFalse(response.data.get('emegency_codes', True)) # Empty List self.assertEqual(len(response.data.get('emegency_codes')), 0) class UpdateEmergencyCodeTest(APITestCaseExtended): """ Test to update an emergency code (POST) """ def setUp(self): self.test_email = "test@example.com" self.test_email_bcrypt = "a" self.test_password = "myPassword" self.test_authkey = "c55066421a559f76d8ed5227622e9f95a0c67df15220e40d7bc98a8a598124fa15373ac553ef3ee27c7" \ "123d6be058e6d43cc71c1b666bdecaf33b734c8583a93" self.test_public_key = "5706a5648debec63e86714c8c489f08aee39477487d1b3f39b0bbb05dbd2c649" self.test_secret_key = "a7d028388e9d80f2679c236ebb2d0fedc5b7b0a28b393f6a20cc8f6be636aa71" self.test_secret_key_enc = "77cde8ff6a5bbead93588fdcd0d6346bb57224b55a49c0f8a22a807bf6414e4d82ff60711422" \ "996e4a26de599982d531eef3098c9a531a05f75878ac0739571d6a242e6bf68c2c28eadf1011" \ "571a48eb" self.test_secret_key_nonce = "f580cc9900ce7ae8b6f7d2bab4627e9e689dca0f13a53e3c" self.test_private_key = "d636f7cc20384475bdc30c3ede98f719ee09d1fd4709276103772dd9479f353c" self.test_private_key_enc = "abddebec9d20cecf7d1cab95ad6c6394db3826856bf21c2c6af9954e9816c2239f5df697e52" \ "d60785eb1136803407b69729c38bb50eefdd2d24f2fa0f104990eee001866ba83704cf4f576" \ "a74b9b2452" self.test_private_key_nonce = "4298a9ab3d9d5d8643dfd4445adc30301b565ab650497fb9" self.test_user_obj = models.User.objects.create( email=self.test_email, email_bcrypt=self.test_email_bcrypt, authkey=make_password(self.test_authkey), public_key=self.test_public_key, private_key=self.test_private_key_enc, private_key_nonce=self.test_private_key_nonce, secret_key=self.test_secret_key_enc, secret_key_nonce=self.test_secret_key_nonce, user_sauce='90272aaf01a2d525223f192aca069e7f5661b3a0f1b1a91f9b16d493fdf15295', is_email_active=True ) def test_put_emergencycode(self): """ Tests to update an emergency code """ url = reverse('emergencycode') data = {} self.client.force_authenticate(user=self.test_user_obj) response = self.client.put(url, data) self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)

42.984402

115

0.695992

2,183

24,802

7.584975

0.078333

0.071989

0.021017

0.024641

0.934835

0.916898

0.913456

0.904215

0.8896

0.874804

0

0.189666

0.227482

24,802

576

116

43.059028

0.67453

0.061164

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0.811671

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0.304622

0.23115

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0.055703

false

0.034483

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0

null

0

1

0

1

0

null

0

7

c1f66cb021f358b7d383b4de73482a2243b6478d

681

py

Python

fragmenstein/victor/_victor_overridables.py

matteoferla/Fragmenstein

151bde01f4ebd930880cb7ad234bab68ac4a3e76

[ "MIT" ]

41

2020-04-09T14:11:39.000Z

2022-03-15T15:44:14.000Z

fragmenstein/victor/_victor_overridables.py

LaYeqa/Fragmenstein

151bde01f4ebd930880cb7ad234bab68ac4a3e76

[ "MIT" ]

13

2020-12-02T13:13:59.000Z

2022-01-14T11:29:46.000Z

fragmenstein/victor/_victor_overridables.py

LaYeqa/Fragmenstein

151bde01f4ebd930880cb7ad234bab68ac4a3e76

[ "MIT" ]

6

2020-09-07T10:47:51.000Z

2021-09-23T14:22:39.000Z

from ._victor_plonk import _VictorPlonk class _VictorOverridables(_VictorPlonk): def post_params_step(self): """ This method is intended for make inherited mods easier. :return: """ pass def post_monster_step(self): """ This method is intended for make inherited mods easier. :return: """ pass def pose_mod_step(self): """ This method is intended for make inherited mods easier. :return: """ pass def post_igor_step(self): """ This method is intended for make inherited mods easier. :return: """ pass

21.967742

63

0.562408

72

681

5.138889

0.375

0.086486

0.12973

0.194595

0.737838

0

0.361233

681

31

64

21.967742

0.850575

0.380323

0

0.4

0

1

0.4

false

0.4

0.1

0

0.6

0

null

0

1

0

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0

null

0

1

0

1

0

1

0

9

a9b8cfbdb5b63265f995b002e7b55f83688c23dc

784

py

Python

za/udp/test3.py

hth945/pytest

83e2aada82a2c6a0fdd1721320e5bf8b8fd59abc

[ "Apache-2.0" ]

null

za/udp/test3.py

hth945/pytest

83e2aada82a2c6a0fdd1721320e5bf8b8fd59abc

[ "Apache-2.0" ]

null

za/udp/test3.py

hth945/pytest

83e2aada82a2c6a0fdd1721320e5bf8b8fd59abc

[ "Apache-2.0" ]

null

#%% ss ='00 80 e1 13 4f 24 04 0e 3c 90 f5 58 08 00 45 00 00 23 a1 c2 00 00 80 11 15 83 c0 a8 01 17 c0 a8 01 1d 17 71 17 71 00 0f f5 6d 31 36 62 62 62 62 62 00 00 00 00 00 00 00 00 00 00 00 ' ss2='00 80 e1 13 4f 24 04 0e 3c 90 f5 58 08 00 45 00 00 23 a1 c6 00 00 80 11 15 7f c0 a8 01 17 c0 a8 01 1d 17 71 17 71 00 0f 17 33 a9 4c 29 10 00 01 00 00 00 00 00 01 20 45 4d 45 42 46 ' ss3='ff ff ff ff ff ff 04 0e 3c 90 f5 58 08 00 45 00 00 60 cf 57 00 00 80 11 e6 ce c0 a8 01 17 c0 a8 01 ff 00 89 00 89 00 4c 17 33 a9 4c 29 10 00 01 00 00 00 00 00 01 20 45 4d 45 42 46 41 46 45 45 50 46 41 43 4e 44 43 46 42 44 46 46 44 46 41 44 43 45 4b 46 43 41 41 00 00 20 00 01 c0 0c 00 20 00 01 00 04 93 e0 00 06 00 00 c0 a8 01 17 ' dd = ss.split(' ') dd.index('0f') # %% 0xf8+40 # %% 256+32 # %%

52.266667

336

0.633929

243

784

2.045267

0.283951

0.209256

0.181087

0.193159

0.607646

0.54326

0.515091

0

0.758945

0.322704

784

14

337

56

0.177024

0.014031

0

0.428571

0.90117

0

1

0.005202

0

1

0

false

0

null

1

0

1

0

1

0

1

0

null

1

0

8

a9f23570f22f0c9d8958a77a80b33c890b5c28d9

2,515

py

Python

tests/endpoints/test_cash_register.py

PJUllrich/Universal-Bunq-API-Python-Wrapper

9e1e0d1477d502c07fb9f31095e55b945b47b062

[ "MIT" ]

null

tests/endpoints/test_cash_register.py

PJUllrich/Universal-Bunq-API-Python-Wrapper

9e1e0d1477d502c07fb9f31095e55b945b47b062

[ "MIT" ]

null

tests/endpoints/test_cash_register.py

PJUllrich/Universal-Bunq-API-Python-Wrapper

9e1e0d1477d502c07fb9f31095e55b945b47b062

[ "MIT" ]

null

from apiwrapper.endpoints.cash_register import CashRegister from tests.endpoints.test_endpoint import EndpointTest class CashRegisterTest(EndpointTest): __base_endpoint_url = "/user/%d/monetary-account/%d/cash-register" @property def _base_endpoint(self): return self.__base_endpoint_url % (self.random_id, self.random_id) def setUp(self): super().setUp(CashRegister) def test_get_base_endpoint(self): endpoint_should_be = self._base_endpoint endpoint_to_check = self.test_class._get_base_endpoint( self.random_id, self.random_id) self.assert_parameters(endpoint_should_be, endpoint_to_check) def test_get_all_cash_registers_for_account(self): endpoint_should_be = self._base_endpoint endpoint_to_check = self.test_class.get_all_cash_registers_for_account( self.random_id, self.random_id) self.assert_parameters(endpoint_should_be, endpoint_to_check) def test_get_cash_register_by_id(self): endpoint_should_be = self._base_endpoint endpoint_should_be += "/%d" % self.random_id endpoint_to_check = self.test_class.get_cash_register_by_id( self.random_id, self.random_id, self.random_id) self.assert_parameters(endpoint_should_be, endpoint_to_check) def test_get_all_qr_codes_for_cash_register(self): endpoint_should_be = self._base_endpoint endpoint_should_be += "/%d/qr-code" % self.random_id endpoint_to_check = self.test_class.get_all_qr_codes_for_cash_register( self.random_id, self.random_id, self.random_id) self.assert_parameters(endpoint_should_be, endpoint_to_check) def test_get_qr_code_by_id(self): endpoint_should_be = self._base_endpoint endpoint_should_be += "/%d/qr-code/%d" % (self.random_id, self.random_id) endpoint_to_check = self.test_class.get_qr_code_by_id( self.random_id, self.random_id, self.random_id, self.random_id) self.assert_parameters(endpoint_should_be, endpoint_to_check) def test_get_content_for_qr_code(self): endpoint_should_be = self._base_endpoint endpoint_should_be += "/%d/qr-code/%d/content" % ( self.random_id, self.random_id ) endpoint_to_check = self.test_class.get_content_for_qr_code( self.random_id, self.random_id, self.random_id, self.random_id) self.assert_parameters(endpoint_should_be, endpoint_to_check)

36.449275

81

0.726839

352

2,515

4.696023

0.119318

0.15729

0.188748

0.203267

0.813067

0.757411

0.729583

0.699335

0

0.192445

2,515

68

82

36.985294

0.813885

0

0.391304

0

0.036581

0.025447

0

0.130435

1

0.173913

false

0

0.043478

0.021739

0.282609

0

null

0

1

0

1

0

null

0

7

e77d068de050af8d157b873cf3f9e8adf3026289

33,055

py

Python

Proyecto/Proyecto.py

rinicro/Machine-Learning-and-Big-Data

b35899c13202d2102a0f093ad2f023a9802b754d

[ "MIT" ]

null

Proyecto/Proyecto.py

rinicro/Machine-Learning-and-Big-Data

b35899c13202d2102a0f093ad2f023a9802b754d

[ "MIT" ]

null

Proyecto/Proyecto.py

rinicro/Machine-Learning-and-Big-Data

b35899c13202d2102a0f093ad2f023a9802b754d

[ "MIT" ]

null

# -*- coding: utf-8 -*- ''' Proyecto de la asignatura Aprendizaje Automático y Big Data Rubén Ruperto Díaz y Rafael Herrera Troca ''' import os import numpy as np import matplotlib.pyplot as plt import pandas as pd from matplotlib import cm from mpl_toolkits.axes_grid1 import make_axes_locatable from sklearn.model_selection import train_test_split from scipy.optimize import fmin_tnc, minimize from sklearn.svm import SVC from sklearn.cluster import KMeans from sklearn.decomposition import PCA os.chdir("./resources") #%% Funciones auxiliares # Función sigmoide def sigmoide(z): return 1 / (1 + np.exp(-z)) # Derivada de la función sigmoide def diffSigmoide(a): return a * (1 - a) ## Funciones para regresión logística (práctica 2): # Función de coste def P1coste(theta, X, Y, reg=0): gXTheta = sigmoide(np.dot(X, theta)) factor = np.dot(np.log(gXTheta).T, Y) + np.dot(np.log(1 - gXTheta).T, 1-Y) return -1 / len(Y) * factor + reg / (2 * len(Y)) * np.sum(theta**2) # Gradiente de la función de coste def P1gradiente(theta, X, Y, reg=0): gXTheta = sigmoide(np.dot(X, theta)) thetaJ = np.concatenate(([0], theta[1:])) return 1 / len(Y) * np.dot(X.T, gXTheta-Y) + reg / len(Y) * thetaJ # Función que devuelve el porcentaje de acierto de un resultado # según el valor real def P1porc_ac(X, Y, theta): gXTheta = sigmoide(np.dot(X, theta)) resultados = [((gXTheta >= 0.5) & (Y == 1)) | ((gXTheta < 0.5) & (Y == 0))] return np.count_nonzero(resultados) / len(Y) * 100 ## Funciones para redes neuronales (práctica 4): # Devuelve una matriz de pesos aleatorios con la dimensión dada def P2randomWeights(l_in, l_out): eps = np.sqrt(6)/np.sqrt(l_in + l_out) rnd = np.random.random((l_out, l_in+1)) * (2*eps) - eps return rnd # Dada la entrada 'X' y los pesos 'theta' de una capa de una red # neuronal, aplica los pesos y devuelve la salida de la capa def P2applyLayer(X, theta): thetaX = np.dot(X, theta.T) return sigmoide(thetaX) # Dada la entrada 'X' y el array de matrices de pesos 'theta', # devuelve la entrada de cada capa y el resultado final devuelto # por la red neuronal def P2applyNet(X, theta): lay = X.copy() a = [] for i in range(len(theta)): lay = np.hstack((np.array([np.ones(len(lay))]).T, lay)) a.append(lay.copy()) lay = P2applyLayer(lay, theta[i]) return lay,a # Calcula la función de coste de una red neuronal para la # salida esperada 'y', el resultado de la red 'h_theta', el array # de matrices de pesos 'theta' y el término de regularización 'reg' def P2coste(y, h_theta, theta, reg): sumandos = -y * np.log(h_theta) - (1-y) * np.log(1-h_theta) regul = 0 for i in range(len(theta)): regul += np.sum(theta[i][:,1:]**2) result = np.sum(sumandos) / len(y) + reg * regul / (2*len(y)) return result # Calcula el gradiente de la función de coste haciendo # retropropagación dada la salida esperada 'y', la entrada # de cada capa 'a', la salida de la red 'h_theta', el array de # matrices de pesos 'theta' y el término de regularización 'reg' def P2gradiente(y, a, h_theta, theta, reg): d = h_theta - y delta = [np.dot(d.T, a[-1]) / len(y)] for i in range(len(theta)-1,0,-1): d = np.dot(d, theta[i]) * diffSigmoide(a[i]) d = d[:,1:] delta.insert(0, np.dot(d.T, a[i-1]) / len(y)) for i in range(len(delta)): delta[i][:,1:] += reg * theta[i][:,1:] / len(y) return delta # Calcula y devuelve el coste y el gradiente de una red neuronal # dados todos los pesos en el array 'param_rn', las dimensiones # de cada capa en 'capas', los datos de entrada 'X', la salida # esperada 'y' y el término de regularización 'reg' def P2backprop(params_rn, capas, X, Y, reg): # Convertimos el vector de todos los pesos en las distintas # matrices theta = [np.reshape(params_rn[:capas[1]*(capas[0]+1)], (capas[1],capas[0]+1))] gastados = capas[1]*(capas[0]+1) for i in range(len(capas)-2): theta.append(np.reshape(params_rn[gastados:gastados+capas[i+2]* (capas[i+1]+1)],(capas[i+2],capas[i+1]+1))) gastados += capas[i+2]*(capas[i+1]+1) # Aplicamos la red neuronal h_theta,a = P2applyNet(X, theta) cost = P2coste(Y, h_theta, theta, reg) grad = P2gradiente(Y, a, h_theta, theta, reg) g = np.array([]) for i in range(len(grad)): g = np.concatenate((g, grad[i].ravel())) return cost, g # Calcula el porcentaje de acierto dada la respuesta de la red y el # resultado real def P2porc_ac(res, Y): resultados = [((res >= 0.5) & (Y == 1)) | ((res < 0.5) & (Y == 0))] return np.count_nonzero(resultados) / len(Y) * 100 #%% Lectura y estudio de los datos np.random.seed(27) data = pd.read_csv('mushrooms.csv') # Transformamos Y = data['class'].replace({'p':0, 'e':1}) X = pd.get_dummies(data.drop('class', axis=1)) # Dividimos los datos en entrenamiento, validación y test Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.2, random_state=0, shuffle=True, stratify=Y) Xtrain, Xval, Ytrain, Yval = train_test_split(Xtrain, Ytrain, test_size=0.25, random_state=0, shuffle=True, stratify=Ytrain) # Preparamos los datos Xtrain2 = np.hstack((np.array([np.ones(len(Ytrain))]).T, Xtrain)) Xval2 = np.hstack((np.array([np.ones(len(Yval))]).T, Xval)) Xtest2 = np.hstack((np.array([np.ones(len(Ytest))]).T, Xtest)) Ytrain2 = np.array([Ytrain]).T Yval2 = np.array([Yval]).T Ytest2 = np.array([Ytest]).T # Representamos un histograma para cada variable según la distribución de # champiñones venenosos y comestibles para cada posible valor for name in data.columns[1:]: plt.figure(figsize=(10,10)) plt.title("Número de venenosos y comestibles según " + name) values = data[name].value_counts().axes[0].to_list() cuentaP = [] cuentaE = [] for v in values: cuentaP.append(len(data[(data[name]==v) & (data['class']=='p')])) cuentaE.append(len(data[(data[name]==v) & (data['class']=='e')])) plt.bar(np.arange(len(values)), cuentaP, 0.4, color='darkorchid') plt.bar(np.arange(len(values))+0.4, cuentaE, 0.4, color='greenyellow') plt.ylabel('Número de casos') plt.xlabel(name) plt.xticks(np.arange(len(values))+0.2, values) plt.savefig("var" + name + ".pdf", format='pdf') plt.show() #%% Parte 1: Regresión logística # Entrenamos la regresión con distintos valores para el término de # regularización theta0 = np.zeros(np.shape(Xtrain2)[1]) regValues = range(-10, 4) thetas = [] errorTrain = [] acTrain = [] errorVal = [] acVal = [] for reg in regValues: theta = fmin_tnc(func=P1coste, x0=theta0, fprime=P1gradiente, args=(Xtrain2, Ytrain, 10**reg))[0] thetas.append(theta) errorTrain.append(P1coste(theta, Xtrain2, Ytrain)) acTrain.append(P1porc_ac(Xtrain2, Ytrain, theta)) errorVal.append(P1coste(theta, Xval2, Yval)) acVal.append(P1porc_ac(Xval2, Yval, theta)) # Comprobamos el error y el pocentaje de acierto según el término de # regularización opt = np.argmin(errorVal) print('El valor óptimo del parámetro de regularización es', 10**regValues[opt]) plt.figure(figsize=(10,10)) plt.plot(regValues, acTrain, 'r', label="Entrenamiento") plt.plot(regValues, acVal, 'b', label="Validación") plt.title(r"Porcentaje de acierto según $\lambda$") plt.xlabel(r"Valor de $\lambda = 10^x$") plt.ylabel("Porcentaje de acierto") plt.legend(loc="lower left") plt.savefig("aciertoLogistica.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.plot(regValues, errorTrain, 'r', label="Entrenamiento") plt.plot(regValues, errorVal, 'b', label="Validación") plt.title(r"Error según $\lambda$") plt.xlabel(r"Valor de $\lambda = 10^x$") plt.ylabel("Error") plt.legend(loc="upper left") plt.savefig("errorLogistica.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido del término de regularización ac = P1porc_ac(Xtest2, Ytest, thetas[opt]) print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 2: Redes neurales # Creamos unas matrices inciales con pesos aleatorios size2 = 25 theta01 = P2randomWeights(np.shape(Xtrain)[1], size2) theta02 = P2randomWeights(size2, 1) theta0 = np.concatenate((theta01.ravel(), theta02.ravel())) regValues = range(-6, 4) itera = range(10, 110, 10) errorTrain = np.zeros((len(regValues), len(itera))) acTrain = np.zeros((len(regValues), len(itera))) errorVal = np.zeros((len(regValues), len(itera))) acVal = np.zeros((len(regValues), len(itera))) for i in range(len(regValues)): for j in range(len(itera)): theta = minimize(fun=P2backprop, x0=theta0, args=((np.shape(Xtrain)[1],size2,1), Xtrain, Ytrain2, 10**regValues[i]), method='TNC', jac=True, options={'maxiter':itera[j]})['x'] theta1 = np.reshape(theta[:size2*(np.shape(Xtrain)[1]+1)], (size2,np.shape(Xtrain)[1]+1)) theta2 = np.reshape(theta[size2*(np.shape(Xtrain)[1]+1):], (1,size2+1)) resTrain = P2applyNet(Xtrain, (theta1, theta2))[0] acTrain[i][j] = P2porc_ac(resTrain, Ytrain2) resVal = P2applyNet(Xval, (theta1, theta2))[0] acVal[i][j] = P2porc_ac(resVal, Yval2) errorTrain[i][j] = P2coste(Ytrain2, resTrain, [theta1,theta2], 0) errorVal[i][j] = P2coste(Yval2, resVal, [theta1,theta2], 0) # Comprobamos el error y el pocentaje de acierto según el término de # regularización y el número de iteraciones opt = np.argmin(errorVal) optReg, optItera = 10**regValues[opt//len(itera)], itera[opt%len(itera)] print('El valor óptimo del parámetro de regularización es', optReg) print('El valor óptimo para el número de iteraciones es', optItera) xLabels = [str(it) for it in itera] yLabels = [r'$10^{' + str(r) + '}$' for r in regValues] plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según el valor de $\lambda$ y" + " el número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(acVal, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(acVal[j][i],2), ha="center", va="center", color=("k" if acVal[j][i] > 93 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("aciertoValNeuronal.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según el valor de $\lambda$ y " + "el número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(acTrain, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(acTrain[j][i],2), ha="center", va="center", color=("k" if acTrain[j][i] > 93 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("aciertoTrainNeuronal.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Error según el valor de $\lambda$ y el número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(np.log10(errorVal), cmap=cm.viridis_r) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(np.log10(errorVal[j][i]),3), ha="center", va="center", color=("k" if np.log10(errorVal[j][i]) < -5 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("errorValNeuronal.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Error según el valor de $\lambda$ y el número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(np.log10(errorTrain), cmap=cm.viridis_r) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(np.log10(errorTrain[j][i]),3), ha="center", va="center", color=("k" if np.log10(errorTrain[j][i]) < -5 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("errorTrainNeuronal.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido del término de regularización y de iteraciones theta = minimize(fun=P2backprop, x0=theta0, args=((np.shape(Xtrain)[1],size2,1), Xtrain, Ytrain2, optReg), method='TNC', jac=True, options={'maxiter':optItera})['x'] theta1 = np.reshape(theta[:size2*(np.shape(Xtrain)[1]+1)], (size2,np.shape(Xtrain)[1]+1)) theta2 = np.reshape(theta[size2*(np.shape(Xtrain)[1]+1):],(1,size2+1)) resTest = P2applyNet(Xtest, (theta1, theta2))[0] ac = P2porc_ac(resTest, Ytest2) print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 3: Máquinas de soporte vectorial # Comenzamos usando kernel lineal y distintos valores de C parValues = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, 300] acTrain = [] acVal = [] for C in parValues: svm = SVC(kernel='linear', C=C) svm.fit(Xtrain,Ytrain) acTrain.append(svm.score(Xtrain,Ytrain) * 100) acVal.append(svm.score(Xval,Yval) * 100) # Comprobamos el porcentaje de acierto según el valor de C opt = np.argmax(acVal) print('El valor óptimo de C es', parValues[opt]) plt.figure(figsize=(10,10)) plt.plot(range(len(parValues)), acTrain, 'r', label="Entrenamiento") plt.plot(range(len(parValues)), acVal, 'b', label="Validación") plt.title(r"Porcentaje de acierto según $C$") plt.xlabel(r"Valor de $C$") plt.xticks(range(len(parValues)), parValues) plt.ylabel("Porcentaje de acierto") plt.legend(loc="lower left") plt.savefig("aciertoSVM.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido de C svm = SVC(kernel='linear', C=parValues[opt]) svm.fit(Xtrain,Ytrain) ac = svm.score(Xtest,Ytest) * 100 print('El porcentaje de acierto sobre los datos de test es', ac, '%') # Probamos ahora con kernel gaussiano utilizando distintos valores de C # y de sigma acTrain = np.zeros((len(regValues), len(regValues))) acVal = np.zeros((len(regValues), len(regValues))) for i in range(len(parValues)): for j in range(len(parValues)): svm = SVC(kernel='rbf', C=parValues[i], gamma=1/(2*parValues[j]**2)) svm.fit(Xtrain,Ytrain) acTrain[i][j] = svm.score(Xtrain,Ytrain) * 100 acVal[i][j] = svm.score(Xval,Yval) * 100 # Comprobamos el pocentaje de acierto según los valores de C y sigma opt = np.argmax(acVal) optC, optSigma = parValues[opt//len(parValues)], parValues[opt%len(parValues)] print('El valor óptimo del parámetro C es', optC) print('El valor óptimo para el parámetro sigma es', optSigma) plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según los valores de $\sigma$ y C") plt.ylabel('$C$') plt.xlabel(r'$\sigma$') fig = plt.subplot() im = fig.imshow(acTrain, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(parValues)): for j in range(len(parValues)): text = fig.text(i, j, round(acTrain[j][i],2), ha="center", va="center", color=("k" if acTrain[j][i] > 70 else "w")) fig.set_xticks(np.arange(len(parValues))) fig.set_yticks(np.arange(len(parValues))) fig.set_xticklabels(parValues) fig.set_yticklabels(parValues) plt.savefig("aciertoTrainSVM.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según los valores de $\sigma$ y C") plt.ylabel('$C$') plt.xlabel(r'$\sigma$') fig = plt.subplot() im = fig.imshow(acVal, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(parValues)): for j in range(len(parValues)): text = fig.text(i, j, round(acVal[j][i],2), ha="center", va="center", color=("k" if acVal[j][i] > 70 else "w")) fig.set_xticks(np.arange(len(parValues))) fig.set_yticks(np.arange(len(parValues))) fig.set_xticklabels(parValues) fig.set_yticklabels(parValues) plt.savefig("aciertoValSVM.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido de C y sigma svm = SVC(kernel='rbf', C=optC, gamma=1/(2*optSigma**2)) svm.fit(Xtrain,Ytrain) ac = svm.score(Xtest,Ytest) * 100 print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 4: K-Medias # Entrenamos un K-Medias con 2 clusters kmeans = KMeans(n_clusters=2, random_state=0).fit(Xtrain) trainLabels = kmeans.labels_ valLabels = kmeans.predict(Xval) # Comprobamos el pocentaje de acierto según la interpretación de las etiquetas acTrainA = np.count_nonzero(trainLabels == Ytrain) / len(Ytrain) * 100 acTrainB = np.count_nonzero(trainLabels != Ytrain) / len(Ytrain) * 100 acValA = np.count_nonzero(valLabels == Yval) / len(Yval) * 100 acValB = np.count_nonzero(valLabels != Yval) / len(Yval) * 100 print('Interpretando las etiquetas de forma directa, el entrenamiento ' + 'obtiene un porcentaje de acierto del ', acTrainA, '%') print('Interpretando las etiquetas de forma inversa, el entrenamiento ' + 'obtiene un porcentaje de acierto del ', acTrainB, '%') print('Interpretando las etiquetas de forma directa, la validación ' + 'obtiene un porcentaje de acierto del ', acValA, '%') print('Interpretando las etiquetas de forma inversa, la validación ' + 'obtiene un porcentaje de acierto del ', acValB, '%') # Calculamos el porcentaje de acierto sobre los datos de test para la # interpretación escogida de las etiquetas testLabels = kmeans.predict(Xtest) ac = (np.count_nonzero(testLabels == Ytest) if acValA > acValB else np.count_nonzero(testLabels != Ytest)) / len(Ytest) * 100 print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 5: Reducción de dimensionalidad # Aplicamos PCA y comprobamos la varianza explicada para cada componente pca = PCA() XtrainR = pca.fit_transform(Xtrain) expVar = pca.explained_variance_ratio_ expVarAcum = [expVar[0]] for i in range(1, len(expVar)): expVarAcum.append(expVar[i] + expVarAcum[-1]) print("La varianza explicada acumulada es:", np.array(expVarAcum) * 100) #%% Alternativa: Eliminar la variable 'olor' correlacionada print("A partir de este punto repetimos los experimentos eliminando la " + "variable 'odor', ya que está fuertemente correlacionada con la " + "variable objetivo") # Preparamos un conjunto adicional de datos eliminando la variable 'odor' odor_lab = [] for c in Xtrain.columns: if c[:4] == 'odor': odor_lab.append(c) Wtrain = Xtrain.copy().drop(odor_lab, 1) Wval = Xval.copy().drop(odor_lab, 1) Wtest = Xtest.copy().drop(odor_lab, 1) Wtrain2 = np.hstack((np.array([np.ones(len(Ytrain))]).T, Wtrain)) Wval2 = np.hstack((np.array([np.ones(len(Yval))]).T, Wval)) Wtest2 = np.hstack((np.array([np.ones(len(Ytest))]).T, Wtest)) #%% Parte 1b: Regresión logística # Entrenamos la regresión con distintos valores para el término de # regularización theta0 = np.zeros(np.shape(Wtrain2)[1]) regValues = range(-10, 4) thetas = [] errorTrain = [] acTrain = [] errorVal = [] acVal = [] for reg in regValues: theta = fmin_tnc(func=P1coste, x0=theta0, fprime=P1gradiente, args=(Wtrain2, Ytrain, 10**reg))[0] thetas.append(theta) errorTrain.append(P1coste(theta, Wtrain2, Ytrain)) acTrain.append(P1porc_ac(Wtrain2, Ytrain, theta)) errorVal.append(P1coste(theta, Wval2, Yval)) acVal.append(P1porc_ac(Wval2, Yval, theta)) # Comprobamos el error y el pocentaje de acierto según el término de # regularización opt = np.argmin(errorVal) print('El valor óptimo del parámetro de regularización es', 10**regValues[opt]) plt.figure(figsize=(10,10)) plt.plot(regValues, acTrain, 'r', label="Entrenamiento") plt.plot(regValues, acVal, 'b', label="Validación") plt.title(r"Porcentaje de acierto según $\lambda$") plt.xlabel(r"Valor de $\lambda = 10^x$") plt.ylabel("Porcentaje de acierto") plt.legend(loc="lower left") plt.savefig("aciertoLogisticaNO.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.plot(regValues, errorTrain, 'r', label="Entrenamiento") plt.plot(regValues, errorVal, 'b', label="Validación") plt.title(r"Error según $\lambda$") plt.xlabel(r"Valor de $\lambda = 10^x$") plt.ylabel("Error") plt.legend(loc="upper left") plt.savefig("errorLogisticaNO.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido del término de regularización ac = P1porc_ac(Wtest2, Ytest, thetas[opt]) print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 2b: Redes neuronales # Creamos unas matrices inciales con pesos aleatorios size2 = 25 theta01 = P2randomWeights(np.shape(Wtrain)[1], size2) theta02 = P2randomWeights(size2, 1) theta0 = np.concatenate((theta01.ravel(), theta02.ravel())) regValues = range(-6, 4) itera = range(10, 110, 10) errorTrain = np.zeros((len(regValues), len(itera))) acTrain = np.zeros((len(regValues), len(itera))) errorVal = np.zeros((len(regValues), len(itera))) acVal = np.zeros((len(regValues), len(itera))) for i in range(len(regValues)): for j in range(len(itera)): theta = minimize(fun=P2backprop, x0=theta0, args=((np.shape(Wtrain)[1],size2,1), Wtrain, Ytrain2, 10**regValues[i]), method='TNC', jac=True, options={'maxiter':itera[j]})['x'] theta1 = np.reshape(theta[:size2*(np.shape(Wtrain)[1]+1)], (size2,np.shape(Wtrain)[1]+1)) theta2 = np.reshape(theta[size2*(np.shape(Wtrain)[1]+1):], (1,size2+1)) resTrain = P2applyNet(Wtrain, (theta1, theta2))[0] acTrain[i][j] = P2porc_ac(resTrain, Ytrain2) resVal = P2applyNet(Wval, (theta1, theta2))[0] acVal[i][j] = P2porc_ac(resVal, Yval2) errorTrain[i][j] = P2coste(Ytrain2, resTrain, [theta1,theta2], 0) errorVal[i][j] = P2coste(Yval2, resVal, [theta1,theta2], 0) # Comprobamos el error y el pocentaje de acierto según el término de # regularización y el número de iteraciones opt = np.argmin(errorVal) optReg, optItera = 10**regValues[opt//len(itera)], itera[opt%len(itera)] print('El valor óptimo del parámetro de regularización es', optReg) print('El valor óptimo para el número de iteraciones es', optItera) xLabels = [str(it) for it in itera] yLabels = [r'$10^{' + str(r) + '}$' for r in regValues] plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según el valor de $\lambda$ y el " + "número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(acVal, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(acVal[j][i],2), ha="center", va="center", color=("k" if acVal[j][i] > 93 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("aciertoValNeuronalNO.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según el valor de $\lambda$ y el " + "número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(acTrain, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(acTrain[j][i],2), ha="center", va="center", color=("k" if acTrain[j][i] > 93 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("aciertoTrainNeuronalNO.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Error según el valor de $\lambda$ y el número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(np.log10(errorVal), cmap=cm.viridis_r) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(np.log10(errorVal[j][i]),3), ha="center", va="center", color=("k" if np.log10(errorVal[j][i]) < -5 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("errorValNeuronalNO.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Error según el valor de $\lambda$ y el número de iteraciones") plt.ylabel(r'$\lambda$') plt.xlabel('Iteraciones') fig = plt.subplot() im = fig.imshow(np.log10(errorTrain), cmap=cm.viridis_r) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(xLabels)): for j in range(len(yLabels)): text = fig.text(i, j, round(np.log10(errorTrain[j][i]),3), ha="center", va="center", color=("k" if np.log10(errorTrain[j][i]) < -5 else "w")) fig.set_xticks(np.arange(len(xLabels))) fig.set_yticks(np.arange(len(yLabels))) fig.set_xticklabels(xLabels) fig.set_yticklabels(yLabels) plt.savefig("errorTrainNeuronalNO.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido del término de regularización y de iteraciones theta = minimize(fun=P2backprop, x0=theta0, args=((np.shape(Wtrain)[1],size2,1), Wtrain, Ytrain2, optReg), method='TNC', jac=True, options={'maxiter':optItera})['x'] theta1 = np.reshape(theta[:size2*(np.shape(Wtrain)[1]+1)], (size2,np.shape(Wtrain)[1]+1)) theta2 = np.reshape(theta[size2*(np.shape(Wtrain)[1]+1):],(1,size2+1)) resTest = P2applyNet(Wtest, (theta1, theta2))[0] ac = P2porc_ac(resTest, Ytest2) print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 3b: Máquinas de soporte vectorial # Comenzamos usando kernel lineal y distintos valores de C parValues = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100, 300] acTrain = [] acVal = [] for C in parValues: svm = SVC(kernel='linear', C=C) svm.fit(Wtrain,Ytrain) acTrain.append(svm.score(Wtrain,Ytrain) * 100) acVal.append(svm.score(Wval,Yval) * 100) # Comprobamos el porcentaje de acierto según el valor de C opt = np.argmax(acVal) print('El valor óptimo de C es', parValues[opt]) plt.figure(figsize=(10,10)) plt.plot(range(len(parValues)), acTrain, 'r', label="Entrenamiento") plt.plot(range(len(parValues)), acVal, 'b', label="Validación") plt.title(r"Porcentaje de acierto según $C$") plt.xlabel(r"Valor de $C$") plt.xticks(range(len(parValues)), parValues) plt.ylabel("Porcentaje de acierto") plt.legend(loc="lower left") plt.savefig("aciertoSVMNO.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido de C svm = SVC(kernel='linear', C=parValues[opt]) svm.fit(Wtrain,Ytrain) ac = svm.score(Wtest,Ytest) * 100 print('El porcentaje de acierto sobre los datos de test es', ac, '%') # Probamos ahora con kernel gaussiano utilizando distintos valores de C # y de sigma acTrain = np.zeros((len(regValues), len(regValues))) acVal = np.zeros((len(regValues), len(regValues))) for i in range(len(parValues)): for j in range(len(parValues)): svm = SVC(kernel='rbf', C=parValues[i], gamma=1/(2*parValues[j]**2)) svm.fit(Wtrain,Ytrain) acTrain[i][j] = svm.score(Wtrain,Ytrain) * 100 acVal[i][j] = svm.score(Wval,Yval) * 100 # Comprobamos el pocentaje de acierto según los valores de C y sigma opt = np.argmax(acVal) optC, optSigma = parValues[opt//len(parValues)], parValues[opt%len(parValues)] print('El valor óptimo del parámetro C es', optC) print('El valor óptimo para el parámetro sigma es', optSigma) plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según los valores de $\sigma$ y C") plt.ylabel('$C$') plt.xlabel(r'$\sigma$') fig = plt.subplot() im = fig.imshow(acTrain, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(parValues)): for j in range(len(parValues)): text = fig.text(i, j, round(acTrain[j][i],2), ha="center", va="center", color=("k" if acTrain[j][i] > 70 else "w")) fig.set_xticks(np.arange(len(parValues))) fig.set_yticks(np.arange(len(parValues))) fig.set_xticklabels(parValues) fig.set_yticklabels(parValues) plt.savefig("aciertoTrainSVMNO.pdf", format='pdf') plt.show() plt.figure(figsize=(10,10)) plt.title(r"Porcentaje de aciertos según los valores de $\sigma$ y C") plt.ylabel('$C$') plt.xlabel(r'$\sigma$') fig = plt.subplot() im = fig.imshow(acVal, cmap=cm.viridis) cax = make_axes_locatable(fig).append_axes("right", size="5%", pad=0.2) plt.colorbar(im, cax=cax) for i in range(len(parValues)): for j in range(len(parValues)): text = fig.text(i, j, round(acVal[j][i],2), ha="center", va="center", color=("k" if acVal[j][i] > 70 else "w")) fig.set_xticks(np.arange(len(parValues))) fig.set_yticks(np.arange(len(parValues))) fig.set_xticklabels(parValues) fig.set_yticklabels(parValues) plt.savefig("aciertoValSVMNO.pdf", format='pdf') plt.show() # Calculamos el porcentaje de acierto sobre los datos de test para el # valor escogido de C y sigma svm = SVC(kernel='rbf', C=optC, gamma=1/(2*optSigma**2)) svm.fit(Wtrain,Ytrain) ac = svm.score(Wtest,Ytest) * 100 print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 4b: K-Medias # Entrenamos un K-Medias con 2 clusters kmeans = KMeans(n_clusters=2, random_state=0).fit(Wtrain) trainLabels = kmeans.labels_ valLabels = kmeans.predict(Wval) # Comprobamos el pocentaje de acierto según la interpretación de las etiquetas acTrainA = np.count_nonzero(trainLabels == Ytrain) / len(Ytrain) * 100 acTrainB = np.count_nonzero(trainLabels != Ytrain) / len(Ytrain) * 100 acValA = np.count_nonzero(valLabels == Yval) / len(Yval) * 100 acValB = np.count_nonzero(valLabels != Yval) / len(Yval) * 100 print('Interpretando las etiquetas de forma directa, el entrenamiento' + ' obtiene un porcentaje de acierto del ', acTrainA, '%') print('Interpretando las etiquetas de forma inversa, el entrenamiento' + ' obtiene un porcentaje de acierto del ', acTrainB, '%') print('Interpretando las etiquetas de forma directa, la validación ' + 'obtiene un porcentaje de acierto del ', acValA, '%') print('Interpretando las etiquetas de forma inversa, la validación ' + 'obtiene un porcentaje de acierto del ', acValB, '%') # Calculamos el porcentaje de acierto sobre los datos de test para la # interpretación escogida de las etiquetas testLabels = kmeans.predict(Wtest) ac = (np.count_nonzero(testLabels == Ytest) if acValA > acValB else np.count_nonzero(testLabels != Ytest)) / len(Ytest) * 100 print('El porcentaje de acierto sobre los datos de test es', ac, '%') #%% Parte 5b: Reducción de dimensionalidad # Aplicamos PCA y comprobamos la varianza explicada para cada componente pca = PCA() WtrainR = pca.fit_transform(Wtrain) expVar = pca.explained_variance_ratio_ expVarAcum = [expVar[0]] for i in range(1, len(expVar)): expVarAcum.append(expVar[i] + expVarAcum[-1]) print("La varianza explicada acumulada es:", np.array(expVarAcum) * 100)

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Python

zeeguu_core/util/__init__.py

simonchristensen1/Zeeguu-Core

76f0e4a73676e00e6023ccbb2017210982670da2

[ "MIT" ]

1

2018-03-22T12:29:49.000Z

zeeguu_core/util/__init__.py

simonchristensen1/Zeeguu-Core

76f0e4a73676e00e6023ccbb2017210982670da2

[ "MIT" ]

82

2017-12-09T16:15:02.000Z

2020-11-12T11:34:09.000Z

zeeguu_core/util/__init__.py

simonchristensen1/Zeeguu-Core

76f0e4a73676e00e6023ccbb2017210982670da2

[ "MIT" ]

9

2017-11-25T11:32:05.000Z

2020-10-26T15:50:13.000Z

#!/usr/bin/env python # -*- coding: utf8 -*- from zeeguu_core.util.encoding import JSONSerializable, encode, encode_error from zeeguu_core.util.hash import text_hash, password_hash

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Python

onpolicy/envs/gridworld/gym_minigrid/envs/__init__.py

zoeyuchao/onpolicy-release

c2cb64e59c5b1f21cce022db76c378b396fd480e

[ "MIT" ]

1

2021-07-04T08:08:30.000Z

onpolicy/envs/gridworld/gym_minigrid/envs/__init__.py

zoeyuchao/onpolicy-release

c2cb64e59c5b1f21cce022db76c378b396fd480e

[ "MIT" ]

1

2021-06-11T15:28:11.000Z

onpolicy/envs/gridworld/gym_minigrid/envs/__init__.py

zoeyuchao/onpolicy-release

c2cb64e59c5b1f21cce022db76c378b396fd480e

[ "MIT" ]

1

2021-05-17T02:00:18.000Z

# from onpolicy.envs.gridworld.gym_minigrid.envs.empty import * # from onpolicy.envs.gridworld.gym_minigrid.envs.doorkey import * # from onpolicy.envs.gridworld.gym_minigrid.envs.multiroom import * # from onpolicy.envs.gridworld.gym_minigrid.envs.fetch import * # from onpolicy.envs.gridworld.gym_minigrid.envs.gotoobject import * # from onpolicy.envs.gridworld.gym_minigrid.envs.gotodoor import * # from onpolicy.envs.gridworld.gym_minigrid.envs.putnear import * # from onpolicy.envs.gridworld.gym_minigrid.envs.lockedroom import * # from onpolicy.envs.gridworld.gym_minigrid.envs.keycorridor import * # from onpolicy.envs.gridworld.gym_minigrid.envs.unlock import * # from onpolicy.envs.gridworld.gym_minigrid.envs.unlockpickup import * # from onpolicy.envs.gridworld.gym_minigrid.envs.blockedunlockpickup import * # from onpolicy.envs.gridworld.gym_minigrid.envs.playground_v0 import * # from onpolicy.envs.gridworld.gym_minigrid.envs.redbluedoors import * # from onpolicy.envs.gridworld.gym_minigrid.envs.obstructedmaze import * # from onpolicy.envs.gridworld.gym_minigrid.envs.memory import * # from onpolicy.envs.gridworld.gym_minigrid.envs.fourrooms import * # from onpolicy.envs.gridworld.gym_minigrid.envs.crossing import * # from onpolicy.envs.gridworld.gym_minigrid.envs.lavagap import * # from onpolicy.envs.gridworld.gym_minigrid.envs.dynamicobstacles import * # from onpolicy.envs.gridworld.gym_minigrid.envs.distshift import * from onpolicy.envs.gridworld.gym_minigrid.envs.human import *

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py

Python

priors/__init__.py

holarchy/Holon

2a557b300bce10fb2c2ab85a1db4bdfd5df470aa

[ "MIT" ]

null

priors/__init__.py

holarchy/Holon

2a557b300bce10fb2c2ab85a1db4bdfd5df470aa

[ "MIT" ]

null

priors/__init__.py

holarchy/Holon

2a557b300bce10fb2c2ab85a1db4bdfd5df470aa

[ "MIT" ]

null

from .priors import make_prior_from_df from .process_priors import make_priors

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7

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py

Python

ui/AboutWidget.py

penglecn/ChiaTools

ca55de3e135c962d46eb821be975444b4654775b

[ "Apache-2.0" ]

6

2021-07-01T21:30:44.000Z

2022-03-25T01:35:41.000Z

ui/AboutWidget.py

penglecn/ChiaTools

ca55de3e135c962d46eb821be975444b4654775b

[ "Apache-2.0" ]

1

2021-07-06T14:05:40.000Z

ui/AboutWidget.py

pengbeicn/ChiaTools

ca55de3e135c962d46eb821be975444b4654775b

[ "Apache-2.0" ]

3

2021-05-07T10:01:18.000Z

2021-05-21T08:38:45.000Z

# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'C:\Users\peng\Desktop\chia-tools\ui\AboutWidget.ui' # # Created by: PyQt5 UI code generator 5.15.4 # # WARNING: Any manual changes made to this file will be lost when pyuic5 is # run again. Do not edit this file unless you know what you are doing. from PyQt5 import QtCore, QtGui, QtWidgets class Ui_AboutWidget(object): def setupUi(self, AboutWidget): AboutWidget.setObjectName("AboutWidget") AboutWidget.resize(689, 489) self.verticalLayout = QtWidgets.QVBoxLayout(AboutWidget) self.verticalLayout.setObjectName("verticalLayout") self.horizontalLayout = QtWidgets.QHBoxLayout() self.horizontalLayout.setObjectName("horizontalLayout") spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem) self.verticalLayout_2 = QtWidgets.QVBoxLayout() self.verticalLayout_2.setObjectName("verticalLayout_2") self.textBrowser = QtWidgets.QTextBrowser(AboutWidget) self.textBrowser.setMinimumSize(QtCore.QSize(600, 0)) self.textBrowser.setStyleSheet("QTextEdit {\n" " background-color: rgba(255, 255, 255, 0);\n" "}") self.textBrowser.setFrameShape(QtWidgets.QFrame.NoFrame) self.textBrowser.setOpenExternalLinks(True) self.textBrowser.setObjectName("textBrowser") self.verticalLayout_2.addWidget(self.textBrowser) self.horizontalLayout.addLayout(self.verticalLayout_2) spacerItem1 = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem1) self.verticalLayout.addLayout(self.horizontalLayout) self.retranslateUi(AboutWidget) QtCore.QMetaObject.connectSlotsByName(AboutWidget) def retranslateUi(self, AboutWidget): _translate = QtCore.QCoreApplication.translate AboutWidget.setWindowTitle(_translate("AboutWidget", "Form")) self.textBrowser.setHtml(_translate("AboutWidget", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n" "<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n" "p, li { white-space: pre-wrap; }\n" "</style></head><body style=\" font-family:\'SimSun\'; font-size:9pt; font-weight:400; font-style:normal;\">\n" "ChiaTools是独立的开源免费软件，旨在帮助Chia矿工们整合各种繁琐的命令行，并提供可视化的操作界面。\n" " \n" "如果你在使用过程中发现了问题，或者有更好的建议，欢迎加入我们的群:\n" "QQ群: 926625265\n" "微信群: 添加我的微信号（penglecn）后拉进群\n" "或者在线提交Issue:\n" "<a href=\"https://gitee.com/devteamcn/chia-tools/issues\">https://gitee.com/devteamcn/chia-tools/issues</a>\n" "<p style=\"-qt-paragraph-type:empty; margin-top:0px; margin-bottom:0px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px; font-size:10pt; text-decoration: underline; color:#0000ff;\"> \n" "开源首页和使用说明\n" "<a href=\"https://gitee.com/devteamcn/chia-tools\">https://gitee.com/devteamcn/chia-tools</a>\n" "最新版本下载地址\n" "<a href=\"https://gitee.com/devteamcn/chia-tools/releases\">https://gitee.com/devteamcn/chia-tools/releases</a>\n" " \n" "多线程P图命令行程序 0.1.5\n" "<a href=\"https://github.com/stotiks/chia-plotter/releases\">https://github.com/stotiks/chia-plotter/releases</a>\n" "<a href=\"https://github.com/stotiks/chia-plotter/releases\">https://github.com/madMAx43v3r/chia-plotter</a>\n" " \n" "HPoolOG老挖矿程序 1.5.3-1\n" "<a href=\"https://github.com/hpool-dev/chia-miner/releases\">https://github.com/hpool-dev/chia-miner/releases</a>\n" " \n" "HPoolPP新挖矿程序 1.5.0-2\n" "<a href=\"https://github.com/hpool-dev/chiapp-miner/releases\">https://github.com/hpool-dev/chiapp-miner/releases</a>\n" " \n" "火币挖矿程序 1.0.0\n" "<a href=\"https://github.com/github-huobipool/HuobiPool-Chia-Miner-release/releases\">https://github.com/github-huobipool/HuobiPool-Chia-Miner-release/releases</a>\n" " \n" "另外，如果你挖矿已经回本，并且该软件帮助你产生了丰厚的收益，请别忘了请作者喝杯咖啡哈！\n" " \n" "感谢你对开源免费软件的支持！\n" " \n" "打赏XCH\n" "xch1zdnvg4xpfzm6smadxfckmg8ma3q7sq0hwsamjapjg75ztsm8fclqqk5tuu\n" " \n" "打赏RMB\n" "<img src=\":/img/donate_alipay\" /> <img src=\":/img/donate_weixin\" />\n" " </body></html>")) import resources_rc

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7

415cd5d07e99c136c58aa3d0c29a0afa95376834

1,788

py

Python

scgv/tests/test_multiplier_error.py

lchorbadjiev/SCGV

7b2fd1fbada7bea49166e37bcb82bd742617fe51

[ "MIT" ]

8

2017-03-31T19:55:36.000Z

2021-01-22T09:11:40.000Z

scgv/tests/test_multiplier_error.py

lchorbadjiev/SCGV

7b2fd1fbada7bea49166e37bcb82bd742617fe51

[ "MIT" ]

null

scgv/tests/test_multiplier_error.py

lchorbadjiev/SCGV

7b2fd1fbada7bea49166e37bcb82bd742617fe51

[ "MIT" ]

2

2019-06-11T09:07:01.000Z

2020-09-25T02:30:22.000Z

''' Created on Jan 4, 2017 @author: lubo ''' import numpy as np import pytest from scgv.views.sample import SamplesViewer def test_multiplier(model_fixture): sample = SamplesViewer(model_fixture) assert sample is not None sample_name = 'CJA5294' assert sample_name in model_fixture.column_labels m1 = sample.calc_ploidy(sample_name) sample_index = np.where(model_fixture.column_labels == sample_name) m2 = model_fixture.multiplier[sample_index] assert len(m2) == 1 print(m1, m2[0]) assert m1 == pytest.approx(m2[0], abs=1E-6) def test_all_mutipliers(model_fixture): sample = SamplesViewer(model_fixture) for sample_name in model_fixture.column_labels: m1 = sample.calc_ploidy(sample_name) sample_index = np.where(model_fixture.column_labels == sample_name) m2 = model_fixture.multiplier[sample_index] assert len(m2) == 1 assert m1 == pytest.approx(m2[0], abs=1E-6) def test_error(model_fixture): sample = SamplesViewer(model_fixture) assert sample is not None sample_name = 'CJA5294' assert sample_name in model_fixture.column_labels e1 = sample.calc_error(sample_name) sample_index = np.where(model_fixture.column_labels == sample_name) e2 = model_fixture.error[sample_index] assert len(e2) == 1 print(e1, e2[0]) assert e1 == pytest.approx(e2[0], abs=1E-6) def test_all_errors(model_fixture): sample = SamplesViewer(model_fixture) for sample_name in model_fixture.column_labels: e1 = sample.calc_error(sample_name) sample_index = np.where(model_fixture.column_labels == sample_name) e2 = model_fixture.error[sample_index] assert len(e2) == 1 assert e1 == pytest.approx(e2[0], abs=1E-6)

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1

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1

0

1

0

null

0

9

41721690f81f9421260c4b33695f76b8360119c3

7,112

py

Python

calcparser.py

kentdlee/GenComp

084da463b00557b4e0181c1a8c8d1554b4c7b2fb

[ "MIT" ]

2

2020-03-03T03:29:56.000Z

2020-06-09T03:13:10.000Z

calcparser.py

kentdlee/GenComp

084da463b00557b4e0181c1a8c8d1554b4c7b2fb

[ "MIT" ]

null

calcparser.py

kentdlee/GenComp

084da463b00557b4e0181c1a8c8d1554b4c7b2fb

[ "MIT" ]

null

from calcbackend import * from genparser import * class calcParser(Parser): def __init__(self): super().__init__({0: LR0State(0,frozenset({LR0Item(0,Production(0,11,[12, 10],'Prog'),0,set()), LR0Item(2,Production(2,12,[],'None'),0,{0, 1, 7, 8, 10}), LR0Item(1,Production(1,12,[12, 13, 9],'None'),0,set())}),{12: 1},False), 1: LR0State(1,frozenset({LR0Item(2,Production(3,13,[14],'print(E)'),0,set()), LR0Item(3,Production(0,11,[12, 10],'Prog'),1,set()), LR0Item(4,Production(5,14,[14, 4, 15],'float(E)-float(T)'),0,set()), LR0Item(3,Production(1,12,[12, 13, 9],'None'),1,set()), LR0Item(3,Production(4,14,[14, 3, 15],'float(E)+float(T)'),0,set()), LR0Item(5,Production(6,14,[15],'T'),0,set()), LR0Item(6,Production(7,15,[15, 5, 16],'float(T)*float(St)'),0,set()), LR0Item(7,Production(8,15,[15, 6, 16],'float(T)/float(St)'),0,set()), LR0Item(8,Production(9,15,[16],'St'),0,set()), LR0Item(9,Production(10,16,[7, 17],'memory.store(float(F))'),0,set()), LR0Item(10,Production(11,16,[17],'F'),0,set()), LR0Item(13,Production(14,17,[8],'memory.recall()'),0,set()), LR0Item(12,Production(13,17,[1, 14, 2],'E'),0,set()), LR0Item(11,Production(12,17,[0],'number'),0,set())}),{14: 2, 10: 3, 13: 4, 15: 5, 16: 6, 7: 7, 17: 8, 8: 9, 1: 10, 0: 11},False), 2: LR0State(2,frozenset({LR0Item(14,Production(3,13,[14],'print(E)'),1,{9}), LR0Item(14,Production(4,14,[14, 3, 15],'float(E)+float(T)'),1,set()), LR0Item(14,Production(5,14,[14, 4, 15],'float(E)-float(T)'),1,set())}),{3: 13, 4: 15},False), 3: LR0State(3,frozenset({LR0Item(14,Production(0,11,[12, 10],'Prog'),2,set())}),{},True), 4: LR0State(4,frozenset({LR0Item(14,Production(1,12,[12, 13, 9],'None'),2,set())}),{9: 23},False), 5: LR0State(5,frozenset({LR0Item(14,Production(6,14,[15],'T'),1,{2, 3, 4, 5, 6, 9}), LR0Item(14,Production(7,15,[15, 5, 16],'float(T)*float(St)'),1,set()), LR0Item(14,Production(8,15,[15, 6, 16],'float(T)/float(St)'),1,set())}),{5: 17, 6: 18},False), 6: LR0State(6,frozenset({LR0Item(14,Production(9,15,[16],'St'),1,{2, 3, 4, 5, 6, 9})}),{},False), 7: LR0State(7,frozenset({LR0Item(1,Production(12,17,[0],'number'),0,set()), LR0Item(14,Production(10,16,[7, 17],'memory.store(float(F))'),1,set()), LR0Item(2,Production(13,17,[1, 14, 2],'E'),0,set()), LR0Item(3,Production(14,17,[8],'memory.recall()'),0,set())}),{0: 11, 17: 22, 1: 10, 8: 9},False), 8: LR0State(8,frozenset({LR0Item(14,Production(11,16,[17],'F'),1,{2, 3, 4, 5, 6, 9})}),{},False), 9: LR0State(9,frozenset({LR0Item(14,Production(14,17,[8],'memory.recall()'),1,{2, 3, 4, 5, 6, 9})}),{},False), 10: LR0State(10,frozenset({LR0Item(1,Production(4,14,[14, 3, 15],'float(E)+float(T)'),0,set()), LR0Item(14,Production(13,17,[1, 14, 2],'E'),1,set()), LR0Item(2,Production(5,14,[14, 4, 15],'float(E)-float(T)'),0,set()), LR0Item(4,Production(7,15,[15, 5, 16],'float(T)*float(St)'),0,set()), LR0Item(3,Production(6,14,[15],'T'),0,set()), LR0Item(5,Production(8,15,[15, 6, 16],'float(T)/float(St)'),0,set()), LR0Item(6,Production(9,15,[16],'St'),0,set()), LR0Item(7,Production(10,16,[7, 17],'memory.store(float(F))'),0,set()), LR0Item(8,Production(11,16,[17],'F'),0,set()), LR0Item(9,Production(12,17,[0],'number'),0,set()), LR0Item(11,Production(14,17,[8],'memory.recall()'),0,set()), LR0Item(10,Production(13,17,[1, 14, 2],'E'),0,set())}),{14: 12, 15: 5, 16: 6, 7: 7, 17: 8, 0: 11, 8: 9, 1: 10},False), 11: LR0State(11,frozenset({LR0Item(14,Production(12,17,[0],'number'),1,{2, 3, 4, 5, 6, 9})}),{},False), 12: LR0State(12,frozenset({LR0Item(12,Production(4,14,[14, 3, 15],'float(E)+float(T)'),1,set()), LR0Item(12,Production(13,17,[1, 14, 2],'E'),2,set()), LR0Item(12,Production(5,14,[14, 4, 15],'float(E)-float(T)'),1,set())}),{3: 13, 2: 14, 4: 15},False), 13: LR0State(13,frozenset({LR0Item(1,Production(7,15,[15, 5, 16],'float(T)*float(St)'),0,set()), LR0Item(2,Production(8,15,[15, 6, 16],'float(T)/float(St)'),0,set()), LR0Item(3,Production(4,14,[14, 3, 15],'float(E)+float(T)'),2,set()), LR0Item(4,Production(10,16,[7, 17],'memory.store(float(F))'),0,set()), LR0Item(3,Production(9,15,[16],'St'),0,set()), LR0Item(5,Production(11,16,[17],'F'),0,set()), LR0Item(6,Production(12,17,[0],'number'),0,set()), LR0Item(7,Production(13,17,[1, 14, 2],'E'),0,set()), LR0Item(8,Production(14,17,[8],'memory.recall()'),0,set())}),{15: 21, 7: 7, 16: 6, 17: 8, 0: 11, 1: 10, 8: 9},False), 14: LR0State(14,frozenset({LR0Item(3,Production(13,17,[1, 14, 2],'E'),3,{2, 3, 4, 5, 6, 9})}),{},False), 15: LR0State(15,frozenset({LR0Item(1,Production(7,15,[15, 5, 16],'float(T)*float(St)'),0,set()), LR0Item(2,Production(8,15,[15, 6, 16],'float(T)/float(St)'),0,set()), LR0Item(3,Production(5,14,[14, 4, 15],'float(E)-float(T)'),2,set()), LR0Item(4,Production(10,16,[7, 17],'memory.store(float(F))'),0,set()), LR0Item(3,Production(9,15,[16],'St'),0,set()), LR0Item(5,Production(11,16,[17],'F'),0,set()), LR0Item(6,Production(12,17,[0],'number'),0,set()), LR0Item(7,Production(13,17,[1, 14, 2],'E'),0,set()), LR0Item(8,Production(14,17,[8],'memory.recall()'),0,set())}),{15: 16, 7: 7, 16: 6, 17: 8, 0: 11, 1: 10, 8: 9},False), 16: LR0State(16,frozenset({LR0Item(9,Production(7,15,[15, 5, 16],'float(T)*float(St)'),1,set()), LR0Item(9,Production(5,14,[14, 4, 15],'float(E)-float(T)'),3,{2, 3, 4, 5, 6, 9}), LR0Item(9,Production(8,15,[15, 6, 16],'float(T)/float(St)'),1,set())}),{5: 17, 6: 18},False), 17: LR0State(17,frozenset({LR0Item(1,Production(10,16,[7, 17],'memory.store(float(F))'),0,set()), LR0Item(4,Production(13,17,[1, 14, 2],'E'),0,set()), LR0Item(3,Production(7,15,[15, 5, 16],'float(T)*float(St)'),2,set()), LR0Item(2,Production(11,16,[17],'F'),0,set()), LR0Item(3,Production(12,17,[0],'number'),0,set()), LR0Item(5,Production(14,17,[8],'memory.recall()'),0,set())}),{7: 7, 1: 10, 16: 20, 17: 8, 0: 11, 8: 9},False), 18: LR0State(18,frozenset({LR0Item(1,Production(10,16,[7, 17],'memory.store(float(F))'),0,set()), LR0Item(4,Production(13,17,[1, 14, 2],'E'),0,set()), LR0Item(3,Production(8,15,[15, 6, 16],'float(T)/float(St)'),2,set()), LR0Item(2,Production(11,16,[17],'F'),0,set()), LR0Item(3,Production(12,17,[0],'number'),0,set()), LR0Item(5,Production(14,17,[8],'memory.recall()'),0,set())}),{7: 7, 1: 10, 16: 19, 17: 8, 0: 11, 8: 9},False), 19: LR0State(19,frozenset({LR0Item(6,Production(8,15,[15, 6, 16],'float(T)/float(St)'),3,{2, 3, 4, 5, 6, 9})}),{},False), 20: LR0State(20,frozenset({LR0Item(6,Production(7,15,[15, 5, 16],'float(T)*float(St)'),3,{2, 3, 4, 5, 6, 9})}),{},False), 21: LR0State(21,frozenset({LR0Item(9,Production(7,15,[15, 5, 16],'float(T)*float(St)'),1,set()), LR0Item(9,Production(4,14,[14, 3, 15],'float(E)+float(T)'),3,{2, 3, 4, 5, 6, 9}), LR0Item(9,Production(8,15,[15, 6, 16],'float(T)/float(St)'),1,set())}),{5: 17, 6: 18},False), 22: LR0State(22,frozenset({LR0Item(4,Production(10,16,[7, 17],'memory.store(float(F))'),2,{2, 3, 4, 5, 6, 9})}),{},False), 23: LR0State(23,frozenset({LR0Item(1,Production(1,12,[12, 13, 9],'None'),3,{0, 1, 7, 8, 10})}),{},False)},['number', "'('", "')'", "'+'", "'-'", "'*'", "'/'", "'S'", "'R'", "';'", 'endoffile', 'Start', 'Prog', 'Stmt', 'E', 'T', 'St', 'F']) def eval(self,expression): return eval(expression)

711.2

6,958

0.604331

1,360

7,112

3.154412

0.045588

0.13986

0.117949

0.054545

0.796737

0.758974

0.72331

0.68648

0.612821

0.570163

0

0.186752

0.059618

7,112

9

6,959

790.222222

0.454695

0

0.142014

0.024747

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false

0

0.285714

0.142857

0.857143

0.142857

0

null

0

1

0

1

0

null

0

1

0

1

0

9

6b644ee301a53a04062058ddfbb47b61b78cb41e

607

py

Python

accounts/models.py

mugagambi/retail-system

82bd9f243836aadee001fa7f17d1d93441214aa8

[ "MIT" ]

1

2019-10-08T13:53:49.000Z

accounts/models.py

mugagambi/retail-system

82bd9f243836aadee001fa7f17d1d93441214aa8

[ "MIT" ]

null

accounts/models.py

mugagambi/retail-system

82bd9f243836aadee001fa7f17d1d93441214aa8

[ "MIT" ]

null

from django.db import models from django.utils import timezone # Create your models here. class IncomeAccount(models.Model): name = models.CharField(max_length=100) amount = models.DecimalField(max_digits=15, decimal_places=2) created_at = models.DateTimeField(default=timezone.now) def __str__(self): return self.name class ExpenditureAccount(models.Model): name = models.CharField(max_length=100) amount = models.DecimalField(max_digits=15, decimal_places=2) created_at = models.DateTimeField(default=timezone.now) def __str__(self): return self.name

27.590909

65

0.744646

78

607

5.589744

0.448718

0.045872

0.068807

0.09633

0.738532

0

0.023622

0.163097

607

21

66

28.904762

0.834646

0.039539

0

0.714286

0

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0.142857

false

0

0.142857

1

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null

0

1

0

1

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null

0

1

0

8

6bf74296fa6be1435a27cc694b5427076bfca326

774

py

Python

core/apps/kubeops_api/models/item_resource.py

r4b3rt/KubeOperator

1fef19816ada64d8b25f87a5e3356ea5f161d7e5

[ "Apache-2.0" ]

3

2020-04-05T04:53:24.000Z

2020-04-05T04:53:34.000Z

core/apps/kubeops_api/models/item_resource.py

r4b3rt/KubeOperator

1fef19816ada64d8b25f87a5e3356ea5f161d7e5

[ "Apache-2.0" ]

27

2021-05-05T02:51:26.000Z

2022-01-04T21:30:21.000Z

core/apps/kubeops_api/models/item_resource.py

r4b3rt/KubeOperator

1fef19816ada64d8b25f87a5e3356ea5f161d7e5

[ "Apache-2.0" ]

1

2020-07-06T04:53:51.000Z

import uuid from django.db import models __all__ = ["ItemResource"] class ItemResource(models.Model): RESOURCE_TYPE_CLUSTER = 'CLUSTER' RESOURCE_TYPE_HOST = 'HOST' RESOURCE_TYPE_PLAN = 'PLAN' RESOURCE_TYPE_BACKUP_STORAGE = 'BACKUP_STORAGE' RESOURCE_TYPE_STORAGE = 'STORAGE' RESOURCE_TYPE_CHOICES = ( (RESOURCE_TYPE_CLUSTER,'CLUSTER'), (RESOURCE_TYPE_HOST,'HOST'), (RESOURCE_TYPE_PLAN,'PLAN'), (RESOURCE_TYPE_BACKUP_STORAGE,'BACKUP_STORAGE'), (RESOURCE_TYPE_STORAGE,'STORAGE') ) item_id = models.UUIDField(max_length=255, default=uuid.uuid4) resource_id = models.UUIDField(max_length=255, default=uuid.uuid4) resource_type = models.CharField(max_length=64,choices=RESOURCE_TYPE_CHOICES)

28.666667

81

0.723514

91

774

5.736264

0.307692

0.298851

0.109195

0.099617

0.701149

0

0.015625

0.173127

774

26

82

29.769231

0.8

0

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0

1

0

false

0

0.105263

0

0.631579

0

null

1

0

1

0

null

0

1

0

7

d445986ab273203a10192235933257890754de6d

11,577

py

Python

src/openprocurement/tender/core/procedure/models/auction.py

ProzorroUKR/openprocurement.api

2855a99aa8738fb832ee0dbad4e9590bd3643511

[ "Apache-2.0" ]

10

2020-02-18T01:56:21.000Z

2022-03-28T00:32:57.000Z

src/openprocurement/tender/core/procedure/models/auction.py

quintagroup/openprocurement.api

2855a99aa8738fb832ee0dbad4e9590bd3643511

[ "Apache-2.0" ]

26

2018-07-16T09:30:44.000Z

2021-02-02T17:51:30.000Z

src/openprocurement/tender/core/procedure/models/auction.py

ProzorroUKR/openprocurement.api

2855a99aa8738fb832ee0dbad4e9590bd3643511

[ "Apache-2.0" ]

15

2019-08-08T10:50:47.000Z

2022-02-05T14:13:36.000Z

from openprocurement.tender.core.procedure.models.base import Model, ListType, ModelType from openprocurement.tender.core.procedure.context import get_request, get_tender from openprocurement.api.models import IsoDateTimeType from schematics.exceptions import ValidationError from schematics.types import URLType, MD5Type, FloatType, StringType, BooleanType from itertools import zip_longest # set urls class LotAuctionUrl(Model): id = MD5Type() auctionUrl = URLType() class ParticipationUrl(Model): id = MD5Type() participationUrl = URLType() # required ? class LotValueUrl(Model): relatedLot = MD5Type() participationUrl = URLType() class AuctionUrls(Model): auctionUrl = URLType() # required ? bids = ListType(ModelType(ParticipationUrl, required=True)) def validate_bids(self, _, bids): """ example input "bids": [{}, {"participationUrl": "http://..."}, {}] """ bid_ids = [b["id"] for b in get_tender().get("bids", "")] passed_ids = [] for bid, positional_bid_id in zip_longest(bids, bid_ids): if None in (positional_bid_id, bid): raise ValidationError("Number of bids did not match the number of tender bids") if bid.id is None: bid.id = positional_bid_id # For now we allow to skip passing id of update object # Also empty objects {} do not appear in result when we call serialise() method # there is a way to hack schematics to do so (see def openprocurement.api.Model.to_patch) # but I don't want to stick to this version of schematics and to any version of it passed_ids.append(bid.id) if passed_ids != bid_ids: raise ValidationError("Auction bids should be identical to the tender bids") return bids class BidLotValue(Model): id = MD5Type() lotValues = ListType(ModelType(LotValueUrl, required=True)) # optional? bid may be cancelled or something.. class LotAuctionUrls(Model): # auctionUrl = URLType() lots = ListType(ModelType(LotAuctionUrl, required=True), required=True) bids = ListType(ModelType(BidLotValue, required=True), required=True) def validate_lots(self, _, lots): """ example input "lots": [{}, {"auctionUrl": "http://auction.."}, {}] """ lot_id = get_request().matchdict.get("auction_lot_id") lot_ids = [l["id"] for l in get_tender().get("lots", "")] passed_ids = [] for lot, positional_lot_id in zip_longest(lots, lot_ids): if None in (positional_lot_id, lot): raise ValidationError("Number of lots did not match the number of tender lots") if lot.id is None: lot.id = positional_lot_id # For now we allow to skip passing id of update object # Also empty objects {} do not appear in result when we call serialise() method # there is a way to hack schematics to do so (see def openprocurement.api.Model.to_patch) # but I don't want to stick to this version of schematics and to any version of it if lot.id == lot_id: if lot.auctionUrl is None: raise ValidationError("Auction url required") else: # post to /auctions/{lot_id} updates only related lots for f in lot: if f != "id": lot[f] = None passed_ids.append(lot.id) if passed_ids != lot_ids: raise ValidationError("Auction lots should be identical to the tender lots") return lots def validate_bids(self, _, bids): """ example input "bids": [{}, {"lotValues": [{}, {"participationUrl": "http://..."}, {}]}, {}] """ lot_id = get_request().matchdict.get("auction_lot_id") bid_ids = [b["id"] for b in get_tender().get("bids", "")] tender_bids = {b["id"]: b for b in get_tender().get("bids", "")} passed_ids = [] for bid, positional_bid_id in zip_longest(bids, bid_ids): if None in (positional_bid_id, bid): raise ValidationError("Number of auction results did not match the number of tender bids") if bid.id is None: bid.id = positional_bid_id elif bid.id not in tender_bids: raise ValidationError("Auction bids should be identical to the tender bids") # For now we allow to skip passing id of update object # Also empty objects {} do not appear in result when we call serialise() method # there is a way to hack schematics to do so (see def openprocurement.api.Model.to_patch) # but I don't want to stick to this version of schematics and to any version of it passed_ids.append(bid.id) # lotValues check --- if bid.lotValues: passed_related_lots = [] tender_related_lots = [v["relatedLot"] for v in tender_bids[bid.id]["lotValues"]] for value, positional_related_lot in zip_longest(bid.lotValues, tender_related_lots): if positional_related_lot is None: raise ValidationError( "Number of lots of auction results did not match the number of tender lots") if value is None: # passed list actually can be shorter continue if value.relatedLot is None: value.relatedLot = positional_related_lot if value.relatedLot == lot_id: if value.participationUrl is None: raise ValidationError("Auction participation url required") else: # post to /auctions/{lot_id} updates only related lotValues for f in value: if f != "relatedLot": value[f] = None passed_related_lots.append(value.relatedLot) if passed_related_lots != tender_related_lots[:len(passed_related_lots)]: # passed can be shorter raise ValidationError("Auction bid.lotValues should be identical to the tender bid.lotValues") # -- lotValues check if passed_ids != bid_ids: raise ValidationError("Auction bids should be identical to the tender bids") return bids # auction results class ValueResult(Model): amount = FloatType(min_value=0) date = IsoDateTimeType() # these two required by tests and maybe "old" auctions TODO: rm them after new auctions currency = StringType() valueAddedTaxIncluded = BooleanType() class WeightedValueResult(Model): amount = FloatType(min_value=0) date = IsoDateTimeType() # these two required by tests and maybe "old" auctions TODO: rm them after new auctions currency = StringType() valueAddedTaxIncluded = BooleanType() class BidResult(Model): id = MD5Type() value = ModelType(ValueResult) weightedValue = ModelType(WeightedValueResult) date = IsoDateTimeType() class AuctionResults(Model): bids = ListType(ModelType(BidResult, required=True)) def validate_bids(self, _, bids): """ example input "bids": [{}, {"value": 1, "date": "2020-..."}, {}] """ bid_ids = [b["id"] for b in get_tender().get("bids", "")] passed_ids = [] for bid, positional_bid_id in zip_longest(bids, bid_ids): if None in (positional_bid_id, bid): raise ValidationError("Number of auction results did not match the number of tender bids") if bid.id is None: bid.id = positional_bid_id # For now we allow to skip passing id of update object # Also empty objects {} do not appear in result when we call serialise() method # there is a way to hack schematics to do so (see def openprocurement.api.Model.to_patch) # but I don't want to stick to this version of schematics and to any version of it passed_ids.append(bid.id) if passed_ids != bid_ids: raise ValidationError("Auction bids should be identical to the tender bids") return bids # auction lot results class LotResult(Model): relatedLot = MD5Type() value = ModelType(ValueResult) weightedValue = ModelType(WeightedValueResult) date = IsoDateTimeType() class BidLotResult(Model): id = MD5Type() lotValues = ListType(ModelType(LotResult, required=True)) class AuctionLotResults(Model): bids = ListType(ModelType(BidLotResult, required=True), required=True) def validate_bids(self, _, bids): """ example input "bids": [{}, {"lotValues": [{}, {"value": 23, "date": "..."}, {}]}, {}] """ lot_id = get_request().matchdict.get("auction_lot_id") bid_ids = [b["id"] for b in get_tender().get("bids", "")] tender_bids = {b["id"]: b for b in get_tender().get("bids", "")} passed_ids = [] for bid, positional_bid_id in zip_longest(bids, bid_ids): if None in (positional_bid_id, bid): raise ValidationError("Number of auction results did not match the number of tender bids") if bid.id is None: bid.id = positional_bid_id elif bid.id not in tender_bids: raise ValidationError("Auction bids should be identical to the tender bids") # For now we allow to skip passing id of update object # Also empty objects {} do not appear in result when we call serialise() method # there is a way to hack schematics to do so (see def openprocurement.api.Model.to_patch) # but I don't want to stick to this version of schematics and to any version of it passed_ids.append(bid.id) # lotValues check --- if bid.lotValues: passed_related_lots = [] tender_related_lots = [v["relatedLot"] for v in tender_bids[bid.id]["lotValues"]] for value, positional_related_lot in zip_longest(bid.lotValues, tender_related_lots): if positional_related_lot is None: raise ValidationError( "Number of lots of auction results did not match the number of tender lots") if value is None: # passed list actually can be shorter continue if value.relatedLot is None: value.relatedLot = positional_related_lot passed_related_lots.append(value.relatedLot) # patch to /auctions/{lot_id} updates only related lotValues if value.relatedLot != lot_id: for f in value: if f != "relatedLot": value[f] = None if passed_related_lots != tender_related_lots[:len(passed_related_lots)]: # passed can be shorter raise ValidationError("Auction bid.lotValues should be identical to the tender bid.lotValues") # -- lotValues check if passed_ids != bid_ids: raise ValidationError("Auction bids should be identical to the tender bids") return bids

43.197761

114

0.60171

1,389

11,577

4.901368

0.11951

0.020564

0.026439

0.025118

0.79671

0.761604

0.727673

0.720182

0.701087

0

0.002136

0.31243

11,577

267

115

43.359551

0.853141

0.220264

0

0.710843

0

0.129303

0

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1

0.03012

false

0.126506

0.036145

0

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null

0

1

0

1

0

null

0

1

0

7

d44e2233c193afbe9a42b253be5890048de0cc5d

1,800

py

Python

algorithm7.py

kipkat/AMSTK7

3c82053e8033eaac265d99fe961d2c3db3ea3c71

[ "MIT" ]

null

algorithm7.py

kipkat/AMSTK7

3c82053e8033eaac265d99fe961d2c3db3ea3c71

[ "MIT" ]

null

algorithm7.py

kipkat/AMSTK7

3c82053e8033eaac265d99fe961d2c3db3ea3c71

[ "MIT" ]

null

import random as nature def amstk7(s: str, chars: int = 64): seed = len(s) leg = len(s) e = '' for x in s: asc = ord(x) seed += asc e += chr(asc * leg * len(str(asc)) % 26 + 65) e += str((asc * 3 * leg + 42 * len(str(asc))) % 10 + len(e)) for x in e: asc = ord(x) e += chr(asc * leg * seed % 10 * len(str(asc)) % 26 + 65) e += str((asc * 3 * leg + 42 * len(str(asc))) % 10 + len(e) + seed % 10) e += chr(len(e) % 26 + 65) seed *= int.from_bytes(e.encode(), 'big') seed += int.from_bytes(s.encode(), 'big') nature.seed(seed) str_var = list(e) nature.shuffle(str_var) e = ''.join(str_var) for x in range(1, 6): str_var = list(e) nature.shuffle(str_var) e += ''.join(str_var) return e[-chars:] def seed_amstk7(s: str, seed2: int, chars: int = 64): seed = len(s) leg = len(s) e = '' for x in s: asc = ord(x) seed += ord(x) e += chr(asc * leg * len(str(asc)) % 26 + 65) e += str((asc * 3 * leg + 42 * len(str(asc))) % 10 + len(e)) for x in e: asc = ord(x) e += chr(asc * leg * seed % 10 * len(str(asc)) % 26 + 65) e += str((asc * 3 * leg + 42 * len(str(asc))) % 10 + len(e) + seed % 10) e += chr(len(e) % 26 + 65) seed *= int.from_bytes(e.encode(), 'big') seed += int.from_bytes(s.encode(), 'big') nature.seed(seed + seed2) str_var = list(e) nature.shuffle(str_var) e = ''.join(str_var) for x in range(1, 6): str_var = list(e) nature.shuffle(str_var) e += ''.join(str_var) return e[-chars:] def repeat_amstk7(s: str, repeats: int, chars: int = 64): for x in range(repeats): s = amstk7(s, chars) return s

29.508197

80

0.488333

294

1,800

2.928571

0.139456

0.083624

0.03252

0.840883

0.836237

0

0.056106

0.326667

1,800

60

81

30

0.65429

0

0.8

0

0.006667

0

1

0.054545

false

0

0.018182

0

0.127273

0

null

0

1

0

1

0

null

0

7

d46dde59c40b21daf8053344583db83375c83c90

294

py

Python

HOZO/hozo/__init__.py

jsgubin/HOZOG

cae6ac386d1b43c70d269e47e10ba4a4ac7aed4a

[ "MIT" ]

4

2021-04-19T21:01:56.000Z

2021-09-05T06:54:47.000Z

HOZO/hozo/__init__.py

jsgubin/HOZOG

cae6ac386d1b43c70d269e47e10ba4a4ac7aed4a

[ "MIT" ]

1

2022-01-24T21:32:42.000Z

HOZO/hozo/__init__.py

jsgubin/HOZOG

cae6ac386d1b43c70d269e47e10ba4a4ac7aed4a

[ "MIT" ]

null

from hozo.logistic import * from hozo.utils import * from hozo.hozo import * # from hozo.datasets import Dataset, Datasets from hozo.models import * from hozo.data_hyper_cleaning_ho import * from hozo.data_hyper_cleaning_bo import * from hozo.ZOG import * from hozo.data_cleaning_model import *

32.666667

45

0.809524

46

294

5

0.326087

0.313043

0.426087

0.234783

0.269565

0

0.12585

294

9

46

32.666667

0.894942

0.146259

0

1

0

true

0

1

0

1

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null

1

0

1

0

null

0

1

0

1

0

1

0

7

d476076839bc5f4132bbb9b8507dafd40035d591

116

py

Python

policy_driven_attack/policy/imagenet/__init__.py

machanic/TangentAttack

17c1a8e93f9bbd03e209e8650631af744a0ff6b8

[ "Apache-2.0" ]

4

2021-11-12T04:06:32.000Z

2022-01-27T09:01:41.000Z

policy_driven_attack/policy/imagenet/__init__.py

machanic/TangentAttack

17c1a8e93f9bbd03e209e8650631af744a0ff6b8

[ "Apache-2.0" ]

1

2022-02-22T14:00:59.000Z

2022-02-25T08:57:29.000Z

policy_driven_attack/policy/imagenet/__init__.py

machanic/TangentAttack

17c1a8e93f9bbd03e209e8650631af744a0ff6b8

[ "Apache-2.0" ]

null

from policy_driven_attack.policy.imagenet.empty import * from policy_driven_attack.policy.imagenet.vgg_inv import *

38.666667

58

0.862069

17

116

5.588235

0.529412

0.210526

0.336842

0.463158

0.757895

0

0.068966

116

2

59

58

0.87963

0

1

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true

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1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

9

d477f7bb49ff7acd2594eb383b9133fc9dacd4e6

178

py

Python

Configuration/StandardSequences/python/FrontierConditions_GlobalTag_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

Configuration/StandardSequences/python/FrontierConditions_GlobalTag_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

Configuration/StandardSequences/python/FrontierConditions_GlobalTag_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms from Configuration.StandardSequences.CondDBESSource_cff import * from Configuration.StandardSequences.AdditionalConditions_cff import *

35.6

70

0.882022

18

178

8.611111

0.666667

0.219355

0.43871

0

0.073034

178

4

71

44.5

0.939394

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

5ceb2c5bf2d4c18dd016e1dfa1274031ff035037

54

py

Python

cv19gm/models/__init__.py

DLab/covid19geomodeller

a3a9eedf064078b21be0928ee41b41c902938eff

[ "MIT" ]

null

cv19gm/models/__init__.py

DLab/covid19geomodeller

a3a9eedf064078b21be0928ee41b41c902938eff

[ "MIT" ]

null

cv19gm/models/__init__.py

DLab/covid19geomodeller

a3a9eedf064078b21be0928ee41b41c902938eff

[ "MIT" ]

null

import cv19gm.models.seir import cv19gm.models.seirhvd

27

28

0.87037

8

54

5.875

0.625

0.510638

0.765957

0

0.078431

0.055556

54

2

28

27

0.843137

0

1

0

true

0

1

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1

0

1

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null

1

0

1

0

null

0

1

0

1

0

1

0

7

d8fe5f4663457bac94156103b7110322cbe61db7

101

py

Python

src/kidmaya/core/__init__.py

KidKaboom/Kid-Maya-2022

0daec301a63438d681cc4c3a5df6d4efdc70daef

[ "MIT" ]

null

src/kidmaya/core/__init__.py

KidKaboom/Kid-Maya-2022

0daec301a63438d681cc4c3a5df6d4efdc70daef

[ "MIT" ]

null

src/kidmaya/core/__init__.py

KidKaboom/Kid-Maya-2022

0daec301a63438d681cc4c3a5df6d4efdc70daef

[ "MIT" ]

null

# :coding: utf-8 from kidmaya.core.kmcommand import KMCommand from kidmaya.core.kmtool import KMTool

25.25

44

0.811881

15

101

5.466667

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0.268293

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0

0.011111

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101

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45

33.666667

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true

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1

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null

0

1

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1

0

1

0

7

996d710cba1b717ed5a682ad2c0ddbb9b26bc8c9

28,336

py

Python

skidl/libs/digital-audio_sklib.py

arjenroodselaar/skidl

0bf801bd3b74e6ef94bd9aa1b68eef756b568276

[ "MIT" ]

700

2016-08-16T21:12:50.000Z

2021-10-10T02:15:18.000Z

skidl/libs/digital-audio_sklib.py

0dvictor/skidl

458709a10b28a864d25ae2c2b44c6103d4ddb291

[ "MIT" ]

118

2016-08-16T20:51:05.000Z

2021-10-10T08:07:18.000Z

skidl/libs/digital-audio_sklib.py

0dvictor/skidl

458709a10b28a864d25ae2c2b44c6103d4ddb291

[ "MIT" ]

94

2016-08-25T14:02:28.000Z

2021-09-12T05:17:08.000Z

from skidl import SKIDL, TEMPLATE, Part, Pin, SchLib SKIDL_lib_version = '0.0.1' digital_audio = SchLib(tool=SKIDL).add_parts(*[ Part(name='AK5392VS',dest=TEMPLATE,tool=SKIDL,keywords='24bit Sigma Delta Audio ADC 2ch',description='AK5392-VS, Enhanced Audio ADC, 2 channels Sigma Delta, 24bit, SO28',ref_prefix='U',num_units=1,fplist=['SO*'],do_erc=True,pins=[ Pin(num='1',name='VREFL',func=Pin.OUTPUT,do_erc=True), Pin(num='2',name='GNDL',func=Pin.PWRIN,do_erc=True), Pin(num='3',name='VCOML',func=Pin.OUTPUT,do_erc=True), Pin(num='4',name='AINL+',do_erc=True), Pin(num='5',name='AINL-',do_erc=True), Pin(num='6',name='ZCAL',do_erc=True), Pin(num='7',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='CAL',func=Pin.OUTPUT,do_erc=True), Pin(num='10',name='~RST~',do_erc=True), Pin(num='20',name='TEST',do_erc=True), Pin(num='11',name='SMODE2',do_erc=True), Pin(num='21',name='BGND',func=Pin.PWRIN,do_erc=True), Pin(num='12',name='SMODE1',do_erc=True), Pin(num='22',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='13',name='LRCK',func=Pin.BIDIR,do_erc=True), Pin(num='23',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='SCLK',func=Pin.BIDIR,do_erc=True), Pin(num='24',name='AINR-',do_erc=True), Pin(num='15',name='SDATA',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='AINR+',do_erc=True), Pin(num='16',name='FSYNC',func=Pin.BIDIR,do_erc=True), Pin(num='26',name='VCOMR',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='CLK',do_erc=True), Pin(num='27',name='GNDR',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='CMODE',do_erc=True), Pin(num='28',name='VREFR',func=Pin.OUTPUT,do_erc=True), Pin(num='19',name='HPFE',do_erc=True)]), Part(name='AK5393VS',dest=TEMPLATE,tool=SKIDL,keywords='96kHz 24bit Sigma Delta Audio ADC 2ch',description='Enhanced Audio ADC, 2 channels Sigma Delta, 24bit 96kHz, SO28',ref_prefix='U',num_units=1,fplist=['SO*'],do_erc=True,pins=[ Pin(num='1',name='VREFL',func=Pin.OUTPUT,do_erc=True), Pin(num='2',name='GNDL',func=Pin.PWRIN,do_erc=True), Pin(num='3',name='VCOML',func=Pin.OUTPUT,do_erc=True), Pin(num='4',name='AINL+',do_erc=True), Pin(num='5',name='AINL-',do_erc=True), Pin(num='6',name='ZCAL',do_erc=True), Pin(num='7',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='CAL',func=Pin.OUTPUT,do_erc=True), Pin(num='10',name='~RST~',do_erc=True), Pin(num='20',name='TEST',do_erc=True), Pin(num='11',name='SMODE2',do_erc=True), Pin(num='21',name='BGND',func=Pin.PWRIN,do_erc=True), Pin(num='12',name='SMODE1',do_erc=True), Pin(num='22',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='13',name='LRCK',func=Pin.BIDIR,do_erc=True), Pin(num='23',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='SCLK',func=Pin.BIDIR,do_erc=True), Pin(num='24',name='AINR-',do_erc=True), Pin(num='15',name='SDATA',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='AINR+',do_erc=True), Pin(num='16',name='FSYNC',func=Pin.BIDIR,do_erc=True), Pin(num='26',name='VCOMR',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='MCLK',do_erc=True), Pin(num='27',name='GNDR',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='DFS',do_erc=True), Pin(num='28',name='VREFR',func=Pin.OUTPUT,do_erc=True), Pin(num='19',name='HPFE',do_erc=True)]), Part(name='AK5394AVS',dest=TEMPLATE,tool=SKIDL,keywords='192kHz 24bit Sigma Delta Audio ADC 2ch',description='Super High Performance Audio ADC, 2 channels Sigma Delta, 24bit 192kHz, SO28',ref_prefix='U',num_units=1,fplist=['SO*'],do_erc=True,pins=[ Pin(num='1',name='VREFL+',func=Pin.OUTPUT,do_erc=True), Pin(num='2',name='VREFL-',func=Pin.OUTPUT,do_erc=True), Pin(num='3',name='VCOML',func=Pin.OUTPUT,do_erc=True), Pin(num='4',name='AINL+',do_erc=True), Pin(num='5',name='AINL-',do_erc=True), Pin(num='6',name='ZCAL',do_erc=True), Pin(num='7',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='CAL',func=Pin.OUTPUT,do_erc=True), Pin(num='10',name='~RST~',do_erc=True), Pin(num='20',name='DFS1',do_erc=True), Pin(num='11',name='SMODE2',do_erc=True), Pin(num='21',name='BGND',func=Pin.PWRIN,do_erc=True), Pin(num='12',name='SMODE1',do_erc=True), Pin(num='22',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='13',name='LRCK',func=Pin.BIDIR,do_erc=True), Pin(num='23',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='SCLK',func=Pin.BIDIR,do_erc=True), Pin(num='24',name='AINR-',do_erc=True), Pin(num='15',name='SDATA',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='AINR+',do_erc=True), Pin(num='16',name='FSYNC',func=Pin.BIDIR,do_erc=True), Pin(num='26',name='VCOMR',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='MCLK',do_erc=True), Pin(num='27',name='VREFR-',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='DFS0',do_erc=True), Pin(num='28',name='VREFR+',func=Pin.OUTPUT,do_erc=True), Pin(num='19',name='HPFE',do_erc=True)]), Part(name='CS4245',dest=TEMPLATE,tool=SKIDL,keywords='CS4245 stereo audio codec',description='Stereo Audio CODEC, 104 dB, 24-Bit, 192 kHz',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='1',name='SDA/CDOUT',func=Pin.BIDIR,do_erc=True), Pin(num='2',name='SCL/CCLK',do_erc=True), Pin(num='3',name='AD0/~CS',do_erc=True), Pin(num='4',name='AD1/CDIN',do_erc=True), Pin(num='5',name='VLC',do_erc=True), Pin(num='6',name='~RESET',do_erc=True), Pin(num='7',name='AIN3A',do_erc=True), Pin(num='8',name='AIN3B',do_erc=True), Pin(num='9',name='AIN2A',do_erc=True), Pin(num='10',name='AIN2B',do_erc=True), Pin(num='20',name='FILT2+',func=Pin.OUTPUT,do_erc=True), Pin(num='30',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='40',name='MCLK2',do_erc=True), Pin(num='11',name='AIN1A',do_erc=True), Pin(num='21',name='AIN4A/MICIN1',do_erc=True), Pin(num='31',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='41',name='SDOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='12',name='AIN1B',do_erc=True), Pin(num='22',name='AIN4B/MICIN2',do_erc=True), Pin(num='32',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='42',name='SCLK1',func=Pin.BIDIR,do_erc=True), Pin(num='13',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='23',name='AIN5A',do_erc=True), Pin(num='33',name='AOUTA',func=Pin.OUTPUT,do_erc=True), Pin(num='43',name='LRCK1',func=Pin.BIDIR,do_erc=True), Pin(num='14',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='24',name='AIN5B',do_erc=True), Pin(num='34',name='AOUTB',func=Pin.OUTPUT,do_erc=True), Pin(num='44',name='MCLK1',do_erc=True), Pin(num='15',name='AFILTA',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='MICBIAS',func=Pin.OUTPUT,do_erc=True), Pin(num='35',name='~MUTEC',func=Pin.OUTPUT,do_erc=True), Pin(num='45',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='16',name='AFILTB',func=Pin.OUTPUT,do_erc=True), Pin(num='26',name='AIN6A',do_erc=True), Pin(num='36',name='VLS',func=Pin.PWRIN,do_erc=True), Pin(num='46',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='17',name='VQ1',func=Pin.OUTPUT,do_erc=True), Pin(num='27',name='AIN6B',do_erc=True), Pin(num='37',name='SDIN',do_erc=True), Pin(num='47',name='INT',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='VQ2',func=Pin.OUTPUT,do_erc=True), Pin(num='28',name='AUXOUTA',func=Pin.OUTPUT,do_erc=True), Pin(num='38',name='SCLK2',func=Pin.BIDIR,do_erc=True), Pin(num='48',name='OVFL',func=Pin.OUTPUT,do_erc=True), Pin(num='19',name='FILT1+',func=Pin.OUTPUT,do_erc=True), Pin(num='29',name='AUXOUTB',func=Pin.OUTPUT,do_erc=True), Pin(num='39',name='LRCK2',func=Pin.BIDIR,do_erc=True)]), Part(name='CS43L21',dest=TEMPLATE,tool=SKIDL,keywords='stereo audio dac',description='Stereo Audio DAC, 24-bit, 96 kHz, 98 dB',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='1',name='LRCK',func=Pin.BIDIR,do_erc=True), Pin(num='2',name='SDA/MCLKDIV2',func=Pin.BIDIR,do_erc=True), Pin(num='3',name='SCL/CCLK/I2S/~LJ',do_erc=True), Pin(num='4',name='AD0/~CS~/DEM',do_erc=True), Pin(num='5',name='VA_HP',do_erc=True), Pin(num='6',name='FLYP',do_erc=True), Pin(num='7',name='GND_HP',do_erc=True), Pin(num='8',name='FLYN',do_erc=True), Pin(num='9',name='VSS_HP',func=Pin.OUTPUT,do_erc=True), Pin(num='10',name='AOUTB',func=Pin.OUTPUT,do_erc=True), Pin(num='30',name='MCLK',do_erc=True), Pin(num='11',name='AOUTA',func=Pin.OUTPUT,do_erc=True), Pin(num='31',name='SCLK',do_erc=True), Pin(num='12',name='VA',do_erc=True), Pin(num='32',name='SDIN',do_erc=True), Pin(num='13',name='AGND',do_erc=True), Pin(num='14',name='FILT+',func=Pin.OUTPUT,do_erc=True), Pin(num='15',name='VQ',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='~RESET',do_erc=True), Pin(num='26',name='VL',do_erc=True), Pin(num='27',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='28',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='29',name='TESTO/M/~S',do_erc=True)]), Part(name='CS5361',dest=TEMPLATE,tool=SKIDL,keywords='stereo audio adc',description='Stereo Audio ADC, 24 bits, 192 kHz, 114 dB',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='1',name='RST',do_erc=True), Pin(num='2',name='M/~S',do_erc=True), Pin(num='3',name='LRCK',func=Pin.BIDIR,do_erc=True), Pin(num='4',name='SCLK',func=Pin.BIDIR,do_erc=True), Pin(num='5',name='MCLK',do_erc=True), Pin(num='6',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='7',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='VL',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='SDOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='10',name='MDIV',do_erc=True), Pin(num='20',name='AINR-',do_erc=True), Pin(num='11',name='~HPF',do_erc=True), Pin(num='21',name='AINR+',do_erc=True), Pin(num='12',name='I2S/~LJ',do_erc=True), Pin(num='22',name='VQ',func=Pin.PWROUT,do_erc=True), Pin(num='13',name='M0',do_erc=True), Pin(num='23',name='REFGND',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='M1',do_erc=True), Pin(num='24',name='FILT+',func=Pin.PWROUT,do_erc=True), Pin(num='15',name='~OVFL',do_erc=True), Pin(num='16',name='AINL+',do_erc=True), Pin(num='17',name='AINL-',do_erc=True), Pin(num='18',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='19',name='VA',func=Pin.PWRIN,do_erc=True)]), Part(name='CS8406',dest=TEMPLATE,tool=SKIDL,keywords='digital audio interface transmitter spdif',description='192 kHz Digital Audio Interface Transmitter (SOIC-28)',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='6',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='~RST',do_erc=True), Pin(num='21',name='OMCK',do_erc=True), Pin(num='12',name='ILRCK',func=Pin.BIDIR,do_erc=True), Pin(num='22',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='13',name='ISCLK',func=Pin.BIDIR,do_erc=True), Pin(num='23',name='VL',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='SDIN',do_erc=True), Pin(num='24',name='H/~S',do_erc=True), Pin(num='25',name='TXN',func=Pin.OUTPUT,do_erc=True), Pin(num='26',name='TXP',func=Pin.OUTPUT,do_erc=True), Pin(num='1',name='COPY/C',do_erc=True), Pin(num='3',name='~EMPH',do_erc=True), Pin(num='4',name='SFMT0',do_erc=True), Pin(num='5',name='SFMT1',do_erc=True), Pin(num='10',name='APMS',do_erc=True), Pin(num='20',name='HWCK0',do_erc=True), Pin(num='11',name='TCBLD',do_erc=True), Pin(num='15',name='TCBL',func=Pin.BIDIR,do_erc=True), Pin(num='16',name='CEN',do_erc=True), Pin(num='17',name='U',do_erc=True), Pin(num='27',name='HWCK1',do_erc=True), Pin(num='18',name='V',do_erc=True), Pin(num='28',name='ORIG',do_erc=True), Pin(num='19',name='~AUDIO',func=Pin.OUTPUT,do_erc=True), Pin(num='1',name='SDA/CDOUT',do_erc=True), Pin(num='2',name='AD0/CS',do_erc=True), Pin(num='3',name='AD2',do_erc=True), Pin(num='4',name='RXP',do_erc=True), Pin(num='5',name='TEST',do_erc=True), Pin(num='10',name='TEST',do_erc=True), Pin(num='20',name='Bit_User',do_erc=True), Pin(num='11',name='TEST',do_erc=True), Pin(num='15',name='TCBL',func=Pin.BIDIR,do_erc=True), Pin(num='16',name='TEST',do_erc=True), Pin(num='17',name='TEST',do_erc=True), Pin(num='27',name='AD1/CDIN',do_erc=True), Pin(num='18',name='TEST',do_erc=True), Pin(num='28',name='SCL/CCLK',do_erc=True), Pin(num='19',name='INT',func=Pin.OUTPUT,do_erc=True)]), Part(name='CS8414',dest=TEMPLATE,tool=SKIDL,keywords='digital audio interface receiver spdif',description='96KHz Digital Audio Receiver',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='1',name='C',func=Pin.OUTPUT,do_erc=True), Pin(num='2',name='CD/F1',func=Pin.OUTPUT,do_erc=True), Pin(num='3',name='CC/F0',func=Pin.OUTPUT,do_erc=True), Pin(num='4',name='CB/E2',func=Pin.OUTPUT,do_erc=True), Pin(num='5',name='CA/E1',func=Pin.OUTPUT,do_erc=True), Pin(num='6',name='C0/E0',func=Pin.OUTPUT,do_erc=True), Pin(num='7',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='RXP',do_erc=True), Pin(num='10',name='RXN',do_erc=True), Pin(num='20',name='FILT',do_erc=True), Pin(num='11',name='FSYNC',func=Pin.BIDIR,do_erc=True), Pin(num='21',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='12',name='SCK',func=Pin.BIDIR,do_erc=True), Pin(num='22',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='13',name='CS12/FCK',do_erc=True), Pin(num='23',name='M0',do_erc=True), Pin(num='14',name='U',func=Pin.OUTPUT,do_erc=True), Pin(num='24',name='M1',do_erc=True), Pin(num='15',name='CBL',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='ERF',func=Pin.OUTPUT,do_erc=True), Pin(num='16',name='SEL',do_erc=True), Pin(num='26',name='SDATA',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='M3',do_erc=True), Pin(num='27',name='CE/F2',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='M2',do_erc=True), Pin(num='28',name='VERF',func=Pin.OUTPUT,do_erc=True), Pin(num='19',name='MCK',func=Pin.OUTPUT,do_erc=True)]), Part(name='CS8416-N',dest=TEMPLATE,tool=SKIDL,keywords='digital audio interface receiver spdif',description='192 kHz Digital Audio Interface Receiver (QFN-28)',ref_prefix='U',num_units=1,fplist=['QFN*28*'],do_erc=True,pins=[ Pin(num='1',name='RXP0',do_erc=True), Pin(num='2',name='RXN',do_erc=True), Pin(num='3',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='4',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='5',name='FILT',func=Pin.PASSIVE,do_erc=True), Pin(num='6',name='~RESET',do_erc=True), Pin(num='7',name='RXP4/RXSEL1',do_erc=True), Pin(num='8',name='RXP5/RXSEL0',do_erc=True), Pin(num='9',name='RXP6/TXSEL1',do_erc=True), Pin(num='10',name='RXP7/TXSEL0',do_erc=True), Pin(num='20',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='11',name='AD0/~CS~/NV/RERR',do_erc=True), Pin(num='21',name='RMCK',func=Pin.OUTPUT,do_erc=True), Pin(num='12',name='AD1/CDIN/~AUDIO',do_erc=True), Pin(num='22',name='OMCK',do_erc=True), Pin(num='13',name='SCL/CCLK/96KHZ',do_erc=True), Pin(num='23',name='SDOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='14',name='SDA/CDOUT/RCBL',func=Pin.BIDIR,do_erc=True), Pin(num='24',name='OSCLK',func=Pin.BIDIR,do_erc=True), Pin(num='15',name='U/AD2/GPO2',func=Pin.OUTPUT,do_erc=True), Pin(num='25',name='OLRCK',func=Pin.BIDIR,do_erc=True), Pin(num='16',name='C/GPO1',func=Pin.OUTPUT,do_erc=True), Pin(num='26',name='RXP3',do_erc=True), Pin(num='17',name='TX/GPO0',func=Pin.OUTPUT,do_erc=True), Pin(num='27',name='RXP2',do_erc=True), Pin(num='18',name='VL',func=Pin.PWRIN,do_erc=True), Pin(num='28',name='RXP1',do_erc=True), Pin(num='19',name='DGND',func=Pin.PWRIN,do_erc=True)]), Part(name='CS8416-Z',dest=TEMPLATE,tool=SKIDL,keywords='digital audio interface receiver spdif',description='192 kHz Digital Audio Interface Receiver (TSSOP-28)',ref_prefix='U',num_units=1,fplist=['SOIC*28*', '*SSOP*28*'],do_erc=True,aliases=['CS8416-S', 'CS8416'],pins=[ Pin(num='1',name='RXP3',do_erc=True), Pin(num='2',name='RXP2',do_erc=True), Pin(num='3',name='RXP1',do_erc=True), Pin(num='4',name='RXP0',do_erc=True), Pin(num='5',name='RXN',do_erc=True), Pin(num='6',name='VA',func=Pin.PWRIN,do_erc=True), Pin(num='7',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='FILT',func=Pin.PASSIVE,do_erc=True), Pin(num='9',name='~RESET',do_erc=True), Pin(num='10',name='RXP4/RXSEL1',do_erc=True), Pin(num='20',name='TX/GPO0',func=Pin.OUTPUT,do_erc=True), Pin(num='11',name='RXP5/RXSEL0',do_erc=True), Pin(num='21',name='VL',func=Pin.PWRIN,do_erc=True), Pin(num='12',name='RXP6/TXSEL1',do_erc=True), Pin(num='22',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='13',name='RXP7/TXSEL0',do_erc=True), Pin(num='23',name='VD',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='AD0/~CS~/NV/RERR',do_erc=True), Pin(num='24',name='RMCK',func=Pin.OUTPUT,do_erc=True), Pin(num='15',name='AD1/CDIN/~AUDIO',do_erc=True), Pin(num='25',name='OMCK',do_erc=True), Pin(num='16',name='SCL/CCLK/96KHZ',do_erc=True), Pin(num='26',name='SDOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='SDA/CDOUT/RCBL',func=Pin.BIDIR,do_erc=True), Pin(num='27',name='OSCLK',func=Pin.BIDIR,do_erc=True), Pin(num='18',name='U/AD2/GPO2',func=Pin.OUTPUT,do_erc=True), Pin(num='28',name='OLRCK',func=Pin.BIDIR,do_erc=True), Pin(num='19',name='C/GPO1',func=Pin.OUTPUT,do_erc=True)]), Part(name='CS8420_SOFT',dest=TEMPLATE,tool=SKIDL,keywords='digital audio sample rate converter transceiver',description='Digital Audio Sample Rate Converter and Transceiver',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='1',name='SDA/CDOUT',func=Pin.BIDIR,do_erc=True), Pin(num='2',name='AD0/CS-',do_erc=True), Pin(num='3',name='EMPH-/AD2',func=Pin.OUTPUT,do_erc=True), Pin(num='4',name='RXP',do_erc=True), Pin(num='5',name='RXN',do_erc=True), Pin(num='6',name='VA',do_erc=True), Pin(num='7',name='AGND',do_erc=True), Pin(num='8',name='FILT',do_erc=True), Pin(num='9',name='RST',do_erc=True), Pin(num='10',name='RMCK',func=Pin.BIDIR,do_erc=True), Pin(num='20',name='U',func=Pin.BIDIR,do_erc=True), Pin(num='11',name='RERR',func=Pin.OUTPUT,do_erc=True), Pin(num='21',name='OMCK',do_erc=True), Pin(num='12',name='ILRCK',func=Pin.BIDIR,do_erc=True), Pin(num='22',name='DGND',do_erc=True), Pin(num='13',name='ISCLK',func=Pin.BIDIR,do_erc=True), Pin(num='23',name='VD',do_erc=True), Pin(num='14',name='SDIN',do_erc=True), Pin(num='24',name='H/S-',do_erc=True), Pin(num='15',name='TCBL',func=Pin.BIDIR,do_erc=True), Pin(num='25',name='TXN',func=Pin.OUTPUT,do_erc=True), Pin(num='16',name='OSCLK',func=Pin.BIDIR,do_erc=True), Pin(num='26',name='TXP',func=Pin.OUTPUT,do_erc=True), Pin(num='17',name='OLRCK',func=Pin.BIDIR,do_erc=True), Pin(num='27',name='AD1/CDIN',do_erc=True), Pin(num='18',name='SDOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='28',name='SCL/CCLK',func=Pin.BIDIR,do_erc=True), Pin(num='19',name='INT',func=Pin.OPENCOLL,do_erc=True)]), Part(name='LM4811',dest=TEMPLATE,tool=SKIDL,keywords='headphone amplifier digital volume',description='Dual105mW Headphone Amplifier, Digital Volume Control, Shutdown Mode',ref_prefix='U',num_units=1,fplist=['VSSOP*', 'WSON*', 'SON*'],do_erc=True,pins=[ Pin(num='1',name='VOUT1',func=Pin.OUTPUT,do_erc=True), Pin(num='2',name='VIN1',do_erc=True), Pin(num='3',name='BYPASS',func=Pin.PASSIVE,do_erc=True), Pin(num='4',name='CLOCK',do_erc=True), Pin(num='5',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='6',name='UP/DN',do_erc=True), Pin(num='7',name='SHDN',do_erc=True), Pin(num='8',name='VIN2',do_erc=True), Pin(num='9',name='VOUT2',func=Pin.OUTPUT,do_erc=True), Pin(num='10',name='VDD',func=Pin.PWRIN,do_erc=True)]), Part(name='TLV320AIC23BPW',dest=TEMPLATE,tool=SKIDL,keywords='Stero Audio CODEC 96kHz Headphone',description='8-96kHz Stero Audio CODEC w/ Headphone Amp, TSSOP28',ref_prefix='U',num_units=1,fplist=['TSSOP*'],do_erc=True,pins=[ Pin(num='1',name='BVDD',func=Pin.PWRIN,do_erc=True), Pin(num='2',name='CLKOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='3',name='BCLK',func=Pin.BIDIR,do_erc=True), Pin(num='4',name='DIN',do_erc=True), Pin(num='5',name='LRCIN',do_erc=True), Pin(num='6',name='DOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='7',name='LRCOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='8',name='HPVDD',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='LHPOUT',func=Pin.PASSIVE,do_erc=True), Pin(num='10',name='RHPOUT',func=Pin.PASSIVE,do_erc=True), Pin(num='20',name='LLINEIN',func=Pin.PASSIVE,do_erc=True), Pin(num='11',name='HPGND',func=Pin.PWRIN,do_erc=True), Pin(num='21',name='~CS~',do_erc=True), Pin(num='12',name='LOUT',func=Pin.PASSIVE,do_erc=True), Pin(num='22',name='MODE',do_erc=True), Pin(num='13',name='ROUT',func=Pin.PASSIVE,do_erc=True), Pin(num='23',name='SDIN',do_erc=True), Pin(num='14',name='AVDD',func=Pin.PWRIN,do_erc=True), Pin(num='24',name='SCLK',do_erc=True), Pin(num='15',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='25',name='XTI/MCK',func=Pin.PASSIVE,do_erc=True), Pin(num='16',name='VMID',func=Pin.PASSIVE,do_erc=True), Pin(num='26',name='XTO',func=Pin.PASSIVE,do_erc=True), Pin(num='17',name='MICBIAS',func=Pin.PASSIVE,do_erc=True), Pin(num='27',name='DVDD',func=Pin.PWRIN,do_erc=True), Pin(num='18',name='MICIN',func=Pin.PASSIVE,do_erc=True), Pin(num='28',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='19',name='RLINEIN',func=Pin.PASSIVE,do_erc=True)]), Part(name='TLV320AIC23BRHD',dest=TEMPLATE,tool=SKIDL,keywords='Stero Audio CODEC 96kHz Headphone',description='8-96kHz Stero Audio CODEC w/ Headphone Amp, QFN28',ref_prefix='U',num_units=1,fplist=['QFN*'],do_erc=True,pins=[ Pin(num='1',name='DIN',do_erc=True), Pin(num='2',name='LRCIN',do_erc=True), Pin(num='3',name='DOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='4',name='LRCOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='5',name='HPVDD',func=Pin.PWRIN,do_erc=True), Pin(num='6',name='LHPOUT',func=Pin.PASSIVE,do_erc=True), Pin(num='7',name='RHPOUT',func=Pin.PASSIVE,do_erc=True), Pin(num='8',name='HPGND',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='LOUT',func=Pin.PASSIVE,do_erc=True), Pin(num='10',name='ROUT',func=Pin.PASSIVE,do_erc=True), Pin(num='20',name='SDIN',do_erc=True), Pin(num='11',name='AVDD',func=Pin.PWRIN,do_erc=True), Pin(num='21',name='SCLK',do_erc=True), Pin(num='12',name='AGND',func=Pin.PWRIN,do_erc=True), Pin(num='22',name='XTI/MCK',func=Pin.PASSIVE,do_erc=True), Pin(num='13',name='VMID',func=Pin.PASSIVE,do_erc=True), Pin(num='23',name='XTO',func=Pin.PASSIVE,do_erc=True), Pin(num='14',name='MICBIAS',func=Pin.PASSIVE,do_erc=True), Pin(num='24',name='DVDD',func=Pin.PWRIN,do_erc=True), Pin(num='15',name='MICIN',func=Pin.PASSIVE,do_erc=True), Pin(num='25',name='DGND',func=Pin.PWRIN,do_erc=True), Pin(num='16',name='RLINEIN',func=Pin.PASSIVE,do_erc=True), Pin(num='26',name='BVDD',func=Pin.PWRIN,do_erc=True), Pin(num='17',name='LLINEIN',func=Pin.PASSIVE,do_erc=True), Pin(num='27',name='CLKOUT',func=Pin.OUTPUT,do_erc=True), Pin(num='18',name='~CS~',do_erc=True), Pin(num='28',name='BCLK',func=Pin.BIDIR,do_erc=True), Pin(num='19',name='MODE',do_erc=True)]), Part(name='TPA5050',dest=TEMPLATE,tool=SKIDL,keywords='AUDIO',description='Stereo Digital Audio Delay Processor With I2C Control',ref_prefix='U',num_units=1,do_erc=True,pins=[ Pin(num='1',name='LRCLK',do_erc=True), Pin(num='2',name='DATA',do_erc=True), Pin(num='3',name='SCL',do_erc=True), Pin(num='4',name='SDA',func=Pin.BIDIR,do_erc=True), Pin(num='5',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='6',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='7',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='8',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='9',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='10',name='ADD0',do_erc=True), Pin(num='11',name='ADD1',do_erc=True), Pin(num='12',name='ADD2',do_erc=True), Pin(num='13',name='VDD',func=Pin.PWRIN,do_erc=True), Pin(num='14',name='GND',func=Pin.PWRIN,do_erc=True), Pin(num='15',name='DATA_OUT',func=Pin.OUTPUT,do_erc=True), Pin(num='16',name='BCLK',do_erc=True)])])

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py

Python

silver/migrations/0001_initial.py

atkinsond/silver

7e88db324ea7380dbc1b03cf18911a614a51e2b3

[ "Apache-2.0" ]

null

silver/migrations/0001_initial.py

atkinsond/silver

7e88db324ea7380dbc1b03cf18911a614a51e2b3

[ "Apache-2.0" ]

null

silver/migrations/0001_initial.py

atkinsond/silver

7e88db324ea7380dbc1b03cf18911a614a51e2b3

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.13 on 2018-06-18 06:25 from __future__ import unicode_literals import annoying.fields from decimal import Decimal import django.core.validators from django.db import migrations, models import django.db.models.deletion import django.utils.timezone import django_fsm import json import livefield.fields import silver.models.documents.base import silver.models.documents.pdf import silver.utils.models import uuid class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='BillingDocumentBase', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('kind', models.CharField(db_index=True, max_length=8, verbose_name=silver.models.documents.base.get_billing_documents_kinds)), ('series', models.CharField(blank=True, db_index=True, max_length=20, null=True)), ('number', models.IntegerField(blank=True, db_index=True, null=True)), ('archived_customer', annoying.fields.JSONField(blank=True, default=dict, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('archived_provider', annoying.fields.JSONField(blank=True, default=dict, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('due_date', models.DateField(blank=True, null=True)), ('issue_date', models.DateField(blank=True, db_index=True, null=True)), ('paid_date', models.DateField(blank=True, null=True)), ('cancel_date', models.DateField(blank=True, null=True)), ('sales_tax_percent', models.DecimalField(blank=True, decimal_places=2, max_digits=4, null=True, validators=[django.core.validators.MinValueValidator(0.0)])), ('sales_tax_name', models.CharField(blank=True, max_length=64, null=True)), ('currency', models.CharField(choices=[('AED', 'AED (UAE Dirham)'), ('AFN', 'AFN (Afghani)'), ('ALL', 'ALL (Lek)'), ('AMD', 'AMD (Armenian Dram)'), ('ANG', 'ANG (Netherlands Antillean Guilder)'), ('AOA', 'AOA (Kwanza)'), ('ARS', 'ARS (Argentine Peso)'), ('AUD', 'AUD (Australian Dollar)'), ('AWG', 'AWG (Aruban Florin)'), ('AZN', 'AZN (Azerbaijanian Manat)'), ('BAM', 'BAM (Convertible Mark)'), ('BBD', 'BBD (Barbados Dollar)'), ('BDT', 'BDT (Taka)'), ('BGN', 'BGN (Bulgarian Lev)'), ('BHD', 'BHD (Bahraini Dinar)'), ('BIF', 'BIF (Burundi Franc)'), ('BMD', 'BMD (Bermudian Dollar)'), ('BND', 'BND (Brunei Dollar)'), ('BOB', 'BOB (Boliviano)'), ('BRL', 'BRL (Brazilian Real)'), ('BSD', 'BSD (Bahamian Dollar)'), ('BTN', 'BTN (Ngultrum)'), ('BWP', 'BWP (Pula)'), ('BYN', 'BYN (Belarusian Ruble)'), ('BZD', 'BZD (Belize Dollar)'), ('CAD', 'CAD (Canadian Dollar)'), ('CDF', 'CDF (Congolese Franc)'), ('CHF', 'CHF (Swiss Franc)'), ('CLP', 'CLP (Chilean Peso)'), ('CNY', 'CNY (Yuan Renminbi)'), ('COP', 'COP (Colombian Peso)'), ('CRC', 'CRC (Costa Rican Colon)'), ('CUC', 'CUC (Peso Convertible)'), ('CUP', 'CUP (Cuban Peso)'), ('CVE', 'CVE (Cabo Verde Escudo)'), ('CZK', 'CZK (Czech Koruna)'), ('DJF', 'DJF (Djibouti Franc)'), ('DKK', 'DKK (Danish Krone)'), ('DOP', 'DOP (Dominican Peso)'), ('DZD', 'DZD (Algerian Dinar)'), ('EGP', 'EGP (Egyptian Pound)'), ('ERN', 'ERN (Nakfa)'), ('ETB', 'ETB (Ethiopian Birr)'), ('EUR', 'EUR (Euro)'), ('FJD', 'FJD (Fiji Dollar)'), ('FKP', 'FKP (Falkland Islands Pound)'), ('GBP', 'GBP (Pound Sterling)'), ('GEL', 'GEL (Lari)'), ('GHS', 'GHS (Ghana Cedi)'), ('GIP', 'GIP (Gibraltar Pound)'), ('GMD', 'GMD (Dalasi)'), ('GNF', 'GNF (Guinea Franc)'), ('GTQ', 'GTQ (Quetzal)'), ('GYD', 'GYD (Guyana Dollar)'), ('HKD', 'HKD (Hong Kong Dollar)'), ('HNL', 'HNL (Lempira)'), ('HRK', 'HRK (Kuna)'), ('HTG', 'HTG (Gourde)'), ('HUF', 'HUF (Forint)'), ('IDR', 'IDR (Rupiah)'), ('ILS', 'ILS (New Israeli Sheqel)'), ('INR', 'INR (Indian Rupee)'), ('IQD', 'IQD (Iraqi Dinar)'), ('IRR', 'IRR (Iranian Rial)'), ('ISK', 'ISK (Iceland Krona)'), ('JMD', 'JMD (Jamaican Dollar)'), ('JOD', 'JOD (Jordanian Dinar)'), ('JPY', 'JPY (Yen)'), ('KES', 'KES (Kenyan Shilling)'), ('KGS', 'KGS (Som)'), ('KHR', 'KHR (Riel)'), ('KMF', 'KMF (Comoro Franc)'), ('KPW', 'KPW (North Korean Won)'), ('KRW', 'KRW (Won)'), ('KWD', 'KWD (Kuwaiti Dinar)'), ('KYD', 'KYD (Cayman Islands Dollar)'), ('KZT', 'KZT (Tenge)'), ('LAK', 'LAK (Kip)'), ('LBP', 'LBP (Lebanese Pound)'), ('LKR', 'LKR (Sri Lanka Rupee)'), ('LRD', 'LRD (Liberian Dollar)'), ('LSL', 'LSL (Loti)'), ('LYD', 'LYD (Libyan Dinar)'), ('MAD', 'MAD (Moroccan Dirham)'), ('MDL', 'MDL (Moldovan Leu)'), ('MGA', 'MGA (Malagasy Ariary)'), ('MKD', 'MKD (Denar)'), ('MMK', 'MMK (Kyat)'), ('MNT', 'MNT (Tugrik)'), ('MOP', 'MOP (Pataca)'), ('MRO', 'MRO (Ouguiya)'), ('MUR', 'MUR (Mauritius Rupee)'), ('MVR', 'MVR (Rufiyaa)'), ('MWK', 'MWK (Malawi Kwacha)'), ('MXN', 'MXN (Mexican Peso)'), ('MYR', 'MYR (Malaysian Ringgit)'), ('MZN', 'MZN (Mozambique Metical)'), ('NAD', 'NAD (Namibia Dollar)'), ('NGN', 'NGN (Naira)'), ('NIO', 'NIO (Cordoba Oro)'), ('NOK', 'NOK (Norwegian Krone)'), ('NPR', 'NPR (Nepalese Rupee)'), ('NZD', 'NZD (New Zealand Dollar)'), ('OMR', 'OMR (Rial Omani)'), ('PAB', 'PAB (Balboa)'), ('PEN', 'PEN (Sol)'), ('PGK', 'PGK (Kina)'), ('PHP', 'PHP (Philippine Peso)'), ('PKR', 'PKR (Pakistan Rupee)'), ('PLN', 'PLN (Zloty)'), ('PYG', 'PYG (Guarani)'), ('QAR', 'QAR (Qatari Rial)'), ('RON', 'RON (Romanian Leu)'), ('RSD', 'RSD (Serbian Dinar)'), ('RUB', 'RUB (Russian Ruble)'), ('RWF', 'RWF (Rwanda Franc)'), ('SAR', 'SAR (Saudi Riyal)'), ('SBD', 'SBD (Solomon Islands Dollar)'), ('SCR', 'SCR (Seychelles Rupee)'), ('SDG', 'SDG (Sudanese Pound)'), ('SEK', 'SEK (Swedish Krona)'), ('SGD', 'SGD (Singapore Dollar)'), ('SHP', 'SHP (Saint Helena Pound)'), ('SLL', 'SLL (Leone)'), ('SOS', 'SOS (Somali Shilling)'), ('SRD', 'SRD (Surinam Dollar)'), ('SSP', 'SSP (South Sudanese Pound)'), ('STD', 'STD (Dobra)'), ('SVC', 'SVC (El Salvador Colon)'), ('SYP', 'SYP (Syrian Pound)'), ('SZL', 'SZL (Lilangeni)'), ('THB', 'THB (Baht)'), ('TJS', 'TJS (Somoni)'), ('TMT', 'TMT (Turkmenistan New Manat)'), ('TND', 'TND (Tunisian Dinar)'), ('TOP', 'TOP (Pa’anga)'), ('TRY', 'TRY (Turkish Lira)'), ('TTD', 'TTD (Trinidad and Tobago Dollar)'), ('TWD', 'TWD (New Taiwan Dollar)'), ('TZS', 'TZS (Tanzanian Shilling)'), ('UAH', 'UAH (Hryvnia)'), ('UGX', 'UGX (Uganda Shilling)'), ('USD', 'USD (US Dollar)'), ('UYU', 'UYU (Peso Uruguayo)'), ('UZS', 'UZS (Uzbekistan Sum)'), ('VEF', 'VEF (Bolívar)'), ('VND', 'VND (Dong)'), ('VUV', 'VUV (Vatu)'), ('WST', 'WST (Tala)'), ('XAF', 'XAF (CFA Franc BEAC)'), ('XAG', 'XAG (Silver)'), ('XAU', 'XAU (Gold)'), ('XBA', 'XBA (Bond Markets Unit European Composite Unit (EURCO))'), ('XBB', 'XBB (Bond Markets Unit European Monetary Unit (E.M.U.-6))'), ('XBC', 'XBC (Bond Markets Unit European Unit of Account 9 (E.U.A.-9))'), ('XBD', 'XBD (Bond Markets Unit European Unit of Account 17 (E.U.A.-17))'), ('XCD', 'XCD (East Caribbean Dollar)'), ('XDR', 'XDR (SDR (Special Drawing Right))'), ('XOF', 'XOF (CFA Franc BCEAO)'), ('XPD', 'XPD (Palladium)'), ('XPF', 'XPF (CFP Franc)'), ('XPT', 'XPT (Platinum)'), ('XSU', 'XSU (Sucre)'), ('XTS', 'XTS (Codes specifically reserved for testing purposes)'), ('XUA', 'XUA (ADB Unit of Account)'), ('XXX', 'XXX (The codes assigned for transactions where no currency is involved)'), ('YER', 'YER (Yemeni Rial)'), ('ZAR', 'ZAR (Rand)'), ('ZMW', 'ZMW (Zambian Kwacha)'), ('ZWL', 'ZWL (Zimbabwe Dollar)')], default='USD', help_text='The currency used for billing.', max_length=4)), ('transaction_currency', models.CharField(choices=[('AED', 'AED (UAE Dirham)'), ('AFN', 'AFN (Afghani)'), ('ALL', 'ALL (Lek)'), ('AMD', 'AMD (Armenian Dram)'), ('ANG', 'ANG (Netherlands Antillean Guilder)'), ('AOA', 'AOA (Kwanza)'), ('ARS', 'ARS (Argentine Peso)'), ('AUD', 'AUD (Australian Dollar)'), ('AWG', 'AWG (Aruban Florin)'), ('AZN', 'AZN (Azerbaijanian Manat)'), ('BAM', 'BAM (Convertible Mark)'), ('BBD', 'BBD (Barbados Dollar)'), ('BDT', 'BDT (Taka)'), ('BGN', 'BGN (Bulgarian Lev)'), ('BHD', 'BHD (Bahraini Dinar)'), ('BIF', 'BIF (Burundi Franc)'), ('BMD', 'BMD (Bermudian Dollar)'), ('BND', 'BND (Brunei Dollar)'), ('BOB', 'BOB (Boliviano)'), ('BRL', 'BRL (Brazilian Real)'), ('BSD', 'BSD (Bahamian Dollar)'), ('BTN', 'BTN (Ngultrum)'), ('BWP', 'BWP (Pula)'), ('BYN', 'BYN (Belarusian Ruble)'), ('BZD', 'BZD (Belize Dollar)'), ('CAD', 'CAD (Canadian Dollar)'), ('CDF', 'CDF (Congolese Franc)'), ('CHF', 'CHF (Swiss Franc)'), ('CLP', 'CLP (Chilean Peso)'), ('CNY', 'CNY (Yuan Renminbi)'), ('COP', 'COP (Colombian Peso)'), ('CRC', 'CRC (Costa Rican Colon)'), ('CUC', 'CUC (Peso Convertible)'), ('CUP', 'CUP (Cuban Peso)'), ('CVE', 'CVE (Cabo Verde Escudo)'), ('CZK', 'CZK (Czech Koruna)'), ('DJF', 'DJF (Djibouti Franc)'), ('DKK', 'DKK (Danish Krone)'), ('DOP', 'DOP (Dominican Peso)'), ('DZD', 'DZD (Algerian Dinar)'), ('EGP', 'EGP (Egyptian Pound)'), ('ERN', 'ERN (Nakfa)'), ('ETB', 'ETB (Ethiopian Birr)'), ('EUR', 'EUR (Euro)'), ('FJD', 'FJD (Fiji Dollar)'), ('FKP', 'FKP (Falkland Islands Pound)'), ('GBP', 'GBP (Pound Sterling)'), ('GEL', 'GEL (Lari)'), ('GHS', 'GHS (Ghana Cedi)'), ('GIP', 'GIP (Gibraltar Pound)'), ('GMD', 'GMD (Dalasi)'), ('GNF', 'GNF (Guinea Franc)'), ('GTQ', 'GTQ (Quetzal)'), ('GYD', 'GYD (Guyana Dollar)'), ('HKD', 'HKD (Hong Kong Dollar)'), ('HNL', 'HNL (Lempira)'), ('HRK', 'HRK (Kuna)'), ('HTG', 'HTG (Gourde)'), ('HUF', 'HUF (Forint)'), ('IDR', 'IDR (Rupiah)'), ('ILS', 'ILS (New Israeli Sheqel)'), ('INR', 'INR (Indian Rupee)'), ('IQD', 'IQD (Iraqi Dinar)'), ('IRR', 'IRR (Iranian Rial)'), ('ISK', 'ISK (Iceland Krona)'), ('JMD', 'JMD (Jamaican Dollar)'), ('JOD', 'JOD (Jordanian Dinar)'), ('JPY', 'JPY (Yen)'), ('KES', 'KES (Kenyan Shilling)'), ('KGS', 'KGS (Som)'), ('KHR', 'KHR (Riel)'), ('KMF', 'KMF (Comoro Franc)'), ('KPW', 'KPW (North Korean Won)'), ('KRW', 'KRW (Won)'), ('KWD', 'KWD (Kuwaiti Dinar)'), ('KYD', 'KYD (Cayman Islands Dollar)'), ('KZT', 'KZT (Tenge)'), ('LAK', 'LAK (Kip)'), ('LBP', 'LBP (Lebanese Pound)'), ('LKR', 'LKR (Sri Lanka Rupee)'), ('LRD', 'LRD (Liberian Dollar)'), ('LSL', 'LSL (Loti)'), ('LYD', 'LYD (Libyan Dinar)'), ('MAD', 'MAD (Moroccan Dirham)'), ('MDL', 'MDL (Moldovan Leu)'), ('MGA', 'MGA (Malagasy Ariary)'), ('MKD', 'MKD (Denar)'), ('MMK', 'MMK (Kyat)'), ('MNT', 'MNT (Tugrik)'), ('MOP', 'MOP (Pataca)'), ('MRO', 'MRO (Ouguiya)'), ('MUR', 'MUR (Mauritius Rupee)'), ('MVR', 'MVR (Rufiyaa)'), ('MWK', 'MWK (Malawi Kwacha)'), ('MXN', 'MXN (Mexican Peso)'), ('MYR', 'MYR (Malaysian Ringgit)'), ('MZN', 'MZN (Mozambique Metical)'), ('NAD', 'NAD (Namibia Dollar)'), ('NGN', 'NGN (Naira)'), ('NIO', 'NIO (Cordoba Oro)'), ('NOK', 'NOK (Norwegian Krone)'), ('NPR', 'NPR (Nepalese Rupee)'), ('NZD', 'NZD (New Zealand Dollar)'), ('OMR', 'OMR (Rial Omani)'), ('PAB', 'PAB (Balboa)'), ('PEN', 'PEN (Sol)'), ('PGK', 'PGK (Kina)'), ('PHP', 'PHP (Philippine Peso)'), ('PKR', 'PKR (Pakistan Rupee)'), ('PLN', 'PLN (Zloty)'), ('PYG', 'PYG (Guarani)'), ('QAR', 'QAR (Qatari Rial)'), ('RON', 'RON (Romanian Leu)'), ('RSD', 'RSD (Serbian Dinar)'), ('RUB', 'RUB (Russian Ruble)'), ('RWF', 'RWF (Rwanda Franc)'), ('SAR', 'SAR (Saudi Riyal)'), ('SBD', 'SBD (Solomon Islands Dollar)'), ('SCR', 'SCR (Seychelles Rupee)'), ('SDG', 'SDG (Sudanese Pound)'), ('SEK', 'SEK (Swedish Krona)'), ('SGD', 'SGD (Singapore Dollar)'), ('SHP', 'SHP (Saint Helena Pound)'), ('SLL', 'SLL (Leone)'), ('SOS', 'SOS (Somali Shilling)'), ('SRD', 'SRD (Surinam Dollar)'), ('SSP', 'SSP (South Sudanese Pound)'), ('STD', 'STD (Dobra)'), ('SVC', 'SVC (El Salvador Colon)'), ('SYP', 'SYP (Syrian Pound)'), ('SZL', 'SZL (Lilangeni)'), ('THB', 'THB (Baht)'), ('TJS', 'TJS (Somoni)'), ('TMT', 'TMT (Turkmenistan New Manat)'), ('TND', 'TND (Tunisian Dinar)'), ('TOP', 'TOP (Pa’anga)'), ('TRY', 'TRY (Turkish Lira)'), ('TTD', 'TTD (Trinidad and Tobago Dollar)'), ('TWD', 'TWD (New Taiwan Dollar)'), ('TZS', 'TZS (Tanzanian Shilling)'), ('UAH', 'UAH (Hryvnia)'), ('UGX', 'UGX (Uganda Shilling)'), ('USD', 'USD (US Dollar)'), ('UYU', 'UYU (Peso Uruguayo)'), ('UZS', 'UZS (Uzbekistan Sum)'), ('VEF', 'VEF (Bolívar)'), ('VND', 'VND (Dong)'), ('VUV', 'VUV (Vatu)'), ('WST', 'WST (Tala)'), ('XAF', 'XAF (CFA Franc BEAC)'), ('XAG', 'XAG (Silver)'), ('XAU', 'XAU (Gold)'), ('XBA', 'XBA (Bond Markets Unit European Composite Unit (EURCO))'), ('XBB', 'XBB (Bond Markets Unit European Monetary Unit (E.M.U.-6))'), ('XBC', 'XBC (Bond Markets Unit European Unit of Account 9 (E.U.A.-9))'), ('XBD', 'XBD (Bond Markets Unit European Unit of Account 17 (E.U.A.-17))'), ('XCD', 'XCD (East Caribbean Dollar)'), ('XDR', 'XDR (SDR (Special Drawing Right))'), ('XOF', 'XOF (CFA Franc BCEAO)'), ('XPD', 'XPD (Palladium)'), ('XPF', 'XPF (CFP Franc)'), ('XPT', 'XPT (Platinum)'), ('XSU', 'XSU (Sucre)'), ('XTS', 'XTS (Codes specifically reserved for testing purposes)'), ('XUA', 'XUA (ADB Unit of Account)'), ('XXX', 'XXX (The codes assigned for transactions where no currency is involved)'), ('YER', 'YER (Yemeni Rial)'), ('ZAR', 'ZAR (Rand)'), ('ZMW', 'ZMW (Zambian Kwacha)'), ('ZWL', 'ZWL (Zimbabwe Dollar)')], help_text='The currency used when making a transaction.', max_length=4)), ('transaction_xe_rate', models.DecimalField(blank=True, decimal_places=4, help_text='Currency exchange rate from document currency to transaction_currency.', max_digits=16, null=True)), ('transaction_xe_date', models.DateField(blank=True, help_text='Date of the transaction exchange rate.', null=True)), ('state', django_fsm.FSMField(choices=[('draft', 'Draft'), ('issued', 'Issued'), ('paid', 'Paid'), ('canceled', 'Canceled')], default='draft', help_text='The state the invoice is in.', max_length=10, verbose_name='State')), ('_total', models.DecimalField(blank=True, decimal_places=2, max_digits=19, null=True)), ('_total_in_transaction_currency', models.DecimalField(blank=True, decimal_places=2, max_digits=19, null=True)), ], options={ 'ordering': ('-issue_date', 'series', '-number'), }, ), migrations.CreateModel( name='BillingLog', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('billing_date', models.DateField(help_text='The date when the invoice/proforma was issued.')), ('plan_billed_up_to', models.DateField(help_text='The date up to which the plan base amount has been billed.')), ('metered_features_billed_up_to', models.DateField(help_text='The date up to which the metered features have been billed.')), ('total', models.DecimalField(blank=True, decimal_places=2, max_digits=12, null=True)), ('plan_amount', models.DecimalField(blank=True, decimal_places=2, max_digits=12, null=True)), ('metered_features_amount', models.DecimalField(blank=True, decimal_places=2, max_digits=12, null=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('invoice', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='invoice_billing_logs', to='silver.BillingDocumentBase')), ('proforma', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='proforma_billing_logs', to='silver.BillingDocumentBase')), ], options={ 'ordering': ['-billing_date'], }, ), migrations.CreateModel( name='Customer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('live', livefield.fields.LiveField(default=True)), ('company', models.CharField(blank=True, max_length=128, null=True)), ('address_1', models.CharField(max_length=128)), ('address_2', models.CharField(blank=True, max_length=128, null=True)), ('country', models.CharField(choices=[('AD', 'Andorra'), ('AE', 'United Arab Emirates'), ('AF', 'Afghanistan'), ('AG', 'Antigua and Barbuda'), ('AI', 'Anguilla'), ('AL', 'Albania'), ('AM', 'Armenia'), ('AO', 'Angola'), ('AQ', 'Antarctica'), ('AR', 'Argentina'), ('AS', 'American Samoa'), ('AT', 'Austria'), ('AU', 'Australia'), ('AW', 'Aruba'), ('AX', 'Åland Islands'), ('AZ', 'Azerbaijan'), ('BA', 'Bosnia and Herzegovina'), ('BB', 'Barbados'), ('BD', 'Bangladesh'), ('BE', 'Belgium'), ('BF', 'Burkina Faso'), ('BG', 'Bulgaria'), ('BH', 'Bahrain'), ('BI', 'Burundi'), ('BJ', 'Benin'), ('BL', 'Saint Barthélemy'), ('BM', 'Bermuda'), ('BN', 'Brunei Darussalam'), ('BO', 'Bolivia, Plurinational State of'), ('BQ', 'Bonaire, Sint Eustatius and Saba'), ('BR', 'Brazil'), ('BS', 'Bahamas'), ('BT', 'Bhutan'), ('BV', 'Bouvet Island'), ('BW', 'Botswana'), ('BY', 'Belarus'), ('BZ', 'Belize'), ('CA', 'Canada'), ('CC', 'Cocos (Keeling) Islands'), ('CD', 'Congo, The Democratic Republic of the'), ('CF', 'Central African Republic'), ('CG', 'Congo'), ('CH', 'Switzerland'), ('CI', "Côte d'Ivoire"), ('CK', 'Cook Islands'), ('CL', 'Chile'), ('CM', 'Cameroon'), ('CN', 'China'), ('CO', 'Colombia'), ('CR', 'Costa Rica'), ('CU', 'Cuba'), ('CV', 'Cabo Verde'), ('CW', 'Curaçao'), ('CX', 'Christmas Island'), ('CY', 'Cyprus'), ('CZ', 'Czechia'), ('DE', 'Germany'), ('DJ', 'Djibouti'), ('DK', 'Denmark'), ('DM', 'Dominica'), ('DO', 'Dominican Republic'), ('DZ', 'Algeria'), ('EC', 'Ecuador'), ('EE', 'Estonia'), ('EG', 'Egypt'), ('EH', 'Western Sahara'), ('ER', 'Eritrea'), ('ES', 'Spain'), ('ET', 'Ethiopia'), ('FI', 'Finland'), ('FJ', 'Fiji'), ('FK', 'Falkland Islands (Malvinas)'), ('FM', 'Micronesia, Federated States of'), ('FO', 'Faroe Islands'), ('FR', 'France'), ('GA', 'Gabon'), ('GB', 'United Kingdom'), ('GD', 'Grenada'), ('GE', 'Georgia'), ('GF', 'French Guiana'), ('GG', 'Guernsey'), ('GH', 'Ghana'), ('GI', 'Gibraltar'), ('GL', 'Greenland'), ('GM', 'Gambia'), ('GN', 'Guinea'), ('GP', 'Guadeloupe'), ('GQ', 'Equatorial Guinea'), ('GR', 'Greece'), ('GS', 'South Georgia and the South Sandwich Islands'), ('GT', 'Guatemala'), ('GU', 'Guam'), ('GW', 'Guinea-Bissau'), ('GY', 'Guyana'), ('HK', 'Hong Kong'), ('HM', 'Heard Island and McDonald Islands'), ('HN', 'Honduras'), ('HR', 'Croatia'), ('HT', 'Haiti'), ('HU', 'Hungary'), ('ID', 'Indonesia'), ('IE', 'Ireland'), ('IL', 'Israel'), ('IM', 'Isle of Man'), ('IN', 'India'), ('IO', 'British Indian Ocean Territory'), ('IQ', 'Iraq'), ('IR', 'Iran, Islamic Republic of'), ('IS', 'Iceland'), ('IT', 'Italy'), ('JE', 'Jersey'), ('JM', 'Jamaica'), ('JO', 'Jordan'), ('JP', 'Japan'), ('KE', 'Kenya'), ('KG', 'Kyrgyzstan'), ('KH', 'Cambodia'), ('KI', 'Kiribati'), ('KM', 'Comoros'), ('KN', 'Saint Kitts and Nevis'), ('KP', "Korea, Democratic People's Republic of"), ('KR', 'Korea, Republic of'), ('KW', 'Kuwait'), ('KY', 'Cayman Islands'), ('KZ', 'Kazakhstan'), ('LA', "Lao People's Democratic Republic"), ('LB', 'Lebanon'), ('LC', 'Saint Lucia'), ('LI', 'Liechtenstein'), ('LK', 'Sri Lanka'), ('LR', 'Liberia'), ('LS', 'Lesotho'), ('LT', 'Lithuania'), ('LU', 'Luxembourg'), ('LV', 'Latvia'), ('LY', 'Libya'), ('MA', 'Morocco'), ('MC', 'Monaco'), ('MD', 'Moldova, Republic of'), ('ME', 'Montenegro'), ('MF', 'Saint Martin (French part)'), ('MG', 'Madagascar'), ('MH', 'Marshall Islands'), ('MK', 'Macedonia, Republic of'), ('ML', 'Mali'), ('MM', 'Myanmar'), ('MN', 'Mongolia'), ('MO', 'Macao'), ('MP', 'Northern Mariana Islands'), ('MQ', 'Martinique'), ('MR', 'Mauritania'), ('MS', 'Montserrat'), ('MT', 'Malta'), ('MU', 'Mauritius'), ('MV', 'Maldives'), ('MW', 'Malawi'), ('MX', 'Mexico'), ('MY', 'Malaysia'), ('MZ', 'Mozambique'), ('NA', 'Namibia'), ('NC', 'New Caledonia'), ('NE', 'Niger'), ('NF', 'Norfolk Island'), ('NG', 'Nigeria'), ('NI', 'Nicaragua'), ('NL', 'Netherlands'), ('NO', 'Norway'), ('NP', 'Nepal'), ('NR', 'Nauru'), ('NU', 'Niue'), ('NZ', 'New Zealand'), ('OM', 'Oman'), ('PA', 'Panama'), ('PE', 'Peru'), ('PF', 'French Polynesia'), ('PG', 'Papua New Guinea'), ('PH', 'Philippines'), ('PK', 'Pakistan'), ('PL', 'Poland'), ('PM', 'Saint Pierre and Miquelon'), ('PN', 'Pitcairn'), ('PR', 'Puerto Rico'), ('PS', 'Palestine, State of'), ('PT', 'Portugal'), ('PW', 'Palau'), ('PY', 'Paraguay'), ('QA', 'Qatar'), ('RE', 'Réunion'), ('RO', 'Romania'), ('RS', 'Serbia'), ('RU', 'Russian Federation'), ('RW', 'Rwanda'), ('SA', 'Saudi Arabia'), ('SB', 'Solomon Islands'), ('SC', 'Seychelles'), ('SD', 'Sudan'), ('SE', 'Sweden'), ('SG', 'Singapore'), ('SH', 'Saint Helena, Ascension and Tristan da Cunha'), ('SI', 'Slovenia'), ('SJ', 'Svalbard and Jan Mayen'), ('SK', 'Slovakia'), ('SL', 'Sierra Leone'), ('SM', 'San Marino'), ('SN', 'Senegal'), ('SO', 'Somalia'), ('SR', 'Suriname'), ('SS', 'South Sudan'), ('ST', 'Sao Tome and Principe'), ('SV', 'El Salvador'), ('SX', 'Sint Maarten (Dutch part)'), ('SY', 'Syrian Arab Republic'), ('SZ', 'Swaziland'), ('TC', 'Turks and Caicos Islands'), ('TD', 'Chad'), ('TF', 'French Southern Territories'), ('TG', 'Togo'), ('TH', 'Thailand'), ('TJ', 'Tajikistan'), ('TK', 'Tokelau'), ('TL', 'Timor-Leste'), ('TM', 'Turkmenistan'), ('TN', 'Tunisia'), ('TO', 'Tonga'), ('TR', 'Turkey'), ('TT', 'Trinidad and Tobago'), ('TV', 'Tuvalu'), ('TW', 'Taiwan, Province of China'), ('TZ', 'Tanzania, United Republic of'), ('UA', 'Ukraine'), ('UG', 'Uganda'), ('UM', 'United States Minor Outlying Islands'), ('US', 'United States'), ('UY', 'Uruguay'), ('UZ', 'Uzbekistan'), ('VA', 'Holy See (Vatican City State)'), ('VC', 'Saint Vincent and the Grenadines'), ('VE', 'Venezuela, Bolivarian Republic of'), ('VG', 'Virgin Islands, British'), ('VI', 'Virgin Islands, U.S.'), ('VN', 'Viet Nam'), ('VU', 'Vanuatu'), ('WF', 'Wallis and Futuna'), ('WS', 'Samoa'), ('YE', 'Yemen'), ('YT', 'Mayotte'), ('ZA', 'South Africa'), ('ZM', 'Zambia'), ('ZW', 'Zimbabwe')], max_length=3)), ('phone', models.CharField(blank=True, max_length=32, null=True)), ('email', models.CharField(blank=True, max_length=254, null=True)), ('city', models.CharField(max_length=128)), ('state', models.CharField(blank=True, max_length=128, null=True)), ('zip_code', models.CharField(blank=True, max_length=32, null=True)), ('extra', models.TextField(blank=True, help_text='Extra information to display on the invoice (markdown formatted).', null=True)), ('meta', annoying.fields.JSONField(blank=True, default={}, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('first_name', models.CharField(help_text="The customer's first name.", max_length=128)), ('last_name', models.CharField(help_text="The customer's last name.", max_length=128)), ('payment_due_days', models.PositiveIntegerField(default=5, help_text='Due days for generated proforma/invoice.')), ('consolidated_billing', models.BooleanField(default=False, help_text='A flag indicating consolidated billing.')), ('customer_reference', models.CharField(blank=True, help_text="It's a reference to be passed between silver and clients. It usually points to an account ID.", max_length=256, null=True, validators=[django.core.validators.RegexValidator(message='Reference must not contain commas.', regex='^[^,]*$')])), ('sales_tax_number', models.CharField(blank=True, max_length=64, null=True)), ('sales_tax_percent', models.DecimalField(blank=True, decimal_places=2, help_text="Whenever to add sales tax. If null, it won't show up on the invoice.", max_digits=4, null=True, validators=[django.core.validators.MinValueValidator(0.0)])), ('sales_tax_name', models.CharField(blank=True, help_text="Sales tax name (eg. 'sales tax' or 'VAT').", max_length=64, null=True)), ('currency', models.CharField(blank=True, choices=[('AED', 'AED (UAE Dirham)'), ('AFN', 'AFN (Afghani)'), ('ALL', 'ALL (Lek)'), ('AMD', 'AMD (Armenian Dram)'), ('ANG', 'ANG (Netherlands Antillean Guilder)'), ('AOA', 'AOA (Kwanza)'), ('ARS', 'ARS (Argentine Peso)'), ('AUD', 'AUD (Australian Dollar)'), ('AWG', 'AWG (Aruban Florin)'), ('AZN', 'AZN (Azerbaijanian Manat)'), ('BAM', 'BAM (Convertible Mark)'), ('BBD', 'BBD (Barbados Dollar)'), ('BDT', 'BDT (Taka)'), ('BGN', 'BGN (Bulgarian Lev)'), ('BHD', 'BHD (Bahraini Dinar)'), ('BIF', 'BIF (Burundi Franc)'), ('BMD', 'BMD (Bermudian Dollar)'), ('BND', 'BND (Brunei Dollar)'), ('BOB', 'BOB (Boliviano)'), ('BRL', 'BRL (Brazilian Real)'), ('BSD', 'BSD (Bahamian Dollar)'), ('BTN', 'BTN (Ngultrum)'), ('BWP', 'BWP (Pula)'), ('BYN', 'BYN (Belarusian Ruble)'), ('BZD', 'BZD (Belize Dollar)'), ('CAD', 'CAD (Canadian Dollar)'), ('CDF', 'CDF (Congolese Franc)'), ('CHF', 'CHF (Swiss Franc)'), ('CLP', 'CLP (Chilean Peso)'), ('CNY', 'CNY (Yuan Renminbi)'), ('COP', 'COP (Colombian Peso)'), ('CRC', 'CRC (Costa Rican Colon)'), ('CUC', 'CUC (Peso Convertible)'), ('CUP', 'CUP (Cuban Peso)'), ('CVE', 'CVE (Cabo Verde Escudo)'), ('CZK', 'CZK (Czech Koruna)'), ('DJF', 'DJF (Djibouti Franc)'), ('DKK', 'DKK (Danish Krone)'), ('DOP', 'DOP (Dominican Peso)'), ('DZD', 'DZD (Algerian Dinar)'), ('EGP', 'EGP (Egyptian Pound)'), ('ERN', 'ERN (Nakfa)'), ('ETB', 'ETB (Ethiopian Birr)'), ('EUR', 'EUR (Euro)'), ('FJD', 'FJD (Fiji Dollar)'), ('FKP', 'FKP (Falkland Islands Pound)'), ('GBP', 'GBP (Pound Sterling)'), ('GEL', 'GEL (Lari)'), ('GHS', 'GHS (Ghana Cedi)'), ('GIP', 'GIP (Gibraltar Pound)'), ('GMD', 'GMD (Dalasi)'), ('GNF', 'GNF (Guinea Franc)'), ('GTQ', 'GTQ (Quetzal)'), ('GYD', 'GYD (Guyana Dollar)'), ('HKD', 'HKD (Hong Kong Dollar)'), ('HNL', 'HNL (Lempira)'), ('HRK', 'HRK (Kuna)'), ('HTG', 'HTG (Gourde)'), ('HUF', 'HUF (Forint)'), ('IDR', 'IDR (Rupiah)'), ('ILS', 'ILS (New Israeli Sheqel)'), ('INR', 'INR (Indian Rupee)'), ('IQD', 'IQD (Iraqi Dinar)'), ('IRR', 'IRR (Iranian Rial)'), ('ISK', 'ISK (Iceland Krona)'), ('JMD', 'JMD (Jamaican Dollar)'), ('JOD', 'JOD (Jordanian Dinar)'), ('JPY', 'JPY (Yen)'), ('KES', 'KES (Kenyan Shilling)'), ('KGS', 'KGS (Som)'), ('KHR', 'KHR (Riel)'), ('KMF', 'KMF (Comoro Franc)'), ('KPW', 'KPW (North Korean Won)'), ('KRW', 'KRW (Won)'), ('KWD', 'KWD (Kuwaiti Dinar)'), ('KYD', 'KYD (Cayman Islands Dollar)'), ('KZT', 'KZT (Tenge)'), ('LAK', 'LAK (Kip)'), ('LBP', 'LBP (Lebanese Pound)'), ('LKR', 'LKR (Sri Lanka Rupee)'), ('LRD', 'LRD (Liberian Dollar)'), ('LSL', 'LSL (Loti)'), ('LYD', 'LYD (Libyan Dinar)'), ('MAD', 'MAD (Moroccan Dirham)'), ('MDL', 'MDL (Moldovan Leu)'), ('MGA', 'MGA (Malagasy Ariary)'), ('MKD', 'MKD (Denar)'), ('MMK', 'MMK (Kyat)'), ('MNT', 'MNT (Tugrik)'), ('MOP', 'MOP (Pataca)'), ('MRO', 'MRO (Ouguiya)'), ('MUR', 'MUR (Mauritius Rupee)'), ('MVR', 'MVR (Rufiyaa)'), ('MWK', 'MWK (Malawi Kwacha)'), ('MXN', 'MXN (Mexican Peso)'), ('MYR', 'MYR (Malaysian Ringgit)'), ('MZN', 'MZN (Mozambique Metical)'), ('NAD', 'NAD (Namibia Dollar)'), ('NGN', 'NGN (Naira)'), ('NIO', 'NIO (Cordoba Oro)'), ('NOK', 'NOK (Norwegian Krone)'), ('NPR', 'NPR (Nepalese Rupee)'), ('NZD', 'NZD (New Zealand Dollar)'), ('OMR', 'OMR (Rial Omani)'), ('PAB', 'PAB (Balboa)'), ('PEN', 'PEN (Sol)'), ('PGK', 'PGK (Kina)'), ('PHP', 'PHP (Philippine Peso)'), ('PKR', 'PKR (Pakistan Rupee)'), ('PLN', 'PLN (Zloty)'), ('PYG', 'PYG (Guarani)'), ('QAR', 'QAR (Qatari Rial)'), ('RON', 'RON (Romanian Leu)'), ('RSD', 'RSD (Serbian Dinar)'), ('RUB', 'RUB (Russian Ruble)'), ('RWF', 'RWF (Rwanda Franc)'), ('SAR', 'SAR (Saudi Riyal)'), ('SBD', 'SBD (Solomon Islands Dollar)'), ('SCR', 'SCR (Seychelles Rupee)'), ('SDG', 'SDG (Sudanese Pound)'), ('SEK', 'SEK (Swedish Krona)'), ('SGD', 'SGD (Singapore Dollar)'), ('SHP', 'SHP (Saint Helena Pound)'), ('SLL', 'SLL (Leone)'), ('SOS', 'SOS (Somali Shilling)'), ('SRD', 'SRD (Surinam Dollar)'), ('SSP', 'SSP (South Sudanese Pound)'), ('STD', 'STD (Dobra)'), ('SVC', 'SVC (El Salvador Colon)'), ('SYP', 'SYP (Syrian Pound)'), ('SZL', 'SZL (Lilangeni)'), ('THB', 'THB (Baht)'), ('TJS', 'TJS (Somoni)'), ('TMT', 'TMT (Turkmenistan New Manat)'), ('TND', 'TND (Tunisian Dinar)'), ('TOP', 'TOP (Pa’anga)'), ('TRY', 'TRY (Turkish Lira)'), ('TTD', 'TTD (Trinidad and Tobago Dollar)'), ('TWD', 'TWD (New Taiwan Dollar)'), ('TZS', 'TZS (Tanzanian Shilling)'), ('UAH', 'UAH (Hryvnia)'), ('UGX', 'UGX (Uganda Shilling)'), ('USD', 'USD (US Dollar)'), ('UYU', 'UYU (Peso Uruguayo)'), ('UZS', 'UZS (Uzbekistan Sum)'), ('VEF', 'VEF (Bolívar)'), ('VND', 'VND (Dong)'), ('VUV', 'VUV (Vatu)'), ('WST', 'WST (Tala)'), ('XAF', 'XAF (CFA Franc BEAC)'), ('XAG', 'XAG (Silver)'), ('XAU', 'XAU (Gold)'), ('XBA', 'XBA (Bond Markets Unit European Composite Unit (EURCO))'), ('XBB', 'XBB (Bond Markets Unit European Monetary Unit (E.M.U.-6))'), ('XBC', 'XBC (Bond Markets Unit European Unit of Account 9 (E.U.A.-9))'), ('XBD', 'XBD (Bond Markets Unit European Unit of Account 17 (E.U.A.-17))'), ('XCD', 'XCD (East Caribbean Dollar)'), ('XDR', 'XDR (SDR (Special Drawing Right))'), ('XOF', 'XOF (CFA Franc BCEAO)'), ('XPD', 'XPD (Palladium)'), ('XPF', 'XPF (CFP Franc)'), ('XPT', 'XPT (Platinum)'), ('XSU', 'XSU (Sucre)'), ('XTS', 'XTS (Codes specifically reserved for testing purposes)'), ('XUA', 'XUA (ADB Unit of Account)'), ('XXX', 'XXX (The codes assigned for transactions where no currency is involved)'), ('YER', 'YER (Yemeni Rial)'), ('ZAR', 'ZAR (Rand)'), ('ZMW', 'ZMW (Zambian Kwacha)'), ('ZWL', 'ZWL (Zimbabwe Dollar)')], help_text='Used to enforce a certain currency when making transactionsfor the customer.', max_length=4, null=True)), ], options={ 'ordering': ['first_name', 'last_name', 'company'], }, ), migrations.CreateModel( name='DocumentEntry', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.CharField(max_length=1024)), ('unit', models.CharField(blank=True, max_length=1024, null=True)), ('quantity', models.DecimalField(decimal_places=4, max_digits=19, validators=[django.core.validators.MinValueValidator(0.0)])), ('unit_price', models.DecimalField(decimal_places=4, max_digits=19)), ('start_date', models.DateField(blank=True, null=True)), ('end_date', models.DateField(blank=True, null=True)), ('prorated', models.BooleanField(default=False)), ('invoice', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='invoice_entries', to='silver.BillingDocumentBase')), ], options={ 'verbose_name': 'Entry', 'verbose_name_plural': 'Entries', }, ), migrations.CreateModel( name='MeteredFeature', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(db_index=True, help_text='The feature display name.', max_length=200)), ('unit', models.CharField(max_length=20)), ('price_per_unit', models.DecimalField(decimal_places=4, help_text='The price per unit.', max_digits=19, validators=[django.core.validators.MinValueValidator(0.0)])), ('included_units', models.DecimalField(decimal_places=4, help_text='The number of included units per plan interval.', max_digits=19, validators=[django.core.validators.MinValueValidator(0.0)])), ('included_units_during_trial', models.DecimalField(blank=True, decimal_places=4, help_text='The number of included units during the trial period.', max_digits=19, null=True, validators=[django.core.validators.MinValueValidator(0.0)])), ], options={ 'ordering': ('name',), }, ), migrations.CreateModel( name='MeteredFeatureUnitsLog', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('consumed_units', models.DecimalField(decimal_places=4, max_digits=19, validators=[django.core.validators.MinValueValidator(0.0)])), ('start_date', models.DateField(editable=False)), ('end_date', models.DateField(editable=False)), ('metered_feature', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='consumed', to='silver.MeteredFeature')), ], ), migrations.CreateModel( name='PaymentMethod', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('payment_processor', models.CharField(choices=[('manual', 'manual')], max_length=256)), ('added_at', models.DateTimeField(default=django.utils.timezone.now)), ('data', annoying.fields.JSONField(blank=True, default={}, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('verified', models.BooleanField(default=False)), ('canceled', models.BooleanField(default=False)), ('valid_until', models.DateTimeField(blank=True, null=True)), ('display_info', models.CharField(blank=True, max_length=256, null=True)), ('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='silver.Customer')), ], options={ 'ordering': ['-id'], }, ), migrations.CreateModel( name='PDF', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('uuid', models.UUIDField(default=uuid.uuid4, unique=True)), ('pdf_file', models.FileField(blank=True, editable=False, null=True, upload_to=silver.models.documents.pdf.get_upload_path)), ('dirty', models.PositiveIntegerField(default=0)), ('upload_path', models.TextField(blank=True, null=True)), ], ), migrations.CreateModel( name='Plan', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(db_index=True, help_text='Display name of the plan.', max_length=200)), ('interval', models.CharField(choices=[('day', 'Day'), ('week', 'Week'), ('month', 'Month'), ('year', 'Year')], default='month', help_text='The frequency with which a subscription should be billed.', max_length=12)), ('interval_count', models.PositiveIntegerField(help_text='The number of intervals between each subscription billing')), ('amount', models.DecimalField(decimal_places=4, help_text='The amount in the specified currency to be charged on the interval specified.', max_digits=19, validators=[django.core.validators.MinValueValidator(0.0)])), ('currency', models.CharField(choices=[('AED', 'AED (UAE Dirham)'), ('AFN', 'AFN (Afghani)'), ('ALL', 'ALL (Lek)'), ('AMD', 'AMD (Armenian Dram)'), ('ANG', 'ANG (Netherlands Antillean Guilder)'), ('AOA', 'AOA (Kwanza)'), ('ARS', 'ARS (Argentine Peso)'), ('AUD', 'AUD (Australian Dollar)'), ('AWG', 'AWG (Aruban Florin)'), ('AZN', 'AZN (Azerbaijanian Manat)'), ('BAM', 'BAM (Convertible Mark)'), ('BBD', 'BBD (Barbados Dollar)'), ('BDT', 'BDT (Taka)'), ('BGN', 'BGN (Bulgarian Lev)'), ('BHD', 'BHD (Bahraini Dinar)'), ('BIF', 'BIF (Burundi Franc)'), ('BMD', 'BMD (Bermudian Dollar)'), ('BND', 'BND (Brunei Dollar)'), ('BOB', 'BOB (Boliviano)'), ('BRL', 'BRL (Brazilian Real)'), ('BSD', 'BSD (Bahamian Dollar)'), ('BTN', 'BTN (Ngultrum)'), ('BWP', 'BWP (Pula)'), ('BYN', 'BYN (Belarusian Ruble)'), ('BZD', 'BZD (Belize Dollar)'), ('CAD', 'CAD (Canadian Dollar)'), ('CDF', 'CDF (Congolese Franc)'), ('CHF', 'CHF (Swiss Franc)'), ('CLP', 'CLP (Chilean Peso)'), ('CNY', 'CNY (Yuan Renminbi)'), ('COP', 'COP (Colombian Peso)'), ('CRC', 'CRC (Costa Rican Colon)'), ('CUC', 'CUC (Peso Convertible)'), ('CUP', 'CUP (Cuban Peso)'), ('CVE', 'CVE (Cabo Verde Escudo)'), ('CZK', 'CZK (Czech Koruna)'), ('DJF', 'DJF (Djibouti Franc)'), ('DKK', 'DKK (Danish Krone)'), ('DOP', 'DOP (Dominican Peso)'), ('DZD', 'DZD (Algerian Dinar)'), ('EGP', 'EGP (Egyptian Pound)'), ('ERN', 'ERN (Nakfa)'), ('ETB', 'ETB (Ethiopian Birr)'), ('EUR', 'EUR (Euro)'), ('FJD', 'FJD (Fiji Dollar)'), ('FKP', 'FKP (Falkland Islands Pound)'), ('GBP', 'GBP (Pound Sterling)'), ('GEL', 'GEL (Lari)'), ('GHS', 'GHS (Ghana Cedi)'), ('GIP', 'GIP (Gibraltar Pound)'), ('GMD', 'GMD (Dalasi)'), ('GNF', 'GNF (Guinea Franc)'), ('GTQ', 'GTQ (Quetzal)'), ('GYD', 'GYD (Guyana Dollar)'), ('HKD', 'HKD (Hong Kong Dollar)'), ('HNL', 'HNL (Lempira)'), ('HRK', 'HRK (Kuna)'), ('HTG', 'HTG (Gourde)'), ('HUF', 'HUF (Forint)'), ('IDR', 'IDR (Rupiah)'), ('ILS', 'ILS (New Israeli Sheqel)'), ('INR', 'INR (Indian Rupee)'), ('IQD', 'IQD (Iraqi Dinar)'), ('IRR', 'IRR (Iranian Rial)'), ('ISK', 'ISK (Iceland Krona)'), ('JMD', 'JMD (Jamaican Dollar)'), ('JOD', 'JOD (Jordanian Dinar)'), ('JPY', 'JPY (Yen)'), ('KES', 'KES (Kenyan Shilling)'), ('KGS', 'KGS (Som)'), ('KHR', 'KHR (Riel)'), ('KMF', 'KMF (Comoro Franc)'), ('KPW', 'KPW (North Korean Won)'), ('KRW', 'KRW (Won)'), ('KWD', 'KWD (Kuwaiti Dinar)'), ('KYD', 'KYD (Cayman Islands Dollar)'), ('KZT', 'KZT (Tenge)'), ('LAK', 'LAK (Kip)'), ('LBP', 'LBP (Lebanese Pound)'), ('LKR', 'LKR (Sri Lanka Rupee)'), ('LRD', 'LRD (Liberian Dollar)'), ('LSL', 'LSL (Loti)'), ('LYD', 'LYD (Libyan Dinar)'), ('MAD', 'MAD (Moroccan Dirham)'), ('MDL', 'MDL (Moldovan Leu)'), ('MGA', 'MGA (Malagasy Ariary)'), ('MKD', 'MKD (Denar)'), ('MMK', 'MMK (Kyat)'), ('MNT', 'MNT (Tugrik)'), ('MOP', 'MOP (Pataca)'), ('MRO', 'MRO (Ouguiya)'), ('MUR', 'MUR (Mauritius Rupee)'), ('MVR', 'MVR (Rufiyaa)'), ('MWK', 'MWK (Malawi Kwacha)'), ('MXN', 'MXN (Mexican Peso)'), ('MYR', 'MYR (Malaysian Ringgit)'), ('MZN', 'MZN (Mozambique Metical)'), ('NAD', 'NAD (Namibia Dollar)'), ('NGN', 'NGN (Naira)'), ('NIO', 'NIO (Cordoba Oro)'), ('NOK', 'NOK (Norwegian Krone)'), ('NPR', 'NPR (Nepalese Rupee)'), ('NZD', 'NZD (New Zealand Dollar)'), ('OMR', 'OMR (Rial Omani)'), ('PAB', 'PAB (Balboa)'), ('PEN', 'PEN (Sol)'), ('PGK', 'PGK (Kina)'), ('PHP', 'PHP (Philippine Peso)'), ('PKR', 'PKR (Pakistan Rupee)'), ('PLN', 'PLN (Zloty)'), ('PYG', 'PYG (Guarani)'), ('QAR', 'QAR (Qatari Rial)'), ('RON', 'RON (Romanian Leu)'), ('RSD', 'RSD (Serbian Dinar)'), ('RUB', 'RUB (Russian Ruble)'), ('RWF', 'RWF (Rwanda Franc)'), ('SAR', 'SAR (Saudi Riyal)'), ('SBD', 'SBD (Solomon Islands Dollar)'), ('SCR', 'SCR (Seychelles Rupee)'), ('SDG', 'SDG (Sudanese Pound)'), ('SEK', 'SEK (Swedish Krona)'), ('SGD', 'SGD (Singapore Dollar)'), ('SHP', 'SHP (Saint Helena Pound)'), ('SLL', 'SLL (Leone)'), ('SOS', 'SOS (Somali Shilling)'), ('SRD', 'SRD (Surinam Dollar)'), ('SSP', 'SSP (South Sudanese Pound)'), ('STD', 'STD (Dobra)'), ('SVC', 'SVC (El Salvador Colon)'), ('SYP', 'SYP (Syrian Pound)'), ('SZL', 'SZL (Lilangeni)'), ('THB', 'THB (Baht)'), ('TJS', 'TJS (Somoni)'), ('TMT', 'TMT (Turkmenistan New Manat)'), ('TND', 'TND (Tunisian Dinar)'), ('TOP', 'TOP (Pa’anga)'), ('TRY', 'TRY (Turkish Lira)'), ('TTD', 'TTD (Trinidad and Tobago Dollar)'), ('TWD', 'TWD (New Taiwan Dollar)'), ('TZS', 'TZS (Tanzanian Shilling)'), ('UAH', 'UAH (Hryvnia)'), ('UGX', 'UGX (Uganda Shilling)'), ('USD', 'USD (US Dollar)'), ('UYU', 'UYU (Peso Uruguayo)'), ('UZS', 'UZS (Uzbekistan Sum)'), ('VEF', 'VEF (Bolívar)'), ('VND', 'VND (Dong)'), ('VUV', 'VUV (Vatu)'), ('WST', 'WST (Tala)'), ('XAF', 'XAF (CFA Franc BEAC)'), ('XAG', 'XAG (Silver)'), ('XAU', 'XAU (Gold)'), ('XBA', 'XBA (Bond Markets Unit European Composite Unit (EURCO))'), ('XBB', 'XBB (Bond Markets Unit European Monetary Unit (E.M.U.-6))'), ('XBC', 'XBC (Bond Markets Unit European Unit of Account 9 (E.U.A.-9))'), ('XBD', 'XBD (Bond Markets Unit European Unit of Account 17 (E.U.A.-17))'), ('XCD', 'XCD (East Caribbean Dollar)'), ('XDR', 'XDR (SDR (Special Drawing Right))'), ('XOF', 'XOF (CFA Franc BCEAO)'), ('XPD', 'XPD (Palladium)'), ('XPF', 'XPF (CFP Franc)'), ('XPT', 'XPT (Platinum)'), ('XSU', 'XSU (Sucre)'), ('XTS', 'XTS (Codes specifically reserved for testing purposes)'), ('XUA', 'XUA (ADB Unit of Account)'), ('XXX', 'XXX (The codes assigned for transactions where no currency is involved)'), ('YER', 'YER (Yemeni Rial)'), ('ZAR', 'ZAR (Rand)'), ('ZMW', 'ZMW (Zambian Kwacha)'), ('ZWL', 'ZWL (Zimbabwe Dollar)')], default='USD', help_text='The currency in which the subscription will be charged.', max_length=4)), ('trial_period_days', models.PositiveIntegerField(blank=True, help_text='Number of trial period days granted when subscribing a customer to this plan.', null=True, verbose_name='Trial days')), ('generate_documents_on_trial_end', models.NullBooleanField(help_text='If this is set to True, then billing documents will be generated when the subscription trial ends, instead of waiting for the end of the billing cycle.')), ('separate_cycles_during_trial', models.NullBooleanField(help_text='If this is set to True, then the trial period cycle will be split if it spans across multiple billing intervals.')), ('prebill_plan', models.NullBooleanField(help_text='If this is set to True, then the plan base amount will be billed at thebeginning of the billing cycle rather than after the end.')), ('generate_after', models.PositiveIntegerField(default=0, help_text='Number of seconds to wait after current billing cycle ends before generating the invoice. This can be used to allow systems to finish updating feature counters.')), ('cycle_billing_duration', models.DurationField(blank=True, help_text="This can be used to ensure that the billing date doesn't pass a certain date.\nFor example if this field is set to 2 days, for a monthly subscription, the billing date will never surpass the 2nd day of the month. Billing documents can still be generated after that day during the billing cycle, but their billing date will appear to be the end of the cycle billing duration.", null=True)), ('enabled', models.BooleanField(default=True, help_text='Whether to accept subscriptions.')), ('private', models.BooleanField(default=False, help_text='Indicates if a plan is private.')), ('metered_features', models.ManyToManyField(blank=True, help_text="A list of the plan's metered features.", to='silver.MeteredFeature')), ], options={ 'ordering': ('name',), }, ), migrations.CreateModel( name='ProductCode', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('value', models.CharField(max_length=128, unique=True)), ], ), migrations.CreateModel( name='Provider', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('live', livefield.fields.LiveField(default=True)), ('company', models.CharField(blank=True, max_length=128, null=True)), ('address_1', models.CharField(max_length=128)), ('address_2', models.CharField(blank=True, max_length=128, null=True)), ('country', models.CharField(choices=[('AD', 'Andorra'), ('AE', 'United Arab Emirates'), ('AF', 'Afghanistan'), ('AG', 'Antigua and Barbuda'), ('AI', 'Anguilla'), ('AL', 'Albania'), ('AM', 'Armenia'), ('AO', 'Angola'), ('AQ', 'Antarctica'), ('AR', 'Argentina'), ('AS', 'American Samoa'), ('AT', 'Austria'), ('AU', 'Australia'), ('AW', 'Aruba'), ('AX', 'Åland Islands'), ('AZ', 'Azerbaijan'), ('BA', 'Bosnia and Herzegovina'), ('BB', 'Barbados'), ('BD', 'Bangladesh'), ('BE', 'Belgium'), ('BF', 'Burkina Faso'), ('BG', 'Bulgaria'), ('BH', 'Bahrain'), ('BI', 'Burundi'), ('BJ', 'Benin'), ('BL', 'Saint Barthélemy'), ('BM', 'Bermuda'), ('BN', 'Brunei Darussalam'), ('BO', 'Bolivia, Plurinational State of'), ('BQ', 'Bonaire, Sint Eustatius and Saba'), ('BR', 'Brazil'), ('BS', 'Bahamas'), ('BT', 'Bhutan'), ('BV', 'Bouvet Island'), ('BW', 'Botswana'), ('BY', 'Belarus'), ('BZ', 'Belize'), ('CA', 'Canada'), ('CC', 'Cocos (Keeling) Islands'), ('CD', 'Congo, The Democratic Republic of the'), ('CF', 'Central African Republic'), ('CG', 'Congo'), ('CH', 'Switzerland'), ('CI', "Côte d'Ivoire"), ('CK', 'Cook Islands'), ('CL', 'Chile'), ('CM', 'Cameroon'), ('CN', 'China'), ('CO', 'Colombia'), ('CR', 'Costa Rica'), ('CU', 'Cuba'), ('CV', 'Cabo Verde'), ('CW', 'Curaçao'), ('CX', 'Christmas Island'), ('CY', 'Cyprus'), ('CZ', 'Czechia'), ('DE', 'Germany'), ('DJ', 'Djibouti'), ('DK', 'Denmark'), ('DM', 'Dominica'), ('DO', 'Dominican Republic'), ('DZ', 'Algeria'), ('EC', 'Ecuador'), ('EE', 'Estonia'), ('EG', 'Egypt'), ('EH', 'Western Sahara'), ('ER', 'Eritrea'), ('ES', 'Spain'), ('ET', 'Ethiopia'), ('FI', 'Finland'), ('FJ', 'Fiji'), ('FK', 'Falkland Islands (Malvinas)'), ('FM', 'Micronesia, Federated States of'), ('FO', 'Faroe Islands'), ('FR', 'France'), ('GA', 'Gabon'), ('GB', 'United Kingdom'), ('GD', 'Grenada'), ('GE', 'Georgia'), ('GF', 'French Guiana'), ('GG', 'Guernsey'), ('GH', 'Ghana'), ('GI', 'Gibraltar'), ('GL', 'Greenland'), ('GM', 'Gambia'), ('GN', 'Guinea'), ('GP', 'Guadeloupe'), ('GQ', 'Equatorial Guinea'), ('GR', 'Greece'), ('GS', 'South Georgia and the South Sandwich Islands'), ('GT', 'Guatemala'), ('GU', 'Guam'), ('GW', 'Guinea-Bissau'), ('GY', 'Guyana'), ('HK', 'Hong Kong'), ('HM', 'Heard Island and McDonald Islands'), ('HN', 'Honduras'), ('HR', 'Croatia'), ('HT', 'Haiti'), ('HU', 'Hungary'), ('ID', 'Indonesia'), ('IE', 'Ireland'), ('IL', 'Israel'), ('IM', 'Isle of Man'), ('IN', 'India'), ('IO', 'British Indian Ocean Territory'), ('IQ', 'Iraq'), ('IR', 'Iran, Islamic Republic of'), ('IS', 'Iceland'), ('IT', 'Italy'), ('JE', 'Jersey'), ('JM', 'Jamaica'), ('JO', 'Jordan'), ('JP', 'Japan'), ('KE', 'Kenya'), ('KG', 'Kyrgyzstan'), ('KH', 'Cambodia'), ('KI', 'Kiribati'), ('KM', 'Comoros'), ('KN', 'Saint Kitts and Nevis'), ('KP', "Korea, Democratic People's Republic of"), ('KR', 'Korea, Republic of'), ('KW', 'Kuwait'), ('KY', 'Cayman Islands'), ('KZ', 'Kazakhstan'), ('LA', "Lao People's Democratic Republic"), ('LB', 'Lebanon'), ('LC', 'Saint Lucia'), ('LI', 'Liechtenstein'), ('LK', 'Sri Lanka'), ('LR', 'Liberia'), ('LS', 'Lesotho'), ('LT', 'Lithuania'), ('LU', 'Luxembourg'), ('LV', 'Latvia'), ('LY', 'Libya'), ('MA', 'Morocco'), ('MC', 'Monaco'), ('MD', 'Moldova, Republic of'), ('ME', 'Montenegro'), ('MF', 'Saint Martin (French part)'), ('MG', 'Madagascar'), ('MH', 'Marshall Islands'), ('MK', 'Macedonia, Republic of'), ('ML', 'Mali'), ('MM', 'Myanmar'), ('MN', 'Mongolia'), ('MO', 'Macao'), ('MP', 'Northern Mariana Islands'), ('MQ', 'Martinique'), ('MR', 'Mauritania'), ('MS', 'Montserrat'), ('MT', 'Malta'), ('MU', 'Mauritius'), ('MV', 'Maldives'), ('MW', 'Malawi'), ('MX', 'Mexico'), ('MY', 'Malaysia'), ('MZ', 'Mozambique'), ('NA', 'Namibia'), ('NC', 'New Caledonia'), ('NE', 'Niger'), ('NF', 'Norfolk Island'), ('NG', 'Nigeria'), ('NI', 'Nicaragua'), ('NL', 'Netherlands'), ('NO', 'Norway'), ('NP', 'Nepal'), ('NR', 'Nauru'), ('NU', 'Niue'), ('NZ', 'New Zealand'), ('OM', 'Oman'), ('PA', 'Panama'), ('PE', 'Peru'), ('PF', 'French Polynesia'), ('PG', 'Papua New Guinea'), ('PH', 'Philippines'), ('PK', 'Pakistan'), ('PL', 'Poland'), ('PM', 'Saint Pierre and Miquelon'), ('PN', 'Pitcairn'), ('PR', 'Puerto Rico'), ('PS', 'Palestine, State of'), ('PT', 'Portugal'), ('PW', 'Palau'), ('PY', 'Paraguay'), ('QA', 'Qatar'), ('RE', 'Réunion'), ('RO', 'Romania'), ('RS', 'Serbia'), ('RU', 'Russian Federation'), ('RW', 'Rwanda'), ('SA', 'Saudi Arabia'), ('SB', 'Solomon Islands'), ('SC', 'Seychelles'), ('SD', 'Sudan'), ('SE', 'Sweden'), ('SG', 'Singapore'), ('SH', 'Saint Helena, Ascension and Tristan da Cunha'), ('SI', 'Slovenia'), ('SJ', 'Svalbard and Jan Mayen'), ('SK', 'Slovakia'), ('SL', 'Sierra Leone'), ('SM', 'San Marino'), ('SN', 'Senegal'), ('SO', 'Somalia'), ('SR', 'Suriname'), ('SS', 'South Sudan'), ('ST', 'Sao Tome and Principe'), ('SV', 'El Salvador'), ('SX', 'Sint Maarten (Dutch part)'), ('SY', 'Syrian Arab Republic'), ('SZ', 'Swaziland'), ('TC', 'Turks and Caicos Islands'), ('TD', 'Chad'), ('TF', 'French Southern Territories'), ('TG', 'Togo'), ('TH', 'Thailand'), ('TJ', 'Tajikistan'), ('TK', 'Tokelau'), ('TL', 'Timor-Leste'), ('TM', 'Turkmenistan'), ('TN', 'Tunisia'), ('TO', 'Tonga'), ('TR', 'Turkey'), ('TT', 'Trinidad and Tobago'), ('TV', 'Tuvalu'), ('TW', 'Taiwan, Province of China'), ('TZ', 'Tanzania, United Republic of'), ('UA', 'Ukraine'), ('UG', 'Uganda'), ('UM', 'United States Minor Outlying Islands'), ('US', 'United States'), ('UY', 'Uruguay'), ('UZ', 'Uzbekistan'), ('VA', 'Holy See (Vatican City State)'), ('VC', 'Saint Vincent and the Grenadines'), ('VE', 'Venezuela, Bolivarian Republic of'), ('VG', 'Virgin Islands, British'), ('VI', 'Virgin Islands, U.S.'), ('VN', 'Viet Nam'), ('VU', 'Vanuatu'), ('WF', 'Wallis and Futuna'), ('WS', 'Samoa'), ('YE', 'Yemen'), ('YT', 'Mayotte'), ('ZA', 'South Africa'), ('ZM', 'Zambia'), ('ZW', 'Zimbabwe')], max_length=3)), ('phone', models.CharField(blank=True, max_length=32, null=True)), ('email', models.CharField(blank=True, max_length=254, null=True)), ('city', models.CharField(max_length=128)), ('state', models.CharField(blank=True, max_length=128, null=True)), ('zip_code', models.CharField(blank=True, max_length=32, null=True)), ('extra', models.TextField(blank=True, help_text='Extra information to display on the invoice (markdown formatted).', null=True)), ('meta', annoying.fields.JSONField(blank=True, default={}, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('name', models.CharField(help_text='The name to be used for billing purposes.', max_length=128)), ('flow', models.CharField(choices=[('proforma', 'Proforma'), ('invoice', 'Invoice')], default='proforma', help_text='One of the available workflows for generating proformas and invoices (see the documentation for more details).', max_length=10)), ('invoice_series', models.CharField(help_text='The series that will be used on every invoice generated by this provider.', max_length=20)), ('invoice_starting_number', models.PositiveIntegerField()), ('proforma_series', models.CharField(blank=True, help_text='The series that will be used on every proforma generated by this provider.', max_length=20, null=True)), ('proforma_starting_number', models.PositiveIntegerField(blank=True, null=True)), ('default_document_state', models.CharField(choices=[('draft', 'Draft'), ('issued', 'Issued')], default='draft', help_text='The default state of the auto-generated documents.', max_length=10)), ('generate_documents_on_trial_end', models.BooleanField(default=True, help_text='If this is set to True, then billing documents will be generated when the subscription trial ends, instead of waiting for the end of the billing cycle.')), ('separate_cycles_during_trial', models.BooleanField(default=False, help_text='If this is set to True, then the trial period cycle will be split if it spans across multiple billing intervals.')), ('prebill_plan', models.BooleanField(default=True, help_text='If this is set to True, then the plan base amount will be billed at thebeginning of the billing cycle rather than after the end.')), ('cycle_billing_duration', models.DurationField(blank=True, help_text="This can be used to ensure that the billing date doesn't pass a certain date.\nFor example if this field is set to 2 days, for a monthly subscription, the billing date will never surpass the 2nd day of the month. Billing documents can still be generated after that day during the billing cycle, but their billing date will appear to be the end of the cycle billing duration.", null=True)), ], options={ 'ordering': ['name', 'company'], }, ), migrations.CreateModel( name='Subscription', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('description', models.CharField(blank=True, max_length=1024, null=True)), ('trial_end', models.DateField(blank=True, help_text='The date at which the trial ends. If set, overrides the computed trial end date from the plan.', null=True)), ('start_date', models.DateField(blank=True, help_text='The starting date for the subscription.', null=True)), ('cancel_date', models.DateField(blank=True, help_text='The date when the subscription was canceled.', null=True)), ('ended_at', models.DateField(blank=True, help_text='The date when the subscription ended.', null=True)), ('reference', models.CharField(blank=True, help_text="The subscription's reference in an external system.", max_length=128, null=True, validators=[django.core.validators.RegexValidator(message='Reference must not contain commas.', regex='^[^,]*$')])), ('state', django_fsm.FSMField(choices=[('active', 'Active'), ('inactive', 'Inactive'), ('canceled', 'Canceled'), ('ended', 'Ended')], default='inactive', help_text='The state the subscription is in.', max_length=12, protected=True)), ('meta', annoying.fields.JSONField(blank=True, default={}, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('customer', models.ForeignKey(help_text='The customer who is subscribed to the plan.', on_delete=django.db.models.deletion.CASCADE, related_name='subscriptions', to='silver.Customer')), ('plan', models.ForeignKey(help_text='The plan the customer is subscribed to.', on_delete=django.db.models.deletion.CASCADE, to='silver.Plan')), ], ), migrations.CreateModel( name='Transaction', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('amount', models.DecimalField(decimal_places=2, max_digits=12, validators=[django.core.validators.MinValueValidator(Decimal('0.00'))])), ('currency', models.CharField(choices=[('AED', 'AED (UAE Dirham)'), ('AFN', 'AFN (Afghani)'), ('ALL', 'ALL (Lek)'), ('AMD', 'AMD (Armenian Dram)'), ('ANG', 'ANG (Netherlands Antillean Guilder)'), ('AOA', 'AOA (Kwanza)'), ('ARS', 'ARS (Argentine Peso)'), ('AUD', 'AUD (Australian Dollar)'), ('AWG', 'AWG (Aruban Florin)'), ('AZN', 'AZN (Azerbaijanian Manat)'), ('BAM', 'BAM (Convertible Mark)'), ('BBD', 'BBD (Barbados Dollar)'), ('BDT', 'BDT (Taka)'), ('BGN', 'BGN (Bulgarian Lev)'), ('BHD', 'BHD (Bahraini Dinar)'), ('BIF', 'BIF (Burundi Franc)'), ('BMD', 'BMD (Bermudian Dollar)'), ('BND', 'BND (Brunei Dollar)'), ('BOB', 'BOB (Boliviano)'), ('BRL', 'BRL (Brazilian Real)'), ('BSD', 'BSD (Bahamian Dollar)'), ('BTN', 'BTN (Ngultrum)'), ('BWP', 'BWP (Pula)'), ('BYN', 'BYN (Belarusian Ruble)'), ('BZD', 'BZD (Belize Dollar)'), ('CAD', 'CAD (Canadian Dollar)'), ('CDF', 'CDF (Congolese Franc)'), ('CHF', 'CHF (Swiss Franc)'), ('CLP', 'CLP (Chilean Peso)'), ('CNY', 'CNY (Yuan Renminbi)'), ('COP', 'COP (Colombian Peso)'), ('CRC', 'CRC (Costa Rican Colon)'), ('CUC', 'CUC (Peso Convertible)'), ('CUP', 'CUP (Cuban Peso)'), ('CVE', 'CVE (Cabo Verde Escudo)'), ('CZK', 'CZK (Czech Koruna)'), ('DJF', 'DJF (Djibouti Franc)'), ('DKK', 'DKK (Danish Krone)'), ('DOP', 'DOP (Dominican Peso)'), ('DZD', 'DZD (Algerian Dinar)'), ('EGP', 'EGP (Egyptian Pound)'), ('ERN', 'ERN (Nakfa)'), ('ETB', 'ETB (Ethiopian Birr)'), ('EUR', 'EUR (Euro)'), ('FJD', 'FJD (Fiji Dollar)'), ('FKP', 'FKP (Falkland Islands Pound)'), ('GBP', 'GBP (Pound Sterling)'), ('GEL', 'GEL (Lari)'), ('GHS', 'GHS (Ghana Cedi)'), ('GIP', 'GIP (Gibraltar Pound)'), ('GMD', 'GMD (Dalasi)'), ('GNF', 'GNF (Guinea Franc)'), ('GTQ', 'GTQ (Quetzal)'), ('GYD', 'GYD (Guyana Dollar)'), ('HKD', 'HKD (Hong Kong Dollar)'), ('HNL', 'HNL (Lempira)'), ('HRK', 'HRK (Kuna)'), ('HTG', 'HTG (Gourde)'), ('HUF', 'HUF (Forint)'), ('IDR', 'IDR (Rupiah)'), ('ILS', 'ILS (New Israeli Sheqel)'), ('INR', 'INR (Indian Rupee)'), ('IQD', 'IQD (Iraqi Dinar)'), ('IRR', 'IRR (Iranian Rial)'), ('ISK', 'ISK (Iceland Krona)'), ('JMD', 'JMD (Jamaican Dollar)'), ('JOD', 'JOD (Jordanian Dinar)'), ('JPY', 'JPY (Yen)'), ('KES', 'KES (Kenyan Shilling)'), ('KGS', 'KGS (Som)'), ('KHR', 'KHR (Riel)'), ('KMF', 'KMF (Comoro Franc)'), ('KPW', 'KPW (North Korean Won)'), ('KRW', 'KRW (Won)'), ('KWD', 'KWD (Kuwaiti Dinar)'), ('KYD', 'KYD (Cayman Islands Dollar)'), ('KZT', 'KZT (Tenge)'), ('LAK', 'LAK (Kip)'), ('LBP', 'LBP (Lebanese Pound)'), ('LKR', 'LKR (Sri Lanka Rupee)'), ('LRD', 'LRD (Liberian Dollar)'), ('LSL', 'LSL (Loti)'), ('LYD', 'LYD (Libyan Dinar)'), ('MAD', 'MAD (Moroccan Dirham)'), ('MDL', 'MDL (Moldovan Leu)'), ('MGA', 'MGA (Malagasy Ariary)'), ('MKD', 'MKD (Denar)'), ('MMK', 'MMK (Kyat)'), ('MNT', 'MNT (Tugrik)'), ('MOP', 'MOP (Pataca)'), ('MRO', 'MRO (Ouguiya)'), ('MUR', 'MUR (Mauritius Rupee)'), ('MVR', 'MVR (Rufiyaa)'), ('MWK', 'MWK (Malawi Kwacha)'), ('MXN', 'MXN (Mexican Peso)'), ('MYR', 'MYR (Malaysian Ringgit)'), ('MZN', 'MZN (Mozambique Metical)'), ('NAD', 'NAD (Namibia Dollar)'), ('NGN', 'NGN (Naira)'), ('NIO', 'NIO (Cordoba Oro)'), ('NOK', 'NOK (Norwegian Krone)'), ('NPR', 'NPR (Nepalese Rupee)'), ('NZD', 'NZD (New Zealand Dollar)'), ('OMR', 'OMR (Rial Omani)'), ('PAB', 'PAB (Balboa)'), ('PEN', 'PEN (Sol)'), ('PGK', 'PGK (Kina)'), ('PHP', 'PHP (Philippine Peso)'), ('PKR', 'PKR (Pakistan Rupee)'), ('PLN', 'PLN (Zloty)'), ('PYG', 'PYG (Guarani)'), ('QAR', 'QAR (Qatari Rial)'), ('RON', 'RON (Romanian Leu)'), ('RSD', 'RSD (Serbian Dinar)'), ('RUB', 'RUB (Russian Ruble)'), ('RWF', 'RWF (Rwanda Franc)'), ('SAR', 'SAR (Saudi Riyal)'), ('SBD', 'SBD (Solomon Islands Dollar)'), ('SCR', 'SCR (Seychelles Rupee)'), ('SDG', 'SDG (Sudanese Pound)'), ('SEK', 'SEK (Swedish Krona)'), ('SGD', 'SGD (Singapore Dollar)'), ('SHP', 'SHP (Saint Helena Pound)'), ('SLL', 'SLL (Leone)'), ('SOS', 'SOS (Somali Shilling)'), ('SRD', 'SRD (Surinam Dollar)'), ('SSP', 'SSP (South Sudanese Pound)'), ('STD', 'STD (Dobra)'), ('SVC', 'SVC (El Salvador Colon)'), ('SYP', 'SYP (Syrian Pound)'), ('SZL', 'SZL (Lilangeni)'), ('THB', 'THB (Baht)'), ('TJS', 'TJS (Somoni)'), ('TMT', 'TMT (Turkmenistan New Manat)'), ('TND', 'TND (Tunisian Dinar)'), ('TOP', 'TOP (Pa’anga)'), ('TRY', 'TRY (Turkish Lira)'), ('TTD', 'TTD (Trinidad and Tobago Dollar)'), ('TWD', 'TWD (New Taiwan Dollar)'), ('TZS', 'TZS (Tanzanian Shilling)'), ('UAH', 'UAH (Hryvnia)'), ('UGX', 'UGX (Uganda Shilling)'), ('USD', 'USD (US Dollar)'), ('UYU', 'UYU (Peso Uruguayo)'), ('UZS', 'UZS (Uzbekistan Sum)'), ('VEF', 'VEF (Bolívar)'), ('VND', 'VND (Dong)'), ('VUV', 'VUV (Vatu)'), ('WST', 'WST (Tala)'), ('XAF', 'XAF (CFA Franc BEAC)'), ('XAG', 'XAG (Silver)'), ('XAU', 'XAU (Gold)'), ('XBA', 'XBA (Bond Markets Unit European Composite Unit (EURCO))'), ('XBB', 'XBB (Bond Markets Unit European Monetary Unit (E.M.U.-6))'), ('XBC', 'XBC (Bond Markets Unit European Unit of Account 9 (E.U.A.-9))'), ('XBD', 'XBD (Bond Markets Unit European Unit of Account 17 (E.U.A.-17))'), ('XCD', 'XCD (East Caribbean Dollar)'), ('XDR', 'XDR (SDR (Special Drawing Right))'), ('XOF', 'XOF (CFA Franc BCEAO)'), ('XPD', 'XPD (Palladium)'), ('XPF', 'XPF (CFP Franc)'), ('XPT', 'XPT (Platinum)'), ('XSU', 'XSU (Sucre)'), ('XTS', 'XTS (Codes specifically reserved for testing purposes)'), ('XUA', 'XUA (ADB Unit of Account)'), ('XXX', 'XXX (The codes assigned for transactions where no currency is involved)'), ('YER', 'YER (Yemeni Rial)'), ('ZAR', 'ZAR (Rand)'), ('ZMW', 'ZMW (Zambian Kwacha)'), ('ZWL', 'ZWL (Zimbabwe Dollar)')], help_text='The currency used for billing.', max_length=4)), ('external_reference', models.CharField(blank=True, max_length=256, null=True)), ('data', annoying.fields.JSONField(blank=True, default={}, deserializer=json.loads, null=True, serializer=annoying.fields.dumps)), ('state', django_fsm.FSMField(choices=[('initial', 'Initial'), ('pending', 'Pending'), ('settled', 'Settled'), ('failed', 'Failed'), ('canceled', 'Canceled'), ('refunded', 'Refunded')], default='initial', max_length=8)), ('uuid', models.UUIDField(default=uuid.uuid4)), ('valid_until', models.DateTimeField(blank=True, null=True)), ('last_access', models.DateTimeField(blank=True, null=True)), ('created_at', models.DateTimeField(default=django.utils.timezone.now)), ('updated_at', silver.utils.models.AutoDateTimeField(default=django.utils.timezone.now)), ('fail_code', models.CharField(blank=True, choices=[('default', 'default'), ('insufficient_funds', 'insufficient_funds'), ('expired_payment_method', 'expired_payment_method'), ('expired_card', 'expired_card'), ('invalid_payment_method', 'invalid_payment_method'), ('invalid_card', 'invalid_card'), ('limit_exceeded', 'limit_exceeded'), ('transaction_declined', 'transaction_declined'), ('transaction_declined_by_bank', 'transaction_declined_by_bank'), ('transaction_hard_declined', 'transaction_hard_declined'), ('transaction_hard_declined_by_bank', 'transaction_hard_declined_by_bank')], max_length=32, null=True)), ('refund_code', models.CharField(blank=True, choices=[('default', 'default')], max_length=32, null=True)), ('cancel_code', models.CharField(blank=True, choices=[('default', 'default')], max_length=32, null=True)), ('invoice', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='invoice_transactions', to='silver.BillingDocumentBase')), ('payment_method', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='silver.PaymentMethod')), ('proforma', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='proforma_transactions', to='silver.BillingDocumentBase')), ], options={ 'ordering': ['-id'], }, ), migrations.AlterIndexTogether( name='provider', index_together=set([('name', 'company')]), ), migrations.AddField( model_name='plan', name='product_code', field=models.ForeignKey(help_text='The product code for this plan.', on_delete=django.db.models.deletion.CASCADE, to='silver.ProductCode'), ), migrations.AddField( model_name='plan', name='provider', field=models.ForeignKey(help_text='The provider which provides the plan.', on_delete=django.db.models.deletion.CASCADE, related_name='plans', to='silver.Provider'), ), migrations.AddField( model_name='meteredfeatureunitslog', name='subscription', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='mf_log_entries', to='silver.Subscription'), ), migrations.AddField( model_name='meteredfeature', name='product_code', field=silver.utils.models.UnsavedForeignKey(help_text='The product code for this plan.', on_delete=django.db.models.deletion.CASCADE, to='silver.ProductCode'), ), migrations.AddField( model_name='documententry', name='product_code', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='invoices', to='silver.ProductCode'), ), migrations.AddField( model_name='documententry', name='proforma', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='proforma_entries', to='silver.BillingDocumentBase'), ), migrations.AlterIndexTogether( name='customer', index_together=set([('first_name', 'last_name', 'company')]), ), migrations.AddField( model_name='billinglog', name='subscription', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='billing_logs', to='silver.Subscription'), ), migrations.AddField( model_name='billingdocumentbase', name='customer', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='silver.Customer'), ), migrations.AddField( model_name='billingdocumentbase', name='pdf', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='silver.PDF'), ), migrations.AddField( model_name='billingdocumentbase', name='provider', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='silver.Provider'), ), migrations.AddField( model_name='billingdocumentbase', name='related_document', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='reverse_related_document', to='silver.BillingDocumentBase'), ), migrations.CreateModel( name='Invoice', fields=[ ], options={ 'proxy': True, 'indexes': [], }, bases=('silver.billingdocumentbase',), ), migrations.CreateModel( name='Proforma', fields=[ ], options={ 'proxy': True, 'indexes': [], }, bases=('silver.billingdocumentbase',), ), migrations.AlterUniqueTogether( name='meteredfeatureunitslog', unique_together=set([('metered_feature', 'subscription', 'start_date', 'end_date')]), ), migrations.AlterUniqueTogether( name='billingdocumentbase', unique_together=set([('kind', 'provider', 'series', 'number')]), ), ]

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41fe8cbf83da61053b9d68cdeb3dd6d38bcc9877

16,872

py

Python

tb_api_client/swagger_client/apis/tenant_controller_api.py

MOSAIC-LoPoW/oss7-thingsboard-backend-example

9b289dd7fdbb6e932ca338ad497a7bb1fc84d010

[ "Apache-2.0" ]

5

2017-11-27T15:48:16.000Z

2020-09-21T04:18:47.000Z

tb_api_client/swagger_client/apis/tenant_controller_api.py

MOSAIC-LoPoW/oss7-thingsboard-backend-example

9b289dd7fdbb6e932ca338ad497a7bb1fc84d010

[ "Apache-2.0" ]

null

tb_api_client/swagger_client/apis/tenant_controller_api.py

MOSAIC-LoPoW/oss7-thingsboard-backend-example

9b289dd7fdbb6e932ca338ad497a7bb1fc84d010

[ "Apache-2.0" ]

6

2018-01-14T17:23:46.000Z

2019-06-24T13:38:54.000Z

# coding: utf-8 """ Thingsboard REST API For instructions how to authorize requests please visit <a href='http://thingsboard.io/docs/reference/rest-api/'>REST API documentation page</a>. OpenAPI spec version: 2.0 Contact: info@thingsboard.io Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import sys import os import re # python 2 and python 3 compatibility library from six import iteritems from ..api_client import ApiClient class TenantControllerApi(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def delete_tenant_using_delete(self, tenant_id, **kwargs): """ deleteTenant This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.delete_tenant_using_delete(tenant_id, async=True) >>> result = thread.get() :param async bool :param str tenant_id: tenantId (required) :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.delete_tenant_using_delete_with_http_info(tenant_id, **kwargs) else: (data) = self.delete_tenant_using_delete_with_http_info(tenant_id, **kwargs) return data def delete_tenant_using_delete_with_http_info(self, tenant_id, **kwargs): """ deleteTenant This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.delete_tenant_using_delete_with_http_info(tenant_id, async=True) >>> result = thread.get() :param async bool :param str tenant_id: tenantId (required) :return: None If the method is called asynchronously, returns the request thread. """ all_params = ['tenant_id'] all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method delete_tenant_using_delete" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'tenant_id' is set if ('tenant_id' not in params) or (params['tenant_id'] is None): raise ValueError("Missing the required parameter `tenant_id` when calling `delete_tenant_using_delete`") collection_formats = {} path_params = {} if 'tenant_id' in params: path_params['tenantId'] = params['tenant_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['*/*']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['application/json']) # Authentication setting auth_settings = ['X-Authorization'] return self.api_client.call_api('/api/tenant/{tenantId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenant_by_id_using_get(self, tenant_id, **kwargs): """ getTenantById This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_tenant_by_id_using_get(tenant_id, async=True) >>> result = thread.get() :param async bool :param str tenant_id: tenantId (required) :return: Tenant If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.get_tenant_by_id_using_get_with_http_info(tenant_id, **kwargs) else: (data) = self.get_tenant_by_id_using_get_with_http_info(tenant_id, **kwargs) return data def get_tenant_by_id_using_get_with_http_info(self, tenant_id, **kwargs): """ getTenantById This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_tenant_by_id_using_get_with_http_info(tenant_id, async=True) >>> result = thread.get() :param async bool :param str tenant_id: tenantId (required) :return: Tenant If the method is called asynchronously, returns the request thread. """ all_params = ['tenant_id'] all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenant_by_id_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'tenant_id' is set if ('tenant_id' not in params) or (params['tenant_id'] is None): raise ValueError("Missing the required parameter `tenant_id` when calling `get_tenant_by_id_using_get`") collection_formats = {} path_params = {} if 'tenant_id' in params: path_params['tenantId'] = params['tenant_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['*/*']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['application/json']) # Authentication setting auth_settings = ['X-Authorization'] return self.api_client.call_api('/api/tenant/{tenantId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Tenant', auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_tenants_using_get(self, limit, **kwargs): """ getTenants This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_tenants_using_get(limit, async=True) >>> result = thread.get() :param async bool :param str limit: limit (required) :param str text_search: textSearch :param str id_offset: idOffset :param str text_offset: textOffset :return: TextPageDataTenant If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.get_tenants_using_get_with_http_info(limit, **kwargs) else: (data) = self.get_tenants_using_get_with_http_info(limit, **kwargs) return data def get_tenants_using_get_with_http_info(self, limit, **kwargs): """ getTenants This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.get_tenants_using_get_with_http_info(limit, async=True) >>> result = thread.get() :param async bool :param str limit: limit (required) :param str text_search: textSearch :param str id_offset: idOffset :param str text_offset: textOffset :return: TextPageDataTenant If the method is called asynchronously, returns the request thread. """ all_params = ['limit', 'text_search', 'id_offset', 'text_offset'] all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_tenants_using_get" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'limit' is set if ('limit' not in params) or (params['limit'] is None): raise ValueError("Missing the required parameter `limit` when calling `get_tenants_using_get`") collection_formats = {} path_params = {} query_params = [] if 'text_search' in params: query_params.append(('textSearch', params['text_search'])) if 'id_offset' in params: query_params.append(('idOffset', params['id_offset'])) if 'text_offset' in params: query_params.append(('textOffset', params['text_offset'])) if 'limit' in params: query_params.append(('limit', params['limit'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['*/*']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['application/json']) # Authentication setting auth_settings = ['X-Authorization'] return self.api_client.call_api('/api/tenants', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='TextPageDataTenant', auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def save_tenant_using_post(self, tenant, **kwargs): """ saveTenant This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.save_tenant_using_post(tenant, async=True) >>> result = thread.get() :param async bool :param Tenant tenant: tenant (required) :return: Tenant If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('async'): return self.save_tenant_using_post_with_http_info(tenant, **kwargs) else: (data) = self.save_tenant_using_post_with_http_info(tenant, **kwargs) return data def save_tenant_using_post_with_http_info(self, tenant, **kwargs): """ saveTenant This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async=True >>> thread = api.save_tenant_using_post_with_http_info(tenant, async=True) >>> result = thread.get() :param async bool :param Tenant tenant: tenant (required) :return: Tenant If the method is called asynchronously, returns the request thread. """ all_params = ['tenant'] all_params.append('async') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method save_tenant_using_post" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'tenant' is set if ('tenant' not in params) or (params['tenant'] is None): raise ValueError("Missing the required parameter `tenant` when calling `save_tenant_using_post`") collection_formats = {} path_params = {} query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None if 'tenant' in params: body_params = params['tenant'] # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['*/*']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['application/json']) # Authentication setting auth_settings = ['X-Authorization'] return self.api_client.call_api('/api/tenant', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Tenant', auth_settings=auth_settings, async=params.get('async'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

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8

5119e9e1bf0c97f24b4d156355297e70ff282020

18,504

py

Python

napalm_yang/models/openconfig/network_instances/network_instance/mpls/signaling_protocols/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

64

2016-10-20T15:47:18.000Z

2021-11-11T11:57:32.000Z

napalm_yang/models/openconfig/network_instances/network_instance/mpls/signaling_protocols/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

126

2016-10-05T10:36:14.000Z

2019-05-15T08:43:23.000Z

napalm_yang/models/openconfig/network_instances/network_instance/mpls/signaling_protocols/__init__.py

ckishimo/napalm-yang

8f2bd907bd3afcde3c2f8e985192de74748baf6c

[ "Apache-2.0" ]

63

2016-11-07T15:23:08.000Z

2021-09-22T14:41:16.000Z

# -*- coding: utf-8 -*- from operator import attrgetter from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType from pyangbind.lib.yangtypes import RestrictedClassType from pyangbind.lib.yangtypes import TypedListType from pyangbind.lib.yangtypes import YANGBool from pyangbind.lib.yangtypes import YANGListType from pyangbind.lib.yangtypes import YANGDynClass from pyangbind.lib.yangtypes import ReferenceType from pyangbind.lib.base import PybindBase from collections import OrderedDict from decimal import Decimal from bitarray import bitarray import six # PY3 support of some PY2 keywords (needs improved) if six.PY3: import builtins as __builtin__ long = int elif six.PY2: import __builtin__ from . import rsvp_te from . import segment_routing class signaling_protocols(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module openconfig-network-instance - based on the path /network-instances/network-instance/mpls/signaling-protocols. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: top-level signaling protocol configuration """ __slots__ = ("_path_helper", "_extmethods", "__rsvp_te", "__segment_routing") _yang_name = "signaling-protocols" _pybind_generated_by = "container" def __init__(self, *args, **kwargs): self._path_helper = False self._extmethods = False self.__rsvp_te = YANGDynClass( base=rsvp_te.rsvp_te, is_container="container", yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__segment_routing = YANGDynClass( base=segment_routing.segment_routing, is_container="container", yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path() + [self._yang_name] else: return [ "network-instances", "network-instance", "mpls", "signaling-protocols" ] def _get_rsvp_te(self): """ Getter method for rsvp_te, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/rsvp_te (container) YANG Description: RSVP-TE global signaling protocol configuration """ return self.__rsvp_te def _set_rsvp_te(self, v, load=False): """ Setter method for rsvp_te, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/rsvp_te (container) If this variable is read-only (config: false) in the source YANG file, then _set_rsvp_te is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rsvp_te() directly. YANG Description: RSVP-TE global signaling protocol configuration """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=rsvp_te.rsvp_te, is_container="container", yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """rsvp_te must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=rsvp_te.rsvp_te, is_container='container', yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__rsvp_te = t if hasattr(self, "_set"): self._set() def _unset_rsvp_te(self): self.__rsvp_te = YANGDynClass( base=rsvp_te.rsvp_te, is_container="container", yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_segment_routing(self): """ Getter method for segment_routing, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/segment_routing (container) YANG Description: MPLS-specific Segment Routing configuration and operational state parameters """ return self.__segment_routing def _set_segment_routing(self, v, load=False): """ Setter method for segment_routing, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/segment_routing (container) If this variable is read-only (config: false) in the source YANG file, then _set_segment_routing is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_segment_routing() directly. YANG Description: MPLS-specific Segment Routing configuration and operational state parameters """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=segment_routing.segment_routing, is_container="container", yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """segment_routing must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=segment_routing.segment_routing, is_container='container', yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__segment_routing = t if hasattr(self, "_set"): self._set() def _unset_segment_routing(self): self.__segment_routing = YANGDynClass( base=segment_routing.segment_routing, is_container="container", yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) rsvp_te = __builtin__.property(_get_rsvp_te, _set_rsvp_te) segment_routing = __builtin__.property(_get_segment_routing, _set_segment_routing) _pyangbind_elements = OrderedDict( [("rsvp_te", rsvp_te), ("segment_routing", segment_routing)] ) from . import rsvp_te from . import segment_routing class signaling_protocols(PybindBase): """ This class was auto-generated by the PythonClass plugin for PYANG from YANG module openconfig-network-instance-l2 - based on the path /network-instances/network-instance/mpls/signaling-protocols. Each member element of the container is represented as a class variable - with a specific YANG type. YANG Description: top-level signaling protocol configuration """ __slots__ = ("_path_helper", "_extmethods", "__rsvp_te", "__segment_routing") _yang_name = "signaling-protocols" _pybind_generated_by = "container" def __init__(self, *args, **kwargs): self._path_helper = False self._extmethods = False self.__rsvp_te = YANGDynClass( base=rsvp_te.rsvp_te, is_container="container", yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) self.__segment_routing = YANGDynClass( base=segment_routing.segment_routing, is_container="container", yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) load = kwargs.pop("load", None) if args: if len(args) > 1: raise TypeError("cannot create a YANG container with >1 argument") all_attr = True for e in self._pyangbind_elements: if not hasattr(args[0], e): all_attr = False break if not all_attr: raise ValueError("Supplied object did not have the correct attributes") for e in self._pyangbind_elements: nobj = getattr(args[0], e) if nobj._changed() is False: continue setmethod = getattr(self, "_set_%s" % e) if load is None: setmethod(getattr(args[0], e)) else: setmethod(getattr(args[0], e), load=load) def _path(self): if hasattr(self, "_parent"): return self._parent._path() + [self._yang_name] else: return [ "network-instances", "network-instance", "mpls", "signaling-protocols" ] def _get_rsvp_te(self): """ Getter method for rsvp_te, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/rsvp_te (container) YANG Description: RSVP-TE global signaling protocol configuration """ return self.__rsvp_te def _set_rsvp_te(self, v, load=False): """ Setter method for rsvp_te, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/rsvp_te (container) If this variable is read-only (config: false) in the source YANG file, then _set_rsvp_te is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_rsvp_te() directly. YANG Description: RSVP-TE global signaling protocol configuration """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=rsvp_te.rsvp_te, is_container="container", yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """rsvp_te must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=rsvp_te.rsvp_te, is_container='container', yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__rsvp_te = t if hasattr(self, "_set"): self._set() def _unset_rsvp_te(self): self.__rsvp_te = YANGDynClass( base=rsvp_te.rsvp_te, is_container="container", yang_name="rsvp-te", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) def _get_segment_routing(self): """ Getter method for segment_routing, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/segment_routing (container) YANG Description: MPLS-specific Segment Routing configuration and operational state parameters """ return self.__segment_routing def _set_segment_routing(self, v, load=False): """ Setter method for segment_routing, mapped from YANG variable /network_instances/network_instance/mpls/signaling_protocols/segment_routing (container) If this variable is read-only (config: false) in the source YANG file, then _set_segment_routing is considered as a private method. Backends looking to populate this variable should do so via calling thisObj._set_segment_routing() directly. YANG Description: MPLS-specific Segment Routing configuration and operational state parameters """ if hasattr(v, "_utype"): v = v._utype(v) try: t = YANGDynClass( v, base=segment_routing.segment_routing, is_container="container", yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) except (TypeError, ValueError): raise ValueError( { "error-string": """segment_routing must be of a type compatible with container""", "defined-type": "container", "generated-type": """YANGDynClass(base=segment_routing.segment_routing, is_container='container', yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace='http://openconfig.net/yang/network-instance', defining_module='openconfig-network-instance', yang_type='container', is_config=True)""", } ) self.__segment_routing = t if hasattr(self, "_set"): self._set() def _unset_segment_routing(self): self.__segment_routing = YANGDynClass( base=segment_routing.segment_routing, is_container="container", yang_name="segment-routing", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, extensions=None, namespace="http://openconfig.net/yang/network-instance", defining_module="openconfig-network-instance", yang_type="container", is_config=True, ) rsvp_te = __builtin__.property(_get_rsvp_te, _set_rsvp_te) segment_routing = __builtin__.property(_get_segment_routing, _set_segment_routing) _pyangbind_elements = OrderedDict( [("rsvp_te", rsvp_te), ("segment_routing", segment_routing)] )

39.708155

404

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5.544534

0.091093

0.038335

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0.050931

0.965772

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0.289775

18,504

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39.793548

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0

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false

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0

null

0

1

0

1

0

null

0

7

5140071a8ad46404186cee1d2f6c448428abfa12

2,417

py

Python

S4/S4 Library/simulation/server_commands/video_commands.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

1

2021-05-20T19:33:37.000Z

S4/S4 Library/simulation/server_commands/video_commands.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

S4/S4 Library/simulation/server_commands/video_commands.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

from objects.components import types import objects import services import sims4.commands @sims4.commands.Command('video.object_info') def get_video_object_info(obj_id:int, _connection=None): manager = services.object_manager() obj = None if obj_id in manager: obj = manager.get(obj_id) else: sims4.commands.output('Object ID {} not present in the object manager.'.format(obj_id), _connection) if obj is not None: sims4.commands.output('Object {} ({})'.format(obj_id, obj.__class__.__name__), _connection) v = obj.get_component(types.VIDEO_COMPONENT) if v is not None: sims4.commands.output(' ' + repr(v), _connection) else: sims4.commands.output(' Object does not have video playback capabilities.', _connection) @sims4.commands.Command('video.set_clips') def set_video_clips(obj_id:int, *clip_names, _connection=None): manager = services.object_manager() obj = None if obj_id in manager: obj = manager.get(obj_id) else: sims4.commands.output('Object ID {} not present in the object manager.'.format(obj_id), _connection) if obj is not None: sims4.commands.output('Object {} ({})'.format(obj_id, obj.__class__.__name__), _connection) v = obj.get_component(types.VIDEO_COMPONENT) if v is not None: v.set_video_clips(clip_names, False) sims4.commands.output(' Added {} clip(s).'.format(len(clip_names)), _connection) else: sims4.commands.output(' Object does not have video playback capabilities.', _connection) @sims4.commands.Command('video.add_clips') def add_video_clips(obj_id:int, *clip_names, _connection=None): manager = services.object_manager() obj = None if obj_id in manager: obj = manager.get(obj_id) else: sims4.commands.output('Object ID {} not present in the object manager.'.format(obj_id), _connection) if obj is not None: sims4.commands.output('Object {} ({})'.format(obj_id, obj.__class__.__name__), _connection) v = obj.get_component(types.VIDEO_COMPONENT) if v is not None: v.add_video_clips(clip_names, False) sims4.commands.output(' Added {} clip(s).'.format(len(clip_names)), _connection) else: sims4.commands.output(' Object does not have video playback capabilities.', _connection)

43.945455

108

0.675217

320

2,417

4.840625

0.146875

0.13428

0.147192

0.145255

0.88315

0.870884

0

0.008381

0.210178

2,417

54

109

44.759259

0.803038

0

0.745098

0

0.17501

0

1

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false

0

0.078431

0

0.137255

0

null

0

1

0

1

0

null

0

7

5aead2bb7acb0edf5fb4880fe085e737776e4cf7

1,548

py

Python

runner/tests/run/json/test_json.py

PC-Trip/runner

e13291e25a2bc2962523a1de2d13725609497cb5

[ "MIT" ]

null

runner/tests/run/json/test_json.py

PC-Trip/runner

e13291e25a2bc2962523a1de2d13725609497cb5

[ "MIT" ]

null

runner/tests/run/json/test_json.py

PC-Trip/runner

e13291e25a2bc2962523a1de2d13725609497cb5

[ "MIT" ]

null

import pytest @pytest.mark.parametrize("run", ["sequence.json"], indirect=True) def test_sequence(run): assert run == 0 @pytest.mark.parametrize("run", ["thread.json"], indirect=True) def test_thread(run): assert run == 0 @pytest.mark.parametrize("run", ["process.json"], indirect=True) def test_process(run): assert run == 0 @pytest.mark.parametrize("run", ["thread_jobs.json"], indirect=True) def test_thread_jobs(run): assert run == 0 @pytest.mark.parametrize("run", ["thread_jobs_broadcast.json"], indirect=True) def test_thread_jobs_broadcast(run): assert run == 0 @pytest.mark.parametrize("run", ["thread_jobs_workers.json"], indirect=True) def test_thread_jobs_workers(run): assert run == 0 @pytest.mark.parametrize("run", ["thread_jobs_broadcast_workers.json"], indirect=True) def test_thread_jobs_broadcast_workers(run): assert run == 0 @pytest.mark.parametrize("run", ["process_jobs.json"], indirect=True) def test_process_jobs(run): assert run == 0 @pytest.mark.parametrize("run", ["process_jobs_broadcast.json"], indirect=True) def test_process_jobs_broadcast(run): assert run == 0 @pytest.mark.parametrize("run", ["process_jobs_workers.json"], indirect=True) def test_process_jobs_workers(run): assert run == 0 @pytest.mark.parametrize("run", ["process_jobs_broadcast_workers.json"], indirect=True) def test_process_jobs_broadcast_workers(run): assert run == 0 @pytest.mark.parametrize("run", ["action.json"], indirect=True) def test_action(run): assert run == 0

24.967742

87

0.72739

214

1,548

5.056075

0.088785

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5175e60c7f1183ee1d14456eeb409ef841bebe39

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py

Python

heat/tests/test_stack_lock.py

redhat-openstack/heat

6b9be0a868b857e942c1cc90594d0f3a0d0725d0

[ "Apache-2.0" ]

null

heat/tests/test_stack_lock.py

redhat-openstack/heat

6b9be0a868b857e942c1cc90594d0f3a0d0725d0

[ "Apache-2.0" ]

null

heat/tests/test_stack_lock.py

redhat-openstack/heat

6b9be0a868b857e942c1cc90594d0f3a0d0725d0

[ "Apache-2.0" ]

null

# # 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. import mock from oslo import messaging from heat.common import exception from heat.db import api as db_api from heat.engine import stack_lock from heat.tests.common import HeatTestCase from heat.tests import utils class StackLockTest(HeatTestCase): def setUp(self): super(StackLockTest, self).setUp() self.context = utils.dummy_context() self.stack = self.m.CreateMockAnything() self.stack.id = "aae01f2d-52ae-47ac-8a0d-3fde3d220fea" self.stack.name = "test_stack" self.stack.action = "CREATE" self.engine_id = stack_lock.StackLock.generate_engine_id() class TestThreadLockException(Exception): pass def test_successful_acquire_new_lock(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn(None) self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) slock.acquire() self.m.VerifyAll() def test_failed_acquire_existing_lock_current_engine(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn(self.engine_id) self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) self.assertRaises(exception.ActionInProgress, slock.acquire) self.m.VerifyAll() def test_successful_acquire_existing_lock_engine_dead(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") self.m.StubOutWithMock(db_api, "stack_lock_steal") db_api.stack_lock_steal(self.stack.id, "fake-engine-id", self.engine_id).AndReturn(None) self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) self.patchobject(slock, 'engine_alive', return_value=False) slock.acquire() self.m.VerifyAll() def test_failed_acquire_existing_lock_engine_alive(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) self.patchobject(slock, 'engine_alive', return_value=True) self.assertRaises(exception.ActionInProgress, slock.acquire) self.m.VerifyAll() def test_failed_acquire_existing_lock_engine_dead(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") self.m.StubOutWithMock(db_api, "stack_lock_steal") db_api.stack_lock_steal( self.stack.id, "fake-engine-id", self.engine_id).AndReturn("fake-engine-id2") self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) self.patchobject(slock, 'engine_alive', return_value=False) self.assertRaises(exception.ActionInProgress, slock.acquire) self.m.VerifyAll() def test_successful_acquire_with_retry(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") self.m.StubOutWithMock(db_api, "stack_lock_steal") db_api.stack_lock_steal( self.stack.id, "fake-engine-id", self.engine_id).AndReturn(True) db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") db_api.stack_lock_steal( self.stack.id, "fake-engine-id", self.engine_id).AndReturn(None) self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) self.patchobject(slock, 'engine_alive', return_value=False) slock.acquire() self.m.VerifyAll() def test_failed_acquire_one_retry_only(self): self.m.StubOutWithMock(db_api, "stack_lock_create") db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") self.m.StubOutWithMock(db_api, "stack_lock_steal") db_api.stack_lock_steal( self.stack.id, "fake-engine-id", self.engine_id).AndReturn(True) db_api.stack_lock_create( self.stack.id, self.engine_id).AndReturn("fake-engine-id") db_api.stack_lock_steal( self.stack.id, "fake-engine-id", self.engine_id).AndReturn(True) self.m.ReplayAll() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) self.patchobject(slock, 'engine_alive', return_value=False) self.assertRaises(exception.ActionInProgress, slock.acquire) self.m.VerifyAll() def test_thread_lock_context_mgr_exception_acquire_success(self): db_api.stack_lock_create = mock.Mock(return_value=None) db_api.stack_lock_release = mock.Mock(return_value=None) slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) def check_thread_lock(): with slock.thread_lock(self.stack.id): self.assertEqual(1, db_api.stack_lock_create.call_count) raise self.TestThreadLockException self.assertRaises(self.TestThreadLockException, check_thread_lock) self.assertEqual(1, db_api.stack_lock_release.call_count) def test_thread_lock_context_mgr_exception_acquire_fail(self): db_api.stack_lock_create = mock.Mock(return_value=self.engine_id) db_api.stack_lock_release = mock.Mock() slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) def check_thread_lock(): with slock.thread_lock(self.stack.id): self.assertEqual(1, db_api.stack_lock_create.call_count) raise exception.ActionInProgress self.assertRaises(exception.ActionInProgress, check_thread_lock) assert not db_api.stack_lock_release.called def test_thread_lock_context_mgr_no_exception(self): db_api.stack_lock_create = mock.Mock(return_value=None) db_api.stack_lock_release = mock.Mock(return_value=None) slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) with slock.thread_lock(self.stack.id): self.assertEqual(1, db_api.stack_lock_create.call_count) assert not db_api.stack_lock_release.called def test_try_thread_lock_context_mgr_exception(self): db_api.stack_lock_create = mock.Mock(return_value=None) db_api.stack_lock_release = mock.Mock(return_value=None) slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) def check_thread_lock(): with slock.try_thread_lock(self.stack.id): self.assertEqual(1, db_api.stack_lock_create.call_count) raise self.TestThreadLockException self.assertRaises(self.TestThreadLockException, check_thread_lock) self.assertEqual(1, db_api.stack_lock_release.call_count) def test_try_thread_lock_context_mgr_no_exception(self): db_api.stack_lock_create = mock.Mock(return_value=None) db_api.stack_lock_release = mock.Mock(return_value=None) slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) with slock.try_thread_lock(self.stack.id): self.assertEqual(1, db_api.stack_lock_create.call_count) assert not db_api.stack_lock_release.called def test_try_thread_lock_context_mgr_existing_lock(self): db_api.stack_lock_create = mock.Mock(return_value=1234) db_api.stack_lock_release = mock.Mock(return_value=None) slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) def check_thread_lock(): with slock.try_thread_lock(self.stack.id): self.assertEqual(1, db_api.stack_lock_create.call_count) raise self.TestThreadLockException self.assertRaises(self.TestThreadLockException, check_thread_lock) assert not db_api.stack_lock_release.called def test_engine_alive_ok(self): slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) mget_client = self.patchobject(stack_lock.rpc_messaging, 'get_rpc_client') mclient = mget_client.return_value mclient_ctx = mclient.prepare.return_value mclient_ctx.call.return_value = True ret = slock.engine_alive(self.context, self.engine_id) self.assertTrue(ret) mclient.prepare.assert_called_once_with(timeout=2) mclient_ctx.call.assert_called_once_with(self.context, 'listening') def test_engine_alive_timeout(self): slock = stack_lock.StackLock(self.context, self.stack, self.engine_id) mget_client = self.patchobject(stack_lock.rpc_messaging, 'get_rpc_client') mclient = mget_client.return_value mclient_ctx = mclient.prepare.return_value mclient_ctx.call.side_effect = messaging.MessagingTimeout('too slow') ret = slock.engine_alive(self.context, self.engine_id) self.assertIs(False, ret) mclient.prepare.assert_called_once_with(timeout=2) mclient_ctx.call.assert_called_once_with(self.context, 'listening')

43.32766

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10,182

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null

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1

0

1

0

null

0

7

51772a9f4c6eed51a4a91106905aeb0e434a8422

166

py

Python

accounts/views.py

meiordac/eCommerce

0efce9ebf5ecb55378890445b0bed16c07613121

[ "MIT" ]

2

2017-01-13T12:39:18.000Z

2020-05-28T21:27:26.000Z

accounts/views.py

meiordac/eCommerce

0efce9ebf5ecb55378890445b0bed16c07613121

[ "MIT" ]

1

2020-05-28T21:31:14.000Z

accounts/views.py

meiordac/eCommerce

0efce9ebf5ecb55378890445b0bed16c07613121

[ "MIT" ]

1

2017-10-16T08:30:59.000Z

from django.shortcuts import render def login(request): return render(request, 'login.html') def logout(request): return render(request, 'logout.html')

20.75

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0.716867

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0.2

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1

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null

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1

0

1

0

8

51d956f2e81e99145eecf963abe0ebaf17acc9ae

18,174

py

Python

ajuste-bayes_mcmc.py

jpacuna99/AcunaJuan_ejercicio07

1a68387a25ed42aa31997a46b7d9b9d70481457f

[ "MIT" ]

null

ajuste-bayes_mcmc.py

jpacuna99/AcunaJuan_ejercicio07

1a68387a25ed42aa31997a46b7d9b9d70481457f

[ "MIT" ]

null

ajuste-bayes_mcmc.py

jpacuna99/AcunaJuan_ejercicio07

1a68387a25ed42aa31997a46b7d9b9d70481457f

[ "MIT" ]

null

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70\u001b[0m \u001b[0mprint\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx_walk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mposterior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_guess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mposterior\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_walk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 72\u001b[0m \u001b[0;32mif\u001b[0m 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"text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "def prior(mu):\n", " \"\"\"\n", " Densidad de probabilidad de mu\n", " \"\"\"\n", " p = np.ones(len(mu))/(mu.max()-mu.min())\n", " return p\n", "\n", "def like(x, sigma, mu,a):\n", " \"\"\"\n", " Likelihod de tener un dato x e incertidumbre sigma\n", " \"\"\"\n", " L = np.ones(len(mu))\n", " \n", " \"\"\"\n", " for x_i in (x):\n", " L *= (1.0/np.sqrt(2.0*np.pi*sigma**2))*np.exp(-0.5*(x_i-mu)**2/(sigma**2))\n", " return L\n", " \"\"\"\n", " for i in range(len(x)):\n", " L += np.sum((a[1:]*x[i]+a[0]-y[i])**2)**((len(y)*-1.)/-2.)\n", " return L\n", "\n", "def posterior(mu, x, sigma,a):\n", " \"\"\"\n", " Posterior calculado con la normalizacion adecuada\n", " \"\"\"\n", " post = like(x, sigma, mu,a) * prior(mu)\n", " evidencia = np.trapz(post, mu)\n", " return post/evidencia\n", "\n", "def estimados(x,y,sigma,a):\n", " w=2./sigma**2\n", " alfa=w*np.sum(x**2)\n", " beta=len(x)*w\n", " gamma=np.sum(x)*w\n", " p=np.sum(x*y)*w\n", " q=np.sum(y)*w\n", " m=(beta*p-gamma*q)/(alfa*beta-gamma**2)\n", " c=(alfa*q-gamma*p)/(alfa*beta-gamma**2)\n", " return m,c\n", "\n", "def maximo_incertidumbre(x, y):\n", " deltax = x[1] - x[0]\n", "\n", " # maximo de y\n", " ii = np.argmax(y)\n", "\n", " # segunda derivada\n", " d = (y[ii+1] - 2*y[ii] + y[ii-1]) / (deltax**2)\n", "\n", " return x[ii], 1.0/np.sqrt(-d)\n", " \n", "def newsigma(a,b,x,y):\n", " return np.sum((a*x+c-y)**2)**(2)/(len/(y)-1)\n", "\n", "x = np.linspace(-4.0,4.0,1000)\n", "y = np.linspace(-4.0,4.0,1000)\n", "\n", "def metropolis(x):\n", " \n", " x_walk=np.empty([0,5])\n", " x0=[(np.random.random()-0.5),(np.random.random()-0.5),(np.random.random()-0.5),(np.random.random()-0.5),(np.random.random()-0.5)]\n", " x_walk=np.vstack((x_walk,x0))\n", " \n", " for i in range(20000):\n", " x_guess=[np.random.normal(x_walk[i],0.1),np.random.normal(x_walk[i],0.1),np.random.normal(x_walk[i],0.1),np.random.normal(x_walk[i],0.1),np.random.normal(x_walk[i],0.1)]\n", " print (x_walk[i])\n", " \n", " a=posterior(mu,x,sigma,x_guess)/posterior(mu,x,sigma,x_walk[i])\n", " if a>=1.:\n", " x_walk=np.vstack((x_walk,x_guess))\n", " \n", " else:\n", " b=np.random.random()\n", " if a>=b:\n", " x_walk=np.vstack((x_walk,x_guess))\n", " else:\n", " x_walk=np.vstack((x_walk,x_walk[i]))\n", " return x_walk()\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", " \n", "\n", "data = np.loadtxt(\"notas_andes.dat\", skiprows=1)\n", "Y = data[:,4]\n", "X = data[:,:4]\n", "mu = np.linspace(1E-4, 10.0, 1000)\n", "sigma=0.1\n", "\n", "\n", "plt.figure()\n", "for i in range(5):\n", " plt.subplot(2,2,i+1)\n", " betas=metropolis(X)\n", " plt.hist(betas[:,i],20)\n", " plt.title(r\"$m_{}={:.2f}$ $c_{}={:.2f}$\".format(i+1,m,i+1,c))\n", " plt.xlabel(\"x\")\n", " \n", "\n", "plt.subplots_adjust(hspace=0.55)\n", "plt.savefig(\"ajuste_bayes-mcmc.png\", bbox_inches='tight')\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "m1,c1=estimados(X[:,0],Y,sigma)\n", "\n", "print (m1,c1)\n", "\n", "\n", "plt.figure()\n", "for i in range(4):\n", " plt.subplot(2,2,i+1)\n", " xf=np.linspace(np.amin(X[:,i]),np.amax(X[:,i]),1000)\n", " m,c=estimados(X[:,i],Y,sigma)\n", " plt.scatter(X[:,i], Y)\n", " plt.plot(xf,m*xf+c)\n", " plt.title(r\"$m_{}={:.2f}$ $c_{}={:.2f}$\".format(i+1,m,i+1,c))\n", " plt.xlabel(\"x\")\n", " \n", "\n", "plt.subplots_adjust(hspace=0.55)\n", "plt.savefig(\"bayes.png\", bbox_inches='tight')\n", "\n", "\n", "\n", "\n", "\n", "\n", "\"\"\"\n", "post = posterior(mu, X[:,0], sigma)\n", "max, incertidumbre = maximo_incertidumbre(mu, np.log(post))\n", "plt.figure()\n", "plt.plot(mu, post)\n", "plt.title('$\\mu$= {:.2f} $\\pm$ {:.2f}'.format(max, incertidumbre))\n", "plt.xlabel('$\\mu$')\n", "plt.ylabel('prob($\\mu$|datos)')\n", "plt.savefig('mean.png')\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }

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51efc0d579439f913806f46824ac9f38aeda1fb9

472

py

Python

narwhallet/core/kcl/bip_utils/bip44/__init__.py

Snider/narwhallet

0d528763c735f1e68b8264e302854d41e7cf1956

[ "MIT" ]

3

2021-12-29T11:25:13.000Z

2022-01-16T13:57:17.000Z

narwhallet/core/kcl/bip_utils/bip44/__init__.py

Snider/narwhallet

0d528763c735f1e68b8264e302854d41e7cf1956

[ "MIT" ]

null

narwhallet/core/kcl/bip_utils/bip44/__init__.py

Snider/narwhallet

0d528763c735f1e68b8264e302854d41e7cf1956

[ "MIT" ]

1

2022-01-16T13:57:20.000Z

from narwhallet.core.kcl.bip_utils.bip44.bip44_base_ex import Bip44DepthError, Bip44CoinNotAllowedError from narwhallet.core.kcl.bip_utils.bip44.bip44_base import Bip44Changes, Bip44Coins, Bip44Levels from narwhallet.core.kcl.bip_utils.bip44.bip44_keys import Bip44PublicKey, Bip44PrivateKey from narwhallet.core.kcl.bip_utils.bip44.bip44 import Bip44 from narwhallet.core.kcl.bip_utils.bip44.bip49 import Bip49 from narwhallet.core.kcl.bip_utils.bip44.bip84 import Bip84

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5c508fac9b57431d3270a882b9e47454f2cdd279

3,937

py

Python

resources/test_cases/python/cryptography/TestRule5.py

stg-tud/licma

b899e6e682f7716d19e79d6ce7b73c28c6efd4cf

[ "MIT" ]

5

2021-09-13T11:24:13.000Z

2022-03-18T21:56:58.000Z

resources/test_cases/python/cryptography/TestRule5.py

stg-tud/licma

b899e6e682f7716d19e79d6ce7b73c28c6efd4cf

[ "MIT" ]

null

resources/test_cases/python/cryptography/TestRule5.py

stg-tud/licma

b899e6e682f7716d19e79d6ce7b73c28c6efd4cf

[ "MIT" ]

1

2021-09-13T06:02:20.000Z

from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC from cryptography.hazmat.primitives import hashes from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes from cryptography.hazmat.backends import default_backend g_backend = default_backend() g_count = 999 def p_example1_hard_coded(password, data): kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=999, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def p_example2_local_variable(password, data): count = 999 kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=count, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def p_example3_nested_local_variable(password, data): count1 = 999 count2 = count1 count3 = count2 kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=count3, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def p_example_method_call(password, count, data): kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=count, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def p_example_nested_method_call(password, count, data): return p_example_method_call(password, count, data) def p_example4_direct_method_call(password, data): count = 999 return p_example_method_call(password, count, data) def p_example5_nested_method_call(password, data): count = 999 return p_example_nested_method_call(password, count, data) def p_example6_direct_g_variable_access(password, data): kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=g_count, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def p_example7_indirect_g_variable_access(password, data): count = g_count kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=count, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def p_example8_warning_parameter_not_resolvable(password, count, data): kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=count, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text def n_example1_iterations_eq_1000(password, data): kdf = PBKDF2HMAC(algorithm=hashes.SHA256(), length=16, salt=b"12345678", iterations=1000, backend=g_backend) key = kdf.derive(password) cipher = Cipher(algorithms.AES(key), modes.ECB(), backend=g_backend) encryptor = cipher.encryptor() cipher_text = encryptor.update(data) + encryptor.finalize() return cipher_text

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7

5c8fc229e2d0019f171db64ae70c454b317fe8d2

125

py

Python

utils.py

alan-toledo/Python-Unit-Testing

46acc2478faa2ed2a5932e54ad04c0cf57d62994

[ "MIT" ]

null

utils.py

alan-toledo/Python-Unit-Testing

46acc2478faa2ed2a5932e54ad04c0cf57d62994

[ "MIT" ]

null

utils.py

alan-toledo/Python-Unit-Testing

46acc2478faa2ed2a5932e54ad04c0cf57d62994

[ "MIT" ]

null

def get_max(lst): return max(lst) def get_min(lst): return min(lst) def get_avg(lst): return sum(lst)/len(lst)

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py

Python

tests/unit/test_FlowEntryManager.py

aristanetworks/DirectFlowAssist

16c594cb42edd8aa084c6dbb931c87bbdff81ed0

[ "Apache-2.0" ]

null

tests/unit/test_FlowEntryManager.py

aristanetworks/DirectFlowAssist

16c594cb42edd8aa084c6dbb931c87bbdff81ed0

[ "Apache-2.0" ]

null

tests/unit/test_FlowEntryManager.py

aristanetworks/DirectFlowAssist

16c594cb42edd8aa084c6dbb931c87bbdff81ed0

[ "Apache-2.0" ]

null

# # Copyright (c) 2015 Arista Networks, Inc. All rights reserved. # Arista Networks, Inc. Confidential and Proprietary. # # pylint: disable = line-too-long import sys # sys.path.extend(['../..','../../persist_common', '../../persist_pan']) import unittest import logging import config from mock import Mock, patch from directflow_assist import FlowEntryManager TCAM_STATS = {'num_avail': 1000, 'num_used': 500, 'pct_used': 50} ACTIVE_FLOWS_1 = [ { "priority": 40, "matchPackets": 20, "matchBytes": 0, "bridgeMacAddr": "00:1c:73:74:81:9e", "name": "BYPASS_FW_ping_ICMP_172-22-28-42_172-22-225-127_May26_11:19:20_RSP", "action": { "outputNormal": False, "outputLocal": False, "ipTos": 8, "loopback": False, "outInterfaces": [ "Port-Channel10" ], "vlanPCP": 3, "egrMirrorInterfaces": [], "outputAll": False, "outputController": False, "outputDrop": False, "outputFlood": False, "ingrMirrorInterfaces": [] }, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "match": { "inInterfaces": [ "Port-Channel20" ], "unknownL3V4MulticastAddress": False, "ethType": 2048, "ethTypeMask": 65535, "tcpSyn": False, "ipSrc": { "mask": "255.255.255.255", "ip": "172.22.28.42" }, "tcpPsh": False, "tcpUrg": False, "tcpFin": False, "tcpRst": False, "ipProto": 1, "unknownL2V4MulticastAddress": False, "tcpAck": False, "ipDst": { "mask": "255.255.255.255", "ip": "172.22.225.127" } } }, { "priority": 40, "matchPackets": 10, "matchBytes": 0, "bridgeMacAddr": "00:1c:73:74:81:9e", "name": "BYPASS_FW_ping_ICMP_172-22-225-127_172-22-28-42_May26_11:19:20_INI", "action": { "outputNormal": False, "outputLocal": False, "ipTos": 8, "loopback": False, "outInterfaces": [ "Port-Channel20" ], "vlanPCP": 3, "egrMirrorInterfaces": [], "outputAll": False, "outputController": False, "outputDrop": False, "outputFlood": False, "ingrMirrorInterfaces": [] }, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "match": { "inInterfaces": [ "Port-Channel10" ], "unknownL3V4MulticastAddress": False, "ethType": 2048, "ethTypeMask": 65535, "tcpSyn": False, "ipSrc": { "mask": "255.255.255.255", "ip": "172.22.225.127" }, "tcpPsh": False, "tcpUrg": False, "tcpFin": False, "tcpRst": False, "ipProto": 1, "unknownL2V4MulticastAddress": False, "tcpAck": False, "ipDst": { "mask": "255.255.255.255", "ip": "172.22.28.42" } } }] ACTIVE_FLOWS_2 = [ { "priority": 40, "matchPackets": 620, "matchBytes": 0, "bridgeMacAddr": "00:1c:73:74:81:9e", "name": "BYPASS_FW_ping_ICMP_172-22-28-42_172-22-225-127_May26_11:19:20_RSP", "action": { "outputNormal": False, "outputLocal": False, "ipTos": 8, "loopback": False, "outInterfaces": [ "Port-Channel10" ], "vlanPCP": 3, "egrMirrorInterfaces": [], "outputAll": False, "outputController": False, "outputDrop": False, "outputFlood": False, "ingrMirrorInterfaces": [] }, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "match": { "inInterfaces": [ "Port-Channel20" ], "unknownL3V4MulticastAddress": False, "ethType": 2048, "ethTypeMask": 65535, "tcpSyn": False, "ipSrc": { "mask": "255.255.255.255", "ip": "172.22.28.42" }, "tcpPsh": False, "tcpUrg": False, "tcpFin": False, "tcpRst": False, "ipProto": 1, "unknownL2V4MulticastAddress": False, "tcpAck": False, "ipDst": { "mask": "255.255.255.255", "ip": "172.22.225.127" } } }, { "priority": 40, "matchPackets": 310, "matchBytes": 0, "bridgeMacAddr": "00:1c:73:74:81:9e", "name": "BYPASS_FW_ping_ICMP_172-22-225-127_172-22-28-42_May26_11:19:20_INI", "action": { "outputNormal": False, "outputLocal": False, "ipTos": 8, "loopback": False, "outInterfaces": [ "Port-Channel20" ], "vlanPCP": 3, "egrMirrorInterfaces": [], "outputAll": False, "outputController": False, "outputDrop": False, "outputFlood": False, "ingrMirrorInterfaces": [] }, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "match": { "inInterfaces": [ "Port-Channel10" ], "unknownL3V4MulticastAddress": False, "ethType": 2048, "ethTypeMask": 65535, "tcpSyn": False, "ipSrc": { "mask": "255.255.255.255", "ip": "172.22.225.127" }, "tcpPsh": False, "tcpUrg": False, "tcpFin": False, "tcpRst": False, "ipProto": 1, "unknownL2V4MulticastAddress": False, "tcpAck": False, "ipDst": { "mask": "255.255.255.255", "ip": "172.22.28.42" } } }] ACTIVE_FLOWS_3 = [ {"name": "BYPASS_FW_1_INI", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.1"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.2"}}}, {"name": "BYPASS_FW_1_RSP", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.2"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.1"}}}, {"name": "BYPASS_FW_2_INI", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.3"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.4"}}}, {"name": "BYPASS_FW_2_RSP", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.4"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.3"}}}, {"name": "BYPASS_FW_3_INI", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.5"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.6"}}}, {"name": "BYPASS_FW_3_RSP", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.6"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.5"}}}, {"name": "BYPASS_FW_4_INI", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.7"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.8"}}}, {"name": "BYPASS_FW_4_RSP", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.8"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.7"}}}, {"name": "BYPASS_FW_5_INI", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.9"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.10"}}}, {"name": "BYPASS_FW_5_RSP", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.10"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.9"}}}, ] ACTIVE_FLOWS_4 = [ {"name": "BYPASS_FW_1_INI", "priority": 40, "matchPackets": 6000, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.1"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.2"}}}, {"name": "BYPASS_FW_1_RSP", "priority": 40, "matchPackets": 6000, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.2"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.1"}}}, {"name": "BYPASS_FW_2_INI", "priority": 40, "matchPackets": 3000, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.3"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.4"}}}, {"name": "BYPASS_FW_2_RSP", "priority": 40, "matchPackets": 3000, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.4"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.3"}}}, {"name": "BYPASS_FW_3_INI", "priority": 40, "matchPackets": 600, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.5"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.6"}}}, {"name": "BYPASS_FW_3_RSP", "priority": 40, "matchPackets": 600, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.6"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.5"}}}, {"name": "BYPASS_FW_4_INI", "priority": 40, "matchPackets": 300, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.7"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.8"}}}, {"name": "BYPASS_FW_4_RSP", "priority": 40, "matchPackets": 300, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.8"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.7"}}}, {"name": "BYPASS_FW_5_INI", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel10"]}, "match": {"inInterfaces": ["Port-Channel20"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.9"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.10"}}}, {"name": "BYPASS_FW_5_RSP", "priority": 40, "matchPackets": 0, "hardTimeout": 600, "idleTimeout": 300, "persistent": False, "action": {"outInterfaces": ["Port-Channel20"]}, "match": {"inInterfaces": ["Port-Channel10"], "ipSrc": {"mask": "255.255.255.255", "ip": "1.1.1.10"}, "ipProto": 6, "ipDst": {"mask": "255.255.255.255", "ip": "1.1.1.9"}}}, ] DBG1= False class TestFlowEntryManager(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def test_update_flow_rates_cache_and_rate_calcs(self): fem = FlowEntryManager.FlowEntryMgr() fem.directflow_switch.get_active_flows = Mock(return_value=ACTIVE_FLOWS_1) fem.update_flow_rates_cache() fem.directflow_switch.get_active_flows.assert_called_with() key = 'ICMP_172.22.225.127:_172.22.28.42:' self.assertTrue(key in fem.flow_rates_cache) cache_entry = fem.flow_rates_cache[key] self.assertTrue(cache_entry.is_current) self.assertTrue(cache_entry.is_bypass) self.assertEqual(cache_entry.rate, -1) flow_ini_key = 'BYPASS_FW_ping_ICMP_172-22-225-127_172-22-28-42_May26_11:19:20_INI' flow_rsp_key = 'BYPASS_FW_ping_ICMP_172-22-28-42_172-22-225-127_May26_11:19:20_RSP' self.assertTrue(flow_ini_key in cache_entry.flows) self.assertTrue(flow_rsp_key in cache_entry.flows) fem.directflow_switch.get_active_flows = Mock(return_value=ACTIVE_FLOWS_2) fem.update_flow_rates_cache() self.assertEqual(cache_entry.rate, 7) def test_reap_least_active_flows(self): fem = FlowEntryManager.FlowEntryMgr() fem.directflow_switch.get_active_flows = Mock(return_value=ACTIVE_FLOWS_3) fem.update_flow_rates_cache() if DBG1: for k,v in fem.flow_rates_cache.items(): print 'A***flow_rates_cache: %s %s' % (k,v) config.TCAM_REAP_THRESHOLD_PCT = 50 config.TCAM_REAP_LEAST_ACTIVE_PCT = 40 tcam_stats = {'num_avail': 20, 'num_used': 10, 'pct_used': 50} fem.directflow_switch.tcam_directflow_utilization = Mock(return_value=tcam_stats) fem.directflow_switch.get_active_flows = Mock(return_value=ACTIVE_FLOWS_4) fem.directflow_switch.delete_flows = Mock() fem.reap_least_active_flows(tcam_stats) if DBG1: print ('TCAM_REAP_THRESHOaLD_PCT: %d, TCAM_REAP_LEAST_ACTIVE_PCT: %d' %(config.TCAM_REAP_THRESHOLD_PCT, config.TCAM_REAP_LEAST_ACTIVE_PCT)) for k,v in fem.flow_rates_cache.items(): print 'B***flow_rates_cache: %s %s' % (k,v) # self.assertTrue(False) # force buffer dump (unittest -b option) least_active = ['BYPASS_FW_5_INI', 'BYPASS_FW_5_RSP', 'BYPASS_FW_4_INI', 'BYPASS_FW_4_RSP'] fem.directflow_switch.delete_flows.assert_called_with(least_active) if __name__ == '__main__': unittest.main()

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Python

L1TriggerConfig/L1GtConfigProducers/python/L1GtConfig_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

L1TriggerConfig/L1GtConfigProducers/python/L1GtConfig_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

L1TriggerConfig/L1GtConfigProducers/python/L1GtConfig_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms # cff file grouping all the L1 GT parameters from L1TriggerConfig.L1GtConfigProducers.L1GtStableParametersConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtParametersConfig_cff import * # from L1TriggerConfig.L1GtConfigProducers.L1GtPrescaleFactorsAlgoTrigConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtPrescaleFactorsTechTrigConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtTriggerMaskAlgoTrigConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtTriggerMaskTechTrigConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtTriggerMaskVetoAlgoTrigConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtTriggerMaskVetoTechTrigConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtBoardMapsConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtPsbSetupConfig_cff import * from L1TriggerConfig.L1GtConfigProducers.L1GtTriggerMenuConfig_cff import *

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py

Python

PhysicsTools/PatAlgos/python/triggerLayer1/triggerMatcherExamples_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

PhysicsTools/PatAlgos/python/triggerLayer1/triggerMatcherExamples_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

PhysicsTools/PatAlgos/python/triggerLayer1/triggerMatcherExamples_cfi.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

import FWCore.ParameterSet.Config as cms # Examples for configurations of the trigger match for various physics objects # # A detailed description is given in # https://twiki.cern.ch/twiki/bin/view/CMS/SWGuidePATTrigger#PATTriggerMatcher # Cuts on the parameters # - 'maxDPtRel' and # - 'maxDeltaR' # are NOT tuned (using old values from TQAF MC match, January 2008) ## Example matches ## # firing trigger objects used in succeeding HLT path 'HLT_Mu17' somePatMuonTriggerMatchHLTMu17 = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatMuons" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_Mu17_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_DoubleMu5_IsoMu5' somePatMuonTriggerMatchHLTDoubleMu5IsoMu5 = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatMuons" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_DoubleMu5_IsoMu5_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_Photon26_Photon18' somePatPhotonTriggerMatchHLTPhoton26Photon18 = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatPhotons" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_Photon26_Photon18_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_Ele17_CaloIdT_CaloIsoVL_TrkIdVL_TrkIsoVL' somePatElectronTriggerMatchHLTEle17CaloIdTCaloIsoVLTrkIdVLTrkIsoVL = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatElectrons" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_Ele17_CaloIdT_CaloIsoVL_TrkIdVL_TrkIsoVL_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_DoubleMediumIsoPFTau30_Trk1_eta2p1' somePatTauTriggerMatchHLTDoubleMediumIsoPFTau30Trk1eta2p1 = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatTaus" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_DoubleMediumIsoPFTau30_Trk1_eta2p1_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_PFJet40' somePatJetTriggerMatchHLTPFJet40 = cms.EDProducer( "PATTriggerMatcherDRLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( 'selectedPatJets' ) , matched = cms.InputTag( 'patTrigger' ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_PFJet40_v*" )' ) , maxDPtRel = cms.double( 3.0 ) , maxDeltaR = cms.double( 0.4 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_MET120' somePatMetTriggerMatchHLTMET120 = cms.EDProducer( "PATTriggerMatcherDRLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( 'patMETs' ) , matched = cms.InputTag( 'patTrigger' ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_MET120_v*" )' ) , maxDPtRel = cms.double( 3.0 ) , maxDeltaR = cms.double( 0.4 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # firing trigger objects used in succeeding HLT path 'HLT_Mu8_DiJet30' (x-trigger) somePatMuonTriggerMatchHLTMu8DiJet30 = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatMuons" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'type( "TriggerMuon" ) && path( "HLT_Mu8_DiJet30_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) somePatJetTriggerMatchHLTMu8DiJet30 = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( "selectedPatJets" ) , matched = cms.InputTag( "patTrigger" ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'type( "TriggerJet" ) && path( "HLT_Mu8_DiJet30_v*" )' ) , maxDPtRel = cms.double( 3.0 ) , maxDeltaR = cms.double( 0.4 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) _exampleTriggerMatchers = [ 'somePatMuonTriggerMatchHLTMu17' , 'somePatMuonTriggerMatchHLTDoubleMu5IsoMu5' , 'somePatPhotonTriggerMatchHLTPhoton26Photon18' , 'somePatElectronTriggerMatchHLTEle17CaloIdTCaloIsoVLTrkIdVLTrkIsoVL' , 'somePatTauTriggerMatchHLTDoubleMediumIsoPFTau30Trk1eta2p1' , 'somePatJetTriggerMatchHLTPFJet40' , 'somePatMetTriggerMatchHLTMET120' , 'somePatMuonTriggerMatchHLTMu8DiJet30' , 'somePatJetTriggerMatchHLTMu8DiJet30' ] ## Further examples ## # L1 e/gammas by original collection somePatElectronTriggerMatchL1EGammaCollection = cms.EDProducer( "PATTriggerMatcherDRLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( 'selectedPatElectrons' ) , matched = cms.InputTag( 'patTrigger' ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'coll( "l1extraParticles:NonIsolated" ) || coll( "l1extraParticles:Isolated" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( False ) # take first match found per reco object ) # L1 and HLT muons by ID somePatMuonTriggerMatchTriggerMuon = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR and DeltaPt, best match by DeltaR , src = cms.InputTag( 'selectedPatMuons' ) , matched = cms.InputTag( 'patTrigger' ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'type( "TriggerL1Mu" ) || type( "TriggerMuon" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( False ) # take first match found per reco object ) # firing trigger objects used in succeeding HLT paths of PD /SingleMu somePatMuonTriggerMatchPDSingleMu = cms.EDProducer( "PATTriggerMatcherDRDPtLessByR" # match by DeltaR and DeltaPt, best match by DeltaR , src = cms.InputTag( 'selectedPatMuons' ) , matched = cms.InputTag( 'patTrigger' ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_RelIso1p0Mu5_v*" ) || path( "HLT_RelIso1p0Mu20_v*" ) || path( "HLT_Mu5_v*" ) || path( "HLT_Mu50_eta2p1_v*" ) || path( "HLT_Mu40_v*" ) || path( "HLT_Mu40_eta2p1_v*" ) || path( "HLT_Mu40_eta2p1_Track60_dEdx3p7_v*" ) || path( "HLT_Mu40_eta2p1_Track50_dEdx3p6_v*" ) || path( "HLT_Mu30_v*" ) || path( "HLT_Mu30_eta2p1_v*" ) || path( "HLT_Mu24_v*" ) || path( "HLT_Mu24_eta2p1_v*" ) || path( "HLT_Mu24_PFJet30_PFJet25_Deta3_CentralPFJet25_v*" ) || path( "HLT_Mu24_CentralPFJet30_CentralPFJet25_v*" ) || path( "HLT_Mu24_CentralPFJet30_CentralPFJet25_v*" ) || path( "HLT_Mu17_eta2p1_TriCentralPFNoPUJet45_35_25_v*" ) || path( "HLT_Mu17_eta2p1_CentralPFNoPUJet30_BTagIPIter_v*" ) || path( "HLT_Mu15_eta2p1_v*" ) || path( "HLT_Mu15_eta2p1_TriCentral_40_20_20_v*" ) || path( "HLT_Mu15_eta2p1_TriCentral_40_20_20_DiBTagIP3D1stTrack_v*" ) || path( "HLT_Mu15_eta2p1_TriCentral_40_20_20_BTagIP3D1stTrack_v*" ) || path( "HLT_Mu15_eta2p1_L1Mu10erJetC12WdEtaPhi1DiJetsC_v*" ) || path( "HLT_Mu12_v*" ) || path( "HLT_Mu12_eta2p1_L1Mu10erJetC12WdEtaPhi1DiJetsC_v*" ) || path( "HLT_Mu12_eta2p1_DiCentral_40_20_v*" ) || path( "HLT_Mu12_eta2p1_DiCentral_40_20_DiBTagIP3D1stTrack_v*" ) || path( "HLT_Mu12_eta2p1_DiCentral_20_v*" ) || path( "HLT_L2Mu70_2Cha_eta2p1_PFMET60_v*" ) || path( "HLT_L2Mu70_2Cha_eta2p1_PFMET55_v*" ) || path( "HLT_IsoMu40_eta2p1_v*" ) || path( "HLT_IsoMu34_eta2p1_v*" ) || path( "HLT_IsoMu30_v*" ) || path( "HLT_IsoMu30_eta2p1_v*" ) || path( "HLT_IsoMu24_v*" ) || path( "HLT_IsoMu24_eta2p1_v*" ) || path( "HLT_IsoMu24_PFJet30_PFJet25_Deta3_CentralPFJet25_v*" ) || path( "HLT_IsoMu24_CentralPFJet30_CentralPFJet25_v*" ) || path( "HLT_IsoMu24_CentralPFJet30_CentralPFJet25_PFMET20_v*" ) || path( "HLT_IsoMu20_eta2p1_v*" ) || path( "HLT_IsoMu20_eta2p1_CentralPFJet80_v*" ) || path( "HLT_IsoMu20_WCandPt80_v*" ) || path( "HLT_IsoMu17_eta2p1_TriCentralPFNoPUJet45_35_25_v*" ) || path( "HLT_IsoMu17_eta2p1_TriCentralPFNoPUJet30_v*" ) || path( "HLT_IsoMu17_eta2p1_DiCentralPFNoPUJet30_v*" ) || path( "HLT_IsoMu17_eta2p1_CentralPFNoPUJet30_v*" ) || path( "HLT_IsoMu17_eta2p1_CentralPFNoPUJet30_BTagIPIter_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) # all trigger objects used in HLT path 'HLT_Mu17' (fake MET) somePatMetTriggerMatchHLTMu17 = cms.EDProducer( "PATTriggerMatcherDRLessByR" # match by DeltaR only, best match by DeltaR , src = cms.InputTag( 'patMETs' ) , matched = cms.InputTag( 'patTrigger' ) # default producer label as defined in PhysicsTools/PatAlgos/python/triggerLayer1/triggerProducer_cfi.py , matchedCuts = cms.string( 'path( "HLT_Mu17_v*" )' ) , maxDPtRel = cms.double( 0.5 ) , maxDeltaR = cms.double( 0.5 ) , resolveAmbiguities = cms.bool( True ) # only one match per trigger object , resolveByMatchQuality = cms.bool( True ) # take best match found per reco object: by DeltaR here (s. above) ) triggerMatcherExamplesTask = cms.Task( somePatMuonTriggerMatchHLTMu17, somePatMuonTriggerMatchHLTDoubleMu5IsoMu5, somePatPhotonTriggerMatchHLTPhoton26Photon18, somePatElectronTriggerMatchHLTEle17CaloIdTCaloIsoVLTrkIdVLTrkIsoVL, somePatTauTriggerMatchHLTDoubleMediumIsoPFTau30Trk1eta2p1, somePatJetTriggerMatchHLTPFJet40, somePatMetTriggerMatchHLTMET120, somePatMuonTriggerMatchHLTMu8DiJet30, somePatJetTriggerMatchHLTMu8DiJet30, somePatElectronTriggerMatchL1EGammaCollection, somePatMuonTriggerMatchTriggerMuon, somePatMuonTriggerMatchPDSingleMu, somePatMetTriggerMatchHLTMu17 )

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24,592

py

Python

sdk/python/pulumi_snowflake/pipe.py

Hacker0x01/pulumi-snowflake

f6ebcf2c3f73b103a7c2001fae231998ce1323b2

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

3

2021-07-01T17:03:33.000Z

2022-03-01T19:29:04.000Z

sdk/python/pulumi_snowflake/pipe.py

Hacker0x01/pulumi-snowflake

f6ebcf2c3f73b103a7c2001fae231998ce1323b2

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

102

2021-07-14T13:12:58.000Z

2022-03-31T18:34:04.000Z

sdk/python/pulumi_snowflake/pipe.py

Hacker0x01/pulumi-snowflake

f6ebcf2c3f73b103a7c2001fae231998ce1323b2

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

1

2022-03-25T07:24:45.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['PipeArgs', 'Pipe'] @pulumi.input_type class PipeArgs: def __init__(__self__, *, copy_statement: pulumi.Input[str], database: pulumi.Input[str], schema: pulumi.Input[str], auto_ingest: Optional[pulumi.Input[bool]] = None, aws_sns_topic_arn: Optional[pulumi.Input[str]] = None, comment: Optional[pulumi.Input[str]] = None, error_integration: Optional[pulumi.Input[str]] = None, integration: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a Pipe resource. :param pulumi.Input[str] copy_statement: Specifies the copy statement for the pipe. :param pulumi.Input[str] database: The database in which to create the pipe. :param pulumi.Input[str] schema: The schema in which to create the pipe. :param pulumi.Input[bool] auto_ingest: Specifies a auto_ingest param for the pipe. :param pulumi.Input[str] aws_sns_topic_arn: Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. :param pulumi.Input[str] comment: Specifies a comment for the pipe. :param pulumi.Input[str] error_integration: Specifies the name of the notification integration used for error notifications. :param pulumi.Input[str] integration: Specifies an integration for the pipe. :param pulumi.Input[str] name: Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. """ pulumi.set(__self__, "copy_statement", copy_statement) pulumi.set(__self__, "database", database) pulumi.set(__self__, "schema", schema) if auto_ingest is not None: pulumi.set(__self__, "auto_ingest", auto_ingest) if aws_sns_topic_arn is not None: pulumi.set(__self__, "aws_sns_topic_arn", aws_sns_topic_arn) if comment is not None: pulumi.set(__self__, "comment", comment) if error_integration is not None: pulumi.set(__self__, "error_integration", error_integration) if integration is not None: pulumi.set(__self__, "integration", integration) if name is not None: pulumi.set(__self__, "name", name) @property @pulumi.getter(name="copyStatement") def copy_statement(self) -> pulumi.Input[str]: """ Specifies the copy statement for the pipe. """ return pulumi.get(self, "copy_statement") @copy_statement.setter def copy_statement(self, value: pulumi.Input[str]): pulumi.set(self, "copy_statement", value) @property @pulumi.getter def database(self) -> pulumi.Input[str]: """ The database in which to create the pipe. """ return pulumi.get(self, "database") @database.setter def database(self, value: pulumi.Input[str]): pulumi.set(self, "database", value) @property @pulumi.getter def schema(self) -> pulumi.Input[str]: """ The schema in which to create the pipe. """ return pulumi.get(self, "schema") @schema.setter def schema(self, value: pulumi.Input[str]): pulumi.set(self, "schema", value) @property @pulumi.getter(name="autoIngest") def auto_ingest(self) -> Optional[pulumi.Input[bool]]: """ Specifies a auto_ingest param for the pipe. """ return pulumi.get(self, "auto_ingest") @auto_ingest.setter def auto_ingest(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "auto_ingest", value) @property @pulumi.getter(name="awsSnsTopicArn") def aws_sns_topic_arn(self) -> Optional[pulumi.Input[str]]: """ Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. """ return pulumi.get(self, "aws_sns_topic_arn") @aws_sns_topic_arn.setter def aws_sns_topic_arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "aws_sns_topic_arn", value) @property @pulumi.getter def comment(self) -> Optional[pulumi.Input[str]]: """ Specifies a comment for the pipe. """ return pulumi.get(self, "comment") @comment.setter def comment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "comment", value) @property @pulumi.getter(name="errorIntegration") def error_integration(self) -> Optional[pulumi.Input[str]]: """ Specifies the name of the notification integration used for error notifications. """ return pulumi.get(self, "error_integration") @error_integration.setter def error_integration(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "error_integration", value) @property @pulumi.getter def integration(self) -> Optional[pulumi.Input[str]]: """ Specifies an integration for the pipe. """ return pulumi.get(self, "integration") @integration.setter def integration(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "integration", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @pulumi.input_type class _PipeState: def __init__(__self__, *, auto_ingest: Optional[pulumi.Input[bool]] = None, aws_sns_topic_arn: Optional[pulumi.Input[str]] = None, comment: Optional[pulumi.Input[str]] = None, copy_statement: Optional[pulumi.Input[str]] = None, database: Optional[pulumi.Input[str]] = None, error_integration: Optional[pulumi.Input[str]] = None, integration: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, notification_channel: Optional[pulumi.Input[str]] = None, owner: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering Pipe resources. :param pulumi.Input[bool] auto_ingest: Specifies a auto_ingest param for the pipe. :param pulumi.Input[str] aws_sns_topic_arn: Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. :param pulumi.Input[str] comment: Specifies a comment for the pipe. :param pulumi.Input[str] copy_statement: Specifies the copy statement for the pipe. :param pulumi.Input[str] database: The database in which to create the pipe. :param pulumi.Input[str] error_integration: Specifies the name of the notification integration used for error notifications. :param pulumi.Input[str] integration: Specifies an integration for the pipe. :param pulumi.Input[str] name: Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. :param pulumi.Input[str] notification_channel: Amazon Resource Name of the Amazon SQS queue for the stage named in the DEFINITION column. :param pulumi.Input[str] owner: Name of the role that owns the pipe. :param pulumi.Input[str] schema: The schema in which to create the pipe. """ if auto_ingest is not None: pulumi.set(__self__, "auto_ingest", auto_ingest) if aws_sns_topic_arn is not None: pulumi.set(__self__, "aws_sns_topic_arn", aws_sns_topic_arn) if comment is not None: pulumi.set(__self__, "comment", comment) if copy_statement is not None: pulumi.set(__self__, "copy_statement", copy_statement) if database is not None: pulumi.set(__self__, "database", database) if error_integration is not None: pulumi.set(__self__, "error_integration", error_integration) if integration is not None: pulumi.set(__self__, "integration", integration) if name is not None: pulumi.set(__self__, "name", name) if notification_channel is not None: pulumi.set(__self__, "notification_channel", notification_channel) if owner is not None: pulumi.set(__self__, "owner", owner) if schema is not None: pulumi.set(__self__, "schema", schema) @property @pulumi.getter(name="autoIngest") def auto_ingest(self) -> Optional[pulumi.Input[bool]]: """ Specifies a auto_ingest param for the pipe. """ return pulumi.get(self, "auto_ingest") @auto_ingest.setter def auto_ingest(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "auto_ingest", value) @property @pulumi.getter(name="awsSnsTopicArn") def aws_sns_topic_arn(self) -> Optional[pulumi.Input[str]]: """ Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. """ return pulumi.get(self, "aws_sns_topic_arn") @aws_sns_topic_arn.setter def aws_sns_topic_arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "aws_sns_topic_arn", value) @property @pulumi.getter def comment(self) -> Optional[pulumi.Input[str]]: """ Specifies a comment for the pipe. """ return pulumi.get(self, "comment") @comment.setter def comment(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "comment", value) @property @pulumi.getter(name="copyStatement") def copy_statement(self) -> Optional[pulumi.Input[str]]: """ Specifies the copy statement for the pipe. """ return pulumi.get(self, "copy_statement") @copy_statement.setter def copy_statement(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "copy_statement", value) @property @pulumi.getter def database(self) -> Optional[pulumi.Input[str]]: """ The database in which to create the pipe. """ return pulumi.get(self, "database") @database.setter def database(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "database", value) @property @pulumi.getter(name="errorIntegration") def error_integration(self) -> Optional[pulumi.Input[str]]: """ Specifies the name of the notification integration used for error notifications. """ return pulumi.get(self, "error_integration") @error_integration.setter def error_integration(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "error_integration", value) @property @pulumi.getter def integration(self) -> Optional[pulumi.Input[str]]: """ Specifies an integration for the pipe. """ return pulumi.get(self, "integration") @integration.setter def integration(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "integration", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter(name="notificationChannel") def notification_channel(self) -> Optional[pulumi.Input[str]]: """ Amazon Resource Name of the Amazon SQS queue for the stage named in the DEFINITION column. """ return pulumi.get(self, "notification_channel") @notification_channel.setter def notification_channel(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "notification_channel", value) @property @pulumi.getter def owner(self) -> Optional[pulumi.Input[str]]: """ Name of the role that owns the pipe. """ return pulumi.get(self, "owner") @owner.setter def owner(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "owner", value) @property @pulumi.getter def schema(self) -> Optional[pulumi.Input[str]]: """ The schema in which to create the pipe. """ return pulumi.get(self, "schema") @schema.setter def schema(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "schema", value) class Pipe(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, auto_ingest: Optional[pulumi.Input[bool]] = None, aws_sns_topic_arn: Optional[pulumi.Input[str]] = None, comment: Optional[pulumi.Input[str]] = None, copy_statement: Optional[pulumi.Input[str]] = None, database: Optional[pulumi.Input[str]] = None, error_integration: Optional[pulumi.Input[str]] = None, integration: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[str]] = None, __props__=None): """ ## Import # format is database name | schema name | pipe name ```sh $ pulumi import snowflake:index/pipe:Pipe example 'dbName|schemaName|pipeName' ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] auto_ingest: Specifies a auto_ingest param for the pipe. :param pulumi.Input[str] aws_sns_topic_arn: Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. :param pulumi.Input[str] comment: Specifies a comment for the pipe. :param pulumi.Input[str] copy_statement: Specifies the copy statement for the pipe. :param pulumi.Input[str] database: The database in which to create the pipe. :param pulumi.Input[str] error_integration: Specifies the name of the notification integration used for error notifications. :param pulumi.Input[str] integration: Specifies an integration for the pipe. :param pulumi.Input[str] name: Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. :param pulumi.Input[str] schema: The schema in which to create the pipe. """ ... @overload def __init__(__self__, resource_name: str, args: PipeArgs, opts: Optional[pulumi.ResourceOptions] = None): """ ## Import # format is database name | schema name | pipe name ```sh $ pulumi import snowflake:index/pipe:Pipe example 'dbName|schemaName|pipeName' ``` :param str resource_name: The name of the resource. :param PipeArgs 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(PipeArgs, 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, auto_ingest: Optional[pulumi.Input[bool]] = None, aws_sns_topic_arn: Optional[pulumi.Input[str]] = None, comment: Optional[pulumi.Input[str]] = None, copy_statement: Optional[pulumi.Input[str]] = None, database: Optional[pulumi.Input[str]] = None, error_integration: Optional[pulumi.Input[str]] = None, integration: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = PipeArgs.__new__(PipeArgs) __props__.__dict__["auto_ingest"] = auto_ingest __props__.__dict__["aws_sns_topic_arn"] = aws_sns_topic_arn __props__.__dict__["comment"] = comment if copy_statement is None and not opts.urn: raise TypeError("Missing required property 'copy_statement'") __props__.__dict__["copy_statement"] = copy_statement if database is None and not opts.urn: raise TypeError("Missing required property 'database'") __props__.__dict__["database"] = database __props__.__dict__["error_integration"] = error_integration __props__.__dict__["integration"] = integration __props__.__dict__["name"] = name if schema is None and not opts.urn: raise TypeError("Missing required property 'schema'") __props__.__dict__["schema"] = schema __props__.__dict__["notification_channel"] = None __props__.__dict__["owner"] = None super(Pipe, __self__).__init__( 'snowflake:index/pipe:Pipe', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, auto_ingest: Optional[pulumi.Input[bool]] = None, aws_sns_topic_arn: Optional[pulumi.Input[str]] = None, comment: Optional[pulumi.Input[str]] = None, copy_statement: Optional[pulumi.Input[str]] = None, database: Optional[pulumi.Input[str]] = None, error_integration: Optional[pulumi.Input[str]] = None, integration: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, notification_channel: Optional[pulumi.Input[str]] = None, owner: Optional[pulumi.Input[str]] = None, schema: Optional[pulumi.Input[str]] = None) -> 'Pipe': """ Get an existing Pipe resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[bool] auto_ingest: Specifies a auto_ingest param for the pipe. :param pulumi.Input[str] aws_sns_topic_arn: Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. :param pulumi.Input[str] comment: Specifies a comment for the pipe. :param pulumi.Input[str] copy_statement: Specifies the copy statement for the pipe. :param pulumi.Input[str] database: The database in which to create the pipe. :param pulumi.Input[str] error_integration: Specifies the name of the notification integration used for error notifications. :param pulumi.Input[str] integration: Specifies an integration for the pipe. :param pulumi.Input[str] name: Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. :param pulumi.Input[str] notification_channel: Amazon Resource Name of the Amazon SQS queue for the stage named in the DEFINITION column. :param pulumi.Input[str] owner: Name of the role that owns the pipe. :param pulumi.Input[str] schema: The schema in which to create the pipe. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _PipeState.__new__(_PipeState) __props__.__dict__["auto_ingest"] = auto_ingest __props__.__dict__["aws_sns_topic_arn"] = aws_sns_topic_arn __props__.__dict__["comment"] = comment __props__.__dict__["copy_statement"] = copy_statement __props__.__dict__["database"] = database __props__.__dict__["error_integration"] = error_integration __props__.__dict__["integration"] = integration __props__.__dict__["name"] = name __props__.__dict__["notification_channel"] = notification_channel __props__.__dict__["owner"] = owner __props__.__dict__["schema"] = schema return Pipe(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="autoIngest") def auto_ingest(self) -> pulumi.Output[Optional[bool]]: """ Specifies a auto_ingest param for the pipe. """ return pulumi.get(self, "auto_ingest") @property @pulumi.getter(name="awsSnsTopicArn") def aws_sns_topic_arn(self) -> pulumi.Output[Optional[str]]: """ Specifies the Amazon Resource Name (ARN) for the SNS topic for your S3 bucket. """ return pulumi.get(self, "aws_sns_topic_arn") @property @pulumi.getter def comment(self) -> pulumi.Output[Optional[str]]: """ Specifies a comment for the pipe. """ return pulumi.get(self, "comment") @property @pulumi.getter(name="copyStatement") def copy_statement(self) -> pulumi.Output[str]: """ Specifies the copy statement for the pipe. """ return pulumi.get(self, "copy_statement") @property @pulumi.getter def database(self) -> pulumi.Output[str]: """ The database in which to create the pipe. """ return pulumi.get(self, "database") @property @pulumi.getter(name="errorIntegration") def error_integration(self) -> pulumi.Output[Optional[str]]: """ Specifies the name of the notification integration used for error notifications. """ return pulumi.get(self, "error_integration") @property @pulumi.getter def integration(self) -> pulumi.Output[Optional[str]]: """ Specifies an integration for the pipe. """ return pulumi.get(self, "integration") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Specifies the identifier for the pipe; must be unique for the database and schema in which the pipe is created. """ return pulumi.get(self, "name") @property @pulumi.getter(name="notificationChannel") def notification_channel(self) -> pulumi.Output[str]: """ Amazon Resource Name of the Amazon SQS queue for the stage named in the DEFINITION column. """ return pulumi.get(self, "notification_channel") @property @pulumi.getter def owner(self) -> pulumi.Output[str]: """ Name of the role that owns the pipe. """ return pulumi.get(self, "owner") @property @pulumi.getter def schema(self) -> pulumi.Output[str]: """ The schema in which to create the pipe. """ return pulumi.get(self, "schema")

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8fb24d4688c7013dba9ee5ff51a0a8adde676d3d

101

py

Python

external/synth/tests/test__nudging.py

jacnugent/fv3net

84958651bdd17784fdab98f87ad0d65414c03368

[ "MIT" ]

5

2021-03-20T22:42:40.000Z

2021-06-30T18:39:36.000Z

external/synth/tests/test__nudging.py

jacnugent/fv3net

84958651bdd17784fdab98f87ad0d65414c03368

[ "MIT" ]

195

2021-09-16T05:47:18.000Z

2022-03-31T22:03:15.000Z

external/synth/tests/test__nudging.py

ai2cm/fv3net

e62038aee0a97d6207e66baabd8938467838cf51

[ "MIT" ]

1

2021-06-16T22:04:24.000Z

from synth import generate_nudging def test_generate_nudging(tmpdir): generate_nudging(tmpdir)

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8

8fdbf4f9fb66c1fad6bd44e31e94f5e9bb50ae71

16,054

py

Python

api/controller/toolchain_unittest.py

khromiumos/chromiumos-chromite

a42a85481cdd9d635dc40a04585e427f89f3bb3f

[ "BSD-3-Clause" ]

null

api/controller/toolchain_unittest.py

khromiumos/chromiumos-chromite

a42a85481cdd9d635dc40a04585e427f89f3bb3f

[ "BSD-3-Clause" ]

2

2021-03-26T00:29:32.000Z

2021-04-30T21:29:33.000Z

api/controller/toolchain_unittest.py

khromiumos/chromiumos-chromite

a42a85481cdd9d635dc40a04585e427f89f3bb3f

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Copyright 2019 The Chromium OS Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Unittests for Toolchain-related operations.""" from __future__ import print_function from chromite.api import api_config from chromite.api import controller from chromite.api.controller import toolchain from chromite.api.gen.chromite.api import artifacts_pb2 from chromite.api.gen.chromite.api import sysroot_pb2 from chromite.api.gen.chromite.api import toolchain_pb2 from chromite.api.gen.chromiumos.builder_config_pb2 import BuilderConfig from chromite.api.gen.chromiumos import common_pb2 from chromite.lib import cros_build_lib from chromite.lib import cros_test_lib from chromite.lib import toolchain_util # pylint: disable=protected-access class UpdateEbuildWithAFDOArtifactsTest(cros_test_lib.MockTestCase, api_config.ApiConfigMixin): """Unittests for UpdateEbuildWithAFDOArtifacts.""" @staticmethod def mock_die(message, *args): raise cros_build_lib.DieSystemExit(message % args) def setUp(self): self.board = 'board' self.response = toolchain_pb2.VerifyAFDOArtifactsResponse() self.invalid_artifact_type = toolchain_pb2.BENCHMARK_AFDO self.orderfile_command = self.PatchObject( toolchain_util, 'OrderfileUpdateChromeEbuild', return_value=True) self.kernel_command = self.PatchObject( toolchain_util, 'AFDOUpdateKernelEbuild', return_value=True) self.chrome_command = self.PatchObject( toolchain_util, 'AFDOUpdateChromeEbuild', return_value=True) self.PatchObject(cros_build_lib, 'Die', new=self.mock_die) def _GetRequest(self, build_target=None, artifact_type=None): return toolchain_pb2.VerifyAFDOArtifactsRequest( build_target={'name': build_target}, artifact_type=artifact_type, ) def testValidateOnly(self): """Sanity check that a validate only call does not execute any logic.""" patch = self.PatchObject(toolchain_util, 'OrderfileUpdateChromeEbuild') request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.ORDERFILE) toolchain.UpdateEbuildWithAFDOArtifacts(request, self.response, self.validate_only_config) patch.assert_not_called() def testMockCall(self): """Test that a mock call does not execute logic, returns mock value.""" patch = self.PatchObject(toolchain_util, 'OrderfileUpdateChromeEbuild') request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.ORDERFILE) toolchain.UpdateEbuildWithAFDOArtifacts(request, self.response, self.mock_call_config) patch.assert_not_called() self.assertEqual(self.response.status, True) def testWrongArtifactType(self): """Test passing wrong artifact type.""" request = self._GetRequest( build_target=self.board, artifact_type=self.invalid_artifact_type) with self.assertRaises(cros_build_lib.DieSystemExit) as context: toolchain.UpdateEbuildWithAFDOArtifacts(request, self.response, self.api_config) self.assertIn('artifact_type (%d) must be in' % self.invalid_artifact_type, str(context.exception)) def testOrderfileSuccess(self): """Test the command is called correctly with orderfile.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.ORDERFILE) toolchain.UpdateEbuildWithAFDOArtifacts(request, self.response, self.api_config) self.orderfile_command.assert_called_once_with(self.board) self.kernel_command.assert_not_called() self.chrome_command.assert_not_called() def testKernelAFDOSuccess(self): """Test the command is called correctly with kernel afdo.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.KERNEL_AFDO) toolchain.UpdateEbuildWithAFDOArtifacts(request, self.response, self.api_config) self.kernel_command.assert_called_once_with(self.board) self.orderfile_command.assert_not_called() self.chrome_command.assert_not_called() def testChromeAFDOSuccess(self): """Test the command is called correctly with Chrome afdo.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.CHROME_AFDO) toolchain.UpdateEbuildWithAFDOArtifacts(request, self.response, self.api_config) self.chrome_command.assert_called_once_with(self.board) self.orderfile_command.assert_not_called() self.kernel_command.assert_not_called() class UploadVettedFDOArtifactsTest(UpdateEbuildWithAFDOArtifactsTest): """Unittests for UploadVettedAFDOArtifacts.""" @staticmethod def mock_die(message, *args): raise cros_build_lib.DieSystemExit(message % args) def setUp(self): self.board = 'board' self.response = toolchain_pb2.VerifyAFDOArtifactsResponse() self.invalid_artifact_type = toolchain_pb2.BENCHMARK_AFDO self.command = self.PatchObject( toolchain_util, 'UploadAndPublishVettedAFDOArtifacts', return_value=True) self.PatchObject(cros_build_lib, 'Die', new=self.mock_die) def testValidateOnly(self): """Sanity check that a validate only call does not execute any logic.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.ORDERFILE) toolchain.UploadVettedAFDOArtifacts(request, self.response, self.validate_only_config) self.command.assert_not_called() def testMockCall(self): """Test that a mock call does not execute logic, returns mock value.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.ORDERFILE) toolchain.UploadVettedAFDOArtifacts(request, self.response, self.mock_call_config) self.command.assert_not_called() self.assertEqual(self.response.status, True) def testWrongArtifactType(self): """Test passing wrong artifact type.""" request = self._GetRequest( build_target=self.board, artifact_type=self.invalid_artifact_type) with self.assertRaises(cros_build_lib.DieSystemExit) as context: toolchain.UploadVettedAFDOArtifacts(request, self.response, self.api_config) self.assertIn('artifact_type (%d) must be in' % self.invalid_artifact_type, str(context.exception)) def testOrderfileSuccess(self): """Test the command is called correctly with orderfile.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.ORDERFILE) toolchain.UploadVettedAFDOArtifacts(request, self.response, self.api_config) self.command.assert_called_once_with('orderfile', self.board) def testKernelAFDOSuccess(self): """Test the command is called correctly with kernel afdo.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.KERNEL_AFDO) toolchain.UploadVettedAFDOArtifacts(request, self.response, self.api_config) self.command.assert_called_once_with('kernel_afdo', self.board) def testChromeAFDOSuccess(self): """Test the command is called correctly with Chrome afdo.""" request = self._GetRequest( build_target=self.board, artifact_type=toolchain_pb2.CHROME_AFDO) toolchain.UploadVettedAFDOArtifacts(request, self.response, self.api_config) self.command.assert_called_once_with('chrome_afdo', self.board) class PrepareForBuildTest(cros_test_lib.MockTempDirTestCase, api_config.ApiConfigMixin): """Unittests for PrepareForBuild.""" def setUp(self): self.response = toolchain_pb2.PrepareForToolchainBuildResponse() self.prep = self.PatchObject( toolchain_util, 'PrepareForBuild', return_value=toolchain_util.PrepareForBuildReturn.NEEDED) self.bundle = self.PatchObject( toolchain_util, 'BundleArtifacts', return_value=[]) self.PatchObject(toolchain, '_TOOLCHAIN_ARTIFACT_HANDLERS', { BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE: toolchain._Handlers('UnverifiedChromeLlvmOrderfile', self.prep, self.bundle), }) def _GetRequest( self, artifact_types=None, input_artifacts=None, additional_args=None): chroot = common_pb2.Chroot(path=self.tempdir) sysroot = sysroot_pb2.Sysroot( path='/build/board', build_target=common_pb2.BuildTarget(name='board')) return toolchain_pb2.PrepareForToolchainBuildRequest( artifact_types=artifact_types, chroot=chroot, sysroot=sysroot, input_artifacts=input_artifacts, additional_args=additional_args) def testRaisesForUnknown(self): request = self._GetRequest([BuilderConfig.Artifacts.IMAGE_ARCHIVES]) self.assertRaises( KeyError, toolchain.PrepareForBuild, request, self.response, self.api_config) def testAcceptsNone(self): request = toolchain_pb2.PrepareForToolchainBuildRequest( artifact_types=[ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE], chroot=None, sysroot=None) toolchain.PrepareForBuild(request, self.response, self.api_config) self.prep.assert_called_once_with( 'UnverifiedChromeLlvmOrderfile', None, '', '', {}, {}) def testHandlesUnknownInputArtifacts(self): request = toolchain_pb2.PrepareForToolchainBuildRequest( artifact_types=[ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE], chroot=None, sysroot=None, input_artifacts=[ BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=BuilderConfig.Artifacts.IMAGE_ZIP, input_artifact_gs_locations=['path1']), ]) toolchain.PrepareForBuild(request, self.response, self.api_config) self.prep.assert_called_once_with( 'UnverifiedChromeLlvmOrderfile', None, '', '', {}, {}) def testPassesProfileInfo(self): request = toolchain_pb2.PrepareForToolchainBuildRequest( artifact_types=[ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE], chroot=None, sysroot=None, input_artifacts=[ BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=\ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE, input_artifact_gs_locations=['path1', 'path2']), BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=\ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE, input_artifact_gs_locations=['path3']), ], profile_info=common_pb2.ArtifactProfileInfo( chrome_cwp_profile='CWPVERSION'), ) toolchain.PrepareForBuild(request, self.response, self.api_config) self.prep.assert_called_once_with( 'UnverifiedChromeLlvmOrderfile', None, '', '', { 'UnverifiedChromeLlvmOrderfile': [ 'gs://path1', 'gs://path2', 'gs://path3'], }, {'chrome_cwp_profile': 'CWPVERSION'}) def testPassesProfileInfoAfdoRelease(self): request = toolchain_pb2.PrepareForToolchainBuildRequest( artifact_types=[ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE], chroot=None, sysroot=None, input_artifacts=[ BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=\ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE, input_artifact_gs_locations=['path1', 'path2']), BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=\ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE, input_artifact_gs_locations=['path3']), ], profile_info=common_pb2.ArtifactProfileInfo( afdo_release=common_pb2.AfdoRelease( chrome_cwp_profile='CWPVERSION', image_build_id=1234)), ) toolchain.PrepareForBuild(request, self.response, self.api_config) self.prep.assert_called_once_with( 'UnverifiedChromeLlvmOrderfile', None, '', '', { 'UnverifiedChromeLlvmOrderfile': [ 'gs://path1', 'gs://path2', 'gs://path3'], }, {'chrome_cwp_profile': 'CWPVERSION', 'image_build_id': 1234}) def testHandlesDuplicateInputArtifacts(self): request = toolchain_pb2.PrepareForToolchainBuildRequest( artifact_types=[ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE], chroot=None, sysroot=None, input_artifacts=[ BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=\ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE, input_artifact_gs_locations=['path1', 'path2']), BuilderConfig.Artifacts.InputArtifactInfo( input_artifact_type=\ BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE, input_artifact_gs_locations=['path3']), ]) toolchain.PrepareForBuild(request, self.response, self.api_config) self.prep.assert_called_once_with( 'UnverifiedChromeLlvmOrderfile', None, '', '', { 'UnverifiedChromeLlvmOrderfile': [ 'gs://path1', 'gs://path2', 'gs://path3'], }, {}) class BundleToolchainTest(cros_test_lib.MockTempDirTestCase, api_config.ApiConfigMixin): """Unittests for BundleToolchain.""" def setUp(self): self.response = toolchain_pb2.BundleToolchainResponse() self.prep = self.PatchObject( toolchain_util, 'PrepareForBuild', return_value=toolchain_util.PrepareForBuildReturn.NEEDED) self.bundle = self.PatchObject( toolchain_util, 'BundleArtifacts', return_value=[]) self.PatchObject(toolchain, '_TOOLCHAIN_ARTIFACT_HANDLERS', { BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE: toolchain._Handlers('UnverifiedChromeLlvmOrderfile', self.prep, self.bundle), }) def _GetRequest(self, artifact_types=None): chroot = common_pb2.Chroot(path=self.tempdir) sysroot = sysroot_pb2.Sysroot( path='/build/board', build_target=common_pb2.BuildTarget(name='board')) return toolchain_pb2.BundleToolchainRequest( chroot=chroot, sysroot=sysroot, output_dir=self.tempdir, artifact_types=artifact_types, ) def testRaisesForUnknown(self): request = self._GetRequest([BuilderConfig.Artifacts.IMAGE_ARCHIVES]) self.assertEqual( controller.RETURN_CODE_UNRECOVERABLE, toolchain.BundleArtifacts(request, self.response, self.api_config)) def testValidateOnly(self): """Sanity check that a validate only call does not execute any logic.""" request = self._GetRequest( [BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE]) toolchain.BundleArtifacts(request, self.response, self.validate_only_config) self.bundle.assert_not_called() def testSetsArtifactsInfo(self): request = self._GetRequest( [BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE]) self.bundle.return_value = ['artifact.xz'] toolchain.BundleArtifacts(request, self.response, self.api_config) self.assertEqual(1, len(self.response.artifacts_info)) self.assertEqual( self.response.artifacts_info[0], toolchain_pb2.ArtifactInfo( artifact_type=( BuilderConfig.Artifacts.UNVERIFIED_CHROME_LLVM_ORDERFILE), artifacts=[ artifacts_pb2.Artifact(path=self.bundle.return_value[0])]))

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0.771232

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0.698672

0

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false

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null

0

1

0

1

0

null

0

7

8fe1ec688bc27e9ee174fbb1920bc715a998d9e2

25

py

Python

src/auto_upload_demo_111/__init__.py

pganssle/auto_upload_demo_111

ffae8901f47614603097bf0aaee312b937ff67d8

[ "Apache-2.0" ]

null

src/auto_upload_demo_111/__init__.py

pganssle/auto_upload_demo_111

ffae8901f47614603097bf0aaee312b937ff67d8

[ "Apache-2.0" ]

null

src/auto_upload_demo_111/__init__.py

pganssle/auto_upload_demo_111

ffae8901f47614603097bf0aaee312b937ff67d8

[ "Apache-2.0" ]

null

def f(): return "⸘‽"

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py

Python

pycozmo/audiokinetic/__init__.py

nalbion/pycozmo

35ee1ea741ecf7a39affc38d4ff5ad17865fea16

[ "MIT" ]

123

2019-08-25T21:28:23.000Z

2022-03-12T13:54:59.000Z

pycozmo/audiokinetic/__init__.py

nalbion/pycozmo

35ee1ea741ecf7a39affc38d4ff5ad17865fea16

[ "MIT" ]

41

2019-08-25T21:21:37.000Z

2022-02-09T14:20:54.000Z

pycozmo/audiokinetic/__init__.py

nalbion/pycozmo

35ee1ea741ecf7a39affc38d4ff5ad17865fea16

[ "MIT" ]

51

2019-09-04T13:30:02.000Z

2022-01-09T01:20:24.000Z

from . import exception # noqa from . import soundbank # noqa from . import soundbanksinfo # noqa from . import wem # noqa

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py

Python

zentral/contrib/osquery/views/__init__.py

gwhitehawk/zentral

156134aed3d7ff8a7cb40ab6f2269a763c316459

[ "Apache-2.0" ]

1

2019-06-10T06:11:27.000Z

zentral/contrib/osquery/views/__init__.py

gwhitehawk/zentral

156134aed3d7ff8a7cb40ab6f2269a763c316459

[ "Apache-2.0" ]

null

zentral/contrib/osquery/views/__init__.py

gwhitehawk/zentral

156134aed3d7ff8a7cb40ab6f2269a763c316459

[ "Apache-2.0" ]

1

2020-09-09T19:26:04.000Z

from .api import * # NOQA from .osquery_compliance_probe import * # NOQA from .osquery_distributed_query_probe import * # NOQA from .osquery_file_carve_probe import * # NOQA from .osquery_fim_probe import * # NOQA from .osquery_probe import * # NOQA from .setup import * # NOQA

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56e2d95cd755eca2597617bb6dcb51e0b0cee69a

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py

Python

pymtl3/stdlib/queues/test/valrdy_queues_test.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

null

pymtl3/stdlib/queues/test/valrdy_queues_test.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

null

pymtl3/stdlib/queues/test/valrdy_queues_test.py

kevinyuan/pymtl3

5949e6a4acc625c0ccbbb25be3af1d0db683df3c

[ "BSD-3-Clause" ]

null

from pymtl3 import * from pymtl3.stdlib.ifcs import InValRdyIfc, OutValRdyIfc from pymtl3.stdlib.test_utils import TestVectorSimulator from ..valrdy_queues import * def run_test_queue( model, test_vectors ): # Define functions mapping the test vector to ports in model def tv_in( model, tv ): model.enq.val @= tv[0] model.enq.msg @= tv[2] model.deq.rdy @= tv[4] def tv_out( model, tv ): if tv[1] != '?': assert model.enq.rdy == tv[1] if tv[3] != '?': assert model.deq.val == tv[3] if tv[5] != '?': assert model.deq.msg == tv[5] # Run the test sim = TestVectorSimulator( model, test_vectors, tv_in, tv_out ) sim.run_test() def test_bypass_Bits(): B1 = mk_bits(1) B32 = mk_bits(32) run_test_queue( BypassQueue1RTL( Bits32 ), [ # enq.val enq.rdy enq.msg deq.val deq.rdy deq.msg [ B1(1) , B1(1) ,B32(123), B1(1) , B1(1) ,B32(123) ], [ B1(1) , B1(1) ,B32(345), B1(1) , B1(0) ,B32(345) ], [ B1(1) , B1(0) ,B32(567), B1(1) , B1(0) ,B32(345) ], [ B1(1) , B1(0) ,B32(567), B1(1) , B1(1) ,B32(345) ], [ B1(1) , B1(1) ,B32(567), B1(1) , B1(1) ,B32(567) ], [ B1(0) , B1(1) ,B32(0 ), B1(0) , B1(1) , '?' ], [ B1(0) , B1(1) ,B32(0 ), B1(0) , B1(0) , '?' ], ] ) def test_pipe_Bits(): B1 = mk_bits(1) B32 = mk_bits(32) run_test_queue( PipeQueue1RTL( Bits32 ), [ # enq.val enq.rdy enq.msg deq.val deq.rdy deq.msg [ B1(1) , B1(1) ,B32(123), B1(0) , B1(1) , '?' ], [ B1(1) , B1(0) ,B32(345), B1(1) , B1(0) ,B32(123) ], [ B1(1) , B1(0) ,B32(567), B1(1) , B1(0) ,B32(123) ], [ B1(1) , B1(1) ,B32(567), B1(1) , B1(1) ,B32(123) ], [ B1(1) , B1(1) ,B32(789), B1(1) , B1(1) ,B32(567) ], [ B1(0) , B1(1) ,B32(0 ), B1(1) , B1(1) ,B32(789) ], [ B1(0) , B1(1) ,B32(0 ), B1(0) , B1(0) , '?' ], ] ) def test_normal_Bits(): B1 = mk_bits(1) B32 = mk_bits(32) run_test_queue( NormalQueue1RTL( Bits32 ), [ # enq.val enq.rdy enq.msg deq.val deq.rdy deq.msg [ B1(1) , B1(1) ,B32(123), B1(0) , B1(1) , '?' ], [ B1(1) , B1(0) ,B32(345), B1(1) , B1(0) ,B32(123) ], [ B1(1) , B1(0) ,B32(567), B1(1) , B1(0) ,B32(123) ], [ B1(1) , B1(0) ,B32(567), B1(1) , B1(1) ,B32(123) ], [ B1(1) , B1(1) ,B32(567), B1(0) , B1(1) ,B32(123) ], [ B1(0) , B1(0) ,B32(0 ), B1(1) , B1(1) ,B32(567) ], [ B1(0) , B1(1) ,B32(0 ), B1(0) , B1(0) , '?' ], ] ) def test_2entry_normal_Bits(): """Two Element Normal Queue.""" B1 = mk_bits(1) B32 = mk_bits(32) run_test_queue( NormalQueueRTL( 2, Bits32 ), [ # Enqueue one element and then dequeue it # enq_val enq_rdy enq_bits deq_val deq_rdy deq_bits [ B1(1), B1(1), B32(0x0001), B1(0), B1(1), '?' ], [ B1(0), B1(1), B32(0x0000), B1(1), B1(1), B32(0x0001) ], [ B1(0), B1(1), B32(0x0000), B1(0), B1(0), '?' ], # Fill in the queue and enq/deq at the same time # enq_val enq_rdy enq_bits deq_val deq_rdy deq_bits [ B1(1), B1(1), B32(0x0002), B1(0), B1(0), '?' ], [ B1(1), B1(1), B32(0x0003), B1(1), B1(0), B32(0x0002) ], [ B1(0), B1(0), B32(0x0003), B1(1), B1(0), B32(0x0002) ], [ B1(1), B1(0), B32(0x0003), B1(1), B1(0), B32(0x0002) ], [ B1(1), B1(0), B32(0x0003), B1(1), B1(1), B32(0x0002) ], [ B1(1), B1(1), B32(0x0004), B1(1), B1(0), '?' ], [ B1(1), B1(0), B32(0x0004), B1(1), B1(1), B32(0x0003) ], [ B1(1), B1(1), B32(0x0005), B1(1), B1(0), '?' ], [ B1(0), B1(0), B32(0x0005), B1(1), B1(1), B32(0x0004) ], [ B1(0), B1(1), B32(0x0005), B1(1), B1(1), B32(0x0005) ], [ B1(0), B1(1), B32(0x0005), B1(0), B1(1), '?' ], ]) def test_3entry_normal_Bits(): """Three Element Queue.""" B1 = mk_bits(1) B32 = mk_bits(32) run_test_queue( NormalQueueRTL( 3, Bits32 ), [ # Enqueue one element and then dequeue it # enq_val enq_rdy enq_bits deq_val deq_rdy deq_bits [ B1(1), B1(1), B32(0x0001), B1(0), B1(1), '?' ], [ B1(0), B1(1), B32(0x0000), B1(1), B1(1), B32(0x0001) ], [ B1(0), B1(1), B32(0x0000), B1(0), B1(0), '?' ], # Fill in the queue and enq/deq at the same time # enq_val enq_rdy enq_bits deq_val deq_rdy deq_bits [ B1(1), B1(1), B32(0x0002), B1(0), B1(0), '?' ], [ B1(1), B1(1), B32(0x0003), B1(1), B1(0), B32(0x0002) ], [ B1(1), B1(1), B32(0x0004), B1(1), B1(0), B32(0x0002) ], [ B1(1), B1(0), B32(0x0005), B1(1), B1(0), B32(0x0002) ], [ B1(0), B1(0), B32(0x0005), B1(1), B1(0), B32(0x0002) ], [ B1(1), B1(0), B32(0x0005), B1(1), B1(1), B32(0x0002) ], [ B1(1), B1(1), B32(0x0005), B1(1), B1(1), B32(0x0003) ], [ B1(1), B1(1), B32(0x0006), B1(1), B1(1), B32(0x0004) ], [ B1(1), B1(1), B32(0x0007), B1(1), B1(1), B32(0x0005) ], [ B1(0), B1(1), B32(0x0000), B1(1), B1(1), B32(0x0006) ], [ B1(0), B1(1), B32(0x0000), B1(1), B1(1), B32(0x0007) ], [ B1(0), B1(1), B32(0x0000), B1(0), B1(1), '?' ], ])

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py

Python

python/ql/src/Classes/MaybeUndefinedClassAttribute.py

vadi2/codeql

a806a4f08696d241ab295a286999251b56a6860c

[ "MIT" ]

4,036

2020-04-29T00:09:57.000Z

2022-03-31T14:16:38.000Z

python/ql/src/Classes/MaybeUndefinedClassAttribute.py

vadi2/codeql

a806a4f08696d241ab295a286999251b56a6860c

[ "MIT" ]

2,970

2020-04-28T17:24:18.000Z

2022-03-31T22:40:46.000Z

python/ql/src/Classes/MaybeUndefinedClassAttribute.py

ScriptBox99/github-codeql

2ecf0d3264db8fb4904b2056964da469372a235c

[ "MIT" ]

794

2020-04-29T00:28:25.000Z

2022-03-30T08:21:46.000Z

class Spam: def __init__(self): self.spam = 'spam, spam, spam' def set_eggs(eggs): self.eggs = eggs def __str__(self): return '%s and %s' % (self.spam, self.eggs) # Maybe uninitialized attribute 'eggs' #Fixed version class Spam: def __init__(self): self.spam = 'spam, spam, spam' self.eggs = None def set_eggs(eggs): self.eggs = eggs def __str__(self): return '%s and %s' % (self.spam, self.eggs) # OK

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56f86b642c8ab327dc906048df6e13350acbc852

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py

Python

userextensions/migrations/0005_auto_20201114_0548.py

davidslusser/django-userprofile

f98559f87a4759d8d6047c20a7b35b53ba25cf49

[ "Apache-2.0" ]

2

2020-01-29T20:18:44.000Z

2020-08-28T16:12:36.000Z

userextensions/migrations/0005_auto_20201114_0548.py

davidslusser/django-userprofile

f98559f87a4759d8d6047c20a7b35b53ba25cf49

[ "Apache-2.0" ]

5

2021-05-08T21:40:31.000Z

2022-03-10T22:54:50.000Z

userextensions/migrations/0005_auto_20201114_0548.py

davidslusser/django-userprofile

f98559f87a4759d8d6047c20a7b35b53ba25cf49

[ "Apache-2.0" ]

2

2020-07-19T00:17:14.000Z

2021-04-02T15:42:13.000Z

# Generated by Django 2.2.15 on 2020-11-14 05:48 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('userextensions', '0004_serviceaccount_serviceaccounttokenhistory'), ] operations = [ migrations.AddField( model_name='serviceaccount', name='admin_enabled', field=models.BooleanField(default=True, help_text='admin enable/disable state of service account'), ), migrations.AlterField( model_name='serviceaccount', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='serviceaccount', name='enabled', field=models.BooleanField(default=True, help_text='owner enable/disable state of service account'), ), migrations.AlterField( model_name='serviceaccount', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='theme', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='theme', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='userfavorite', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='userfavorite', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='userpreference', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='userpreference', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='userrecent', name='created_at', field=models.DateTimeField(auto_now_add=True), ), migrations.AlterField( model_name='userrecent', name='updated_at', field=models.DateTimeField(auto_now=True), ), ]

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py

Python

sdk/python/pulumi_aws_native/frauddetector/_inputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

29

2021-09-30T19:32:07.000Z

2022-03-22T21:06:08.000Z

sdk/python/pulumi_aws_native/frauddetector/_inputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

232

2021-09-30T19:26:26.000Z

2022-03-31T23:22:06.000Z

sdk/python/pulumi_aws_native/frauddetector/_inputs.py

AaronFriel/pulumi-aws-native

5621690373ac44accdbd20b11bae3be1baf022d1

[ "Apache-2.0" ]

4

2021-11-10T19:42:01.000Z

2022-02-05T10:15:49.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from ._enums import * __all__ = [ 'DetectorEntityTypeArgs', 'DetectorEventTypeArgs', 'DetectorEventVariableArgs', 'DetectorLabelArgs', 'DetectorModelArgs', 'DetectorOutcomeArgs', 'DetectorRuleArgs', 'DetectorTagArgs', 'EntityTypeTagArgs', 'EventTypeEntityTypeArgs', 'EventTypeEventVariableArgs', 'EventTypeLabelArgs', 'EventTypeTagArgs', 'LabelTagArgs', 'OutcomeTagArgs', 'VariableTagArgs', ] @pulumi.input_type class DetectorEntityTypeArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]] = None): """ :param pulumi.Input[str] created_time: The time when the entity type was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the entity type was last updated. :param pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]] tags: Tags associated with this entity type. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the entity type was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the entity type was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]: """ Tags associated with this entity type. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class DetectorEventTypeArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, entity_types: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorEntityTypeArgs']]]] = None, event_variables: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorEventVariableArgs']]]] = None, inline: Optional[pulumi.Input[bool]] = None, labels: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorLabelArgs']]]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]] = None): """ :param pulumi.Input[str] arn: The ARN of the event type. :param pulumi.Input[str] created_time: The time when the event type was created. :param pulumi.Input[str] description: The description of the event type. :param pulumi.Input[str] last_updated_time: The time when the event type was last updated. :param pulumi.Input[str] name: The name for the event type :param pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]] tags: Tags associated with this event type. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if entity_types is not None: pulumi.set(__self__, "entity_types", entity_types) if event_variables is not None: pulumi.set(__self__, "event_variables", event_variables) if inline is not None: pulumi.set(__self__, "inline", inline) if labels is not None: pulumi.set(__self__, "labels", labels) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: """ The ARN of the event type. """ return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description of the event type. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="entityTypes") def entity_types(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorEntityTypeArgs']]]]: return pulumi.get(self, "entity_types") @entity_types.setter def entity_types(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorEntityTypeArgs']]]]): pulumi.set(self, "entity_types", value) @property @pulumi.getter(name="eventVariables") def event_variables(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorEventVariableArgs']]]]: return pulumi.get(self, "event_variables") @event_variables.setter def event_variables(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorEventVariableArgs']]]]): pulumi.set(self, "event_variables", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter def labels(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorLabelArgs']]]]: return pulumi.get(self, "labels") @labels.setter def labels(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorLabelArgs']]]]): pulumi.set(self, "labels", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: """ The name for the event type """ return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]: """ Tags associated with this event type. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class DetectorEventVariableArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, data_source: Optional[pulumi.Input['DetectorEventVariableDataSource']] = None, data_type: Optional[pulumi.Input['DetectorEventVariableDataType']] = None, default_value: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]] = None, variable_type: Optional[pulumi.Input['DetectorEventVariableVariableType']] = None): """ :param pulumi.Input[str] created_time: The time when the event variable was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the event variable was last updated. :param pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]] tags: Tags associated with this event variable. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if data_source is not None: pulumi.set(__self__, "data_source", data_source) if data_type is not None: pulumi.set(__self__, "data_type", data_type) if default_value is not None: pulumi.set(__self__, "default_value", default_value) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) if variable_type is not None: pulumi.set(__self__, "variable_type", variable_type) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event variable was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter(name="dataSource") def data_source(self) -> Optional[pulumi.Input['DetectorEventVariableDataSource']]: return pulumi.get(self, "data_source") @data_source.setter def data_source(self, value: Optional[pulumi.Input['DetectorEventVariableDataSource']]): pulumi.set(self, "data_source", value) @property @pulumi.getter(name="dataType") def data_type(self) -> Optional[pulumi.Input['DetectorEventVariableDataType']]: return pulumi.get(self, "data_type") @data_type.setter def data_type(self, value: Optional[pulumi.Input['DetectorEventVariableDataType']]): pulumi.set(self, "data_type", value) @property @pulumi.getter(name="defaultValue") def default_value(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "default_value") @default_value.setter def default_value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "default_value", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event variable was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]: """ Tags associated with this event variable. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="variableType") def variable_type(self) -> Optional[pulumi.Input['DetectorEventVariableVariableType']]: return pulumi.get(self, "variable_type") @variable_type.setter def variable_type(self, value: Optional[pulumi.Input['DetectorEventVariableVariableType']]): pulumi.set(self, "variable_type", value) @pulumi.input_type class DetectorLabelArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]] = None): """ :param pulumi.Input[str] created_time: The time when the label was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the label was last updated. :param pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]] tags: Tags associated with this label. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the label was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the label was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]: """ Tags associated with this label. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class DetectorModelArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None): """ A model to associate with a detector. """ if arn is not None: pulumi.set(__self__, "arn", arn) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @pulumi.input_type class DetectorOutcomeArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]] = None): """ :param pulumi.Input[str] created_time: The time when the outcome was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the outcome was last updated. :param pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]] tags: Tags associated with this outcome. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the outcome was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the outcome was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]: """ Tags associated with this outcome. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class DetectorRuleArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, detector_id: Optional[pulumi.Input[str]] = None, expression: Optional[pulumi.Input[str]] = None, language: Optional[pulumi.Input['DetectorRuleLanguage']] = None, last_updated_time: Optional[pulumi.Input[str]] = None, outcomes: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorOutcomeArgs']]]] = None, rule_id: Optional[pulumi.Input[str]] = None, rule_version: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]] = None): """ :param pulumi.Input[str] created_time: The time when the event type was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the event type was last updated. :param pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]] tags: Tags associated with this event type. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if detector_id is not None: pulumi.set(__self__, "detector_id", detector_id) if expression is not None: pulumi.set(__self__, "expression", expression) if language is not None: pulumi.set(__self__, "language", language) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if outcomes is not None: pulumi.set(__self__, "outcomes", outcomes) if rule_id is not None: pulumi.set(__self__, "rule_id", rule_id) if rule_version is not None: pulumi.set(__self__, "rule_version", rule_version) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter(name="detectorId") def detector_id(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "detector_id") @detector_id.setter def detector_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "detector_id", value) @property @pulumi.getter def expression(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "expression") @expression.setter def expression(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "expression", value) @property @pulumi.getter def language(self) -> Optional[pulumi.Input['DetectorRuleLanguage']]: return pulumi.get(self, "language") @language.setter def language(self, value: Optional[pulumi.Input['DetectorRuleLanguage']]): pulumi.set(self, "language", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def outcomes(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorOutcomeArgs']]]]: return pulumi.get(self, "outcomes") @outcomes.setter def outcomes(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorOutcomeArgs']]]]): pulumi.set(self, "outcomes", value) @property @pulumi.getter(name="ruleId") def rule_id(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "rule_id") @rule_id.setter def rule_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "rule_id", value) @property @pulumi.getter(name="ruleVersion") def rule_version(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "rule_version") @rule_version.setter def rule_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "rule_version", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]: """ Tags associated with this event type. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['DetectorTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class DetectorTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class EntityTypeTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class EventTypeEntityTypeArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]] = None): """ :param pulumi.Input[str] created_time: The time when the event type was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the event type was last updated. :param pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]] tags: Tags associated with this event type. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]]: """ Tags associated with this event type. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class EventTypeEventVariableArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, data_source: Optional[pulumi.Input['EventTypeEventVariableDataSource']] = None, data_type: Optional[pulumi.Input['EventTypeEventVariableDataType']] = None, default_value: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]] = None, variable_type: Optional[pulumi.Input['EventTypeEventVariableVariableType']] = None): """ :param pulumi.Input[str] created_time: The time when the event type was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the event type was last updated. :param pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]] tags: Tags associated with this event type. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if data_source is not None: pulumi.set(__self__, "data_source", data_source) if data_type is not None: pulumi.set(__self__, "data_type", data_type) if default_value is not None: pulumi.set(__self__, "default_value", default_value) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) if variable_type is not None: pulumi.set(__self__, "variable_type", variable_type) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter(name="dataSource") def data_source(self) -> Optional[pulumi.Input['EventTypeEventVariableDataSource']]: return pulumi.get(self, "data_source") @data_source.setter def data_source(self, value: Optional[pulumi.Input['EventTypeEventVariableDataSource']]): pulumi.set(self, "data_source", value) @property @pulumi.getter(name="dataType") def data_type(self) -> Optional[pulumi.Input['EventTypeEventVariableDataType']]: return pulumi.get(self, "data_type") @data_type.setter def data_type(self, value: Optional[pulumi.Input['EventTypeEventVariableDataType']]): pulumi.set(self, "data_type", value) @property @pulumi.getter(name="defaultValue") def default_value(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "default_value") @default_value.setter def default_value(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "default_value", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]]: """ Tags associated with this event type. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="variableType") def variable_type(self) -> Optional[pulumi.Input['EventTypeEventVariableVariableType']]: return pulumi.get(self, "variable_type") @variable_type.setter def variable_type(self, value: Optional[pulumi.Input['EventTypeEventVariableVariableType']]): pulumi.set(self, "variable_type", value) @pulumi.input_type class EventTypeLabelArgs: def __init__(__self__, *, arn: Optional[pulumi.Input[str]] = None, created_time: Optional[pulumi.Input[str]] = None, description: Optional[pulumi.Input[str]] = None, inline: Optional[pulumi.Input[bool]] = None, last_updated_time: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]] = None): """ :param pulumi.Input[str] created_time: The time when the event type was created. :param pulumi.Input[str] description: The description. :param pulumi.Input[str] last_updated_time: The time when the event type was last updated. :param pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]] tags: Tags associated with this event type. """ if arn is not None: pulumi.set(__self__, "arn", arn) if created_time is not None: pulumi.set(__self__, "created_time", created_time) if description is not None: pulumi.set(__self__, "description", description) if inline is not None: pulumi.set(__self__, "inline", inline) if last_updated_time is not None: pulumi.set(__self__, "last_updated_time", last_updated_time) if name is not None: pulumi.set(__self__, "name", name) if tags is not None: pulumi.set(__self__, "tags", tags) @property @pulumi.getter def arn(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "arn") @arn.setter def arn(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "arn", value) @property @pulumi.getter(name="createdTime") def created_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was created. """ return pulumi.get(self, "created_time") @created_time.setter def created_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "created_time", value) @property @pulumi.getter def description(self) -> Optional[pulumi.Input[str]]: """ The description. """ return pulumi.get(self, "description") @description.setter def description(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "description", value) @property @pulumi.getter def inline(self) -> Optional[pulumi.Input[bool]]: return pulumi.get(self, "inline") @inline.setter def inline(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "inline", value) @property @pulumi.getter(name="lastUpdatedTime") def last_updated_time(self) -> Optional[pulumi.Input[str]]: """ The time when the event type was last updated. """ return pulumi.get(self, "last_updated_time") @last_updated_time.setter def last_updated_time(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "last_updated_time", value) @property @pulumi.getter def name(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "name") @name.setter def name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "name", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]]: """ Tags associated with this event type. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['EventTypeTagArgs']]]]): pulumi.set(self, "tags", value) @pulumi.input_type class EventTypeTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class LabelTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class OutcomeTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value) @pulumi.input_type class VariableTagArgs: def __init__(__self__, *, key: pulumi.Input[str], value: pulumi.Input[str]): pulumi.set(__self__, "key", key) pulumi.set(__self__, "value", value) @property @pulumi.getter def key(self) -> pulumi.Input[str]: return pulumi.get(self, "key") @key.setter def key(self, value: pulumi.Input[str]): pulumi.set(self, "key", value) @property @pulumi.getter def value(self) -> pulumi.Input[str]: return pulumi.get(self, "value") @value.setter def value(self, value: pulumi.Input[str]): pulumi.set(self, "value", value)

34.970674

118

0.627778

5,533

47,700

5.249232

0.023315

0.142026

0.155041

0.115893

0.919467

0.90177

0.888548

0.864172

0.854875

0.848402

0

0.000028

0.244046

47,700

1,363

119

34.996332

0.80543

0.098407

0

0.857732

1

0

0.099796

0.019801

0

1

0.204124

false

0

0.006186

0.054639

0.320619

0

null

0

1

0

1

0

null

0

1

0

9

85729873a942c7b96c7c25e93e815b2628d4e959

6,545

py

Python

loldib/getratings/models/NA/na_khazix/na_khazix_mid.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_khazix/na_khazix_mid.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_khazix/na_khazix_mid.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from getratings.models.ratings import Ratings class NA_Khazix_Mid_Aatrox(Ratings): pass class NA_Khazix_Mid_Ahri(Ratings): pass class NA_Khazix_Mid_Akali(Ratings): pass class NA_Khazix_Mid_Alistar(Ratings): pass class NA_Khazix_Mid_Amumu(Ratings): pass class NA_Khazix_Mid_Anivia(Ratings): pass class NA_Khazix_Mid_Annie(Ratings): pass class NA_Khazix_Mid_Ashe(Ratings): pass class NA_Khazix_Mid_AurelionSol(Ratings): pass class NA_Khazix_Mid_Azir(Ratings): pass class NA_Khazix_Mid_Bard(Ratings): pass class NA_Khazix_Mid_Blitzcrank(Ratings): pass class NA_Khazix_Mid_Brand(Ratings): pass class NA_Khazix_Mid_Braum(Ratings): pass class NA_Khazix_Mid_Caitlyn(Ratings): pass class NA_Khazix_Mid_Camille(Ratings): pass class NA_Khazix_Mid_Cassiopeia(Ratings): pass class NA_Khazix_Mid_Chogath(Ratings): pass class NA_Khazix_Mid_Corki(Ratings): pass class NA_Khazix_Mid_Darius(Ratings): pass class NA_Khazix_Mid_Diana(Ratings): pass class NA_Khazix_Mid_Draven(Ratings): pass class NA_Khazix_Mid_DrMundo(Ratings): pass class NA_Khazix_Mid_Ekko(Ratings): pass class NA_Khazix_Mid_Elise(Ratings): pass class NA_Khazix_Mid_Evelynn(Ratings): pass class NA_Khazix_Mid_Ezreal(Ratings): pass class NA_Khazix_Mid_Fiddlesticks(Ratings): pass class NA_Khazix_Mid_Fiora(Ratings): pass class NA_Khazix_Mid_Fizz(Ratings): pass class NA_Khazix_Mid_Galio(Ratings): pass class NA_Khazix_Mid_Gangplank(Ratings): pass class NA_Khazix_Mid_Garen(Ratings): pass class NA_Khazix_Mid_Gnar(Ratings): pass class NA_Khazix_Mid_Gragas(Ratings): pass class NA_Khazix_Mid_Graves(Ratings): pass class NA_Khazix_Mid_Hecarim(Ratings): pass class NA_Khazix_Mid_Heimerdinger(Ratings): pass class NA_Khazix_Mid_Illaoi(Ratings): pass class NA_Khazix_Mid_Irelia(Ratings): pass class NA_Khazix_Mid_Ivern(Ratings): pass class NA_Khazix_Mid_Janna(Ratings): pass class NA_Khazix_Mid_JarvanIV(Ratings): pass class NA_Khazix_Mid_Jax(Ratings): pass class NA_Khazix_Mid_Jayce(Ratings): pass class NA_Khazix_Mid_Jhin(Ratings): pass class NA_Khazix_Mid_Jinx(Ratings): pass class NA_Khazix_Mid_Kalista(Ratings): pass class NA_Khazix_Mid_Karma(Ratings): pass class NA_Khazix_Mid_Karthus(Ratings): pass class NA_Khazix_Mid_Kassadin(Ratings): pass class NA_Khazix_Mid_Katarina(Ratings): pass class NA_Khazix_Mid_Kayle(Ratings): pass class NA_Khazix_Mid_Kayn(Ratings): pass class NA_Khazix_Mid_Kennen(Ratings): pass class NA_Khazix_Mid_Khazix(Ratings): pass class NA_Khazix_Mid_Kindred(Ratings): pass class NA_Khazix_Mid_Kled(Ratings): pass class NA_Khazix_Mid_KogMaw(Ratings): pass class NA_Khazix_Mid_Leblanc(Ratings): pass class NA_Khazix_Mid_LeeSin(Ratings): pass class NA_Khazix_Mid_Leona(Ratings): pass class NA_Khazix_Mid_Lissandra(Ratings): pass class NA_Khazix_Mid_Lucian(Ratings): pass class NA_Khazix_Mid_Lulu(Ratings): pass class NA_Khazix_Mid_Lux(Ratings): pass class NA_Khazix_Mid_Malphite(Ratings): pass class NA_Khazix_Mid_Malzahar(Ratings): pass class NA_Khazix_Mid_Maokai(Ratings): pass class NA_Khazix_Mid_MasterYi(Ratings): pass class NA_Khazix_Mid_MissFortune(Ratings): pass class NA_Khazix_Mid_MonkeyKing(Ratings): pass class NA_Khazix_Mid_Mordekaiser(Ratings): pass class NA_Khazix_Mid_Morgana(Ratings): pass class NA_Khazix_Mid_Nami(Ratings): pass class NA_Khazix_Mid_Nasus(Ratings): pass class NA_Khazix_Mid_Nautilus(Ratings): pass class NA_Khazix_Mid_Nidalee(Ratings): pass class NA_Khazix_Mid_Nocturne(Ratings): pass class NA_Khazix_Mid_Nunu(Ratings): pass class NA_Khazix_Mid_Olaf(Ratings): pass class NA_Khazix_Mid_Orianna(Ratings): pass class NA_Khazix_Mid_Ornn(Ratings): pass class NA_Khazix_Mid_Pantheon(Ratings): pass class NA_Khazix_Mid_Poppy(Ratings): pass class NA_Khazix_Mid_Quinn(Ratings): pass class NA_Khazix_Mid_Rakan(Ratings): pass class NA_Khazix_Mid_Rammus(Ratings): pass class NA_Khazix_Mid_RekSai(Ratings): pass class NA_Khazix_Mid_Renekton(Ratings): pass class NA_Khazix_Mid_Rengar(Ratings): pass class NA_Khazix_Mid_Riven(Ratings): pass class NA_Khazix_Mid_Rumble(Ratings): pass class NA_Khazix_Mid_Ryze(Ratings): pass class NA_Khazix_Mid_Sejuani(Ratings): pass class NA_Khazix_Mid_Shaco(Ratings): pass class NA_Khazix_Mid_Shen(Ratings): pass class NA_Khazix_Mid_Shyvana(Ratings): pass class NA_Khazix_Mid_Singed(Ratings): pass class NA_Khazix_Mid_Sion(Ratings): pass class NA_Khazix_Mid_Sivir(Ratings): pass class NA_Khazix_Mid_Skarner(Ratings): pass class NA_Khazix_Mid_Sona(Ratings): pass class NA_Khazix_Mid_Soraka(Ratings): pass class NA_Khazix_Mid_Swain(Ratings): pass class NA_Khazix_Mid_Syndra(Ratings): pass class NA_Khazix_Mid_TahmKench(Ratings): pass class NA_Khazix_Mid_Taliyah(Ratings): pass class NA_Khazix_Mid_Talon(Ratings): pass class NA_Khazix_Mid_Taric(Ratings): pass class NA_Khazix_Mid_Teemo(Ratings): pass class NA_Khazix_Mid_Thresh(Ratings): pass class NA_Khazix_Mid_Tristana(Ratings): pass class NA_Khazix_Mid_Trundle(Ratings): pass class NA_Khazix_Mid_Tryndamere(Ratings): pass class NA_Khazix_Mid_TwistedFate(Ratings): pass class NA_Khazix_Mid_Twitch(Ratings): pass class NA_Khazix_Mid_Udyr(Ratings): pass class NA_Khazix_Mid_Urgot(Ratings): pass class NA_Khazix_Mid_Varus(Ratings): pass class NA_Khazix_Mid_Vayne(Ratings): pass class NA_Khazix_Mid_Veigar(Ratings): pass class NA_Khazix_Mid_Velkoz(Ratings): pass class NA_Khazix_Mid_Vi(Ratings): pass class NA_Khazix_Mid_Viktor(Ratings): pass class NA_Khazix_Mid_Vladimir(Ratings): pass class NA_Khazix_Mid_Volibear(Ratings): pass class NA_Khazix_Mid_Warwick(Ratings): pass class NA_Khazix_Mid_Xayah(Ratings): pass class NA_Khazix_Mid_Xerath(Ratings): pass class NA_Khazix_Mid_XinZhao(Ratings): pass class NA_Khazix_Mid_Yasuo(Ratings): pass class NA_Khazix_Mid_Yorick(Ratings): pass class NA_Khazix_Mid_Zac(Ratings): pass class NA_Khazix_Mid_Zed(Ratings): pass class NA_Khazix_Mid_Ziggs(Ratings): pass class NA_Khazix_Mid_Zilean(Ratings): pass class NA_Khazix_Mid_Zyra(Ratings): pass

15.695444

46

0.766692

972

6,545

4.736626

0.151235

0.209818

0.389661

0.479583

0.803432

0

0.169748

6,545

416

47

15.733173

0.847258

0

0.498195

0

1

0

true

0.498195

0.00361

0

0.501805

0

null

1

0

null

0

1

0

1

0

8

85821392c09e1139f86fefd3c8ac45be23741603

194

py

Python

bdbc/lib/python3.5/site-packages/cryptoconditions/lib/__init__.py

entropyx/fiduchain-blockchain-interface

07336a5eebfaa9cddb148edb94461a8fd57562b1

[ "MIT" ]

null

bdbc/lib/python3.5/site-packages/cryptoconditions/lib/__init__.py

entropyx/fiduchain-blockchain-interface

07336a5eebfaa9cddb148edb94461a8fd57562b1

[ "MIT" ]

null

bdbc/lib/python3.5/site-packages/cryptoconditions/lib/__init__.py

entropyx/fiduchain-blockchain-interface

07336a5eebfaa9cddb148edb94461a8fd57562b1

[ "MIT" ]

null

from cryptoconditions.lib.hasher import Hasher from cryptoconditions.lib.writer import Writer from cryptoconditions.lib.reader import Reader from cryptoconditions.lib.predictor import Predictor

38.8

52

0.876289

24

194

7.083333

0.333333

0.470588

0.541176

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85a7738bb1ef52179bb19322f4c8baeca9533e96

216

py

Python

rh_pathfinding/src/rh_pathfinding/engine/geometry/obstacle/__init__.py

RhinohawkUAV/rh_ros

e13077060bdfcc231adee9731ebfddadcd8d6b4a

[ "MIT" ]

4

2020-05-13T19:34:27.000Z

2021-09-20T09:01:10.000Z

rh_pathfinding/src/rh_pathfinding/engine/geometry/obstacle/__init__.py

RhinohawkUAV/rh_ros

e13077060bdfcc231adee9731ebfddadcd8d6b4a

[ "MIT" ]

null

rh_pathfinding/src/rh_pathfinding/engine/geometry/obstacle/__init__.py

RhinohawkUAV/rh_ros

e13077060bdfcc231adee9731ebfddadcd8d6b4a

[ "MIT" ]

2

2019-09-14T14:45:09.000Z

2020-11-22T01:46:59.000Z

from arcFinder import ArcSegmentFinder from intersectionDetector import * from lineFinder import LineSegmentFinder from obstacleCourse import ObstacleCourse import obstacleCourse from pathSegment import PathSegment

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a44960bd85f4ff25799e7078bbe153539f1243ba

266,820

py

Python

pyboto3/autoscaling.py

gehad-shaat/pyboto3

4a0c2851a8bc04fb1c71c36086f7bb257e48181d

[ "MIT" ]

91

2016-12-31T11:38:37.000Z

2021-09-16T19:33:23.000Z

pyboto3/autoscaling.py

gehad-shaat/pyboto3

4a0c2851a8bc04fb1c71c36086f7bb257e48181d

[ "MIT" ]

7

2017-01-02T18:54:23.000Z

2020-08-11T13:54:02.000Z

pyboto3/autoscaling.py

gehad-shaat/pyboto3

4a0c2851a8bc04fb1c71c36086f7bb257e48181d

[ "MIT" ]

26

2016-12-31T13:11:00.000Z

2022-03-03T21:01:12.000Z

''' The MIT License (MIT) Copyright (c) 2016 WavyCloud Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' def attach_instances(InstanceIds=None, AutoScalingGroupName=None): """ Attaches one or more EC2 instances to the specified Auto Scaling group. When you attach instances, Amazon EC2 Auto Scaling increases the desired capacity of the group by the number of instances being attached. If the number of instances being attached plus the desired capacity of the group exceeds the maximum size of the group, the operation fails. If there is a Classic Load Balancer attached to your Auto Scaling group, the instances are also registered with the load balancer. If there are target groups attached to your Auto Scaling group, the instances are also registered with the target groups. For more information, see Attach EC2 Instances to Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example attaches the specified instance to the specified Auto Scaling group. Expected Output: :example: response = client.attach_instances( InstanceIds=[ 'string', ], AutoScalingGroupName='string' ) :type InstanceIds: list :param InstanceIds: The IDs of the instances. You can specify up to 20 instances.\n\n(string) --\n\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :return: response = client.attach_instances( AutoScalingGroupName='my-auto-scaling-group', InstanceIds=[ 'i-93633f9b', ], ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure """ pass def attach_load_balancer_target_groups(AutoScalingGroupName=None, TargetGroupARNs=None): """ Attaches one or more target groups to the specified Auto Scaling group. To describe the target groups for an Auto Scaling group, call the DescribeLoadBalancerTargetGroups API. To detach the target group from the Auto Scaling group, call the DetachLoadBalancerTargetGroups API. With Application Load Balancers and Network Load Balancers, instances are registered as targets with a target group. With Classic Load Balancers, instances are registered with the load balancer. For more information, see Attaching a Load Balancer to Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example attaches the specified target group to the specified Auto Scaling group. Expected Output: :example: response = client.attach_load_balancer_target_groups( AutoScalingGroupName='string', TargetGroupARNs=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type TargetGroupARNs: list :param TargetGroupARNs: [REQUIRED]\nThe Amazon Resource Names (ARN) of the target groups. You can specify up to 10 target groups.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure Examples This example attaches the specified target group to the specified Auto Scaling group. response = client.attach_load_balancer_target_groups( AutoScalingGroupName='my-auto-scaling-group', TargetGroupARNs=[ 'arn:aws:elasticloadbalancing:us-west-2:123456789012:targetgroup/my-targets/73e2d6bc24d8a067', ], ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: (dict) -- """ pass def attach_load_balancers(AutoScalingGroupName=None, LoadBalancerNames=None): """ Attaches one or more Classic Load Balancers to the specified Auto Scaling group. Amazon EC2 Auto Scaling registers the running instances with these Classic Load Balancers. To describe the load balancers for an Auto Scaling group, call the DescribeLoadBalancers API. To detach the load balancer from the Auto Scaling group, call the DetachLoadBalancers API. For more information, see Attaching a Load Balancer to Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example attaches the specified load balancer to the specified Auto Scaling group. Expected Output: :example: response = client.attach_load_balancers( AutoScalingGroupName='string', LoadBalancerNames=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LoadBalancerNames: list :param LoadBalancerNames: [REQUIRED]\nThe names of the load balancers. You can specify up to 10 load balancers.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure Examples This example attaches the specified load balancer to the specified Auto Scaling group. response = client.attach_load_balancers( AutoScalingGroupName='my-auto-scaling-group', LoadBalancerNames=[ 'my-load-balancer', ], ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: (dict) -- """ pass def batch_delete_scheduled_action(AutoScalingGroupName=None, ScheduledActionNames=None): """ Deletes one or more scheduled actions for the specified Auto Scaling group. See also: AWS API Documentation Exceptions :example: response = client.batch_delete_scheduled_action( AutoScalingGroupName='string', ScheduledActionNames=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ScheduledActionNames: list :param ScheduledActionNames: [REQUIRED]\nThe names of the scheduled actions to delete. The maximum number allowed is 50.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'FailedScheduledActions': [ { 'ScheduledActionName': 'string', 'ErrorCode': 'string', 'ErrorMessage': 'string' }, ] } Response Structure (dict) -- FailedScheduledActions (list) -- The names of the scheduled actions that could not be deleted, including an error message. (dict) -- Describes a scheduled action that could not be created, updated, or deleted. ScheduledActionName (string) -- The name of the scheduled action. ErrorCode (string) -- The error code. ErrorMessage (string) -- The error message accompanying the error code. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault :return: { 'FailedScheduledActions': [ { 'ScheduledActionName': 'string', 'ErrorCode': 'string', 'ErrorMessage': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def batch_put_scheduled_update_group_action(AutoScalingGroupName=None, ScheduledUpdateGroupActions=None): """ Creates or updates one or more scheduled scaling actions for an Auto Scaling group. If you leave a parameter unspecified when updating a scheduled scaling action, the corresponding value remains unchanged. See also: AWS API Documentation Exceptions :example: response = client.batch_put_scheduled_update_group_action( AutoScalingGroupName='string', ScheduledUpdateGroupActions=[ { 'ScheduledActionName': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'Recurrence': 'string', 'MinSize': 123, 'MaxSize': 123, 'DesiredCapacity': 123 }, ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ScheduledUpdateGroupActions: list :param ScheduledUpdateGroupActions: [REQUIRED]\nOne or more scheduled actions. The maximum number allowed is 50.\n\n(dict) --Describes information used for one or more scheduled scaling action updates in a BatchPutScheduledUpdateGroupAction operation.\nWhen updating a scheduled scaling action, all optional parameters are left unchanged if not specified.\n\nScheduledActionName (string) -- [REQUIRED]The name of the scaling action.\n\nStartTime (datetime) --The date and time for the action to start, in YYYY-MM-DDThh:mm:ssZ format in UTC/GMT only and in quotes (for example, '2019-06-01T00:00:00Z' ).\nIf you specify Recurrence and StartTime , Amazon EC2 Auto Scaling performs the action at this time, and then performs the action based on the specified recurrence.\nIf you try to schedule the action in the past, Amazon EC2 Auto Scaling returns an error message.\n\nEndTime (datetime) --The date and time for the recurring schedule to end. Amazon EC2 Auto Scaling does not perform the action after this time.\n\nRecurrence (string) --The recurring schedule for the action, in Unix cron syntax format. This format consists of five fields separated by white spaces: [Minute] [Hour] [Day_of_Month] [Month_of_Year] [Day_of_Week]. The value must be in quotes (for example, '30 0 1 1,6,12 *' ). For more information about this format, see Crontab .\nWhen StartTime and EndTime are specified with Recurrence , they form the boundaries of when the recurring action starts and stops.\n\nMinSize (integer) --The minimum size of the Auto Scaling group.\n\nMaxSize (integer) --The maximum size of the Auto Scaling group.\n\nDesiredCapacity (integer) --The desired capacity is the initial capacity of the Auto Scaling group after the scheduled action runs and the capacity it attempts to maintain.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'FailedScheduledUpdateGroupActions': [ { 'ScheduledActionName': 'string', 'ErrorCode': 'string', 'ErrorMessage': 'string' }, ] } Response Structure (dict) -- FailedScheduledUpdateGroupActions (list) -- The names of the scheduled actions that could not be created or updated, including an error message. (dict) -- Describes a scheduled action that could not be created, updated, or deleted. ScheduledActionName (string) -- The name of the scheduled action. ErrorCode (string) -- The error code. ErrorMessage (string) -- The error message accompanying the error code. Exceptions AutoScaling.Client.exceptions.AlreadyExistsFault AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault :return: { 'FailedScheduledUpdateGroupActions': [ { 'ScheduledActionName': 'string', 'ErrorCode': 'string', 'ErrorMessage': 'string' }, ] } :returns: AutoScaling.Client.exceptions.AlreadyExistsFault AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def can_paginate(operation_name=None): """ Check if an operation can be paginated. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). """ pass def complete_lifecycle_action(LifecycleHookName=None, AutoScalingGroupName=None, LifecycleActionToken=None, LifecycleActionResult=None, InstanceId=None): """ Completes the lifecycle action for the specified token or instance with the specified result. This step is a part of the procedure for adding a lifecycle hook to an Auto Scaling group: For more information, see Amazon EC2 Auto Scaling Lifecycle Hooks in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example notifies Auto Scaling that the specified lifecycle action is complete so that it can finish launching or terminating the instance. Expected Output: :example: response = client.complete_lifecycle_action( LifecycleHookName='string', AutoScalingGroupName='string', LifecycleActionToken='string', LifecycleActionResult='string', InstanceId='string' ) :type LifecycleHookName: string :param LifecycleHookName: [REQUIRED]\nThe name of the lifecycle hook.\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LifecycleActionToken: string :param LifecycleActionToken: A universally unique identifier (UUID) that identifies a specific lifecycle action associated with an instance. Amazon EC2 Auto Scaling sends this token to the notification target you specified when you created the lifecycle hook. :type LifecycleActionResult: string :param LifecycleActionResult: [REQUIRED]\nThe action for the group to take. This parameter can be either CONTINUE or ABANDON .\n :type InstanceId: string :param InstanceId: The ID of the instance. :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example notifies Auto Scaling that the specified lifecycle action is complete so that it can finish launching or terminating the instance. response = client.complete_lifecycle_action( AutoScalingGroupName='my-auto-scaling-group', LifecycleActionResult='CONTINUE', LifecycleActionToken='bcd2f1b8-9a78-44d3-8a7a-4dd07d7cf635', LifecycleHookName='my-lifecycle-hook', ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: LifecycleHookName (string) -- [REQUIRED] The name of the lifecycle hook. AutoScalingGroupName (string) -- [REQUIRED] The name of the Auto Scaling group. LifecycleActionToken (string) -- A universally unique identifier (UUID) that identifies a specific lifecycle action associated with an instance. Amazon EC2 Auto Scaling sends this token to the notification target you specified when you created the lifecycle hook. LifecycleActionResult (string) -- [REQUIRED] The action for the group to take. This parameter can be either CONTINUE or ABANDON . InstanceId (string) -- The ID of the instance. """ pass def create_auto_scaling_group(AutoScalingGroupName=None, LaunchConfigurationName=None, LaunchTemplate=None, MixedInstancesPolicy=None, InstanceId=None, MinSize=None, MaxSize=None, DesiredCapacity=None, DefaultCooldown=None, AvailabilityZones=None, LoadBalancerNames=None, TargetGroupARNs=None, HealthCheckType=None, HealthCheckGracePeriod=None, PlacementGroup=None, VPCZoneIdentifier=None, TerminationPolicies=None, NewInstancesProtectedFromScaleIn=None, LifecycleHookSpecificationList=None, Tags=None, ServiceLinkedRoleARN=None, MaxInstanceLifetime=None): """ Creates an Auto Scaling group with the specified name and attributes. If you exceed your maximum limit of Auto Scaling groups, the call fails. To query this limit, call the DescribeAccountLimits API. For information about updating this limit, see Amazon EC2 Auto Scaling Service Quotas in the Amazon EC2 Auto Scaling User Guide . For introductory exercises for creating an Auto Scaling group, see Getting Started with Amazon EC2 Auto Scaling and Tutorial: Set Up a Scaled and Load-Balanced Application in the Amazon EC2 Auto Scaling User Guide . For more information, see Auto Scaling Groups in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example creates an Auto Scaling group. Expected Output: This example creates an Auto Scaling group and attaches the specified Classic Load Balancer. Expected Output: This example creates an Auto Scaling group and attaches the specified target group. Expected Output: :example: response = client.create_auto_scaling_group( AutoScalingGroupName='string', LaunchConfigurationName='string', LaunchTemplate={ 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, MixedInstancesPolicy={ 'LaunchTemplate': { 'LaunchTemplateSpecification': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'Overrides': [ { 'InstanceType': 'string', 'WeightedCapacity': 'string' }, ] }, 'InstancesDistribution': { 'OnDemandAllocationStrategy': 'string', 'OnDemandBaseCapacity': 123, 'OnDemandPercentageAboveBaseCapacity': 123, 'SpotAllocationStrategy': 'string', 'SpotInstancePools': 123, 'SpotMaxPrice': 'string' } }, InstanceId='string', MinSize=123, MaxSize=123, DesiredCapacity=123, DefaultCooldown=123, AvailabilityZones=[ 'string', ], LoadBalancerNames=[ 'string', ], TargetGroupARNs=[ 'string', ], HealthCheckType='string', HealthCheckGracePeriod=123, PlacementGroup='string', VPCZoneIdentifier='string', TerminationPolicies=[ 'string', ], NewInstancesProtectedFromScaleIn=True|False, LifecycleHookSpecificationList=[ { 'LifecycleHookName': 'string', 'LifecycleTransition': 'string', 'NotificationMetadata': 'string', 'HeartbeatTimeout': 123, 'DefaultResult': 'string', 'NotificationTargetARN': 'string', 'RoleARN': 'string' }, ], Tags=[ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ], ServiceLinkedRoleARN='string', MaxInstanceLifetime=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group. This name must be unique per Region per account.\n :type LaunchConfigurationName: string :param LaunchConfigurationName: The name of the launch configuration to use when an instance is launched. To get the launch configuration name, use the DescribeLaunchConfigurations API operation. New launch configurations can be created with the CreateLaunchConfiguration API.\nYou must specify one of the following parameters in your request: LaunchConfigurationName , LaunchTemplate , InstanceId , or MixedInstancesPolicy .\n :type LaunchTemplate: dict :param LaunchTemplate: Parameters used to specify the launch template and version to use when an instance is launched.\nFor more information, see LaunchTemplateSpecification in the Amazon EC2 Auto Scaling API Reference .\nYou can alternatively associate a launch template to the Auto Scaling group by using the MixedInstancesPolicy parameter.\nYou must specify one of the following parameters in your request: LaunchConfigurationName , LaunchTemplate , InstanceId , or MixedInstancesPolicy .\n\nLaunchTemplateId (string) --The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nLaunchTemplateName (string) --The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nVersion (string) --The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API.\nIf the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default .\n\n\n :type MixedInstancesPolicy: dict :param MixedInstancesPolicy: An embedded object that specifies a mixed instances policy. The required parameters must be specified. If optional parameters are unspecified, their default values are used.\nThe policy includes parameters that not only define the distribution of On-Demand Instances and Spot Instances, the maximum price to pay for Spot Instances, and how the Auto Scaling group allocates instance types to fulfill On-Demand and Spot capacity, but also the parameters that specify the instance configuration information\xe2\x80\x94the launch template and instance types.\nFor more information, see MixedInstancesPolicy in the Amazon EC2 Auto Scaling API Reference and Auto Scaling Groups with Multiple Instance Types and Purchase Options in the Amazon EC2 Auto Scaling User Guide .\nYou must specify one of the following parameters in your request: LaunchConfigurationName , LaunchTemplate , InstanceId , or MixedInstancesPolicy .\n\nLaunchTemplate (dict) --The launch template and instance types (overrides).\nThis parameter must be specified when creating a mixed instances policy.\n\nLaunchTemplateSpecification (dict) --The launch template to use. You must specify either the launch template ID or launch template name in the request.\n\nLaunchTemplateId (string) --The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nLaunchTemplateName (string) --The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nVersion (string) --The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API.\nIf the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default .\n\n\n\nOverrides (list) --Any parameters that you specify override the same parameters in the launch template. Currently, the only supported override is instance type. You can specify between 1 and 20 instance types.\nIf not provided, Amazon EC2 Auto Scaling will use the instance type specified in the launch template to launch instances.\n\n(dict) --Describes an override for a launch template. Currently, the only supported override is instance type.\nThe maximum number of instance type overrides that can be associated with an Auto Scaling group is 20.\n\nInstanceType (string) --The instance type. You must use an instance type that is supported in your requested Region and Availability Zones.\nFor information about available instance types, see Available Instance Types in the Amazon Elastic Compute Cloud User Guide.\n\nWeightedCapacity (string) --The number of capacity units, which gives the instance type a proportional weight to other instance types. For example, larger instance types are generally weighted more than smaller instance types. These are the same units that you chose to set the desired capacity in terms of instances, or a performance attribute such as vCPUs, memory, or I/O.\nFor more information, see Instance Weighting for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide .\nValid Range: Minimum value of 1. Maximum value of 999.\n\n\n\n\n\n\n\nInstancesDistribution (dict) --The instances distribution to use.\nIf you leave this parameter unspecified, the value for each parameter in InstancesDistribution uses a default value.\n\nOnDemandAllocationStrategy (string) --Indicates how to allocate instance types to fulfill On-Demand capacity.\nThe only valid value is prioritized , which is also the default value. This strategy uses the order of instance type overrides for the LaunchTemplate to define the launch priority of each instance type. The first instance type in the array is prioritized higher than the last. If all your On-Demand capacity cannot be fulfilled using your highest priority instance, then the Auto Scaling groups launches the remaining capacity using the second priority instance type, and so on.\n\nOnDemandBaseCapacity (integer) --The minimum amount of the Auto Scaling group\'s capacity that must be fulfilled by On-Demand Instances. This base portion is provisioned first as your group scales.\nDefault if not set is 0. If you leave it set to 0, On-Demand Instances are launched as a percentage of the Auto Scaling group\'s desired capacity, per the OnDemandPercentageAboveBaseCapacity setting.\n\nNote\nAn update to this setting means a gradual replacement of instances to maintain the specified number of On-Demand Instances for your base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones.\n\n\nOnDemandPercentageAboveBaseCapacity (integer) --Controls the percentages of On-Demand Instances and Spot Instances for your additional capacity beyond OnDemandBaseCapacity .\nDefault if not set is 100. If you leave it set to 100, the percentages are 100% for On-Demand Instances and 0% for Spot Instances.\n\nNote\nAn update to this setting means a gradual replacement of instances to maintain the percentage of On-Demand Instances for your additional capacity above the base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones.\n\nValid Range: Minimum value of 0. Maximum value of 100.\n\nSpotAllocationStrategy (string) --Indicates how to allocate instances across Spot Instance pools.\nIf the allocation strategy is lowest-price , the Auto Scaling group launches instances using the Spot pools with the lowest price, and evenly allocates your instances across the number of Spot pools that you specify. If the allocation strategy is capacity-optimized , the Auto Scaling group launches instances using Spot pools that are optimally chosen based on the available Spot capacity.\nThe default Spot allocation strategy for calls that you make through the API, the AWS CLI, or the AWS SDKs is lowest-price . The default Spot allocation strategy for the AWS Management Console is capacity-optimized .\nValid values: lowest-price | capacity-optimized\n\nSpotInstancePools (integer) --The number of Spot Instance pools across which to allocate your Spot Instances. The Spot pools are determined from the different instance types in the Overrides array of LaunchTemplate . Default if not set is 2.\nUsed only when the Spot allocation strategy is lowest-price .\nValid Range: Minimum value of 1. Maximum value of 20.\n\nSpotMaxPrice (string) --The maximum price per unit hour that you are willing to pay for a Spot Instance. If you leave the value of this parameter blank (which is the default), the maximum Spot price is set at the On-Demand price.\nTo remove a value that you previously set, include the parameter but leave the value blank.\n\n\n\n\n :type InstanceId: string :param InstanceId: The ID of the instance used to create a launch configuration for the group. To get the instance ID, use the Amazon EC2 DescribeInstances API operation.\nWhen you specify an ID of an instance, Amazon EC2 Auto Scaling creates a new launch configuration and associates it with the group. This launch configuration derives its attributes from the specified instance, except for the block device mapping.\nYou must specify one of the following parameters in your request: LaunchConfigurationName , LaunchTemplate , InstanceId , or MixedInstancesPolicy .\n :type MinSize: integer :param MinSize: [REQUIRED]\nThe minimum size of the group.\n :type MaxSize: integer :param MaxSize: [REQUIRED]\nThe maximum size of the group.\n\nNote\nWith a mixed instances policy that uses instance weighting, Amazon EC2 Auto Scaling may need to go above MaxSize to meet your capacity requirements. In this event, Amazon EC2 Auto Scaling will never go above MaxSize by more than your maximum instance weight (weights that define how many capacity units each instance contributes to the capacity of the group).\n\n :type DesiredCapacity: integer :param DesiredCapacity: The desired capacity is the initial capacity of the Auto Scaling group at the time of its creation and the capacity it attempts to maintain. It can scale beyond this capacity if you configure automatic scaling.\nThis number must be greater than or equal to the minimum size of the group and less than or equal to the maximum size of the group. If you do not specify a desired capacity, the default is the minimum size of the group.\n :type DefaultCooldown: integer :param DefaultCooldown: The amount of time, in seconds, after a scaling activity completes before another scaling activity can start. The default value is 300 .\nFor more information, see Scaling Cooldowns in the Amazon EC2 Auto Scaling User Guide .\n :type AvailabilityZones: list :param AvailabilityZones: One or more Availability Zones for the group. This parameter is optional if you specify one or more subnets for VPCZoneIdentifier .\nConditional: If your account supports EC2-Classic and VPC, this parameter is required to launch instances into EC2-Classic.\n\n(string) --\n\n :type LoadBalancerNames: list :param LoadBalancerNames: A list of Classic Load Balancers associated with this Auto Scaling group. For Application Load Balancers and Network Load Balancers, specify a list of target groups using the TargetGroupARNs property instead.\nFor more information, see Using a Load Balancer with an Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide .\n\n(string) --\n\n :type TargetGroupARNs: list :param TargetGroupARNs: The Amazon Resource Names (ARN) of the target groups to associate with the Auto Scaling group. Instances are registered as targets in a target group, and traffic is routed to the target group.\nFor more information, see Using a Load Balancer with an Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide .\n\n(string) --\n\n :type HealthCheckType: string :param HealthCheckType: The service to use for the health checks. The valid values are EC2 and ELB . The default value is EC2 . If you configure an Auto Scaling group to use ELB health checks, it considers the instance unhealthy if it fails either the EC2 status checks or the load balancer health checks.\nFor more information, see Health Checks for Auto Scaling Instances in the Amazon EC2 Auto Scaling User Guide .\n :type HealthCheckGracePeriod: integer :param HealthCheckGracePeriod: The amount of time, in seconds, that Amazon EC2 Auto Scaling waits before checking the health status of an EC2 instance that has come into service. During this time, any health check failures for the instance are ignored. The default value is 0 .\nFor more information, see Health Check Grace Period in the Amazon EC2 Auto Scaling User Guide .\nConditional: This parameter is required if you are adding an ELB health check.\n :type PlacementGroup: string :param PlacementGroup: The name of the placement group into which to launch your instances, if any. A placement group is a logical grouping of instances within a single Availability Zone. You cannot specify multiple Availability Zones and a placement group. For more information, see Placement Groups in the Amazon EC2 User Guide for Linux Instances . :type VPCZoneIdentifier: string :param VPCZoneIdentifier: A comma-separated list of subnet IDs for your virtual private cloud (VPC).\nIf you specify VPCZoneIdentifier with AvailabilityZones , the subnets that you specify for this parameter must reside in those Availability Zones.\nConditional: If your account supports EC2-Classic and VPC, this parameter is required to launch instances into a VPC.\n :type TerminationPolicies: list :param TerminationPolicies: One or more termination policies used to select the instance to terminate. These policies are executed in the order that they are listed.\nFor more information, see Controlling Which Instances Auto Scaling Terminates During Scale In in the Amazon EC2 Auto Scaling User Guide .\n\n(string) --\n\n :type NewInstancesProtectedFromScaleIn: boolean :param NewInstancesProtectedFromScaleIn: Indicates whether newly launched instances are protected from termination by Amazon EC2 Auto Scaling when scaling in.\nFor more information about preventing instances from terminating on scale in, see Instance Protection in the Amazon EC2 Auto Scaling User Guide .\n :type LifecycleHookSpecificationList: list :param LifecycleHookSpecificationList: One or more lifecycle hooks.\n\n(dict) --Describes information used to specify a lifecycle hook for an Auto Scaling group.\nA lifecycle hook tells Amazon EC2 Auto Scaling to perform an action on an instance when the instance launches (before it is put into service) or as the instance terminates (before it is fully terminated).\nThis step is a part of the procedure for creating a lifecycle hook for an Auto Scaling group:\n\n(Optional) Create a Lambda function and a rule that allows CloudWatch Events to invoke your Lambda function when Amazon EC2 Auto Scaling launches or terminates instances.\n(Optional) Create a notification target and an IAM role. The target can be either an Amazon SQS queue or an Amazon SNS topic. The role allows Amazon EC2 Auto Scaling to publish lifecycle notifications to the target.\nCreate the lifecycle hook. Specify whether the hook is used when the instances launch or terminate.\nIf you need more time, record the lifecycle action heartbeat to keep the instance in a pending state.\nIf you finish before the timeout period ends, complete the lifecycle action.\n\nFor more information, see Amazon EC2 Auto Scaling Lifecycle Hooks in the Amazon EC2 Auto Scaling User Guide .\n\nLifecycleHookName (string) -- [REQUIRED]The name of the lifecycle hook.\n\nLifecycleTransition (string) -- [REQUIRED]The state of the EC2 instance to which you want to attach the lifecycle hook. The valid values are:\n\nautoscaling:EC2_INSTANCE_LAUNCHING\nautoscaling:EC2_INSTANCE_TERMINATING\n\n\nNotificationMetadata (string) --Additional information that you want to include any time Amazon EC2 Auto Scaling sends a message to the notification target.\n\nHeartbeatTimeout (integer) --The maximum time, in seconds, that can elapse before the lifecycle hook times out.\nIf the lifecycle hook times out, Amazon EC2 Auto Scaling performs the action that you specified in the DefaultResult parameter. You can prevent the lifecycle hook from timing out by calling RecordLifecycleActionHeartbeat .\n\nDefaultResult (string) --Defines the action the Auto Scaling group should take when the lifecycle hook timeout elapses or if an unexpected failure occurs. The valid values are CONTINUE and ABANDON . The default value is ABANDON .\n\nNotificationTargetARN (string) --The ARN of the target that Amazon EC2 Auto Scaling sends notifications to when an instance is in the transition state for the lifecycle hook. The notification target can be either an SQS queue or an SNS topic.\n\nRoleARN (string) --The ARN of the IAM role that allows the Auto Scaling group to publish to the specified notification target, for example, an Amazon SNS topic or an Amazon SQS queue.\n\n\n\n\n :type Tags: list :param Tags: One or more tags. You can tag your Auto Scaling group and propagate the tags to the Amazon EC2 instances it launches.\nTags are not propagated to Amazon EBS volumes. To add tags to Amazon EBS volumes, specify the tags in a launch template but use caution. If the launch template specifies an instance tag with a key that is also specified for the Auto Scaling group, Amazon EC2 Auto Scaling overrides the value of that instance tag with the value specified by the Auto Scaling group.\nFor more information, see Tagging Auto Scaling Groups and Instances in the Amazon EC2 Auto Scaling User Guide .\n\n(dict) --Describes a tag for an Auto Scaling group.\n\nResourceId (string) --The name of the group.\n\nResourceType (string) --The type of resource. The only supported value is auto-scaling-group .\n\nKey (string) -- [REQUIRED]The tag key.\n\nValue (string) --The tag value.\n\nPropagateAtLaunch (boolean) --Determines whether the tag is added to new instances as they are launched in the group.\n\n\n\n\n :type ServiceLinkedRoleARN: string :param ServiceLinkedRoleARN: The Amazon Resource Name (ARN) of the service-linked role that the Auto Scaling group uses to call other AWS services on your behalf. By default, Amazon EC2 Auto Scaling uses a service-linked role named AWSServiceRoleForAutoScaling, which it creates if it does not exist. For more information, see Service-Linked Roles in the Amazon EC2 Auto Scaling User Guide . :type MaxInstanceLifetime: integer :param MaxInstanceLifetime: The maximum amount of time, in seconds, that an instance can be in service. The default is null.\nThis parameter is optional, but if you specify a value for it, you must specify a value of at least 604,800 seconds (7 days). To clear a previously set value, specify a new value of 0.\nFor more information, see Replacing Auto Scaling Instances Based on Maximum Instance Lifetime in the Amazon EC2 Auto Scaling User Guide .\nValid Range: Minimum value of 0.\n :return: response = client.create_auto_scaling_group( AutoScalingGroupName='my-auto-scaling-group', LaunchConfigurationName='my-launch-config', MaxSize=3, MinSize=1, VPCZoneIdentifier='subnet-4176792c', ) print(response) :returns: AutoScaling.Client.exceptions.AlreadyExistsFault AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure """ pass def create_launch_configuration(LaunchConfigurationName=None, ImageId=None, KeyName=None, SecurityGroups=None, ClassicLinkVPCId=None, ClassicLinkVPCSecurityGroups=None, UserData=None, InstanceId=None, InstanceType=None, KernelId=None, RamdiskId=None, BlockDeviceMappings=None, InstanceMonitoring=None, SpotPrice=None, IamInstanceProfile=None, EbsOptimized=None, AssociatePublicIpAddress=None, PlacementTenancy=None): """ Creates a launch configuration. If you exceed your maximum limit of launch configurations, the call fails. To query this limit, call the DescribeAccountLimits API. For information about updating this limit, see Amazon EC2 Auto Scaling Service Quotas in the Amazon EC2 Auto Scaling User Guide . For more information, see Launch Configurations in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example creates a launch configuration. Expected Output: :example: response = client.create_launch_configuration( LaunchConfigurationName='string', ImageId='string', KeyName='string', SecurityGroups=[ 'string', ], ClassicLinkVPCId='string', ClassicLinkVPCSecurityGroups=[ 'string', ], UserData='string', InstanceId='string', InstanceType='string', KernelId='string', RamdiskId='string', BlockDeviceMappings=[ { 'VirtualName': 'string', 'DeviceName': 'string', 'Ebs': { 'SnapshotId': 'string', 'VolumeSize': 123, 'VolumeType': 'string', 'DeleteOnTermination': True|False, 'Iops': 123, 'Encrypted': True|False }, 'NoDevice': True|False }, ], InstanceMonitoring={ 'Enabled': True|False }, SpotPrice='string', IamInstanceProfile='string', EbsOptimized=True|False, AssociatePublicIpAddress=True|False, PlacementTenancy='string' ) :type LaunchConfigurationName: string :param LaunchConfigurationName: [REQUIRED]\nThe name of the launch configuration. This name must be unique per Region per account.\n :type ImageId: string :param ImageId: The ID of the Amazon Machine Image (AMI) that was assigned during registration. For more information, see Finding an AMI in the Amazon EC2 User Guide for Linux Instances .\nIf you do not specify InstanceId , you must specify ImageId .\n :type KeyName: string :param KeyName: The name of the key pair. For more information, see Amazon EC2 Key Pairs in the Amazon EC2 User Guide for Linux Instances . :type SecurityGroups: list :param SecurityGroups: A list that contains the security groups to assign to the instances in the Auto Scaling group.\n[EC2-VPC] Specify the security group IDs. For more information, see Security Groups for Your VPC in the Amazon Virtual Private Cloud User Guide .\n[EC2-Classic] Specify either the security group names or the security group IDs. For more information, see Amazon EC2 Security Groups in the Amazon EC2 User Guide for Linux Instances .\n\n(string) --\n\n :type ClassicLinkVPCId: string :param ClassicLinkVPCId: The ID of a ClassicLink-enabled VPC to link your EC2-Classic instances to. For more information, see ClassicLink in the Amazon EC2 User Guide for Linux Instances and Linking EC2-Classic Instances to a VPC in the Amazon EC2 Auto Scaling User Guide .\nThis parameter can only be used if you are launching EC2-Classic instances.\n :type ClassicLinkVPCSecurityGroups: list :param ClassicLinkVPCSecurityGroups: The IDs of one or more security groups for the specified ClassicLink-enabled VPC. For more information, see ClassicLink in the Amazon EC2 User Guide for Linux Instances and Linking EC2-Classic Instances to a VPC in the Amazon EC2 Auto Scaling User Guide .\nIf you specify the ClassicLinkVPCId parameter, you must specify this parameter.\n\n(string) --\n\n :type UserData: string :param UserData: The Base64-encoded user data to make available to the launched EC2 instances. For more information, see Instance Metadata and User Data in the Amazon EC2 User Guide for Linux Instances .\n\nThis value will be base64 encoded automatically. Do not base64 encode this value prior to performing the operation.\n :type InstanceId: string :param InstanceId: The ID of the instance to use to create the launch configuration. The new launch configuration derives attributes from the instance, except for the block device mapping.\nTo create a launch configuration with a block device mapping or override any other instance attributes, specify them as part of the same request.\nFor more information, see Create a Launch Configuration Using an EC2 Instance in the Amazon EC2 Auto Scaling User Guide .\nIf you do not specify InstanceId , you must specify both ImageId and InstanceType .\n :type InstanceType: string :param InstanceType: Specifies the instance type of the EC2 instance.\nFor information about available instance types, see Available Instance Types in the Amazon EC2 User Guide for Linux Instances.\nIf you do not specify InstanceId , you must specify InstanceType .\n :type KernelId: string :param KernelId: The ID of the kernel associated with the AMI. :type RamdiskId: string :param RamdiskId: The ID of the RAM disk to select. :type BlockDeviceMappings: list :param BlockDeviceMappings: A block device mapping, which specifies the block devices for the instance. You can specify virtual devices and EBS volumes. For more information, see Block Device Mapping in the Amazon EC2 User Guide for Linux Instances .\n\n(dict) --Describes a block device mapping.\n\nVirtualName (string) --The name of the virtual device (for example, ephemeral0 ).\nYou can specify either VirtualName or Ebs , but not both.\n\nDeviceName (string) -- [REQUIRED]The device name exposed to the EC2 instance (for example, /dev/sdh or xvdh ). For more information, see Device Naming on Linux Instances in the Amazon EC2 User Guide for Linux Instances .\n\nEbs (dict) --Parameters used to automatically set up EBS volumes when an instance is launched.\nYou can specify either VirtualName or Ebs , but not both.\n\nSnapshotId (string) --The snapshot ID of the volume to use.\nConditional: This parameter is optional if you specify a volume size. If you specify both SnapshotId and VolumeSize , VolumeSize must be equal or greater than the size of the snapshot.\n\nVolumeSize (integer) --The volume size, in Gibibytes (GiB).\nThis can be a number from 1-1,024 for standard , 4-16,384 for io1 , 1-16,384 for gp2 , and 500-16,384 for st1 and sc1 . If you specify a snapshot, the volume size must be equal to or larger than the snapshot size.\nDefault: If you create a volume from a snapshot and you don\'t specify a volume size, the default is the snapshot size.\n\nNote\nAt least one of VolumeSize or SnapshotId is required.\n\n\nVolumeType (string) --The volume type, which can be standard for Magnetic, io1 for Provisioned IOPS SSD, gp2 for General Purpose SSD, st1 for Throughput Optimized HDD, or sc1 for Cold HDD. For more information, see Amazon EBS Volume Types in the Amazon EC2 User Guide for Linux Instances .\nValid Values: standard | io1 | gp2 | st1 | sc1\n\nDeleteOnTermination (boolean) --Indicates whether the volume is deleted on instance termination. For Amazon EC2 Auto Scaling, the default value is true .\n\nIops (integer) --The number of I/O operations per second (IOPS) to provision for the volume. The maximum ratio of IOPS to volume size (in GiB) is 50:1. For more information, see Amazon EBS Volume Types in the Amazon EC2 User Guide for Linux Instances .\nConditional: This parameter is required when the volume type is io1 . (Not used with standard , gp2 , st1 , or sc1 volumes.)\n\nEncrypted (boolean) --Specifies whether the volume should be encrypted. Encrypted EBS volumes can only be attached to instances that support Amazon EBS encryption. For more information, see Supported Instance Types . If your AMI uses encrypted volumes, you can also only launch it on supported instance types.\n\nNote\nIf you are creating a volume from a snapshot, you cannot specify an encryption value. Volumes that are created from encrypted snapshots are automatically encrypted, and volumes that are created from unencrypted snapshots are automatically unencrypted. By default, encrypted snapshots use the AWS managed CMK that is used for EBS encryption, but you can specify a custom CMK when you create the snapshot. The ability to encrypt a snapshot during copying also allows you to apply a new CMK to an already-encrypted snapshot. Volumes restored from the resulting copy are only accessible using the new CMK.\nEnabling encryption by default results in all EBS volumes being encrypted with the AWS managed CMK or a customer managed CMK, whether or not the snapshot was encrypted.\n\nFor more information, see Using Encryption with EBS-Backed AMIs in the Amazon EC2 User Guide for Linux Instances and Required CMK Key Policy for Use with Encrypted Volumes in the Amazon EC2 Auto Scaling User Guide .\n\n\n\nNoDevice (boolean) --Setting this value to true suppresses the specified device included in the block device mapping of the AMI.\nIf NoDevice is true for the root device, instances might fail the EC2 health check. In that case, Amazon EC2 Auto Scaling launches replacement instances.\nIf you specify NoDevice , you cannot specify Ebs .\n\n\n\n\n :type InstanceMonitoring: dict :param InstanceMonitoring: Controls whether instances in this group are launched with detailed (true ) or basic (false ) monitoring.\nThe default value is true (enabled).\n\nWarning\nWhen detailed monitoring is enabled, Amazon CloudWatch generates metrics every minute and your account is charged a fee. When you disable detailed monitoring, CloudWatch generates metrics every 5 minutes. For more information, see Configure Monitoring for Auto Scaling Instances in the Amazon EC2 Auto Scaling User Guide .\n\n\nEnabled (boolean) --If true , detailed monitoring is enabled. Otherwise, basic monitoring is enabled.\n\n\n :type SpotPrice: string :param SpotPrice: The maximum hourly price to be paid for any Spot Instance launched to fulfill the request. Spot Instances are launched when the price you specify exceeds the current Spot price. For more information, see Launching Spot Instances in Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide .\n\nNote\nWhen you change your maximum price by creating a new launch configuration, running instances will continue to run as long as the maximum price for those running instances is higher than the current Spot price.\n\n :type IamInstanceProfile: string :param IamInstanceProfile: The name or the Amazon Resource Name (ARN) of the instance profile associated with the IAM role for the instance. The instance profile contains the IAM role.\nFor more information, see IAM Role for Applications That Run on Amazon EC2 Instances in the Amazon EC2 Auto Scaling User Guide .\n :type EbsOptimized: boolean :param EbsOptimized: Specifies whether the launch configuration is optimized for EBS I/O (true ) or not (false ). The optimization provides dedicated throughput to Amazon EBS and an optimized configuration stack to provide optimal I/O performance. This optimization is not available with all instance types. Additional fees are incurred when you enable EBS optimization for an instance type that is not EBS-optimized by default. For more information, see Amazon EBS-Optimized Instances in the Amazon EC2 User Guide for Linux Instances .\nThe default value is false .\n :type AssociatePublicIpAddress: boolean :param AssociatePublicIpAddress: For Auto Scaling groups that are running in a virtual private cloud (VPC), specifies whether to assign a public IP address to the group\'s instances. If you specify true , each instance in the Auto Scaling group receives a unique public IP address. For more information, see Launching Auto Scaling Instances in a VPC in the Amazon EC2 Auto Scaling User Guide .\nIf you specify this parameter, you must specify at least one subnet for VPCZoneIdentifier when you create your group.\n\nNote\nIf the instance is launched into a default subnet, the default is to assign a public IP address, unless you disabled the option to assign a public IP address on the subnet. If the instance is launched into a nondefault subnet, the default is not to assign a public IP address, unless you enabled the option to assign a public IP address on the subnet.\n\n :type PlacementTenancy: string :param PlacementTenancy: The tenancy of the instance. An instance with dedicated tenancy runs on isolated, single-tenant hardware and can only be launched into a VPC.\nTo launch dedicated instances into a shared tenancy VPC (a VPC with the instance placement tenancy attribute set to default ), you must set the value of this parameter to dedicated .\nIf you specify PlacementTenancy , you must specify at least one subnet for VPCZoneIdentifier when you create your group.\nFor more information, see Instance Placement Tenancy in the Amazon EC2 Auto Scaling User Guide .\nValid Values: default | dedicated\n :return: response = client.create_launch_configuration( IamInstanceProfile='my-iam-role', ImageId='ami-12345678', InstanceType='m3.medium', LaunchConfigurationName='my-launch-config', SecurityGroups=[ 'sg-eb2af88e', ], ) print(response) :returns: AutoScaling.Client.exceptions.AlreadyExistsFault AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def create_or_update_tags(Tags=None): """ Creates or updates tags for the specified Auto Scaling group. When you specify a tag with a key that already exists, the operation overwrites the previous tag definition, and you do not get an error message. For more information, see Tagging Auto Scaling Groups and Instances in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example adds two tags to the specified Auto Scaling group. Expected Output: :example: response = client.create_or_update_tags( Tags=[ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ] ) :type Tags: list :param Tags: [REQUIRED]\nOne or more tags.\n\n(dict) --Describes a tag for an Auto Scaling group.\n\nResourceId (string) --The name of the group.\n\nResourceType (string) --The type of resource. The only supported value is auto-scaling-group .\n\nKey (string) -- [REQUIRED]The tag key.\n\nValue (string) --The tag value.\n\nPropagateAtLaunch (boolean) --Determines whether the tag is added to new instances as they are launched in the group.\n\n\n\n\n :return: response = client.create_or_update_tags( Tags=[ { 'Key': 'Role', 'PropagateAtLaunch': True, 'ResourceId': 'my-auto-scaling-group', 'ResourceType': 'auto-scaling-group', 'Value': 'WebServer', }, { 'Key': 'Dept', 'PropagateAtLaunch': True, 'ResourceId': 'my-auto-scaling-group', 'ResourceType': 'auto-scaling-group', 'Value': 'Research', }, ], ) print(response) """ pass def delete_auto_scaling_group(AutoScalingGroupName=None, ForceDelete=None): """ Deletes the specified Auto Scaling group. If the group has instances or scaling activities in progress, you must specify the option to force the deletion in order for it to succeed. If the group has policies, deleting the group deletes the policies, the underlying alarm actions, and any alarm that no longer has an associated action. To remove instances from the Auto Scaling group before deleting it, call the DetachInstances API with the list of instances and the option to decrement the desired capacity. This ensures that Amazon EC2 Auto Scaling does not launch replacement instances. To terminate all instances before deleting the Auto Scaling group, call the UpdateAutoScalingGroup API and set the minimum size and desired capacity of the Auto Scaling group to zero. See also: AWS API Documentation Exceptions Examples This example deletes the specified Auto Scaling group. Expected Output: This example deletes the specified Auto Scaling group and all its instances. Expected Output: :example: response = client.delete_auto_scaling_group( AutoScalingGroupName='string', ForceDelete=True|False ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ForceDelete: boolean :param ForceDelete: Specifies that the group is to be deleted along with all instances associated with the group, without waiting for all instances to be terminated. This parameter also deletes any lifecycle actions associated with the group. :return: response = client.delete_auto_scaling_group( AutoScalingGroupName='my-auto-scaling-group', ) print(response) :returns: AutoScaling.Client.exceptions.ScalingActivityInProgressFault AutoScaling.Client.exceptions.ResourceInUseFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def delete_launch_configuration(LaunchConfigurationName=None): """ Deletes the specified launch configuration. The launch configuration must not be attached to an Auto Scaling group. When this call completes, the launch configuration is no longer available for use. See also: AWS API Documentation Exceptions Examples This example deletes the specified launch configuration. Expected Output: :example: response = client.delete_launch_configuration( LaunchConfigurationName='string' ) :type LaunchConfigurationName: string :param LaunchConfigurationName: [REQUIRED]\nThe name of the launch configuration.\n :return: response = client.delete_launch_configuration( LaunchConfigurationName='my-launch-config', ) print(response) """ pass def delete_lifecycle_hook(LifecycleHookName=None, AutoScalingGroupName=None): """ Deletes the specified lifecycle hook. If there are any outstanding lifecycle actions, they are completed first (ABANDON for launching instances, CONTINUE for terminating instances). See also: AWS API Documentation Exceptions Examples This example deletes the specified lifecycle hook. Expected Output: :example: response = client.delete_lifecycle_hook( LifecycleHookName='string', AutoScalingGroupName='string' ) :type LifecycleHookName: string :param LifecycleHookName: [REQUIRED]\nThe name of the lifecycle hook.\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example deletes the specified lifecycle hook. response = client.delete_lifecycle_hook( AutoScalingGroupName='my-auto-scaling-group', LifecycleHookName='my-lifecycle-hook', ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: (dict) -- """ pass def delete_notification_configuration(AutoScalingGroupName=None, TopicARN=None): """ Deletes the specified notification. See also: AWS API Documentation Exceptions Examples This example deletes the specified notification from the specified Auto Scaling group. Expected Output: :example: response = client.delete_notification_configuration( AutoScalingGroupName='string', TopicARN='string' ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type TopicARN: string :param TopicARN: [REQUIRED]\nThe Amazon Resource Name (ARN) of the Amazon Simple Notification Service (Amazon SNS) topic.\n :return: response = client.delete_notification_configuration( AutoScalingGroupName='my-auto-scaling-group', TopicARN='arn:aws:sns:us-west-2:123456789012:my-sns-topic', ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def delete_policy(AutoScalingGroupName=None, PolicyName=None): """ Deletes the specified scaling policy. Deleting either a step scaling policy or a simple scaling policy deletes the underlying alarm action, but does not delete the alarm, even if it no longer has an associated action. For more information, see Deleting a Scaling Policy in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example deletes the specified Auto Scaling policy. Expected Output: :example: response = client.delete_policy( AutoScalingGroupName='string', PolicyName='string' ) :type AutoScalingGroupName: string :param AutoScalingGroupName: The name of the Auto Scaling group. :type PolicyName: string :param PolicyName: [REQUIRED]\nThe name or Amazon Resource Name (ARN) of the policy.\n :return: response = client.delete_policy( AutoScalingGroupName='my-auto-scaling-group', PolicyName='ScaleIn', ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure """ pass def delete_scheduled_action(AutoScalingGroupName=None, ScheduledActionName=None): """ Deletes the specified scheduled action. See also: AWS API Documentation Exceptions Examples This example deletes the specified scheduled action from the specified Auto Scaling group. Expected Output: :example: response = client.delete_scheduled_action( AutoScalingGroupName='string', ScheduledActionName='string' ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ScheduledActionName: string :param ScheduledActionName: [REQUIRED]\nThe name of the action to delete.\n :return: response = client.delete_scheduled_action( AutoScalingGroupName='my-auto-scaling-group', ScheduledActionName='my-scheduled-action', ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def delete_tags(Tags=None): """ Deletes the specified tags. See also: AWS API Documentation Exceptions Examples This example deletes the specified tag from the specified Auto Scaling group. Expected Output: :example: response = client.delete_tags( Tags=[ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ] ) :type Tags: list :param Tags: [REQUIRED]\nOne or more tags.\n\n(dict) --Describes a tag for an Auto Scaling group.\n\nResourceId (string) --The name of the group.\n\nResourceType (string) --The type of resource. The only supported value is auto-scaling-group .\n\nKey (string) -- [REQUIRED]The tag key.\n\nValue (string) --The tag value.\n\nPropagateAtLaunch (boolean) --Determines whether the tag is added to new instances as they are launched in the group.\n\n\n\n\n :return: response = client.delete_tags( Tags=[ { 'Key': 'Dept', 'ResourceId': 'my-auto-scaling-group', 'ResourceType': 'auto-scaling-group', 'Value': 'Research', }, ], ) print(response) """ pass def describe_account_limits(): """ Describes the current Amazon EC2 Auto Scaling resource quotas for your AWS account. For information about requesting an increase, see Amazon EC2 Auto Scaling Service Quotas in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example describes the Auto Scaling limits for your AWS account. Expected Output: :example: response = client.describe_account_limits() :rtype: dict ReturnsResponse Syntax{ 'MaxNumberOfAutoScalingGroups': 123, 'MaxNumberOfLaunchConfigurations': 123, 'NumberOfAutoScalingGroups': 123, 'NumberOfLaunchConfigurations': 123 } Response Structure (dict) -- MaxNumberOfAutoScalingGroups (integer) --The maximum number of groups allowed for your AWS account. The default is 200 groups per AWS Region. MaxNumberOfLaunchConfigurations (integer) --The maximum number of launch configurations allowed for your AWS account. The default is 200 launch configurations per AWS Region. NumberOfAutoScalingGroups (integer) --The current number of groups for your AWS account. NumberOfLaunchConfigurations (integer) --The current number of launch configurations for your AWS account. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the Auto Scaling limits for your AWS account. response = client.describe_account_limits( ) print(response) Expected Output: { 'MaxNumberOfAutoScalingGroups': 20, 'MaxNumberOfLaunchConfigurations': 100, 'NumberOfAutoScalingGroups': 3, 'NumberOfLaunchConfigurations': 5, 'ResponseMetadata': { '...': '...', }, } :return: { 'MaxNumberOfAutoScalingGroups': 123, 'MaxNumberOfLaunchConfigurations': 123, 'NumberOfAutoScalingGroups': 123, 'NumberOfLaunchConfigurations': 123 } """ pass def describe_adjustment_types(): """ Describes the available adjustment types for Amazon EC2 Auto Scaling scaling policies. These settings apply to step scaling policies and simple scaling policies; they do not apply to target tracking scaling policies. The following adjustment types are supported: See also: AWS API Documentation Exceptions Examples This example describes the available adjustment types. Expected Output: :example: response = client.describe_adjustment_types() :rtype: dict ReturnsResponse Syntax{ 'AdjustmentTypes': [ { 'AdjustmentType': 'string' }, ] } Response Structure (dict) -- AdjustmentTypes (list) --The policy adjustment types. (dict) --Describes a policy adjustment type. AdjustmentType (string) --The policy adjustment type. The valid values are ChangeInCapacity , ExactCapacity , and PercentChangeInCapacity . Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the available adjustment types. response = client.describe_adjustment_types( ) print(response) Expected Output: { 'AdjustmentTypes': [ { 'AdjustmentType': 'ChangeInCapacity', }, { 'AdjustmentType': 'ExactCapcity', }, { 'AdjustmentType': 'PercentChangeInCapacity', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'AdjustmentTypes': [ { 'AdjustmentType': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_auto_scaling_groups(AutoScalingGroupNames=None, NextToken=None, MaxRecords=None): """ Describes one or more Auto Scaling groups. See also: AWS API Documentation Exceptions Examples This example describes the specified Auto Scaling group. Expected Output: :example: response = client.describe_auto_scaling_groups( AutoScalingGroupNames=[ 'string', ], NextToken='string', MaxRecords=123 ) :type AutoScalingGroupNames: list :param AutoScalingGroupNames: The names of the Auto Scaling groups. Each name can be a maximum of 1600 characters. By default, you can only specify up to 50 names. You can optionally increase this limit using the MaxRecords parameter.\nIf you omit this parameter, all Auto Scaling groups are described.\n\n(string) --\n\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 50 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'AutoScalingGroups': [ { 'AutoScalingGroupName': 'string', 'AutoScalingGroupARN': 'string', 'LaunchConfigurationName': 'string', 'LaunchTemplate': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'MixedInstancesPolicy': { 'LaunchTemplate': { 'LaunchTemplateSpecification': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'Overrides': [ { 'InstanceType': 'string', 'WeightedCapacity': 'string' }, ] }, 'InstancesDistribution': { 'OnDemandAllocationStrategy': 'string', 'OnDemandBaseCapacity': 123, 'OnDemandPercentageAboveBaseCapacity': 123, 'SpotAllocationStrategy': 'string', 'SpotInstancePools': 123, 'SpotMaxPrice': 'string' } }, 'MinSize': 123, 'MaxSize': 123, 'DesiredCapacity': 123, 'DefaultCooldown': 123, 'AvailabilityZones': [ 'string', ], 'LoadBalancerNames': [ 'string', ], 'TargetGroupARNs': [ 'string', ], 'HealthCheckType': 'string', 'HealthCheckGracePeriod': 123, 'Instances': [ { 'InstanceId': 'string', 'InstanceType': 'string', 'AvailabilityZone': 'string', 'LifecycleState': 'Pending'|'Pending:Wait'|'Pending:Proceed'|'Quarantined'|'InService'|'Terminating'|'Terminating:Wait'|'Terminating:Proceed'|'Terminated'|'Detaching'|'Detached'|'EnteringStandby'|'Standby', 'HealthStatus': 'string', 'LaunchConfigurationName': 'string', 'LaunchTemplate': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'ProtectedFromScaleIn': True|False, 'WeightedCapacity': 'string' }, ], 'CreatedTime': datetime(2015, 1, 1), 'SuspendedProcesses': [ { 'ProcessName': 'string', 'SuspensionReason': 'string' }, ], 'PlacementGroup': 'string', 'VPCZoneIdentifier': 'string', 'EnabledMetrics': [ { 'Metric': 'string', 'Granularity': 'string' }, ], 'Status': 'string', 'Tags': [ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ], 'TerminationPolicies': [ 'string', ], 'NewInstancesProtectedFromScaleIn': True|False, 'ServiceLinkedRoleARN': 'string', 'MaxInstanceLifetime': 123 }, ], 'NextToken': 'string' } Response Structure (dict) -- AutoScalingGroups (list) -- The groups. (dict) -- Describes an Auto Scaling group. AutoScalingGroupName (string) -- The name of the Auto Scaling group. AutoScalingGroupARN (string) -- The Amazon Resource Name (ARN) of the Auto Scaling group. LaunchConfigurationName (string) -- The name of the associated launch configuration. LaunchTemplate (dict) -- The launch template for the group. LaunchTemplateId (string) -- The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. LaunchTemplateName (string) -- The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. Version (string) -- The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API. If the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default . MixedInstancesPolicy (dict) -- The mixed instances policy for the group. LaunchTemplate (dict) -- The launch template and instance types (overrides). This parameter must be specified when creating a mixed instances policy. LaunchTemplateSpecification (dict) -- The launch template to use. You must specify either the launch template ID or launch template name in the request. LaunchTemplateId (string) -- The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. LaunchTemplateName (string) -- The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. Version (string) -- The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API. If the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default . Overrides (list) -- Any parameters that you specify override the same parameters in the launch template. Currently, the only supported override is instance type. You can specify between 1 and 20 instance types. If not provided, Amazon EC2 Auto Scaling will use the instance type specified in the launch template to launch instances. (dict) -- Describes an override for a launch template. Currently, the only supported override is instance type. The maximum number of instance type overrides that can be associated with an Auto Scaling group is 20. InstanceType (string) -- The instance type. You must use an instance type that is supported in your requested Region and Availability Zones. For information about available instance types, see Available Instance Types in the Amazon Elastic Compute Cloud User Guide. WeightedCapacity (string) -- The number of capacity units, which gives the instance type a proportional weight to other instance types. For example, larger instance types are generally weighted more than smaller instance types. These are the same units that you chose to set the desired capacity in terms of instances, or a performance attribute such as vCPUs, memory, or I/O. For more information, see Instance Weighting for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide . Valid Range: Minimum value of 1. Maximum value of 999. InstancesDistribution (dict) -- The instances distribution to use. If you leave this parameter unspecified, the value for each parameter in InstancesDistribution uses a default value. OnDemandAllocationStrategy (string) -- Indicates how to allocate instance types to fulfill On-Demand capacity. The only valid value is prioritized , which is also the default value. This strategy uses the order of instance type overrides for the LaunchTemplate to define the launch priority of each instance type. The first instance type in the array is prioritized higher than the last. If all your On-Demand capacity cannot be fulfilled using your highest priority instance, then the Auto Scaling groups launches the remaining capacity using the second priority instance type, and so on. OnDemandBaseCapacity (integer) -- The minimum amount of the Auto Scaling group\'s capacity that must be fulfilled by On-Demand Instances. This base portion is provisioned first as your group scales. Default if not set is 0. If you leave it set to 0, On-Demand Instances are launched as a percentage of the Auto Scaling group\'s desired capacity, per the OnDemandPercentageAboveBaseCapacity setting. Note An update to this setting means a gradual replacement of instances to maintain the specified number of On-Demand Instances for your base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones. OnDemandPercentageAboveBaseCapacity (integer) -- Controls the percentages of On-Demand Instances and Spot Instances for your additional capacity beyond OnDemandBaseCapacity . Default if not set is 100. If you leave it set to 100, the percentages are 100% for On-Demand Instances and 0% for Spot Instances. Note An update to this setting means a gradual replacement of instances to maintain the percentage of On-Demand Instances for your additional capacity above the base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones. Valid Range: Minimum value of 0. Maximum value of 100. SpotAllocationStrategy (string) -- Indicates how to allocate instances across Spot Instance pools. If the allocation strategy is lowest-price , the Auto Scaling group launches instances using the Spot pools with the lowest price, and evenly allocates your instances across the number of Spot pools that you specify. If the allocation strategy is capacity-optimized , the Auto Scaling group launches instances using Spot pools that are optimally chosen based on the available Spot capacity. The default Spot allocation strategy for calls that you make through the API, the AWS CLI, or the AWS SDKs is lowest-price . The default Spot allocation strategy for the AWS Management Console is capacity-optimized . Valid values: lowest-price | capacity-optimized SpotInstancePools (integer) -- The number of Spot Instance pools across which to allocate your Spot Instances. The Spot pools are determined from the different instance types in the Overrides array of LaunchTemplate . Default if not set is 2. Used only when the Spot allocation strategy is lowest-price . Valid Range: Minimum value of 1. Maximum value of 20. SpotMaxPrice (string) -- The maximum price per unit hour that you are willing to pay for a Spot Instance. If you leave the value of this parameter blank (which is the default), the maximum Spot price is set at the On-Demand price. To remove a value that you previously set, include the parameter but leave the value blank. MinSize (integer) -- The minimum size of the group. MaxSize (integer) -- The maximum size of the group. DesiredCapacity (integer) -- The desired size of the group. DefaultCooldown (integer) -- The amount of time, in seconds, after a scaling activity completes before another scaling activity can start. AvailabilityZones (list) -- One or more Availability Zones for the group. (string) -- LoadBalancerNames (list) -- One or more load balancers associated with the group. (string) -- TargetGroupARNs (list) -- The Amazon Resource Names (ARN) of the target groups for your load balancer. (string) -- HealthCheckType (string) -- The service to use for the health checks. The valid values are EC2 and ELB . If you configure an Auto Scaling group to use ELB health checks, it considers the instance unhealthy if it fails either the EC2 status checks or the load balancer health checks. HealthCheckGracePeriod (integer) -- The amount of time, in seconds, that Amazon EC2 Auto Scaling waits before checking the health status of an EC2 instance that has come into service. Instances (list) -- The EC2 instances associated with the group. (dict) -- Describes an EC2 instance. InstanceId (string) -- The ID of the instance. InstanceType (string) -- The instance type of the EC2 instance. AvailabilityZone (string) -- The Availability Zone in which the instance is running. LifecycleState (string) -- A description of the current lifecycle state. The Quarantined state is not used. HealthStatus (string) -- The last reported health status of the instance. "Healthy" means that the instance is healthy and should remain in service. "Unhealthy" means that the instance is unhealthy and that Amazon EC2 Auto Scaling should terminate and replace it. LaunchConfigurationName (string) -- The launch configuration associated with the instance. LaunchTemplate (dict) -- The launch template for the instance. LaunchTemplateId (string) -- The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. LaunchTemplateName (string) -- The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. Version (string) -- The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API. If the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default . ProtectedFromScaleIn (boolean) -- Indicates whether the instance is protected from termination by Amazon EC2 Auto Scaling when scaling in. WeightedCapacity (string) -- The number of capacity units contributed by the instance based on its instance type. Valid Range: Minimum value of 1. Maximum value of 999. CreatedTime (datetime) -- The date and time the group was created. SuspendedProcesses (list) -- The suspended processes associated with the group. (dict) -- Describes an automatic scaling process that has been suspended. For more information, see Scaling Processes in the Amazon EC2 Auto Scaling User Guide . ProcessName (string) -- The name of the suspended process. SuspensionReason (string) -- The reason that the process was suspended. PlacementGroup (string) -- The name of the placement group into which to launch your instances, if any. VPCZoneIdentifier (string) -- One or more subnet IDs, if applicable, separated by commas. EnabledMetrics (list) -- The metrics enabled for the group. (dict) -- Describes an enabled metric. Metric (string) -- One of the following metrics: GroupMinSize GroupMaxSize GroupDesiredCapacity GroupInServiceInstances GroupPendingInstances GroupStandbyInstances GroupTerminatingInstances GroupTotalInstances GroupInServiceCapacity GroupPendingCapacity GroupStandbyCapacity GroupTerminatingCapacity GroupTotalCapacity Granularity (string) -- The granularity of the metric. The only valid value is 1Minute . Status (string) -- The current state of the group when the DeleteAutoScalingGroup operation is in progress. Tags (list) -- The tags for the group. (dict) -- Describes a tag for an Auto Scaling group. ResourceId (string) -- The name of the group. ResourceType (string) -- The type of resource. The only supported value is auto-scaling-group . Key (string) -- The tag key. Value (string) -- The tag value. PropagateAtLaunch (boolean) -- Determines whether the tag is added to new instances as they are launched in the group. TerminationPolicies (list) -- The termination policies for the group. (string) -- NewInstancesProtectedFromScaleIn (boolean) -- Indicates whether newly launched instances are protected from termination by Amazon EC2 Auto Scaling when scaling in. ServiceLinkedRoleARN (string) -- The Amazon Resource Name (ARN) of the service-linked role that the Auto Scaling group uses to call other AWS services on your behalf. MaxInstanceLifetime (integer) -- The maximum amount of time, in seconds, that an instance can be in service. Valid Range: Minimum value of 0. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the specified Auto Scaling group. response = client.describe_auto_scaling_groups( AutoScalingGroupNames=[ 'my-auto-scaling-group', ], ) print(response) Expected Output: { 'AutoScalingGroups': [ { 'AutoScalingGroupARN': 'arn:aws:autoscaling:us-west-2:123456789012:autoScalingGroup:930d940e-891e-4781-a11a-7b0acd480f03:autoScalingGroupName/my-auto-scaling-group', 'AutoScalingGroupName': 'my-auto-scaling-group', 'AvailabilityZones': [ 'us-west-2c', ], 'CreatedTime': datetime(2013, 8, 19, 20, 53, 25, 0, 231, 0), 'DefaultCooldown': 300, 'DesiredCapacity': 1, 'EnabledMetrics': [ ], 'HealthCheckGracePeriod': 300, 'HealthCheckType': 'EC2', 'Instances': [ { 'AvailabilityZone': 'us-west-2c', 'HealthStatus': 'Healthy', 'InstanceId': 'i-4ba0837f', 'LaunchConfigurationName': 'my-launch-config', 'LifecycleState': 'InService', 'ProtectedFromScaleIn': False, }, ], 'LaunchConfigurationName': 'my-launch-config', 'LoadBalancerNames': [ ], 'MaxSize': 1, 'MinSize': 0, 'NewInstancesProtectedFromScaleIn': False, 'SuspendedProcesses': [ ], 'Tags': [ ], 'TerminationPolicies': [ 'Default', ], 'VPCZoneIdentifier': 'subnet-12345678', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'AutoScalingGroups': [ { 'AutoScalingGroupName': 'string', 'AutoScalingGroupARN': 'string', 'LaunchConfigurationName': 'string', 'LaunchTemplate': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'MixedInstancesPolicy': { 'LaunchTemplate': { 'LaunchTemplateSpecification': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'Overrides': [ { 'InstanceType': 'string', 'WeightedCapacity': 'string' }, ] }, 'InstancesDistribution': { 'OnDemandAllocationStrategy': 'string', 'OnDemandBaseCapacity': 123, 'OnDemandPercentageAboveBaseCapacity': 123, 'SpotAllocationStrategy': 'string', 'SpotInstancePools': 123, 'SpotMaxPrice': 'string' } }, 'MinSize': 123, 'MaxSize': 123, 'DesiredCapacity': 123, 'DefaultCooldown': 123, 'AvailabilityZones': [ 'string', ], 'LoadBalancerNames': [ 'string', ], 'TargetGroupARNs': [ 'string', ], 'HealthCheckType': 'string', 'HealthCheckGracePeriod': 123, 'Instances': [ { 'InstanceId': 'string', 'InstanceType': 'string', 'AvailabilityZone': 'string', 'LifecycleState': 'Pending'|'Pending:Wait'|'Pending:Proceed'|'Quarantined'|'InService'|'Terminating'|'Terminating:Wait'|'Terminating:Proceed'|'Terminated'|'Detaching'|'Detached'|'EnteringStandby'|'Standby', 'HealthStatus': 'string', 'LaunchConfigurationName': 'string', 'LaunchTemplate': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'ProtectedFromScaleIn': True|False, 'WeightedCapacity': 'string' }, ], 'CreatedTime': datetime(2015, 1, 1), 'SuspendedProcesses': [ { 'ProcessName': 'string', 'SuspensionReason': 'string' }, ], 'PlacementGroup': 'string', 'VPCZoneIdentifier': 'string', 'EnabledMetrics': [ { 'Metric': 'string', 'Granularity': 'string' }, ], 'Status': 'string', 'Tags': [ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ], 'TerminationPolicies': [ 'string', ], 'NewInstancesProtectedFromScaleIn': True|False, 'ServiceLinkedRoleARN': 'string', 'MaxInstanceLifetime': 123 }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def describe_auto_scaling_instances(InstanceIds=None, MaxRecords=None, NextToken=None): """ Describes one or more Auto Scaling instances. See also: AWS API Documentation Exceptions Examples This example describes the specified Auto Scaling instance. Expected Output: :example: response = client.describe_auto_scaling_instances( InstanceIds=[ 'string', ], MaxRecords=123, NextToken='string' ) :type InstanceIds: list :param InstanceIds: The IDs of the instances. You can specify up to MaxRecords IDs. If you omit this parameter, all Auto Scaling instances are described. If you specify an ID that does not exist, it is ignored with no error.\n\n(string) --\n\n :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 50 and the maximum value is 50 . :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :rtype: dict ReturnsResponse Syntax { 'AutoScalingInstances': [ { 'InstanceId': 'string', 'InstanceType': 'string', 'AutoScalingGroupName': 'string', 'AvailabilityZone': 'string', 'LifecycleState': 'string', 'HealthStatus': 'string', 'LaunchConfigurationName': 'string', 'LaunchTemplate': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'ProtectedFromScaleIn': True|False, 'WeightedCapacity': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- AutoScalingInstances (list) -- The instances. (dict) -- Describes an EC2 instance associated with an Auto Scaling group. InstanceId (string) -- The ID of the instance. InstanceType (string) -- The instance type of the EC2 instance. AutoScalingGroupName (string) -- The name of the Auto Scaling group for the instance. AvailabilityZone (string) -- The Availability Zone for the instance. LifecycleState (string) -- The lifecycle state for the instance. HealthStatus (string) -- The last reported health status of this instance. "Healthy" means that the instance is healthy and should remain in service. "Unhealthy" means that the instance is unhealthy and Amazon EC2 Auto Scaling should terminate and replace it. LaunchConfigurationName (string) -- The launch configuration used to launch the instance. This value is not available if you attached the instance to the Auto Scaling group. LaunchTemplate (dict) -- The launch template for the instance. LaunchTemplateId (string) -- The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. LaunchTemplateName (string) -- The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. Version (string) -- The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API. If the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default . ProtectedFromScaleIn (boolean) -- Indicates whether the instance is protected from termination by Amazon EC2 Auto Scaling when scaling in. WeightedCapacity (string) -- The number of capacity units contributed by the instance based on its instance type. Valid Range: Minimum value of 1. Maximum value of 999. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the specified Auto Scaling instance. response = client.describe_auto_scaling_instances( InstanceIds=[ 'i-4ba0837f', ], ) print(response) Expected Output: { 'AutoScalingInstances': [ { 'AutoScalingGroupName': 'my-auto-scaling-group', 'AvailabilityZone': 'us-west-2c', 'HealthStatus': 'HEALTHY', 'InstanceId': 'i-4ba0837f', 'LaunchConfigurationName': 'my-launch-config', 'LifecycleState': 'InService', 'ProtectedFromScaleIn': False, }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'AutoScalingInstances': [ { 'InstanceId': 'string', 'InstanceType': 'string', 'AutoScalingGroupName': 'string', 'AvailabilityZone': 'string', 'LifecycleState': 'string', 'HealthStatus': 'string', 'LaunchConfigurationName': 'string', 'LaunchTemplate': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'ProtectedFromScaleIn': True|False, 'WeightedCapacity': 'string' }, ], 'NextToken': 'string' } :returns: AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_auto_scaling_notification_types(): """ Describes the notification types that are supported by Amazon EC2 Auto Scaling. See also: AWS API Documentation Exceptions Examples This example describes the available notification types. Expected Output: :example: response = client.describe_auto_scaling_notification_types() :rtype: dict ReturnsResponse Syntax{ 'AutoScalingNotificationTypes': [ 'string', ] } Response Structure (dict) -- AutoScalingNotificationTypes (list) --The notification types. (string) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the available notification types. response = client.describe_auto_scaling_notification_types( ) print(response) Expected Output: { 'AutoScalingNotificationTypes': [ 'autoscaling:EC2_INSTANCE_LAUNCH', 'autoscaling:EC2_INSTANCE_LAUNCH_ERROR', 'autoscaling:EC2_INSTANCE_TERMINATE', 'autoscaling:EC2_INSTANCE_TERMINATE_ERROR', 'autoscaling:TEST_NOTIFICATION', ], 'ResponseMetadata': { '...': '...', }, } :return: { 'AutoScalingNotificationTypes': [ 'string', ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_launch_configurations(LaunchConfigurationNames=None, NextToken=None, MaxRecords=None): """ Describes one or more launch configurations. See also: AWS API Documentation Exceptions Examples This example describes the specified launch configuration. Expected Output: :example: response = client.describe_launch_configurations( LaunchConfigurationNames=[ 'string', ], NextToken='string', MaxRecords=123 ) :type LaunchConfigurationNames: list :param LaunchConfigurationNames: The launch configuration names. If you omit this parameter, all launch configurations are described.\n\n(string) --\n\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 50 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'LaunchConfigurations': [ { 'LaunchConfigurationName': 'string', 'LaunchConfigurationARN': 'string', 'ImageId': 'string', 'KeyName': 'string', 'SecurityGroups': [ 'string', ], 'ClassicLinkVPCId': 'string', 'ClassicLinkVPCSecurityGroups': [ 'string', ], 'UserData': 'string', 'InstanceType': 'string', 'KernelId': 'string', 'RamdiskId': 'string', 'BlockDeviceMappings': [ { 'VirtualName': 'string', 'DeviceName': 'string', 'Ebs': { 'SnapshotId': 'string', 'VolumeSize': 123, 'VolumeType': 'string', 'DeleteOnTermination': True|False, 'Iops': 123, 'Encrypted': True|False }, 'NoDevice': True|False }, ], 'InstanceMonitoring': { 'Enabled': True|False }, 'SpotPrice': 'string', 'IamInstanceProfile': 'string', 'CreatedTime': datetime(2015, 1, 1), 'EbsOptimized': True|False, 'AssociatePublicIpAddress': True|False, 'PlacementTenancy': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- LaunchConfigurations (list) -- The launch configurations. (dict) -- Describes a launch configuration. LaunchConfigurationName (string) -- The name of the launch configuration. LaunchConfigurationARN (string) -- The Amazon Resource Name (ARN) of the launch configuration. ImageId (string) -- The ID of the Amazon Machine Image (AMI) to use to launch your EC2 instances. For more information, see Finding an AMI in the Amazon EC2 User Guide for Linux Instances . KeyName (string) -- The name of the key pair. For more information, see Amazon EC2 Key Pairs in the Amazon EC2 User Guide for Linux Instances . SecurityGroups (list) -- A list that contains the security groups to assign to the instances in the Auto Scaling group. For more information, see Security Groups for Your VPC in the Amazon Virtual Private Cloud User Guide . (string) -- ClassicLinkVPCId (string) -- The ID of a ClassicLink-enabled VPC to link your EC2-Classic instances to. For more information, see ClassicLink in the Amazon EC2 User Guide for Linux Instances and Linking EC2-Classic Instances to a VPC in the Amazon EC2 Auto Scaling User Guide . ClassicLinkVPCSecurityGroups (list) -- The IDs of one or more security groups for the VPC specified in ClassicLinkVPCId . For more information, see ClassicLink in the Amazon EC2 User Guide for Linux Instances and Linking EC2-Classic Instances to a VPC in the Amazon EC2 Auto Scaling User Guide . (string) -- UserData (string) -- The Base64-encoded user data to make available to the launched EC2 instances. For more information, see Instance Metadata and User Data in the Amazon EC2 User Guide for Linux Instances . InstanceType (string) -- The instance type for the instances. For information about available instance types, see Available Instance Types in the Amazon EC2 User Guide for Linux Instances. KernelId (string) -- The ID of the kernel associated with the AMI. RamdiskId (string) -- The ID of the RAM disk associated with the AMI. BlockDeviceMappings (list) -- A block device mapping, which specifies the block devices for the instance. For more information, see Block Device Mapping in the Amazon EC2 User Guide for Linux Instances . (dict) -- Describes a block device mapping. VirtualName (string) -- The name of the virtual device (for example, ephemeral0 ). You can specify either VirtualName or Ebs , but not both. DeviceName (string) -- The device name exposed to the EC2 instance (for example, /dev/sdh or xvdh ). For more information, see Device Naming on Linux Instances in the Amazon EC2 User Guide for Linux Instances . Ebs (dict) -- Parameters used to automatically set up EBS volumes when an instance is launched. You can specify either VirtualName or Ebs , but not both. SnapshotId (string) -- The snapshot ID of the volume to use. Conditional: This parameter is optional if you specify a volume size. If you specify both SnapshotId and VolumeSize , VolumeSize must be equal or greater than the size of the snapshot. VolumeSize (integer) -- The volume size, in Gibibytes (GiB). This can be a number from 1-1,024 for standard , 4-16,384 for io1 , 1-16,384 for gp2 , and 500-16,384 for st1 and sc1 . If you specify a snapshot, the volume size must be equal to or larger than the snapshot size. Default: If you create a volume from a snapshot and you don\'t specify a volume size, the default is the snapshot size. Note At least one of VolumeSize or SnapshotId is required. VolumeType (string) -- The volume type, which can be standard for Magnetic, io1 for Provisioned IOPS SSD, gp2 for General Purpose SSD, st1 for Throughput Optimized HDD, or sc1 for Cold HDD. For more information, see Amazon EBS Volume Types in the Amazon EC2 User Guide for Linux Instances . Valid Values: standard | io1 | gp2 | st1 | sc1 DeleteOnTermination (boolean) -- Indicates whether the volume is deleted on instance termination. For Amazon EC2 Auto Scaling, the default value is true . Iops (integer) -- The number of I/O operations per second (IOPS) to provision for the volume. The maximum ratio of IOPS to volume size (in GiB) is 50:1. For more information, see Amazon EBS Volume Types in the Amazon EC2 User Guide for Linux Instances . Conditional: This parameter is required when the volume type is io1 . (Not used with standard , gp2 , st1 , or sc1 volumes.) Encrypted (boolean) -- Specifies whether the volume should be encrypted. Encrypted EBS volumes can only be attached to instances that support Amazon EBS encryption. For more information, see Supported Instance Types . If your AMI uses encrypted volumes, you can also only launch it on supported instance types. Note If you are creating a volume from a snapshot, you cannot specify an encryption value. Volumes that are created from encrypted snapshots are automatically encrypted, and volumes that are created from unencrypted snapshots are automatically unencrypted. By default, encrypted snapshots use the AWS managed CMK that is used for EBS encryption, but you can specify a custom CMK when you create the snapshot. The ability to encrypt a snapshot during copying also allows you to apply a new CMK to an already-encrypted snapshot. Volumes restored from the resulting copy are only accessible using the new CMK. Enabling encryption by default results in all EBS volumes being encrypted with the AWS managed CMK or a customer managed CMK, whether or not the snapshot was encrypted. For more information, see Using Encryption with EBS-Backed AMIs in the Amazon EC2 User Guide for Linux Instances and Required CMK Key Policy for Use with Encrypted Volumes in the Amazon EC2 Auto Scaling User Guide . NoDevice (boolean) -- Setting this value to true suppresses the specified device included in the block device mapping of the AMI. If NoDevice is true for the root device, instances might fail the EC2 health check. In that case, Amazon EC2 Auto Scaling launches replacement instances. If you specify NoDevice , you cannot specify Ebs . InstanceMonitoring (dict) -- Controls whether instances in this group are launched with detailed (true ) or basic (false ) monitoring. For more information, see Configure Monitoring for Auto Scaling Instances in the Amazon EC2 Auto Scaling User Guide . Enabled (boolean) -- If true , detailed monitoring is enabled. Otherwise, basic monitoring is enabled. SpotPrice (string) -- The maximum hourly price to be paid for any Spot Instance launched to fulfill the request. Spot Instances are launched when the price you specify exceeds the current Spot price. For more information, see Launching Spot Instances in Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . IamInstanceProfile (string) -- The name or the Amazon Resource Name (ARN) of the instance profile associated with the IAM role for the instance. The instance profile contains the IAM role. For more information, see IAM Role for Applications That Run on Amazon EC2 Instances in the Amazon EC2 Auto Scaling User Guide . CreatedTime (datetime) -- The creation date and time for the launch configuration. EbsOptimized (boolean) -- Specifies whether the launch configuration is optimized for EBS I/O (true ) or not (false ). For more information, see Amazon EBS-Optimized Instances in the Amazon EC2 User Guide for Linux Instances . AssociatePublicIpAddress (boolean) -- For Auto Scaling groups that are running in a VPC, specifies whether to assign a public IP address to the group\'s instances. For more information, see Launching Auto Scaling Instances in a VPC in the Amazon EC2 Auto Scaling User Guide . PlacementTenancy (string) -- The tenancy of the instance, either default or dedicated . An instance with dedicated tenancy runs on isolated, single-tenant hardware and can only be launched into a VPC. For more information, see Instance Placement Tenancy in the Amazon EC2 Auto Scaling User Guide . NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the specified launch configuration. response = client.describe_launch_configurations( LaunchConfigurationNames=[ 'my-launch-config', ], ) print(response) Expected Output: { 'LaunchConfigurations': [ { 'AssociatePublicIpAddress': True, 'BlockDeviceMappings': [ ], 'CreatedTime': datetime(2014, 5, 7, 17, 39, 28, 2, 127, 0), 'EbsOptimized': False, 'ImageId': 'ami-043a5034', 'InstanceMonitoring': { 'Enabled': True, }, 'InstanceType': 't1.micro', 'LaunchConfigurationARN': 'arn:aws:autoscaling:us-west-2:123456789012:launchConfiguration:98d3b196-4cf9-4e88-8ca1-8547c24ced8b:launchConfigurationName/my-launch-config', 'LaunchConfigurationName': 'my-launch-config', 'SecurityGroups': [ 'sg-67ef0308', ], }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'LaunchConfigurations': [ { 'LaunchConfigurationName': 'string', 'LaunchConfigurationARN': 'string', 'ImageId': 'string', 'KeyName': 'string', 'SecurityGroups': [ 'string', ], 'ClassicLinkVPCId': 'string', 'ClassicLinkVPCSecurityGroups': [ 'string', ], 'UserData': 'string', 'InstanceType': 'string', 'KernelId': 'string', 'RamdiskId': 'string', 'BlockDeviceMappings': [ { 'VirtualName': 'string', 'DeviceName': 'string', 'Ebs': { 'SnapshotId': 'string', 'VolumeSize': 123, 'VolumeType': 'string', 'DeleteOnTermination': True|False, 'Iops': 123, 'Encrypted': True|False }, 'NoDevice': True|False }, ], 'InstanceMonitoring': { 'Enabled': True|False }, 'SpotPrice': 'string', 'IamInstanceProfile': 'string', 'CreatedTime': datetime(2015, 1, 1), 'EbsOptimized': True|False, 'AssociatePublicIpAddress': True|False, 'PlacementTenancy': 'string' }, ], 'NextToken': 'string' } :returns: (string) -- """ pass def describe_lifecycle_hook_types(): """ Describes the available types of lifecycle hooks. The following hook types are supported: See also: AWS API Documentation Exceptions Examples This example describes the available lifecycle hook types. Expected Output: :example: response = client.describe_lifecycle_hook_types() :rtype: dict ReturnsResponse Syntax{ 'LifecycleHookTypes': [ 'string', ] } Response Structure (dict) -- LifecycleHookTypes (list) --The lifecycle hook types. (string) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the available lifecycle hook types. response = client.describe_lifecycle_hook_types( ) print(response) Expected Output: { 'LifecycleHookTypes': [ 'autoscaling:EC2_INSTANCE_LAUNCHING', 'autoscaling:EC2_INSTANCE_TERMINATING', ], 'ResponseMetadata': { '...': '...', }, } :return: { 'LifecycleHookTypes': [ 'string', ] } :returns: (string) -- """ pass def describe_lifecycle_hooks(AutoScalingGroupName=None, LifecycleHookNames=None): """ Describes the lifecycle hooks for the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example describes the lifecycle hooks for the specified Auto Scaling group. Expected Output: :example: response = client.describe_lifecycle_hooks( AutoScalingGroupName='string', LifecycleHookNames=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LifecycleHookNames: list :param LifecycleHookNames: The names of one or more lifecycle hooks. If you omit this parameter, all lifecycle hooks are described.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax { 'LifecycleHooks': [ { 'LifecycleHookName': 'string', 'AutoScalingGroupName': 'string', 'LifecycleTransition': 'string', 'NotificationTargetARN': 'string', 'RoleARN': 'string', 'NotificationMetadata': 'string', 'HeartbeatTimeout': 123, 'GlobalTimeout': 123, 'DefaultResult': 'string' }, ] } Response Structure (dict) -- LifecycleHooks (list) -- The lifecycle hooks for the specified group. (dict) -- Describes a lifecycle hook, which tells Amazon EC2 Auto Scaling that you want to perform an action whenever it launches instances or terminates instances. LifecycleHookName (string) -- The name of the lifecycle hook. AutoScalingGroupName (string) -- The name of the Auto Scaling group for the lifecycle hook. LifecycleTransition (string) -- The state of the EC2 instance to which to attach the lifecycle hook. The following are possible values: autoscaling:EC2_INSTANCE_LAUNCHING autoscaling:EC2_INSTANCE_TERMINATING NotificationTargetARN (string) -- The ARN of the target that Amazon EC2 Auto Scaling sends notifications to when an instance is in the transition state for the lifecycle hook. The notification target can be either an SQS queue or an SNS topic. RoleARN (string) -- The ARN of the IAM role that allows the Auto Scaling group to publish to the specified notification target. NotificationMetadata (string) -- Additional information that is included any time Amazon EC2 Auto Scaling sends a message to the notification target. HeartbeatTimeout (integer) -- The maximum time, in seconds, that can elapse before the lifecycle hook times out. If the lifecycle hook times out, Amazon EC2 Auto Scaling performs the action that you specified in the DefaultResult parameter. GlobalTimeout (integer) -- The maximum time, in seconds, that an instance can remain in a Pending:Wait or Terminating:Wait state. The maximum is 172800 seconds (48 hours) or 100 times HeartbeatTimeout , whichever is smaller. DefaultResult (string) -- Defines the action the Auto Scaling group should take when the lifecycle hook timeout elapses or if an unexpected failure occurs. The possible values are CONTINUE and ABANDON . Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the lifecycle hooks for the specified Auto Scaling group. response = client.describe_lifecycle_hooks( AutoScalingGroupName='my-auto-scaling-group', ) print(response) Expected Output: { 'LifecycleHooks': [ { 'AutoScalingGroupName': 'my-auto-scaling-group', 'DefaultResult': 'ABANDON', 'GlobalTimeout': 172800, 'HeartbeatTimeout': 3600, 'LifecycleHookName': 'my-lifecycle-hook', 'LifecycleTransition': 'autoscaling:EC2_INSTANCE_LAUNCHING', 'NotificationTargetARN': 'arn:aws:sns:us-west-2:123456789012:my-sns-topic', 'RoleARN': 'arn:aws:iam::123456789012:role/my-auto-scaling-role', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'LifecycleHooks': [ { 'LifecycleHookName': 'string', 'AutoScalingGroupName': 'string', 'LifecycleTransition': 'string', 'NotificationTargetARN': 'string', 'RoleARN': 'string', 'NotificationMetadata': 'string', 'HeartbeatTimeout': 123, 'GlobalTimeout': 123, 'DefaultResult': 'string' }, ] } :returns: autoscaling:EC2_INSTANCE_LAUNCHING autoscaling:EC2_INSTANCE_TERMINATING """ pass def describe_load_balancer_target_groups(AutoScalingGroupName=None, NextToken=None, MaxRecords=None): """ Describes the target groups for the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example describes the target groups attached to the specified Auto Scaling group. Expected Output: :example: response = client.describe_load_balancer_target_groups( AutoScalingGroupName='string', NextToken='string', MaxRecords=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 100 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'LoadBalancerTargetGroups': [ { 'LoadBalancerTargetGroupARN': 'string', 'State': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- LoadBalancerTargetGroups (list) -- Information about the target groups. (dict) -- Describes the state of a target group. If you attach a target group to an existing Auto Scaling group, the initial state is Adding . The state transitions to Added after all Auto Scaling instances are registered with the target group. If Elastic Load Balancing health checks are enabled, the state transitions to InService after at least one Auto Scaling instance passes the health check. If EC2 health checks are enabled instead, the target group remains in the Added state. LoadBalancerTargetGroupARN (string) -- The Amazon Resource Name (ARN) of the target group. State (string) -- The state of the target group. Adding - The Auto Scaling instances are being registered with the target group. Added - All Auto Scaling instances are registered with the target group. InService - At least one Auto Scaling instance passed an ELB health check. Removing - The Auto Scaling instances are being deregistered from the target group. If connection draining is enabled, Elastic Load Balancing waits for in-flight requests to complete before deregistering the instances. Removed - All Auto Scaling instances are deregistered from the target group. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the target groups attached to the specified Auto Scaling group. response = client.describe_load_balancer_target_groups( AutoScalingGroupName='my-auto-scaling-group', ) print(response) Expected Output: { 'LoadBalancerTargetGroups': [ { 'LoadBalancerTargetGroupARN': 'arn:aws:elasticloadbalancing:us-west-2:123456789012:targetgroup/my-targets/73e2d6bc24d8a067', 'State': 'Added', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'LoadBalancerTargetGroups': [ { 'LoadBalancerTargetGroupARN': 'string', 'State': 'string' }, ], 'NextToken': 'string' } :returns: Adding - The Auto Scaling instances are being registered with the target group. Added - All Auto Scaling instances are registered with the target group. InService - At least one Auto Scaling instance passed an ELB health check. Removing - The Auto Scaling instances are being deregistered from the target group. If connection draining is enabled, Elastic Load Balancing waits for in-flight requests to complete before deregistering the instances. Removed - All Auto Scaling instances are deregistered from the target group. """ pass def describe_load_balancers(AutoScalingGroupName=None, NextToken=None, MaxRecords=None): """ Describes the load balancers for the specified Auto Scaling group. This operation describes only Classic Load Balancers. If you have Application Load Balancers or Network Load Balancers, use the DescribeLoadBalancerTargetGroups API instead. See also: AWS API Documentation Exceptions Examples This example describes the load balancers attached to the specified Auto Scaling group. Expected Output: :example: response = client.describe_load_balancers( AutoScalingGroupName='string', NextToken='string', MaxRecords=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 100 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'LoadBalancers': [ { 'LoadBalancerName': 'string', 'State': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- LoadBalancers (list) -- The load balancers. (dict) -- Describes the state of a Classic Load Balancer. If you specify a load balancer when creating the Auto Scaling group, the state of the load balancer is InService . If you attach a load balancer to an existing Auto Scaling group, the initial state is Adding . The state transitions to Added after all instances in the group are registered with the load balancer. If Elastic Load Balancing health checks are enabled for the load balancer, the state transitions to InService after at least one instance in the group passes the health check. If EC2 health checks are enabled instead, the load balancer remains in the Added state. LoadBalancerName (string) -- The name of the load balancer. State (string) -- One of the following load balancer states: Adding - The instances in the group are being registered with the load balancer. Added - All instances in the group are registered with the load balancer. InService - At least one instance in the group passed an ELB health check. Removing - The instances in the group are being deregistered from the load balancer. If connection draining is enabled, Elastic Load Balancing waits for in-flight requests to complete before deregistering the instances. Removed - All instances in the group are deregistered from the load balancer. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the load balancers attached to the specified Auto Scaling group. response = client.describe_load_balancers( AutoScalingGroupName='my-auto-scaling-group', ) print(response) Expected Output: { 'LoadBalancers': [ { 'LoadBalancerName': 'my-load-balancer', 'State': 'Added', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'LoadBalancers': [ { 'LoadBalancerName': 'string', 'State': 'string' }, ], 'NextToken': 'string' } :returns: Adding - The instances in the group are being registered with the load balancer. Added - All instances in the group are registered with the load balancer. InService - At least one instance in the group passed an ELB health check. Removing - The instances in the group are being deregistered from the load balancer. If connection draining is enabled, Elastic Load Balancing waits for in-flight requests to complete before deregistering the instances. Removed - All instances in the group are deregistered from the load balancer. """ pass def describe_metric_collection_types(): """ Describes the available CloudWatch metrics for Amazon EC2 Auto Scaling. The GroupStandbyInstances metric is not returned by default. You must explicitly request this metric when calling the EnableMetricsCollection API. See also: AWS API Documentation Exceptions Examples This example describes the available metric collection types. Expected Output: :example: response = client.describe_metric_collection_types() :rtype: dict ReturnsResponse Syntax{ 'Metrics': [ { 'Metric': 'string' }, ], 'Granularities': [ { 'Granularity': 'string' }, ] } Response Structure (dict) -- Metrics (list) --One or more metrics. (dict) --Describes a metric. Metric (string) --One of the following metrics: GroupMinSize GroupMaxSize GroupDesiredCapacity GroupInServiceInstances GroupPendingInstances GroupStandbyInstances GroupTerminatingInstances GroupTotalInstances Granularities (list) --The granularities for the metrics. (dict) --Describes a granularity of a metric. Granularity (string) --The granularity. The only valid value is 1Minute . Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the available metric collection types. response = client.describe_metric_collection_types( ) print(response) Expected Output: { 'Granularities': [ { 'Granularity': '1Minute', }, ], 'Metrics': [ { 'Metric': 'GroupMinSize', }, { 'Metric': 'GroupMaxSize', }, { 'Metric': 'GroupDesiredCapacity', }, { 'Metric': 'GroupInServiceInstances', }, { 'Metric': 'GroupPendingInstances', }, { 'Metric': 'GroupTerminatingInstances', }, { 'Metric': 'GroupStandbyInstances', }, { 'Metric': 'GroupTotalInstances', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Metrics': [ { 'Metric': 'string' }, ], 'Granularities': [ { 'Granularity': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_notification_configurations(AutoScalingGroupNames=None, NextToken=None, MaxRecords=None): """ Describes the notification actions associated with the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example describes the notification configurations for the specified Auto Scaling group. Expected Output: :example: response = client.describe_notification_configurations( AutoScalingGroupNames=[ 'string', ], NextToken='string', MaxRecords=123 ) :type AutoScalingGroupNames: list :param AutoScalingGroupNames: The name of the Auto Scaling group.\n\n(string) --\n\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 50 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'NotificationConfigurations': [ { 'AutoScalingGroupName': 'string', 'TopicARN': 'string', 'NotificationType': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- NotificationConfigurations (list) -- The notification configurations. (dict) -- Describes a notification. AutoScalingGroupName (string) -- The name of the Auto Scaling group. TopicARN (string) -- The Amazon Resource Name (ARN) of the Amazon Simple Notification Service (Amazon SNS) topic. NotificationType (string) -- One of the following event notification types: autoscaling:EC2_INSTANCE_LAUNCH autoscaling:EC2_INSTANCE_LAUNCH_ERROR autoscaling:EC2_INSTANCE_TERMINATE autoscaling:EC2_INSTANCE_TERMINATE_ERROR autoscaling:TEST_NOTIFICATION NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the notification configurations for the specified Auto Scaling group. response = client.describe_notification_configurations( AutoScalingGroupNames=[ 'my-auto-scaling-group', ], ) print(response) Expected Output: { 'NotificationConfigurations': [ { 'AutoScalingGroupName': 'my-auto-scaling-group', 'NotificationType': 'autoscaling:TEST_NOTIFICATION', 'TopicARN': 'arn:aws:sns:us-west-2:123456789012:my-sns-topic-2', }, { 'AutoScalingGroupName': 'my-auto-scaling-group', 'NotificationType': 'autoscaling:TEST_NOTIFICATION', 'TopicARN': 'arn:aws:sns:us-west-2:123456789012:my-sns-topic', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'NotificationConfigurations': [ { 'AutoScalingGroupName': 'string', 'TopicARN': 'string', 'NotificationType': 'string' }, ], 'NextToken': 'string' } :returns: autoscaling:EC2_INSTANCE_LAUNCH autoscaling:EC2_INSTANCE_LAUNCH_ERROR autoscaling:EC2_INSTANCE_TERMINATE autoscaling:EC2_INSTANCE_TERMINATE_ERROR autoscaling:TEST_NOTIFICATION """ pass def describe_policies(AutoScalingGroupName=None, PolicyNames=None, PolicyTypes=None, NextToken=None, MaxRecords=None): """ Describes the policies for the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example describes the policies for the specified Auto Scaling group. Expected Output: :example: response = client.describe_policies( AutoScalingGroupName='string', PolicyNames=[ 'string', ], PolicyTypes=[ 'string', ], NextToken='string', MaxRecords=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: The name of the Auto Scaling group. :type PolicyNames: list :param PolicyNames: The names of one or more policies. If you omit this parameter, all policies are described. If a group name is provided, the results are limited to that group. This list is limited to 50 items. If you specify an unknown policy name, it is ignored with no error.\n\n(string) --\n\n :type PolicyTypes: list :param PolicyTypes: One or more policy types. The valid values are SimpleScaling , StepScaling , and TargetTrackingScaling .\n\n(string) --\n\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to be returned with each call. The default value is 50 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'ScalingPolicies': [ { 'AutoScalingGroupName': 'string', 'PolicyName': 'string', 'PolicyARN': 'string', 'PolicyType': 'string', 'AdjustmentType': 'string', 'MinAdjustmentStep': 123, 'MinAdjustmentMagnitude': 123, 'ScalingAdjustment': 123, 'Cooldown': 123, 'StepAdjustments': [ { 'MetricIntervalLowerBound': 123.0, 'MetricIntervalUpperBound': 123.0, 'ScalingAdjustment': 123 }, ], 'MetricAggregationType': 'string', 'EstimatedInstanceWarmup': 123, 'Alarms': [ { 'AlarmName': 'string', 'AlarmARN': 'string' }, ], 'TargetTrackingConfiguration': { 'PredefinedMetricSpecification': { 'PredefinedMetricType': 'ASGAverageCPUUtilization'|'ASGAverageNetworkIn'|'ASGAverageNetworkOut'|'ALBRequestCountPerTarget', 'ResourceLabel': 'string' }, 'CustomizedMetricSpecification': { 'MetricName': 'string', 'Namespace': 'string', 'Dimensions': [ { 'Name': 'string', 'Value': 'string' }, ], 'Statistic': 'Average'|'Minimum'|'Maximum'|'SampleCount'|'Sum', 'Unit': 'string' }, 'TargetValue': 123.0, 'DisableScaleIn': True|False }, 'Enabled': True|False }, ], 'NextToken': 'string' } Response Structure (dict) -- ScalingPolicies (list) -- The scaling policies. (dict) -- Describes a scaling policy. AutoScalingGroupName (string) -- The name of the Auto Scaling group. PolicyName (string) -- The name of the scaling policy. PolicyARN (string) -- The Amazon Resource Name (ARN) of the policy. PolicyType (string) -- The policy type. The valid values are SimpleScaling , StepScaling , and TargetTrackingScaling . AdjustmentType (string) -- The adjustment type, which specifies how ScalingAdjustment is interpreted. The valid values are ChangeInCapacity , ExactCapacity , and PercentChangeInCapacity . MinAdjustmentStep (integer) -- Available for backward compatibility. Use MinAdjustmentMagnitude instead. MinAdjustmentMagnitude (integer) -- The minimum number of instances to scale. If the value of AdjustmentType is PercentChangeInCapacity , the scaling policy changes the DesiredCapacity of the Auto Scaling group by at least this many instances. Otherwise, the error is ValidationError . ScalingAdjustment (integer) -- The amount by which to scale, based on the specified adjustment type. A positive value adds to the current capacity while a negative number removes from the current capacity. Cooldown (integer) -- The amount of time, in seconds, after a scaling activity completes before any further dynamic scaling activities can start. StepAdjustments (list) -- A set of adjustments that enable you to scale based on the size of the alarm breach. (dict) -- Describes information used to create a step adjustment for a step scaling policy. For the following examples, suppose that you have an alarm with a breach threshold of 50: To trigger the adjustment when the metric is greater than or equal to 50 and less than 60, specify a lower bound of 0 and an upper bound of 10. To trigger the adjustment when the metric is greater than 40 and less than or equal to 50, specify a lower bound of -10 and an upper bound of 0. There are a few rules for the step adjustments for your step policy: The ranges of your step adjustments can\'t overlap or have a gap. At most, one step adjustment can have a null lower bound. If one step adjustment has a negative lower bound, then there must be a step adjustment with a null lower bound. At most, one step adjustment can have a null upper bound. If one step adjustment has a positive upper bound, then there must be a step adjustment with a null upper bound. The upper and lower bound can\'t be null in the same step adjustment. For more information, see Step Adjustments in the Amazon EC2 Auto Scaling User Guide . MetricIntervalLowerBound (float) -- The lower bound for the difference between the alarm threshold and the CloudWatch metric. If the metric value is above the breach threshold, the lower bound is inclusive (the metric must be greater than or equal to the threshold plus the lower bound). Otherwise, it is exclusive (the metric must be greater than the threshold plus the lower bound). A null value indicates negative infinity. MetricIntervalUpperBound (float) -- The upper bound for the difference between the alarm threshold and the CloudWatch metric. If the metric value is above the breach threshold, the upper bound is exclusive (the metric must be less than the threshold plus the upper bound). Otherwise, it is inclusive (the metric must be less than or equal to the threshold plus the upper bound). A null value indicates positive infinity. The upper bound must be greater than the lower bound. ScalingAdjustment (integer) -- The amount by which to scale, based on the specified adjustment type. A positive value adds to the current capacity while a negative number removes from the current capacity. MetricAggregationType (string) -- The aggregation type for the CloudWatch metrics. The valid values are Minimum , Maximum , and Average . EstimatedInstanceWarmup (integer) -- The estimated time, in seconds, until a newly launched instance can contribute to the CloudWatch metrics. Alarms (list) -- The CloudWatch alarms related to the policy. (dict) -- Describes an alarm. AlarmName (string) -- The name of the alarm. AlarmARN (string) -- The Amazon Resource Name (ARN) of the alarm. TargetTrackingConfiguration (dict) -- A target tracking scaling policy. PredefinedMetricSpecification (dict) -- A predefined metric. You must specify either a predefined metric or a customized metric. PredefinedMetricType (string) -- The metric type. The following predefined metrics are available: ASGAverageCPUUtilization - Average CPU utilization of the Auto Scaling group. ASGAverageNetworkIn - Average number of bytes received on all network interfaces by the Auto Scaling group. ASGAverageNetworkOut - Average number of bytes sent out on all network interfaces by the Auto Scaling group. ALBRequestCountPerTarget - Number of requests completed per target in an Application Load Balancer target group. ResourceLabel (string) -- Identifies the resource associated with the metric type. You can\'t specify a resource label unless the metric type is ALBRequestCountPerTarget and there is a target group attached to the Auto Scaling group. The format is ``app/load-balancer-name /load-balancer-id /targetgroup/target-group-name /target-group-id `` , where ``app/load-balancer-name /load-balancer-id `` is the final portion of the load balancer ARN, and ``targetgroup/target-group-name /target-group-id `` is the final portion of the target group ARN. CustomizedMetricSpecification (dict) -- A customized metric. You must specify either a predefined metric or a customized metric. MetricName (string) -- The name of the metric. Namespace (string) -- The namespace of the metric. Dimensions (list) -- The dimensions of the metric. Conditional: If you published your metric with dimensions, you must specify the same dimensions in your scaling policy. (dict) -- Describes the dimension of a metric. Name (string) -- The name of the dimension. Value (string) -- The value of the dimension. Statistic (string) -- The statistic of the metric. Unit (string) -- The unit of the metric. TargetValue (float) -- The target value for the metric. DisableScaleIn (boolean) -- Indicates whether scaling in by the target tracking scaling policy is disabled. If scaling in is disabled, the target tracking scaling policy doesn\'t remove instances from the Auto Scaling group. Otherwise, the target tracking scaling policy can remove instances from the Auto Scaling group. The default is false . Enabled (boolean) -- Indicates whether the policy is enabled (true ) or disabled (false ). NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure Examples This example describes the policies for the specified Auto Scaling group. response = client.describe_policies( AutoScalingGroupName='my-auto-scaling-group', ) print(response) Expected Output: { 'ScalingPolicies': [ { 'AdjustmentType': 'ChangeInCapacity', 'Alarms': [ ], 'AutoScalingGroupName': 'my-auto-scaling-group', 'PolicyARN': 'arn:aws:autoscaling:us-west-2:123456789012:scalingPolicy:2233f3d7-6290-403b-b632-93c553560106:autoScalingGroupName/my-auto-scaling-group:policyName/ScaleIn', 'PolicyName': 'ScaleIn', 'ScalingAdjustment': -1, }, { 'AdjustmentType': 'PercentChangeInCapacity', 'Alarms': [ ], 'AutoScalingGroupName': 'my-auto-scaling-group', 'Cooldown': 60, 'MinAdjustmentStep': 2, 'PolicyARN': 'arn:aws:autoscaling:us-west-2:123456789012:scalingPolicy:2b435159-cf77-4e89-8c0e-d63b497baad7:autoScalingGroupName/my-auto-scaling-group:policyName/ScalePercentChange', 'PolicyName': 'ScalePercentChange', 'ScalingAdjustment': 25, }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'ScalingPolicies': [ { 'AutoScalingGroupName': 'string', 'PolicyName': 'string', 'PolicyARN': 'string', 'PolicyType': 'string', 'AdjustmentType': 'string', 'MinAdjustmentStep': 123, 'MinAdjustmentMagnitude': 123, 'ScalingAdjustment': 123, 'Cooldown': 123, 'StepAdjustments': [ { 'MetricIntervalLowerBound': 123.0, 'MetricIntervalUpperBound': 123.0, 'ScalingAdjustment': 123 }, ], 'MetricAggregationType': 'string', 'EstimatedInstanceWarmup': 123, 'Alarms': [ { 'AlarmName': 'string', 'AlarmARN': 'string' }, ], 'TargetTrackingConfiguration': { 'PredefinedMetricSpecification': { 'PredefinedMetricType': 'ASGAverageCPUUtilization'|'ASGAverageNetworkIn'|'ASGAverageNetworkOut'|'ALBRequestCountPerTarget', 'ResourceLabel': 'string' }, 'CustomizedMetricSpecification': { 'MetricName': 'string', 'Namespace': 'string', 'Dimensions': [ { 'Name': 'string', 'Value': 'string' }, ], 'Statistic': 'Average'|'Minimum'|'Maximum'|'SampleCount'|'Sum', 'Unit': 'string' }, 'TargetValue': 123.0, 'DisableScaleIn': True|False }, 'Enabled': True|False }, ], 'NextToken': 'string' } :returns: To trigger the adjustment when the metric is greater than or equal to 50 and less than 60, specify a lower bound of 0 and an upper bound of 10. To trigger the adjustment when the metric is greater than 40 and less than or equal to 50, specify a lower bound of -10 and an upper bound of 0. """ pass def describe_scaling_activities(ActivityIds=None, AutoScalingGroupName=None, MaxRecords=None, NextToken=None): """ Describes one or more scaling activities for the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example describes the scaling activities for the specified Auto Scaling group. Expected Output: :example: response = client.describe_scaling_activities( ActivityIds=[ 'string', ], AutoScalingGroupName='string', MaxRecords=123, NextToken='string' ) :type ActivityIds: list :param ActivityIds: The activity IDs of the desired scaling activities. You can specify up to 50 IDs. If you omit this parameter, all activities for the past six weeks are described. If unknown activities are requested, they are ignored with no error. If you specify an Auto Scaling group, the results are limited to that group.\n\n(string) --\n\n :type AutoScalingGroupName: string :param AutoScalingGroupName: The name of the Auto Scaling group. :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 100 and the maximum value is 100 . :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :rtype: dict ReturnsResponse Syntax { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ], 'NextToken': 'string' } Response Structure (dict) -- Activities (list) -- The scaling activities. Activities are sorted by start time. Activities still in progress are described first. (dict) -- Describes scaling activity, which is a long-running process that represents a change to your Auto Scaling group, such as changing its size or replacing an instance. ActivityId (string) -- The ID of the activity. AutoScalingGroupName (string) -- The name of the Auto Scaling group. Description (string) -- A friendly, more verbose description of the activity. Cause (string) -- The reason the activity began. StartTime (datetime) -- The start time of the activity. EndTime (datetime) -- The end time of the activity. StatusCode (string) -- The current status of the activity. StatusMessage (string) -- A friendly, more verbose description of the activity status. Progress (integer) -- A value between 0 and 100 that indicates the progress of the activity. Details (string) -- The details about the activity. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the scaling activities for the specified Auto Scaling group. response = client.describe_scaling_activities( AutoScalingGroupName='my-auto-scaling-group', ) print(response) Expected Output: { 'Activities': [ { 'ActivityId': 'f9f2d65b-f1f2-43e7-b46d-d86756459699', 'AutoScalingGroupName': 'my-auto-scaling-group', 'Cause': 'At 2013-08-19T20:53:25Z a user request created an AutoScalingGroup changing the desired capacity from 0 to 1. At 2013-08-19T20:53:29Z an instance was started in response to a difference between desired and actual capacity, increasing the capacity from 0 to 1.', 'Description': 'Launching a new EC2 instance: i-4ba0837f', 'Details': 'details', 'EndTime': datetime(2013, 8, 19, 20, 54, 2, 0, 231, 0), 'Progress': 100, 'StartTime': datetime(2013, 8, 19, 20, 53, 29, 0, 231, 0), 'StatusCode': 'Successful', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ], 'NextToken': 'string' } :returns: AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_scaling_process_types(): """ Describes the scaling process types for use with the ResumeProcesses and SuspendProcesses APIs. See also: AWS API Documentation Exceptions Examples This example describes the Auto Scaling process types. Expected Output: :example: response = client.describe_scaling_process_types() :rtype: dict ReturnsResponse Syntax{ 'Processes': [ { 'ProcessName': 'string' }, ] } Response Structure (dict) -- Processes (list) --The names of the process types. (dict) --Describes a process type. For more information, see Scaling Processes in the Amazon EC2 Auto Scaling User Guide . ProcessName (string) --One of the following processes: Launch Terminate AddToLoadBalancer AlarmNotification AZRebalance HealthCheck ReplaceUnhealthy ScheduledActions Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the Auto Scaling process types. response = client.describe_scaling_process_types( ) print(response) Expected Output: { 'Processes': [ { 'ProcessName': 'AZRebalance', }, { 'ProcessName': 'AddToLoadBalancer', }, { 'ProcessName': 'AlarmNotification', }, { 'ProcessName': 'HealthCheck', }, { 'ProcessName': 'Launch', }, { 'ProcessName': 'ReplaceUnhealthy', }, { 'ProcessName': 'ScheduledActions', }, { 'ProcessName': 'Terminate', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Processes': [ { 'ProcessName': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_scheduled_actions(AutoScalingGroupName=None, ScheduledActionNames=None, StartTime=None, EndTime=None, NextToken=None, MaxRecords=None): """ Describes the actions scheduled for your Auto Scaling group that haven\'t run or that have not reached their end time. To describe the actions that have already run, call the DescribeScalingActivities API. See also: AWS API Documentation Exceptions Examples This example describes the scheduled actions for the specified Auto Scaling group. Expected Output: :example: response = client.describe_scheduled_actions( AutoScalingGroupName='string', ScheduledActionNames=[ 'string', ], StartTime=datetime(2015, 1, 1), EndTime=datetime(2015, 1, 1), NextToken='string', MaxRecords=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: The name of the Auto Scaling group. :type ScheduledActionNames: list :param ScheduledActionNames: The names of one or more scheduled actions. You can specify up to 50 actions. If you omit this parameter, all scheduled actions are described. If you specify an unknown scheduled action, it is ignored with no error.\n\n(string) --\n\n :type StartTime: datetime :param StartTime: The earliest scheduled start time to return. If scheduled action names are provided, this parameter is ignored. :type EndTime: datetime :param EndTime: The latest scheduled start time to return. If scheduled action names are provided, this parameter is ignored. :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 50 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'ScheduledUpdateGroupActions': [ { 'AutoScalingGroupName': 'string', 'ScheduledActionName': 'string', 'ScheduledActionARN': 'string', 'Time': datetime(2015, 1, 1), 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'Recurrence': 'string', 'MinSize': 123, 'MaxSize': 123, 'DesiredCapacity': 123 }, ], 'NextToken': 'string' } Response Structure (dict) -- ScheduledUpdateGroupActions (list) -- The scheduled actions. (dict) -- Describes a scheduled scaling action. AutoScalingGroupName (string) -- The name of the Auto Scaling group. ScheduledActionName (string) -- The name of the scheduled action. ScheduledActionARN (string) -- The Amazon Resource Name (ARN) of the scheduled action. Time (datetime) -- This parameter is no longer used. StartTime (datetime) -- The date and time in UTC for this action to start. For example, "2019-06-01T00:00:00Z" . EndTime (datetime) -- The date and time in UTC for the recurring schedule to end. For example, "2019-06-01T00:00:00Z" . Recurrence (string) -- The recurring schedule for the action, in Unix cron syntax format. When StartTime and EndTime are specified with Recurrence , they form the boundaries of when the recurring action starts and stops. MinSize (integer) -- The minimum size of the Auto Scaling group. MaxSize (integer) -- The maximum size of the Auto Scaling group. DesiredCapacity (integer) -- The desired capacity is the initial capacity of the Auto Scaling group after the scheduled action runs and the capacity it attempts to maintain. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the scheduled actions for the specified Auto Scaling group. response = client.describe_scheduled_actions( AutoScalingGroupName='my-auto-scaling-group', ) print(response) Expected Output: { 'ScheduledUpdateGroupActions': [ { 'AutoScalingGroupName': 'my-auto-scaling-group', 'DesiredCapacity': 4, 'MaxSize': 6, 'MinSize': 2, 'Recurrence': '30 0 1 12 0', 'ScheduledActionARN': 'arn:aws:autoscaling:us-west-2:123456789012:scheduledUpdateGroupAction:8e86b655-b2e6-4410-8f29-b4f094d6871c:autoScalingGroupName/my-auto-scaling-group:scheduledActionName/my-scheduled-action', 'ScheduledActionName': 'my-scheduled-action', 'StartTime': datetime(2016, 12, 1, 0, 30, 0, 3, 336, 0), 'Time': datetime(2016, 12, 1, 0, 30, 0, 3, 336, 0), }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'ScheduledUpdateGroupActions': [ { 'AutoScalingGroupName': 'string', 'ScheduledActionName': 'string', 'ScheduledActionARN': 'string', 'Time': datetime(2015, 1, 1), 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'Recurrence': 'string', 'MinSize': 123, 'MaxSize': 123, 'DesiredCapacity': 123 }, ], 'NextToken': 'string' } :returns: AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_tags(Filters=None, NextToken=None, MaxRecords=None): """ Describes the specified tags. You can use filters to limit the results. For example, you can query for the tags for a specific Auto Scaling group. You can specify multiple values for a filter. A tag must match at least one of the specified values for it to be included in the results. You can also specify multiple filters. The result includes information for a particular tag only if it matches all the filters. If there\'s no match, no special message is returned. For more information, see Tagging Auto Scaling Groups and Instances in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example describes the tags for the specified Auto Scaling group. Expected Output: :example: response = client.describe_tags( Filters=[ { 'Name': 'string', 'Values': [ 'string', ] }, ], NextToken='string', MaxRecords=123 ) :type Filters: list :param Filters: One or more filters to scope the tags to return. The maximum number of filters per filter type (for example, auto-scaling-group ) is 1000.\n\n(dict) --Describes a filter that is used to return a more specific list of results when describing tags.\nFor more information, see Tagging Auto Scaling Groups and Instances in the Amazon EC2 Auto Scaling User Guide .\n\nName (string) --The name of the filter. The valid values are: auto-scaling-group , key , value , and propagate-at-launch .\n\nValues (list) --One or more filter values. Filter values are case-sensitive.\n\n(string) --\n\n\n\n\n\n :type NextToken: string :param NextToken: The token for the next set of items to return. (You received this token from a previous call.) :type MaxRecords: integer :param MaxRecords: The maximum number of items to return with this call. The default value is 50 and the maximum value is 100 . :rtype: dict ReturnsResponse Syntax { 'Tags': [ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ], 'NextToken': 'string' } Response Structure (dict) -- Tags (list) -- One or more tags. (dict) -- Describes a tag for an Auto Scaling group. ResourceId (string) -- The name of the group. ResourceType (string) -- The type of resource. The only supported value is auto-scaling-group . Key (string) -- The tag key. Value (string) -- The tag value. PropagateAtLaunch (boolean) -- Determines whether the tag is added to new instances as they are launched in the group. NextToken (string) -- A string that indicates that the response contains more items than can be returned in a single response. To receive additional items, specify this string for the NextToken value when requesting the next set of items. This value is null when there are no more items to return. Exceptions AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the tags for the specified Auto Scaling group. response = client.describe_tags( Filters=[ { 'Name': 'auto-scaling-group', 'Values': [ 'my-auto-scaling-group', ], }, ], ) print(response) Expected Output: { 'Tags': [ { 'Key': 'Dept', 'PropagateAtLaunch': True, 'ResourceId': 'my-auto-scaling-group', 'ResourceType': 'auto-scaling-group', 'Value': 'Research', }, { 'Key': 'Role', 'PropagateAtLaunch': True, 'ResourceId': 'my-auto-scaling-group', 'ResourceType': 'auto-scaling-group', 'Value': 'WebServer', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Tags': [ { 'ResourceId': 'string', 'ResourceType': 'string', 'Key': 'string', 'Value': 'string', 'PropagateAtLaunch': True|False }, ], 'NextToken': 'string' } :returns: AutoScaling.Client.exceptions.InvalidNextToken AutoScaling.Client.exceptions.ResourceContentionFault """ pass def describe_termination_policy_types(): """ Describes the termination policies supported by Amazon EC2 Auto Scaling. For more information, see Controlling Which Auto Scaling Instances Terminate During Scale In in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example describes the available termination policy types. Expected Output: :example: response = client.describe_termination_policy_types() :rtype: dict ReturnsResponse Syntax{ 'TerminationPolicyTypes': [ 'string', ] } Response Structure (dict) -- TerminationPolicyTypes (list) --The termination policies supported by Amazon EC2 Auto Scaling: OldestInstance , OldestLaunchConfiguration , NewestInstance , ClosestToNextInstanceHour , Default , OldestLaunchTemplate , and AllocationStrategy . (string) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example describes the available termination policy types. response = client.describe_termination_policy_types( ) print(response) Expected Output: { 'TerminationPolicyTypes': [ 'ClosestToNextInstanceHour', 'Default', 'NewestInstance', 'OldestInstance', 'OldestLaunchConfiguration', ], 'ResponseMetadata': { '...': '...', }, } :return: { 'TerminationPolicyTypes': [ 'string', ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def detach_instances(InstanceIds=None, AutoScalingGroupName=None, ShouldDecrementDesiredCapacity=None): """ Removes one or more instances from the specified Auto Scaling group. After the instances are detached, you can manage them independent of the Auto Scaling group. If you do not specify the option to decrement the desired capacity, Amazon EC2 Auto Scaling launches instances to replace the ones that are detached. If there is a Classic Load Balancer attached to the Auto Scaling group, the instances are deregistered from the load balancer. If there are target groups attached to the Auto Scaling group, the instances are deregistered from the target groups. For more information, see Detach EC2 Instances from Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example detaches the specified instance from the specified Auto Scaling group. Expected Output: :example: response = client.detach_instances( InstanceIds=[ 'string', ], AutoScalingGroupName='string', ShouldDecrementDesiredCapacity=True|False ) :type InstanceIds: list :param InstanceIds: The IDs of the instances. You can specify up to 20 instances.\n\n(string) --\n\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ShouldDecrementDesiredCapacity: boolean :param ShouldDecrementDesiredCapacity: [REQUIRED]\nIndicates whether the Auto Scaling group decrements the desired capacity value by the number of instances detached.\n :rtype: dict ReturnsResponse Syntax { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ] } Response Structure (dict) -- Activities (list) -- The activities related to detaching the instances from the Auto Scaling group. (dict) -- Describes scaling activity, which is a long-running process that represents a change to your Auto Scaling group, such as changing its size or replacing an instance. ActivityId (string) -- The ID of the activity. AutoScalingGroupName (string) -- The name of the Auto Scaling group. Description (string) -- A friendly, more verbose description of the activity. Cause (string) -- The reason the activity began. StartTime (datetime) -- The start time of the activity. EndTime (datetime) -- The end time of the activity. StatusCode (string) -- The current status of the activity. StatusMessage (string) -- A friendly, more verbose description of the activity status. Progress (integer) -- A value between 0 and 100 that indicates the progress of the activity. Details (string) -- The details about the activity. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example detaches the specified instance from the specified Auto Scaling group. response = client.detach_instances( AutoScalingGroupName='my-auto-scaling-group', InstanceIds=[ 'i-93633f9b', ], ShouldDecrementDesiredCapacity=True, ) print(response) Expected Output: { 'Activities': [ { 'ActivityId': '5091cb52-547a-47ce-a236-c9ccbc2cb2c9', 'AutoScalingGroupName': 'my-auto-scaling-group', 'Cause': 'At 2015-04-12T15:02:16Z instance i-93633f9b was detached in response to a user request, shrinking the capacity from 2 to 1.', 'Description': 'Detaching EC2 instance: i-93633f9b', 'Details': 'details', 'Progress': 50, 'StartTime': datetime(2015, 4, 12, 15, 2, 16, 6, 102, 0), 'StatusCode': 'InProgress', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def detach_load_balancer_target_groups(AutoScalingGroupName=None, TargetGroupARNs=None): """ Detaches one or more target groups from the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example detaches the specified target group from the specified Auto Scaling group Expected Output: :example: response = client.detach_load_balancer_target_groups( AutoScalingGroupName='string', TargetGroupARNs=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type TargetGroupARNs: list :param TargetGroupARNs: [REQUIRED]\nThe Amazon Resource Names (ARN) of the target groups. You can specify up to 10 target groups.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example detaches the specified target group from the specified Auto Scaling group response = client.detach_load_balancer_target_groups( AutoScalingGroupName='my-auto-scaling-group', TargetGroupARNs=[ 'arn:aws:elasticloadbalancing:us-west-2:123456789012:targetgroup/my-targets/73e2d6bc24d8a067', ], ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: (dict) -- """ pass def detach_load_balancers(AutoScalingGroupName=None, LoadBalancerNames=None): """ Detaches one or more Classic Load Balancers from the specified Auto Scaling group. This operation detaches only Classic Load Balancers. If you have Application Load Balancers or Network Load Balancers, use the DetachLoadBalancerTargetGroups API instead. When you detach a load balancer, it enters the Removing state while deregistering the instances in the group. When all instances are deregistered, then you can no longer describe the load balancer using the DescribeLoadBalancers API call. The instances remain running. See also: AWS API Documentation Exceptions Examples This example detaches the specified load balancer from the specified Auto Scaling group. Expected Output: :example: response = client.detach_load_balancers( AutoScalingGroupName='string', LoadBalancerNames=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LoadBalancerNames: list :param LoadBalancerNames: [REQUIRED]\nThe names of the load balancers. You can specify up to 10 load balancers.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example detaches the specified load balancer from the specified Auto Scaling group. response = client.detach_load_balancers( AutoScalingGroupName='my-auto-scaling-group', LoadBalancerNames=[ 'my-load-balancer', ], ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: (dict) -- """ pass def disable_metrics_collection(AutoScalingGroupName=None, Metrics=None): """ Disables group metrics for the specified Auto Scaling group. See also: AWS API Documentation Exceptions Examples This example disables collecting data for the GroupDesiredCapacity metric for the specified Auto Scaling group. Expected Output: :example: response = client.disable_metrics_collection( AutoScalingGroupName='string', Metrics=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type Metrics: list :param Metrics: Specifies one or more of the following metrics:\n\nGroupMinSize\nGroupMaxSize\nGroupDesiredCapacity\nGroupInServiceInstances\nGroupPendingInstances\nGroupStandbyInstances\nGroupTerminatingInstances\nGroupTotalInstances\nGroupInServiceCapacity\nGroupPendingCapacity\nGroupStandbyCapacity\nGroupTerminatingCapacity\nGroupTotalCapacity\n\nIf you omit this parameter, all metrics are disabled.\n\n(string) --\n\n :return: response = client.disable_metrics_collection( AutoScalingGroupName='my-auto-scaling-group', Metrics=[ 'GroupDesiredCapacity', ], ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def enable_metrics_collection(AutoScalingGroupName=None, Metrics=None, Granularity=None): """ Enables group metrics for the specified Auto Scaling group. For more information, see Monitoring Your Auto Scaling Groups and Instances in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example enables data collection for the specified Auto Scaling group. Expected Output: :example: response = client.enable_metrics_collection( AutoScalingGroupName='string', Metrics=[ 'string', ], Granularity='string' ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type Metrics: list :param Metrics: Specifies which group-level metrics to start collecting. You can specify one or more of the following metrics:\n\nGroupMinSize\nGroupMaxSize\nGroupDesiredCapacity\nGroupInServiceInstances\nGroupPendingInstances\nGroupStandbyInstances\nGroupTerminatingInstances\nGroupTotalInstances\n\nThe instance weighting feature supports the following additional metrics:\n\nGroupInServiceCapacity\nGroupPendingCapacity\nGroupStandbyCapacity\nGroupTerminatingCapacity\nGroupTotalCapacity\n\nIf you omit this parameter, all metrics are enabled.\n\n(string) --\n\n :type Granularity: string :param Granularity: [REQUIRED]\nThe granularity to associate with the metrics to collect. The only valid value is 1Minute .\n :return: response = client.enable_metrics_collection( AutoScalingGroupName='my-auto-scaling-group', Granularity='1Minute', ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def enter_standby(InstanceIds=None, AutoScalingGroupName=None, ShouldDecrementDesiredCapacity=None): """ Moves the specified instances into the standby state. If you choose to decrement the desired capacity of the Auto Scaling group, the instances can enter standby as long as the desired capacity of the Auto Scaling group after the instances are placed into standby is equal to or greater than the minimum capacity of the group. If you choose not to decrement the desired capacity of the Auto Scaling group, the Auto Scaling group launches new instances to replace the instances on standby. For more information, see Temporarily Removing Instances from Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example puts the specified instance into standby mode. Expected Output: :example: response = client.enter_standby( InstanceIds=[ 'string', ], AutoScalingGroupName='string', ShouldDecrementDesiredCapacity=True|False ) :type InstanceIds: list :param InstanceIds: The IDs of the instances. You can specify up to 20 instances.\n\n(string) --\n\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ShouldDecrementDesiredCapacity: boolean :param ShouldDecrementDesiredCapacity: [REQUIRED]\nIndicates whether to decrement the desired capacity of the Auto Scaling group by the number of instances moved to Standby mode.\n :rtype: dict ReturnsResponse Syntax { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ] } Response Structure (dict) -- Activities (list) -- The activities related to moving instances into Standby mode. (dict) -- Describes scaling activity, which is a long-running process that represents a change to your Auto Scaling group, such as changing its size or replacing an instance. ActivityId (string) -- The ID of the activity. AutoScalingGroupName (string) -- The name of the Auto Scaling group. Description (string) -- A friendly, more verbose description of the activity. Cause (string) -- The reason the activity began. StartTime (datetime) -- The start time of the activity. EndTime (datetime) -- The end time of the activity. StatusCode (string) -- The current status of the activity. StatusMessage (string) -- A friendly, more verbose description of the activity status. Progress (integer) -- A value between 0 and 100 that indicates the progress of the activity. Details (string) -- The details about the activity. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example puts the specified instance into standby mode. response = client.enter_standby( AutoScalingGroupName='my-auto-scaling-group', InstanceIds=[ 'i-93633f9b', ], ShouldDecrementDesiredCapacity=True, ) print(response) Expected Output: { 'Activities': [ { 'ActivityId': 'ffa056b4-6ed3-41ba-ae7c-249dfae6eba1', 'AutoScalingGroupName': 'my-auto-scaling-group', 'Cause': 'At 2015-04-12T15:10:23Z instance i-93633f9b was moved to standby in response to a user request, shrinking the capacity from 2 to 1.', 'Description': 'Moving EC2 instance to Standby: i-93633f9b', 'Details': 'details', 'Progress': 50, 'StartTime': datetime(2015, 4, 12, 15, 10, 23, 6, 102, 0), 'StatusCode': 'InProgress', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def execute_policy(AutoScalingGroupName=None, PolicyName=None, HonorCooldown=None, MetricValue=None, BreachThreshold=None): """ Executes the specified policy. See also: AWS API Documentation Exceptions Examples This example executes the specified Auto Scaling policy for the specified Auto Scaling group. Expected Output: :example: response = client.execute_policy( AutoScalingGroupName='string', PolicyName='string', HonorCooldown=True|False, MetricValue=123.0, BreachThreshold=123.0 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: The name of the Auto Scaling group. :type PolicyName: string :param PolicyName: [REQUIRED]\nThe name or ARN of the policy.\n :type HonorCooldown: boolean :param HonorCooldown: Indicates whether Amazon EC2 Auto Scaling waits for the cooldown period to complete before executing the policy.\nThis parameter is not supported if the policy type is StepScaling or TargetTrackingScaling .\nFor more information, see Scaling Cooldowns in the Amazon EC2 Auto Scaling User Guide .\n :type MetricValue: float :param MetricValue: The metric value to compare to BreachThreshold . This enables you to execute a policy of type StepScaling and determine which step adjustment to use. For example, if the breach threshold is 50 and you want to use a step adjustment with a lower bound of 0 and an upper bound of 10, you can set the metric value to 59.\nIf you specify a metric value that doesn\'t correspond to a step adjustment for the policy, the call returns an error.\nConditional: This parameter is required if the policy type is StepScaling and not supported otherwise.\n :type BreachThreshold: float :param BreachThreshold: The breach threshold for the alarm.\nConditional: This parameter is required if the policy type is StepScaling and not supported otherwise.\n :return: response = client.execute_policy( AutoScalingGroupName='my-auto-scaling-group', HonorCooldown=True, PolicyName='ScaleIn', ) print(response) :returns: AutoScaling.Client.exceptions.ScalingActivityInProgressFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def exit_standby(InstanceIds=None, AutoScalingGroupName=None): """ Moves the specified instances out of the standby state. After you put the instances back in service, the desired capacity is incremented. For more information, see Temporarily Removing Instances from Your Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example moves the specified instance out of standby mode. Expected Output: :example: response = client.exit_standby( InstanceIds=[ 'string', ], AutoScalingGroupName='string' ) :type InstanceIds: list :param InstanceIds: The IDs of the instances. You can specify up to 20 instances.\n\n(string) --\n\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :rtype: dict ReturnsResponse Syntax { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ] } Response Structure (dict) -- Activities (list) -- The activities related to moving instances out of Standby mode. (dict) -- Describes scaling activity, which is a long-running process that represents a change to your Auto Scaling group, such as changing its size or replacing an instance. ActivityId (string) -- The ID of the activity. AutoScalingGroupName (string) -- The name of the Auto Scaling group. Description (string) -- A friendly, more verbose description of the activity. Cause (string) -- The reason the activity began. StartTime (datetime) -- The start time of the activity. EndTime (datetime) -- The end time of the activity. StatusCode (string) -- The current status of the activity. StatusMessage (string) -- A friendly, more verbose description of the activity status. Progress (integer) -- A value between 0 and 100 that indicates the progress of the activity. Details (string) -- The details about the activity. Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example moves the specified instance out of standby mode. response = client.exit_standby( AutoScalingGroupName='my-auto-scaling-group', InstanceIds=[ 'i-93633f9b', ], ) print(response) Expected Output: { 'Activities': [ { 'ActivityId': '142928e1-a2dc-453a-9b24-b85ad6735928', 'AutoScalingGroupName': 'my-auto-scaling-group', 'Cause': 'At 2015-04-12T15:14:29Z instance i-93633f9b was moved out of standby in response to a user request, increasing the capacity from 1 to 2.', 'Description': 'Moving EC2 instance out of Standby: i-93633f9b', 'Details': 'details', 'Progress': 30, 'StartTime': datetime(2015, 4, 12, 15, 14, 29, 6, 102, 0), 'StatusCode': 'PreInService', }, ], 'ResponseMetadata': { '...': '...', }, } :return: { 'Activities': [ { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' }, ] } :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def generate_presigned_url(ClientMethod=None, Params=None, ExpiresIn=None, HttpMethod=None): """ Generate a presigned url given a client, its method, and arguments :type ClientMethod: string :param ClientMethod: The client method to presign for :type Params: dict :param Params: The parameters normally passed to\nClientMethod. :type ExpiresIn: int :param ExpiresIn: The number of seconds the presigned url is valid\nfor. By default it expires in an hour (3600 seconds) :type HttpMethod: string :param HttpMethod: The http method to use on the generated url. By\ndefault, the http method is whatever is used in the method\'s model. """ pass def get_paginator(operation_name=None): """ Create a paginator for an operation. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). :rtype: L{botocore.paginate.Paginator} ReturnsA paginator object. """ pass def get_waiter(waiter_name=None): """ Returns an object that can wait for some condition. :type waiter_name: str :param waiter_name: The name of the waiter to get. See the waiters\nsection of the service docs for a list of available waiters. :rtype: botocore.waiter.Waiter """ pass def put_lifecycle_hook(LifecycleHookName=None, AutoScalingGroupName=None, LifecycleTransition=None, RoleARN=None, NotificationTargetARN=None, NotificationMetadata=None, HeartbeatTimeout=None, DefaultResult=None): """ Creates or updates a lifecycle hook for the specified Auto Scaling group. A lifecycle hook tells Amazon EC2 Auto Scaling to perform an action on an instance when the instance launches (before it is put into service) or as the instance terminates (before it is fully terminated). This step is a part of the procedure for adding a lifecycle hook to an Auto Scaling group: For more information, see Amazon EC2 Auto Scaling Lifecycle Hooks in the Amazon EC2 Auto Scaling User Guide . If you exceed your maximum limit of lifecycle hooks, which by default is 50 per Auto Scaling group, the call fails. You can view the lifecycle hooks for an Auto Scaling group using the DescribeLifecycleHooks API call. If you are no longer using a lifecycle hook, you can delete it by calling the DeleteLifecycleHook API. See also: AWS API Documentation Exceptions Examples This example creates a lifecycle hook. Expected Output: :example: response = client.put_lifecycle_hook( LifecycleHookName='string', AutoScalingGroupName='string', LifecycleTransition='string', RoleARN='string', NotificationTargetARN='string', NotificationMetadata='string', HeartbeatTimeout=123, DefaultResult='string' ) :type LifecycleHookName: string :param LifecycleHookName: [REQUIRED]\nThe name of the lifecycle hook.\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LifecycleTransition: string :param LifecycleTransition: The instance state to which you want to attach the lifecycle hook. The valid values are:\n\nautoscaling:EC2_INSTANCE_LAUNCHING\nautoscaling:EC2_INSTANCE_TERMINATING\n\nConditional: This parameter is required for new lifecycle hooks, but optional when updating existing hooks.\n :type RoleARN: string :param RoleARN: The ARN of the IAM role that allows the Auto Scaling group to publish to the specified notification target, for example, an Amazon SNS topic or an Amazon SQS queue.\nConditional: This parameter is required for new lifecycle hooks, but optional when updating existing hooks.\n :type NotificationTargetARN: string :param NotificationTargetARN: The ARN of the notification target that Amazon EC2 Auto Scaling uses to notify you when an instance is in the transition state for the lifecycle hook. This target can be either an SQS queue or an SNS topic.\nIf you specify an empty string, this overrides the current ARN.\nThis operation uses the JSON format when sending notifications to an Amazon SQS queue, and an email key-value pair format when sending notifications to an Amazon SNS topic.\nWhen you specify a notification target, Amazon EC2 Auto Scaling sends it a test message. Test messages contain the following additional key-value pair: 'Event': 'autoscaling:TEST_NOTIFICATION' .\n :type NotificationMetadata: string :param NotificationMetadata: Additional information that you want to include any time Amazon EC2 Auto Scaling sends a message to the notification target. :type HeartbeatTimeout: integer :param HeartbeatTimeout: The maximum time, in seconds, that can elapse before the lifecycle hook times out. The range is from 30 to 7200 seconds. The default value is 3600 seconds (1 hour).\nIf the lifecycle hook times out, Amazon EC2 Auto Scaling performs the action that you specified in the DefaultResult parameter. You can prevent the lifecycle hook from timing out by calling the RecordLifecycleActionHeartbeat API.\n :type DefaultResult: string :param DefaultResult: Defines the action the Auto Scaling group should take when the lifecycle hook timeout elapses or if an unexpected failure occurs. This parameter can be either CONTINUE or ABANDON . The default value is ABANDON . :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault Examples This example creates a lifecycle hook. response = client.put_lifecycle_hook( AutoScalingGroupName='my-auto-scaling-group', LifecycleHookName='my-lifecycle-hook', LifecycleTransition='autoscaling:EC2_INSTANCE_LAUNCHING', NotificationTargetARN='arn:aws:sns:us-west-2:123456789012:my-sns-topic --role-arn', RoleARN='arn:aws:iam::123456789012:role/my-auto-scaling-role', ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: LifecycleHookName (string) -- [REQUIRED] The name of the lifecycle hook. AutoScalingGroupName (string) -- [REQUIRED] The name of the Auto Scaling group. LifecycleTransition (string) -- The instance state to which you want to attach the lifecycle hook. The valid values are: autoscaling:EC2_INSTANCE_LAUNCHING autoscaling:EC2_INSTANCE_TERMINATING Conditional: This parameter is required for new lifecycle hooks, but optional when updating existing hooks. RoleARN (string) -- The ARN of the IAM role that allows the Auto Scaling group to publish to the specified notification target, for example, an Amazon SNS topic or an Amazon SQS queue. Conditional: This parameter is required for new lifecycle hooks, but optional when updating existing hooks. NotificationTargetARN (string) -- The ARN of the notification target that Amazon EC2 Auto Scaling uses to notify you when an instance is in the transition state for the lifecycle hook. This target can be either an SQS queue or an SNS topic. If you specify an empty string, this overrides the current ARN. This operation uses the JSON format when sending notifications to an Amazon SQS queue, and an email key-value pair format when sending notifications to an Amazon SNS topic. When you specify a notification target, Amazon EC2 Auto Scaling sends it a test message. Test messages contain the following additional key-value pair: "Event": "autoscaling:TEST_NOTIFICATION" . NotificationMetadata (string) -- Additional information that you want to include any time Amazon EC2 Auto Scaling sends a message to the notification target. HeartbeatTimeout (integer) -- The maximum time, in seconds, that can elapse before the lifecycle hook times out. The range is from 30 to 7200 seconds. The default value is 3600 seconds (1 hour). If the lifecycle hook times out, Amazon EC2 Auto Scaling performs the action that you specified in the DefaultResult parameter. You can prevent the lifecycle hook from timing out by calling the RecordLifecycleActionHeartbeat API. DefaultResult (string) -- Defines the action the Auto Scaling group should take when the lifecycle hook timeout elapses or if an unexpected failure occurs. This parameter can be either CONTINUE or ABANDON . The default value is ABANDON . """ pass def put_notification_configuration(AutoScalingGroupName=None, TopicARN=None, NotificationTypes=None): """ Configures an Auto Scaling group to send notifications when specified events take place. Subscribers to the specified topic can have messages delivered to an endpoint such as a web server or an email address. This configuration overwrites any existing configuration. For more information, see Getting Amazon SNS Notifications When Your Auto Scaling Group Scales in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example adds the specified notification to the specified Auto Scaling group. Expected Output: :example: response = client.put_notification_configuration( AutoScalingGroupName='string', TopicARN='string', NotificationTypes=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type TopicARN: string :param TopicARN: [REQUIRED]\nThe Amazon Resource Name (ARN) of the Amazon Simple Notification Service (Amazon SNS) topic.\n :type NotificationTypes: list :param NotificationTypes: [REQUIRED]\nThe type of event that causes the notification to be sent. To query the notification types supported by Amazon EC2 Auto Scaling, call the DescribeAutoScalingNotificationTypes API.\n\n(string) --\n\n :return: response = client.put_notification_configuration( AutoScalingGroupName='my-auto-scaling-group', NotificationTypes=[ 'autoscaling:TEST_NOTIFICATION', ], TopicARN='arn:aws:sns:us-west-2:123456789012:my-sns-topic', ) print(response) :returns: AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure """ pass def put_scaling_policy(AutoScalingGroupName=None, PolicyName=None, PolicyType=None, AdjustmentType=None, MinAdjustmentStep=None, MinAdjustmentMagnitude=None, ScalingAdjustment=None, Cooldown=None, MetricAggregationType=None, StepAdjustments=None, EstimatedInstanceWarmup=None, TargetTrackingConfiguration=None, Enabled=None): """ Creates or updates a scaling policy for an Auto Scaling group. For more information about using scaling policies to scale your Auto Scaling group, see Target Tracking Scaling Policies and Step and Simple Scaling Policies in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example adds the specified policy to the specified Auto Scaling group. Expected Output: :example: response = client.put_scaling_policy( AutoScalingGroupName='string', PolicyName='string', PolicyType='string', AdjustmentType='string', MinAdjustmentStep=123, MinAdjustmentMagnitude=123, ScalingAdjustment=123, Cooldown=123, MetricAggregationType='string', StepAdjustments=[ { 'MetricIntervalLowerBound': 123.0, 'MetricIntervalUpperBound': 123.0, 'ScalingAdjustment': 123 }, ], EstimatedInstanceWarmup=123, TargetTrackingConfiguration={ 'PredefinedMetricSpecification': { 'PredefinedMetricType': 'ASGAverageCPUUtilization'|'ASGAverageNetworkIn'|'ASGAverageNetworkOut'|'ALBRequestCountPerTarget', 'ResourceLabel': 'string' }, 'CustomizedMetricSpecification': { 'MetricName': 'string', 'Namespace': 'string', 'Dimensions': [ { 'Name': 'string', 'Value': 'string' }, ], 'Statistic': 'Average'|'Minimum'|'Maximum'|'SampleCount'|'Sum', 'Unit': 'string' }, 'TargetValue': 123.0, 'DisableScaleIn': True|False }, Enabled=True|False ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type PolicyName: string :param PolicyName: [REQUIRED]\nThe name of the policy.\n :type PolicyType: string :param PolicyType: The policy type. The valid values are SimpleScaling , StepScaling , and TargetTrackingScaling . If the policy type is null, the value is treated as SimpleScaling . :type AdjustmentType: string :param AdjustmentType: Specifies whether the ScalingAdjustment parameter is an absolute number or a percentage of the current capacity. The valid values are ChangeInCapacity , ExactCapacity , and PercentChangeInCapacity .\nValid only if the policy type is StepScaling or SimpleScaling . For more information, see Scaling Adjustment Types in the Amazon EC2 Auto Scaling User Guide .\n :type MinAdjustmentStep: integer :param MinAdjustmentStep: Available for backward compatibility. Use MinAdjustmentMagnitude instead. :type MinAdjustmentMagnitude: integer :param MinAdjustmentMagnitude: The minimum value to scale by when scaling by percentages. For example, suppose that you create a step scaling policy to scale out an Auto Scaling group by 25 percent and you specify a MinAdjustmentMagnitude of 2. If the group has 4 instances and the scaling policy is performed, 25 percent of 4 is 1. However, because you specified a MinAdjustmentMagnitude of 2, Amazon EC2 Auto Scaling scales out the group by 2 instances.\nValid only if the policy type is StepScaling or SimpleScaling and the adjustment type is PercentChangeInCapacity . For more information, see Scaling Adjustment Types in the Amazon EC2 Auto Scaling User Guide .\n :type ScalingAdjustment: integer :param ScalingAdjustment: The amount by which a simple scaling policy scales the Auto Scaling group in response to an alarm breach. The adjustment is based on the value that you specified in the AdjustmentType parameter (either an absolute number or a percentage). A positive value adds to the current capacity and a negative value subtracts from the current capacity. For exact capacity, you must specify a positive value.\nConditional: If you specify SimpleScaling for the policy type, you must specify this parameter. (Not used with any other policy type.)\n :type Cooldown: integer :param Cooldown: The amount of time, in seconds, after a scaling activity completes before any further dynamic scaling activities can start. If this parameter is not specified, the default cooldown period for the group applies.\nValid only if the policy type is SimpleScaling . For more information, see Scaling Cooldowns in the Amazon EC2 Auto Scaling User Guide .\n :type MetricAggregationType: string :param MetricAggregationType: The aggregation type for the CloudWatch metrics. The valid values are Minimum , Maximum , and Average . If the aggregation type is null, the value is treated as Average .\nValid only if the policy type is StepScaling .\n :type StepAdjustments: list :param StepAdjustments: A set of adjustments that enable you to scale based on the size of the alarm breach.\nConditional: If you specify StepScaling for the policy type, you must specify this parameter. (Not used with any other policy type.)\n\n(dict) --Describes information used to create a step adjustment for a step scaling policy.\nFor the following examples, suppose that you have an alarm with a breach threshold of 50:\n\nTo trigger the adjustment when the metric is greater than or equal to 50 and less than 60, specify a lower bound of 0 and an upper bound of 10.\nTo trigger the adjustment when the metric is greater than 40 and less than or equal to 50, specify a lower bound of -10 and an upper bound of 0.\n\nThere are a few rules for the step adjustments for your step policy:\n\nThe ranges of your step adjustments can\'t overlap or have a gap.\nAt most, one step adjustment can have a null lower bound. If one step adjustment has a negative lower bound, then there must be a step adjustment with a null lower bound.\nAt most, one step adjustment can have a null upper bound. If one step adjustment has a positive upper bound, then there must be a step adjustment with a null upper bound.\nThe upper and lower bound can\'t be null in the same step adjustment.\n\nFor more information, see Step Adjustments in the Amazon EC2 Auto Scaling User Guide .\n\nMetricIntervalLowerBound (float) --The lower bound for the difference between the alarm threshold and the CloudWatch metric. If the metric value is above the breach threshold, the lower bound is inclusive (the metric must be greater than or equal to the threshold plus the lower bound). Otherwise, it is exclusive (the metric must be greater than the threshold plus the lower bound). A null value indicates negative infinity.\n\nMetricIntervalUpperBound (float) --The upper bound for the difference between the alarm threshold and the CloudWatch metric. If the metric value is above the breach threshold, the upper bound is exclusive (the metric must be less than the threshold plus the upper bound). Otherwise, it is inclusive (the metric must be less than or equal to the threshold plus the upper bound). A null value indicates positive infinity.\nThe upper bound must be greater than the lower bound.\n\nScalingAdjustment (integer) -- [REQUIRED]The amount by which to scale, based on the specified adjustment type. A positive value adds to the current capacity while a negative number removes from the current capacity.\n\n\n\n\n :type EstimatedInstanceWarmup: integer :param EstimatedInstanceWarmup: The estimated time, in seconds, until a newly launched instance can contribute to the CloudWatch metrics. The default is to use the value specified for the default cooldown period for the group.\nValid only if the policy type is StepScaling or TargetTrackingScaling .\n :type TargetTrackingConfiguration: dict :param TargetTrackingConfiguration: A target tracking scaling policy. Includes support for predefined or customized metrics.\nFor more information, see TargetTrackingConfiguration in the Amazon EC2 Auto Scaling API Reference .\nConditional: If you specify TargetTrackingScaling for the policy type, you must specify this parameter. (Not used with any other policy type.)\n\nPredefinedMetricSpecification (dict) --A predefined metric. You must specify either a predefined metric or a customized metric.\n\nPredefinedMetricType (string) -- [REQUIRED]The metric type. The following predefined metrics are available:\n\nASGAverageCPUUtilization - Average CPU utilization of the Auto Scaling group.\nASGAverageNetworkIn - Average number of bytes received on all network interfaces by the Auto Scaling group.\nASGAverageNetworkOut - Average number of bytes sent out on all network interfaces by the Auto Scaling group.\nALBRequestCountPerTarget - Number of requests completed per target in an Application Load Balancer target group.\n\n\nResourceLabel (string) --Identifies the resource associated with the metric type. You can\'t specify a resource label unless the metric type is ALBRequestCountPerTarget and there is a target group attached to the Auto Scaling group.\nThe format is ``app/load-balancer-name /load-balancer-id /targetgroup/target-group-name /target-group-id `` , where\n\n``app/load-balancer-name /load-balancer-id `` is the final portion of the load balancer ARN, and\n``targetgroup/target-group-name /target-group-id `` is the final portion of the target group ARN.\n\n\n\n\nCustomizedMetricSpecification (dict) --A customized metric. You must specify either a predefined metric or a customized metric.\n\nMetricName (string) -- [REQUIRED]The name of the metric.\n\nNamespace (string) -- [REQUIRED]The namespace of the metric.\n\nDimensions (list) --The dimensions of the metric.\nConditional: If you published your metric with dimensions, you must specify the same dimensions in your scaling policy.\n\n(dict) --Describes the dimension of a metric.\n\nName (string) -- [REQUIRED]The name of the dimension.\n\nValue (string) -- [REQUIRED]The value of the dimension.\n\n\n\n\n\nStatistic (string) -- [REQUIRED]The statistic of the metric.\n\nUnit (string) --The unit of the metric.\n\n\n\nTargetValue (float) -- [REQUIRED]The target value for the metric.\n\nDisableScaleIn (boolean) --Indicates whether scaling in by the target tracking scaling policy is disabled. If scaling in is disabled, the target tracking scaling policy doesn\'t remove instances from the Auto Scaling group. Otherwise, the target tracking scaling policy can remove instances from the Auto Scaling group. The default is false .\n\n\n :type Enabled: boolean :param Enabled: Indicates whether the scaling policy is enabled or disabled. The default is enabled. For more information, see Disabling a Scaling Policy for an Auto Scaling Group in the Amazon EC2 Auto Scaling User Guide . :rtype: dict ReturnsResponse Syntax { 'PolicyARN': 'string', 'Alarms': [ { 'AlarmName': 'string', 'AlarmARN': 'string' }, ] } Response Structure (dict) -- Contains the output of PutScalingPolicy. PolicyARN (string) -- The Amazon Resource Name (ARN) of the policy. Alarms (list) -- The CloudWatch alarms created for the target tracking scaling policy. (dict) -- Describes an alarm. AlarmName (string) -- The name of the alarm. AlarmARN (string) -- The Amazon Resource Name (ARN) of the alarm. Exceptions AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure Examples This example adds the specified policy to the specified Auto Scaling group. response = client.put_scaling_policy( AdjustmentType='ChangeInCapacity', AutoScalingGroupName='my-auto-scaling-group', PolicyName='ScaleIn', ScalingAdjustment=-1, ) print(response) Expected Output: { 'PolicyARN': 'arn:aws:autoscaling:us-west-2:123456789012:scalingPolicy:2233f3d7-6290-403b-b632-93c553560106:autoScalingGroupName/my-auto-scaling-group:policyName/ScaleIn', 'ResponseMetadata': { '...': '...', }, } :return: { 'PolicyARN': 'string', 'Alarms': [ { 'AlarmName': 'string', 'AlarmARN': 'string' }, ] } :returns: AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault AutoScaling.Client.exceptions.ServiceLinkedRoleFailure """ pass def put_scheduled_update_group_action(AutoScalingGroupName=None, ScheduledActionName=None, Time=None, StartTime=None, EndTime=None, Recurrence=None, MinSize=None, MaxSize=None, DesiredCapacity=None): """ Creates or updates a scheduled scaling action for an Auto Scaling group. If you leave a parameter unspecified when updating a scheduled scaling action, the corresponding value remains unchanged. For more information, see Scheduled Scaling in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example adds the specified scheduled action to the specified Auto Scaling group. Expected Output: :example: response = client.put_scheduled_update_group_action( AutoScalingGroupName='string', ScheduledActionName='string', Time=datetime(2015, 1, 1), StartTime=datetime(2015, 1, 1), EndTime=datetime(2015, 1, 1), Recurrence='string', MinSize=123, MaxSize=123, DesiredCapacity=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ScheduledActionName: string :param ScheduledActionName: [REQUIRED]\nThe name of this scaling action.\n :type Time: datetime :param Time: This parameter is no longer used. :type StartTime: datetime :param StartTime: The date and time for this action to start, in YYYY-MM-DDThh:mm:ssZ format in UTC/GMT only and in quotes (for example, '2019-06-01T00:00:00Z' ).\nIf you specify Recurrence and StartTime , Amazon EC2 Auto Scaling performs the action at this time, and then performs the action based on the specified recurrence.\nIf you try to schedule your action in the past, Amazon EC2 Auto Scaling returns an error message.\n :type EndTime: datetime :param EndTime: The date and time for the recurring schedule to end. Amazon EC2 Auto Scaling does not perform the action after this time. :type Recurrence: string :param Recurrence: The recurring schedule for this action, in Unix cron syntax format. This format consists of five fields separated by white spaces: [Minute] [Hour] [Day_of_Month] [Month_of_Year] [Day_of_Week]. The value must be in quotes (for example, '30 0 1 1,6,12 *' ). For more information about this format, see Crontab .\nWhen StartTime and EndTime are specified with Recurrence , they form the boundaries of when the recurring action starts and stops.\n :type MinSize: integer :param MinSize: The minimum size of the Auto Scaling group. :type MaxSize: integer :param MaxSize: The maximum size of the Auto Scaling group. :type DesiredCapacity: integer :param DesiredCapacity: The desired capacity is the initial capacity of the Auto Scaling group after the scheduled action runs and the capacity it attempts to maintain. It can scale beyond this capacity if you add more scaling conditions. :return: response = client.put_scheduled_update_group_action( AutoScalingGroupName='my-auto-scaling-group', DesiredCapacity=4, EndTime=datetime(2014, 5, 12, 8, 0, 0, 0, 132, 0), MaxSize=6, MinSize=2, ScheduledActionName='my-scheduled-action', StartTime=datetime(2014, 5, 12, 8, 0, 0, 0, 132, 0), ) print(response) :returns: AutoScaling.Client.exceptions.AlreadyExistsFault AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def record_lifecycle_action_heartbeat(LifecycleHookName=None, AutoScalingGroupName=None, LifecycleActionToken=None, InstanceId=None): """ Records a heartbeat for the lifecycle action associated with the specified token or instance. This extends the timeout by the length of time defined using the PutLifecycleHook API call. This step is a part of the procedure for adding a lifecycle hook to an Auto Scaling group: For more information, see Auto Scaling Lifecycle in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example records a lifecycle action heartbeat to keep the instance in a pending state. Expected Output: :example: response = client.record_lifecycle_action_heartbeat( LifecycleHookName='string', AutoScalingGroupName='string', LifecycleActionToken='string', InstanceId='string' ) :type LifecycleHookName: string :param LifecycleHookName: [REQUIRED]\nThe name of the lifecycle hook.\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LifecycleActionToken: string :param LifecycleActionToken: A token that uniquely identifies a specific lifecycle action associated with an instance. Amazon EC2 Auto Scaling sends this token to the notification target that you specified when you created the lifecycle hook. :type InstanceId: string :param InstanceId: The ID of the instance. :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.ResourceContentionFault Examples This example records a lifecycle action heartbeat to keep the instance in a pending state. response = client.record_lifecycle_action_heartbeat( AutoScalingGroupName='my-auto-scaling-group', LifecycleActionToken='bcd2f1b8-9a78-44d3-8a7a-4dd07d7cf635', LifecycleHookName='my-lifecycle-hook', ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: LifecycleHookName (string) -- [REQUIRED] The name of the lifecycle hook. AutoScalingGroupName (string) -- [REQUIRED] The name of the Auto Scaling group. LifecycleActionToken (string) -- A token that uniquely identifies a specific lifecycle action associated with an instance. Amazon EC2 Auto Scaling sends this token to the notification target that you specified when you created the lifecycle hook. InstanceId (string) -- The ID of the instance. """ pass def resume_processes(AutoScalingGroupName=None, ScalingProcesses=None): """ Resumes the specified suspended automatic scaling processes, or all suspended process, for the specified Auto Scaling group. For more information, see Suspending and Resuming Scaling Processes in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example resumes the specified suspended scaling process for the specified Auto Scaling group. Expected Output: :example: response = client.resume_processes( AutoScalingGroupName='string', ScalingProcesses=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ScalingProcesses: list :param ScalingProcesses: One or more of the following processes. If you omit this parameter, all processes are specified.\n\nLaunch\nTerminate\nHealthCheck\nReplaceUnhealthy\nAZRebalance\nAlarmNotification\nScheduledActions\nAddToLoadBalancer\n\n\n(string) --\n\n :return: response = client.resume_processes( AutoScalingGroupName='my-auto-scaling-group', ScalingProcesses=[ 'AlarmNotification', ], ) print(response) :returns: AutoScaling.Client.exceptions.ResourceInUseFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def set_desired_capacity(AutoScalingGroupName=None, DesiredCapacity=None, HonorCooldown=None): """ Sets the size of the specified Auto Scaling group. If a scale-in activity occurs as a result of a new DesiredCapacity value that is lower than the current size of the group, the Auto Scaling group uses its termination policy to determine which instances to terminate. For more information, see Manual Scaling in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example sets the desired capacity for the specified Auto Scaling group. Expected Output: :example: response = client.set_desired_capacity( AutoScalingGroupName='string', DesiredCapacity=123, HonorCooldown=True|False ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type DesiredCapacity: integer :param DesiredCapacity: [REQUIRED]\nThe desired capacity is the initial capacity of the Auto Scaling group after this operation completes and the capacity it attempts to maintain.\n :type HonorCooldown: boolean :param HonorCooldown: Indicates whether Amazon EC2 Auto Scaling waits for the cooldown period to complete before initiating a scaling activity to set your Auto Scaling group to its new capacity. By default, Amazon EC2 Auto Scaling does not honor the cooldown period during manual scaling activities. :return: response = client.set_desired_capacity( AutoScalingGroupName='my-auto-scaling-group', DesiredCapacity=2, HonorCooldown=True, ) print(response) :returns: AutoScaling.Client.exceptions.ScalingActivityInProgressFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def set_instance_health(InstanceId=None, HealthStatus=None, ShouldRespectGracePeriod=None): """ Sets the health status of the specified instance. For more information, see Health Checks for Auto Scaling Instances in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example sets the health status of the specified instance to Unhealthy. Expected Output: :example: response = client.set_instance_health( InstanceId='string', HealthStatus='string', ShouldRespectGracePeriod=True|False ) :type InstanceId: string :param InstanceId: [REQUIRED]\nThe ID of the instance.\n :type HealthStatus: string :param HealthStatus: [REQUIRED]\nThe health status of the instance. Set to Healthy to have the instance remain in service. Set to Unhealthy to have the instance be out of service. Amazon EC2 Auto Scaling terminates and replaces the unhealthy instance.\n :type ShouldRespectGracePeriod: boolean :param ShouldRespectGracePeriod: If the Auto Scaling group of the specified instance has a HealthCheckGracePeriod specified for the group, by default, this call respects the grace period. Set this to False , to have the call not respect the grace period associated with the group.\nFor more information about the health check grace period, see CreateAutoScalingGroup in the Amazon EC2 Auto Scaling API Reference .\n :return: response = client.set_instance_health( HealthStatus='Unhealthy', InstanceId='i-93633f9b', ) print(response) :returns: AutoScaling.Client.exceptions.ResourceContentionFault """ pass def set_instance_protection(InstanceIds=None, AutoScalingGroupName=None, ProtectedFromScaleIn=None): """ Updates the instance protection settings of the specified instances. For more information about preventing instances that are part of an Auto Scaling group from terminating on scale in, see Instance Protection in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example enables instance protection for the specified instance. Expected Output: This example disables instance protection for the specified instance. Expected Output: :example: response = client.set_instance_protection( InstanceIds=[ 'string', ], AutoScalingGroupName='string', ProtectedFromScaleIn=True|False ) :type InstanceIds: list :param InstanceIds: [REQUIRED]\nOne or more instance IDs.\n\n(string) --\n\n :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ProtectedFromScaleIn: boolean :param ProtectedFromScaleIn: [REQUIRED]\nIndicates whether the instance is protected from termination by Amazon EC2 Auto Scaling when scaling in.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions AutoScaling.Client.exceptions.LimitExceededFault AutoScaling.Client.exceptions.ResourceContentionFault Examples This example enables instance protection for the specified instance. response = client.set_instance_protection( AutoScalingGroupName='my-auto-scaling-group', InstanceIds=[ 'i-93633f9b', ], ProtectedFromScaleIn=True, ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } This example disables instance protection for the specified instance. response = client.set_instance_protection( AutoScalingGroupName='my-auto-scaling-group', InstanceIds=[ 'i-93633f9b', ], ProtectedFromScaleIn=False, ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: {} :returns: (dict) -- """ pass def suspend_processes(AutoScalingGroupName=None, ScalingProcesses=None): """ Suspends the specified automatic scaling processes, or all processes, for the specified Auto Scaling group. If you suspend either the Launch or Terminate process types, it can prevent other process types from functioning properly. For more information, see Suspending and Resuming Scaling Processes in the Amazon EC2 Auto Scaling User Guide . To resume processes that have been suspended, call the ResumeProcesses API. See also: AWS API Documentation Exceptions Examples This example suspends the specified scaling process for the specified Auto Scaling group. Expected Output: :example: response = client.suspend_processes( AutoScalingGroupName='string', ScalingProcesses=[ 'string', ] ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type ScalingProcesses: list :param ScalingProcesses: One or more of the following processes. If you omit this parameter, all processes are specified.\n\nLaunch\nTerminate\nHealthCheck\nReplaceUnhealthy\nAZRebalance\nAlarmNotification\nScheduledActions\nAddToLoadBalancer\n\n\n(string) --\n\n :return: response = client.suspend_processes( AutoScalingGroupName='my-auto-scaling-group', ScalingProcesses=[ 'AlarmNotification', ], ) print(response) :returns: AutoScaling.Client.exceptions.ResourceInUseFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def terminate_instance_in_auto_scaling_group(InstanceId=None, ShouldDecrementDesiredCapacity=None): """ Terminates the specified instance and optionally adjusts the desired group size. This call simply makes a termination request. The instance is not terminated immediately. When an instance is terminated, the instance status changes to terminated . You can\'t connect to or start an instance after you\'ve terminated it. If you do not specify the option to decrement the desired capacity, Amazon EC2 Auto Scaling launches instances to replace the ones that are terminated. By default, Amazon EC2 Auto Scaling balances instances across all Availability Zones. If you decrement the desired capacity, your Auto Scaling group can become unbalanced between Availability Zones. Amazon EC2 Auto Scaling tries to rebalance the group, and rebalancing might terminate instances in other zones. For more information, see Rebalancing Activities in the Amazon EC2 Auto Scaling User Guide . See also: AWS API Documentation Exceptions Examples This example terminates the specified instance from the specified Auto Scaling group without updating the size of the group. Auto Scaling launches a replacement instance after the specified instance terminates. Expected Output: :example: response = client.terminate_instance_in_auto_scaling_group( InstanceId='string', ShouldDecrementDesiredCapacity=True|False ) :type InstanceId: string :param InstanceId: [REQUIRED]\nThe ID of the instance.\n :type ShouldDecrementDesiredCapacity: boolean :param ShouldDecrementDesiredCapacity: [REQUIRED]\nIndicates whether terminating the instance also decrements the size of the Auto Scaling group.\n :rtype: dict ReturnsResponse Syntax { 'Activity': { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' } } Response Structure (dict) -- Activity (dict) -- A scaling activity. ActivityId (string) -- The ID of the activity. AutoScalingGroupName (string) -- The name of the Auto Scaling group. Description (string) -- A friendly, more verbose description of the activity. Cause (string) -- The reason the activity began. StartTime (datetime) -- The start time of the activity. EndTime (datetime) -- The end time of the activity. StatusCode (string) -- The current status of the activity. StatusMessage (string) -- A friendly, more verbose description of the activity status. Progress (integer) -- A value between 0 and 100 that indicates the progress of the activity. Details (string) -- The details about the activity. Exceptions AutoScaling.Client.exceptions.ScalingActivityInProgressFault AutoScaling.Client.exceptions.ResourceContentionFault Examples This example terminates the specified instance from the specified Auto Scaling group without updating the size of the group. Auto Scaling launches a replacement instance after the specified instance terminates. response = client.terminate_instance_in_auto_scaling_group( InstanceId='i-93633f9b', ShouldDecrementDesiredCapacity=False, ) print(response) Expected Output: { 'ResponseMetadata': { '...': '...', }, } :return: { 'Activity': { 'ActivityId': 'string', 'AutoScalingGroupName': 'string', 'Description': 'string', 'Cause': 'string', 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'StatusCode': 'PendingSpotBidPlacement'|'WaitingForSpotInstanceRequestId'|'WaitingForSpotInstanceId'|'WaitingForInstanceId'|'PreInService'|'InProgress'|'WaitingForELBConnectionDraining'|'MidLifecycleAction'|'WaitingForInstanceWarmup'|'Successful'|'Failed'|'Cancelled', 'StatusMessage': 'string', 'Progress': 123, 'Details': 'string' } } :returns: AutoScaling.Client.exceptions.ScalingActivityInProgressFault AutoScaling.Client.exceptions.ResourceContentionFault """ pass def update_auto_scaling_group(AutoScalingGroupName=None, LaunchConfigurationName=None, LaunchTemplate=None, MixedInstancesPolicy=None, MinSize=None, MaxSize=None, DesiredCapacity=None, DefaultCooldown=None, AvailabilityZones=None, HealthCheckType=None, HealthCheckGracePeriod=None, PlacementGroup=None, VPCZoneIdentifier=None, TerminationPolicies=None, NewInstancesProtectedFromScaleIn=None, ServiceLinkedRoleARN=None, MaxInstanceLifetime=None): """ Updates the configuration for the specified Auto Scaling group. To update an Auto Scaling group, specify the name of the group and the parameter that you want to change. Any parameters that you don\'t specify are not changed by this update request. The new settings take effect on any scaling activities after this call returns. If you associate a new launch configuration or template with an Auto Scaling group, all new instances will get the updated configuration. Existing instances continue to run with the configuration that they were originally launched with. When you update a group to specify a mixed instances policy instead of a launch configuration or template, existing instances may be replaced to match the new purchasing options that you specified in the policy. For example, if the group currently has 100% On-Demand capacity and the policy specifies 50% Spot capacity, this means that half of your instances will be gradually terminated and relaunched as Spot Instances. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones, so that updating your group does not compromise the performance or availability of your application. Note the following about changing DesiredCapacity , MaxSize , or MinSize : To see which parameters have been set, call the DescribeAutoScalingGroups API. To view the scaling policies for an Auto Scaling group, call the DescribePolicies API. If the group has scaling policies, you can update them by calling the PutScalingPolicy API. See also: AWS API Documentation Exceptions Examples This example updates the launch configuration of the specified Auto Scaling group. Expected Output: This example updates the minimum size and maximum size of the specified Auto Scaling group. Expected Output: This example enables instance protection for the specified Auto Scaling group. Expected Output: :example: response = client.update_auto_scaling_group( AutoScalingGroupName='string', LaunchConfigurationName='string', LaunchTemplate={ 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, MixedInstancesPolicy={ 'LaunchTemplate': { 'LaunchTemplateSpecification': { 'LaunchTemplateId': 'string', 'LaunchTemplateName': 'string', 'Version': 'string' }, 'Overrides': [ { 'InstanceType': 'string', 'WeightedCapacity': 'string' }, ] }, 'InstancesDistribution': { 'OnDemandAllocationStrategy': 'string', 'OnDemandBaseCapacity': 123, 'OnDemandPercentageAboveBaseCapacity': 123, 'SpotAllocationStrategy': 'string', 'SpotInstancePools': 123, 'SpotMaxPrice': 'string' } }, MinSize=123, MaxSize=123, DesiredCapacity=123, DefaultCooldown=123, AvailabilityZones=[ 'string', ], HealthCheckType='string', HealthCheckGracePeriod=123, PlacementGroup='string', VPCZoneIdentifier='string', TerminationPolicies=[ 'string', ], NewInstancesProtectedFromScaleIn=True|False, ServiceLinkedRoleARN='string', MaxInstanceLifetime=123 ) :type AutoScalingGroupName: string :param AutoScalingGroupName: [REQUIRED]\nThe name of the Auto Scaling group.\n :type LaunchConfigurationName: string :param LaunchConfigurationName: The name of the launch configuration. If you specify LaunchConfigurationName in your update request, you can\'t specify LaunchTemplate or MixedInstancesPolicy . :type LaunchTemplate: dict :param LaunchTemplate: The launch template and version to use to specify the updates. If you specify LaunchTemplate in your update request, you can\'t specify LaunchConfigurationName or MixedInstancesPolicy .\nFor more information, see LaunchTemplateSpecification in the Amazon EC2 Auto Scaling API Reference .\n\nLaunchTemplateId (string) --The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nLaunchTemplateName (string) --The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nVersion (string) --The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API.\nIf the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default .\n\n\n :type MixedInstancesPolicy: dict :param MixedInstancesPolicy: An embedded object that specifies a mixed instances policy.\nIn your call to UpdateAutoScalingGroup , you can make changes to the policy that is specified. All optional parameters are left unchanged if not specified.\nFor more information, see MixedInstancesPolicy in the Amazon EC2 Auto Scaling API Reference and Auto Scaling Groups with Multiple Instance Types and Purchase Options in the Amazon EC2 Auto Scaling User Guide .\n\nLaunchTemplate (dict) --The launch template and instance types (overrides).\nThis parameter must be specified when creating a mixed instances policy.\n\nLaunchTemplateSpecification (dict) --The launch template to use. You must specify either the launch template ID or launch template name in the request.\n\nLaunchTemplateId (string) --The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nLaunchTemplateName (string) --The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API.\nYou must specify either a template ID or a template name.\n\nVersion (string) --The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API.\nIf the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default .\n\n\n\nOverrides (list) --Any parameters that you specify override the same parameters in the launch template. Currently, the only supported override is instance type. You can specify between 1 and 20 instance types.\nIf not provided, Amazon EC2 Auto Scaling will use the instance type specified in the launch template to launch instances.\n\n(dict) --Describes an override for a launch template. Currently, the only supported override is instance type.\nThe maximum number of instance type overrides that can be associated with an Auto Scaling group is 20.\n\nInstanceType (string) --The instance type. You must use an instance type that is supported in your requested Region and Availability Zones.\nFor information about available instance types, see Available Instance Types in the Amazon Elastic Compute Cloud User Guide.\n\nWeightedCapacity (string) --The number of capacity units, which gives the instance type a proportional weight to other instance types. For example, larger instance types are generally weighted more than smaller instance types. These are the same units that you chose to set the desired capacity in terms of instances, or a performance attribute such as vCPUs, memory, or I/O.\nFor more information, see Instance Weighting for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide .\nValid Range: Minimum value of 1. Maximum value of 999.\n\n\n\n\n\n\n\nInstancesDistribution (dict) --The instances distribution to use.\nIf you leave this parameter unspecified, the value for each parameter in InstancesDistribution uses a default value.\n\nOnDemandAllocationStrategy (string) --Indicates how to allocate instance types to fulfill On-Demand capacity.\nThe only valid value is prioritized , which is also the default value. This strategy uses the order of instance type overrides for the LaunchTemplate to define the launch priority of each instance type. The first instance type in the array is prioritized higher than the last. If all your On-Demand capacity cannot be fulfilled using your highest priority instance, then the Auto Scaling groups launches the remaining capacity using the second priority instance type, and so on.\n\nOnDemandBaseCapacity (integer) --The minimum amount of the Auto Scaling group\'s capacity that must be fulfilled by On-Demand Instances. This base portion is provisioned first as your group scales.\nDefault if not set is 0. If you leave it set to 0, On-Demand Instances are launched as a percentage of the Auto Scaling group\'s desired capacity, per the OnDemandPercentageAboveBaseCapacity setting.\n\nNote\nAn update to this setting means a gradual replacement of instances to maintain the specified number of On-Demand Instances for your base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones.\n\n\nOnDemandPercentageAboveBaseCapacity (integer) --Controls the percentages of On-Demand Instances and Spot Instances for your additional capacity beyond OnDemandBaseCapacity .\nDefault if not set is 100. If you leave it set to 100, the percentages are 100% for On-Demand Instances and 0% for Spot Instances.\n\nNote\nAn update to this setting means a gradual replacement of instances to maintain the percentage of On-Demand Instances for your additional capacity above the base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones.\n\nValid Range: Minimum value of 0. Maximum value of 100.\n\nSpotAllocationStrategy (string) --Indicates how to allocate instances across Spot Instance pools.\nIf the allocation strategy is lowest-price , the Auto Scaling group launches instances using the Spot pools with the lowest price, and evenly allocates your instances across the number of Spot pools that you specify. If the allocation strategy is capacity-optimized , the Auto Scaling group launches instances using Spot pools that are optimally chosen based on the available Spot capacity.\nThe default Spot allocation strategy for calls that you make through the API, the AWS CLI, or the AWS SDKs is lowest-price . The default Spot allocation strategy for the AWS Management Console is capacity-optimized .\nValid values: lowest-price | capacity-optimized\n\nSpotInstancePools (integer) --The number of Spot Instance pools across which to allocate your Spot Instances. The Spot pools are determined from the different instance types in the Overrides array of LaunchTemplate . Default if not set is 2.\nUsed only when the Spot allocation strategy is lowest-price .\nValid Range: Minimum value of 1. Maximum value of 20.\n\nSpotMaxPrice (string) --The maximum price per unit hour that you are willing to pay for a Spot Instance. If you leave the value of this parameter blank (which is the default), the maximum Spot price is set at the On-Demand price.\nTo remove a value that you previously set, include the parameter but leave the value blank.\n\n\n\n\n :type MinSize: integer :param MinSize: The minimum size of the Auto Scaling group. :type MaxSize: integer :param MaxSize: The maximum size of the Auto Scaling group.\n\nNote\nWith a mixed instances policy that uses instance weighting, Amazon EC2 Auto Scaling may need to go above MaxSize to meet your capacity requirements. In this event, Amazon EC2 Auto Scaling will never go above MaxSize by more than your maximum instance weight (weights that define how many capacity units each instance contributes to the capacity of the group).\n\n :type DesiredCapacity: integer :param DesiredCapacity: The desired capacity is the initial capacity of the Auto Scaling group after this operation completes and the capacity it attempts to maintain.\nThis number must be greater than or equal to the minimum size of the group and less than or equal to the maximum size of the group.\n :type DefaultCooldown: integer :param DefaultCooldown: The amount of time, in seconds, after a scaling activity completes before another scaling activity can start. The default value is 300 . This cooldown period is not used when a scaling-specific cooldown is specified.\nCooldown periods are not supported for target tracking scaling policies, step scaling policies, or scheduled scaling. For more information, see Scaling Cooldowns in the Amazon EC2 Auto Scaling User Guide .\n :type AvailabilityZones: list :param AvailabilityZones: One or more Availability Zones for the group.\n\n(string) --\n\n :type HealthCheckType: string :param HealthCheckType: The service to use for the health checks. The valid values are EC2 and ELB . If you configure an Auto Scaling group to use ELB health checks, it considers the instance unhealthy if it fails either the EC2 status checks or the load balancer health checks. :type HealthCheckGracePeriod: integer :param HealthCheckGracePeriod: The amount of time, in seconds, that Amazon EC2 Auto Scaling waits before checking the health status of an EC2 instance that has come into service. The default value is 0 .\nFor more information, see Health Check Grace Period in the Amazon EC2 Auto Scaling User Guide .\nConditional: This parameter is required if you are adding an ELB health check.\n :type PlacementGroup: string :param PlacementGroup: The name of the placement group into which to launch your instances, if any. A placement group is a logical grouping of instances within a single Availability Zone. You cannot specify multiple Availability Zones and a placement group. For more information, see Placement Groups in the Amazon EC2 User Guide for Linux Instances . :type VPCZoneIdentifier: string :param VPCZoneIdentifier: A comma-separated list of subnet IDs for virtual private cloud (VPC).\nIf you specify VPCZoneIdentifier with AvailabilityZones , the subnets that you specify for this parameter must reside in those Availability Zones.\n :type TerminationPolicies: list :param TerminationPolicies: A standalone termination policy or a list of termination policies used to select the instance to terminate. The policies are executed in the order that they are listed.\nFor more information, see Controlling Which Instances Auto Scaling Terminates During Scale In in the Amazon EC2 Auto Scaling User Guide .\n\n(string) --\n\n :type NewInstancesProtectedFromScaleIn: boolean :param NewInstancesProtectedFromScaleIn: Indicates whether newly launched instances are protected from termination by Amazon EC2 Auto Scaling when scaling in.\nFor more information about preventing instances from terminating on scale in, see Instance Protection in the Amazon EC2 Auto Scaling User Guide .\n :type ServiceLinkedRoleARN: string :param ServiceLinkedRoleARN: The Amazon Resource Name (ARN) of the service-linked role that the Auto Scaling group uses to call other AWS services on your behalf. For more information, see Service-Linked Roles in the Amazon EC2 Auto Scaling User Guide . :type MaxInstanceLifetime: integer :param MaxInstanceLifetime: The maximum amount of time, in seconds, that an instance can be in service. The default is null.\nThis parameter is optional, but if you specify a value for it, you must specify a value of at least 604,800 seconds (7 days). To clear a previously set value, specify a new value of 0.\nFor more information, see Replacing Auto Scaling Instances Based on Maximum Instance Lifetime in the Amazon EC2 Auto Scaling User Guide .\nValid Range: Minimum value of 0.\n :return: response = client.update_auto_scaling_group( AutoScalingGroupName='my-auto-scaling-group', LaunchConfigurationName='new-launch-config', ) print(response) :returns: AutoScalingGroupName (string) -- [REQUIRED] The name of the Auto Scaling group. LaunchConfigurationName (string) -- The name of the launch configuration. If you specify LaunchConfigurationName in your update request, you can\'t specify LaunchTemplate or MixedInstancesPolicy . LaunchTemplate (dict) -- The launch template and version to use to specify the updates. If you specify LaunchTemplate in your update request, you can\'t specify LaunchConfigurationName or MixedInstancesPolicy . For more information, see LaunchTemplateSpecification in the Amazon EC2 Auto Scaling API Reference . LaunchTemplateId (string) --The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. LaunchTemplateName (string) --The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. Version (string) --The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API. If the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default . MixedInstancesPolicy (dict) -- An embedded object that specifies a mixed instances policy. In your call to UpdateAutoScalingGroup , you can make changes to the policy that is specified. All optional parameters are left unchanged if not specified. For more information, see MixedInstancesPolicy in the Amazon EC2 Auto Scaling API Reference and Auto Scaling Groups with Multiple Instance Types and Purchase Options in the Amazon EC2 Auto Scaling User Guide . LaunchTemplate (dict) --The launch template and instance types (overrides). This parameter must be specified when creating a mixed instances policy. LaunchTemplateSpecification (dict) --The launch template to use. You must specify either the launch template ID or launch template name in the request. LaunchTemplateId (string) --The ID of the launch template. To get the template ID, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. LaunchTemplateName (string) --The name of the launch template. To get the template name, use the Amazon EC2 DescribeLaunchTemplates API operation. New launch templates can be created using the Amazon EC2 CreateLaunchTemplate API. You must specify either a template ID or a template name. Version (string) --The version number, $Latest , or $Default . To get the version number, use the Amazon EC2 DescribeLaunchTemplateVersions API operation. New launch template versions can be created using the Amazon EC2 CreateLaunchTemplateVersion API. If the value is $Latest , Amazon EC2 Auto Scaling selects the latest version of the launch template when launching instances. If the value is $Default , Amazon EC2 Auto Scaling selects the default version of the launch template when launching instances. The default value is $Default . Overrides (list) --Any parameters that you specify override the same parameters in the launch template. Currently, the only supported override is instance type. You can specify between 1 and 20 instance types. If not provided, Amazon EC2 Auto Scaling will use the instance type specified in the launch template to launch instances. (dict) --Describes an override for a launch template. Currently, the only supported override is instance type. The maximum number of instance type overrides that can be associated with an Auto Scaling group is 20. InstanceType (string) --The instance type. You must use an instance type that is supported in your requested Region and Availability Zones. For information about available instance types, see Available Instance Types in the Amazon Elastic Compute Cloud User Guide. WeightedCapacity (string) --The number of capacity units, which gives the instance type a proportional weight to other instance types. For example, larger instance types are generally weighted more than smaller instance types. These are the same units that you chose to set the desired capacity in terms of instances, or a performance attribute such as vCPUs, memory, or I/O. For more information, see Instance Weighting for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide . Valid Range: Minimum value of 1. Maximum value of 999. InstancesDistribution (dict) --The instances distribution to use. If you leave this parameter unspecified, the value for each parameter in InstancesDistribution uses a default value. OnDemandAllocationStrategy (string) --Indicates how to allocate instance types to fulfill On-Demand capacity. The only valid value is prioritized , which is also the default value. This strategy uses the order of instance type overrides for the LaunchTemplate to define the launch priority of each instance type. The first instance type in the array is prioritized higher than the last. If all your On-Demand capacity cannot be fulfilled using your highest priority instance, then the Auto Scaling groups launches the remaining capacity using the second priority instance type, and so on. OnDemandBaseCapacity (integer) --The minimum amount of the Auto Scaling group\'s capacity that must be fulfilled by On-Demand Instances. This base portion is provisioned first as your group scales. Default if not set is 0. If you leave it set to 0, On-Demand Instances are launched as a percentage of the Auto Scaling group\'s desired capacity, per the OnDemandPercentageAboveBaseCapacity setting. Note An update to this setting means a gradual replacement of instances to maintain the specified number of On-Demand Instances for your base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones. OnDemandPercentageAboveBaseCapacity (integer) --Controls the percentages of On-Demand Instances and Spot Instances for your additional capacity beyond OnDemandBaseCapacity . Default if not set is 100. If you leave it set to 100, the percentages are 100% for On-Demand Instances and 0% for Spot Instances. Note An update to this setting means a gradual replacement of instances to maintain the percentage of On-Demand Instances for your additional capacity above the base capacity. When replacing instances, Amazon EC2 Auto Scaling launches new instances before terminating the old ones. Valid Range: Minimum value of 0. Maximum value of 100. SpotAllocationStrategy (string) --Indicates how to allocate instances across Spot Instance pools. If the allocation strategy is lowest-price , the Auto Scaling group launches instances using the Spot pools with the lowest price, and evenly allocates your instances across the number of Spot pools that you specify. If the allocation strategy is capacity-optimized , the Auto Scaling group launches instances using Spot pools that are optimally chosen based on the available Spot capacity. The default Spot allocation strategy for calls that you make through the API, the AWS CLI, or the AWS SDKs is lowest-price . The default Spot allocation strategy for the AWS Management Console is capacity-optimized . Valid values: lowest-price | capacity-optimized SpotInstancePools (integer) --The number of Spot Instance pools across which to allocate your Spot Instances. The Spot pools are determined from the different instance types in the Overrides array of LaunchTemplate . Default if not set is 2. Used only when the Spot allocation strategy is lowest-price . Valid Range: Minimum value of 1. Maximum value of 20. SpotMaxPrice (string) --The maximum price per unit hour that you are willing to pay for a Spot Instance. If you leave the value of this parameter blank (which is the default), the maximum Spot price is set at the On-Demand price. To remove a value that you previously set, include the parameter but leave the value blank. MinSize (integer) -- The minimum size of the Auto Scaling group. MaxSize (integer) -- The maximum size of the Auto Scaling group. Note With a mixed instances policy that uses instance weighting, Amazon EC2 Auto Scaling may need to go above MaxSize to meet your capacity requirements. In this event, Amazon EC2 Auto Scaling will never go above MaxSize by more than your maximum instance weight (weights that define how many capacity units each instance contributes to the capacity of the group). DesiredCapacity (integer) -- The desired capacity is the initial capacity of the Auto Scaling group after this operation completes and the capacity it attempts to maintain. This number must be greater than or equal to the minimum size of the group and less than or equal to the maximum size of the group. DefaultCooldown (integer) -- The amount of time, in seconds, after a scaling activity completes before another scaling activity can start. The default value is 300 . This cooldown period is not used when a scaling-specific cooldown is specified. Cooldown periods are not supported for target tracking scaling policies, step scaling policies, or scheduled scaling. For more information, see Scaling Cooldowns in the Amazon EC2 Auto Scaling User Guide . AvailabilityZones (list) -- One or more Availability Zones for the group. (string) -- HealthCheckType (string) -- The service to use for the health checks. The valid values are EC2 and ELB . If you configure an Auto Scaling group to use ELB health checks, it considers the instance unhealthy if it fails either the EC2 status checks or the load balancer health checks. HealthCheckGracePeriod (integer) -- The amount of time, in seconds, that Amazon EC2 Auto Scaling waits before checking the health status of an EC2 instance that has come into service. The default value is 0 . For more information, see Health Check Grace Period in the Amazon EC2 Auto Scaling User Guide . Conditional: This parameter is required if you are adding an ELB health check. PlacementGroup (string) -- The name of the placement group into which to launch your instances, if any. A placement group is a logical grouping of instances within a single Availability Zone. You cannot specify multiple Availability Zones and a placement group. For more information, see Placement Groups in the Amazon EC2 User Guide for Linux Instances . VPCZoneIdentifier (string) -- A comma-separated list of subnet IDs for virtual private cloud (VPC). If you specify VPCZoneIdentifier with AvailabilityZones , the subnets that you specify for this parameter must reside in those Availability Zones. TerminationPolicies (list) -- A standalone termination policy or a list of termination policies used to select the instance to terminate. The policies are executed in the order that they are listed. For more information, see Controlling Which Instances Auto Scaling Terminates During Scale In in the Amazon EC2 Auto Scaling User Guide . (string) -- NewInstancesProtectedFromScaleIn (boolean) -- Indicates whether newly launched instances are protected from termination by Amazon EC2 Auto Scaling when scaling in. For more information about preventing instances from terminating on scale in, see Instance Protection in the Amazon EC2 Auto Scaling User Guide . ServiceLinkedRoleARN (string) -- The Amazon Resource Name (ARN) of the service-linked role that the Auto Scaling group uses to call other AWS services on your behalf. For more information, see Service-Linked Roles in the Amazon EC2 Auto Scaling User Guide . MaxInstanceLifetime (integer) -- The maximum amount of time, in seconds, that an instance can be in service. The default is null. This parameter is optional, but if you specify a value for it, you must specify a value of at least 604,800 seconds (7 days). To clear a previously set value, specify a new value of 0. For more information, see Replacing Auto Scaling Instances Based on Maximum Instance Lifetime in the Amazon EC2 Auto Scaling User Guide . Valid Range: Minimum value of 0. """ pass

41.716698

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0.706619

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0.047985

0.037784

0.030967

0.022545

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null

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1

0

1

0

10

a46923ea53f4c0436157a6281c7c0e2f28c4a684

78

py

Python

tdtax/test/test_validate.py

sydneyjenkins/timedomain-taxonomy

9b1dd7e8fa0fa48648a25ede613ce443fa37a88b

[ "MIT" ]

null

tdtax/test/test_validate.py

sydneyjenkins/timedomain-taxonomy

9b1dd7e8fa0fa48648a25ede613ce443fa37a88b

[ "MIT" ]

null

tdtax/test/test_validate.py

sydneyjenkins/timedomain-taxonomy

9b1dd7e8fa0fa48648a25ede613ce443fa37a88b

[ "MIT" ]

null

import tdtax def test(): tdtax.validate(tdtax.taxonomy, tdtax.taxonomy)

13

50

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10

78

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0.45614

0

0.153846

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f1131326ad9a9d570b04646dc34c69e02398e517

8,858

py

Python

service/agent/service/agent/__init__.py

plvhx/qiscus-programming-test

3b6480c41a787ee89770d1375f3a28bbc5b4f453

[ "BSD-2-Clause" ]

null

service/agent/service/agent/__init__.py

plvhx/qiscus-programming-test

3b6480c41a787ee89770d1375f3a28bbc5b4f453

[ "BSD-2-Clause" ]

null

service/agent/service/agent/__init__.py

plvhx/qiscus-programming-test

3b6480c41a787ee89770d1375f3a28bbc5b4f453

[ "BSD-2-Clause" ]

null

from service.abstract import Abstract from service.exception import IncompleteRequiredHeaderException from service.exception import HeaderValueViolationException from service.exception import TypeErrorException class Agent(Abstract): def hand_over(self, data, headers=None): url = "/api/v1/agent/service/assign_agent" if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Content-Type"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Content-Type' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e try: headers["Qiscus-Secret-Key"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-Secret-Key' from headers list." ) from e try: headers["Qiscus-User-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-Header-Id' from headers list." ) from e if headers["Content-Type"] != "application/x-www-form-urlencoded": raise HeaderValueViolationException( "Content-Type value must be 'application/x-www-form-urlencoded'." ) url = self.get_client().get_base_url() + url return self.get_client().post(url, data) def mark_as_resolved(self, data, headers=None): url = "/api/v1/agent/service/mark_as_resolved" if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Content-Type"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Content-Type' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e url = self.get_client().get_base_url() + url return self.get_client().post(url, data) def takeover_status(self, headers=None): url = "/api/v1/app/config/agent_takeover" if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e url = self.get_client().get_base_url() + url return self.get_client().get(url) def available_agents( self, room_id, limit=None, cursor_after=None, cursor_before=None, is_available_in_room=False, headers=None, ): url = "/api/v2/agent/service/available_agents" query = [] if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e if not isinstance(room_id, int): raise TypeErrorException("'room_id' must be an integer.") query.append("room_id=%d" % (room_id)) if isinstance(limit, int): query.append("limit=%d" % (limit)) if isinstance(cursor_after, int): query.append("cursor_after=%d" % (cursor_after)) if isinstance(cursor_before, int): query.append("cursor_before=%d" % (cursor_before)) if isinstance(is_available_in_room, bool): query.append( "is_available_in_room=%s" % ("true" if is_available_in_room == True else "false") ) url = url + "?" + "&".join(query) url = self.get_client().get_base_url() + url return self.get_client().get(url) def get_total_unserved(self, headers=None): url = "/api/v2/agent/service/total_unserved" if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e url = self.get_client().get_base_url() + url return self.get_client().get(url) def takeover_unserved(self, headers=None): url = "/api/v2/agent/service/takeover_unserved" if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e url = self.get_client().get_base_url() + url return self.get_client().post(url) def add_agent(self, data, headers=None): url = "/api/v2/agent/service/add_agent" if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e url = self.get_client().get_base_url() + url return self.get_client().post(url) def get_other_agents( self, cursor_after=None, cursor_before=None, room_id=None, limit=None, headers=None, ): url = "/api/v2/agent/service/other_agents" query = [] if isinstance(headers, dict): self.get_client().set_headers(headers) headers = self.get_client().get_headers() try: headers["Authorization"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Authorization' from headers list." ) from e try: headers["Qiscus-App-Id"] except KeyError as e: raise IncompleteRequiredHeaderException( "Missing header 'Qiscus-App-Id' from headers list." ) from e if isinstance(cursor_after, int): query.append("cursor_after=%d" % (cursor_after)) if isinstance(cursor_before, int): query.append("cursor_before=%d" % (cursor_before)) if isinstance(room_id, int): query.append("room_id=%d" % (room_id)) if isinstance(limit, int): query.append("limit=%d" % (limit)) url = url + "?" + "&".join(query) url = self.get_client().get_base_url() + url return self.get_client().get(url)

30.335616

81

0.579589

922

8,858

5.437093

0.095445

0.044684

0.082984

0.063834

0.860363

0.844205

0.830241

0.806902

0.796529

0.749052

0

0.001333

0.322308

8,858

291

82

30.439863

0.83375

0

0.818182

0

0.196433

0.042335

0

1

0.036364

false

0

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null

0

1

0

1

0

null

0

7

f18b2e4f3cb9cbb2aa3d2516362d0d15bdde6c1b

10,445

py

Python

src/asai_conversion.py

AlagarPrabu/Tamil_asai

ec757ca22dfdb154758f126e3d731628c0f3c1a4

[ "MIT" ]

null

src/asai_conversion.py

AlagarPrabu/Tamil_asai

ec757ca22dfdb154758f126e3d731628c0f3c1a4

[ "MIT" ]

null

src/asai_conversion.py

AlagarPrabu/Tamil_asai

ec757ca22dfdb154758f126e3d731628c0f3c1a4

[ "MIT" ]

null

### # Tamil_Asai # Copyright 2021 The Author Alagar Prabu ### import character rule_1 = ["KKOOO", "KNOOO"] rule_2 = ["KKOO", "KNOO"] rule_3 = ["KKO", "KNO"] rule_4 = ["KK", "KN"] rule_5 = ["KOOO", "NOOO"] rule_6 = ["KOO", "NOO"] rule_7 = ["KO", "NO"] rule_8 = ["K", "N"] formula_formation = "" splitted_formation = "" initial_length = 0 def word_convert(word): converted_word = "" global formula_formation formula_formation ="" get_letters = character.character(word) for get_letter in get_letters: letter_conversion = character.kuril(get_letter) if(letter_conversion != False): converted_word += letter_conversion else: print("Unknown tamil characters found") exit() return formula_verify(converted_word) def formula_verify(actual_text): global formula_formation global splitted_formation if len(actual_text) != 0: length_actual_word = len(actual_text) rule_word = actual_text if(length_actual_word >= 5): word_specific_length = rule_word[initial_length:5] if word_specific_length in rule_1: formula_formation += "NIRAI " cut_length = 5 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:4] if word_specific_length in rule_2: formula_formation += "NIRAI " cut_length = 4 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:4] if word_specific_length in rule_5: formula_formation += "NER " cut_length = 4 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:3] if word_specific_length in rule_3: formula_formation += "NIRAI " cut_length = 3 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:3] if word_specific_length in rule_6: formula_formation += "NER " cut_length = 3 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_4: formula_formation += "NIRAI " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_7: formula_formation += "NER " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:1] if word_specific_length in rule_8: formula_formation += "NER " cut_length = 1 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) elif(length_actual_word == 4): word_specific_length = rule_word[initial_length:4] if word_specific_length in rule_2: formula_formation += "NIRAI " cut_length = 4 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:4] if word_specific_length in rule_5: formula_formation += "NER " cut_length = 4 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:3] if word_specific_length in rule_3: formula_formation += "NIRAI " cut_length = 3 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:3] if word_specific_length in rule_6: formula_formation += "NER " cut_length = 3 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_4: formula_formation += "NIRAI " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_7: formula_formation += "NER " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:1] if word_specific_length in rule_8: formula_formation += "NER " cut_length = 1 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) elif(length_actual_word == 3): word_specific_length = rule_word[initial_length:3] if word_specific_length in rule_3: formula_formation += "NIRAI " cut_length = 3 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:3] if word_specific_length in rule_6: formula_formation += "NER " cut_length = 3 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_4: formula_formation += "NIRAI " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_7: formula_formation += "NER " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:1] if word_specific_length in rule_8: formula_formation += "NER " cut_length = 1 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) elif(length_actual_word == 2): word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_4: formula_formation += "NIRAI " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:2] if word_specific_length in rule_7: formula_formation += "NER " cut_length = 2 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) else: word_specific_length = rule_word[initial_length:1] if word_specific_length in rule_8: formula_formation += "NER " cut_length = 1 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) elif(length_actual_word == 1): word_specific_length = rule_word[initial_length:1] if word_specific_length in rule_8: formula_formation += "NER " cut_length = 1 splitted_formation = actual_text[cut_length:] formula_verify(splitted_formation) return formula_formation else: return False

46.838565

90

0.490282

917

10,445

5.166848

0.077426

0.179401

0.182355

0.111439

0.857113

0.850781

0.845293

0.837062

0

0.016035

0.456678

10,445

222

91

47.04955

0.818855

0.004691

0

0.821782

0

0.018677

0

1

0.009901

false

0

0.004951

0

0.029703

0.004951

0

null

0

1

0

1

0

1

0

null

0

8

2d07693669a430ec211c20d581992c8b269c417b

2,007

py

Python

Projeto-em-DRF/site_adocao/models.py

Projetointegradorunivesp/projeto_integrador_site

9d71b7c28b66e3c98210d4500454d0ef843a33c0

[ "MIT" ]

null

Projeto-em-DRF/site_adocao/models.py

Projetointegradorunivesp/projeto_integrador_site

9d71b7c28b66e3c98210d4500454d0ef843a33c0

[ "MIT" ]

null

Projeto-em-DRF/site_adocao/models.py

Projetointegradorunivesp/projeto_integrador_site

9d71b7c28b66e3c98210d4500454d0ef843a33c0

[ "MIT" ]

null

from django.db import models class Apoio(models.Model): nome = models.CharField(max_length=50) contato = models.CharField(max_length=11, default="") logo = models.ImageField(upload_to='media', blank=True) def __str__(self): return self.nome class Cachorro(models.Model): numero_chip = models.IntegerField(max_length=6) nome = models.CharField(max_length=30) raca = models.CharField(max_length=30) sexo = models.CharField(max_length=1) idade = models.IntegerField(max_length=4) cor = models.CharField(max_length=30) descricao = models.CharField(max_length=100) foto1 = models.ImageField(upload_to='media', blank=True) foto2 = models.ImageField(upload_to='media', blank=True) foto3 = models.ImageField(upload_to='media', blank=True) def __str__(self): return self.nome class Gato(models.Model): numero_chip = models.IntegerField(max_length=6) nome = models.CharField(max_length=30) raca = models.CharField(max_length=30) sexo = models.CharField(max_length=1) idade = models.IntegerField(max_length=4) cor = models.CharField(max_length=30) descricao = models.CharField(max_length=100) foto1 = models.ImageField(upload_to='media', blank=True) foto2 = models.ImageField(upload_to='media', blank=True) foto3 = models.ImageField(upload_to='media', blank=True) def __str__(self): return self.nome class Outro(models.Model): numero_chip = models.IntegerField(max_length=6) nome = models.CharField(max_length=30) raca = models.CharField(max_length=30) sexo = models.CharField(max_length=1) idade = models.IntegerField(max_length=4) cor = models.CharField(max_length=30) descricao = models.CharField(max_length=100) foto1 = models.ImageField(upload_to='media', blank=True) foto2 = models.ImageField(upload_to='media', blank=True) foto3 = models.ImageField(upload_to='media', blank=True) def __str__(self): return self.nome

32.370968

60

0.713503

267

2,007

5.168539

0.172285

0.15

0.221739

0.295652

0.921739

0.901449

0

0.029271

0.165919

2,007

61

32.901639

0.795102

0

0.826087

0

0.024938

0

1

0.086957

false

0

0.021739

0.086957

1

0

null

0

1

0

1

0

null

0

1

0

10

2d314fb6ed78c5e1bd9ad42df1d5b3bd7cc0061c

33,938

py

Python

openprocurement/auction/texas/tests/unit/datasources/test_openprocurement_datasource.py

oleksiyVeretiuk/openprocurement.auction.gong

783ea355c1633e084aaf26a1d6128cc77ae6f642

[ "Apache-2.0" ]

null

openprocurement/auction/texas/tests/unit/datasources/test_openprocurement_datasource.py

oleksiyVeretiuk/openprocurement.auction.gong

783ea355c1633e084aaf26a1d6128cc77ae6f642

[ "Apache-2.0" ]

null

openprocurement/auction/texas/tests/unit/datasources/test_openprocurement_datasource.py

oleksiyVeretiuk/openprocurement.auction.gong

783ea355c1633e084aaf26a1d6128cc77ae6f642

[ "Apache-2.0" ]

null

import unittest import mock from uuid import uuid4 from openprocurement.auction.texas.datasource import OpenProcurementAPIDataSource class TestOpenProcurementAPIDataSource(unittest.TestCase): datasource_class = OpenProcurementAPIDataSource def setUp(self): self.config = { 'resource_api_server': 'https://lb.api-sandbox.ea.openprocurement.org/', 'resource_api_version': '2.4', 'resource_name': 'auction', 'auction_id': '1' * 32, 'resource_api_token': 'api_token', 'AUCTIONS_URL': 'localhost:8090', 'HASH_SECRET': 'secret', } class TestInit(TestOpenProcurementAPIDataSource): def setUp(self): super(TestInit, self).setUp() self.request_session = mock.MagicMock() self.patch_request_session = mock.patch('openprocurement.auction.texas.datasource.RequestsSession') self.mocked_request_session = self.patch_request_session.start() self.mocked_request_session.return_value = self.request_session def tearDown(self): self.patch_request_session.stop() def test_init_with_docservice(self): self.config['with_document_service'] = True ds_service_config = { 'username': 'username', 'password': 'password', 'url': 'http://docservice_url' } self.config['DOCUMENT_SERVICE'] = ds_service_config datasource = self.datasource_class(self.config) self.assertEqual(datasource.api_token, self.config['resource_api_token']) url = '{}api/{}/{}/{}'.format( self.config['resource_api_server'], self.config['resource_api_version'], self.config['resource_name'], self.config['auction_id'] ) self.assertEqual(datasource.api_url, url) self.assertIs(datasource.session, self.request_session) self.assertIs(datasource.session_ds, self.request_session) self.assertEqual(self.mocked_request_session.call_count, 2) def test_init_without_docservice(self): self.config['with_document_service'] = False datasource = self.datasource_class(self.config) self.assertEqual(datasource.api_token, self.config['resource_api_token']) url = '{}api/{}/{}/{}'.format( self.config['resource_api_server'], self.config['resource_api_version'], self.config['resource_name'], self.config['auction_id'] ) self.assertEqual(datasource.api_url, url) self.assertEqual(self.config['AUCTIONS_URL'], datasource.auction_url) self.assertEqual(self.config['HASH_SECRET'], datasource.hash_secret) self.assertIs(datasource.session, self.request_session) self.assertIs(hasattr(datasource, 'session_ds'), False) self.assertEqual(self.mocked_request_session.call_count, 1) class TestUpdateSourceObject(TestOpenProcurementAPIDataSource): def setUp(self): super(TestUpdateSourceObject, self).setUp() self.datasource = self.datasource_class(self.config) self.external_data = {'external': 'data'} self.db_document = {'db': 'document'} self.history_document = {'auction': 'protocol'} self.request_session = mock.MagicMock() self.patch_get_active_bids = mock.patch('openprocurement.auction.texas.datasource.get_active_bids') self.patch_open_bidders_name = mock.patch('openprocurement.auction.texas.datasource.open_bidders_name') self.patch_upload_history = mock.patch.object(self.datasource, 'upload_auction_history_document') self.patch_post_results = mock.patch.object(self.datasource, '_post_results_data') self.mocked_get_active_bids = self.patch_get_active_bids.start() self.mocked_open_bidders_name = self.patch_open_bidders_name.start() self.mocked_upload_history = self.patch_upload_history.start() self.mocked_post_results = self.patch_post_results.start() def tearDown(self): self.patch_get_active_bids.stop() self.patch_open_bidders_name.stop() self.patch_upload_history.stop() self.patch_post_results.stop() def test_update_source_object_with_bad_document_upload(self): self.mocked_upload_history.return_value = None post_response_data = {'response': 'data'} self.mocked_post_results.return_value = post_response_data bids_result_data = {'bids': 'result'} self.mocked_get_active_bids.return_value = bids_result_data new_db_document = {'db_document': 'with opened names'} self.mocked_open_bidders_name.return_value = new_db_document result = self.datasource.update_source_object(self.external_data, self.db_document, self.history_document) self.assertEqual(result, None) self.assertEqual(self.mocked_upload_history.call_count, 1) self.mocked_upload_history.assert_called_with(self.history_document) self.assertEqual(self.mocked_post_results.call_count, 1) self.mocked_post_results.assert_called_with(self.external_data, self.db_document) self.assertEqual(self.mocked_get_active_bids.call_count, 1) self.mocked_get_active_bids.assert_called_with(post_response_data) self.assertEqual(self.mocked_open_bidders_name.call_count, 1) self.mocked_open_bidders_name.assert_called_with(self.db_document, bids_result_data) def test_update_source_object_with_bad_api_post(self): doc_id = '1' * 32 self.mocked_upload_history.return_value = doc_id self.mocked_post_results.return_value = None result = self.datasource.update_source_object(self.external_data, self.db_document, self.history_document) self.assertEqual(result, None) self.assertEqual(self.mocked_upload_history.call_count, 1) self.mocked_upload_history.assert_called_with(self.history_document) self.assertEqual(self.mocked_post_results.call_count, 1) self.mocked_post_results.assert_called_with(self.external_data, self.db_document) self.assertEqual(self.mocked_get_active_bids.call_count, 0) self.assertEqual(self.mocked_open_bidders_name.call_count, 0) def test_update_source_object_with_bad_document_upload_and_api_post(self): self.mocked_upload_history.return_value = None self.mocked_post_results.return_value = None result = self.datasource.update_source_object(self.external_data, self.db_document, self.history_document) self.assertEqual(result, None) self.assertEqual(self.mocked_upload_history.call_count, 1) self.mocked_upload_history.assert_called_with(self.history_document) self.assertEqual(self.mocked_post_results.call_count, 1) self.mocked_post_results.assert_called_with(self.external_data, self.db_document) self.assertEqual(self.mocked_get_active_bids.call_count, 0) self.assertEqual(self.mocked_open_bidders_name.call_count, 0) def test_update_source_object_with_second_bad_document_upload(self): doc_id = '1' * 32 self.mocked_upload_history.side_effect = iter([ doc_id, None ]) post_response_data = {'response': 'data'} self.mocked_post_results.return_value = post_response_data bids_result_data = {'bids': 'result'} self.mocked_get_active_bids.return_value = bids_result_data new_db_document = {'db_document': 'with opened names'} self.mocked_open_bidders_name.return_value = new_db_document result = self.datasource.update_source_object(self.external_data, self.db_document, self.history_document) self.assertEqual(result, new_db_document) self.assertEqual(self.mocked_upload_history.call_count, 2) self.mocked_upload_history.assert_called_with(self.history_document, doc_id) self.assertEqual(self.mocked_post_results.call_count, 1) self.mocked_post_results.assert_called_with(self.external_data, self.db_document) self.assertEqual(self.mocked_get_active_bids.call_count, 1) self.mocked_get_active_bids.assert_called_with(post_response_data) self.assertEqual(self.mocked_open_bidders_name.call_count, 1) self.mocked_open_bidders_name.assert_called_with(self.db_document, bids_result_data) def test_successful_update(self): doc_id = '1' * 32 self.mocked_upload_history.side_effect = iter([ doc_id, doc_id ]) post_response_data = {'response': 'data'} self.mocked_post_results.return_value = post_response_data bids_result_data = {'bids': 'result'} self.mocked_get_active_bids.return_value = bids_result_data new_db_document = {'db_document': 'with opened names'} self.mocked_open_bidders_name.return_value = new_db_document result = self.datasource.update_source_object(self.external_data, self.db_document, self.history_document) self.assertEqual(result, new_db_document) self.assertEqual(self.mocked_upload_history.call_count, 2) self.mocked_upload_history.assert_called_with(self.history_document, doc_id) self.assertEqual(self.mocked_post_results.call_count, 1) self.mocked_post_results.assert_called_with(self.external_data, self.db_document) self.assertEqual(self.mocked_get_active_bids.call_count, 1) self.mocked_get_active_bids.assert_called_with(post_response_data) self.assertEqual(self.mocked_open_bidders_name.call_count, 1) self.mocked_open_bidders_name.assert_called_with(self.db_document, bids_result_data) class TestPostResultData(TestOpenProcurementAPIDataSource): def setUp(self): super(TestPostResultData, self).setUp() self.datasource = self.datasource_class(self.config) self.session = mock.MagicMock() self.datasource.session = self.session self.db_document = {'results': []} self.request_session = mock.MagicMock() self.patch_make_request = mock.patch('openprocurement.auction.texas.datasource.make_request') self.patch_generate_request_id = mock.patch('openprocurement.auction.texas.datasource.generate_request_id') self.patch_get_latest_bid_for_bidder = mock.patch('openprocurement.auction.texas.datasource.get_latest_bid_for_bidder') self.mocked_make_request = self.patch_make_request.start() self.mocked_generate_request_id = self.patch_generate_request_id.start() self.request_id = uuid4().hex self.mocked_generate_request_id.return_value = self.request_id self.mocked_get_latest_bid_for_bidder = self.patch_get_latest_bid_for_bidder.start() def tearDown(self): self.patch_make_request.stop() self.patch_generate_request_id.stop() self.patch_get_latest_bid_for_bidder.stop() def test_post_results_data_with_bids_in_active(self): external_data = {'data': { 'bids': [ { 'status': 'draft', }, { 'value': {'amount': 1000}, 'date': 'bid create date', 'status': 'active', 'id': '2' * 32 }, ] }} last_bid_of_active_bidder = { 'amount': 10000, 'time': 'time of bid', 'id': '2' * 32 } self.mocked_get_latest_bid_for_bidder.return_value = last_bid_of_active_bidder data_with_results = { 'data': { 'bids': [ { 'status': 'draft', }, { 'value': {'amount': last_bid_of_active_bidder['amount']}, 'date': last_bid_of_active_bidder['time'], 'status': 'active', 'id': '2' * 32 } ] } } self.datasource._post_results_data(external_data, self.db_document) self.assertEqual(self.mocked_get_latest_bid_for_bidder.call_count, 1) self.mocked_get_latest_bid_for_bidder.assert_called_with(self.db_document['results'], last_bid_of_active_bidder['id']) self.assertEqual(self.mocked_make_request.call_count, 1) self.mocked_make_request.assert_called_with( self.datasource.api_url + '/auction', data=data_with_results, user=self.datasource.api_token, method='post', request_id=self.request_id, session=self.datasource.session ) def test_post_results_data_with_bid_without_status(self): external_data = {'data': { 'bids': [ { 'status': 'draft', }, { 'value': {'amount': 1000}, 'date': 'bid create date', 'id': '2' * 32 }, ] }} last_bid_of_active_bidder = { 'amount': 10000, 'time': 'time of bid', 'id': '2' * 32 } self.mocked_get_latest_bid_for_bidder.return_value = last_bid_of_active_bidder data_with_results = { 'data': { 'bids': [ { 'status': 'draft', }, { 'value': {'amount': last_bid_of_active_bidder['amount']}, 'date': last_bid_of_active_bidder['time'], 'id': '2' * 32 } ] } } self.datasource._post_results_data(external_data, self.db_document) self.assertEqual(self.mocked_get_latest_bid_for_bidder.call_count, 1) self.mocked_get_latest_bid_for_bidder.assert_called_with( self.db_document['results'], last_bid_of_active_bidder['id'] ) self.assertEqual(self.mocked_make_request.call_count, 1) self.mocked_make_request.assert_called_with( self.datasource.api_url + '/auction', data=data_with_results, user=self.datasource.api_token, method='post', request_id=self.request_id, session=self.datasource.session ) class TestUploadHistoryDocument(TestOpenProcurementAPIDataSource): def setUp(self): super(TestUploadHistoryDocument, self).setUp() self.history_data = {'auction': 'protocol'} self.patch_request_session = mock.patch('openprocurement.auction.texas.datasource.RequestsSession') self.mocked_request_session = self.patch_request_session.start() self.request_session = mock.MagicMock() self.mocked_request_session.return_value = self.request_session self.datasource = self.datasource_class(self.config) self.patch_upload_audit_with_ds = mock.patch.object( self.datasource, '_upload_audit_file_with_document_service' ) self.patch_upload_audit_without_ds = mock.patch.object( self.datasource, '_upload_audit_file_without_document_service' ) self.mocked_upload_audit_with_ds = self.patch_upload_audit_with_ds.start() self.mocked_upload_audit_without_ds = self.patch_upload_audit_without_ds.start() def tearDown(self): self.patch_request_session.stop() self.mocked_request_session.stop() self.patch_upload_audit_with_ds.stop() self.patch_upload_audit_without_ds.stop() def test_upload_history_document_with_ds(self): self.datasource.with_document_service = True self.mocked_upload_audit_with_ds.return_value = None result = self.datasource.upload_auction_history_document(self.history_data) self.assertIsNone(result) self.assertEqual(self.mocked_upload_audit_with_ds.call_count, 1) self.mocked_upload_audit_with_ds.assert_called_with(self.history_data, None) self.assertEqual(self.mocked_upload_audit_without_ds.call_count, 0) # With doc id doc_id = '1' * 32 result = self.datasource.upload_auction_history_document(self.history_data, doc_id) self.assertIsNone(result) self.assertEqual(self.mocked_upload_audit_with_ds.call_count, 2) self.mocked_upload_audit_with_ds.assert_called_with(self.history_data, doc_id) self.assertEqual(self.mocked_upload_audit_without_ds.call_count, 0) def test_upload_history_document_without_ds(self): self.datasource.with_document_service = False self.mocked_upload_audit_without_ds.return_value = None result = self.datasource.upload_auction_history_document(self.history_data) self.assertIsNone(result) self.assertEqual(self.mocked_upload_audit_without_ds.call_count, 1) self.mocked_upload_audit_without_ds.assert_called_with(self.history_data, None) self.assertEqual(self.mocked_upload_audit_with_ds.call_count, 0) # With doc id doc_id = '1' * 32 result = self.datasource.upload_auction_history_document(self.history_data, doc_id) self.assertIsNone(result) self.assertEqual(self.mocked_upload_audit_without_ds.call_count, 2) self.mocked_upload_audit_without_ds.assert_called_with(self.history_data, doc_id) self.assertEqual(self.mocked_upload_audit_with_ds.call_count, 0) def test_successful_upload_with_ds(self): self.datasource.with_document_service = True doc_id = '1' * 32 self.mocked_upload_audit_with_ds.return_value = doc_id result = self.datasource.upload_auction_history_document(self.history_data) self.assertEqual(result, doc_id) self.assertEqual(self.mocked_upload_audit_with_ds.call_count, 1) self.mocked_upload_audit_with_ds.assert_called_with(self.history_data, None) self.assertEqual(self.mocked_upload_audit_without_ds.call_count, 0) def test_successful_upload_without_ds(self): self.datasource.with_document_service = False doc_id = '1' * 32 self.mocked_upload_audit_without_ds.return_value = doc_id result = self.datasource.upload_auction_history_document(self.history_data) self.assertEqual(result, doc_id) self.assertEqual(self.mocked_upload_audit_without_ds.call_count, 1) self.mocked_upload_audit_without_ds.assert_called_with(self.history_data, None) self.assertEqual(self.mocked_upload_audit_with_ds.call_count, 0) class TestUploadFileWithDS(TestOpenProcurementAPIDataSource): def setUp(self): super(TestUploadFileWithDS, self).setUp() self.ds_service_config = { 'username': 'username', 'password': 'password', 'url': 'http://docservice_url' } self.config['DOCUMENT_SERVICE'] = self.ds_service_config self.config['with_document_service'] = True self.datasource = self.datasource_class(self.config) self.history_data = {'auction': 'protocol'} self.session = mock.MagicMock() self.session_ds = mock.MagicMock() self.datasource.session = self.session self.datasource.session_ds = self.session_ds self.patch_make_request = mock.patch('openprocurement.auction.texas.datasource.make_request') self.patch_yaml_dump = mock.patch('openprocurement.auction.texas.datasource.yaml_dump') self.patch_generate_request_id = mock.patch('openprocurement.auction.texas.datasource.generate_request_id') self.mock_make_request = self.patch_make_request.start() self.mock_yaml_dump = self.patch_yaml_dump.start() self.yaml_doc = {'yaml': 'data'} self.mock_yaml_dump.return_value = self.yaml_doc self.mock_generate_request_id = self.patch_generate_request_id.start() self.request_id = uuid4().hex self.mock_generate_request_id.return_value = self.request_id def tearDown(self): self.patch_generate_request_id.stop() self.patch_yaml_dump.stop() self.patch_make_request.stop() def test_upload_with_doc_id(self): success_put_data_response = {'data': {'id': '1' * 32}} ds_response = {'ds': 'response'} self.mock_make_request.side_effect = iter([ ds_response, success_put_data_response ]) doc_id = uuid4().hex result = self.datasource._upload_audit_file_with_document_service(self.history_data, doc_id) self.assertEqual(result, success_put_data_response['data']['id']) self.assertEqual(self.mock_make_request.call_count, 2) ds_request = { 'files': {'file': ('audit_{}.yaml'.format(self.config['auction_id']), self.yaml_doc)}, 'method': 'post', 'user': self.ds_service_config['username'], 'password': self.ds_service_config['password'], 'session': self.session_ds, 'retry_count': 3 } # Really bad practise but only way to make assert_called_with to previous call self.assertEqual( self.mock_make_request.call_args_list[0][0][0], self.ds_service_config['url'] ) self.assertEqual( self.mock_make_request.call_args_list[0][1], ds_request ) self.mock_make_request.assert_called_with( self.datasource.api_url + '/documents/{}'.format(doc_id), data=ds_response, user=self.datasource.api_token, method='put', request_id=self.request_id, session=self.session, retry_count=2 ) def test_upload_without_doc_id(self): success_put_data_response = {'data': {'id': '1' * 32}} ds_response = {'ds': 'response'} self.mock_make_request.side_effect = iter([ ds_response, success_put_data_response ]) result = self.datasource._upload_audit_file_with_document_service(self.history_data) self.assertEqual(result, success_put_data_response['data']['id']) self.assertEqual(self.mock_make_request.call_count, 2) ds_request = { 'files': {'file': ('audit_{}.yaml'.format(self.config['auction_id']), self.yaml_doc)}, 'method': 'post', 'user': self.ds_service_config['username'], 'password': self.ds_service_config['password'], 'session': self.session_ds, 'retry_count': 3 } # Really bad practise but only way to make assert_called_with to previous call self.assertEqual( self.mock_make_request.call_args_list[0][0][0], self.ds_service_config['url'] ) self.assertEqual( self.mock_make_request.call_args_list[0][1], ds_request ) self.mock_make_request.assert_called_with( self.datasource.api_url + '/documents', data=ds_response, user=self.datasource.api_token, method='post', request_id=self.request_id, session=self.session, retry_count=2 ) def test_upload_with_bad_api_request(self): ds_response = {'ds': 'response'} self.mock_make_request.side_effect = iter([ ds_response, None ]) result = self.datasource._upload_audit_file_with_document_service(self.history_data) self.assertEqual(result, None) self.assertEqual(self.mock_make_request.call_count, 2) ds_request = { 'files': {'file': ('audit_{}.yaml'.format(self.config['auction_id']), self.yaml_doc)}, 'method': 'post', 'user': self.ds_service_config['username'], 'password': self.ds_service_config['password'], 'session': self.session_ds, 'retry_count': 3 } # Really bad practise but only way to make assert_called_with to previous call self.assertEqual( self.mock_make_request.call_args_list[0][0][0], self.ds_service_config['url'] ) self.assertEqual( self.mock_make_request.call_args_list[0][1], ds_request ) self.mock_make_request.assert_called_with( self.datasource.api_url + '/documents', data=ds_response, user=self.datasource.api_token, method='post', request_id=self.request_id, session=self.session, retry_count=2 ) class TestUploadFileWithoutDS(TestOpenProcurementAPIDataSource): def setUp(self): super(TestUploadFileWithoutDS, self).setUp() self.datasource = self.datasource_class(self.config) self.history_data = {'auction': 'protocol'} self.session = mock.MagicMock() self.datasource.session = self.session self.patch_make_request = mock.patch('openprocurement.auction.texas.datasource.make_request') self.patch_yaml_dump = mock.patch('openprocurement.auction.texas.datasource.yaml_dump') self.patch_generate_request_id = mock.patch('openprocurement.auction.texas.datasource.generate_request_id') self.mock_make_request = self.patch_make_request.start() self.mock_yaml_dump = self.patch_yaml_dump.start() self.yaml_doc = {'yaml': 'data'} self.mock_yaml_dump.return_value = self.yaml_doc self.mock_generate_request_id = self.patch_generate_request_id.start() self.request_id = uuid4().hex self.mock_generate_request_id.return_value = self.request_id def tearDown(self): self.patch_generate_request_id.stop() self.patch_yaml_dump.stop() self.patch_make_request.stop() def test_upload_with_doc_id(self): success_put_data_response = {'data': {'id': '1' * 32}} self.mock_make_request.side_effect = iter([ success_put_data_response ]) doc_id = uuid4().hex result = self.datasource._upload_audit_file_without_document_service(self.history_data, doc_id) self.assertEqual(result, success_put_data_response['data']['id']) self.assertEqual(self.mock_make_request.call_count, 1) files = {'file': ('audit_{}.yaml'.format(self.config['auction_id']), self.yaml_doc)} self.mock_make_request.assert_called_with( self.datasource.api_url + '/documents/{}'.format(doc_id), files=files, user=self.datasource.api_token, method='put', request_id=self.request_id, session=self.session, retry_count=2 ) def test_upload_without_doc_id(self): success_put_data_response = {'data': {'id': '1' * 32}} self.mock_make_request.side_effect = iter([ success_put_data_response ]) result = self.datasource._upload_audit_file_without_document_service(self.history_data) self.assertEqual(result, success_put_data_response['data']['id']) self.assertEqual(self.mock_make_request.call_count, 1) files = {'file': ('audit_{}.yaml'.format(self.config['auction_id']), self.yaml_doc)} self.mock_make_request.assert_called_with( self.datasource.api_url + '/documents', files=files, user=self.datasource.api_token, method='post', request_id=self.request_id, session=self.session, retry_count=2 ) def test_upload_with_bad_api_request(self): self.mock_make_request.side_effect = iter([ None ]) result = self.datasource._upload_audit_file_without_document_service(self.history_data) self.assertEqual(result, None) self.assertEqual(self.mock_make_request.call_count, 1) files = {'file': ('audit_{}.yaml'.format(self.config['auction_id']), self.yaml_doc)} self.mock_make_request.assert_called_with( self.datasource.api_url + '/documents', files=files, user=self.datasource.api_token, method='post', request_id=self.request_id, session=self.session, retry_count=2 ) class TestSetParticipationUrls(TestOpenProcurementAPIDataSource): def setUp(self): super(TestSetParticipationUrls, self).setUp() self.datasource = self.datasource_class(self.config) self.history_data = {'auction': 'protocol'} self.session = mock.MagicMock() self.datasource.session = self.session self.patch_make_request = mock.patch('openprocurement.auction.texas.datasource.make_request') self.patch_generate_request_id = mock.patch('openprocurement.auction.texas.datasource.generate_request_id') self.patch_calculate_hash = mock.patch('openprocurement.auction.texas.datasource.calculate_hash') self.mock_make_request = self.patch_make_request.start() self.mock_calculate_hash = self.patch_calculate_hash.start() self.hash = 'hash' self.mock_calculate_hash.return_value = self.hash self.mock_generate_request_id = self.patch_generate_request_id.start() self.request_id = uuid4().hex self.mock_generate_request_id.return_value = self.request_id def tearDown(self): self.patch_generate_request_id.stop() self.patch_make_request.stop() def test_bid_in_active_status(self): processed_bid = { 'id': '1' * 32, 'status': 'active' } external_data = { 'data': { 'bids': [processed_bid] } } participation_url = self.datasource.auction_url + '/login?bidder_id={}&hash={}'.format( '1' * 32, self.hash ) expected_patch = { 'data': { 'auctionUrl': self.datasource.auction_url, 'bids': [ { 'id': processed_bid['id'], 'participationUrl': participation_url } ] } } self.datasource.set_participation_urls(external_data) self.assertEqual(self.mock_generate_request_id.call_count, 1) self.assertEqual(self.mock_calculate_hash.call_count, 1) self.mock_calculate_hash.assert_called_with(processed_bid['id'], self.datasource.hash_secret) self.assertEqual(self.mock_make_request.call_count, 1) self.mock_make_request.assert_called_with( self.datasource.api_url + '/auction', expected_patch, user=self.datasource.api_token, request_id=self.request_id, session=self.session ) def test_bid_withous_status(self): processed_bid = { 'id': '1' * 32, } external_data = { 'data': { 'bids': [processed_bid] } } participation_url = self.datasource.auction_url + '/login?bidder_id={}&hash={}'.format( '1' * 32, self.hash ) expected_patch = { 'data': { 'auctionUrl': self.datasource.auction_url, 'bids': [ { 'id': processed_bid['id'], 'participationUrl': participation_url } ] } } self.datasource.set_participation_urls(external_data) self.assertEqual(self.mock_generate_request_id.call_count, 1) self.assertEqual(self.mock_calculate_hash.call_count, 1) self.mock_calculate_hash.assert_called_with(processed_bid['id'], self.datasource.hash_secret) self.assertEqual(self.mock_make_request.call_count, 1) self.mock_make_request.assert_called_with( self.datasource.api_url + '/auction', expected_patch, user=self.datasource.api_token, request_id=self.request_id, session=self.session ) def test_bid_in_not_active_status(self): processed_bid = { 'id': '1' * 32, 'status': 'not_active' } external_data = { 'data': { 'bids': [processed_bid] } } expected_patch = { 'data': { 'auctionUrl': self.datasource.auction_url, 'bids': [ { 'id': processed_bid['id'], } ] } } self.datasource.set_participation_urls(external_data) self.assertEqual(self.mock_generate_request_id.call_count, 1) self.assertEqual(self.mock_calculate_hash.call_count, 0) self.assertEqual(self.mock_make_request.call_count, 1) self.mock_make_request.assert_called_with( self.datasource.api_url + '/auction', expected_patch, user=self.datasource.api_token, request_id=self.request_id, session=self.session ) def suite(): suite = unittest.TestSuite() suite.addTest(unittest.makeSuite(TestInit)) suite.addTest(unittest.makeSuite(TestUpdateSourceObject)) suite.addTest(unittest.makeSuite(TestPostResultData)) suite.addTest(unittest.makeSuite(TestUploadHistoryDocument)) suite.addTest(unittest.makeSuite(TestUploadFileWithDS)) suite.addTest(unittest.makeSuite(TestUploadFileWithoutDS)) suite.addTest(unittest.makeSuite(TestSetParticipationUrls)) return suite

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7

74a4a622b98c53feacd4fd3923bb7e01a4321604

74

py

Python

arm64_tester/__init__.py

luist18/mast-tool

dfa891a95407c6cb4ea58d41237cfa0b974887f7

[ "MIT" ]

1

2021-06-09T03:33:03.000Z

arm64_tester/__init__.py

luist18/mast-tool

dfa891a95407c6cb4ea58d41237cfa0b974887f7

[ "MIT" ]

null

arm64_tester/__init__.py

luist18/mast-tool

dfa891a95407c6cb4ea58d41237cfa0b974887f7

[ "MIT" ]

null

from arm64_tester.test import Test from arm64_tester.tester import Tester

24.666667

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7ae438a16f81fe6c2cebdf304b4264f17d5749e7

30,038

py

Python

ParallelSolvers.py

neu-spiral/GraphMatching

84729b9d793bf8d7aca99dcc1751b99222d8cdc9

[ "MIT" ]

null

ParallelSolvers.py

neu-spiral/GraphMatching

84729b9d793bf8d7aca99dcc1751b99222d8cdc9

[ "MIT" ]

null

ParallelSolvers.py

neu-spiral/GraphMatching

84729b9d793bf8d7aca99dcc1751b99222d8cdc9

[ "MIT" ]

null

#from cvxopt import spmatrix,matrix #from cvxopt.solvers import qp,lp from helpers import identityHash,swap,mergedicts,identityHash import numpy as np from numpy.linalg import solve as linearSystemSolver,inv import logging from debug import logger,Sij_test from numpy.linalg import matrix_rank from pprint import pformat from time import time import argparse from pyspark import SparkContext from operator import add,and_ from proxOp import pnormOp,pnorm_proxop, L1normOp, EuclidiannormOp from LocalSolvers import LocalL1Solver, LocalL2Solver class ParallelSolver(): """ A class for a parallel solver object. This object stores an RDD containing "local" data per partition, captured via a local solver object. The RDD also stores primal and dual variables associated with the arguments of this local solver function, as well as statistics reported by the last computation of the local solver. The class can be used as an interface to add "homogeneous" objectives in the consensus admm algorithm, that can be executed in parallel """ def __init__(self,LocalSolverClass,data,initvalue,N,rho,silent=False,lean=False, RDD=None, D=None, lambda_linear=1.0, prePartFunc=None): """Class constructor. It takes as an argument a local solver class, data (of a form understandable by the local solver class), an initial value for the primal variables, and a boolean value; the latter can be used to suppress the evaluation of the objective. """ self.SolverClass=LocalSolverClass if RDD==None: if D==None: if LocalSolverClass==LocalL1Solver or LocalSolverClass==LocalL2Solver: self.PrimalDualRDD = LocalSolverClass.initializeLocalVariables(Sij=data,initvalue=initvalue,N=N,rho=rho, prePartFunc=prePartFunc).cache() #LocalSolver class should implement class method initializeLocalVariables else: self.PrimalDualRDD = LocalSolverClass.initializeLocalVariables(data,initvalue,N,rho,D,lambda_linear).cache() else: self.PrimalDualRDD = LocalSolverClass.initializeLocalVariables(data,initvalue,N,rho,D,lambda_linear).cache() else: self.PrimalDualRDD = RDD self.N = N self.silent=silent self.lean=lean self.varsToPartitions = self.PrimalDualRDD.flatMapValues( lambda (solver,P,Phi,stats) : P.keys()).map(swap).partitionBy(self.N).cache() def joinAndAdapt(self,ZRDD, alpha, rho,checkpoint = False, forceComp=False): """ Given a ZRDD, adapt the local primal and dual variables. The former are updated via the proximal operator, the latter via gradient ascent. """ toUnpersist = self.PrimalDualRDD #Old RDD is to be uncached def adaptDual(solver, P, Phi, stats, Z, alpha): """Update the dual variables.""" return ( solver, P, dict( [ (key,Phi[key]+alpha*(P[key]-Z[key])) for key in Phi ] ), Z) #Send z to the appropriate partitions ZtoPartitions = ZRDD.join(self.varsToPartitions,numPartitions=self.N).map(lambda (key,(z,splitIndex)): (splitIndex, (key,z))).partitionBy(self.N,partitionFunc=identityHash).groupByKey().mapValues(list).mapValues(dict) PrimalDualOldZ=self.PrimalDualRDD.join(ZtoPartitions,numPartitions=self.N) if not self.silent or forceComp: oldPrimalResidual = np.sqrt(PrimalDualOldZ.values().map(lambda ((solver,P,Phi,stats),Z): sum( ( (P[key]-Z[key])**2 for key in Z) ) ).reduce(add)) oldObjValue = PrimalDualOldZ.values().map(lambda ((solver,P,Phi,stats),Z): solver.evaluate(Z)).reduce(add) #local solver should implement evaluate PrimalNewDualOldZ = PrimalDualOldZ.mapValues(lambda ((solver,P,Phi,stats),Z): adaptDual(solver, P, Phi, stats, Z, alpha)) ZbarAndNewDual = PrimalNewDualOldZ.mapValues(lambda (solver,P,Phi,Z): ( solver, dict( [(key, Z[key]-Phi[key]) for key in Z]), Phi )) self.PrimalDualRDD = ZbarAndNewDual.mapValues( lambda (solver,Zbar,Phi) : (solver,solver.solve(Zbar, rho),Phi)).mapValues(lambda (solver,(P,stats),Phi): (solver,P,Phi,stats)).cache() #Solver should implement solve #Maybe partitioning is not needed? if checkpoint: self.PrimalDualRDD.localCheckpoint() ##Unpersisit commented for now because running time increases. #toUnpersist.unpersist() if not self.silent or forceComp: return (oldPrimalResidual,oldObjValue) else: return None def logstats(self): """ Return statistics from PrimalDualRDD. In particular, this returns the average, min, and maximum value of each statistic. """ rdd = self.PrimalDualRDD statsonly =rdd.map(lambda (partitionid, (solver,P,Phi,stats)): stats).cache() #Checkpoint the RDD # if iteration!=0 and iteration % checkointing_freq == 0: # statsonly.checkpoint() stats = statsonly.reduce(lambda x,y: mergedicts(x,y)) minstats = statsonly.reduce(lambda x,y: mergedicts(x,y,min)) maxstats = statsonly.reduce(lambda x,y: mergedicts(x,y,max)) return " ".join([ key+"= %s (%s/%s)" % (str(1.0*stats[key]/self.N),str(minstats[key]),str(maxstats[key])) for key in stats]) def getVars(self, rho): """Return the primal variables associated with this RDD. To be used to compute the new consensus variable""" return self.PrimalDualRDD.flatMap(lambda (partitionId,(solver,P,Phi,stats)): [ (key, ( rho*( P[key]+Phi[key]), rho)) for key in P ] ) def computeDualResidual(self, ZRDDjoinedOldZRDD): '''Return the squared norm of the dual residual, which is computed as: S = A^TB(Z^(k+1)-Z^(k)) ''' ZRDDjoinedOldZRDD = ZRDDjoinedOldZRDD.mapValues(lambda (z, zOld): (z-zOld)**2) return np.sqrt( self.varsToPartitions.join(ZRDDjoinedOldZRDD).mapValues(lambda (splitID, deltaZ): deltaZ).values().reduce(add) ) class ParallelSolverPnorm(ParallelSolver): """This class is inheritted from ParallelSolver, it updates P and Y vriables for a general p-norm solver via inner ADMM.""" def __init__(self,LocalSolverClass,data,initvalue,N,rho,rho_inner, p, silent=False,lean=False, RDD=None, debug=False, prePartFunc=None): """Class constructor. It takes as an argument a local solver class, data (of a form understandable by the local solver class), an initial value for the primal variables, and a boolean value; the latter can be used to suppress the evaluation of the objective. """ self.SolverClass=LocalSolverClass if RDD==None: self.PrimalDualRDD = LocalSolverClass.initializeLocalVariables(Sij=data,initvalue=initvalue,N=N,rho=rho, rho_inner=rho_inner, prePartFunc=prePartFunc).cache() #LocalSolver class should implement class method initializeLocalVariables else: self.PrimalDualRDD = RDD self.N = N self.silent=silent self.lean=lean self.debug = debug #In debug mode keep track of the obj. val. and residuals self.rho_inner = rho_inner self.p = p self.varsToPartitions = self.PrimalDualRDD.flatMapValues( lambda (solver,P,Y,Phi,Upsilon, stats) : P.keys()).map(swap).partitionBy(self.N).cache() def joinAndAdapt(self,ZRDD, alpha, rho, alpha_inner=1.0, maxiters = 100, residual_tol = 1.e-06, checkpoint = False, logger=None, forceComp=False): rho_inner = self.rho_inner p_param = self.p #In debug mode keep track of the obj. val. and residuals if self.debug: trace = {} #Send z to the appropriate partitions def Fm(objs,P): """ Compute the FPm functions, i.e., FPm = \sum_{ij\in S1} P[(i,j)]-\sum_{ij \in S2} P[(i,j)] """ FPm = {} for edge in objs: (set1, set2) = objs[edge] tmp_val = 0.0 for key in set1: tmp_val += P[key] for key in set2: tmp_val -= P[key] FPm[edge] = tmp_val return FPm ZtoPartitions = ZRDD.join(self.varsToPartitions,numPartitions=self.N).map(lambda (key,(z,splitIndex)): (splitIndex, (key,z))).partitionBy(self.N,partitionFunc=identityHash).groupByKey().mapValues(list).mapValues(dict) PrimalDualOldZ=self.PrimalDualRDD.join(ZtoPartitions,numPartitions=self.N) if not self.silent or forceComp: oldPrimalResidual = np.sqrt(PrimalDualOldZ.values().map(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): sum( ( (P[key]-Z[key])**2 for key in Z) ) ).reduce(add)) oldObjValue = (PrimalDualOldZ.values().map(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): solver.evaluate(Z, p_param)).reduce(add))**(1./p_param) #local solver should compute p-norm to the power p. PrimalNewDualOldZ = PrimalDualOldZ.mapValues(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): ( solver, P, Y,dict( [ (key,Phi[key]+alpha*(P[key]-Z[key])) for key in Phi ] ),Upsilon, stats, Z)) ZbarPrimalDual = PrimalNewDualOldZ.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats, Z): ( solver,P,Y,Phi,Upsilon,stats,dict( [(key, Z[key]-Phi[key]) for key in Z]))) last = time() start_time = time() #Start the inner ADMM iterations for i in range(maxiters): #Compute vectors Fm(Pm) FmZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats,Zbar):(solver, Fm(solver.objectives,P),P,Y,Phi,Upsilon,stats,Zbar)) if not self.lean or (self.lean and i==maxiters-1): #Compute the residual OldinnerResidual = np.sqrt(FmZbarPrimalDual.values().flatMap(lambda (solver, FPm,OldP,Y,Phi,Upsilon,stats,Zbar): [(Y[key]-FPm[key])**2 for key in Y]).reduce(add) ) ##ADMM steps #Adapt the dual varible Upsilon FmYNewUpsilonPPhi = FmZbarPrimalDual.mapValues(lambda (solver, FPm,OldP, Y,Phi,Upsilon,stats,Zbar): (solver, FPm, OldP, Y, Phi, dict( [(key,Upsilon[key]+alpha_inner*(Y[key]-FPm[key])) for key in Y]),stats,Zbar)) #Update Y via prox. op. for p-norm NewYUpsilonPhi, Ynorm = pnormOp(FmYNewUpsilonPPhi.mapValues(lambda (solver, FPm, OldP, Y, Phi, Upsilon, stats, Zbar):(dict([(key,FPm[key]-Upsilon[key]) for key in Upsilon]), (solver, OldP, Y, Phi, Upsilon,stats,Zbar) ) ), p_param, rho_inner, 1.e-6, self.lean and i<maxiters-1 ) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (Y, (solver, OldP, OldY, Phi, Upsilon, stats, Zbar)): (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar) ) if not self.lean or (self.lean and i==maxiters-1): #Compute the dual residual for Y DualInnerResidual_Y = np.sqrt( NewYUpsilonPhi.values().flatMap(lambda (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar): [ (Y[key] -OldY[key])**2 for key in Y]).reduce(add) ) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar):(solver, OldP, Y, Phi, Upsilon,stats, Zbar) ) #Update P via solving a least-square problem ZbarPrimalDual = NewYUpsilonPhi.mapValues(lambda (solver, OldP, Y, Phi,Upsilon,stats,Zbar): (solver,solver.solve(Y, Zbar, Upsilon, rho, rho_inner),OldP, Y, Phi, Upsilon, stats, Zbar)).mapValues(lambda (solver, (P, stats),OldP, Y, Phi, Upsilon, stats_old, Zbar): (solver,P,OldP,Y,Phi,Upsilon, stats, Zbar)) if not self.lean or (self.lean and i==maxiters-1): #Compute the dual residual for P DualInnerResidual_P = np.sqrt( ZbarPrimalDual.values().flatMap(lambda (solver,P,OldP,Y,Phi,Upsilon, stats, Zbar): [ (P[key] -OldP[key])**2 for key in P]).reduce(add) ) #Total dual residual DualInnerResidual = DualInnerResidual_P + DualInnerResidual_Y ZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,OldP,Y,Phi,Upsilon, stats, Zbar): (solver,P,Y,Phi,Upsilon, stats, Zbar)) if not self.lean or (self.lean and i==maxiters-1): objval = ZbarPrimalDual.values().flatMap(lambda (solver,P,Y,Phi,Upsilon, stats, Zbar):[(P[key]-Zbar[key])**2 for key in P]).reduce(lambda x,y:x+y) + Ynorm now = time() if logger != None and ( not self.lean or (self.lean and i==maxiters-1) ): logger.info("Inner ADMM iteration %d, p-norm is %f, objective is %f, residual is %f, dual residual is %f, time is %f" %(i, Ynorm, objval, OldinnerResidual, DualInnerResidual, now-last)) if (not self.lean or (self.lean and i==maxiters-1)) and self.debug: trace[i] = {} trace[i]['OBJ'] = objval trace[i]['PRES'] = OldinnerResidual trace[i]['DRES'] = DualInnerResidual trace[i]['IT_TIME'] = now-last trace[i]['TIME'] = now-start_time last = time() if not self.lean and DualInnerResidual<residual_tol and OldinnerResidual<residual_tol: break self.PrimalDualRDD = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats, Zbar): (solver,P,Y,Phi,Upsilon,stats)).cache() #Checkpointing if checkpoint: self.PrimalDualRDD.localCheckpoint() if self.debug: return trace if not self.silent or forceComp: return (oldPrimalResidual,oldObjValue) else: return None def logstats(self): """ Return statistics from PrimalDualRDD. In particular, this returns the average, min, and maximum value of each statistic. """ rdd = self.PrimalDualRDD statsonly =rdd.map(lambda (partitionid, (solver,P,Y,Phi,Upsilon,stats)): stats).cache() #Checkpoint the RDD # if iteration!=0 and iteration % checkointing_freq == 0: # statsonly.checkpoint() stats = statsonly.reduce(lambda x,y: mergedicts(x,y)) minstats = statsonly.reduce(lambda x,y: mergedicts(x,y,min)) maxstats = statsonly.reduce(lambda x,y: mergedicts(x,y,max)) return " ".join([ key+"= %s (%s/%s)" % (str(1.0*stats[key]/self.N),str(minstats[key]),str(maxstats[key])) for key in stats]) def getVars(self, rho): return self.PrimalDualRDD.flatMap(lambda (partitionId,(solver,P,Y,Phi,Upsilon,stats)): [ (key, ( rho*( P[key]+Phi[key]), rho)) for key in P ] ) class ParallelSolver1norm(ParallelSolverPnorm): def joinAndAdapt(self,ZRDD, alpha, rho,alpha_inner=1.0, maxiters = 100, residual_tol = 1.e-06, checkpoint = False, logger = None, forceComp=False): rho_inner = self.rho_inner p_param = 1 if self.debug: trace = {} #Send z to the appropriate partitions def Fm(objs,P): """ Compute the FPm functions, i.e., FPm = \sum_{ij\in S1} P[(i,j)]-\sum_{ij \in S2} P[(i,j)] """ FPm = {} for edge in objs: (set1, set2) = objs[edge] tmp_val = 0.0 for key in set1: tmp_val += P[key] for key in set2: tmp_val -= P[key] FPm[edge] = tmp_val return FPm ZtoPartitions = ZRDD.join(self.varsToPartitions,numPartitions=self.N).map(lambda (key,(z,splitIndex)): (splitIndex, (key,z))).partitionBy(self.N,partitionFunc=identityHash).groupByKey().mapValues(list).mapValues(dict) PrimalDualOldZ=self.PrimalDualRDD.join(ZtoPartitions,numPartitions=self.N) if not self.silent or forceComp: oldPrimalResidual = np.sqrt(PrimalDualOldZ.values().map(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): sum( ( (P[key]-Z[key])**2 for key in Z) ) ).reduce(add)) oldObjValue = (PrimalDualOldZ.values().map(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): solver.evaluate(Z, p_param)).reduce(add))**(1./p_param) #local solver should compute p-norm to the power p. PrimalNewDualOldZ = PrimalDualOldZ.mapValues(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): ( solver, P, Y,dict( [ (key,Phi[key]+alpha*(P[key]-Z[key])) for key in Phi ] ),Upsilon, stats, Z)) ZbarPrimalDual = PrimalNewDualOldZ.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats, Z): ( solver,P,Y,Phi,Upsilon,stats,dict( [(key, Z[key]-Phi[key]) for key in Z]))) #Initialization for Inner ADMM #initialize Upsilon to 0 # ZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats,Zbar):(solver, P, Y, Phi, dict([(key,0.0) for key in Upsilon]), stats, Zbar)) #initialize P by solving # ZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats,Zbar):(solver, solver.solve(Y, Zbar, Upsilon, rho, rho_inner), Y, Phi,Upsilon,stats,Zbar)).mapValues(lambda (solver, (P, stats0), Y, Phi,Upsilon,stats,Zbar): (solver,P,Y,Phi,Upsilon,stats,Zbar)) last = time() start_time = last #Start the inner ADMM iterations for i in range(maxiters): #Compute vectors Fm(Pm) FmZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats,Zbar):(solver, Fm(solver.objectives,P),P,Y,Phi,Upsilon,stats,Zbar)) if not self.lean or (self.lean and i==maxiters-1): #Compute the residual OldinnerResidual = np.sqrt(FmZbarPrimalDual.values().flatMap(lambda (solver, FPm,OldP,Y,Phi,Upsilon,stats,Zbar): [(Y[key]-FPm[key])**2 for key in Y]).reduce(add) ) ##ADMM steps #Adapt the dual varible Upsilon FmYNewUpsilonPPhi = FmZbarPrimalDual.mapValues(lambda (solver, FPm, OldP,Y,Phi,Upsilon,stats,Zbar): (solver, FPm,OldP, Y, Phi, dict( [(key,Upsilon[key]+alpha_inner*(Y[key]-FPm[key])) for key in Y]),stats,Zbar)) #Update Y via prox. op. for ell_1 norm NewYUpsilonPhi, Ynorm = L1normOp(FmYNewUpsilonPPhi.mapValues(lambda (solver, FPm,OldP, Y, Phi, Upsilon, stats, Zbar):(dict([(key,FPm[key]-Upsilon[key]) for key in Upsilon]), (solver, OldP,Y, Phi, Upsilon,stats,Zbar) ) ), rho_inner , self.lean and i<maxiters-1) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (Y, (solver, OldP, OldY, Phi, Upsilon, stats, Zbar)): (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar) ) if not self.lean or (self.lean and i==maxiters-1): #Compute the dual residual for Y DualInnerResidual_Y = np.sqrt( NewYUpsilonPhi.values().flatMap(lambda (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar): [ (Y[key] -OldY[key])**2 for key in Y]).reduce(add) ) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar):(solver, OldP, Y, Phi, Upsilon,stats, Zbar) ) #Update P via solving a least-square problem ZbarPrimalDual = NewYUpsilonPhi.mapValues(lambda (solver, OldP, Y, Phi,Upsilon,stats,Zbar): (solver,solver.solve(Y, Zbar, Upsilon, rho, rho_inner), OldP, Y, Phi, Upsilon, stats, Zbar)).mapValues(lambda (solver, (P, stats), OldP, Y, Phi, Upsilon, stats_old, Zbar): (solver,OldP,P,Y,Phi,Upsilon, stats, Zbar)) if not self.lean or (self.lean and i==maxiters-1): #Compute the dual residual for P DualInnerResidual_P = np.sqrt( ZbarPrimalDual.values().flatMap(lambda (solver,OldP,P,Y,Phi,Upsilon, stats, Zbar): [ (P[key] -OldP[key])**2 for key in P]).reduce(add) ) #Total dual residual DualInnerResidual = DualInnerResidual_P + DualInnerResidual_Y ZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,OldP,P,Y,Phi,Upsilon, stats, Zbar): (solver,P,Y,Phi,Upsilon, stats, Zbar)) if not self.lean or (self.lean and i==maxiters-1): objval = ZbarPrimalDual.values().flatMap(lambda (solver,P,Y,Phi,Upsilon, stats, Zbar):[(P[key]-Zbar[key])**2 for key in P]).reduce(lambda x,y:x+y) + Ynorm now = time() if logger != None and (not self.lean or (self.lean and i==maxiters-1)): logger.info("Inner ADMM iteration %d, p-norm is %f, objective is %f, residual is %f, dual residual is %f, iteration time is %f" %(i, Ynorm, objval, OldinnerResidual, DualInnerResidual, now-last)) if (not self.lean or (self.lean and i==maxiters-1)) and self.debug: trace[i] = {} trace[i]['OBJ'] = objval trace[i]['PRES'] = OldinnerResidual trace[i]['DRES'] = DualInnerResidual trace[i]['IT_TIME'] = now-last trace[i]['TIME'] = now-start_time last = time() if not self.lean and DualInnerResidual<residual_tol and OldinnerResidual<residual_tol: break self.PrimalDualRDD = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats, Zbar): (solver,P,Y,Phi,Upsilon,stats)).cache() #Checkpointing if checkpoint: self.PrimalDualRDD.localCheckpoint() if self.debug: return trace if not self.silent or forceComp: return (oldPrimalResidual,oldObjValue) else: return None class ParallelSolver2norm(ParallelSolverPnorm): def joinAndAdapt(self,ZRDD, alpha, rho, alpha_inner=1.0, maxiters = 100, residual_tol = 1.e-06, accelerated=False, checkpoint = False, logger = None, forceComp=False): rho_inner = self.rho_inner p_param = 2 if self.debug: trace = {} #Send z to the appropriate partitions def Fm(objs,P): """ Compute the FPm functions, i.e., FPm = \sum_{ij\in S1} P[(i,j)]-\sum_{ij \in S2} P[(i,j)] """ FPm = {} for edge in objs: (set1, set2) = objs[edge] tmp_val = 0.0 for key in set1: tmp_val += P[key] for key in set2: tmp_val -= P[key] FPm[edge] = tmp_val return FPm ZtoPartitions = ZRDD.join(self.varsToPartitions,numPartitions=self.N).map(lambda (key,(z,splitIndex)): (splitIndex, (key,z))).partitionBy(self.N,partitionFunc=identityHash).groupByKey().mapValues(list).mapValues(dict) PrimalDualOldZ=self.PrimalDualRDD.join(ZtoPartitions,numPartitions=self.N) if not self.silent or forceComp: oldPrimalResidual = np.sqrt(PrimalDualOldZ.values().map(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): sum( ( (P[key]-Z[key])**2 for key in Z) ) ).reduce(add)) oldObjValue = (PrimalDualOldZ.values().map(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): solver.evaluate(Z, p_param)).reduce(add))**(1./p_param) #local solver should compute p-norm to the power p. PrimalNewDualOldZ = PrimalDualOldZ.mapValues(lambda ((solver,P,Y,Phi,Upsilon,stats),Z): ( solver, P, Y,dict( [ (key,Phi[key]+alpha*(P[key]-Z[key])) for key in Phi ] ),Upsilon, stats, Z)) ZbarPrimalDual = PrimalNewDualOldZ.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats, Z): ( solver,P,Y,Phi,Upsilon,stats,dict( [(key, Z[key]-Phi[key]) for key in Z]))) last = time() start_time = last #Start the inner ADMM iterations if accelerated: #For accelerated ADMM, keep track of old dual variables Upsilon as well, plus add Upsilon hat. (see Alg. 2 in Accelerated Alternating Direction Method of Multipliers by Kadkhodaie et al.) ZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats,Zbar):(solver,P,Y,Phi,Upsilon,Upsilon,Upsilon,stats,Zbar)) ak = 1. for i in range(maxiters): #Compute vectors Fm(Pm) if accelerated: FmZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,OldUpsilon,HatUpsilon, stats,Zbar):(solver, Fm(solver.objectives,P),P,Y,Phi,Upsilon, OldUpsilon, HatUpsilon, stats,Zbar)) else: FmZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats,Zbar):(solver, Fm(solver.objectives,P),P,Y,Phi,Upsilon,stats,Zbar)) if not self.lean or (self.lean and i==maxiters-1): #Compute the residual OldinnerResidual = np.sqrt(FmZbarPrimalDual.values().flatMap(lambda (solver, FPm,OldP,Y,Phi,Upsilon,stats,Zbar): [(Y[key]-FPm[key])**2 for key in Y]).reduce(add) ) ##ADMM steps #Adapt the dual varible Upsilon if accelerated: #Replace OldUpsilon FmYNewUpsilonPPhi = FmZbarPrimalDual.mapValues(lambda (solver, FPm,OldP, Y,Phi,Upsilon, OldUpsilon, HatUpsilon,stats,Zbar): (solver, FPm,OldP, Y,Phi,Upsilon, Upsilon, HatUpsilon,stats,Zbar)) #Update Upsilon FmYNewUpsilonPPhi = FmZbarPrimalDual.mapValues(lambda (solver, FPm,OldP, Y,Phi,Upsilon, OldUpsilon, HatUpsilon,stats,Zbar): (solver, FPm, OldP, Y, Phi, dict( [(key,HatUpsilon[key]+alpha_inner*(Y[key]-FPm[key])) for key in Y]), OldUpsilon, HatUpsilon, stats,Zbar)) #Update HatUpsilon FmYNewUpsilonPPhi = FmZbarPrimalDual.mapValues(lambda (solver, FPm,OldP, Y,Phi,Upsilon, OldUpsilon, HatUpsilon,stats,Zbar): (solver, FPm,OldP, Y,Phi,Upsilon,OldUpsilon , dict( [(key, Upsilon[key] + (ak-1.)/(ak+1.)*(Upsilon[key]-OldUpsilon[key])) for key in Upsilon]), stats,Zbar)) #Update ak ak = 0.5 * (1. + np.sqrt(1+4*ak**2)) else: FmYNewUpsilonPPhi = FmZbarPrimalDual.mapValues(lambda (solver, FPm,OldP, Y,Phi,Upsilon,stats,Zbar): (solver, FPm, OldP, Y, Phi, dict( [(key,Upsilon[key]+alpha_inner*(Y[key]-FPm[key])) for key in Y]),stats,Zbar)) #Update Y via prox. op. for ell_2 norm if accelerated: NewYUpsilonPhi, Ynorm = EuclidiannormOp(FmYNewUpsilonPPhi.mapValues(lambda (solver, FPm,OldP, Y, Phi, Upsilon, OldUpsilon, HatUpsilon, stats, Zbar):(dict([(key,FPm[key]-Upsilon[key]) for key in Upsilon]), (solver, OldP, Y, Phi, Upsilon, OldUpsilon, HatUpsilon, stats, Zbar) ) ), rho_inner, self.lean and i<maxiters-1) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (Y, (solver, OldP, OldY, Phi, Upsilon, OldUpsilon, HatUpsilon, stats, Zbar)): (solver, OldP, Y, OldY, Phi, Upsilon, OldUpsilon, HatUpsilon, stats, Zbar) ) else: NewYUpsilonPhi, Ynorm = EuclidiannormOp(FmYNewUpsilonPPhi.mapValues(lambda (solver, FPm,OldP, Y, Phi, Upsilon, stats, Zbar):(dict([(key,FPm[key]-Upsilon[key]) for key in Upsilon]), (solver, OldP, Y, Phi, Upsilon,stats,Zbar) ) ), rho_inner, self.lean and i<maxiters-1) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (Y, (solver, OldP, OldY, Phi, Upsilon, stats, Zbar)): (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar) ) #To Be Continued if not self.lean or (self.lean and i==maxiters-1): #Compute the dual residual for Y DualInnerResidual_Y = np.sqrt( NewYUpsilonPhi.values().flatMap(lambda (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar): [ (Y[key] -OldY[key])**2 for key in Y]).reduce(add) ) NewYUpsilonPhi = NewYUpsilonPhi.mapValues(lambda (solver, OldP, Y, OldY, Phi, Upsilon,stats, Zbar):(solver, OldP, Y, Phi, Upsilon,stats, Zbar) ) #Update P via solving a least-square problem ZbarPrimalDual = NewYUpsilonPhi.mapValues(lambda (solver, OldP, Y, Phi,Upsilon,stats,Zbar): (solver,solver.solve(Y, Zbar, Upsilon, rho, rho_inner),OldP, Y, Phi, Upsilon, stats, Zbar)).mapValues(lambda (solver, (P, stats), OldP, Y, Phi, Upsilon, stats_old, Zbar): (solver,OldP,P,Y,Phi,Upsilon, stats, Zbar)) if not self.lean or (self.lean and i==maxiters-1): #Compute the dual residual for P DualInnerResidual_P = np.sqrt( ZbarPrimalDual.values().flatMap(lambda (solver,OldP,P,Y,Phi,Upsilon, stats, Zbar): [ (P[key] -OldP[key])**2 for key in P]).reduce(add) ) #Total dual residual DualInnerResidual = DualInnerResidual_P + DualInnerResidual_Y ZbarPrimalDual = ZbarPrimalDual.mapValues(lambda (solver,OldP,P,Y,Phi,Upsilon, stats, Zbar): (solver,P,Y,Phi,Upsilon, stats, Zbar)) if not self.lean or (self.lean and i==maxiters-1): objval = ZbarPrimalDual.values().flatMap(lambda (solver,P,Y,Phi,Upsilon, stats, Zbar):[(P[key]-Zbar[key])**2 for key in P]).reduce(lambda x,y:x+y) + Ynorm now = time() if logger != None and (not self.lean or (self.lean and i==maxiters-1)): logger.info("Inner ADMM iteration %d, p-norm is %f, objective is %f, residual is %f, dual residual is %f, iteration time is %f" %(i, Ynorm, objval, OldinnerResidual, DualInnerResidual, now-last)) if (not self.lean or (self.lean and i==maxiters-1)) and self.debug: trace[i] = {} trace[i]['OBJ'] = objval trace[i]['PRES'] = OldinnerResidual trace[i]['DRES'] = DualInnerResidual trace[i]['IT_TIME'] = now-last trace[i]['TIME'] = now-start_time last = time() if not self.lean and DualInnerResidual<residual_tol and OldinnerResidual<residual_tol: break self.PrimalDualRDD = ZbarPrimalDual.mapValues(lambda (solver,P,Y,Phi,Upsilon,stats, Zbar): (solver,P,Y,Phi,Upsilon,stats)).cache() #Checkpointing if checkpoint: self.PrimalDualRDD.localCheckpoint() if self.debug: return trace if not self.silent or forceComp: return (oldPrimalResidual,oldObjValue) else: return None

64.046908

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0.053826

0.07124

0.063325

0.858839

0.851662

0.841055

0.83372

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0.005629

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468

337

64.183761

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0

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null

0

1

0

1

0

null

0

1

0

8

7aebe7b18d3ca35925bee6a0877f4de269856a61

19,234

py

Python

sdk/python/pulumi_gcp/compute/router_nat.py

dimpu47/pulumi-gcp

38355de300a5768e11c49d344a8165ba0735deed

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

null

sdk/python/pulumi_gcp/compute/router_nat.py

dimpu47/pulumi-gcp

38355de300a5768e11c49d344a8165ba0735deed

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

null

sdk/python/pulumi_gcp/compute/router_nat.py

dimpu47/pulumi-gcp

38355de300a5768e11c49d344a8165ba0735deed

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

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Dict, List, Mapping, Optional, Tuple, Union from .. import _utilities, _tables from . import outputs from ._inputs import * __all__ = ['RouterNat'] class RouterNat(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, drain_nat_ips: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None, icmp_idle_timeout_sec: Optional[pulumi.Input[float]] = None, log_config: Optional[pulumi.Input[pulumi.InputType['RouterNatLogConfigArgs']]] = None, min_ports_per_vm: Optional[pulumi.Input[float]] = None, name: Optional[pulumi.Input[str]] = None, nat_ip_allocate_option: Optional[pulumi.Input[str]] = None, nat_ips: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None, project: Optional[pulumi.Input[str]] = None, region: Optional[pulumi.Input[str]] = None, router: Optional[pulumi.Input[str]] = None, source_subnetwork_ip_ranges_to_nat: Optional[pulumi.Input[str]] = None, subnetworks: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RouterNatSubnetworkArgs']]]]] = None, tcp_established_idle_timeout_sec: Optional[pulumi.Input[float]] = None, tcp_transitory_idle_timeout_sec: Optional[pulumi.Input[float]] = None, udp_idle_timeout_sec: Optional[pulumi.Input[float]] = None, __props__=None, __name__=None, __opts__=None): """ A NAT service created in a router. To get more information about RouterNat, see: * [API documentation](https://cloud.google.com/compute/docs/reference/rest/v1/routers) * How-to Guides * [Google Cloud Router](https://cloud.google.com/router/docs/) ## Example Usage :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[List[pulumi.Input[str]]] drain_nat_ips: A list of URLs of the IP resources to be drained. These IPs must be valid static external IPs that have been assigned to the NAT. :param pulumi.Input[float] icmp_idle_timeout_sec: Timeout (in seconds) for ICMP connections. Defaults to 30s if not set. :param pulumi.Input[pulumi.InputType['RouterNatLogConfigArgs']] log_config: Configuration for logging on NAT Structure is documented below. :param pulumi.Input[float] min_ports_per_vm: Minimum number of ports allocated to a VM from this NAT. :param pulumi.Input[str] name: Self-link of subnetwork to NAT :param pulumi.Input[str] nat_ip_allocate_option: How external IPs should be allocated for this NAT. Valid values are `AUTO_ONLY` for only allowing NAT IPs allocated by Google Cloud Platform, or `MANUAL_ONLY` for only user-allocated NAT IP addresses. Possible values are `MANUAL_ONLY` and `AUTO_ONLY`. :param pulumi.Input[List[pulumi.Input[str]]] nat_ips: Self-links of NAT IPs. Only valid if natIpAllocateOption is set to MANUAL_ONLY. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. :param pulumi.Input[str] region: Region where the router and NAT reside. :param pulumi.Input[str] router: The name of the Cloud Router in which this NAT will be configured. :param pulumi.Input[str] source_subnetwork_ip_ranges_to_nat: How NAT should be configured per Subnetwork. If `ALL_SUBNETWORKS_ALL_IP_RANGES`, all of the IP ranges in every Subnetwork are allowed to Nat. If `ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES`, all of the primary IP ranges in every Subnetwork are allowed to Nat. `LIST_OF_SUBNETWORKS`: A list of Subnetworks are allowed to Nat (specified in the field subnetwork below). Note that if this field contains ALL_SUBNETWORKS_ALL_IP_RANGES or ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES, then there should not be any other RouterNat section in any Router for this network in this region. Possible values are `ALL_SUBNETWORKS_ALL_IP_RANGES`, `ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES`, and `LIST_OF_SUBNETWORKS`. :param pulumi.Input[List[pulumi.Input[pulumi.InputType['RouterNatSubnetworkArgs']]]] subnetworks: One or more subnetwork NAT configurations. Only used if `source_subnetwork_ip_ranges_to_nat` is set to `LIST_OF_SUBNETWORKS` Structure is documented below. :param pulumi.Input[float] tcp_established_idle_timeout_sec: Timeout (in seconds) for TCP established connections. Defaults to 1200s if not set. :param pulumi.Input[float] tcp_transitory_idle_timeout_sec: Timeout (in seconds) for TCP transitory connections. Defaults to 30s if not set. :param pulumi.Input[float] udp_idle_timeout_sec: Timeout (in seconds) for UDP connections. Defaults to 30s if not set. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ 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__ = dict() __props__['drain_nat_ips'] = drain_nat_ips __props__['icmp_idle_timeout_sec'] = icmp_idle_timeout_sec __props__['log_config'] = log_config __props__['min_ports_per_vm'] = min_ports_per_vm __props__['name'] = name if nat_ip_allocate_option is None: raise TypeError("Missing required property 'nat_ip_allocate_option'") __props__['nat_ip_allocate_option'] = nat_ip_allocate_option __props__['nat_ips'] = nat_ips __props__['project'] = project __props__['region'] = region if router is None: raise TypeError("Missing required property 'router'") __props__['router'] = router if source_subnetwork_ip_ranges_to_nat is None: raise TypeError("Missing required property 'source_subnetwork_ip_ranges_to_nat'") __props__['source_subnetwork_ip_ranges_to_nat'] = source_subnetwork_ip_ranges_to_nat __props__['subnetworks'] = subnetworks __props__['tcp_established_idle_timeout_sec'] = tcp_established_idle_timeout_sec __props__['tcp_transitory_idle_timeout_sec'] = tcp_transitory_idle_timeout_sec __props__['udp_idle_timeout_sec'] = udp_idle_timeout_sec super(RouterNat, __self__).__init__( 'gcp:compute/routerNat:RouterNat', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, drain_nat_ips: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None, icmp_idle_timeout_sec: Optional[pulumi.Input[float]] = None, log_config: Optional[pulumi.Input[pulumi.InputType['RouterNatLogConfigArgs']]] = None, min_ports_per_vm: Optional[pulumi.Input[float]] = None, name: Optional[pulumi.Input[str]] = None, nat_ip_allocate_option: Optional[pulumi.Input[str]] = None, nat_ips: Optional[pulumi.Input[List[pulumi.Input[str]]]] = None, project: Optional[pulumi.Input[str]] = None, region: Optional[pulumi.Input[str]] = None, router: Optional[pulumi.Input[str]] = None, source_subnetwork_ip_ranges_to_nat: Optional[pulumi.Input[str]] = None, subnetworks: Optional[pulumi.Input[List[pulumi.Input[pulumi.InputType['RouterNatSubnetworkArgs']]]]] = None, tcp_established_idle_timeout_sec: Optional[pulumi.Input[float]] = None, tcp_transitory_idle_timeout_sec: Optional[pulumi.Input[float]] = None, udp_idle_timeout_sec: Optional[pulumi.Input[float]] = None) -> 'RouterNat': """ Get an existing RouterNat 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[List[pulumi.Input[str]]] drain_nat_ips: A list of URLs of the IP resources to be drained. These IPs must be valid static external IPs that have been assigned to the NAT. :param pulumi.Input[float] icmp_idle_timeout_sec: Timeout (in seconds) for ICMP connections. Defaults to 30s if not set. :param pulumi.Input[pulumi.InputType['RouterNatLogConfigArgs']] log_config: Configuration for logging on NAT Structure is documented below. :param pulumi.Input[float] min_ports_per_vm: Minimum number of ports allocated to a VM from this NAT. :param pulumi.Input[str] name: Self-link of subnetwork to NAT :param pulumi.Input[str] nat_ip_allocate_option: How external IPs should be allocated for this NAT. Valid values are `AUTO_ONLY` for only allowing NAT IPs allocated by Google Cloud Platform, or `MANUAL_ONLY` for only user-allocated NAT IP addresses. Possible values are `MANUAL_ONLY` and `AUTO_ONLY`. :param pulumi.Input[List[pulumi.Input[str]]] nat_ips: Self-links of NAT IPs. Only valid if natIpAllocateOption is set to MANUAL_ONLY. :param pulumi.Input[str] project: The ID of the project in which the resource belongs. If it is not provided, the provider project is used. :param pulumi.Input[str] region: Region where the router and NAT reside. :param pulumi.Input[str] router: The name of the Cloud Router in which this NAT will be configured. :param pulumi.Input[str] source_subnetwork_ip_ranges_to_nat: How NAT should be configured per Subnetwork. If `ALL_SUBNETWORKS_ALL_IP_RANGES`, all of the IP ranges in every Subnetwork are allowed to Nat. If `ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES`, all of the primary IP ranges in every Subnetwork are allowed to Nat. `LIST_OF_SUBNETWORKS`: A list of Subnetworks are allowed to Nat (specified in the field subnetwork below). Note that if this field contains ALL_SUBNETWORKS_ALL_IP_RANGES or ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES, then there should not be any other RouterNat section in any Router for this network in this region. Possible values are `ALL_SUBNETWORKS_ALL_IP_RANGES`, `ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES`, and `LIST_OF_SUBNETWORKS`. :param pulumi.Input[List[pulumi.Input[pulumi.InputType['RouterNatSubnetworkArgs']]]] subnetworks: One or more subnetwork NAT configurations. Only used if `source_subnetwork_ip_ranges_to_nat` is set to `LIST_OF_SUBNETWORKS` Structure is documented below. :param pulumi.Input[float] tcp_established_idle_timeout_sec: Timeout (in seconds) for TCP established connections. Defaults to 1200s if not set. :param pulumi.Input[float] tcp_transitory_idle_timeout_sec: Timeout (in seconds) for TCP transitory connections. Defaults to 30s if not set. :param pulumi.Input[float] udp_idle_timeout_sec: Timeout (in seconds) for UDP connections. Defaults to 30s if not set. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["drain_nat_ips"] = drain_nat_ips __props__["icmp_idle_timeout_sec"] = icmp_idle_timeout_sec __props__["log_config"] = log_config __props__["min_ports_per_vm"] = min_ports_per_vm __props__["name"] = name __props__["nat_ip_allocate_option"] = nat_ip_allocate_option __props__["nat_ips"] = nat_ips __props__["project"] = project __props__["region"] = region __props__["router"] = router __props__["source_subnetwork_ip_ranges_to_nat"] = source_subnetwork_ip_ranges_to_nat __props__["subnetworks"] = subnetworks __props__["tcp_established_idle_timeout_sec"] = tcp_established_idle_timeout_sec __props__["tcp_transitory_idle_timeout_sec"] = tcp_transitory_idle_timeout_sec __props__["udp_idle_timeout_sec"] = udp_idle_timeout_sec return RouterNat(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="drainNatIps") def drain_nat_ips(self) -> pulumi.Output[Optional[List[str]]]: """ A list of URLs of the IP resources to be drained. These IPs must be valid static external IPs that have been assigned to the NAT. """ return pulumi.get(self, "drain_nat_ips") @property @pulumi.getter(name="icmpIdleTimeoutSec") def icmp_idle_timeout_sec(self) -> pulumi.Output[Optional[float]]: """ Timeout (in seconds) for ICMP connections. Defaults to 30s if not set. """ return pulumi.get(self, "icmp_idle_timeout_sec") @property @pulumi.getter(name="logConfig") def log_config(self) -> pulumi.Output[Optional['outputs.RouterNatLogConfig']]: """ Configuration for logging on NAT Structure is documented below. """ return pulumi.get(self, "log_config") @property @pulumi.getter(name="minPortsPerVm") def min_ports_per_vm(self) -> pulumi.Output[Optional[float]]: """ Minimum number of ports allocated to a VM from this NAT. """ return pulumi.get(self, "min_ports_per_vm") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Self-link of subnetwork to NAT """ return pulumi.get(self, "name") @property @pulumi.getter(name="natIpAllocateOption") def nat_ip_allocate_option(self) -> pulumi.Output[str]: """ How external IPs should be allocated for this NAT. Valid values are `AUTO_ONLY` for only allowing NAT IPs allocated by Google Cloud Platform, or `MANUAL_ONLY` for only user-allocated NAT IP addresses. Possible values are `MANUAL_ONLY` and `AUTO_ONLY`. """ return pulumi.get(self, "nat_ip_allocate_option") @property @pulumi.getter(name="natIps") def nat_ips(self) -> pulumi.Output[Optional[List[str]]]: """ Self-links of NAT IPs. Only valid if natIpAllocateOption is set to MANUAL_ONLY. """ return pulumi.get(self, "nat_ips") @property @pulumi.getter def project(self) -> pulumi.Output[str]: """ The ID of the project in which the resource belongs. If it is not provided, the provider project is used. """ return pulumi.get(self, "project") @property @pulumi.getter def region(self) -> pulumi.Output[str]: """ Region where the router and NAT reside. """ return pulumi.get(self, "region") @property @pulumi.getter def router(self) -> pulumi.Output[str]: """ The name of the Cloud Router in which this NAT will be configured. """ return pulumi.get(self, "router") @property @pulumi.getter(name="sourceSubnetworkIpRangesToNat") def source_subnetwork_ip_ranges_to_nat(self) -> pulumi.Output[str]: """ How NAT should be configured per Subnetwork. If `ALL_SUBNETWORKS_ALL_IP_RANGES`, all of the IP ranges in every Subnetwork are allowed to Nat. If `ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES`, all of the primary IP ranges in every Subnetwork are allowed to Nat. `LIST_OF_SUBNETWORKS`: A list of Subnetworks are allowed to Nat (specified in the field subnetwork below). Note that if this field contains ALL_SUBNETWORKS_ALL_IP_RANGES or ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES, then there should not be any other RouterNat section in any Router for this network in this region. Possible values are `ALL_SUBNETWORKS_ALL_IP_RANGES`, `ALL_SUBNETWORKS_ALL_PRIMARY_IP_RANGES`, and `LIST_OF_SUBNETWORKS`. """ return pulumi.get(self, "source_subnetwork_ip_ranges_to_nat") @property @pulumi.getter def subnetworks(self) -> pulumi.Output[Optional[List['outputs.RouterNatSubnetwork']]]: """ One or more subnetwork NAT configurations. Only used if `source_subnetwork_ip_ranges_to_nat` is set to `LIST_OF_SUBNETWORKS` Structure is documented below. """ return pulumi.get(self, "subnetworks") @property @pulumi.getter(name="tcpEstablishedIdleTimeoutSec") def tcp_established_idle_timeout_sec(self) -> pulumi.Output[Optional[float]]: """ Timeout (in seconds) for TCP established connections. Defaults to 1200s if not set. """ return pulumi.get(self, "tcp_established_idle_timeout_sec") @property @pulumi.getter(name="tcpTransitoryIdleTimeoutSec") def tcp_transitory_idle_timeout_sec(self) -> pulumi.Output[Optional[float]]: """ Timeout (in seconds) for TCP transitory connections. Defaults to 30s if not set. """ return pulumi.get(self, "tcp_transitory_idle_timeout_sec") @property @pulumi.getter(name="udpIdleTimeoutSec") def udp_idle_timeout_sec(self) -> pulumi.Output[Optional[float]]: """ Timeout (in seconds) for UDP connections. Defaults to 30s if not set. """ return pulumi.get(self, "udp_idle_timeout_sec") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

52.986226

161

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2,442

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5.013923

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0.045737

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0.803822

0.772052

0.756534

0.731869

0.712431

0.70802

0

0.002211

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19,234

362

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53.132597

0.843895

0.453052

0

0.300578

1

0

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1

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false

0.00578

0.040462

0.011561

0.260116

0

null

0

1

0

1

0

null

0

7

7af1fd0fce2287d73c91c00361146b409c776db4

10,890

py

Python

cogs/Punish.py

Aspen-The-Deer/Guardian-Deer

53cf4a135b033df11082ee63bf59a359a0f6d362

[ "MIT" ]

null

cogs/Punish.py

Aspen-The-Deer/Guardian-Deer

53cf4a135b033df11082ee63bf59a359a0f6d362

[ "MIT" ]

null

cogs/Punish.py

Aspen-The-Deer/Guardian-Deer

53cf4a135b033df11082ee63bf59a359a0f6d362

[ "MIT" ]

null

import discord import sys import time import random import os import json import datetime from discord.ext import commands class Punishments(commands.Cog): def __init__(self, client): self.client = client @commands.Cog.listener() async def on_ready(self): time.sleep(0.2) print('Punish.py') @commands.command(aliases =["Ban", "b", "B"]) @commands.has_permissions(ban_members=True) async def ban (self, ctx, member:discord.User=None, reason:str=None): logger = discord.utils.get(ctx.guild.channels, name='logs') server = ctx.message.guild mod = ctx.message.author.mention embed2= discord.Embed( colour=(0x629632), title="You have been Banned:" ) embed2.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed2.add_field(name="You have been banned from:", value=str(server), inline=False) embed2.add_field(name="For the Reason:", value=str(reason), inline=False) embed2.add_field(name="You were Banned By:", value=str(mod), inline=False) embed2.set_footer(text="Type '>help' for help options!") if member == None or member == ctx.message.author: embed= discord.Embed( colour=(0x629632), title="User Cannot Banned:" ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User:", value=str(member), inline=False) embed.add_field(name="This happened because:", value="You cannot ban yourself.\nNo user was specified to ban.", inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) return elif reason != None: await ctx.guild.ban(member, reason=reason) embed= discord.Embed( colour=(0x629632), title="User Banned:" ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User:", value=str(member), inline=False) embed.add_field(name="Reason:", value=str(reason), inline=False) embed.add_field(name="Banned By:", value=str(mod), inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) elif reason == None: reason = "No Reason Given" await ctx.guild.ban(member, reason=reason) embed= discord.Embed( colour=(0x629632), title="User Banned:" ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User:", value=str(member), inline=False) embed.add_field(name="Reason:", value=str(reason), inline=False) embed.add_field(name="Banned By:", value=str(mod), inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) try: await logger.send(embed=embed) await member.send(embed=embed2) except AttributeError: print("No logging channel found in "+server+", Ignoring Event.") await member.send(embed=embed2) @ban.error async def ban_error(self, ctx, error): if isinstance(error, commands.MissingPermissions): embed3= discord.Embed( colour=(0x629632), title="Insufficient Permissions..." ) embed3.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed3.add_field(name="You are missing the permissions required to use this command.", value="Error: #001", inline=False) embed3.set_footer(text="Type '>help' for help options!") await ctx.send(embed=embed3) return else: print(error) @commands.command(aliases =["Kick", "k", "K"]) @commands.has_permissions(kick_members=True) async def kick (self, ctx, member:discord.User=None, reason:str=None): logger = discord.utils.get(ctx.guild.channels, name='logs') server = ctx.message.guild mod = ctx.message.author.mention embed2= discord.Embed( colour=(0x629632), title="You have been Kicked:" ) embed2.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed2.add_field(name="You have been Kicked from:", value=str(server), inline=False) embed2.add_field(name="For the Reason:", value=reason, inline=False) embed2.add_field(name="You were kicked By:", value=str(mod), inline=False) embed2.set_footer(text="Type '>help' for help options!") if member == None or member == ctx.message.author: embed= discord.Embed( colour=(0x629632), title="User Cannot Kicked:" ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User:", value=str(member), inline=False) embed.add_field(name="This happened because:", value="You cannot kick yourself.\nNo user was specified to kick.", inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) elif reason != None: await ctx.guild.kick(member, reason=reason) embed= discord.Embed( colour=(0x629632), title="User Kicked:" ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User:", value=str(member), inline=False) embed.add_field(name="Reason:", value=reason, inline=False) embed.add_field(name="Kicked By:", value=str(mod), inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) embed2= discord.Embed( colour=(0x629632), title="You have been Kicked:" ) embed2.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed2.add_field(name="You have been Kicked from:", value=str(server), inline=False) embed2.add_field(name="For the Reason:", value=reason, inline=False) embed2.add_field(name="You were kicked By:", value=str(mod), inline=False) embed2.set_footer(text="Type '>help' for help options!") await member.send(embed=embed2) elif reason == None: reason = "No Reason Given" await ctx.guild.kick(member, reason=reason) embed= discord.Embed( colour=(0x629632), title="User Kicked:" ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User:", value=str(member), inline=False) embed.add_field(name="Reason:", value=str(reason), inline=False) embed.add_field(name="Kicked By:", value=str(mod), inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) try: await logger.send(embed=embed) await member.send(embed=embed2) except AttributeError: print("No logging channel found in "+server+", Ignoring Event.") await member.send(embed=embed2) @kick.error async def kick_error(self, ctx, error): if isinstance(error, commands.MissingPermissions): embed3= discord.Embed( colour=(0x629632), title="Insufficient Permissions..." ) embed3.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed3.add_field(name="You are missing the permissions required to use this command.", value="Error: #001", inline=False) embed3.set_footer(text="Type '>help' for help options!") await ctx.send(embed=embed3) return else: print(error) @commands.command(aliases =["Unban", "u", "U"]) @commands.has_permissions(ban_members=True) @commands.guild_only() async def unban(self, ctx, *, userId): logger = discord.utils.get(ctx.guild.channels, name='logs') mod = ctx.message.author.mention server = ctx.message.guild user = discord.Object(id=userId) await ctx.guild.unban(user) embed= discord.Embed( colour=(0x629632) ) embed.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed.add_field(name="User Pardoned by:", value=str(mod), inline=False) embed.set_footer(text="Type '>help' for help options!") await ctx.channel.send(embed=embed) try: await logger.send(embed=embed) except AttributeError: print("No logging channel found in "+server+", Ignoring Event.") return @unban.error async def unban_error(self, ctx, error): if isinstance(error, commands.MissingPermissions): embed3= discord.Embed( colour=(0x629632), title="Insufficient Permissions..." ) embed3.set_author(name="Guardian Deer", icon_url="https://cdn.discordapp.com/avatars/606855758612660327/98b13ab2d31342848754caa909a653da.png?size=1024") embed3.add_field(name="You are missing the permissions required to use this command.", value="Error: #001", inline=False) embed3.set_footer(text="Type '>help' for help options!") await ctx.send(embed=embed3) return else: print(error) def setup(client): client.add_cog(Punishments(client))

46.340426

164

0.628926

1,266

10,890

5.343602

0.110585

0.034294

0.051441

0.04272

0.912934

0.901404

0.879675

0.878788

0.865188

0

0.086829

0.247016

10,890

235

165

46.340426

0.738171

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0.719212

0

0.271784

0

0.009549

0

1

0.009852

false

0

0.039409

0

0.078818

0.034483

0

null

0

1

0

1

0

null

0

7

bb27099e35560831799e88b7b41511cc784afc17

3,122

py

Python

POM/user.py

Mikhail-QA/IU

ec792502b0a453b410e6c59f38a42b541564a776

[ "Apache-2.0" ]

null

POM/user.py

Mikhail-QA/IU

ec792502b0a453b410e6c59f38a42b541564a776

[ "Apache-2.0" ]

null

POM/user.py

Mikhail-QA/IU

ec792502b0a453b410e6c59f38a42b541564a776

[ "Apache-2.0" ]

null

# authorization user not abonement class AutopaymentMailRu(object): def __init__(self, driver): self.driver = driver def enter_email(self, user_name="autopayment@mail.ru"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password) # registration user buy subscription class PaymentnotMailRu(object): def __init__(self, driver): self.driver = driver def reg_enter_email(self, user_name="payment.not@mail.ru"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def reg_enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password) # registration user with abonement class PaymNotYandexRu(object): def __init__(self, driver): self.driver = driver def reg_enter_email(self, user_name="paym.not@yandex.ru"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def reg_enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password) def enter_email(self, user_name="paym.not@yandex.ru"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password) # registration user with abonement class VratchGlavYandexRu(object): def __init__(self, driver): self.driver = driver def reg_enter_email(self, user_name="vratch.glav@yandex.ru"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def reg_enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password) def enter_email(self, user_name="vratch.glav@yandex.ru"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password) # authorization admin user class Admin(object): def __init__(self, driver): self.driver = driver def enter_email(self, user_name): self.driver.find_element_by_name("user[email]").clear() self.driver.find_element_by_name("user[email]").send_keys("%s" % user_name) def enter_password(self, password): self.driver.find_element_by_id("user_password").clear() self.driver.find_element_by_id("user_password").send_keys("%s" % password) class IuUseryopmail(object): def __init__(self, driver): self.driver = driver def reg_enter_email(self, user_name="iuuser@yopmail.com"): self.driver.find_element_by_xpath("//div/label[1]/input").send_keys(user_name) def reg_enter_password(self, password="123456"): self.driver.find_element_by_xpath("//div/label[2]/input").send_keys(password)

36.729412

86

0.714606

440

3,122

4.761364

0.115909

0.143198

0.120286

0.18043

0.888783

0.88401

0.866826

0.797136

0

0.02095

0.143818

3,122

84

87

37.166667

0.762813

0.050609

0

0.634615

0

0.171738

0.014199

0

1

0.423077

false

0.326923

0

0.538462

0

null

0

1

0

null

0

1

0

1

0

1

0

10

2475acbb21143beca1d0740977818f72c72f50fa

6,127

py

Python

aioamqp/tests/test_exchange.py

tkukushkin/aioamqp

b4f01209794122a4ec3b5d8d437cb5739641fb3e

[ "BSD-3-Clause" ]

284

2015-01-08T20:05:07.000Z

2022-03-28T10:07:31.000Z

aioamqp/tests/test_exchange.py

tkukushkin/aioamqp

b4f01209794122a4ec3b5d8d437cb5739641fb3e

[ "BSD-3-Clause" ]

179

2015-02-16T09:27:53.000Z

2022-03-30T16:01:52.000Z

aioamqp/tests/test_exchange.py

tkukushkin/aioamqp

b4f01209794122a4ec3b5d8d437cb5739641fb3e

[ "BSD-3-Clause" ]

111

2015-02-15T00:27:58.000Z

2022-01-13T05:08:01.000Z

""" Amqp exchange class tests """ import asynctest from . import testcase from .. import exceptions class ExchangeDeclareTestCase(testcase.RabbitTestCaseMixin, asynctest.TestCase): _multiprocess_can_split_ = True async def test_exchange_direct_declare(self): result = await self.channel.exchange_declare( 'exchange_name', type_name='direct') self.assertTrue(result) async def test_exchange_fanout_declare(self): result = await self.channel.exchange_declare( 'exchange_name', type_name='fanout') self.assertTrue(result) async def test_exchange_topic_declare(self): result = await self.channel.exchange_declare( 'exchange_name', type_name='topic') self.assertTrue(result) async def test_exchange_headers_declare(self): result = await self.channel.exchange_declare( 'exchange_name', type_name='headers') self.assertTrue(result) async def test_exchange_declare_wrong_types(self): result = await self.channel.exchange_declare( 'exchange_name', type_name='headers', auto_delete=True, durable=True) self.assertTrue(result) with self.assertRaises(exceptions.ChannelClosed): result = await self.channel.exchange_declare( 'exchange_name', type_name='fanout', auto_delete=False, durable=False) async def test_exchange_declare_passive(self): result = await self.channel.exchange_declare( 'exchange_name', type_name='headers', auto_delete=True, durable=True) self.assertTrue(result) result = await self.channel.exchange_declare( 'exchange_name', type_name='headers', auto_delete=True, durable=True, passive=True) self.assertTrue(result) result = await self.channel.exchange_declare( 'exchange_name', type_name='headers', auto_delete=False, durable=False, passive=True) self.assertTrue(result) async def test_exchange_declare_passive_does_not_exists(self): with self.assertRaises(exceptions.ChannelClosed) as cm: await self.channel.exchange_declare( 'non_existant_exchange', type_name='headers', auto_delete=False, durable=False, passive=True) self.assertEqual(cm.exception.code, 404) async def test_exchange_declare_unknown_type(self): with self.assertRaises(exceptions.ChannelClosed): await self.channel.exchange_declare( 'non_existant_exchange', type_name='unknown_type', auto_delete=False, durable=False, passive=True) class ExchangeDelete(testcase.RabbitTestCaseMixin, asynctest.TestCase): async def test_delete(self): exchange_name = 'exchange_name' await self.channel.exchange_declare(exchange_name, type_name='direct') result = await self.channel.exchange_delete(exchange_name) self.assertTrue(result) with self.assertRaises(exceptions.ChannelClosed) as cm: await self.channel.exchange_declare( exchange_name, type_name='direct', passive=True ) self.assertEqual(cm.exception.code, 404) async def test_double_delete(self): exchange_name = 'exchange_name' await self.channel.exchange_declare(exchange_name, type_name='direct') result = await self.channel.exchange_delete(exchange_name) self.assertTrue(result) if self.server_version() < (3, 3, 5): with self.assertRaises(exceptions.ChannelClosed) as cm: await self.channel.exchange_delete(exchange_name) self.assertEqual(cm.exception.code, 404) else: # weird result from rabbitmq 3.3.5 result = await self.channel.exchange_delete(exchange_name) self.assertTrue(result) class ExchangeBind(testcase.RabbitTestCaseMixin, asynctest.TestCase): async def test_exchange_bind(self): await self.channel.exchange_declare('exchange_destination', type_name='direct') await self.channel.exchange_declare('exchange_source', type_name='direct') result = await self.channel.exchange_bind( 'exchange_destination', 'exchange_source', routing_key='') self.assertTrue(result) async def test_inexistant_exchange_bind(self): with self.assertRaises(exceptions.ChannelClosed) as cm: await self.channel.exchange_bind( 'exchange_destination', 'exchange_source', routing_key='') self.assertEqual(cm.exception.code, 404) class ExchangeUnbind(testcase.RabbitTestCaseMixin, asynctest.TestCase): async def test_exchange_unbind(self): ex_source = 'exchange_source' ex_destination = 'exchange_destination' await self.channel.exchange_declare(ex_destination, type_name='direct') await self.channel.exchange_declare(ex_source, type_name='direct') await self.channel.exchange_bind( ex_destination, ex_source, routing_key='') await self.channel.exchange_unbind( ex_destination, ex_source, routing_key='') async def test_exchange_unbind_reversed(self): ex_source = 'exchange_source' ex_destination = 'exchange_destination' await self.channel.exchange_declare(ex_destination, type_name='direct') await self.channel.exchange_declare(ex_source, type_name='direct') await self.channel.exchange_bind( ex_destination, ex_source, routing_key='') if self.server_version() < (3, 3, 5): with self.assertRaises(exceptions.ChannelClosed) as cm: result = await self.channel.exchange_unbind( ex_source, ex_destination, routing_key='') self.assertEqual(cm.exception.code, 404) else: # weird result from rabbitmq 3.3.5 result = await self.channel.exchange_unbind(ex_source, ex_destination, routing_key='') self.assertTrue(result)

37.588957

98

0.675698

674

6,127

5.896142

0.108309

0.070206

0.124811

0.187217

0.906895

0.882486

0.82612

0.77227

0.689733

0.675642

0

0.005757

0.234536

6,127

162

99

37.820988

0.841578

0.015016

0

0.646552

0

0.081534

0.006974

0

0.215517

1

0

false

0.060345

0.025862

0

0.068966

0

null

0

1

0

1

0

null

0

1

0

7

24c8633e4dff63088afb3aadc34f2d5b0636b7a8

93

py

Python

decisiorama/__init__.py

j-chacon/Hartmann_contaminants

316d543efcdc0bcc4442c56fda6748b405ca2e22

[ "MIT" ]

null

decisiorama/__init__.py

j-chacon/Hartmann_contaminants

316d543efcdc0bcc4442c56fda6748b405ca2e22

[ "MIT" ]

null

decisiorama/__init__.py

j-chacon/Hartmann_contaminants

316d543efcdc0bcc4442c56fda6748b405ca2e22

[ "MIT" ]

null

from decisiorama import pda from decisiorama import sensitivity from decisiorama import utils

31

35

0.88172

12

93

6.833333

0.5

0.54878

0.768293

0

0.11828

93

3

36

31

1

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

7

24f7c70905a68a25e9698adbcf4130537c5fbde0

20,996

py

Python

serial_scripts/vrouter/test_session_logging.py

vkolli/5.0_contrail-test

1793f169a94100400a1b2fafbad21daf5aa4d48a

[ "Apache-2.0" ]

null

serial_scripts/vrouter/test_session_logging.py

vkolli/5.0_contrail-test

1793f169a94100400a1b2fafbad21daf5aa4d48a

[ "Apache-2.0" ]

1

2021-06-01T22:18:29.000Z

serial_scripts/vrouter/test_session_logging.py

vkolli/5.0_contrail-test

1793f169a94100400a1b2fafbad21daf5aa4d48a

[ "Apache-2.0" ]

null

from tcutils.wrappers import preposttest_wrapper from common.sessionlogging.base import * import test import random from tcutils.util import skip_because AF_TEST = 'v6' class SessionLogging(SessionLoggingBase): @classmethod def setUpClass(cls): super(SessionLogging, cls).setUpClass() @classmethod def tearDownClass(cls): super(SessionLogging, cls).tearDownClass() def _test_logging_intra_node(self): self._create_resources(test_type='intra-node') #For intra node traffic there is no tunnel so underlay_proto would be zero underlay_proto = 0 proto_list = [1, 17, 6] self.enable_logging_on_compute(self.client_fixture.vm_node_ip, log_type=AGENT_LOG) #Clear local ips after agent restart self.client_fixture.clear_local_ips() self.server_fixture.clear_local_ips() #Verify Session logs in agent logs for proto in proto_list: self.start_traffic_validate_sessions(self.client_fixture, self.server_fixture, self.policy_fixture, proto=proto, underlay_proto=underlay_proto) self.logger.info("Expected Session logs found in agent log for " "protocol %s" % (proto)) self.enable_logging_on_compute(self.client_fixture.vm_node_ip, log_type=SYS_LOG) #Clear local ips after agent restart self.client_fixture.clear_local_ips() self.server_fixture.clear_local_ips() #Verify Session logs in syslog for proto in proto_list: self.start_traffic_validate_sessions_in_syslog(self.client_fixture, self.server_fixture, self.policy_fixture, proto=proto, underlay_proto=underlay_proto) self.logger.info("Expected Session logs found in syslog for " "protocol %s" % (proto)) def _test_logging_inter_node(self): self._create_resources(test_type='inter-node') underlay_proto = UNDERLAY_PROTO[ self.connections.read_vrouter_config_encap()[0]] proto_list = [1, 17, 6] self.enable_logging_on_compute(self.client_fixture.vm_node_ip, log_type=AGENT_LOG) self.enable_logging_on_compute(self.server_fixture.vm_node_ip, log_type=AGENT_LOG) #Clear local ips after agent restart self.client_fixture.clear_local_ips() self.server_fixture.clear_local_ips() #Verify Session logs in agent logs for proto in proto_list: self.start_traffic_validate_sessions(self.client_fixture, self.server_fixture, self.policy_fixture, proto=proto, underlay_proto=underlay_proto) self.logger.info("Expected Session logs found in agent log for " "protocol %s" % (proto)) self.enable_logging_on_compute(self.client_fixture.vm_node_ip, log_type=SYS_LOG) self.enable_logging_on_compute(self.server_fixture.vm_node_ip, log_type=SYS_LOG) #Clear local ips after agent restart self.client_fixture.clear_local_ips() self.server_fixture.clear_local_ips() #Verify Session logs in syslog for proto in proto_list: self.start_traffic_validate_sessions_in_syslog(self.client_fixture, self.server_fixture, self.policy_fixture, proto=proto, underlay_proto=underlay_proto) self.logger.info("Expected Session logs found in syslog for " "protocol %s" % (proto)) @preposttest_wrapper def test_local_logging_intra_node(self): """ Description: Verify sessions logged for inter-VN intra-Node traffic Steps: 1. create 2 VNs and connect them using policy 2. launch 1 VM in each VN on same compute node 3. start icmp/tcp/udp traffic and verify the session logs in agent log as well as in syslog Pass criteria: step 3 should pass """ self._test_logging_intra_node() @preposttest_wrapper def test_local_logging_inter_node(self): """ Description: Verify sessions logged for inter-VN inter-Node traffic Steps: 1. create 2 VNs and connect them using policy 2. launch 1 VM in each VN on different compute nodes 3. start icmp/tcp/udp traffic and verify the session logs in agent log as well as in syslog Pass criteria: step 3 should pass """ self._test_logging_inter_node() @preposttest_wrapper def test_client_session_aggregation(self): """ Description: Verify client sessions aggregation for tcp and udp """ self._create_resources(test_type='inter-node', no_of_server=3) underlay_proto = UNDERLAY_PROTO[ self.connections.read_vrouter_config_encap()[0]] self.enable_logging_on_compute(self.client_fixture.vm_node_ip, log_type=AGENT_LOG) self.enable_logging_on_compute(self.server_fixture.vm_node_ip, log_type=AGENT_LOG) #Clear local ips after agent restart for vm in self.client_fixtures + self.server_fixtures: vm.clear_local_ips() pkt_count = 100 client_port = random.randint(12000, 65000) service_port = client_port + 1 hping3_obj = {} traffic_stats = {} project_fqname = ':'.join(self.project.project_fq_name) client_vmi_fqname = project_fqname + ':' +\ self.client_fixture.vmi_ids[self.client_fixture.vn_fq_name] server_vn_fq_name = self.server_fixture.vn_fq_name is_client_session = 1 policy_api_obj = self.vnc_lib.network_policy_read( id=self.policy_fixture.get_id()) nw_ace_uuid = policy_api_obj.get_network_policy_entries( ).policy_rule[0].rule_uuid interval = 1 tcp_flags = 0 for proto in [17, 6]: traffic_stats[proto] = {} hping3_obj[proto] = {} udp = True if proto == 17 else False #Start the traffic for idx, server in enumerate(self.server_fixtures): hping3_obj[proto][server] = self.send_hping3_traffic( self.client_fixture, server.vm_ip, client_port+idx, service_port, count=pkt_count, interval=interval, wait=False, stop=False, udp=udp, keep=True)[0] if proto != 6: expected_client_session = SESSION_CLIENT_AGGR % ( client_vmi_fqname,#Client vmi name self.client_fixture.vn_fq_name, FIREWALL_RULE_ID_DEFAULT, server_vn_fq_name, is_client_session, 0, self.client_fixture.vm_node_ip, self.client_fixture.vm_ip, service_port, proto,#Session agg INT_RE, INT_RE, INT_RE, INT_RE, self.server_fixtures[0].vm_ip, client_port,#Server1 INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.client_fixture.vm_id,#Client vm ID self.server_fixtures[0].vm_node_ip, underlay_proto, self.server_fixtures[1].vm_ip, client_port+1,#Server2 INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.client_fixture.vm_id, self.server_fixtures[1].vm_node_ip, underlay_proto, self.server_fixtures[2].vm_ip, client_port+2,#Server3 INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.client_fixture.vm_id, self.server_fixtures[2].vm_node_ip, underlay_proto) #Verify session aggregation on client node result, output = self.search_session_in_agent_log( self.client_fixture.vm_node_ip, expected_client_session) assert result, ("Expected client session not found in agent log " "for protocol %s" % (proto)) #Stop the traffic for idx, server in enumerate(self.server_fixtures): traffic_stats[proto][server] = hping3_obj[proto][server].stop()[0] #Delete all the flows self.delete_all_flows_on_vms_compute( self.client_fixtures + self.server_fixtures) if proto == 6: pkt_count1 = pkt_count2 = pkt_count3 = 1 else: pkt_count1 = traffic_stats[proto][self.server_fixtures[0]]['sent'] pkt_count2 = traffic_stats[proto][self.server_fixtures[1]]['sent'] pkt_count3 = traffic_stats[proto][self.server_fixtures[2]]['sent'] expected_client_session = SESSION_CLIENT_AGGR_TEARDOWN % ( client_vmi_fqname, self.client_fixture.vn_fq_name, FIREWALL_RULE_ID_DEFAULT, server_vn_fq_name, is_client_session, 0, self.client_fixture.vm_node_ip, self.client_fixture.vm_ip, service_port, proto, INT_RE, INT_RE, INT_RE, INT_RE, self.server_fixtures[0].vm_ip, client_port, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, self.client_fixture.vm_id, self.server_fixtures[0].vm_node_ip, underlay_proto, self.server_fixtures[1].vm_ip, client_port+1, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count2, 'pass', UUID_RE, nw_ace_uuid, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count2, 'pass', UUID_RE, nw_ace_uuid, self.client_fixture.vm_id, self.server_fixtures[1].vm_node_ip, underlay_proto, self.server_fixtures[2].vm_ip, client_port+2, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count3, 'pass', UUID_RE, nw_ace_uuid, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count3, 'pass', UUID_RE, nw_ace_uuid, self.client_fixture.vm_id, self.server_fixtures[2].vm_node_ip, underlay_proto) #Verify teardown session after deleting the flows result, output = self.search_session_in_agent_log( self.client_fixture.vm_node_ip, expected_client_session) if ((not result) and (proto == 6)): expected_client_session = SESSION_CLIENT_AGGR_TEARDOWN_TCP % ( client_vmi_fqname, self.client_fixture.vn_fq_name, FIREWALL_RULE_ID_DEFAULT, server_vn_fq_name, is_client_session, 0, self.client_fixture.vm_node_ip, self.client_fixture.vm_ip, service_port, proto, INT_RE, INT_RE, INT_RE, INT_RE, self.server_fixtures[0].vm_ip, client_port,#Server1 INT_RE, pkt_count1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, pkt_count1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.client_fixture.vm_id, self.server_fixtures[0].vm_node_ip, underlay_proto, self.server_fixtures[1].vm_ip, client_port+1,#Server2 INT_RE, pkt_count1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, pkt_count1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.client_fixture.vm_id, self.server_fixtures[1].vm_node_ip, underlay_proto, self.server_fixtures[2].vm_ip, client_port+2,#Server3 INT_RE, pkt_count1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, pkt_count1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.client_fixture.vm_id, self.server_fixtures[2].vm_node_ip, underlay_proto) result_tcp, output = self.search_session_in_agent_log( self.client_fixture.vm_node_ip, expected_client_session) result = result or result_tcp assert result, ("Expected client session not found in agent log " "for protocol %s" % (proto)) self.logger.info("Expected Session logs found in agent log for " "protocol %s" % (proto)) @preposttest_wrapper def test_server_session_aggregation(self): """ Description: Verify server sessions aggregation """ self._create_resources(test_type='inter-node', no_of_client=3) underlay_proto = UNDERLAY_PROTO[ self.connections.read_vrouter_config_encap()[0]] self.enable_logging_on_compute(self.client_fixture.vm_node_ip, log_type=AGENT_LOG) self.enable_logging_on_compute(self.server_fixture.vm_node_ip, log_type=AGENT_LOG) #Clear local ips after agent restart for vm in self.client_fixtures + self.server_fixtures: vm.clear_local_ips() pkt_count = 100 client_port = random.randint(12000, 65000) service_port = client_port + 1 hping3_obj = {} traffic_stats = {} project_fqname = ':'.join(self.project.project_fq_name) server_vmi_fqname = project_fqname + ':' +\ self.server_fixture.vmi_ids[self.server_fixture.vn_fq_name] client_vn_fq_name = self.client_fixture.vn_fq_name is_client_session = 0 policy_api_obj = self.vnc_lib.network_policy_read( id=self.policy_fixture.get_id()) nw_ace_uuid = policy_api_obj.get_network_policy_entries( ).policy_rule[0].rule_uuid interval = 1 tcp_flags = 0 proto = 17 traffic_stats[proto] = {} hping3_obj[proto] = {} udp = True if proto == 17 else False #Start the traffic for idx, client in enumerate(self.client_fixtures): hping3_obj[proto][client] = self.send_hping3_traffic( client, self.server_fixture.vm_ip, client_port+idx, service_port, count=pkt_count, interval=interval, wait=False, stop=False, udp=udp, keep=True)[0] expected_server_session = SESSION_SERVER_AGGR % ( server_vmi_fqname,#Server vmi name self.server_fixture.vn_fq_name, FIREWALL_RULE_ID_DEFAULT, client_vn_fq_name, is_client_session, 0, self.server_fixture.vm_node_ip, self.server_fixture.vm_ip, service_port, proto,#Session agg INT_RE, INT_RE, INT_RE, INT_RE, self.client_fixtures[0].vm_ip, client_port,#Client1 INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.server_fixture.vm_id,#Server vm ID self.client_fixtures[0].vm_node_ip, underlay_proto, self.client_fixtures[1].vm_ip, client_port+1,#Client2 INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.server_fixture.vm_id, self.client_fixtures[1].vm_node_ip, underlay_proto, self.client_fixtures[2].vm_ip, client_port+2,#Client3 INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Fwd flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, INT_RE, 1, UUID_RE, tcp_flags, INT_RE,#Reverse flow info 'pass', UUID_RE, nw_ace_uuid, INT_RE, self.server_fixture.vm_id, self.client_fixtures[2].vm_node_ip, underlay_proto) #Verify session aggregation on client node result, output = self.search_session_in_agent_log( self.server_fixture.vm_node_ip, expected_server_session) assert result, ("Expected server session not found in agent log " "for protocol %s" % (proto)) #Stop the traffic for idx, client in enumerate(self.client_fixtures): traffic_stats[proto][client] = hping3_obj[proto][client].stop()[0] #Delete all the flows self.delete_all_flows_on_vms_compute( self.client_fixtures + self.server_fixtures) pkt_count1 = traffic_stats[proto][self.client_fixtures[0]]['sent'] pkt_count2 = traffic_stats[proto][self.client_fixtures[1]]['sent'] pkt_count3 = traffic_stats[proto][self.client_fixtures[2]]['sent'] expected_server_session = SESSION_CLIENT_AGGR_TEARDOWN % ( server_vmi_fqname, self.server_fixture.vn_fq_name, FIREWALL_RULE_ID_DEFAULT, client_vn_fq_name, is_client_session, 0, self.server_fixture.vm_node_ip, self.server_fixture.vm_ip, service_port, proto, INT_RE, INT_RE, INT_RE, INT_RE, self.client_fixtures[0].vm_ip, client_port, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count1, 'pass', UUID_RE, nw_ace_uuid, self.server_fixture.vm_id, self.client_fixtures[0].vm_node_ip, underlay_proto, self.client_fixtures[1].vm_ip, client_port+1, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count2, 'pass', UUID_RE, nw_ace_uuid, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count2, 'pass', UUID_RE, nw_ace_uuid, self.server_fixture.vm_id, self.client_fixtures[1].vm_node_ip, underlay_proto, self.client_fixtures[2].vm_ip, client_port+2, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count3, 'pass', UUID_RE, nw_ace_uuid, UUID_RE, INT_RE, INT_RE, INT_RE, pkt_count3, 'pass', UUID_RE, nw_ace_uuid, self.server_fixture.vm_id, self.client_fixtures[2].vm_node_ip, underlay_proto) #Verify teardown session after deleting the flows result, output = self.search_session_in_agent_log( self.server_fixture.vm_node_ip, expected_server_session) assert result, ("Expected server session not found in agent log " "for protocol %s" % (proto)) self.logger.info("Expected Session logs found in agent log for " "protocol %s" % (proto)) class SessionLoggingIpv6(SessionLogging): @classmethod def setUpClass(cls): super(SessionLoggingIpv6, cls).setUpClass() cls.inputs.set_af(AF_TEST) def is_test_applicable(self): if (self.inputs.orchestrator == 'vcenter') and ( not self.orch.is_feature_supported('ipv6')): return(False, 'Skipping IPv6 Test on vcenter setup') return (True, None) @preposttest_wrapper @skip_because(address_family = 'v6') def test_client_session_aggregation(self): ''' This test uses hping3 utils which does not support ipv6, so need to skip ''' super(SessionLoggingIpv6, self).test_client_session_aggregation() @preposttest_wrapper @skip_because(address_family = 'v6') def test_server_session_aggregation(self): ''' This test uses hping3 utils which does not support ipv6, so need to skip ''' super(SessionLoggingIpv6, self).test_server_session_aggregation()

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85

0.618165

2,743

20,996

4.374408

0.076559

0.050838

0.038503

0.045004

0.887991

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0.826069

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0.776898

0

0.014112

0.301391

20,996

453

86

46.348786

0.803927

0.095256

0

0.816901

0

0.043762

0

0.011268

1

0.033803

false

0.084507

0.014085

0

0.056338

0

null

0

1

0

1

0

null

0

1

0

8

70049ed00e4c0b7633c512741073cd70fa0db15c

166

py

Python

yawn/models.py

rsalmaso/yawn

5ede07f9015468481c941f8221cd70c99be6e895

[ "MIT" ]

28

2017-01-20T15:07:20.000Z

2022-02-18T17:25:38.000Z

yawn/models.py

rsalmaso/yawn

5ede07f9015468481c941f8221cd70c99be6e895

[ "MIT" ]

83

2017-01-26T16:28:21.000Z

2022-03-08T23:48:04.000Z

yawn/models.py

rsalmaso/yawn

5ede07f9015468481c941f8221cd70c99be6e895

[ "MIT" ]

5

2017-01-23T00:19:38.000Z

2020-05-12T15:36:59.000Z

# a hack to store the models in subfolders from yawn.task.models import * # noqa from yawn.worker.models import * # noqa from yawn.workflow.models import * # noqa

33.2

42

0.740964

26

166

4.730769

0.576923

0.195122

0.390244

0.325203

0.390244

0

0.180723

166

4

43

41.5

0.904412

0.331325

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

7

700e664ee0e556361e34af746991946bb25de7b5

726

py

Python

test.py

bastcazaux/search_myers_IUPAC

8fb3dba7c2313d103bf9eeb6a739e5b27e4ded58

[ "CECILL-B" ]

3

2020-10-15T09:35:30.000Z

2020-12-28T03:02:32.000Z

test.py

bastcazaux/search_myers_IUPAC

8fb3dba7c2313d103bf9eeb6a739e5b27e4ded58

[ "CECILL-B" ]

null

test.py

bastcazaux/search_myers_IUPAC

8fb3dba7c2313d103bf9eeb6a739e5b27e4ded58

[ "CECILL-B" ]

2

2021-02-02T10:45:48.000Z

2021-08-04T08:41:38.000Z

import search_myers_IUPAC print("search_myers_IUPAC.listofpositions('HACTADGTRTG','HYC', 1)") print(search_myers_IUPAC.listofpositions('HACTADGTRTG','HYC', 1)) print("search_myers_IUPAC.listofbestpositions('HACTADGTRTG','HYCAC')") print(search_myers_IUPAC.listofbestpositions('HACTADGTRTG','HYCAC')) print("search_myers_IUPAC.backtrackpositions('HACTADGTRTG','HYCG', 1,10)") print(search_myers_IUPAC.backtrackpositions('HACTADGTRTG','HYCG', 1,10)) print("search_myers_IUPAC.backtrackbestposition('HACTADGTRTG','HYCG', 1,10)") print(search_myers_IUPAC.backtrackbestposition('HACTADGTRTG','HYCG', 1,10)) print("search_myers_IUPAC.tag('HACTADGTRTG','HYCG', 1,8)") print(search_myers_IUPAC.tag('HACTADGTRTG','HYCG', 1,8)) #

38.210526

77

0.790634

88

726

6.272727

0.193182

0.219203

0.318841

0.380435

0.960145

0.849638

0

0.025751

0.03719

726

18

78

40.333333

0.763949

0

0.518621

0.387586

0

1

0

true

0

0.090909

0

0.090909

0.909091

0

null

1

0

null

0

1

0

1

0

11

703459fc2adb300c9565840ab76077f238507d5f

4,063

py

Python

falcon_call.py

ashish-1801/falcon2.0

2560d0de4bd2dd138c0af985af5ea98beb3021b9

[ "MIT" ]

null

falcon_call.py

ashish-1801/falcon2.0

2560d0de4bd2dd138c0af985af5ea98beb3021b9

[ "MIT" ]

null

falcon_call.py

ashish-1801/falcon2.0

2560d0de4bd2dd138c0af985af5ea98beb3021b9

[ "MIT" ]

null

import csv import requests headers = {'content-type': 'application/json', 'Accept-Charset': 'UTF-8'} def falcon2_call(text,mode='short'): try: text=text.replace('"','') text=text.replace("'","") if mode=='short': url = 'https://labs.tib.eu/falcon/falcon2/api?mode=short&db=1' entities_wikidata=[] entities_db=[] payload = '{"text":"'+text+'"}' r = requests.post(url, data=payload.encode('utf-8'), headers=headers) if r.status_code == 200: response=r.json() #print(response) for result in response['entities_wikidata']: entities_wikidata.append(result[0]) for result in response['entities_dbpedia']: entities_db.append(result[0]) else: r = requests.post(url, data=payload.encode('utf-8'), headers=headers) if r.status_code == 200: response=r.json() for result in response['entities_wikidata']: entities_wikidata.append(result[0]) for result in response['entities_dbpedia']: entities_db.append(result[0]) if len(entities_wikidata)>0: entities_wikidata=entities_wikidata[0].replace('<','').replace('>','') if len(entities_db)>0: entities_db=entities_db[0] return entities_wikidata,entities_db else: url = 'https://labs.tib.eu/falcon/falcon2/api?mode=long&db=1' entities_wikidata=[] entities_db=[] payload = '{"text":"'+text+'"}' r = requests.post(url, data=payload.encode('utf-8'), headers=headers) if r.status_code == 200: response=r.json() #print(response) return response for result in response['entities_wikidata']: entities_wikidata.append(result) for result in response['entities_dbpedia']: entities_db.append(result) else: r = requests.post(url, data=payload.encode('utf-8'), headers=headers) if r.status_code == 200: response=r.json() return response for result in response['entities_wikidata']: entities_wikidata.append(result) for result in response['entities_dbpedia']: entities_db.append(result) if len(entities_wikidata)>0: entities_wikidata[0][1]=entities_wikidata[0][1].replace('<','').replace('>','') if len(entities_db)>0: entities_db=entities_db return entities_wikidata,entities_db except: raise return -1 def bioFalcon_call(text, mode='short'): text=text.replace('"','') text=text.replace("'","") if mode=='short': url = 'https://labs.tib.eu/sdm/biofalcon/api?mode='+mode payload = '{"text":"'+text+'"}' try: r = requests.post(url, data=payload.encode('utf-8'), headers=headers) if r.status_code == 200: response=r.json() if len(response['entities']) > 1: return response['entities'][1][0] else: return "" else: return "" except: raise return "" else: url = 'https://labs.tib.eu/sdm/biofalcon/api?mode='+mode payload = '{"text":"'+text+'"}' try: r = requests.post(url, data=payload.encode('utf-8'), headers=headers) if r.status_code == 200: response=r.json() return response except: raise return ""

39.446602

94

0.488555

402

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7081c16cd575a3edbe9448426cdfe43761c040d7

22,250

py

Python

alshamelah_api/apps/books/permissions.py

devna-dev/durar-backend

36ea29bafd4cb95098e4057eb71df211dc923008

[ "MIT" ]

null

alshamelah_api/apps/books/permissions.py

devna-dev/durar-backend

36ea29bafd4cb95098e4057eb71df211dc923008

[ "MIT" ]

null

alshamelah_api/apps/books/permissions.py

devna-dev/durar-backend

36ea29bafd4cb95098e4057eb71df211dc923008

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals from rest_framework import permissions from rolepermissions.checkers import has_permission class CanManageBook(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_books) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_books) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_books) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_books): return True parent_permission = super(CanManageBook, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_books): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_books): return True parent_permission = super(CanManageBook, self).has_permission(request, view) if not parent_permission: return False return True class CanSubmitBook(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.submit_books) # 'PUT/PATCH' method update if request.method in ['PUT']: return has_permission(request.user, AppPermissions.submit_books) parent_permission = super(CanSubmitBook, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.submit_books): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True parent_permission = super(CanSubmitBook, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookRating(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_ratings) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_rating) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_rating) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_rating): return True parent_permission = super(CanManageBookRating, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_rating): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_rating): return True parent_permission = super(CanManageBookRating, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookComment(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_comments) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_comment) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_comment) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_comment): return True parent_permission = super(CanManageBookComment, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_comment): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_comment): return True parent_permission = super(CanManageBookComment, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookHighlight(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_highlights) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_highlight) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_highlight) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_highlight): return True parent_permission = super(CanManageBookHighlight, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_highlight): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_highlight): return True parent_permission = super(CanManageBookHighlight, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookMark(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_marks) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_mark) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_mark) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_mark): return True parent_permission = super(CanManageBookMark, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_mark): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_mark): return True parent_permission = super(CanManageBookMark, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookAudio(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_audio) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_audio) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_audio) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_audio): return True parent_permission = super(CanManageBookAudio, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_audio): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_audio): return True parent_permission = super(CanManageBookAudio, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookPdf(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_pdf) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_pdf) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_pdf) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_pdf): return True parent_permission = super(CanManageBookPdf, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_pdf): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_pdf): return True parent_permission = super(CanManageBookPdf, self).has_permission(request, view) if not parent_permission: return False return True class CanManageBookReview(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): from ..users.roles import AppPermissions # Allow list to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True if request.method in permissions.SAFE_METHODS: return has_permission(request.user, AppPermissions.view_book_review) # 'POST' method creation if request.method == 'POST': return has_permission(request.user, AppPermissions.create_book_review) # 'PUT/PATCH' method update if request.method in ['PUT', 'PATCH']: return has_permission(request.user, AppPermissions.edit_book_review) # Deleting Books if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_rating): return True parent_permission = super(CanManageBookReview, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): from ..users.roles import AppPermissions """ Manages only permissions for editing and deleting the objects """ # Allow get to all if request.method in ['GET']: return True # Superuser can manage all the objects if request.user.is_authenticated and request.user.is_superuser: return True # 'PUT' method, editing the rental items if request.method in ['PUT', 'PATCH'] and has_permission(request.user, AppPermissions.edit_book_review): return True # 'PUT' method, editing the rental items # Let user have access to a single object if request.method in permissions.SAFE_METHODS: return True # Deleting rental items if request.method == 'DELETE' and has_permission(request.user, AppPermissions.delete_book_review): return True parent_permission = super(CanManageBookReview, self).has_permission(request, view) if not parent_permission: return False return True class CanManageUserData(permissions.IsAuthenticated): """ Write documentation """ # book_lookup = 'parent_lookup_book' case of parent child def has_permission(self, request, view): # Superuser can manage all the objects if request.user.is_authenticated: return True parent_permission = super(CanManageUserData, self).has_permission(request, view) if not parent_permission: return False return False def has_object_permission(self, request, view, obj): """ Manages only permissions for editing and deleting the objects """ # Superuser can manage all the objects if request.user.is_authenticated: return True parent_permission = super(CanManageUserData, self).has_permission(request, view) if not parent_permission: return False return True

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null

0

1

0

8

565ec712da897a03d3e2e74e541d76385de65132

1,169

py

Python

tests/test_index.py

sgowdaks/mtdata

3db2b9cf035fe1b51fd59658d58fa8ce037a47d9

[ "Apache-2.0" ]

81

2020-04-07T02:55:39.000Z

2022-03-30T05:28:58.000Z

tests/test_index.py

sgowdaks/mtdata

3db2b9cf035fe1b51fd59658d58fa8ce037a47d9

[ "Apache-2.0" ]

77

2020-04-07T19:53:48.000Z

2022-03-22T18:41:08.000Z

tests/test_index.py

sgowdaks/mtdata

3db2b9cf035fe1b51fd59658d58fa8ce037a47d9

[ "Apache-2.0" ]

6

2020-04-16T22:21:19.000Z

2022-02-07T20:52:15.000Z

#!/usr/bin/env python # # # Author: Thamme Gowda # Created: 10/12/21 from mtdata.index import is_compatible, bcp47 def test_is_compatible(): assert is_compatible(bcp47('en'), bcp47('en')) assert is_compatible(bcp47('en'), bcp47('en_US')) assert is_compatible(bcp47('en_US'), bcp47('en')) assert not is_compatible(bcp47('en_US'), bcp47('en_GB')) assert not is_compatible(bcp47('en_US'), bcp47('en_IN')) assert is_compatible(bcp47('en_US'), bcp47('en_Latn')) assert is_compatible(bcp47('en_US'), bcp47('en_Latn_US')) assert is_compatible(bcp47('por_BR'), bcp47('por')) assert is_compatible(bcp47('por_PT'), bcp47('por')) assert not is_compatible(bcp47('por_PT'), bcp47('por_BR')) assert is_compatible(bcp47('kan'), bcp47('kan_IN')) assert is_compatible(bcp47('kan'), bcp47('kan_Knda')) assert is_compatible(bcp47('kan'), bcp47('kan_Knda_IN')) assert not is_compatible(bcp47('kan'), bcp47('kan_Deva_IN')) assert not is_compatible(bcp47('hin_Deva_In'), bcp47('kan_Deva_IN')) assert not is_compatible(bcp47('hin_In'), bcp47('kan_Deva_IN')) assert not is_compatible(bcp47('hin'), bcp47('kan_Deva_IN'))

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1

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8

565f0384e48199694bbe9a81f4dccb107136b8b4

1,359

py

Python

tests/test_log_requests.py

ex-tipsi/pytest-tipsi-testing-backup

26d73933f477571de4ef739a5f3b7d461e49832c

[ "MIT" ]

2

2018-01-25T02:46:07.000Z

2019-04-15T18:27:15.000Z

tests/test_log_requests.py

ex-tipsi/pytest-tipsi-testing-backup

26d73933f477571de4ef739a5f3b7d461e49832c

[ "MIT" ]

1

2019-10-09T13:13:42.000Z

tests/test_log_requests.py

ex-tipsi/pytest-tipsi-testing-backup

26d73933f477571de4ef739a5f3b7d461e49832c

[ "MIT" ]

2

2019-10-09T08:15:22.000Z

2019-10-14T14:32:30.000Z

import os import json from unittest.mock import patch import requests def test_docme(tmpdir, log_requests): with patch.dict('os.environ', {'DOCS_ROOT': tmpdir.strpath}): with log_requests('out'): r = requests.get('http://echo.jsontest.com/key/value/one/two') assert r.status_code == 200, r outfile = tmpdir.join('tests.test_log_requests.out.json') assert os.path.exists(outfile.strpath) data = json.loads(outfile.read()) assert len(data) == 1, data data = data[0] assert data['method'] == 'get', data assert data['response'] == "{'key': 'value', 'one': 'two'}", data def test_docme_second(tmpdir, log_requests): with patch.dict('os.environ', {'DOCS_ROOT': tmpdir.strpath}): with log_requests('out'): r = requests.get('http://echo.jsontest.com/key/value/one/two') assert r.status_code == 200, r with log_requests('out'): r = requests.get('http://echo.jsontest.com/key/value/one/two') assert r.status_code == 200, r outfile = tmpdir.join('tests.test_log_requests.out.json') assert os.path.exists(outfile.strpath) data = json.loads(outfile.read()) assert len(data) == 1, data data = data[0] assert data['method'] == 'get', data assert data['response'] == "{'key': 'value', 'one': 'two'}", data

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null

0

7

569bb446e1bb2fc396db2d44db8bd823b9ea83e4

85

py

Python

ips/ip/ir_recieve/__init__.py

zld012739/zldrepository

5635b78a168956091676ef4dd99fa564be0e5ba0

[ "MIT" ]

null

ips/ip/ir_recieve/__init__.py

zld012739/zldrepository

5635b78a168956091676ef4dd99fa564be0e5ba0

[ "MIT" ]

null

ips/ip/ir_recieve/__init__.py

zld012739/zldrepository

5635b78a168956091676ef4dd99fa564be0e5ba0

[ "MIT" ]

null

from ir_recieve_partial import get_ip_name from ir_recieve_partial import IR_RECIEVE

28.333333

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null

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1

0

1

0

8

3b061aba176bee9c7b313a54d7ecce529a4384a0

6,656

py

Python

decode_exchange_string.py

selplacei/factorio-stuff

c809bcd0312d2568390104e391abbd18a4f03e75

[ "Unlicense" ]

null

decode_exchange_string.py

selplacei/factorio-stuff

c809bcd0312d2568390104e391abbd18a4f03e75

[ "Unlicense" ]

null

decode_exchange_string.py

selplacei/factorio-stuff

c809bcd0312d2568390104e391abbd18a4f03e75

[ "Unlicense" ]

null

import blueprint import json bp = blueprint.Blueprint.from_exchange_string('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') for e in bp.data['blueprint']['entities']: print(json.dumps(e, indent=4))

832

6,546

0.961839

229

6,656

27.947598

0.956332

0

0.158957

0.002855

6,656

7

6,547

950.857143

0.805334

0

0.2

0.978666

0.976112

0

1

0

1

0

false

0

0.4

0

0.4

0.8

0

1

null

0

1

0

1

null

1

0

1

0

1

0

9

3b91e6eaecf8f2c6bd3406c5523b4fe71a918c8f

145

py

Python

loldib/getratings/models/NA/na_riven/__init__.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_riven/__init__.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

loldib/getratings/models/NA/na_riven/__init__.py

koliupy/loldib

c9ab94deb07213cdc42b5a7c26467cdafaf81b7f

[ "Apache-2.0" ]

null

from .na_riven_top import * from .na_riven_jng import * from .na_riven_mid import * from .na_riven_bot import * from .na_riven_sup import *

24.166667

28

0.758621

25

145

4

0.36

0.3

0.55

0.68

0

0.172414

145

5

29

0.833333

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

7

8e8411f03889d4695f356eaa7eac50a9c651cdc9

4,302

py

Python

server/rideshare/migrations/0035_auto_20170517_0555.py

aadabi/tagrides

980a6f7df62f1bb9b396eca79421388c36987f45

[ "Unlicense" ]

2

2018-03-21T23:32:09.000Z

2018-03-22T01:39:51.000Z

server/rideshare/migrations/0035_auto_20170517_0555.py

aadabi/Cruzer

980a6f7df62f1bb9b396eca79421388c36987f45

[ "Unlicense" ]

null

server/rideshare/migrations/0035_auto_20170517_0555.py

aadabi/Cruzer

980a6f7df62f1bb9b396eca79421388c36987f45

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2017-05-17 05:55 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('rideshare', '0034_auto_20170516_0702'), ] operations = [ migrations.AlterField( model_name='driveractive', name='driverod_departure_lat', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='driveractive', name='driverod_departure_lon', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='driveractive', name='driverod_destination_lat', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='driveractive', name='driverod_destination_lon', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='plannedtrips', name='driver_departure_latitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='plannedtrips', name='driver_departure_longitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='plannedtrips', name='driver_destination_latitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='plannedtrips', name='driver_destination_longitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='plannedtrips', name='driver_timeofdeparture_hour', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='plannedtrips', name='driver_timeofdeparture_minute', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='proposedtrips', name='rider_departure_latitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='proposedtrips', name='rider_departure_longitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='proposedtrips', name='rider_destination_latitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='proposedtrips', name='rider_destination_longitude', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='proposedtrips', name='rider_timeofdeparture_hour', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='proposedtrips', name='rider_timeofdeparture_minute', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='rideractive', name='riderod_departure_lat', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='rideractive', name='riderod_departure_lon', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='rideractive', name='riderod_destination_lat', field=models.FloatField(blank=True, max_length=200, null=True), ), migrations.AlterField( model_name='rideractive', name='riderod_destination_lon', field=models.FloatField(blank=True, max_length=200, null=True), ), ]

37.086207

75

0.603673

404

4,302

6.220297

0.14604

0.159172

0.198965

0.2308

0.923199

0.902109

0

0.026118

0.288006

4,302

115

76

37.408696

0.794319

0.015342

0

0.740741

1

0

0.182377

0.123081

0

1

0

false

0

0.018519

0

0.046296

0

null

0

1

0

1

0

null

0

10

d900d27237234bbe12d722aa8c9e77b4afd6f305

2,276

py

Python

test/test_bvr_hi.py

doedotdev/bvr

023fc93424fa6a50c8a3c2ce2fc48b76a041b58c

[ "MIT" ]

null

test/test_bvr_hi.py

doedotdev/bvr

023fc93424fa6a50c8a3c2ce2fc48b76a041b58c

[ "MIT" ]

12

2019-12-07T21:40:23.000Z

2019-12-07T21:43:54.000Z

test/test_bvr_hi.py

doedotdev/bvr

023fc93424fa6a50c8a3c2ce2fc48b76a041b58c

[ "MIT" ]

null

import logging from bvr.bvr_hi import bvr_hi logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def test_bvr_hi_called_as_decorator(caplog): @bvr_hi def hi(): return 2 return_value = hi() assert return_value == 2 assert hi.__name__ == "hi" # Important for decorators to not override method name assert len(caplog.records) == 2 assert caplog.records[0].msg == 'Hi' assert caplog.records[0].levelname == 'INFO' assert caplog.records[1].msg == 'Hi' assert caplog.records[1].levelname == 'INFO' def test_bvr_hi_called_as_callable_returning_decorator(caplog): @bvr_hi() def hi(): return 2 return_value = hi() assert return_value == 2 assert hi.__name__ == "hi" # Important for decorators to not override method name assert len(caplog.records) == 2 assert caplog.records[0].msg == 'Hi' assert caplog.records[0].levelname == 'INFO' assert caplog.records[1].msg == 'Hi' assert caplog.records[1].levelname == 'INFO' def test_bvr_hi_called_as_decorator_with_function_args(caplog): @bvr_hi def hi(msg): logger.info(msg) return msg return_value = hi("Hello") assert return_value == "Hello" assert hi.__name__ == "hi" # Important for decorators to not override method name assert len(caplog.records) == 3 assert caplog.records[0].msg == 'Hi' assert caplog.records[0].levelname == 'INFO' assert caplog.records[1].msg == 'Hello' assert caplog.records[1].levelname == 'INFO' assert caplog.records[2].msg == 'Hi' assert caplog.records[2].levelname == 'INFO' def test_bvr_hi_called_as_callable_returning_decorator_with_function_args(caplog): @bvr_hi() def hi(msg): logger.info(msg) return msg return_value = hi("Hello") assert return_value == "Hello" assert hi.__name__ == "hi" # Important for decorators to not override method name assert len(caplog.records) == 3 assert caplog.records[0].msg == 'Hi' assert caplog.records[0].levelname == 'INFO' assert caplog.records[1].msg == 'Hello' assert caplog.records[1].levelname == 'INFO' assert caplog.records[2].msg == 'Hi' assert caplog.records[2].levelname == 'INFO'

26.776471

86

0.670475

310

2,276

4.716129

0.132258

0.213406

0.259918

0.109439

0.934337

0.920657

0

0.015504

0.206503

2,276

84

87

27.095238

0.79402

0.092707

0

0.862069

0

0.045631

0

0.551724

1

0.137931

false

0

0.034483

0.241379

0

null

1

0

1

0

1

0

null

0

1

0

10

d96d74a5691df6e353d73060064a6f5a63632727

25,737

py

Python

fhir/resources/tests/test_servicerequest.py

mmabey/fhir.resources

cc73718e9762c04726cd7de240c8f2dd5313cbe1

[ "BSD-3-Clause" ]

null

fhir/resources/tests/test_servicerequest.py

mmabey/fhir.resources

cc73718e9762c04726cd7de240c8f2dd5313cbe1

[ "BSD-3-Clause" ]

null

fhir/resources/tests/test_servicerequest.py

mmabey/fhir.resources

cc73718e9762c04726cd7de240c8f2dd5313cbe1

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """ Profile: http://hl7.org/fhir/StructureDefinition/ServiceRequest Release: R4 Version: 4.0.1 Build ID: 9346c8cc45 Last updated: 2019-11-01T09:29:23.356+11:00 """ import io import json import os import unittest import pytest from .. import servicerequest from ..fhirdate import FHIRDate from .fixtures import force_bytes @pytest.mark.usefixtures("base_settings") class ServiceRequestTests(unittest.TestCase): def instantiate_from(self, filename): datadir = os.environ.get("FHIR_UNITTEST_DATADIR") or "" with io.open(os.path.join(datadir, filename), "r", encoding="utf-8") as handle: js = json.load(handle) self.assertEqual("ServiceRequest", js["resourceType"]) return servicerequest.ServiceRequest(js) def testServiceRequest1(self): inst = self.instantiate_from("servicerequest-example2.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest1(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest1(inst2) def implServiceRequest1(self, inst): self.assertEqual( force_bytes(inst.asNeededCodeableConcept.text), force_bytes("as needed to clear mucus"), ) self.assertEqual(inst.authoredOn.date, FHIRDate("2017-02-01T17:23:07Z").date) self.assertEqual(inst.authoredOn.as_json(), "2017-02-01T17:23:07Z") self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("34431008")) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Physiotherapy of chest (regime/therapy) "), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.contained[0].id), force_bytes("signature")) self.assertEqual( force_bytes(inst.contained[1].id), force_bytes("cystic-fibrosis") ) self.assertEqual(force_bytes(inst.id), force_bytes("physiotherapy")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("http://goodhealth.org/placer-ids"), ) self.assertEqual( force_bytes(inst.identifier[0].type.coding[0].code), force_bytes("PLAC") ) self.assertEqual( force_bytes(inst.identifier[0].type.coding[0].display), force_bytes("Placer Identifier"), ) self.assertEqual( force_bytes(inst.identifier[0].type.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v2-0203"), ) self.assertEqual( force_bytes(inst.identifier[0].type.text), force_bytes("Placer") ) self.assertEqual( force_bytes(inst.identifier[0].value), force_bytes("20170201-0001") ) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(inst.occurrenceTiming.repeat.duration, 15) self.assertEqual(inst.occurrenceTiming.repeat.durationMax, 25) self.assertEqual( force_bytes(inst.occurrenceTiming.repeat.durationUnit), force_bytes("min") ) self.assertEqual(inst.occurrenceTiming.repeat.frequency, 1) self.assertEqual(inst.occurrenceTiming.repeat.frequencyMax, 4) self.assertEqual(inst.occurrenceTiming.repeat.period, 1) self.assertEqual( force_bytes(inst.occurrenceTiming.repeat.periodUnit), force_bytes("d") ) self.assertEqual(force_bytes(inst.status), force_bytes("completed")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest2(self): inst = self.instantiate_from("servicerequest-example3.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest2(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest2(inst2) def implServiceRequest2(self, inst): self.assertEqual(inst.authoredOn.date, FHIRDate("2017-02-01T17:23:07Z").date) self.assertEqual(inst.authoredOn.as_json(), "2017-02-01T17:23:07Z") self.assertEqual( force_bytes(inst.code.coding[0].code), force_bytes("359962006") ) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Turning patient in bed (procedure)"), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertTrue(inst.doNotPerform) self.assertEqual(force_bytes(inst.id), force_bytes("do-not-turn")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("http://goodhealth.org/placer-ids"), ) self.assertEqual( force_bytes(inst.identifier[0].value), force_bytes("20170201-0002") ) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(force_bytes(inst.priority), force_bytes("stat")) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest3(self): inst = self.instantiate_from("servicerequest-example-lipid.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest3(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest3(inst2) def implServiceRequest3(self, inst): self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("LIPID")) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://acme.org/tests"), ) self.assertEqual(force_bytes(inst.code.text), force_bytes("Lipid Panel")) self.assertEqual(force_bytes(inst.contained[0].id), force_bytes("fasting")) self.assertEqual(force_bytes(inst.contained[1].id), force_bytes("serum")) self.assertEqual(force_bytes(inst.id), force_bytes("lipid")) self.assertEqual( force_bytes(inst.identifier[0].system), force_bytes("urn:oid:1.3.4.5.6.7") ) self.assertEqual( force_bytes(inst.identifier[0].type.coding[0].code), force_bytes("PLAC") ) self.assertEqual( force_bytes(inst.identifier[0].type.coding[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v2-0203"), ) self.assertEqual( force_bytes(inst.identifier[0].type.text), force_bytes("Placer") ) self.assertEqual( force_bytes(inst.identifier[0].value), force_bytes("2345234234234") ) self.assertEqual(force_bytes(inst.intent), force_bytes("original-order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual( force_bytes(inst.note[0].text), force_bytes("patient is afraid of needles") ) self.assertEqual( inst.occurrenceDateTime.date, FHIRDate("2013-05-02T16:16:00-07:00").date ) self.assertEqual(inst.occurrenceDateTime.as_json(), "2013-05-02T16:16:00-07:00") self.assertEqual( force_bytes(inst.reasonCode[0].coding[0].code), force_bytes("V173") ) self.assertEqual( force_bytes(inst.reasonCode[0].coding[0].display), force_bytes("Fam hx-ischem heart dis"), ) self.assertEqual( force_bytes(inst.reasonCode[0].coding[0].system), force_bytes("http://hl7.org/fhir/sid/icd-9"), ) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest4(self): inst = self.instantiate_from("servicerequest-example-colonoscopy-bx.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest4(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest4(inst2) def implServiceRequest4(self, inst): self.assertEqual(inst.authoredOn.date, FHIRDate("2017-03-05").date) self.assertEqual(inst.authoredOn.as_json(), "2017-03-05") self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("76164006")) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Biopsy of colon (procedure)"), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.code.text), force_bytes("Biopsy of colon")) self.assertEqual(force_bytes(inst.id), force_bytes("colon-biopsy")) self.assertEqual(force_bytes(inst.identifier[0].value), force_bytes("12345")) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual( force_bytes(inst.requisition.system), force_bytes("http://bumc.org/requisitions"), ) self.assertEqual(force_bytes(inst.requisition.value), force_bytes("req12345")) self.assertEqual(force_bytes(inst.status), force_bytes("completed")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest5(self): inst = self.instantiate_from("servicerequest-example4.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest5(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest5(inst2) def implServiceRequest5(self, inst): self.assertEqual( force_bytes(inst.code.coding[0].code), force_bytes("229115003") ) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Bench Press (regime/therapy) "), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.id), force_bytes("benchpress")) self.assertEqual(force_bytes(inst.intent), force_bytes("plan")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(inst.occurrenceTiming.repeat.count, 20) self.assertEqual(inst.occurrenceTiming.repeat.countMax, 30) self.assertEqual(inst.occurrenceTiming.repeat.frequency, 3) self.assertEqual(inst.occurrenceTiming.repeat.period, 1) self.assertEqual( force_bytes(inst.occurrenceTiming.repeat.periodUnit), force_bytes("wk") ) self.assertEqual( force_bytes(inst.patientInstruction), force_bytes( "Start with 30kg 10-15 repetitions for three sets and increase in increments of 5kg when you feel ready" ), ) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest6(self): inst = self.instantiate_from("servicerequest-example-edu.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest6(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest6(inst2) def implServiceRequest6(self, inst): self.assertEqual(inst.authoredOn.date, FHIRDate("2016-08-16").date) self.assertEqual(inst.authoredOn.as_json(), "2016-08-16") self.assertEqual( force_bytes(inst.category[0].coding[0].code), force_bytes("311401005") ) self.assertEqual( force_bytes(inst.category[0].coding[0].display), force_bytes("Patient education (procedure)"), ) self.assertEqual( force_bytes(inst.category[0].coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.category[0].text), force_bytes("Education")) self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("48023004")) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Breast self-examination technique education (procedure)"), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual( force_bytes(inst.code.text), force_bytes("Health education - breast examination"), ) self.assertEqual(force_bytes(inst.id), force_bytes("education")) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(inst.occurrenceDateTime.date, FHIRDate("2014-08-16").date) self.assertEqual(inst.occurrenceDateTime.as_json(), "2014-08-16") self.assertEqual( force_bytes(inst.reasonCode[0].text), force_bytes("early detection of breast mass"), ) self.assertEqual(force_bytes(inst.status), force_bytes("completed")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest7(self): inst = self.instantiate_from("servicerequest-example-ventilation.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest7(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest7(inst2) def implServiceRequest7(self, inst): self.assertEqual(inst.authoredOn.date, FHIRDate("2018-02-20").date) self.assertEqual(inst.authoredOn.as_json(), "2018-02-20") self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("40617009")) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Artificial respiration (procedure)"), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual( force_bytes(inst.code.text), force_bytes("Mechanical Ventilation") ) self.assertEqual(force_bytes(inst.id), force_bytes("vent")) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual( force_bytes(inst.orderDetail[0].coding[0].code), force_bytes("243144002") ) self.assertEqual( force_bytes(inst.orderDetail[0].coding[0].display), force_bytes("Patient triggered inspiratory assistance (procedure)"), ) self.assertEqual( force_bytes(inst.orderDetail[0].coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.orderDetail[0].text), force_bytes("IPPB")) self.assertEqual( force_bytes(inst.orderDetail[1].text), force_bytes( " Initial Settings : Sens: -1 cm H20 Pressure 15 cm H2O moderate flow: Monitor VS every 15 minutes x 4 at the start of mechanical ventilation, then routine for unit OR every 5 hr" ), ) self.assertEqual( force_bytes(inst.reasonCode[0].text), force_bytes("chronic obstructive lung disease (COLD)"), ) self.assertEqual(force_bytes(inst.status), force_bytes("completed")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest8(self): inst = self.instantiate_from("servicerequest-example-ambulation.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest8(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest8(inst2) def implServiceRequest8(self, inst): self.assertEqual(inst.authoredOn.date, FHIRDate("2017-03-05").date) self.assertEqual(inst.authoredOn.as_json(), "2017-03-05") self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("62013009")) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Ambulating patient (procedure)"), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.code.text), force_bytes("Ambulation")) self.assertEqual(force_bytes(inst.id), force_bytes("ambulation")) self.assertEqual(force_bytes(inst.identifier[0].value), force_bytes("45678")) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(force_bytes(inst.status), force_bytes("completed")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest9(self): inst = self.instantiate_from("servicerequest-example-pt.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest9(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest9(inst2) def implServiceRequest9(self, inst): self.assertEqual(inst.authoredOn.date, FHIRDate("2016-09-20").date) self.assertEqual(inst.authoredOn.as_json(), "2016-09-20") self.assertEqual( force_bytes(inst.bodySite[0].coding[0].code), force_bytes("36701003") ) self.assertEqual( force_bytes(inst.bodySite[0].coding[0].display), force_bytes("Both knees (body structure)"), ) self.assertEqual( force_bytes(inst.bodySite[0].coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.bodySite[0].text), force_bytes("Both knees")) self.assertEqual( force_bytes(inst.category[0].coding[0].code), force_bytes("386053000") ) self.assertEqual( force_bytes(inst.category[0].coding[0].display), force_bytes("Evaluation procedure (procedure)"), ) self.assertEqual( force_bytes(inst.category[0].coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual(force_bytes(inst.category[0].text), force_bytes("Evaluation")) self.assertEqual( force_bytes(inst.code.coding[0].code), force_bytes("710830005") ) self.assertEqual( force_bytes(inst.code.coding[0].display), force_bytes("Assessment of passive range of motion (procedure)"), ) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://snomed.info/sct"), ) self.assertEqual( force_bytes(inst.code.text), force_bytes("Assessment of passive range of motion"), ) self.assertEqual(force_bytes(inst.id), force_bytes("physical-therapy")) self.assertEqual(force_bytes(inst.intent), force_bytes("order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual(inst.occurrenceDateTime.date, FHIRDate("2016-09-27").date) self.assertEqual(inst.occurrenceDateTime.as_json(), "2016-09-27") self.assertEqual( force_bytes(inst.reasonCode[0].text), force_bytes("assessment of mobility limitations due to osteoarthritis"), ) self.assertEqual(force_bytes(inst.status), force_bytes("completed")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated")) def testServiceRequest10(self): inst = self.instantiate_from("servicerequest-example-di.json") self.assertIsNotNone(inst, "Must have instantiated a ServiceRequest instance") self.implServiceRequest10(inst) js = inst.as_json() self.assertEqual("ServiceRequest", js["resourceType"]) inst2 = servicerequest.ServiceRequest(js) self.implServiceRequest10(inst2) def implServiceRequest10(self, inst): self.assertEqual(force_bytes(inst.code.coding[0].code), force_bytes("24627-2")) self.assertEqual( force_bytes(inst.code.coding[0].system), force_bytes("http://loinc.org") ) self.assertEqual(force_bytes(inst.code.text), force_bytes("Chest CT")) self.assertEqual(force_bytes(inst.id), force_bytes("di")) self.assertEqual(force_bytes(inst.intent), force_bytes("original-order")) self.assertEqual(force_bytes(inst.meta.tag[0].code), force_bytes("HTEST")) self.assertEqual( force_bytes(inst.meta.tag[0].display), force_bytes("test health data") ) self.assertEqual( force_bytes(inst.meta.tag[0].system), force_bytes("http://terminology.hl7.org/CodeSystem/v3-ActReason"), ) self.assertEqual( inst.occurrenceDateTime.date, FHIRDate("2013-05-08T09:33:27+07:00").date ) self.assertEqual(inst.occurrenceDateTime.as_json(), "2013-05-08T09:33:27+07:00") self.assertEqual( force_bytes(inst.reasonCode[0].text), force_bytes("Check for metastatic disease"), ) self.assertEqual(force_bytes(inst.status), force_bytes("active")) self.assertEqual(force_bytes(inst.text.status), force_bytes("generated"))

45.231986

196

0.64689

2,877

25,737

5.665276

0.117136

0.193263

0.19265

0.240812

0.829253

0.813915

0.788269

0.748267

0.700595

0.664335

0

0.03476

0.220888

25,737

568

197

45.31162

0.778077

0.006916

0

0.499048

0

0.001905

0.172126

0.017534

0

0.4

1

0.04

false

0.00381

0.015238

0

0.059048

0

null

0

1

0

null

0

8

796503261bc0da4dbd38c017915d6293ed46fe21

12,936

py

Python

lithic/types/account_holder_create_params.py

lithic-com/lithic-python

be19d7195ebdf217b45f1ab59b39021d51330989

[ "Apache-2.0" ]

null

lithic/types/account_holder_create_params.py

lithic-com/lithic-python

be19d7195ebdf217b45f1ab59b39021d51330989

[ "Apache-2.0" ]

null

lithic/types/account_holder_create_params.py

lithic-com/lithic-python

be19d7195ebdf217b45f1ab59b39021d51330989

[ "Apache-2.0" ]

null

# File generated from our OpenAPI spec by Stainless. from __future__ import annotations from typing import Optional, Union, List from typing_extensions import Literal, TypedDict, Required from ..types import shared_params __all__ = [ "KYCIndividualAddress", "KYCIndividual", "KYC", "KybBusinessEntityAddress", "KybBusinessEntity", "KybBeneficialOwnerEntitiesAddress", "KybBeneficialOwnerEntities", "KybBeneficialOwnerIndividualsAddress", "KybBeneficialOwnerIndividuals", "KybControlPersonAddress", "KybControlPerson", "Kyb", "AccountHolderCreateParams", ] class KYCIndividualAddress(TypedDict, total=False): address1: Required[str] """Valid deliverable address (no PO boxes).""" city: Required[str] """Name of city.""" country: Required[str] """Valid country code. Only USA is currently supported, entered in uppercase ISO 3166-1 alpha-3 three-character format.""" postal_code: Required[str] """Valid postal code. Only USA ZIP codes are currently supported, entered as a five-digit ZIP or nine-digit ZIP+4.""" state: Required[str] """Valid state code. Only USA state codes are currently supported, entered in uppercase ISO 3166-2 two-character format.""" address2: str """Unit or apartment number (if applicable).""" class KYCIndividual(TypedDict, total=False): address: Required[KYCIndividualAddress] """Individual's current address - PO boxes, UPS drops, and FedEx drops are not acceptable; APO/FPO are acceptable. Only USA addresses are currently supported.""" dob: Required[str] """Individual's date of birth, as an ISO 8601 date.""" email: Required[str] """Individual's email address. If utilizing Lithic for chargeback processing, this customer email address may be used to communicate dispute status and resolution.""" first_name: Required[str] """Individual's first name, as it appears on government-issued identity documents.""" government_id: Required[str] """Government-issued identification number (required for identity verification and compliance with banking regulations). Social Security Numbers (SSN) and Individual Taxpayer Identification Numbers (ITIN) are currently supported, entered as full nine-digits, with or without hyphens""" last_name: Required[str] """Individual's last name, as it appears on government-issued identity documents.""" phone_number: Required[str] """Individual's phone number, entered in E.164 format.""" class KYC(TypedDict, total=False): individual: Required[KYCIndividual] """Information on individual for whom the account is being opened and KYC is being run.""" tos_timestamp: Required[str] """An ISO 8601 timestamp indicating when Lithic's terms of service were accepted by the API customer.""" workflow: Required[Literal["KYC_ADVANCED", "KYC_BASIC", "KYC_BYO"]] """Specifies the type of KYC workflow to run.""" kyc_passed_timestamp: str """An ISO 8601 timestamp indicating when precomputed KYC was completed on the individual with a pass result. This field is required only if workflow type is `KYC_BYO`.""" class KybBusinessEntityAddress(TypedDict, total=False): address1: Required[str] """Valid deliverable address (no PO boxes).""" city: Required[str] """Name of city.""" country: Required[str] """Valid country code. Only USA is currently supported, entered in uppercase ISO 3166-1 alpha-3 three-character format.""" postal_code: Required[str] """Valid postal code. Only USA ZIP codes are currently supported, entered as a five-digit ZIP or nine-digit ZIP+4.""" state: Required[str] """Valid state code. Only USA state codes are currently supported, entered in uppercase ISO 3166-2 two-character format.""" address2: str """Unit or apartment number (if applicable).""" class KybBusinessEntity(TypedDict, total=False): address: Required[KybBusinessEntityAddress] """Business's physical address - PO boxes, UPS drops, and FedEx drops are not acceptable; APO/FPO are acceptable.""" government_id: Required[str] """Government-issued identification number. US Federal Employer Identification Numbers (EIN) are currently supported, entered as full nine-digits, with or without hyphens.""" legal_business_name: Required[str] """Legal (formal) business name.""" phone_numbers: Required[List[str]] """One or more of the business's phone number(s), entered as a list in E.164 format.""" dba_business_name: str """Any name that the business operates under that is not its legal business name (if applicable).""" parent_company: str """Parent company name (if applicable).""" class KybBeneficialOwnerEntitiesAddress(TypedDict, total=False): address1: Required[str] """Valid deliverable address (no PO boxes).""" city: Required[str] """Name of city.""" country: Required[str] """Valid country code. Only USA is currently supported, entered in uppercase ISO 3166-1 alpha-3 three-character format.""" postal_code: Required[str] """Valid postal code. Only USA ZIP codes are currently supported, entered as a five-digit ZIP or nine-digit ZIP+4.""" state: Required[str] """Valid state code. Only USA state codes are currently supported, entered in uppercase ISO 3166-2 two-character format.""" address2: str """Unit or apartment number (if applicable).""" class KybBeneficialOwnerEntities(TypedDict, total=False): address: Required[KybBeneficialOwnerEntitiesAddress] """Business's physical address - PO boxes, UPS drops, and FedEx drops are not acceptable; APO/FPO are acceptable.""" government_id: Required[str] """Government-issued identification number. US Federal Employer Identification Numbers (EIN) are currently supported, entered as full nine-digits, with or without hyphens.""" legal_business_name: Required[str] """Legal (formal) business name.""" phone_numbers: Required[List[str]] """One or more of the business's phone number(s), entered as a list in E.164 format.""" dba_business_name: str """Any name that the business operates under that is not its legal business name (if applicable).""" parent_company: str """Parent company name (if applicable).""" class KybBeneficialOwnerIndividualsAddress(TypedDict, total=False): address1: Required[str] """Valid deliverable address (no PO boxes).""" city: Required[str] """Name of city.""" country: Required[str] """Valid country code. Only USA is currently supported, entered in uppercase ISO 3166-1 alpha-3 three-character format.""" postal_code: Required[str] """Valid postal code. Only USA ZIP codes are currently supported, entered as a five-digit ZIP or nine-digit ZIP+4.""" state: Required[str] """Valid state code. Only USA state codes are currently supported, entered in uppercase ISO 3166-2 two-character format.""" address2: str """Unit or apartment number (if applicable).""" class KybBeneficialOwnerIndividuals(TypedDict, total=False): address: Required[KybBeneficialOwnerIndividualsAddress] """Individual's current address - PO boxes, UPS drops, and FedEx drops are not acceptable; APO/FPO are acceptable. Only USA addresses are currently supported.""" dob: Required[str] """Individual's date of birth, as an ISO 8601 date.""" email: Required[str] """Individual's email address. If utilizing Lithic for chargeback processing, this customer email address may be used to communicate dispute status and resolution.""" first_name: Required[str] """Individual's first name, as it appears on government-issued identity documents.""" government_id: Required[str] """Government-issued identification number (required for identity verification and compliance with banking regulations). Social Security Numbers (SSN) and Individual Taxpayer Identification Numbers (ITIN) are currently supported, entered as full nine-digits, with or without hyphens""" last_name: Required[str] """Individual's last name, as it appears on government-issued identity documents.""" phone_number: Required[str] """Individual's phone number, entered in E.164 format.""" class KybControlPersonAddress(TypedDict, total=False): address1: Required[str] """Valid deliverable address (no PO boxes).""" city: Required[str] """Name of city.""" country: Required[str] """Valid country code. Only USA is currently supported, entered in uppercase ISO 3166-1 alpha-3 three-character format.""" postal_code: Required[str] """Valid postal code. Only USA ZIP codes are currently supported, entered as a five-digit ZIP or nine-digit ZIP+4.""" state: Required[str] """Valid state code. Only USA state codes are currently supported, entered in uppercase ISO 3166-2 two-character format.""" address2: str """Unit or apartment number (if applicable).""" class KybControlPerson(TypedDict, total=False): address: Required[KybControlPersonAddress] """Individual's current address - PO boxes, UPS drops, and FedEx drops are not acceptable; APO/FPO are acceptable. Only USA addresses are currently supported.""" dob: Required[str] """Individual's date of birth, as an ISO 8601 date.""" email: Required[str] """Individual's email address. If utilizing Lithic for chargeback processing, this customer email address may be used to communicate dispute status and resolution.""" first_name: Required[str] """Individual's first name, as it appears on government-issued identity documents.""" government_id: Required[str] """Government-issued identification number (required for identity verification and compliance with banking regulations). Social Security Numbers (SSN) and Individual Taxpayer Identification Numbers (ITIN) are currently supported, entered as full nine-digits, with or without hyphens""" last_name: Required[str] """Individual's last name, as it appears on government-issued identity documents.""" phone_number: Required[str] """Individual's phone number, entered in E.164 format.""" class Kyb(TypedDict, total=False): beneficial_owner_entities: Required[List[KybBeneficialOwnerEntities]] """List of all entities with >25% ownership in the company. If no entity or individual owns >25% of the company, and the largest shareholder is an entity, please identify them in this field. See [FinCEN requirements](https://www.fincen.gov/sites/default/files/shared/CDD_Rev6.7_Sept_2017_Certificate.pdf) (Section I) for more background. If no business owner is an entity, pass in an empty list. However, either this parameter or `beneficial_owner_individuals` must be populated. on entities that should be included.""" beneficial_owner_individuals: Required[List[KybBeneficialOwnerIndividuals]] """List of all individuals with >25% ownership in the company. If no entity or individual owns >25% of the company, and the largest shareholder is an individual, please identify them in this field. See [FinCEN requirements](https://www.fincen.gov/sites/default/files/shared/CDD_Rev6.7_Sept_2017_Certificate.pdf) (Section I) for more background on individuals that should be included. If no individual is an entity, pass in an empty list. However, either this parameter or `beneficial_owner_entities` must be populated.""" business_entity: Required[KybBusinessEntity] """Information for business for which the account is being opened and KYB is being run.""" control_person: Required[KybControlPerson] """An individual with significant responsibility for managing the legal entity (e.g., a Chief Executive Officer, Chief Financial Officer, Chief Operating Officer, Managing Member, General Partner, President, Vice President, or Treasurer). This can be an executive, or someone who will have program-wide access to the cards that Lithic will provide. In some cases, this individual could also be a beneficial owner listed above. See [FinCEN requirements](https://www.fincen.gov/sites/default/files/shared/CDD_Rev6.7_Sept_2017_Certificate.pdf) (Section II) for more background.""" nature_of_business: Required[str] """Short description of the company's line of business (i.e., what does the company do?).""" tos_timestamp: Required[str] """An ISO 8601 timestamp indicating when Lithic's terms of service were accepted by the API customer.""" website_url: Required[str] """Company website URL.""" workflow: Required[Literal["KYB_BASIC", "KYB_BYO"]] """Specifies the type of KYB workflow to run.""" kyb_passed_timestamp: str """An ISO 8601 timestamp indicating when precomputed KYC was completed on the business with a pass result. This field is required only if workflow type is `KYB_BYO`.""" AccountHolderCreateParams = Union[KYC, Kyb]

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7

799279249bf2f662732bddceb61d3225979a7a1c

102

py

Python

src/behavior_tree_learning/core/plotter/print_functions.py

dgerod/behavior_tree_learning

71da80c91ecd48fd5da377f83604b62112ba9629

[ "Apache-2.0" ]

7

2022-02-09T12:51:51.000Z

2022-03-19T14:40:16.000Z

src/behavior_tree_learning/core/plotter/print_functions.py

dgerod/bt_learning_using_gp

ac1fb6ba4dbd6d18b5d002c7ad2647771f8b0fb9

[ "Apache-2.0" ]

6

2021-12-12T15:38:40.000Z

2022-01-31T11:02:12.000Z

src/behavior_tree_learning/core/plotter/print_functions.py

dgerod/bt_learning_using_gp

ac1fb6ba4dbd6d18b5d002c7ad2647771f8b0fb9

[ "Apache-2.0" ]

null

import py_trees as pt def print_ascii_tree(py_tree): print(pt.display.ascii_tree(py_tree.root))

17

46

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1

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9

79942b2d7989666bf2aaeeaa75032db470ecb82b

1,354

py

Python

scripts/Qubit/TimeDomain/swap_batch/batch.py

sourav-majumder/qtlab

96b2a127b1df7b45622c90229bd5ef8a4083614e

[ "MIT" ]

null

scripts/Qubit/TimeDomain/swap_batch/batch.py

sourav-majumder/qtlab

96b2a127b1df7b45622c90229bd5ef8a4083614e

[ "MIT" ]

null

scripts/Qubit/TimeDomain/swap_batch/batch.py

sourav-majumder/qtlab

96b2a127b1df7b45622c90229bd5ef8a4083614e

[ "MIT" ]

null

# execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_30.py') # execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_100.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_200.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_300.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_400.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_500.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_600.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_800.py') #execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_1000.py') execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_2000.py') execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_3000.py') execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_4000.py') execfile(r'C:\qtlab-aalto\scripts\Qubit\TimeDomain\swap_batch\0dbm_swap\swap_with_high_pulse_5000.py')

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11

79951e1e449db66902a7aaacb3a90bafd0c07b7c

1,725

py

Python

tests/perf/test-assign-reg.py

wenq1/duktape

5ed3eee19b291f3b3de0b212cc62c0aba0ab4ecb

[ "MIT" ]

4,268

2015-01-01T17:33:40.000Z

2022-03-31T17:53:31.000Z

tests/perf/test-assign-reg.py

KiraanRK/esp32-duktape

1b7fbcb8bd6bfc346d92df30ec099df7f13b03aa

[ "MIT" ]

1,667

2015-01-01T22:43:03.000Z

2022-02-23T22:27:19.000Z

tests/perf/test-assign-reg.py

KiraanRK/esp32-duktape

1b7fbcb8bd6bfc346d92df30ec099df7f13b03aa

[ "MIT" ]

565

2015-01-08T14:15:28.000Z

2022-03-31T16:29:31.000Z

def test(): r0 = 123.0 r1 = 123.1 r2 = 123.2 r3 = 123.3 r4 = 123.4 r5 = 123.5 r6 = 123.6 r7 = 123.7 r8 = 123.8 r9 = 123.9 i = 0 while i < 1e7: t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 t = r0 t = r1 t = r2 t = r3 t = r4 t = r5 t = r6 t = r7 t = r8 t = r9 i += 1 test()

13.476563

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1

0

1

0

1

0

null

0

10

79ce249ed07f688f366151f9c1a12a13ac191dfe

95,909

py

Python

src/python/k4a/tests/test_functional_ctypes_azurekinect.py

seanyen/Azure-Kinect-Sensor-SDK

d87ef578676c05b9a5d23c097502942753bf3777

[ "MIT" ]

1,120

2019-05-07T06:01:44.000Z

2022-03-28T08:02:29.000Z

src/python/k4a/tests/test_functional_ctypes_azurekinect.py

seanyen/Azure-Kinect-Sensor-SDK

d87ef578676c05b9a5d23c097502942753bf3777

[ "MIT" ]

1,321

2019-05-07T14:37:15.000Z

2022-03-31T12:03:01.000Z

src/python/k4a/tests/test_functional_ctypes_azurekinect.py

seanyen/Azure-Kinect-Sensor-SDK

d87ef578676c05b9a5d23c097502942753bf3777

[ "MIT" ]

529

2019-05-06T22:25:33.000Z

2022-03-31T13:57:26.000Z

''' test_k4a_azurekinect.py Tests for the k4a functions for Azure Kinect device. Copyright (C) Microsoft Corporation. All rights reserved. ''' import unittest import ctypes from time import sleep import k4a import test_config def get_1080p_bgr32_nfov_2x2binned(device_handle): return test_config.get_capture(device_handle, k4a.EImageFormat.COLOR_BGRA32, k4a.EColorResolution.RES_1080P, k4a.EDepthMode.NFOV_2X2BINNED) def k4a_device_get_color_control_capability( device_handle:k4a._bindings.k4a._DeviceHandle, color_control_command:k4a.EColorControlCommand )->k4a.EStatus: supports_auto = ctypes.c_bool(False) min_value = ctypes.c_int32(0) max_value = ctypes.c_int32(0) step_value = ctypes.c_int32(0) default_value = ctypes.c_int32(0) color_control_mode = ctypes.c_int32(k4a.EColorControlMode.AUTO.value) status = k4a._bindings.k4a.k4a_device_get_color_control_capabilities( device_handle, color_control_command, ctypes.byref(supports_auto), ctypes.byref(min_value), ctypes.byref(max_value), ctypes.byref(step_value), ctypes.byref(default_value), ctypes.byref(color_control_mode), ) return status def k4a_device_set_and_get_color_control( device_handle:k4a._bindings.k4a._DeviceHandle, color_control_command:k4a.EColorControlCommand): mode = ctypes.c_int32(k4a.EColorControlMode.MANUAL.value) saved_value = ctypes.c_int32(0) # Get the step size. supports_auto = ctypes.c_bool(False) min_value = ctypes.c_int32(0) max_value = ctypes.c_int32(0) step_value = ctypes.c_int32(0) default_value = ctypes.c_int32(0) color_control_mode = ctypes.c_int32(k4a.EColorControlMode.MANUAL.value) status = k4a._bindings.k4a.k4a_device_get_color_control_capabilities( device_handle, color_control_command, ctypes.byref(supports_auto), ctypes.byref(min_value), ctypes.byref(max_value), ctypes.byref(step_value), ctypes.byref(default_value), ctypes.byref(color_control_mode), ) mode = color_control_mode # Read the original value. status0 = k4a._bindings.k4a.k4a_device_get_color_control( device_handle, ctypes.c_int(color_control_command.value), ctypes.byref(mode), ctypes.byref(saved_value)) # Write a new value. new_value = ctypes.c_int32(0) if (saved_value.value + step_value.value <= max_value.value): new_value = ctypes.c_int32(saved_value.value + step_value.value) else: new_value = ctypes.c_int32(saved_value.value - step_value.value) status1 = k4a._bindings.k4a.k4a_device_set_color_control( device_handle, ctypes.c_int(color_control_command.value), mode, new_value) # Read back the value to check that it was written. new_value_readback = ctypes.c_int32(0) status2 = k4a._bindings.k4a.k4a_device_get_color_control( device_handle, ctypes.c_int(color_control_command.value), ctypes.byref(mode), ctypes.byref(new_value_readback)) # Write the original saved value. status3 = k4a._bindings.k4a.k4a_device_set_color_control( device_handle, ctypes.c_int(color_control_command.value), mode, saved_value) # Read back the value to check that it was written. saved_value_readback = ctypes.c_int32(0) status4 = k4a._bindings.k4a.k4a_device_get_color_control( device_handle, ctypes.c_int(color_control_command.value), ctypes.byref(mode), ctypes.byref(saved_value_readback)) return (status, status0, status1, status2, status3, status4, saved_value, saved_value_readback, new_value, new_value_readback) class Test_Functional_Ctypes_AzureKinect(unittest.TestCase): '''Test k4a functions requiring a device handle for Azure Kinect device. ''' @classmethod def setUpClass(cls): cls.device_handle = k4a._bindings.k4a._DeviceHandle() status = k4a._bindings.k4a.k4a_device_open(ctypes.c_uint32(0), ctypes.byref(cls.device_handle)) assert(k4a.K4A_SUCCEEDED(status)) cls.lock = test_config.glb_lock @classmethod def tearDownClass(cls): if test_config.glb_capture is not None: k4a._bindings.k4a.k4a_capture_release(test_config.glb_capture) test_config.glb_capture = None # Stop the cameras and imus before closing device. k4a._bindings.k4a.k4a_device_stop_cameras(cls.device_handle) k4a._bindings.k4a.k4a_device_stop_imu(cls.device_handle) k4a._bindings.k4a.k4a_device_close(cls.device_handle) def test_functional_fast_ctypes_device_open_twice_expected_fail(self): device_handle_2 = k4a._bindings.k4a._DeviceHandle() status = k4a._bindings.k4a.k4a_device_open(ctypes.c_uint32(0), ctypes.byref(device_handle_2)) self.assertTrue(k4a.K4A_FAILED(status)) status = k4a._bindings.k4a.k4a_device_open(ctypes.c_uint32(1000000), ctypes.byref(device_handle_2)) self.assertTrue(k4a.K4A_FAILED(status)) def test_functional_fast_ctypes_device_get_installed_count(self): device_count = k4a._bindings.k4a.k4a_device_get_installed_count() self.assertGreater(device_count, 0) @unittest.skip def test_functional_fast_ctypes_set_debug_message_handler_NULL_callback(self): status = k4a._bindings.k4a_set_debug_message_handler( ctypes.cast(ctypes.c_void_p(), ctypes.POINTER(k4a.logging_message_cb)), ctypes.c_void_p(), k4a.ELogLevel.TRACE) self.assertTrue(k4a.K4A_SUCCEEDED(status)) @unittest.skip def test_functional_fast_ctypes_set_debug_message_handler_callback(self): logger_cb = k4a.logging_message_cb(glb_print_message) context = ctypes.c_void_p() status = k4a._bindings.k4a_set_debug_message_handler( ctypes.byref(logger_cb), context, k4a.ELogLevel.TRACE ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) def test_functional_fast_ctypes_device_get_capture(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) def test_functional_fast_ctypes_device_get_imu_sample(self): with self.lock: device_config = k4a.DEVICE_CONFIG_BGRA32_1080P_NFOV_2X2BINNED_FPS15 status = k4a._bindings.k4a.k4a_device_start_cameras(self.device_handle, ctypes.byref(device_config)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) status = k4a._bindings.k4a.k4a_device_start_imu(self.device_handle) self.assertTrue(k4a.K4A_SUCCEEDED(status)) imu_sample = k4a.ImuSample() timeout_ms = ctypes.c_int32(1000) status = k4a._bindings.k4a.k4a_device_get_imu_sample( self.device_handle, ctypes.byref(imu_sample), timeout_ms ) # Stop imu and cameras. k4a._bindings.k4a.k4a_device_stop_imu(self.device_handle) k4a._bindings.k4a.k4a_device_stop_cameras(self.device_handle) self.assertEqual(status, k4a.EWaitStatus.SUCCEEDED) self.assertNotAlmostEqual(imu_sample.temperature, 0.0) #self.assertNotAlmostEqual(imu_sample.acc_sample.xyz.x, 0.0) #self.assertNotAlmostEqual(imu_sample.acc_sample.xyz.y, 0.0) #self.assertNotAlmostEqual(imu_sample.acc_sample.xyz.z, 0.0) self.assertNotEqual(imu_sample.acc_timestamp_usec, 0) #self.assertNotAlmostEqual(imu_sample.gyro_sample.xyz.x, 0.0) #self.assertNotAlmostEqual(imu_sample.gyro_sample.xyz.y, 0.0) #self.assertNotAlmostEqual(imu_sample.gyro_sample.xyz.z, 0.0) self.assertNotEqual(imu_sample.gyro_timestamp_usec, 0.0) def test_functional_fast_ctypes_capture_create(self): capture = k4a._bindings.k4a._CaptureHandle() status = k4a._bindings.k4a.k4a_capture_create(ctypes.byref(capture)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_capture_reference(capture) k4a._bindings.k4a.k4a_capture_release(capture) k4a._bindings.k4a.k4a_capture_release(capture) def test_functional_fast_ctypes_capture_get_color_image(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) self.assertIsInstance(color_image, k4a._bindings.k4a._ImageHandle) k4a._bindings.k4a.k4a_image_release(color_image) def test_functional_fast_ctypes_capture_get_depth_image(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) self.assertIsInstance(depth_image, k4a._bindings.k4a._ImageHandle) k4a._bindings.k4a.k4a_image_release(depth_image) def test_functional_fast_ctypes_capture_get_ir_image(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) ir_image = k4a._bindings.k4a.k4a_capture_get_ir_image(capture) self.assertIsInstance(ir_image, k4a._bindings.k4a._ImageHandle) k4a._bindings.k4a.k4a_image_release(ir_image) def test_functional_fast_ctypes_image_create(self): image_format = k4a.EImageFormat.COLOR_BGRA32 width_pixels = 512 height_pixels = 512 stride_pixels = 4*512 image_handle = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create(ctypes.c_int(image_format.value), width_pixels, height_pixels, stride_pixels, ctypes.byref(image_handle)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Check that the created image has properties requested. created_image_format = k4a._bindings.k4a.k4a_image_get_format(image_handle) created_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(image_handle) created_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(image_handle) created_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(image_handle) k4a._bindings.k4a.k4a_image_release(image_handle) self.assertEqual(image_format, created_image_format) self.assertEqual(width_pixels, created_image_width_pixels) self.assertEqual(height_pixels, created_image_height_pixels) self.assertEqual(stride_pixels, created_image_stride_bytes) def test_functional_fast_ctypes_capture_set_color_image(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) # Grab the current color image. saved_color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Create a new image. image_format = k4a.EImageFormat.COLOR_BGRA32 width_pixels = ctypes.c_int(512) height_pixels = ctypes.c_int(512) stride_bytes = ctypes.c_int(4*512) image_handle = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create(ctypes.c_int(image_format.value), width_pixels, height_pixels, stride_bytes, ctypes.byref(image_handle)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Check that the created image has the expected properties. image_format = k4a._bindings.k4a.k4a_image_get_format(image_handle) image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(image_handle) image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(image_handle) image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(image_handle) self.assertEqual(image_format, k4a.EImageFormat.COLOR_BGRA32) self.assertEqual(image_width_pixels, 512) self.assertEqual(image_height_pixels, 512) self.assertEqual(image_stride_bytes, 512*4) # Replace the saved image with the created one. k4a._bindings.k4a.k4a_capture_set_color_image(capture, image_handle) k4a._bindings.k4a.k4a_image_release(image_handle) # Get a new image. It should be identical to the created one. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Test that the new image has characteristics of the created image. color_image_format = k4a._bindings.k4a.k4a_image_get_format(color_image) color_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) color_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) color_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(color_image) k4a._bindings.k4a.k4a_image_release(color_image) # Now put back the saved color image into the capture. k4a._bindings.k4a.k4a_capture_set_color_image(capture, saved_color_image) k4a._bindings.k4a.k4a_image_release(saved_color_image) # Test that the image has characteristics of the saved color image. saved_color_image2 = k4a._bindings.k4a.k4a_capture_get_color_image(capture) saved_color_image_format = k4a._bindings.k4a.k4a_image_get_format(saved_color_image2) saved_color_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(saved_color_image2) saved_color_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(saved_color_image2) saved_color_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(saved_color_image2) k4a._bindings.k4a.k4a_image_release(saved_color_image2) self.assertEqual(color_image_format, k4a.EImageFormat.COLOR_BGRA32) self.assertEqual(color_image_width_pixels, 512) self.assertEqual(color_image_height_pixels, 512) self.assertEqual(color_image_stride_bytes, 512*4) self.assertEqual(saved_color_image_format, k4a.EImageFormat.COLOR_BGRA32) self.assertEqual(saved_color_image_width_pixels, 1920) self.assertEqual(saved_color_image_height_pixels, 1080) self.assertEqual(saved_color_image_stride_bytes, 1920*4) def test_functional_fast_ctypes_capture_set_depth_image(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) # Grab the current depth image and add a reference to it so it is not destroyed. saved_depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) # Create a new image. image_format = k4a.EImageFormat.DEPTH16 width_pixels = ctypes.c_int(512) height_pixels = ctypes.c_int(512) stride_bytes = ctypes.c_int(4*512) image_handle = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create(ctypes.c_int(image_format.value), width_pixels, height_pixels, stride_bytes, ctypes.byref(image_handle)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Replace the saved image with the created one. k4a._bindings.k4a.k4a_capture_set_depth_image(capture, image_handle) k4a._bindings.k4a.k4a_image_release(image_handle) # Get a new image. It should be identical to the created one. depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) # Test that the new image has characteristics of the created image. depth_image_format = k4a._bindings.k4a.k4a_image_get_format(depth_image) depth_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) depth_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) depth_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(depth_image) k4a._bindings.k4a.k4a_image_release(depth_image) # Now put back the saved color image into the capture. k4a._bindings.k4a.k4a_capture_set_depth_image(capture, saved_depth_image) k4a._bindings.k4a.k4a_image_release(saved_depth_image) # Test that the image has characteristics of the saved depth image. saved_depth_image2 = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) saved_depth_image_format = k4a._bindings.k4a.k4a_image_get_format(saved_depth_image2) saved_depth_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(saved_depth_image) saved_depth_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(saved_depth_image) saved_depth_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(saved_depth_image) k4a._bindings.k4a.k4a_image_release(saved_depth_image2) self.assertEqual(depth_image_format, k4a.EImageFormat.DEPTH16) self.assertEqual(depth_image_width_pixels, 512) self.assertEqual(depth_image_height_pixels, 512) self.assertEqual(depth_image_stride_bytes, 512*4) self.assertEqual(saved_depth_image_format, k4a.EImageFormat.DEPTH16) self.assertEqual(saved_depth_image_width_pixels, 320) self.assertEqual(saved_depth_image_height_pixels, 288) self.assertEqual(saved_depth_image_stride_bytes, 320*2) def test_functional_fast_ctypes_capture_set_ir_image(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) # Grab the current depth image and add a reference to it so it is not destroyed. saved_ir_image = k4a._bindings.k4a.k4a_capture_get_ir_image(capture) # Create a new image. image_format = k4a.EImageFormat.IR16 width_pixels = ctypes.c_int(512) height_pixels = ctypes.c_int(512) stride_bytes = ctypes.c_int(4*512) image_handle = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create(ctypes.c_int(image_format.value), width_pixels, height_pixels, stride_bytes, ctypes.byref(image_handle)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Replace the saved image with the created one. k4a._bindings.k4a.k4a_capture_set_ir_image(capture, image_handle) k4a._bindings.k4a.k4a_image_release(image_handle) # Get a new image. It should be identical to the created one. ir_image = k4a._bindings.k4a.k4a_capture_get_ir_image(capture) # Test that the new image has characteristics of the created image. ir_image_format = k4a._bindings.k4a.k4a_image_get_format(ir_image) ir_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(ir_image) ir_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(ir_image) ir_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(ir_image) k4a._bindings.k4a.k4a_image_release(ir_image) # Now put back the saved color image into the capture. k4a._bindings.k4a.k4a_capture_set_ir_image(capture, saved_ir_image) k4a._bindings.k4a.k4a_image_release(saved_ir_image) # Test that the image has characteristics of the saved depth image. saved_ir_image2 = k4a._bindings.k4a.k4a_capture_get_ir_image(capture) saved_ir_image_format = k4a._bindings.k4a.k4a_image_get_format(saved_ir_image2) saved_ir_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(saved_ir_image2) saved_ir_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(saved_ir_image2) saved_ir_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(saved_ir_image2) k4a._bindings.k4a.k4a_image_release(saved_ir_image2) self.assertEqual(ir_image_format, k4a.EImageFormat.IR16) self.assertEqual(ir_image_width_pixels, 512) self.assertEqual(ir_image_height_pixels, 512) self.assertEqual(ir_image_stride_bytes, 512*4) self.assertEqual(saved_ir_image_format, k4a.EImageFormat.IR16) self.assertEqual(saved_ir_image_width_pixels, 320) self.assertEqual(saved_ir_image_height_pixels, 288) self.assertEqual(saved_ir_image_stride_bytes, 320*2) def test_functional_fast_ctypes_capture_get_temperature_c(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) temperature_c = k4a._bindings.k4a.k4a_capture_get_temperature_c(capture) self.assertNotAlmostEqual(temperature_c, 0.0, 2) def test_functional_fast_ctypes_capture_set_temperature_c(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) absolute_zero_temperature_c = -277.15 k4a._bindings.k4a.k4a_capture_set_temperature_c(capture, absolute_zero_temperature_c) temperature_c = k4a._bindings.k4a.k4a_capture_get_temperature_c(capture) self.assertAlmostEqual(temperature_c, absolute_zero_temperature_c, 2) def test_functional_fast_ctypes_image_get_buffer(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) buffer_ptr = k4a._bindings.k4a.k4a_image_get_buffer(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertIsNotNone(ctypes.cast(buffer_ptr, ctypes.c_void_p).value) def test_functional_fast_ctypes_image_get_buffer_None(self): buffer_ptr = k4a._bindings.k4a.k4a_image_get_buffer(None) self.assertIsNone(ctypes.cast(buffer_ptr, ctypes.c_void_p).value) def test_functional_fast_ctypes_image_get_size(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) color_image_size_bytes = k4a._bindings.k4a.k4a_image_get_size(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(color_image_size_bytes, 1080*1920*4) def test_functional_fast_ctypes_image_get_format(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) color_image_format = k4a._bindings.k4a.k4a_image_get_format(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(color_image_format, k4a.EImageFormat.COLOR_BGRA32) def test_functional_fast_ctypes_image_get_width_pixels(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) color_image_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(color_image_width_pixels, 1920) def test_functional_fast_ctypes_image_get_height_pixels(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) color_image_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(color_image_height_pixels, 1080) def test_functional_fast_ctypes_image_get_stride_bytes(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) color_image_stride_bytes = k4a._bindings.k4a.k4a_image_get_stride_bytes(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(color_image_stride_bytes, 1920*4) def test_functional_fast_ctypes_image_get_device_timestamp_usec(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) device_timestamp_usec = k4a._bindings.k4a.k4a_image_get_device_timestamp_usec(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertIsInstance(device_timestamp_usec, int) self.assertNotEqual(device_timestamp_usec, 0) # Strictly not always the case. def test_functional_fast_ctypes_image_get_system_timestamp_nsec(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) system_timestamp_nsec = k4a._bindings.k4a.k4a_image_get_system_timestamp_nsec(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertIsInstance(system_timestamp_nsec, int) self.assertNotEqual(system_timestamp_nsec, 0) # Strictly not always the case. def test_functional_fast_ctypes_image_get_exposure_usec(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) exposure_usec = k4a._bindings.k4a.k4a_image_get_exposure_usec(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertIsInstance(exposure_usec, int) self.assertNotEqual(exposure_usec, 0) # Strictly not always the case. def test_functional_fast_ctypes_image_get_white_balance(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) white_balance = k4a._bindings.k4a.k4a_image_get_white_balance(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertIsInstance(white_balance, int) self.assertNotEqual(white_balance, 0) # Strictly not always the case. def test_functional_fast_ctypes_image_get_iso_speed(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) iso_speed = k4a._bindings.k4a.k4a_image_get_iso_speed(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertIsInstance(iso_speed, int) self.assertNotEqual(iso_speed, 0) # Strictly not always the case. def test_functional_fast_ctypes_image_set_device_timestamp_usec(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Save the original value. saved_value = k4a._bindings.k4a.k4a_image_get_device_timestamp_usec(color_image) # Set a new value and read it back. new_value = saved_value + 1 k4a._bindings.k4a.k4a_image_set_device_timestamp_usec(color_image, new_value) new_value_readback = k4a._bindings.k4a.k4a_image_get_device_timestamp_usec(color_image) # Set the original value on the device and read it back. k4a._bindings.k4a.k4a_image_set_device_timestamp_usec(color_image, saved_value) saved_value_readback = k4a._bindings.k4a.k4a_image_get_device_timestamp_usec(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(new_value_readback, new_value) self.assertEqual(saved_value_readback, saved_value) self.assertNotEqual(new_value, saved_value) self.assertNotEqual(saved_value_readback, new_value_readback) def test_functional_fast_ctypes_image_set_system_timestamp_nsec(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Save the original value. saved_value = k4a._bindings.k4a.k4a_image_get_system_timestamp_nsec(color_image) # Set a new value and read it back. new_value = saved_value + 1 k4a._bindings.k4a.k4a_image_set_system_timestamp_nsec(color_image, new_value) new_value_readback = k4a._bindings.k4a.k4a_image_get_system_timestamp_nsec(color_image) # Set the original value on the device and read it back. k4a._bindings.k4a.k4a_image_set_system_timestamp_nsec(color_image, saved_value) saved_value_readback = k4a._bindings.k4a.k4a_image_get_system_timestamp_nsec(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(new_value_readback, new_value) self.assertEqual(saved_value_readback, saved_value) self.assertNotEqual(new_value, saved_value) self.assertNotEqual(saved_value_readback, new_value_readback) def test_functional_fast_ctypes_image_set_exposure_usec(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Save the original value. saved_value = k4a._bindings.k4a.k4a_image_get_exposure_usec(color_image) # Set a new value and read it back. new_value = saved_value + 1 k4a._bindings.k4a.k4a_image_set_exposure_usec(color_image, new_value) new_value_readback = k4a._bindings.k4a.k4a_image_get_exposure_usec(color_image) # Set the original value on the device and read it back. k4a._bindings.k4a.k4a_image_set_exposure_usec(color_image, saved_value) saved_value_readback = k4a._bindings.k4a.k4a_image_get_exposure_usec(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(new_value_readback, new_value) self.assertEqual(saved_value_readback, saved_value) self.assertNotEqual(new_value, saved_value) self.assertNotEqual(saved_value_readback, new_value_readback) def test_functional_fast_ctypes_image_set_white_balance(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Save the original value. saved_value = k4a._bindings.k4a.k4a_image_get_white_balance(color_image) # Set a new value and read it back. new_value = saved_value + 1 k4a._bindings.k4a.k4a_image_set_white_balance(color_image, new_value) new_value_readback = k4a._bindings.k4a.k4a_image_get_white_balance(color_image) # Set the original value on the device and read it back. k4a._bindings.k4a.k4a_image_set_white_balance(color_image, saved_value) saved_value_readback = k4a._bindings.k4a.k4a_image_get_white_balance(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(new_value_readback, new_value) self.assertEqual(saved_value_readback, saved_value) self.assertNotEqual(new_value, saved_value) self.assertNotEqual(saved_value_readback, new_value_readback) def test_functional_fast_ctypes_image_set_iso_speed(self): with self.lock: capture = get_1080p_bgr32_nfov_2x2binned(self.device_handle) self.assertIsNotNone(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Save the original value. saved_value = k4a._bindings.k4a.k4a_image_get_iso_speed(color_image) # Set a new value and read it back. new_value = saved_value + 1 k4a._bindings.k4a.k4a_image_set_iso_speed(color_image, new_value) new_value_readback = k4a._bindings.k4a.k4a_image_get_iso_speed(color_image) # Set the original value on the device and read it back. k4a._bindings.k4a.k4a_image_set_iso_speed(color_image, saved_value) saved_value_readback = k4a._bindings.k4a.k4a_image_get_iso_speed(color_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertEqual(new_value_readback, new_value) self.assertEqual(saved_value_readback, saved_value) self.assertNotEqual(new_value, saved_value) self.assertNotEqual(saved_value_readback, new_value_readback) def test_functional_fast_ctypes_device_start_cameras_stop_cameras(self): with self.lock: # Start the cameras. device_config = k4a.DeviceConfiguration() device_config.color_format = k4a.EImageFormat.COLOR_BGRA32 device_config.color_resolution = k4a.EColorResolution.RES_1080P device_config.depth_mode = k4a.EDepthMode.NFOV_2X2BINNED device_config.camera_fps = k4a.EFramesPerSecond.FPS_15 device_config.synchronized_images_only = True device_config.depth_delay_off_color_usec = 0 device_config.wired_sync_mode = k4a.EWiredSyncMode.STANDALONE device_config.subordinate_delay_off_master_usec = 0 device_config.disable_streaming_indicator = False status = k4a._bindings.k4a.k4a_device_start_cameras(self.device_handle, ctypes.byref(device_config)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_device_stop_cameras(self.device_handle) def test_functional_fast_ctypes_device_start_cameras_stop_cameras_DEFAULT_DISABLE(self): with self.lock: device_config = k4a.DEVICE_CONFIG_DISABLE_ALL status = k4a._bindings.k4a.k4a_device_start_cameras(self.device_handle, ctypes.byref(device_config)) self.assertTrue(k4a.K4A_FAILED(status)) # Seems to fail when DISABLE_ALL config is used. k4a._bindings.k4a.k4a_device_stop_cameras(self.device_handle) def test_functional_fast_ctypes_device_start_imu_stop_imu(self): with self.lock: device_config = k4a.DEVICE_CONFIG_BGRA32_1080P_NFOV_2X2BINNED_FPS15 status = k4a._bindings.k4a.k4a_device_start_cameras(self.device_handle, ctypes.byref(device_config)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) status = k4a._bindings.k4a.k4a_device_start_imu(self.device_handle) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_device_stop_imu(self.device_handle) k4a._bindings.k4a.k4a_device_stop_cameras(self.device_handle) def test_functional_fast_ctypes_device_get_serialnum(self): strsize = ctypes.c_size_t(32) serial_number = (ctypes.c_char * strsize.value)() status = k4a._bindings.k4a.k4a_device_get_serialnum(self.device_handle, serial_number, ctypes.byref(strsize)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) def test_functional_fast_ctypes_device_get_version(self): hwver = k4a.HardwareVersion() status = k4a._bindings.k4a.k4a_device_get_version(self.device_handle, ctypes.byref(hwver)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Check the versions. self.assertTrue(hwver.rgb.major != 0 or hwver.rgb.minor != 0 or hwver.rgb.iteration != 0) self.assertTrue(hwver.depth.major != 0 or hwver.depth.minor != 0 or hwver.depth.iteration != 0) self.assertTrue(hwver.audio.major != 0 or hwver.audio.minor != 0 or hwver.audio.iteration != 0) self.assertTrue(hwver.depth_sensor.major != 0 or hwver.depth_sensor.minor != 0 or hwver.depth_sensor.iteration != 0) def test_functional_fast_ctypes_device_get_color_control_capabilities(self): color_control_commands = [ k4a.EColorControlCommand.BACKLIGHT_COMPENSATION, k4a.EColorControlCommand.BRIGHTNESS, k4a.EColorControlCommand.CONTRAST, k4a.EColorControlCommand.EXPOSURE_TIME_ABSOLUTE, k4a.EColorControlCommand.GAIN, k4a.EColorControlCommand.POWERLINE_FREQUENCY, k4a.EColorControlCommand.SATURATION, k4a.EColorControlCommand.SHARPNESS, k4a.EColorControlCommand.WHITEBALANCE ] for command in color_control_commands: with self.subTest(command = command): status = k4a_device_get_color_control_capability(self.device_handle, command) self.assertTrue(k4a.K4A_SUCCEEDED(status)) def test_functional_fast_ctypes_device_get_color_control(self): color_control_commands = [ k4a.EColorControlCommand.BACKLIGHT_COMPENSATION, k4a.EColorControlCommand.BRIGHTNESS, k4a.EColorControlCommand.CONTRAST, k4a.EColorControlCommand.EXPOSURE_TIME_ABSOLUTE, k4a.EColorControlCommand.GAIN, k4a.EColorControlCommand.POWERLINE_FREQUENCY, k4a.EColorControlCommand.SATURATION, k4a.EColorControlCommand.SHARPNESS, k4a.EColorControlCommand.WHITEBALANCE ] for command in color_control_commands: with self.subTest(command = command): mode = ctypes.c_int32(k4a.EColorControlMode.AUTO.value) value = ctypes.c_int32(0) status = k4a._bindings.k4a.k4a_device_get_color_control( self.device_handle, ctypes.c_int(command.value), ctypes.byref(mode), ctypes.byref(value) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # For some reason, manually setting EXPOSURE_TIME_ABSOLUTE fails. def test_functional_fast_ctypes_device_set_color_control(self): color_control_commands = [ k4a.EColorControlCommand.BACKLIGHT_COMPENSATION, k4a.EColorControlCommand.BRIGHTNESS, k4a.EColorControlCommand.CONTRAST, #k4a.EColorControlCommand.EXPOSURE_TIME_ABSOLUTE, k4a.EColorControlCommand.GAIN, k4a.EColorControlCommand.POWERLINE_FREQUENCY, k4a.EColorControlCommand.SATURATION, k4a.EColorControlCommand.SHARPNESS, #k4a.EColorControlCommand.WHITEBALANCE ] for command in color_control_commands: with self.subTest(command = command): (status, status0, status1, status2, status3, status4, saved_value, saved_value_readback, new_value, new_value_readback) = \ k4a_device_set_and_get_color_control(self.device_handle, command) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertTrue(k4a.K4A_SUCCEEDED(status0)) self.assertTrue(k4a.K4A_SUCCEEDED(status1)) self.assertTrue(k4a.K4A_SUCCEEDED(status2)) self.assertTrue(k4a.K4A_SUCCEEDED(status3)) self.assertTrue(k4a.K4A_SUCCEEDED(status4)) self.assertEqual(saved_value.value, saved_value_readback.value) self.assertEqual(new_value.value, new_value_readback.value) self.assertNotEqual(saved_value.value, new_value.value) def test_functional_fast_ctypes_device_get_raw_calibration(self): with self.lock: # Get buffer size requirement. buffer_size = ctypes.c_size_t(0) buffer = ctypes.c_uint8(0) status = k4a._bindings.k4a.k4a_device_get_raw_calibration( self.device_handle, ctypes.byref(buffer), ctypes.byref(buffer_size)) self.assertEqual(status, k4a.EBufferStatus.BUFFER_TOO_SMALL) buffer = ctypes.create_string_buffer(buffer_size.value) buffer = ctypes.cast(buffer, ctypes.POINTER(ctypes.c_uint8)) status = k4a._bindings.k4a.k4a_device_get_raw_calibration( self.device_handle, buffer, ctypes.byref(buffer_size)) self.assertEqual(status, k4a.EBufferStatus.SUCCEEDED) def test_functional_fast_ctypes_device_get_calibration(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration = k4a._bindings.k4a._Calibration() for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) def test_functional_fast_ctypes_device_get_sync_jack(self): sync_in = ctypes.c_bool(False) sync_out = ctypes.c_bool(False) status = k4a._bindings.k4a.k4a_device_get_sync_jack( self.device_handle, ctypes.byref(sync_in), ctypes.byref(sync_out)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) def test_functional_fast_ctypes_calibration_get_from_raw(self): with self.lock: # Get buffer size requirement. buffer_size = ctypes.c_size_t(0) buffer = ctypes.c_uint8(0) status = k4a._bindings.k4a.k4a_device_get_raw_calibration( self.device_handle, ctypes.byref(buffer), ctypes.byref(buffer_size)) self.assertEqual(status, k4a.EBufferStatus.BUFFER_TOO_SMALL) buffer = ctypes.create_string_buffer(buffer_size.value) buffer = ctypes.cast(buffer, ctypes.POINTER(ctypes.c_uint8)) status = k4a._bindings.k4a.k4a_device_get_raw_calibration( self.device_handle, buffer, ctypes.byref(buffer_size)) self.assertEqual(status, k4a.EBufferStatus.SUCCEEDED) # Now get the calibration from the buffer. depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] buffer = ctypes.cast(buffer, ctypes.POINTER(ctypes.c_char)) calibration = k4a._bindings.k4a._Calibration() for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_calibration_get_from_raw( buffer, buffer_size, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) def test_functional_fast_ctypes_calibration_3d_to_3d(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P source_camera = k4a.ECalibrationType.COLOR target_camera = k4a.ECalibrationType.DEPTH calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float3(300, 300, 500) target_point = k4a._bindings.k4a._Float3() status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_3d_to_3d( ctypes.byref(calibration), ctypes.byref(source_point), source_camera, target_camera, ctypes.byref(target_point)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) if source_camera == target_camera: self.assertAlmostEqual(source_point.xyz.x, target_point.xyz.x) self.assertAlmostEqual(source_point.xyz.y, target_point.xyz.y) self.assertAlmostEqual(source_point.xyz.z, target_point.xyz.z) def test_functional_fast_ctypes_calibration_2d_to_3d(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P source_camera = k4a.ECalibrationType.COLOR target_camera = k4a.ECalibrationType.DEPTH calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float2(300, 300) depth_mm = 500.0 target_point = k4a._bindings.k4a._Float3() valid_int_flag = ctypes.c_int(0) status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_2d_to_3d( ctypes.byref(calibration), ctypes.byref(source_point), ctypes.c_float(depth_mm), ctypes.c_int(source_camera), ctypes.c_int(target_camera), ctypes.byref(target_point), ctypes.byref(valid_int_flag)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) def test_functional_fast_ctypes_calibration_3d_to_2d(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P source_camera = k4a.ECalibrationType.COLOR target_camera = k4a.ECalibrationType.DEPTH calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float3(300, 300, 500) target_point = k4a._bindings.k4a._Float2() valid_int_flag = ctypes.c_int(0) status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_3d_to_2d( ctypes.byref(calibration), ctypes.byref(source_point), source_camera, target_camera, ctypes.byref(target_point), ctypes.byref(valid_int_flag)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) def test_functional_fast_ctypes_calibration_2d_to_2d(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P source_camera = k4a.ECalibrationType.COLOR target_camera = k4a.ECalibrationType.DEPTH calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float2(300, 300) depth_mm = 500 target_point = k4a._bindings.k4a._Float2() valid_int_flag = ctypes.c_int(0) status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_2d_to_2d( ctypes.byref(calibration), ctypes.byref(source_point), depth_mm, source_camera, target_camera, ctypes.byref(target_point), ctypes.byref(valid_int_flag)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) if source_camera == target_camera: self.assertAlmostEqual(source_point.xy.x, target_point.xy.x) self.assertAlmostEqual(source_point.xy.y, target_point.xy.y) def test_functional_fast_ctypes_calibration_color_2d_to_depth_2d(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P calibration = k4a._bindings.k4a._Calibration() target_point = k4a._bindings.k4a._Float2() valid_int_flag = ctypes.c_int(0) status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Get a depth image. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) self.assertIsNotNone(depth_image) # Get color image width and height to specify the source point. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) source_point = k4a._bindings.k4a._Float2(width_pixels/4, height_pixels/4) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_color_2d_to_depth_2d( ctypes.byref(calibration), ctypes.byref(source_point), depth_image, ctypes.byref(target_point), ctypes.byref(valid_int_flag)) k4a._bindings.k4a.k4a_image_release(depth_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) def test_functional_ctypes_calibration_3d_to_3d(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration_types = [ k4a.ECalibrationType.COLOR, k4a.ECalibrationType.DEPTH ] calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float3(300, 300, 500) target_point = k4a._bindings.k4a._Float3() for depth_mode in depth_modes: for color_resolution in color_resolutions: for source_camera in calibration_types: for target_camera in calibration_types: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution, source_camera = source_camera, target_camera = target_camera): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_3d_to_3d( ctypes.byref(calibration), ctypes.byref(source_point), source_camera, target_camera, ctypes.byref(target_point)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) if source_camera == target_camera: self.assertAlmostEqual(source_point.xyz.x, target_point.xyz.x) self.assertAlmostEqual(source_point.xyz.y, target_point.xyz.y) self.assertAlmostEqual(source_point.xyz.z, target_point.xyz.z) def test_functional_ctypes_calibration_2d_to_3d(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration_types = [ k4a.ECalibrationType.COLOR, k4a.ECalibrationType.DEPTH ] calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float2(300, 300) depth_mm = 500.0 target_point = k4a._bindings.k4a._Float3() valid_int_flag = ctypes.c_int(0) for depth_mode in depth_modes: for color_resolution in color_resolutions: for source_camera in calibration_types: for target_camera in calibration_types: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution, source_camera = source_camera, target_camera = target_camera): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_2d_to_3d( ctypes.byref(calibration), ctypes.byref(source_point), ctypes.c_float(depth_mm), ctypes.c_int(source_camera), ctypes.c_int(target_camera), ctypes.byref(target_point), ctypes.byref(valid_int_flag)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) def test_functional_ctypes_calibration_3d_to_2d(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration_types = [ k4a.ECalibrationType.COLOR, k4a.ECalibrationType.DEPTH ] calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float3(300, 300, 500) target_point = k4a._bindings.k4a._Float2() valid_int_flag = ctypes.c_int(0) for depth_mode in depth_modes: for color_resolution in color_resolutions: for source_camera in calibration_types: for target_camera in calibration_types: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution, source_camera = source_camera, target_camera = target_camera): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_3d_to_2d( ctypes.byref(calibration), ctypes.byref(source_point), source_camera, target_camera, ctypes.byref(target_point), ctypes.byref(valid_int_flag)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) def test_functional_ctypes_calibration_2d_to_2d(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration_types = [ k4a.ECalibrationType.COLOR, k4a.ECalibrationType.DEPTH ] calibration = k4a._bindings.k4a._Calibration() source_point = k4a._bindings.k4a._Float2(300, 300) depth_mm = 500 target_point = k4a._bindings.k4a._Float2() valid_int_flag = ctypes.c_int(0) for depth_mode in depth_modes: for color_resolution in color_resolutions: for source_camera in calibration_types: for target_camera in calibration_types: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution, source_camera = source_camera, target_camera = target_camera): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_2d_to_2d( ctypes.byref(calibration), ctypes.byref(source_point), depth_mm, source_camera, target_camera, ctypes.byref(target_point), ctypes.byref(valid_int_flag)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) if source_camera == target_camera: self.assertAlmostEqual(source_point.xy.x, target_point.xy.x) self.assertAlmostEqual(source_point.xy.y, target_point.xy.y) # This test is data dependent. It may fail based on scene content. # It is favorable to point the camera at a flat wall about 30 cm away. # Perhaps it's better to generate synthetic data. def test_functional_ctypes_calibration_color_2d_to_depth_2d(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration = k4a._bindings.k4a._Calibration() target_point = k4a._bindings.k4a._Float2() valid_int_flag = ctypes.c_int(0) for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Get a depth image. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) self.assertIsNotNone(depth_image) # Get color image width and height to specify the source point. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) source_point = k4a._bindings.k4a._Float2(width_pixels/4, height_pixels/4) # Transform source point from source_camera to target_camera. status = k4a._bindings.k4a.k4a_calibration_color_2d_to_depth_2d( ctypes.byref(calibration), ctypes.byref(source_point), depth_image, ctypes.byref(target_point), ctypes.byref(valid_int_flag)) k4a._bindings.k4a.k4a_image_release(depth_image) k4a._bindings.k4a.k4a_image_release(color_image) self.assertTrue(k4a.K4A_SUCCEEDED(status)) self.assertEqual(valid_int_flag.value, 1) def test_functional_fast_ctypes_transformation_create_destroy(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, k4a.EDepthMode.PASSIVE_IR, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration = k4a._bindings.k4a._Calibration() for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. k4a._bindings.k4a.k4a_transformation_destroy(transformation) def test_functional_fast_ctypes_transformation_depth_image_to_color_camera(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P calibration = k4a._bindings.k4a._Calibration() status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Get a depth image. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) image_format = k4a._bindings.k4a.k4a_image_get_format(depth_image) # Get color image width and height. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) stride_bytes = width_pixels * 2 # Create an output depth image. transformed_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format, width_pixels, height_pixels, stride_bytes, ctypes.byref(transformed_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_depth_image_to_color_camera( transformation, depth_image, transformed_image ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(transformed_image) k4a._bindings.k4a.k4a_image_release(depth_image) def test_functional_fast_ctypes_transformation_depth_image_to_color_camera_custom(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P calibration = k4a._bindings.k4a._Calibration() status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Get a capture. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) # Get color image width and height. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) output_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) output_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) output_stride_bytes = output_width_pixels * 2 # Get a depth image. depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) image_format = k4a._bindings.k4a.k4a_image_get_format(depth_image) input_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) input_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) # Create an output depth image. transformed_depth_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format, output_width_pixels, output_height_pixels, output_stride_bytes, ctypes.byref(transformed_depth_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Create a custom image. image_format = k4a.EImageFormat.CUSTOM16 custom_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format.value, input_width_pixels, input_height_pixels, input_width_pixels * 2, ctypes.byref(custom_image)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Create a transformed custom image. image_format = k4a.EImageFormat.CUSTOM16 transformed_custom_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format.value, output_width_pixels, output_height_pixels, output_width_pixels * 2, ctypes.byref(transformed_custom_image)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_depth_image_to_color_camera_custom( transformation, depth_image, custom_image, transformed_depth_image, transformed_custom_image, k4a.ETransformInterpolationType.LINEAR, 0 ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(depth_image) k4a._bindings.k4a.k4a_image_release(custom_image) k4a._bindings.k4a.k4a_image_release(transformed_depth_image) k4a._bindings.k4a.k4a_image_release(transformed_custom_image) def test_functional_fast_ctypes_transformation_color_image_to_depth_camera(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED color_resolution = k4a.EColorResolution.RES_720P calibration = k4a._bindings.k4a._Calibration() status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Get a capture and depth and color images. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Create an output image. image_format = k4a._bindings.k4a.k4a_image_get_format(color_image) width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) stride_bytes = width_pixels * 4 transformed_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format, width_pixels, height_pixels, stride_bytes, ctypes.byref(transformed_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_color_image_to_depth_camera( transformation, depth_image, color_image, transformed_image ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(transformed_image) k4a._bindings.k4a.k4a_image_release(depth_image) k4a._bindings.k4a.k4a_image_release(color_image) def test_functional_fast_ctypes_transformation_depth_image_to_point_cloud(self): with self.lock: depth_mode = k4a.EDepthMode.NFOV_2X2BINNED calibration = k4a._bindings.k4a._Calibration() # Get a capture and depth image. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, k4a.EColorResolution.RES_1080P, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) # Create an output image. image_format = k4a.EImageFormat.CUSTOM width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) stride_bytes = width_pixels * 6 xyz_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( ctypes.c_int(image_format), ctypes.c_int(width_pixels), ctypes.c_int(height_pixels), ctypes.c_int(stride_bytes), ctypes.byref(xyz_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Get a transformation. status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, k4a.EColorResolution.RES_1080P, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create( ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_depth_image_to_point_cloud( transformation, depth_image, k4a.ECalibrationType.DEPTH, xyz_image ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(xyz_image) k4a._bindings.k4a.k4a_image_release(depth_image) def test_functional_ctypes_transformation_depth_image_to_color_camera(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration = k4a._bindings.k4a._Calibration() for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Get a depth image. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) image_format = k4a._bindings.k4a.k4a_image_get_format(depth_image) # Get color image width and height. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) stride_bytes = width_pixels * 2 # Create an output depth image. transformed_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format, width_pixels, height_pixels, stride_bytes, ctypes.byref(transformed_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_depth_image_to_color_camera( transformation, depth_image, transformed_image ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(transformed_image) k4a._bindings.k4a.k4a_image_release(depth_image) def test_functional_ctypes_transformation_depth_image_to_color_camera_custom(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration = k4a._bindings.k4a._Calibration() for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Get a capture. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) # Get color image width and height. color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) output_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(color_image) output_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(color_image) output_stride_bytes = output_width_pixels * 2 # Get a depth image. depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) image_format = k4a._bindings.k4a.k4a_image_get_format(depth_image) input_width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) input_height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) # Create an output depth image. transformed_depth_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format, output_width_pixels, output_height_pixels, output_stride_bytes, ctypes.byref(transformed_depth_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Create a custom image. image_format = k4a.EImageFormat.CUSTOM16 custom_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format.value, input_width_pixels, input_height_pixels, input_width_pixels * 2, ctypes.byref(custom_image)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Create a transformed custom image. image_format = k4a.EImageFormat.CUSTOM16 transformed_custom_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format.value, output_width_pixels, output_height_pixels, output_width_pixels * 2, ctypes.byref(transformed_custom_image)) self.assertEqual(k4a.EStatus.SUCCEEDED, status) # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_depth_image_to_color_camera_custom( transformation, depth_image, custom_image, transformed_depth_image, transformed_custom_image, k4a.ETransformInterpolationType.LINEAR, 0 ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(depth_image) k4a._bindings.k4a.k4a_image_release(custom_image) k4a._bindings.k4a.k4a_image_release(transformed_depth_image) k4a._bindings.k4a.k4a_image_release(transformed_custom_image) def test_functional_ctypes_transformation_color_image_to_depth_camera(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, ] color_resolutions = [ k4a.EColorResolution.RES_3072P, k4a.EColorResolution.RES_2160P, k4a.EColorResolution.RES_1536P, k4a.EColorResolution.RES_1440P, k4a.EColorResolution.RES_1080P, k4a.EColorResolution.RES_720P, ] calibration = k4a._bindings.k4a._Calibration() for depth_mode in depth_modes: for color_resolution in color_resolutions: with self.subTest(depth_mode = depth_mode, color_resolution = color_resolution): status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, color_resolution, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create(ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Get a capture and depth and color images. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, color_resolution, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) color_image = k4a._bindings.k4a.k4a_capture_get_color_image(capture) # Create an output image. image_format = k4a._bindings.k4a.k4a_image_get_format(color_image) width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) stride_bytes = width_pixels * 4 transformed_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( image_format, width_pixels, height_pixels, stride_bytes, ctypes.byref(transformed_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_color_image_to_depth_camera( transformation, depth_image, color_image, transformed_image ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(transformed_image) k4a._bindings.k4a.k4a_image_release(depth_image) k4a._bindings.k4a.k4a_image_release(color_image) def test_functional_ctypes_transformation_depth_image_to_point_cloud(self): with self.lock: depth_modes = [ k4a.EDepthMode.NFOV_2X2BINNED, k4a.EDepthMode.NFOV_UNBINNED, k4a.EDepthMode.WFOV_2X2BINNED, k4a.EDepthMode.WFOV_UNBINNED, ] for depth_mode in depth_modes: with self.subTest(depth_mode = depth_mode): calibration = k4a._bindings.k4a._Calibration() # Get a capture and depth image. capture = test_config.get_capture(self.device_handle, k4a.EImageFormat.COLOR_BGRA32, k4a.EColorResolution.RES_1080P, depth_mode) depth_image = k4a._bindings.k4a.k4a_capture_get_depth_image(capture) # Create an output image. image_format = k4a.EImageFormat.CUSTOM width_pixels = k4a._bindings.k4a.k4a_image_get_width_pixels(depth_image) height_pixels = k4a._bindings.k4a.k4a_image_get_height_pixels(depth_image) stride_bytes = width_pixels * 6 xyz_image = k4a._bindings.k4a._ImageHandle() status = k4a._bindings.k4a.k4a_image_create( ctypes.c_int(image_format), ctypes.c_int(width_pixels), ctypes.c_int(height_pixels), ctypes.c_int(stride_bytes), ctypes.byref(xyz_image) ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) # Get a transformation. status = k4a._bindings.k4a.k4a_device_get_calibration( self.device_handle, depth_mode, k4a.EColorResolution.RES_1080P, ctypes.byref(calibration)) self.assertTrue(k4a.K4A_SUCCEEDED(status)) transformation = k4a._bindings.k4a.k4a_transformation_create( ctypes.byref(calibration)) self.assertIsNotNone(transformation) # Might not be a valid assert. # Apply the transformation. status = k4a._bindings.k4a.k4a_transformation_depth_image_to_point_cloud( transformation, depth_image, k4a.ECalibrationType.DEPTH, xyz_image ) self.assertTrue(k4a.K4A_SUCCEEDED(status)) k4a._bindings.k4a.k4a_transformation_destroy(transformation) k4a._bindings.k4a.k4a_image_release(xyz_image) k4a._bindings.k4a.k4a_image_release(depth_image) if __name__ == '__main__': unittest.main()

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